From da60630d3a3ee5031293b812e5ca4bc4b77fe210 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 2 Jun 2026 12:48:31 -0700 Subject: [PATCH 01/41] Setup > View settings activity --- site/_quarto.yml | 2 +- site/about/using-the-documentation.qmd | 2 +- site/guide/_sidebar.yaml | 7 +- site/guide/guides.qmd | 3 +- .../tracking-organization-activity.qmd | 20 ++++ .../reporting/view-settings-activity.qmd | 111 ++++++++++++++++++ ...chatbot-product-map-frontend-snapshot.json | 22 +++- site/llm/chatbot-product-map.md | 48 +++++++- 8 files changed, 206 insertions(+), 9 deletions(-) create mode 100644 site/guide/reporting/tracking-organization-activity.qmd create mode 100644 site/guide/reporting/view-settings-activity.qmd diff --git a/site/_quarto.yml b/site/_quarto.yml index 977562cd8..6a0858939 100644 --- a/site/_quarto.yml +++ b/site/_quarto.yml @@ -207,7 +207,7 @@ website:
  • Documents & templates
  • Documentation
  • Validation
    • -
  • Reporting
    • +
  • Reporting & auditing
  • Monitoring
  • Attestation
    • diff --git a/site/about/using-the-documentation.qmd b/site/about/using-the-documentation.qmd index 39f59870e..4ccc935b1 100644 --- a/site/about/using-the-documentation.qmd +++ b/site/about/using-the-documentation.qmd @@ -57,7 +57,7 @@ Step-by-step instructions for platform tasks, organized by feature area. | [Documents & templates](/guide/templates/working-with-documents.qmd) | Creating and customizing documentation | Manage document types, customize templates, use the text block library | | [Documentation](/guide/guides.qmd#documentation) | Authoring and collaborating on documents | Edit content blocks, add test results, manage versions, submit for approval | | [Validation](/guide/guides.qmd#validation) | Reviewing and validating records (models) | Review documentation, assess compliance, manage findings and artifacts | -| [Reporting](/guide/guides.qmd#reporting) | Analyzing and exporting data | View reports, create custom analytics, export inventory and documents | +| [Reporting & auditing](/guide/guides.qmd#reporting) | Analyzing, exporting, and auditing data | View reports, track organization activity, create custom analytics, export inventory and documents | | [Monitoring](/guide/guides.qmd#monitoring) | Tracking record (model) performance over time | Enable monitoring, review results, set thresholds and alerts | | [Attestation](/guide/guides.qmd#attestation) | Managing formal attestations | Create, submit, review, and approve attestations | diff --git a/site/guide/_sidebar.yaml b/site/guide/_sidebar.yaml index ea6b1e502..6c055d831 100644 --- a/site/guide/_sidebar.yaml +++ b/site/guide/_sidebar.yaml @@ -157,8 +157,13 @@ website: - guide/validation/update-artifacts.qmd - guide/validation/customize-artifact-overview-page.qmd - text: "---" - - section: "Reporting" + - section: "Reporting & auditing" contents: + - file: guide/reporting/tracking-organization-activity.qmd + contents: + - text: "View model activity" + file: guide/inventory/view-record-activity.qmd + - guide/reporting/view-settings-activity.qmd - file: guide/reporting/working-with-analytics.qmd contents: - guide/reporting/view-report-data.qmd diff --git a/site/guide/guides.qmd b/site/guide/guides.qmd index 4a14c93eb..91eeeeb82 100644 --- a/site/guide/guides.qmd +++ b/site/guide/guides.qmd @@ -87,6 +87,7 @@ listing: sort: false fields: [title, description] contents: + - reporting/tracking-organization-activity.qmd - reporting/working-with-analytics.qmd - reporting/generating-exports.qmd - id: monitoring @@ -154,7 +155,7 @@ Our guides offer step-by-step instructions for frequent tasks you perform within :::{#validation} ::: -## Reporting +## Reporting & auditing :::{#reporting} ::: diff --git a/site/guide/reporting/tracking-organization-activity.qmd b/site/guide/reporting/tracking-organization-activity.qmd new file mode 100644 index 000000000..7c867850d --- /dev/null +++ b/site/guide/reporting/tracking-organization-activity.qmd @@ -0,0 +1,20 @@ +--- +# Copyright © 2023-2026 ValidMind Inc. All rights reserved. +# Refer to the LICENSE file in the root of this repository for details. +# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial +title: "Tracking organization activity" +date: last-modified +listing: + - id: activity + type: grid + max-description-length: 250 + sort: false + grid-columns: 2 + fields: [title, description] + contents: + - ../inventory/view-record-activity.qmd + - view-settings-activity.qmd +--- + +::::{#activity} +:::: \ No newline at end of file diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd new file mode 100644 index 000000000..f64c5f1f1 --- /dev/null +++ b/site/guide/reporting/view-settings-activity.qmd @@ -0,0 +1,111 @@ +--- +# Copyright © 2023-2026 ValidMind Inc. All rights reserved. +# Refer to the LICENSE file in the root of this repository for details. +# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial +title: "View settings activity" +date: last-modified +--- + +Use settings activity to audit organization-level settings and configuration changes made by admins in the {{< var validmind.platform >}}. + +:::: {.attn} + +## Prerequisites + +- [x] {{< var link.login >}} +- [x] You are a [{{< fa hand >}} Customer Admin]{.bubble} or assigned another role with sufficient permissions to view organization settings activity.[^1] + +:::: + +## About settings activity + +Settings activity records changes to admin-managed configuration across your organization. Use it to review who changed a setting, when the change happened, and which settings area was affected. + +Settings activity can include changes to: + +- Roles and permissions.[^1] +- Users and invites.[^2] +- Organization settings.[^3] +- Groups and business units.[^4] +- Workflows.[^5] +- Record configuration, including record fields and record page layouts.[^6] +- Artifact configuration, including artifact types and custom fields.[^7] +- Custom fields.[^6] +- Governance settings, including validation guidelines, assessment options, regulations, and policies.[^8] +- Document settings, including document types, templates, and checker configuration.[^9] +- Webhook secrets and other integration secrets.[^10] +- Page layouts.[^11] + +## View settings activity + +To review settings activity from the Activity page: + +1. In the left sidebar, click **{{< fa wifi >}} Activity**. + +2. Select **Settings Activity** to view organization-level settings and configuration events. + +3. Review the activity list to see who made each change, when it happened, and which settings area changed. + +To review settings activity from Settings: + +1. In the left sidebar, click **{{< fa gear >}} Settings**. + +2. Open the **Settings Activity Log**. + +3. Review the activity list to audit recent settings and configuration changes. + +## Filter settings activity + +Filter settings activity to narrow the audit trail to the events you need: + +1. Open the settings activity view from the Activity page or Settings activity log. + +2. Click **{{< fa filter >}} Filter**. + +3. Add one or more filter rules, such as category, user, or date range. + +4. Click **Apply Filters**. + +Active filters appear as chips above the activity list. Remove individual chips or clear all filters to return to the full settings activity view. + +## API access + +Customer Admins can retrieve settings activity through the public audit-events API. Non-admin users receive a `403` response. + +Use the public API endpoints at a high level as follows: + +- `POST /organization/audit-events` — Query audit events with filters. +- `/organization/audit-events/filters` — Retrieve available filter options. + +## What's next + +- Use Data Exports to schedule Settings Activity CSV exports to cloud storage.[^12] +- Review model and record audit trails for activity on individual inventory records.[^13] + + + +[^1]: [Manage permissions](/guide/configuration/manage-permissions.qmd) + +[^2]: [Manage users](/guide/configuration/manage-users.qmd) + +[^3]: [Set up your organization](/guide/configuration/set-up-your-organization.qmd) + +[^4]: [Manage groups](/guide/configuration/manage-groups.qmd) + +[^5]: [Configure workflows](/guide/workflows/configure-workflows.qmd) + +[^6]: [Manage inventory fields](/guide/inventory/manage-inventory-fields.qmd) + +[^7]: [Manage artifact fields](/guide/validation/manage-artifact-fields.qmd) + +[^8]: [Customize {{< var validmind.checker >}}](/guide/templates/customize-document-checker.qmd) + +[^9]: [Manage document types](/guide/templates/manage-document-types.qmd) + +[^10]: [Manage secrets](/guide/integrations/manage-secrets.qmd) + +[^11]: [Customize record overview pages](/guide/inventory/customize-record-overview-page.qmd) + +[^12]: [Configure data exports](/guide/integrations/configure-analytics-exports.qmd) + +[^13]: [View record activity](/guide/inventory/view-record-activity.qmd) diff --git a/site/llm/chatbot-product-map-frontend-snapshot.json b/site/llm/chatbot-product-map-frontend-snapshot.json index 4ee66823a..4af1fb714 100644 --- a/site/llm/chatbot-product-map-frontend-snapshot.json +++ b/site/llm/chatbot-product-map-frontend-snapshot.json @@ -1,6 +1,6 @@ { "version": 1, - "generated_at": "2026-05-23T01:45:21.129000+00:00", + "generated_at": "2026-06-02T18:53:33.853540+00:00", "settings": [ { "path": "/settings/profile", @@ -91,7 +91,7 @@ }, { "path": "/settings/integrations/data-exports", - "label": "Analytics Exports", + "label": "Data Exports", "group": "Integrations", "primary_docs": [] }, @@ -125,6 +125,12 @@ "group": "Governance", "primary_docs": [] }, + { + "path": "/settings/risk-tier-templates", + "label": "Risk Tier Templates", + "group": "Governance", + "primary_docs": [] + }, { "path": "/settings/workflows", "label": "Workflows", @@ -143,6 +149,12 @@ "group": "Governance", "primary_docs": [] }, + { + "path": "/settings/documents/general", + "label": "General", + "group": "Documents", + "primary_docs": [] + }, { "path": "/settings/document-types", "label": "Document Types", @@ -193,6 +205,12 @@ "group": "Main navigation", "primary_docs": [] }, + { + "path": "/activity", + "label": "Activity", + "group": "Main navigation", + "primary_docs": [] + }, { "path": "/analytics", "label": "sidebar.analytics", diff --git a/site/llm/chatbot-product-map.md b/site/llm/chatbot-product-map.md index c2abf3e49..fb47cfeb3 100644 --- a/site/llm/chatbot-product-map.md +++ b/site/llm/chatbot-product-map.md @@ -128,6 +128,25 @@ - `/guide/templates/working-with-document-templates.html` - Sections: What's next +#### `/settings/documents/general` — General + +**Docs (related):** + +- `/guide/templates/customize-document-checker.html` + - Sections: Prerequisites; Manage regulations and policies; Manage assessments; Default assessments provided by } cannot be edited, only cloned.; Add or clone assessments; Add or edit assessment questions; Add assessment questions; Edit assessment questions +- `/guide/templates/customize-document-templates.html` + - Sections: Prerequisites; Edit template outlines; Configure assessment options[^4]; Edit YAML templates; Template schema; Troubleshooting YAML templates; Add text blocks to templates; Add text blocks via template outlines +- `/guide/templates/manage-document-templates.html` + - Sections: Prerequisites; View document templates; Edit document template outlines; Swap document templates; View currently applied templates; Swap between templates; Duplicate document templates; Delete document templates +- `/guide/templates/manage-document-types.html` + - Sections: Prerequisites; Add document types; Edit or delete document types; Development, Validation, and Monitoring document types are stock types and cannot be deleted. +- `/guide/templates/manage-documents.html` + - Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents +- `/guide/templates/manage-text-block-library.html` + - Sections: Prerequisites; Add text blocks; Add existing text blocks to library; Duplicate text blocks; Edit text blocks; Delete text blocks + +- *No direct help link in frontend; related docs inferred from keywords.* + ### Organization #### `/settings/email-notifications` — Email Notifications @@ -247,14 +266,12 @@ - *No direct help link in frontend; related docs inferred from keywords.* -#### `/settings/integrations/data-exports` — Analytics Exports +#### `/settings/integrations/data-exports` — Data Exports **Docs (related):** - `/faq/faq-integrations.html` - Sections: Which languages, libraries, and environments do you support?; Currently, we support **Python }** and the most popular AI/ML and data science libraries.; What test ingestion or modeling techniques are supported?; What large language model (LLM) features are offered?; What deployment options are supported by }?; Learn more -- `/faq/faq-reporting.html` - - Sections: What analytic features are offered by }?; Learn more - `/guide/configuration/configure-aws-privatelink.html` - Sections: Prerequisites; VPC service information; Steps; What's next - `/guide/configuration/configure-azure-private-link.html` @@ -263,6 +280,8 @@ - Sections: Prerequisites; VPC service information; Configure your Google Cloud Platform project; Request access from }; Prepare your network for connection; Create an endpoint to connect to }; Steps; Create an endpoint to connect to the } authentication service - `/guide/configuration/customize-your-dashboard.html` - Sections: Prerequisites; Manage dashboards; Add dashboards; Edit or remove dashboards; Manage widgets; Arrange widgets; Add widgets; Remove widgets +- `/guide/configuration/manage-groups.html` + - Sections: Prerequisites; View group details; Add new groups; Remove groups; Add or remove group members - *No direct help link in frontend; related docs inferred from keywords.* @@ -367,6 +386,25 @@ - `/guide/model-validation/manage-validation-guidelines.html` - Note: no matching `.qmd` source found +#### `/settings/risk-tier-templates` — Risk Tier Templates + +**Docs (related):** + +- `/guide/templates/customize-document-checker.html` + - Sections: Prerequisites; Manage regulations and policies; Manage assessments; Default assessments provided by } cannot be edited, only cloned.; Add or clone assessments; Add or edit assessment questions; Add assessment questions; Edit assessment questions +- `/guide/templates/customize-document-templates.html` + - Sections: Prerequisites; Edit template outlines; Configure assessment options[^4]; Edit YAML templates; Template schema; Troubleshooting YAML templates; Add text blocks to templates; Add text blocks via template outlines +- `/guide/templates/manage-document-templates.html` + - Sections: Prerequisites; View document templates; Edit document template outlines; Swap document templates; View currently applied templates; Swap between templates; Duplicate document templates; Delete document templates +- `/guide/templates/manage-document-types.html` + - Sections: Prerequisites; Add document types; Edit or delete document types; Development, Validation, and Monitoring document types are stock types and cannot be deleted. +- `/guide/templates/manage-documents.html` + - Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents +- `/guide/templates/manage-text-block-library.html` + - Sections: Prerequisites; Add text blocks; Add existing text blocks to library; Duplicate text blocks; Edit text blocks; Delete text blocks + +- *No direct help link in frontend; related docs inferred from keywords.* + ### Users & Access #### `/settings/roles` — Roles @@ -488,6 +526,10 @@ ## Main application +#### `/activity` — Activity + +- *No direct help link; content may be covered under scattered guide sections.* + #### `/analytics` — sidebar.analytics **Docs (related):** From b92d116726fe40eca7f61558a941e73caaed1178 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 2 Jun 2026 12:59:11 -0700 Subject: [PATCH 02/41] Integrations analytics WIP --- .../configure-analytics-exports.qmd | 53 +++++++++++-------- .../integrations/managing-integrations.qmd | 2 +- 2 files changed, 31 insertions(+), 24 deletions(-) diff --git a/site/guide/integrations/configure-analytics-exports.qmd b/site/guide/integrations/configure-analytics-exports.qmd index 24ca31981..9bf477f96 100644 --- a/site/guide/integrations/configure-analytics-exports.qmd +++ b/site/guide/integrations/configure-analytics-exports.qmd @@ -2,12 +2,12 @@ # Copyright © 2023-2026 ValidMind Inc. All rights reserved. # Refer to the LICENSE file in the root of this repository for details. # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial -title: "Configure analytics exports" +title: "Configure data exports" date: last-modified --- -Configure scheduled exports of analytics datasets to cloud storage so that BI tools such as Tableau, Snowflake, and Looker can consume models, findings, workflow metrics, and activity events. +Configure scheduled exports of platform datasets to cloud storage so that BI tools such as Tableau, Snowflake, and Looker can consume models, findings, workflow metrics, and activity events. ::: {.attn} @@ -19,26 +19,31 @@ Configure scheduled exports of analytics datasets to cloud storage so that BI to ::: -## Create analytics exports +## Create data exports 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. -3. On the Analytics Exports page, click **{{< fa plus >}} Create Analytics Export**. +3. On the Data Exports page, click **{{< fa plus >}} Create Data Export**. -4. From the **[datasets]{.smallcaps}** dropdown, choose which analytics datasets to export. For example: +4. From the **[datasets]{.smallcaps}** dropdown, choose which datasets to export. For example: - Artifact - Model Limitation - Artifact - Policy Exception - Artifact - Validation Issue - Inventory Models - Model Activity[^4] + - Settings Activity[^5] - Custom artifact types[^3] - Custom inventory record types - ::: {.callout title="Model Activity exports"} - Model Activity exports audit events — comments, workflow status changes, and field updates on inventory records. Use this dataset for compliance reporting or audit trails. Model Activity respects the same export scope (organization, workspace, or selected models) and supports 30, 60, or 90-day window filtering. + ::: {.callout title="Activity exports"} + Model Activity exports audit events — comments, workflow status changes, and field updates on inventory records. Settings Activity exports organization-level settings and configuration changes as CSV files. Use these datasets for compliance reporting or audit trails. + ::: + + ::: {.callout title="Rolling activity windows"} + Activity exports use a rolling window of **Last 30 days**, **Last 60 days**, or **Last 90 days**. A rolling window is required for Settings Activity so scheduled exports do not export the full audit history on each run. ::: ::: {.callout title="Custom types"} @@ -46,7 +51,7 @@ Configure scheduled exports of analytics datasets to cloud storage so that BI to ::: ::: {.column-margin} -![Selecting datasets](configure-analytics-export-step-1.png){fig-alt="Create Analytics Export modal showing Select Datasets step and datasets dropdown." .screenshot} +![Selecting datasets](configure-analytics-export-step-1.png){fig-alt="Create Data Export modal showing Select Datasets step and datasets dropdown." .screenshot} ::: 5. Click **Next** {{< fa angle-right >}}. @@ -63,7 +68,7 @@ Configure scheduled exports of analytics datasets to cloud storage so that BI to ::: {.column-margin} -![Configuring your cloud storage destination](configure-analytics-export-step-2.png){fig-alt="Create Analytics Export modal showing Configure Destination step with cloud storage and output settings." .screenshot} +![Configuring your cloud storage destination](configure-analytics-export-step-2.png){fig-alt="Create Data Export modal showing Configure Destination step with cloud storage and output settings." .screenshot} ::: 7. Click **Next** {{< fa angle-right >}}. @@ -75,10 +80,10 @@ Configure scheduled exports of analytics datasets to cloud storage so that BI to ::: {.column-margin} -![Setting the schedule](configure-analytics-export-step-3.png){fig-alt="Create Analytics Export modal showing Set Schedule step with Daily schedule and 12:00 am UTC." .screenshot} +![Setting the schedule](configure-analytics-export-step-3.png){fig-alt="Create Data Export modal showing Set Schedule step with Daily schedule and 12:00 am UTC." .screenshot} ::: -9. Click **Create Analytics Export**. +9. Click **Create Data Export**. The export is created and runs on the schedule you set. Data is written to the configured path in your cloud storage so BI tools can read it. @@ -86,17 +91,17 @@ The export is created and runs on the schedule you set. Data is written to the c 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. 3. Click the export row you want to view, or hover over it and when the **{{< fa ellipsis-vertical >}}** menu appears, click it and select **{{< fa eye >}} View Details**. You see the destination, schedule, datasets, and next run for that export. -## Edit analytics exports +## Edit data exports 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. 3. Hover over the export you want to change. @@ -104,11 +109,11 @@ You see the destination, schedule, datasets, and next run for that export. 5. In the modal, adjust datasets, destination, or schedule, then click **Save Changes**. -## Run analytics exports manually +## Run data exports manually 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. 3. Hover over the export you want to run. @@ -120,7 +125,7 @@ A one-off export runs in addition to the scheduled runs. The status for a run is 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. 3. Hover over the export you want to inspect. @@ -128,11 +133,11 @@ A one-off export runs in addition to the scheduled runs. The status for a run is You see past runs with their status, start time, duration, rows exported, and files written. -## Disable or enable analytics exports +## Disable or enable data exports 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. 3. Hover over the export you want to pause. @@ -140,15 +145,15 @@ You see past runs with their status, start time, duration, rows exported, and fi The export stops or starts running depending on the action you take. -## Delete analytics export configurations +## Delete data export configurations -::: {.callout-warning title="Deleting an analytics export configuration is permanent."} +::: {.callout-warning title="Deleting a data export configuration is permanent."} Data you exported previously will remain in the cloud storage destination unchanged. ::: 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. 3. Hover over the export you want to remove. @@ -170,3 +175,5 @@ Data you exported previously will remain in the cloud storage destination unchan [^3]: [Manage artifact types](/guide/validation/manage-artifact-types.qmd) [^4]: [View record activity](/guide/inventory/view-record-activity.qmd) + +[^5]: [View settings activity](/guide/reporting/view-settings-activity.qmd) diff --git a/site/guide/integrations/managing-integrations.qmd b/site/guide/integrations/managing-integrations.qmd index 4b251ae34..fdf5f0b31 100644 --- a/site/guide/integrations/managing-integrations.qmd +++ b/site/guide/integrations/managing-integrations.qmd @@ -282,5 +282,5 @@ Connect AI assistants to {{< var vm.product >}} using the Model Context Protocol [^3]: [Introduction to workflows](/guide/workflows/introduction-to-workflows.qmd) [^4]: [Public REST API](/reference/validmind-rest-api-vm.qmd) [^5]: [Testing & documentation](/developer/validmind-library.qmd) -[^6]: [Configure analytics exports](/guide/integrations/configure-analytics-exports.qmd) +[^6]: [Configure data exports](/guide/integrations/configure-analytics-exports.qmd) [^7]: [Connect AI assistants](/guide/mcp/connect-ai-assistants-via-mcp.qmd) \ No newline at end of file From ef3e2fab723a4e5520703505b4cfb67b7d717d2c Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 2 Jun 2026 17:01:20 -0700 Subject: [PATCH 03/41] reverting integrations and LLM changes for now --- .../configure-analytics-exports.qmd | 53 ++++++++----------- .../integrations/managing-integrations.qmd | 2 +- ...chatbot-product-map-frontend-snapshot.json | 22 +------- site/llm/chatbot-product-map.md | 48 ++--------------- 4 files changed, 29 insertions(+), 96 deletions(-) diff --git a/site/guide/integrations/configure-analytics-exports.qmd b/site/guide/integrations/configure-analytics-exports.qmd index 9bf477f96..24ca31981 100644 --- a/site/guide/integrations/configure-analytics-exports.qmd +++ b/site/guide/integrations/configure-analytics-exports.qmd @@ -2,12 +2,12 @@ # Copyright © 2023-2026 ValidMind Inc. All rights reserved. # Refer to the LICENSE file in the root of this repository for details. # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial -title: "Configure data exports" +title: "Configure analytics exports" date: last-modified --- -Configure scheduled exports of platform datasets to cloud storage so that BI tools such as Tableau, Snowflake, and Looker can consume models, findings, workflow metrics, and activity events. +Configure scheduled exports of analytics datasets to cloud storage so that BI tools such as Tableau, Snowflake, and Looker can consume models, findings, workflow metrics, and activity events. ::: {.attn} @@ -19,31 +19,26 @@ Configure scheduled exports of platform datasets to cloud storage so that BI too ::: -## Create data exports +## Create analytics exports 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. -3. On the Data Exports page, click **{{< fa plus >}} Create Data Export**. +3. On the Analytics Exports page, click **{{< fa plus >}} Create Analytics Export**. -4. From the **[datasets]{.smallcaps}** dropdown, choose which datasets to export. For example: +4. From the **[datasets]{.smallcaps}** dropdown, choose which analytics datasets to export. For example: - Artifact - Model Limitation - Artifact - Policy Exception - Artifact - Validation Issue - Inventory Models - Model Activity[^4] - - Settings Activity[^5] - Custom artifact types[^3] - Custom inventory record types - ::: {.callout title="Activity exports"} - Model Activity exports audit events — comments, workflow status changes, and field updates on inventory records. Settings Activity exports organization-level settings and configuration changes as CSV files. Use these datasets for compliance reporting or audit trails. - ::: - - ::: {.callout title="Rolling activity windows"} - Activity exports use a rolling window of **Last 30 days**, **Last 60 days**, or **Last 90 days**. A rolling window is required for Settings Activity so scheduled exports do not export the full audit history on each run. + ::: {.callout title="Model Activity exports"} + Model Activity exports audit events — comments, workflow status changes, and field updates on inventory records. Use this dataset for compliance reporting or audit trails. Model Activity respects the same export scope (organization, workspace, or selected models) and supports 30, 60, or 90-day window filtering. ::: ::: {.callout title="Custom types"} @@ -51,7 +46,7 @@ Configure scheduled exports of platform datasets to cloud storage so that BI too ::: ::: {.column-margin} -![Selecting datasets](configure-analytics-export-step-1.png){fig-alt="Create Data Export modal showing Select Datasets step and datasets dropdown." .screenshot} +![Selecting datasets](configure-analytics-export-step-1.png){fig-alt="Create Analytics Export modal showing Select Datasets step and datasets dropdown." .screenshot} ::: 5. Click **Next** {{< fa angle-right >}}. @@ -68,7 +63,7 @@ Configure scheduled exports of platform datasets to cloud storage so that BI too ::: {.column-margin} -![Configuring your cloud storage destination](configure-analytics-export-step-2.png){fig-alt="Create Data Export modal showing Configure Destination step with cloud storage and output settings." .screenshot} +![Configuring your cloud storage destination](configure-analytics-export-step-2.png){fig-alt="Create Analytics Export modal showing Configure Destination step with cloud storage and output settings." .screenshot} ::: 7. Click **Next** {{< fa angle-right >}}. @@ -80,10 +75,10 @@ Configure scheduled exports of platform datasets to cloud storage so that BI too ::: {.column-margin} -![Setting the schedule](configure-analytics-export-step-3.png){fig-alt="Create Data Export modal showing Set Schedule step with Daily schedule and 12:00 am UTC." .screenshot} +![Setting the schedule](configure-analytics-export-step-3.png){fig-alt="Create Analytics Export modal showing Set Schedule step with Daily schedule and 12:00 am UTC." .screenshot} ::: -9. Click **Create Data Export**. +9. Click **Create Analytics Export**. The export is created and runs on the schedule you set. Data is written to the configured path in your cloud storage so BI tools can read it. @@ -91,17 +86,17 @@ The export is created and runs on the schedule you set. Data is written to the c 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. 3. Click the export row you want to view, or hover over it and when the **{{< fa ellipsis-vertical >}}** menu appears, click it and select **{{< fa eye >}} View Details**. You see the destination, schedule, datasets, and next run for that export. -## Edit data exports +## Edit analytics exports 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. 3. Hover over the export you want to change. @@ -109,11 +104,11 @@ You see the destination, schedule, datasets, and next run for that export. 5. In the modal, adjust datasets, destination, or schedule, then click **Save Changes**. -## Run data exports manually +## Run analytics exports manually 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. 3. Hover over the export you want to run. @@ -125,7 +120,7 @@ A one-off export runs in addition to the scheduled runs. The status for a run is 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. 3. Hover over the export you want to inspect. @@ -133,11 +128,11 @@ A one-off export runs in addition to the scheduled runs. The status for a run is You see past runs with their status, start time, duration, rows exported, and files written. -## Disable or enable data exports +## Disable or enable analytics exports 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. 3. Hover over the export you want to pause. @@ -145,15 +140,15 @@ You see past runs with their status, start time, duration, rows exported, and fi The export stops or starts running depending on the action you take. -## Delete data export configurations +## Delete analytics export configurations -::: {.callout-warning title="Deleting a data export configuration is permanent."} +::: {.callout-warning title="Deleting an analytics export configuration is permanent."} Data you exported previously will remain in the cloud storage destination unchanged. ::: 1. In the left sidebar, click **{{< fa gear >}} Settings**. -2. Under {{< fa puzzle-piece >}} Integrations, select **Data Exports**. +2. Under {{< fa puzzle-piece >}} Integrations, select **Analytics Exports**. 3. Hover over the export you want to remove. @@ -175,5 +170,3 @@ Data you exported previously will remain in the cloud storage destination unchan [^3]: [Manage artifact types](/guide/validation/manage-artifact-types.qmd) [^4]: [View record activity](/guide/inventory/view-record-activity.qmd) - -[^5]: [View settings activity](/guide/reporting/view-settings-activity.qmd) diff --git a/site/guide/integrations/managing-integrations.qmd b/site/guide/integrations/managing-integrations.qmd index fdf5f0b31..4b251ae34 100644 --- a/site/guide/integrations/managing-integrations.qmd +++ b/site/guide/integrations/managing-integrations.qmd @@ -282,5 +282,5 @@ Connect AI assistants to {{< var vm.product >}} using the Model Context Protocol [^3]: [Introduction to workflows](/guide/workflows/introduction-to-workflows.qmd) [^4]: [Public REST API](/reference/validmind-rest-api-vm.qmd) [^5]: [Testing & documentation](/developer/validmind-library.qmd) -[^6]: [Configure data exports](/guide/integrations/configure-analytics-exports.qmd) +[^6]: [Configure analytics exports](/guide/integrations/configure-analytics-exports.qmd) [^7]: [Connect AI assistants](/guide/mcp/connect-ai-assistants-via-mcp.qmd) \ No newline at end of file diff --git a/site/llm/chatbot-product-map-frontend-snapshot.json b/site/llm/chatbot-product-map-frontend-snapshot.json index 4af1fb714..4ee66823a 100644 --- a/site/llm/chatbot-product-map-frontend-snapshot.json +++ b/site/llm/chatbot-product-map-frontend-snapshot.json @@ -1,6 +1,6 @@ { "version": 1, - "generated_at": "2026-06-02T18:53:33.853540+00:00", + "generated_at": "2026-05-23T01:45:21.129000+00:00", "settings": [ { "path": "/settings/profile", @@ -91,7 +91,7 @@ }, { "path": "/settings/integrations/data-exports", - "label": "Data Exports", + "label": "Analytics Exports", "group": "Integrations", "primary_docs": [] }, @@ -125,12 +125,6 @@ "group": "Governance", "primary_docs": [] }, - { - "path": "/settings/risk-tier-templates", - "label": "Risk Tier Templates", - "group": "Governance", - "primary_docs": [] - }, { "path": "/settings/workflows", "label": "Workflows", @@ -149,12 +143,6 @@ "group": "Governance", "primary_docs": [] }, - { - "path": "/settings/documents/general", - "label": "General", - "group": "Documents", - "primary_docs": [] - }, { "path": "/settings/document-types", "label": "Document Types", @@ -205,12 +193,6 @@ "group": "Main navigation", "primary_docs": [] }, - { - "path": "/activity", - "label": "Activity", - "group": "Main navigation", - "primary_docs": [] - }, { "path": "/analytics", "label": "sidebar.analytics", diff --git a/site/llm/chatbot-product-map.md b/site/llm/chatbot-product-map.md index fb47cfeb3..c2abf3e49 100644 --- a/site/llm/chatbot-product-map.md +++ b/site/llm/chatbot-product-map.md @@ -128,25 +128,6 @@ - `/guide/templates/working-with-document-templates.html` - Sections: What's next -#### `/settings/documents/general` — General - -**Docs (related):** - -- `/guide/templates/customize-document-checker.html` - - Sections: Prerequisites; Manage regulations and policies; Manage assessments; Default assessments provided by } cannot be edited, only cloned.; Add or clone assessments; Add or edit assessment questions; Add assessment questions; Edit assessment questions -- `/guide/templates/customize-document-templates.html` - - Sections: Prerequisites; Edit template outlines; Configure assessment options[^4]; Edit YAML templates; Template schema; Troubleshooting YAML templates; Add text blocks to templates; Add text blocks via template outlines -- `/guide/templates/manage-document-templates.html` - - Sections: Prerequisites; View document templates; Edit document template outlines; Swap document templates; View currently applied templates; Swap between templates; Duplicate document templates; Delete document templates -- `/guide/templates/manage-document-types.html` - - Sections: Prerequisites; Add document types; Edit or delete document types; Development, Validation, and Monitoring document types are stock types and cannot be deleted. -- `/guide/templates/manage-documents.html` - - Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents -- `/guide/templates/manage-text-block-library.html` - - Sections: Prerequisites; Add text blocks; Add existing text blocks to library; Duplicate text blocks; Edit text blocks; Delete text blocks - -- *No direct help link in frontend; related docs inferred from keywords.* - ### Organization #### `/settings/email-notifications` — Email Notifications @@ -266,12 +247,14 @@ - *No direct help link in frontend; related docs inferred from keywords.* -#### `/settings/integrations/data-exports` — Data Exports +#### `/settings/integrations/data-exports` — Analytics Exports **Docs (related):** - `/faq/faq-integrations.html` - Sections: Which languages, libraries, and environments do you support?; Currently, we support **Python }** and the most popular AI/ML and data science libraries.; What test ingestion or modeling techniques are supported?; What large language model (LLM) features are offered?; What deployment options are supported by }?; Learn more +- `/faq/faq-reporting.html` + - Sections: What analytic features are offered by }?; Learn more - `/guide/configuration/configure-aws-privatelink.html` - Sections: Prerequisites; VPC service information; Steps; What's next - `/guide/configuration/configure-azure-private-link.html` @@ -280,8 +263,6 @@ - Sections: Prerequisites; VPC service information; Configure your Google Cloud Platform project; Request access from }; Prepare your network for connection; Create an endpoint to connect to }; Steps; Create an endpoint to connect to the } authentication service - `/guide/configuration/customize-your-dashboard.html` - Sections: Prerequisites; Manage dashboards; Add dashboards; Edit or remove dashboards; Manage widgets; Arrange widgets; Add widgets; Remove widgets -- `/guide/configuration/manage-groups.html` - - Sections: Prerequisites; View group details; Add new groups; Remove groups; Add or remove group members - *No direct help link in frontend; related docs inferred from keywords.* @@ -386,25 +367,6 @@ - `/guide/model-validation/manage-validation-guidelines.html` - Note: no matching `.qmd` source found -#### `/settings/risk-tier-templates` — Risk Tier Templates - -**Docs (related):** - -- `/guide/templates/customize-document-checker.html` - - Sections: Prerequisites; Manage regulations and policies; Manage assessments; Default assessments provided by } cannot be edited, only cloned.; Add or clone assessments; Add or edit assessment questions; Add assessment questions; Edit assessment questions -- `/guide/templates/customize-document-templates.html` - - Sections: Prerequisites; Edit template outlines; Configure assessment options[^4]; Edit YAML templates; Template schema; Troubleshooting YAML templates; Add text blocks to templates; Add text blocks via template outlines -- `/guide/templates/manage-document-templates.html` - - Sections: Prerequisites; View document templates; Edit document template outlines; Swap document templates; View currently applied templates; Swap between templates; Duplicate document templates; Delete document templates -- `/guide/templates/manage-document-types.html` - - Sections: Prerequisites; Add document types; Edit or delete document types; Development, Validation, and Monitoring document types are stock types and cannot be deleted. -- `/guide/templates/manage-documents.html` - - Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents -- `/guide/templates/manage-text-block-library.html` - - Sections: Prerequisites; Add text blocks; Add existing text blocks to library; Duplicate text blocks; Edit text blocks; Delete text blocks - -- *No direct help link in frontend; related docs inferred from keywords.* - ### Users & Access #### `/settings/roles` — Roles @@ -526,10 +488,6 @@ ## Main application -#### `/activity` — Activity - -- *No direct help link; content may be covered under scattered guide sections.* - #### `/analytics` — sidebar.analytics **Docs (related):** From c66584d52827157b1718b55cb8cc9261ae8f65b4 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 3 Jun 2026 12:48:08 -0700 Subject: [PATCH 04/41] wip --- site/faq/faq-collaboration.qmd | 2 +- site/faq/faq-privacy.qmd | 2 +- site/guide/_sidebar.yaml | 6 ++-- .../manage-platform-notifications.qmd | 5 +++ site/guide/configuration/managing-users.qmd | 9 ++++-- .../managing-your-organization.qmd | 6 ++++ .../working-with-documentation.qmd | 4 +++ site/guide/guides.qmd | 5 ++- .../inventory/managing-the-inventory.qmd | 4 +-- site/guide/inventory/view-record-activity.qmd | 3 +- .../tracking-organization-activity.qmd | 2 ++ .../reporting/view-settings-activity.qmd | 4 +-- .../reporting/working-with-analytics.qmd | 32 ++++++++++--------- site/guide/validation/managing-validation.qmd | 4 +-- .../preparing-validation-reports.qmd | 1 + 15 files changed, 53 insertions(+), 36 deletions(-) diff --git a/site/faq/faq-collaboration.qmd b/site/faq/faq-collaboration.qmd index 76e1dd16a..7dba26785 100644 --- a/site/faq/faq-collaboration.qmd +++ b/site/faq/faq-collaboration.qmd @@ -12,7 +12,7 @@ listing: sort: false fields: [title, description] contents: - - ../guide/inventory/view-record-activity.qmd + - ../guide/reporting/tracking-organization-activity.qmd - ../guide/documentation/collaborate-with-others.qmd - ../guide/workflows/working-with-workflows.qmd categories: ["real-time collaboration", "documents","documentation", "record activity", "auditing", "workflows", "lifecycle", "validmind platform"] diff --git a/site/faq/faq-privacy.qmd b/site/faq/faq-privacy.qmd index b2468c25e..8163a1969 100644 --- a/site/faq/faq-privacy.qmd +++ b/site/faq/faq-privacy.qmd @@ -18,7 +18,7 @@ listing: contents: - ../about/deployment/deployment-options.qmd - ../about/fine-print/data-privacy-policy.qmd - - ../guide/inventory/view-record-activity.qmd + - ../guide/reporting/tracking-organization-activity.qmd categories: ["data handling", "privacy", "confidentiality", "record activity", "auditing", "validmind platform", "validmind library"] --- diff --git a/site/guide/_sidebar.yaml b/site/guide/_sidebar.yaml index 6c055d831..244ae944e 100644 --- a/site/guide/_sidebar.yaml +++ b/site/guide/_sidebar.yaml @@ -33,11 +33,11 @@ website: - guide/configuration/manage-roles.qmd - guide/configuration/manage-record-stakeholder-types.qmd - guide/configuration/manage-permissions.qmd + - guide/configuration/manage-platform-notifications.qmd - file: guide/configuration/personalizing-validmind.qmd contents: - guide/configuration/manage-your-profile.qmd - guide/configuration/customize-your-dashboard.qmd - - guide/configuration/manage-platform-notifications.qmd - text: "---" - section: "Integrations" contents: @@ -96,7 +96,6 @@ website: contents: - guide/inventory/configure-record-interdependencies.qmd - guide/inventory/archive-delete-records.qmd - - guide/inventory/view-record-activity.qmd - text: "---" - section: "Documents & templates" contents: @@ -161,8 +160,7 @@ website: contents: - file: guide/reporting/tracking-organization-activity.qmd contents: - - text: "View model activity" - file: guide/inventory/view-record-activity.qmd + - guide/inventory/view-record-activity.qmd - guide/reporting/view-settings-activity.qmd - file: guide/reporting/working-with-analytics.qmd contents: diff --git a/site/guide/configuration/manage-platform-notifications.qmd b/site/guide/configuration/manage-platform-notifications.qmd index d4c0bc62d..380e63469 100644 --- a/site/guide/configuration/manage-platform-notifications.qmd +++ b/site/guide/configuration/manage-platform-notifications.qmd @@ -118,6 +118,11 @@ Enable or disable email notifications for specific events across your organizati 4. Click **Save Changes** to apply your adjustments. +## What's next + +- [Tracking organization activity](/guide/reporting/tracking-organization-activity.qmd) +- [Personalizing {{< var vm.product >}}](/guide/configuration/personalizing-validmind.qmd) + diff --git a/site/guide/configuration/managing-users.qmd b/site/guide/configuration/managing-users.qmd index 52bed3a7f..117cd9120 100644 --- a/site/guide/configuration/managing-users.qmd +++ b/site/guide/configuration/managing-users.qmd @@ -5,7 +5,7 @@ title: "Managing users" date: last-modified listing: - - id: whats-next-listing + - id: user-management type: grid grid-columns: 3 max-description-length: 250 @@ -67,5 +67,10 @@ customer admin ## User management -:::{#whats-next-listing} +:::{#user-management} ::: + +## What's next + +- [Personalizing {{< var vm.product >}}](/guide/configuration/personalizing-validmind.qmd) +- [Manage platform notifications](/guide/configuration/manage-platform-notifications.qmd) \ No newline at end of file diff --git a/site/guide/configuration/managing-your-organization.qmd b/site/guide/configuration/managing-your-organization.qmd index 32813a5e1..5d9dcc88b 100644 --- a/site/guide/configuration/managing-your-organization.qmd +++ b/site/guide/configuration/managing-your-organization.qmd @@ -101,6 +101,12 @@ Enabling tracked changes by default for your organization will disable the follo :::{#guides-configuration} ::: +## What's next + +- [Managing users](/guide/configuration/managing-users.qmd) +- [Manage platform notifications](/guide/configuration/manage-platform-notifications.qmd) + + [^1]: Your active organization is displayed under your name and roles in the left sidebar. diff --git a/site/guide/documentation/working-with-documentation.qmd b/site/guide/documentation/working-with-documentation.qmd index d5ada7455..4d88f1afc 100644 --- a/site/guide/documentation/working-with-documentation.qmd +++ b/site/guide/documentation/working-with-documentation.qmd @@ -124,6 +124,10 @@ Wrap up your documentation, collaborate with other stakeholders, and submit your :::{#complete-documentation} ::: +## What's next + +- [Ongoing monitoring](/guide/monitoring/ongoing-monitoring.qmd) + diff --git a/site/guide/guides.qmd b/site/guide/guides.qmd index 91eeeeb82..b55fe63e5 100644 --- a/site/guide/guides.qmd +++ b/site/guide/guides.qmd @@ -25,8 +25,8 @@ listing: contents: - configuration/managing-your-organization.qmd - configuration/managing-users.qmd - - configuration/personalizing-validmind.qmd - configuration/manage-platform-notifications.qmd + - configuration/personalizing-validmind.qmd - id: integrations type: grid max-description-length: 250 @@ -45,7 +45,7 @@ listing: - workflows/working-with-workflows.qmd - id: inventory type: grid - grid-columns: 2 + grid-columns: 3 max-description-length: 250 sort: false fields: [title, description] @@ -53,7 +53,6 @@ listing: - inventory/setting-up-the-inventory.qmd - inventory/working-with-the-inventory.qmd - inventory/managing-the-inventory.qmd - - inventory/view-record-activity.qmd - id: documents-templates type: grid grid-columns: 2 diff --git a/site/guide/inventory/managing-the-inventory.qmd b/site/guide/inventory/managing-the-inventory.qmd index b5095eebb..171dac946 100644 --- a/site/guide/inventory/managing-the-inventory.qmd +++ b/site/guide/inventory/managing-the-inventory.qmd @@ -23,9 +23,7 @@ Once you're familiar with the {{< var vm.product >}} inventory, dive into more a :::{#inventory-management} ::: -
    - ## What's next -- [Working with the inventory](working-with-the-inventory.qmd) - [View record activity](view-record-activity.qmd) +- [Working with documentation](/guide/documentation/working-with-documentation.qmd) diff --git a/site/guide/inventory/view-record-activity.qmd b/site/guide/inventory/view-record-activity.qmd index b710fd595..4d2f1bef5 100644 --- a/site/guide/inventory/view-record-activity.qmd +++ b/site/guide/inventory/view-record-activity.qmd @@ -10,8 +10,7 @@ aliases: - /guide/model-inventory/view-model-activity.html --- -Use the audit trail functionality in the {{< var validmind.platform >}} to track or audit all the information events associated with a specific record. See a record of comments, workflow status changes, and updates made to the record. - +Track information events associated with a specific record. Audit a history of updates made to the record, including stage or workflow transitions, updates on associated documents, activity on logged artifacts, and more. ::: {.attn} diff --git a/site/guide/reporting/tracking-organization-activity.qmd b/site/guide/reporting/tracking-organization-activity.qmd index 7c867850d..4f3160a54 100644 --- a/site/guide/reporting/tracking-organization-activity.qmd +++ b/site/guide/reporting/tracking-organization-activity.qmd @@ -16,5 +16,7 @@ listing: - view-settings-activity.qmd --- +Review activity logs for your organization relating to records and settings configurations. + ::::{#activity} :::: \ No newline at end of file diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index f64c5f1f1..73c2029d2 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -6,7 +6,7 @@ title: "View settings activity" date: last-modified --- -Use settings activity to audit organization-level settings and configuration changes made by admins in the {{< var validmind.platform >}}. +Track information events associated with updates made to your organization's settings and configurations. Audit a history of administrative updates made on your organization, including changes to roles and permissions, users and user invitations, setup options, display layouts, and more. :::: {.attn} @@ -21,7 +21,7 @@ Use settings activity to audit organization-level settings and configuration cha Settings activity records changes to admin-managed configuration across your organization. Use it to review who changed a setting, when the change happened, and which settings area was affected. -Settings activity can include changes to: +Settings activity includes changes to: - Roles and permissions.[^1] - Users and invites.[^2] diff --git a/site/guide/reporting/working-with-analytics.qmd b/site/guide/reporting/working-with-analytics.qmd index b1f644060..a8e567fd5 100644 --- a/site/guide/reporting/working-with-analytics.qmd +++ b/site/guide/reporting/working-with-analytics.qmd @@ -20,9 +20,11 @@ listing: - export-analytics.qmd --- -Analytical reports allow you to gain quick insights into your validation efforts. Reports detail critical artifacts, risk exposure, and compliance status to ensure effective oversight and management of risk. +View, manage, and export analytics that allow you to gain quick insights into your risk governance efforts across your organization. -#### Typical uses for reports +## Typical uses for reports + +Reports detail critical artifacts, risk exposure, and compliance status to ensure effective oversight and management of risk. - **Executive summaries** — Gain a high-level overview of validation activities, focusing on critical artifacts and risk exposure. - **Record (model) tracking** — Get quick insights into each record's validation artifacts, highlighting any major issues or deficiencies. @@ -32,17 +34,7 @@ Analytical reports allow you to gain quick insights into your validation efforts Reports are primarily designed for validators or Chief Risk Officers (CROs) who require an understanding of artifacts related to validation activities. -::: {.attn} - -## Prerequisites - -- [x] {{< var link.login >}} -- [x] There are records registered in the inventory.[^1] -- [x] There are Validation Issues logged on any record.[^2] - -::: - -## Default report pages +### Default report pages The {{< var validmind.platform >}} provides two default report pages to get you started with analytics: @@ -56,7 +48,7 @@ The {{< var validmind.platform >}} provides two default report pages to get you ::: ::: {.w-40-ns} -2. **Validation Issues** +2. **Validation Issues** ![Analytics `Validation Issues` report page](analytics-validation_issues.png){fig-alt="Screenshot of the Analytics `Validation Issues` report page" .screenshot group="reports"} @@ -78,7 +70,7 @@ Total Models : Count of your total models registered with {{< var vm.product >}}. Number of Models by Business Unit and Tier -: Count of your model volume by business unit[^4] further broken down by risk tier. Assists you in pinpointing where in the organization resources should be concentrated on, along with the materiality of risk that unit may pose based on the models they are responsible for. +: Count of your model volume by business unit[^4] further broken down by risk tier. Assists you in pinpointing where in the organization resources should be concentrated on, along with the materiality of risk that unit may pose based on the models they are responsible for. Number of Models by Stage and Business Unit : Count of your model volume by stage[^5] further broken down by business unit. Assists you in pinpointing where in the organization resources may need to be focused, based on the progress and severity of artifacts. @@ -107,6 +99,16 @@ Validation Issues by Model ::: +::: {.attn} + +## Prerequisites + +- [x] {{< var link.login >}} +- [x] There are records registered in the inventory.[^1] +- [x] There are Validation Issues logged on any record.[^2] + +::: + ## Arrange report widgets :::{.callout-important title="Changes are automatically saved and will affect all other users in your organization."} diff --git a/site/guide/validation/managing-validation.qmd b/site/guide/validation/managing-validation.qmd index c42a5853b..d572dfc28 100644 --- a/site/guide/validation/managing-validation.qmd +++ b/site/guide/validation/managing-validation.qmd @@ -40,9 +40,7 @@ Enable your organization to use the {{< var validmind.platform >}} for validatio :::{#artifacts} ::: -
    - ## What's next -- [Working with artifacts](working-with-artifacts.qmd) - [Preparing validation reports](preparing-validation-reports.qmd) +- [Working with artifacts](working-with-artifacts.qmd) diff --git a/site/guide/validation/preparing-validation-reports.qmd b/site/guide/validation/preparing-validation-reports.qmd index df1dc864e..bcdee8ae3 100644 --- a/site/guide/validation/preparing-validation-reports.qmd +++ b/site/guide/validation/preparing-validation-reports.qmd @@ -137,6 +137,7 @@ Wrap up your report, collaborate with other stakeholders, and submit your work f ## What's next - [Working with artifacts](working-with-artifacts.qmd) +- [Working with analytics](/guide/reporting/working-with-analytics.qmd) From 69527a41e939d8f3e6928eb52bd9ae58da7abffd Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Wed, 3 Jun 2026 13:14:34 -0700 Subject: [PATCH 05/41] Save --- .../_view-record-activity-overview.qmd | 2 +- .../reporting/view-settings-activity.qmd | 67 +++++-------------- 2 files changed, 18 insertions(+), 51 deletions(-) diff --git a/site/guide/inventory/_view-record-activity-overview.qmd b/site/guide/inventory/_view-record-activity-overview.qmd index a238d728b..df3cd33d7 100644 --- a/site/guide/inventory/_view-record-activity-overview.qmd +++ b/site/guide/inventory/_view-record-activity-overview.qmd @@ -2,7 +2,7 @@ Refer to the LICENSE file in the root of this repository for details. SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial --> -The **{{< fa wifi >}} Activity** page shows a record of activities for the selected inventory record, including actions performed by users in your organization, activity from automated workflows, and updates generated via the {{< var validmind.developer >}}, such as: +The record **{{< fa wifi >}} Activity** page shows a history of activities for the selected inventory record, including actions performed by users in your organization, activity from automated workflows, and updates generated via the {{< var validmind.developer >}}, such as: :::: {.content-visible unless-format="revealjs"} - Fields updated^[[Edit inventory fields](/guide/inventory/edit-inventory-fields.qmd)] diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index 73c2029d2..2c02b3917 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -6,7 +6,7 @@ title: "View settings activity" date: last-modified --- -Track information events associated with updates made to your organization's settings and configurations. Audit a history of administrative updates made on your organization, including changes to roles and permissions, users and user invitations, setup options, display layouts, and more. +Track information events associated with your organization's settings. Audit a history of administrative updates made on your organization, including changes to users and permissions, workflows and integrations, customization options, and more. :::: {.attn} @@ -17,11 +17,9 @@ Track information events associated with updates made to your organization's set :::: -## About settings activity - -Settings activity records changes to admin-managed configuration across your organization. Use it to review who changed a setting, when the change happened, and which settings area was affected. +## View settings activity -Settings activity includes changes to: +The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as: - Roles and permissions.[^1] - Users and invites.[^2] @@ -36,57 +34,28 @@ Settings activity includes changes to: - Webhook secrets and other integration secrets.[^10] - Page layouts.[^11] -## View settings activity - -To review settings activity from the Activity page: +To review settings activity for your organization: 1. In the left sidebar, click **{{< fa wifi >}} Activity**. -2. Select **Settings Activity** to view organization-level settings and configuration events. - -3. Review the activity list to see who made each change, when it happened, and which settings area changed. - -To review settings activity from Settings: - -1. In the left sidebar, click **{{< fa gear >}} Settings**. - -2. Open the **Settings Activity Log**. - -3. Review the activity list to audit recent settings and configuration changes. - -## Filter settings activity - -Filter settings activity to narrow the audit trail to the events you need: - -1. Open the settings activity view from the Activity page or Settings activity log. - -2. Click **{{< fa filter >}} Filter**. - -3. Add one or more filter rules, such as category, user, or date range. - -4. Click **Apply Filters**. - -Active filters appear as chips above the activity list. Remove individual chips or clear all filters to return to the full settings activity view. - -## API access - -Customer Admins can retrieve settings activity through the public audit-events API. Non-admin users receive a `403` response. - -Use the public API endpoints at a high level as follows: - -- `POST /organization/audit-events` — Query audit events with filters. -- `/organization/audit-events/filters` — Retrieve available filter options. - -## What's next +2. Select the **Settings Activity** tab. To narrow down settings activity by category, click on: -- Use Data Exports to schedule Settings Activity CSV exports to cloud storage.[^12] -- Review model and record audit trails for activity on individual inventory records.[^13] + - **All** to view all settings activity. + - **Page Layouts** to view activity related to page layout customizations.^ + - **Roles** to view activity related to role assignments and permissions.^ + - **Users** to view activity related to user account management and invitations.^ + - **Organization Settings** to view activity related to organization-level settings and configuration.^ + - **Groups and Business Units** to view activity related to group and business unit management.^ + - **Workflows** to view activity related to workflow configuration and execution.^ + - **Record Configuration** to view activity related to record field and layout customizations.^ + - **Artifact Configuration** to view activity related to artifact field and layout customizations.^ + - **Custom Fields** to view activity related to custom field management and usage.^ [^1]: [Manage permissions](/guide/configuration/manage-permissions.qmd) -[^2]: [Manage users](/guide/configuration/manage-users.qmd) + -[^12]: [Configure data exports](/guide/integrations/configure-analytics-exports.qmd) -[^13]: [View record activity](/guide/inventory/view-record-activity.qmd) From 37c6eb350fc745a6b20e033288d00c2ea17453d9 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Thu, 4 Jun 2026 11:34:34 -0700 Subject: [PATCH 06/41] Save --- site/guide/inventory/view-record-activity.qmd | 6 +- .../reporting/view-settings-activity.qmd | 58 ++++++++++++------- 2 files changed, 40 insertions(+), 24 deletions(-) diff --git a/site/guide/inventory/view-record-activity.qmd b/site/guide/inventory/view-record-activity.qmd index 4d2f1bef5..49f336589 100644 --- a/site/guide/inventory/view-record-activity.qmd +++ b/site/guide/inventory/view-record-activity.qmd @@ -12,6 +12,10 @@ aliases: Track information events associated with a specific record. Audit a history of updates made to the record, including stage or workflow transitions, updates on associated documents, activity on logged artifacts, and more. +## What is tracked on record activity? + +{{< include _view-record-activity-overview.qmd >}} + ::: {.attn} ## Prerequisites @@ -23,8 +27,6 @@ Track information events associated with a specific record. Audit a history of u ## View record activity -{{< include _view-record-activity-overview.qmd >}} - {{< include _view-record-activity-steps.qmd >}} ### View individual field activity diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index 2c02b3917..ca13d13af 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -8,6 +8,23 @@ date: last-modified Track information events associated with your organization's settings. Audit a history of administrative updates made on your organization, including changes to users and permissions, workflows and integrations, customization options, and more. +## What is tracked on settings activity? + +The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as:* + +- Roles and permissions. +- Users and invites. +- Organization settings. +- Groups and business units. +- Workflows. +- Record configuration, including record fields and record page layouts. +- Artifact configuration, including artifact types and custom fields. +- Custom fields. +- Governance settings, including validation guidelines, assessment options, regulations, and policies. +- Document settings, including document types, templates, and checker configuration. +- Webhook secrets and other integration secrets. +- Page layouts. + :::: {.attn} ## Prerequisites @@ -19,37 +36,34 @@ Track information events associated with your organization's settings. Audit a h ## View settings activity -The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as: - -- Roles and permissions.[^1] -- Users and invites.[^2] -- Organization settings.[^3] -- Groups and business units.[^4] -- Workflows.[^5] -- Record configuration, including record fields and record page layouts.[^6] -- Artifact configuration, including artifact types and custom fields.[^7] -- Custom fields.[^6] -- Governance settings, including validation guidelines, assessment options, regulations, and policies.[^8] -- Document settings, including document types, templates, and checker configuration.[^9] -- Webhook secrets and other integration secrets.[^10] -- Page layouts.[^11] - To review settings activity for your organization: 1. In the left sidebar, click **{{< fa wifi >}} Activity**. -2. Select the **Settings Activity** tab. To narrow down settings activity by category, click on: +2. Select the **Settings Activity** tab. + +3. To narrow down settings activity by category, click: - - **All** to view all settings activity. - - **Page Layouts** to view activity related to page layout customizations.^ - - **Roles** to view activity related to role assignments and permissions.^ - - **Users** to view activity related to user account management and invitations.^ - - **Organization Settings** to view activity related to organization-level settings and configuration.^ + - **All** to view all activity. + - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types, severities, and fields. + - **Business Units** to view activity related to business unit creation, updates, and deletions. + - **Documents** to view activity related to creation of, updates to, and deletions of document types, templates, and library blocks. + - **Groups** to view activity related to group creation, updates, deletions, and membership changes. + - **Inventory Record Types** to view activity related to inventory record type creation, updates, and deletions. + - **Organization Settings** to view activity related to authentication configuration, email notifications, and general settings for documents. + - **Permissions** to view activity related to permission changes on roles. + - **Roles** to view activity related to role creation, updates, deletions, and user assignments. + - **Users** to view activity related to user information updates and user invitations. + + + + To view more details about specific activity, click {{< fa chevron-down >}} when the option is available on an entry. + From 5a749db1ef0f51893b01fc5eeaa46f74e195ea74 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 12:55:00 -0700 Subject: [PATCH 07/41] save --- .../reporting/view-settings-activity.qmd | 42 ++++++++----------- 1 file changed, 18 insertions(+), 24 deletions(-) diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index ca13d13af..fdd027809 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -10,20 +10,17 @@ Track information events associated with your organization's settings. Audit a h ## What is tracked on settings activity? -The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as:* - -- Roles and permissions. -- Users and invites. -- Organization settings. -- Groups and business units. -- Workflows. -- Record configuration, including record fields and record page layouts. -- Artifact configuration, including artifact types and custom fields. -- Custom fields. -- Governance settings, including validation guidelines, assessment options, regulations, and policies. -- Document settings, including document types, templates, and checker configuration. -- Webhook secrets and other integration secrets. -- Page layouts. +The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as activity related to: + +- Organization settings, including business units, authentication configuration, email notifications, and general settings for documents +- Groups and group membership, users and invitations, permissions and roles +- Inventory record settings, including inventory record types and record fields +- Artifact settings, including artifact types, severities, and fields +- Document settings, including document types, templates, and library blocks +- Governance settings, including regulations, policies, validation guidelines, risk areas, and assessment questions +- Workflow settings, including workflow creations, publications, and deletions +- Integration settings, including integration secret creation, revocation, and deletion +- Webhook settings, including webhook secret creation, revocation, and deletion :::: {.attn} @@ -48,19 +45,16 @@ To review settings activity for your organization: - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types, severities, and fields. - **Business Units** to view activity related to business unit creation, updates, and deletions. - **Documents** to view activity related to creation of, updates to, and deletions of document types, templates, and library blocks. - - **Groups** to view activity related to group creation, updates, deletions, and membership changes. - - **Inventory Record Types** to view activity related to inventory record type creation, updates, and deletions. + - **Governance** to view activity related to creation of, updates to, and deletions of regulations, policies, validation guidelines, risk areas, and assessment questions.* + - **Groups** to view activity related to group creation, updates, deletions, and group membership changes. + - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - **Organization Settings** to view activity related to authentication configuration, email notifications, and general settings for documents. + - **Inventory Records** to view activity related to inventory record type or record field creation, updates, and deletions. - **Permissions** to view activity related to permission changes on roles. - - **Roles** to view activity related to role creation, updates, deletions, and user assignments. + - **Roles** to view activity related to role creation, updates, deletions, and user role assignments. - **Users** to view activity related to user information updates and user invitations. - - + - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion. + - **Workflows** to view activity related to workflow creation, publications, and deletions. To view more details about specific activity, click {{< fa chevron-down >}} when the option is available on an entry. From 36edee020e105a2f69017b697760800d1e368c47 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:14:01 -0700 Subject: [PATCH 08/41] save --- .../managing-your-organization.qmd | 35 ---------------- .../templates/working-with-documents.qmd | 40 ++++++++++++++++++- 2 files changed, 39 insertions(+), 36 deletions(-) diff --git a/site/guide/configuration/managing-your-organization.qmd b/site/guide/configuration/managing-your-organization.qmd index 5d9dcc88b..751a77089 100644 --- a/site/guide/configuration/managing-your-organization.qmd +++ b/site/guide/configuration/managing-your-organization.qmd @@ -64,38 +64,6 @@ By default, the [{{< fa hand >}} Customer Admin]{.bubble} role has these permiss 3. Under **Organization Name**, type in your new name and click **Update** to save your changes. -## Manage document defaults - -::: {.panel-tabset} - -### Tracked changes - -::: {.callout-important} -Enabling tracked changes by default for your organization will disable the following within simple text blocks:[^5] - -- The ability to reference field values in the form of variables -- Content draft generation with AI - -::: - -1. In the left sidebar, click **{{< fa gear >}} Settings**. - -2. Under {{< fa building >}} Organization, select **Organization**. - -3. Under Document Defaults, toggle whether you would like to **Enable Track Changes by default**[^6] for all content blocks within documents for your current active organization. - -### Numbered table and figure captions - -1. In the left sidebar, click **{{< fa gear >}} Settings**. - -2. Under {{< fa building >}} Organization, select **Organization**. - -3. Under Document Defaults, toggle whether you would like to **Enable numbered table and figure captions** for test results inserted within documents[^7] for your current active organization. - - -::: - - ## Organization setup :::{#guides-configuration} @@ -120,8 +88,5 @@ Enabling tracked changes by default for your organization will disable the follo [^4]: [Manage permissions](/guide/configuration/manage-permissions.qmd) -[^5]: [Work with content blocks](/guide/documentation/work-with-content-blocks.qmd#add-content-blocks) -[^6]: [Collaborate with others](/guide/documentation/collaborate-with-others.qmd#tracking-changes) -[^7]: [Work with test results](/guide/documentation/work-with-test-results.qmd) diff --git a/site/guide/templates/working-with-documents.qmd b/site/guide/templates/working-with-documents.qmd index e598a9a23..70a63077c 100644 --- a/site/guide/templates/working-with-documents.qmd +++ b/site/guide/templates/working-with-documents.qmd @@ -20,8 +20,37 @@ aliases: Set up document types, categories of record-related content, for use on records across your organization, and the regulations and policies that oversee your documents within the {{< var validmind.platform >}}. +## Manage document defaults -:::{#documents} +To change the default settings for documents within your organization: + +1. In the left sidebar, click **{{< fa gear >}} Settings**. + +2. Under {{< fa file >}} Documents, select **General** to enable or disable tracked changes by default or numbered table and figure captions: + + :::: {.panel-tabset} + + ### Tracked changes + + ::: {.callout-important} + Enabling tracked changes by default for your organization will disable the following within simple text blocks:[^1] + + - The ability to reference field values in the form of variables + - Content draft generation with AI + + ::: + + Under Document Defaults, toggle whether you would like to **Enable Track Changes by default**[^2] for all content blocks within documents for your current active organization. + + ### Numbered table and figure captions + + Under Document Defaults, toggle whether you would like to **Enable numbered table and figure captions** for test results inserted within documents[^3] for your current active organization. + + :::: + +## Work with documents + +::: {#documents} :::
    @@ -31,3 +60,12 @@ Set up document types, categories of record-related content, for use on records - [Working with document templates](working-with-document-templates.qmd) - [Manage text block library](manage-text-block-library.qmd) - [Customize {{< var validmind.checker >}}](customize-document-checker.qmd) + + + + +[^1]: [Work with content blocks](/guide/documentation/work-with-content-blocks.qmd#add-content-blocks) + +[^2]: [Collaborate with others](/guide/documentation/collaborate-with-others.qmd#tracking-changes) + +[^3]: [Work with test results](/guide/documentation/work-with-test-results.qmd) From 5d5f4c4fd34ee5746c7f362914f3d6b61dfc660e Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:26:33 -0700 Subject: [PATCH 09/41] Working with documents > Managing documents --- site/about/using-the-documentation.qmd | 2 +- .../install-and-initialize-validmind-library.qmd | 2 +- .../quickstart/store-credentials-in-env-file.qmd | 2 +- site/guide/_sidebar.yaml | 2 +- site/guide/documentation/_add-content-blocks.qmd | 2 +- site/guide/documentation/_add-test-results.qmd | 2 +- site/guide/documentation/_check-documents.qmd | 2 +- .../documentation/_locate-document-overview.qmd | 2 +- .../guide/documentation/collaborate-with-others.qmd | 2 +- .../documentation/work-with-content-blocks.qmd | 4 ++-- .../documentation/work-with-document-versions.qmd | 6 +++--- site/guide/documentation/work-with-test-results.qmd | 2 +- site/guide/guides.qmd | 2 +- site/guide/monitoring/enable-monitoring.qmd | 2 +- site/guide/monitoring/review-monitoring-results.qmd | 2 +- .../monitoring/work-with-metrics-over-time.qmd | 4 ++-- site/guide/reporting/export-documents.qmd | 2 +- .../templates/_customize-document-templates.qmd | 6 +++--- site/guide/templates/_duplicate-template.qmd | 4 ++-- site/guide/templates/_view-document-templates.qmd | 6 +++--- .../templates/customize-document-templates.qmd | 2 +- site/guide/templates/manage-document-templates.qmd | 2 +- site/guide/templates/manage-text-block-library.qmd | 2 +- ...ng-with-documents.qmd => managing-documents.qmd} | 13 +++++++------ .../templates/working-with-document-templates.qmd | 2 +- .../validation/preparing-validation-reports.qmd | 2 +- 26 files changed, 41 insertions(+), 40 deletions(-) rename site/guide/templates/{working-with-documents.qmd => managing-documents.qmd} (83%) diff --git a/site/about/using-the-documentation.qmd b/site/about/using-the-documentation.qmd index 4ccc935b1..733a6541e 100644 --- a/site/about/using-the-documentation.qmd +++ b/site/about/using-the-documentation.qmd @@ -54,7 +54,7 @@ Step-by-step instructions for platform tasks, organized by feature area. | [Integrations](/guide/integrations/managing-integrations.qmd) | Connecting {{< var vm.product >}} to external systems | Manage secrets, configure connections, link external records (models) | | [Workflows](/guide/guides.qmd#workflows) | Automating lifecycle processes | Configure workflow steps, manage transitions, set up approvals | | [Inventory](/guide/guides.qmd#inventory) | Managing your records (models) and record inventory | Register records, edit fields, configure interdependencies | -| [Documents & templates](/guide/templates/working-with-documents.qmd) | Creating and customizing documentation | Manage document types, customize templates, use the text block library | +| [Documents & templates](/guide/templates/managing-documents.qmd) | Creating and customizing documentation | Manage document types, customize templates, use the text block library | | [Documentation](/guide/guides.qmd#documentation) | Authoring and collaborating on documents | Edit content blocks, add test results, manage versions, submit for approval | | [Validation](/guide/guides.qmd#validation) | Reviewing and validating records (models) | Review documentation, assess compliance, manage findings and artifacts | | [Reporting & auditing](/guide/guides.qmd#reporting) | Analyzing, exporting, and auditing data | View reports, track organization activity, create custom analytics, export inventory and documents | diff --git a/site/developer/quickstart/install-and-initialize-validmind-library.qmd b/site/developer/quickstart/install-and-initialize-validmind-library.qmd index e0eb1656f..a18f91b00 100644 --- a/site/developer/quickstart/install-and-initialize-validmind-library.qmd +++ b/site/developer/quickstart/install-and-initialize-validmind-library.qmd @@ -159,7 +159,7 @@ Version: 2.11.0 [^5]: [Working with the inventory](/guide/inventory/working-with-the-inventory.qmd#search-filter-and-sort-records) -[^6]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^6]: [Managing documents](/guide/templates/managing-documents.qmd) [^7]: [Upgrade the {{< var validmind.developer >}}](#upgrade-validmind) diff --git a/site/developer/quickstart/store-credentials-in-env-file.qmd b/site/developer/quickstart/store-credentials-in-env-file.qmd index c59d30396..ab8ac1bf4 100644 --- a/site/developer/quickstart/store-credentials-in-env-file.qmd +++ b/site/developer/quickstart/store-credentials-in-env-file.qmd @@ -164,7 +164,7 @@ c. Run the cell. [^4]: [Working with the inventory](/guide/inventory/working-with-the-inventory.qmd#search-filter-and-sort-records) -[^5]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^5]: [Managing documents](/guide/templates/managing-documents.qmd) [^6]: diff --git a/site/guide/_sidebar.yaml b/site/guide/_sidebar.yaml index 244ae944e..af55c8a34 100644 --- a/site/guide/_sidebar.yaml +++ b/site/guide/_sidebar.yaml @@ -99,7 +99,7 @@ website: - text: "---" - section: "Documents & templates" contents: - - file: guide/templates/working-with-documents.qmd + - file: guide/templates/managing-documents.qmd contents: - guide/templates/manage-document-types.qmd - guide/templates/manage-documents.qmd diff --git a/site/guide/documentation/_add-content-blocks.qmd b/site/guide/documentation/_add-content-blocks.qmd index 6a694403e..3fe25621f 100644 --- a/site/guide/documentation/_add-content-blocks.qmd +++ b/site/guide/documentation/_add-content-blocks.qmd @@ -9,7 +9,7 @@ SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial --> 1. In the left sidebar that appears for your record, click **{{< fa file >}} Documents** and select the **Latest** tab.^[[Work with document versions](/guide/documentation/work-with-document-versions.qmd)] -1. Click on the document file you want to add a content block to.^[[Working with documents](/guide/templates/working-with-documents.qmd)] +1. Click on the document file you want to add a content block to.^[[Managing documents](/guide/templates/managing-documents.qmd)] 1. Click on a section header to expand that section and add content. diff --git a/site/guide/documentation/_add-test-results.qmd b/site/guide/documentation/_add-test-results.qmd index bf9e22965..03efd0ae5 100644 --- a/site/guide/documentation/_add-test-results.qmd +++ b/site/guide/documentation/_add-test-results.qmd @@ -9,7 +9,7 @@ SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial --> 1. In the left sidebar that appears for your record, click **{{< fa file >}} Documents** and select the **Latest** tab.^[[Work with document versions](/guide/documentation/work-with-document-versions.qmd)] -1. Click on the document file you want to add test results to.^[[Working with documents](/guide/templates/working-with-documents.qmd)] +1. Click on the document file you want to add test results to.^[[Managing documents](/guide/templates/managing-documents.qmd)] 1. Click on a section header to expand that section and add content. diff --git a/site/guide/documentation/_check-documents.qmd b/site/guide/documentation/_check-documents.qmd index af38364c4..d17923a06 100644 --- a/site/guide/documentation/_check-documents.qmd +++ b/site/guide/documentation/_check-documents.qmd @@ -11,7 +11,7 @@ a. Select a record or find your record by applying a filter or searching for it. a. In the left sidebar that appears for your record, click **{{< fa file >}} Documents** and select the **Latest** or **Saved Versions** tab.^[[Work with document versions](/guide/documentation/work-with-document-versions.qmd)] -a. Click on the file you want to check:^[[Working with documents](/guide/templates/working-with-documents.qmd)] +a. Click on the file you want to check:^[[Managing documents](/guide/templates/managing-documents.qmd)] ::: {.panel-tabset} diff --git a/site/guide/documentation/_locate-document-overview.qmd b/site/guide/documentation/_locate-document-overview.qmd index af25cd9fe..62f839f6e 100644 --- a/site/guide/documentation/_locate-document-overview.qmd +++ b/site/guide/documentation/_locate-document-overview.qmd @@ -11,7 +11,7 @@ To locate your document overview for a record: 1. In the left sidebar that appears for your record, click **{{< fa file >}} Documents** and select the **Latest** or **Saved Versions** tab.^[[Work with document versions](/guide/documentation/work-with-document-versions.qmd)] -1. Click on a Development type file.^[[Working with documents](/guide/templates/working-with-documents.qmd)] +1. Click on a Development type file.^[[Managing documents](/guide/templates/managing-documents.qmd)] :::: diff --git a/site/guide/documentation/collaborate-with-others.qmd b/site/guide/documentation/collaborate-with-others.qmd index a8f9bf43a..2a8384bce 100644 --- a/site/guide/documentation/collaborate-with-others.qmd +++ b/site/guide/documentation/collaborate-with-others.qmd @@ -146,7 +146,7 @@ To delete a comment in a resolved thread, follow the same steps but click the ** [^2]: [Register records in the inventory](/guide/inventory/register-records-in-inventory.qmd) -[^3]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^3]: [Managing documents](/guide/templates/managing-documents.qmd) [^4]: [Manage permissions](/guide/configuration/manage-permissions.qmd) diff --git a/site/guide/documentation/work-with-content-blocks.qmd b/site/guide/documentation/work-with-content-blocks.qmd index 6ec599dba..7d9a0af7d 100644 --- a/site/guide/documentation/work-with-content-blocks.qmd +++ b/site/guide/documentation/work-with-content-blocks.qmd @@ -218,7 +218,7 @@ Test-driven or metric over time blocks can be re-added later on but **text block [^7]: [Register records in the inventory](/guide/inventory/register-records-in-inventory.qmd) -[^8]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^8]: [Managing documents](/guide/templates/managing-documents.qmd) [^9]: [Manage permissions](/guide/configuration/manage-permissions.qmd) @@ -244,4 +244,4 @@ Test-driven or metric over time blocks can be re-added later on but **text block [^20]: [Work with document versions](/guide/documentation/work-with-document-versions.qmd) -[^21]: [Working with documents](/guide/templates/working-with-documents.qmd) \ No newline at end of file +[^21]: [Managing documents](/guide/templates/managing-documents.qmd) \ No newline at end of file diff --git a/site/guide/documentation/work-with-document-versions.qmd b/site/guide/documentation/work-with-document-versions.qmd index 15ee59055..af12c0e14 100644 --- a/site/guide/documentation/work-with-document-versions.qmd +++ b/site/guide/documentation/work-with-document-versions.qmd @@ -56,15 +56,15 @@ If you save another version before 5 minutes is up, you'll receive a warning toa [^1]: [Register records in the inventory](/guide/inventory/register-records-in-inventory.qmd) -[^2]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^2]: [Managing documents](/guide/templates/managing-documents.qmd) [^3]: [Manage permissions](/guide/configuration/manage-permissions.qmd) [^4]: [Working with the inventory](/guide/inventory/working-with-the-inventory.qmd#search-filter-and-sort-records) -[^5]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^5]: [Managing documents](/guide/templates/managing-documents.qmd) [^6]: [Working with the inventory](/guide/inventory/working-with-the-inventory.qmd#search-filter-and-sort-records) -[^7]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^7]: [Managing documents](/guide/templates/managing-documents.qmd) diff --git a/site/guide/documentation/work-with-test-results.qmd b/site/guide/documentation/work-with-test-results.qmd index 778e152aa..8a33ac55e 100644 --- a/site/guide/documentation/work-with-test-results.qmd +++ b/site/guide/documentation/work-with-test-results.qmd @@ -123,4 +123,4 @@ Filters can be removed from the list of test result metadata by clicking on the [^7]: [Work with document versions](/guide/documentation/work-with-document-versions.qmd) -[^8]: [Working with documents](/guide/templates/working-with-documents.qmd) \ No newline at end of file +[^8]: [Managing documents](/guide/templates/managing-documents.qmd) \ No newline at end of file diff --git a/site/guide/guides.qmd b/site/guide/guides.qmd index b55fe63e5..fdbac6daa 100644 --- a/site/guide/guides.qmd +++ b/site/guide/guides.qmd @@ -60,7 +60,7 @@ listing: sort: false fields: [title, description] contents: - - templates/working-with-documents.qmd + - templates/managing-documents.qmd - templates/working-with-document-templates.qmd - templates/manage-text-block-library.qmd - templates/customize-document-checker.qmd diff --git a/site/guide/monitoring/enable-monitoring.qmd b/site/guide/monitoring/enable-monitoring.qmd index 07e64065a..7a289faa5 100644 --- a/site/guide/monitoring/enable-monitoring.qmd +++ b/site/guide/monitoring/enable-monitoring.qmd @@ -150,6 +150,6 @@ After you have enabled ongoing monitoring and run your code to generate some out [^7]: [Work with document versions](/guide/documentation/work-with-document-versions.qmd) -[^8]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^8]: [Managing documents](/guide/templates/managing-documents.qmd) [^9]: [Review monitoring results](review-monitoring-results.qmd) \ No newline at end of file diff --git a/site/guide/monitoring/review-monitoring-results.qmd b/site/guide/monitoring/review-monitoring-results.qmd index 589707303..994a67ed7 100644 --- a/site/guide/monitoring/review-monitoring-results.qmd +++ b/site/guide/monitoring/review-monitoring-results.qmd @@ -125,5 +125,5 @@ The final assessment is that the model requires attention: [^6]: [Work with document versions](/guide/documentation/work-with-document-versions.qmd) -[^7]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^7]: [Managing documents](/guide/templates/managing-documents.qmd) diff --git a/site/guide/monitoring/work-with-metrics-over-time.qmd b/site/guide/monitoring/work-with-metrics-over-time.qmd index 09ba39ddc..792c0306b 100644 --- a/site/guide/monitoring/work-with-metrics-over-time.qmd +++ b/site/guide/monitoring/work-with-metrics-over-time.qmd @@ -152,7 +152,7 @@ After you have added metrics over time to your document, you can view the follow [^5]: [Work with document versions](/guide/documentation/work-with-document-versions.qmd) -[^6]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^6]: [Managing documents](/guide/templates/managing-documents.qmd) [^7]: [Work with content blocks](/guide/documentation/work-with-content-blocks.qmd) @@ -160,7 +160,7 @@ After you have added metrics over time to your document, you can view the follow [^9]: [Work with document versions](/guide/documentation/work-with-document-versions.qmd) -[^10]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^10]: [Managing documents](/guide/templates/managing-documents.qmd) [^11]: [Configure connections](/guide/integrations/configure-connections.qmd) and [Link external models](/guide/integrations/link-external-models.qmd) diff --git a/site/guide/reporting/export-documents.qmd b/site/guide/reporting/export-documents.qmd index 4d698115a..7004363ec 100644 --- a/site/guide/reporting/export-documents.qmd +++ b/site/guide/reporting/export-documents.qmd @@ -65,7 +65,7 @@ Documents exported from {{< var vm.product >}} are compatible with Word 365, Wor [^1]: [Register records in the inventory](/guide/inventory/register-records-in-inventory.qmd) -[^2]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^2]: [Managing documents](/guide/templates/managing-documents.qmd) [^3]: [Manage permissions](/guide/configuration/manage-permissions.qmd) diff --git a/site/guide/templates/_customize-document-templates.qmd b/site/guide/templates/_customize-document-templates.qmd index 1f37129c2..d7a08e8bd 100644 --- a/site/guide/templates/_customize-document-templates.qmd +++ b/site/guide/templates/_customize-document-templates.qmd @@ -27,7 +27,7 @@ Editing outlines for document templates from **{{< fa gear >}} Settings** will c - **Development**^[[Working with documentation](/guide/documentation/working-with-documentation.qmd)] - **Validation**^[[Preparing validation reports](/guide/validation/preparing-validation-reports.qmd)] - **Monitoring**^[[Ongoing monitoring](/guide/monitoring/ongoing-monitoring.qmd)] - - **Custom**^[[Working with documents](/guide/templates/working-with-documents.qmd)] + - **Custom**^[[Managing documents](/guide/templates/managing-documents.qmd)] 1. Click the template to edit and on the template details page, select **{{< fa pencil >}} Edit Outline**. @@ -49,7 +49,7 @@ Editing outlines for templates on documents will only apply changes to that docu 1. In the left sidebar that appears for your record, click **{{< fa file >}} Documents** and select the **Latest** or **Saved Versions** tab.^[[Work with document versions](/guide/documentation/work-with-document-versions.qmd)] -1. Click on the document you would like to customize the template outline for.^[[Working with documents](/guide/templates/working-with-documents.qmd)] +1. Click on the document you would like to customize the template outline for.^[[Managing documents](/guide/templates/managing-documents.qmd)] 1. On the template details page, click **{{< fa pencil >}} Edit Outline**. @@ -76,7 +76,7 @@ Edit outlines for templates directly on documents to only apply changes to that 1. In the left sidebar that appears for your record, click **{{< fa file >}} Documents** and select the **Latest** or **Saved Versions** tab.^[[Work with document versions](/guide/documentation/work-with-document-versions.qmd)] -1. Click on the document you would like to customize the template outline for.^[[Working with documents](/guide/templates/working-with-documents.qmd)] +1. Click on the document you would like to customize the template outline for.^[[Managing documents](/guide/templates/managing-documents.qmd)] 1. On the template details page, click **{{< fa pencil >}} Edit Outline**. diff --git a/site/guide/templates/_duplicate-template.qmd b/site/guide/templates/_duplicate-template.qmd index f2f3c0420..dddfb3add 100644 --- a/site/guide/templates/_duplicate-template.qmd +++ b/site/guide/templates/_duplicate-template.qmd @@ -14,7 +14,7 @@ To duplicate an existing template and start with version one of that new templat - **Development**^[[Working with documentation](/guide/documentation/working-with-documentation.qmd)] - **Validation**^[[Preparing validation reports](/guide/validation/preparing-validation-reports.qmd)] - **Monitoring**^[[Ongoing monitoring](/guide/monitoring/ongoing-monitoring.qmd)] - - **Custom**^[[Working with documents](/guide/templates/working-with-documents.qmd)] + - **Custom**^[[Managing documents](/guide/templates/managing-documents.qmd)] 1. Click on the template to duplicate and on the template details page, select **{{< fa copy >}} Duplicate Template**. @@ -34,7 +34,7 @@ To duplicate an existing template and start with version one of that new templat 1. Under {{< fa file >}} Documents, select **Templates**. -1. Select one of the tabs for the [type of template you want to duplicate](/guide/templates/working-with-documents.qmd){target="_blank"}. +1. Select one of the tabs for the [type of template you want to duplicate](/guide/templates/managing-documents.qmd){target="_blank"}. 1. Click on the template to duplicate and on the template details page, select **{{< fa copy >}} Duplicate Template**. diff --git a/site/guide/templates/_view-document-templates.qmd b/site/guide/templates/_view-document-templates.qmd index a2bc6d93e..830f2a6ec 100644 --- a/site/guide/templates/_view-document-templates.qmd +++ b/site/guide/templates/_view-document-templates.qmd @@ -14,7 +14,7 @@ To review the existing templates available to your organization: - **Development**^[[Working with documentation](/guide/documentation/working-with-documentation.qmd)] - **Validation**^[[Preparing validation reports](/guide/validation/preparing-validation-reports.qmd)] - **Monitoring**^[[Ongoing monitoring](/guide/monitoring/ongoing-monitoring.qmd)] - - **Custom**^[[Working with documents](/guide/templates/working-with-documents.qmd)] + - **Custom**^[[Managing documents](/guide/templates/managing-documents.qmd)] Displayed in list view is an overview of information for templates under that specific document type. @@ -55,7 +55,7 @@ To review the existing templates available to your organization: [^template-info]: - **Versions & outlines:** [Customize document templates](/guide/templates/customize-document-templates.qmd#edit-template-outlines) - - **Types:** [Working with documents](/guide/templates/working-with-documents.qmd) + - **Types:** [Managing documents](/guide/templates/managing-documents.qmd) :::: @@ -65,7 +65,7 @@ To review the existing templates available to your organization: 1. Under {{< fa file >}} Documents, select **Templates**. -1. Select one of the tabs for [the document type with the templates you want to view](/guide/templates/working-with-documents.qmd){target="_blank"}. +1. Select one of the tabs for [the document type with the templates you want to view](/guide/templates/managing-documents.qmd){target="_blank"}. 1. Select one of the available templates to view detailed information about the template. diff --git a/site/guide/templates/customize-document-templates.qmd b/site/guide/templates/customize-document-templates.qmd index 80cc00805..cd8da00d4 100644 --- a/site/guide/templates/customize-document-templates.qmd +++ b/site/guide/templates/customize-document-templates.qmd @@ -156,7 +156,7 @@ c. Finish editing your template, then save a new version. [^4]: [validation report templates only]{.smallcaps .pink} -[^5]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^5]: [Managing documents](/guide/templates/managing-documents.qmd) [^6]: [Template schema](#template-schema) diff --git a/site/guide/templates/manage-document-templates.qmd b/site/guide/templates/manage-document-templates.qmd index da5aee54a..bf27c6bc0 100644 --- a/site/guide/templates/manage-document-templates.qmd +++ b/site/guide/templates/manage-document-templates.qmd @@ -158,7 +158,7 @@ Delete templates no longer required by your organization: [^10]: [Ongoing monitoring](/guide/monitoring/ongoing-monitoring.qmd) -[^11]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^11]: [Managing documents](/guide/templates/managing-documents.qmd) [^12]: diff --git a/site/guide/templates/manage-text-block-library.qmd b/site/guide/templates/manage-text-block-library.qmd index b43d1ac6c..8b31aecb5 100644 --- a/site/guide/templates/manage-text-block-library.qmd +++ b/site/guide/templates/manage-text-block-library.qmd @@ -98,7 +98,7 @@ To remove an existing text block from your block library: [^3]: [Work with document versions](/guide/documentation/work-with-document-versions.qmd) -[^4]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^4]: [Managing documents](/guide/templates/managing-documents.qmd) [^5]: [Work with content blocks](/guide/documentation/work-with-content-blocks.qmd#add-content-blocks) diff --git a/site/guide/templates/working-with-documents.qmd b/site/guide/templates/managing-documents.qmd similarity index 83% rename from site/guide/templates/working-with-documents.qmd rename to site/guide/templates/managing-documents.qmd index 70a63077c..acb1f431d 100644 --- a/site/guide/templates/working-with-documents.qmd +++ b/site/guide/templates/managing-documents.qmd @@ -2,7 +2,7 @@ # Copyright © 2023-2026 ValidMind Inc. All rights reserved. # Refer to the LICENSE file in the root of this repository for details. # SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial -title: "Working with documents" +title: "Managing documents" date: last-modified listing: - id: documents @@ -15,14 +15,15 @@ listing: - manage-document-types.qmd - manage-documents.qmd aliases: + - /guide/templates/working-with-documents.html - /guide/templates/working-with-model-documents.html --- -Set up document types, categories of record-related content, for use on records across your organization, and the regulations and policies that oversee your documents within the {{< var validmind.platform >}}. +Set up and manage documents and document types, categories of record-related content, for use on records across your organization within the {{< var validmind.platform >}}. ## Manage document defaults -To change the default settings for documents within your organization: +To change the default settings for all documents within your organization: 1. In the left sidebar, click **{{< fa gear >}} Settings**. @@ -48,10 +49,10 @@ To change the default settings for documents within your organization: :::: -## Work with documents +## Manage document types and documents -::: {#documents} -::: +:::: {#documents} +::::
    diff --git a/site/guide/templates/working-with-document-templates.qmd b/site/guide/templates/working-with-document-templates.qmd index 57aed31de..dc22e7765 100644 --- a/site/guide/templates/working-with-document-templates.qmd +++ b/site/guide/templates/working-with-document-templates.qmd @@ -30,5 +30,5 @@ aliases: ## What's next -- [Working with documents](working-with-documents.qmd) +- [Managing documents](managing-documents.qmd) - [Manage text block library](manage-text-block-library.qmd) \ No newline at end of file diff --git a/site/guide/validation/preparing-validation-reports.qmd b/site/guide/validation/preparing-validation-reports.qmd index bcdee8ae3..df3c6a6c9 100644 --- a/site/guide/validation/preparing-validation-reports.qmd +++ b/site/guide/validation/preparing-validation-reports.qmd @@ -158,7 +158,7 @@ Wrap up your report, collaborate with other stakeholders, and submit your work f [^8]: [Work with document versions](/guide/documentation/work-with-document-versions.qmd) -[^9]: [Working with documents](/guide/templates/working-with-documents.qmd) +[^9]: [Managing documents](/guide/templates/managing-documents.qmd) [^10]: [Manage documents](/guide/templates/manage-documents.qmd#add-record-documents) From a15081081b938c767c89701dbbc2f92a91bf42bb Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:34:53 -0700 Subject: [PATCH 10/41] updated member removal from group --- .../_add-remove-group-members.qmd | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/site/guide/configuration/_add-remove-group-members.qmd b/site/guide/configuration/_add-remove-group-members.qmd index c111aab92..0073a3ded 100644 --- a/site/guide/configuration/_add-remove-group-members.qmd +++ b/site/guide/configuration/_add-remove-group-members.qmd @@ -9,7 +9,7 @@ SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial --> ::: {.panel-tabset} -#### Add members +### Add members a. Click on the group you want to add members to. @@ -21,17 +21,26 @@ a. Click **Add # User(s) Group**. Confirm that the new members are added to the correct group. -#### Remove members +### Remove members -a. Click on the group you want to remove members from. +Click on the group you want to remove members from. -a. Select members to be removed from the group by checking the boxes next to their names. +#### Remove a single member + +a. Hover over the member you want to remove from that group. + +a. Under the Actions column, click **{{< fa ellipsis-vertical >}}** and select **{{< fa user-minus >}} Remove Member**. + +a. Click **{{< fa user-minus >}} Remove 1 member** to confirm removal of the selected member from that group. + +#### Remove multiple members + +a. Select several members to be removed from the group by checking the boxes next to their names. a. Review the number of selected members to be removed, then click **{{< fa user-minus >}} Remove**. a. Click **{{< fa user-minus >}} Remove # member(s)** to confirm removal of selected members from that group. - ::: :::: From d632155783aa32c6d8f6280be711d287f51aa24e Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:42:10 -0700 Subject: [PATCH 11/41] edit --- site/guide/configuration/_add-remove-group-members.qmd | 2 +- site/guide/reporting/view-settings-activity.qmd | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/site/guide/configuration/_add-remove-group-members.qmd b/site/guide/configuration/_add-remove-group-members.qmd index 0073a3ded..089375870 100644 --- a/site/guide/configuration/_add-remove-group-members.qmd +++ b/site/guide/configuration/_add-remove-group-members.qmd @@ -23,7 +23,7 @@ a. Click **Add # User(s) Group**. ### Remove members -Click on the group you want to remove members from. +Click on the group you want to remove members from to remove one or multiple members from that group: #### Remove a single member diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index fdd027809..89beb6cf6 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -19,8 +19,8 @@ The organization **{{< fa bolt >}} Activity** page shows a history of updates to - Document settings, including document types, templates, and library blocks - Governance settings, including regulations, policies, validation guidelines, risk areas, and assessment questions - Workflow settings, including workflow creations, publications, and deletions -- Integration settings, including integration secret creation, revocation, and deletion -- Webhook settings, including webhook secret creation, revocation, and deletion +- Integration settings, including integration setup and secret management +- Webhook settings, including webhook secret management :::: {.attn} @@ -47,6 +47,7 @@ To review settings activity for your organization: - **Documents** to view activity related to creation of, updates to, and deletions of document types, templates, and library blocks. - **Governance** to view activity related to creation of, updates to, and deletions of regulations, policies, validation guidelines, risk areas, and assessment questions.* - **Groups** to view activity related to group creation, updates, deletions, and group membership changes. + - **Integration Connections** to view activity related to integration connection creation, update, and removal. - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - **Organization Settings** to view activity related to authentication configuration, email notifications, and general settings for documents. - **Inventory Records** to view activity related to inventory record type or record field creation, updates, and deletions. From 5d2a4a168c837b2438dfaf6db914cc025a4c6c37 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:43:38 -0700 Subject: [PATCH 12/41] save --- site/guide/reporting/view-settings-activity.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index 89beb6cf6..65f83b151 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -49,8 +49,8 @@ To review settings activity for your organization: - **Groups** to view activity related to group creation, updates, deletions, and group membership changes. - **Integration Connections** to view activity related to integration connection creation, update, and removal. - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - - **Organization Settings** to view activity related to authentication configuration, email notifications, and general settings for documents. - **Inventory Records** to view activity related to inventory record type or record field creation, updates, and deletions. + - **Organization Settings** to view activity related to authentication configuration, email notifications, and general settings for documents. - **Permissions** to view activity related to permission changes on roles. - **Roles** to view activity related to role creation, updates, deletions, and user role assignments. - **Users** to view activity related to user information updates and user invitations. From 75bb35a87a3de9e6bdb8bd0417179d548ff06abb Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:48:30 -0700 Subject: [PATCH 13/41] save --- .../reporting/view-settings-activity.qmd | 64 +++++++------------ 1 file changed, 22 insertions(+), 42 deletions(-) diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index 65f83b151..06ae7a43b 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -12,15 +12,15 @@ Track information events associated with your organization's settings. Audit a h The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as activity related to: -- Organization settings, including business units, authentication configuration, email notifications, and general settings for documents -- Groups and group membership, users and invitations, permissions and roles -- Inventory record settings, including inventory record types and record fields -- Artifact settings, including artifact types, severities, and fields -- Document settings, including document types, templates, and library blocks -- Governance settings, including regulations, policies, validation guidelines, risk areas, and assessment questions -- Workflow settings, including workflow creations, publications, and deletions -- Integration settings, including integration setup and secret management -- Webhook settings, including webhook secret management +- Organization settings, including business units,^ authentication configuration, email notifications,^ and general settings for documents^ +- Groups and group membership,^ users and invitations,^ permissions^ and roles^ +- Inventory record settings, including inventory record types^ and record fields^ +- Artifact settings, including artifact types,^ severities,^ and fields^ +- Document settings, including document types,^ templates,^ and library blocks^ +- Governance settings, including regulations or polices and related assessments,^ as well as risk areas and validation guidelines +- Workflow settings,^ including workflow creations, publications, and deletions +- Integration settings,^ including integration setup and secret management +- Webhook settings,^ including webhook secret management :::: {.attn} @@ -42,20 +42,20 @@ To review settings activity for your organization: 3. To narrow down settings activity by category, click: - **All** to view all activity. - - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types, severities, and fields. - - **Business Units** to view activity related to business unit creation, updates, and deletions. - - **Documents** to view activity related to creation of, updates to, and deletions of document types, templates, and library blocks. - - **Governance** to view activity related to creation of, updates to, and deletions of regulations, policies, validation guidelines, risk areas, and assessment questions.* - - **Groups** to view activity related to group creation, updates, deletions, and group membership changes. - - **Integration Connections** to view activity related to integration connection creation, update, and removal. + - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,^ severities,^ and fields.^ + - **Business Units** to view activity related to business unit creation, updates, and deletions.^ + - **Documents** to view activity related to creation of, updates to, and deletions of document types,^ templates,^ and library blocks.^ + - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,^ as well as risk areas and validation guidelines.^ + - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.^ + - **Integration Connections** to view activity related to integration connection creation, update, and removal.^ - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - - **Inventory Records** to view activity related to inventory record type or record field creation, updates, and deletions. - - **Organization Settings** to view activity related to authentication configuration, email notifications, and general settings for documents. - - **Permissions** to view activity related to permission changes on roles. - - **Roles** to view activity related to role creation, updates, deletions, and user role assignments. - - **Users** to view activity related to user information updates and user invitations. - - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion. - - **Workflows** to view activity related to workflow creation, publications, and deletions. + - **Inventory Records** to view activity related to inventory record type or record field creation, updates, and deletions.^ + - **Organization Settings** to view activity related to authentication configuration, email notifications,^ and general settings for documents.^ + - **Permissions** to view activity related to permission changes on roles.^ + - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.^ + - **Users** to view activity related to user information updates and user invitations.^ + - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.^ + - **Workflows** to view activity related to workflow creation, publications, and deletions.^ To view more details about specific activity, click {{< fa chevron-down >}} when the option is available on an entry. @@ -64,24 +64,4 @@ To review settings activity for your organization: [^1]: [Manage permissions](/guide/configuration/manage-permissions.qmd) - - From 49c91d9df3acc1a6efbe241ee22ce57ef9759902 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 13:58:58 -0700 Subject: [PATCH 14/41] View settings activity --- .../reporting/view-settings-activity.qmd | 84 ++++++++++++++----- 1 file changed, 62 insertions(+), 22 deletions(-) diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index 06ae7a43b..75d47c422 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -12,15 +12,15 @@ Track information events associated with your organization's settings. Audit a h The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as activity related to: -- Organization settings, including business units,^ authentication configuration, email notifications,^ and general settings for documents^ -- Groups and group membership,^ users and invitations,^ permissions^ and roles^ -- Inventory record settings, including inventory record types^ and record fields^ -- Artifact settings, including artifact types,^ severities,^ and fields^ -- Document settings, including document types,^ templates,^ and library blocks^ -- Governance settings, including regulations or polices and related assessments,^ as well as risk areas and validation guidelines -- Workflow settings,^ including workflow creations, publications, and deletions -- Integration settings,^ including integration setup and secret management -- Webhook settings,^ including webhook secret management +- Organization settings, including management of business units, authentication configuration, email notifications, and general settings for documents +- Management of groups and group membership, users and invitations, permissions and roles +- Inventory record settings, including management of inventory record types and record fields +- Artifact settings, including management of artifact types, severities, and fields +- Document settings, including management of document types, templates, and library blocks +- Governance settings, including management of regulations or polices and related assessments, as well as risk areas and validation guidelines +- Workflow settings, including workflow creations, publications, and deletions +- Integration settings, including integration setup and secret management +- Webhook settings, including webhook secret management :::: {.attn} @@ -42,20 +42,20 @@ To review settings activity for your organization: 3. To narrow down settings activity by category, click: - **All** to view all activity. - - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,^ severities,^ and fields.^ - - **Business Units** to view activity related to business unit creation, updates, and deletions.^ - - **Documents** to view activity related to creation of, updates to, and deletions of document types,^ templates,^ and library blocks.^ - - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,^ as well as risk areas and validation guidelines.^ - - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.^ - - **Integration Connections** to view activity related to integration connection creation, update, and removal.^ + - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,[^2] severities,[^3] and fields.[^4] + - **Business Units** to view activity related to business unit creation, updates, and deletions.[^5] + - **Documents** to view activity related to creation of, updates to, and deletions of document types,[^6] templates,[^7] and library blocks.[^8] + - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,[^9] as well as risk areas and validation guidelines.[^10] + - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.[^11] + - **Integration Connections** to view activity related to integration connection creation, update, and removal.[^12] - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - - **Inventory Records** to view activity related to inventory record type or record field creation, updates, and deletions.^ - - **Organization Settings** to view activity related to authentication configuration, email notifications,^ and general settings for documents.^ - - **Permissions** to view activity related to permission changes on roles.^ - - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.^ - - **Users** to view activity related to user information updates and user invitations.^ - - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.^ - - **Workflows** to view activity related to workflow creation, publications, and deletions.^ + - **Inventory Records** to view activity related to inventory record type[^13] or record field[^14] creation, updates, and deletions. + - **Organization Settings** to view activity related to authentication configuration, email notifications,[^15] and general settings for documents.[^16] + - **Permissions** to view activity related to permission changes on roles.[^17] + - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.[^18] + - **Users** to view activity related to user information updates and user invitations.[^19] + - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.[^20] + - **Workflows** to view activity related to workflow creation, publications, and deletions.[^21] To view more details about specific activity, click {{< fa chevron-down >}} when the option is available on an entry. @@ -64,4 +64,44 @@ To review settings activity for your organization: [^1]: [Manage permissions](/guide/configuration/manage-permissions.qmd) +[^2]: [Manage artifact types](/guide/validation/manage-artifact-types.qmd) + +[^3]: [Manage artifact severities](/guide/validation/manage-artifact-severities.qmd) + +[^4]: [Manage artifact fields](/guide/validation/manage-artifact-fields.qmd) + +[^5]: [Set up your organization](/guide/configuration/set-up-your-organization.qmd#manage-business-units) + +[^6]: [Manage document types](/guide/templates/manage-document-types.qmd) + +[^7]: [Manage document templates](/guide/templates/manage-document-templates.qmd) + +[^8]: [Manage text block library](/guide/templates/manage-text-block-library.qmd) + +[^9]: [Customize {{< var validmind.checker >}}](/guide/templates/customize-document-checker.qmd) + +[^10]: [Manage validation guidelines](/guide/validation/manage-validation-guidelines.qmd) + +[^11]: [Manage groups](/guide/configuration/manage-groups.qmd) + +[^12]: [Managing integrations](/guide/integrations/managing-integrations.qmd) + +[^13]: [Manage inventory record types](/guide/inventory/manage-inventory-record-types.qmd) + +[^14]: [Manage inventory fields](/guide/inventory/manage-inventory-fields.qmd) + +[^15]: [Manage platform notifications](/guide/configuration/manage-platform-notifications.qmd#customize-email-notifications) + +[^16]: [Managing documents](/guide/templates/managing-documents.qmd#manage-document-defaults) + +[^17]: [Manage permissions](/guide/configuration/manage-permissions.qmd) + +[^18]: [Manage roles](/guide/configuration/manage-roles.qmd) + +[^19]: [Manage users](/guide/configuration/manage-users.qmd) + +[^20]: [Manage secrets](/guide/integrations/manage-secrets.qmd#webhook-secrets) + +[^21]: [Setting up workflows](/guide/workflows/setting-up-workflows.qmd) + From 1adc4b30f094349274ab042318deb3c180cf5155 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Mon, 8 Jun 2026 14:00:57 -0700 Subject: [PATCH 15/41] save --- .../inventory/_view-record-activity-overview.qmd | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/site/guide/inventory/_view-record-activity-overview.qmd b/site/guide/inventory/_view-record-activity-overview.qmd index df3cd33d7..9884a359c 100644 --- a/site/guide/inventory/_view-record-activity-overview.qmd +++ b/site/guide/inventory/_view-record-activity-overview.qmd @@ -4,19 +4,6 @@ SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial --> The record **{{< fa wifi >}} Activity** page shows a history of activities for the selected inventory record, including actions performed by users in your organization, activity from automated workflows, and updates generated via the {{< var validmind.developer >}}, such as: -:::: {.content-visible unless-format="revealjs"} -- Fields updated^[[Edit inventory fields](/guide/inventory/edit-inventory-fields.qmd)] -- Stage transitions^[[Manage record stages](/guide/workflows/manage-record-stages.qmd)] -- Updates to documents: documentation,^[[Working with documentation](/guide/documentation/working-with-documentation.qmd)] validation reports,^[[Preparing validation reports](/guide/validation/preparing-validation-reports.qmd)] ongoing monitoring reports^[[Ongoing monitoring](/guide/monitoring/ongoing-monitoring.qmd)], or custom documents -- Test results or metrics added via the {{< var vm.developer >}}^[[{{< var validmind.developer >}}](/developer/validmind-library.qmd)] -- Artifacts added, updated, or removed^[[Working with artifacts](/guide/validation/working-with-artifacts.qmd)] -- User comment creation and replies on documents^[[Collaborate with others](/guide/documentation/collaborate-with-others.qmd#commenting)] -- Stakeholders added or removed^[[Manage record stakeholder types](/guide/configuration/manage-record-stakeholder-types.qmd#manage-stakeholder-types-on-records)] -- Workflow activity^[[Working with workflows](/guide/workflows/working-with-workflows.qmd)] - -:::: - -:::: {.content-hidden unless-format="revealjs"} - Fields updated - Stage transitions - Updates to documents: documentation, validation reports, ongoing monitoring reports, or custom documents @@ -26,4 +13,3 @@ The record **{{< fa wifi >}} Activity** page shows a history of activities for t - Stakeholders added or removed - Workflow activity -:::: \ No newline at end of file From 2769128ba1afd0b6c3785a87186c3df62bba2fe6 Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 9 Jun 2026 10:39:56 -0700 Subject: [PATCH 16/41] setup for training --- .../_view-settings-activity-overview.qmd | 15 ++++++ .../_view-settings-activity-steps.qmd | 49 +++++++++++++++++++ .../reporting/view-settings-activity.qmd | 42 ++-------------- 3 files changed, 68 insertions(+), 38 deletions(-) create mode 100644 site/guide/reporting/_view-settings-activity-overview.qmd create mode 100644 site/guide/reporting/_view-settings-activity-steps.qmd diff --git a/site/guide/reporting/_view-settings-activity-overview.qmd b/site/guide/reporting/_view-settings-activity-overview.qmd new file mode 100644 index 000000000..3b89b8e2d --- /dev/null +++ b/site/guide/reporting/_view-settings-activity-overview.qmd @@ -0,0 +1,15 @@ + + +The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as activity related to: + +- Organization settings, including management of business units, authentication configuration, email notifications, and general settings for documents +- Management of groups and group membership, users and invitations, permissions and roles +- Inventory record settings, including management of inventory record types and record fields +- Artifact settings, including management of artifact types, severities, and fields +- Document settings, including management of document types, templates, and library blocks +- Governance settings, including management of regulations or polices and related assessments, as well as risk areas and validation guidelines +- Workflow settings, including workflow creations, publications, and deletions +- Integration settings, including integration setup and secret management +- Webhook settings, including webhook secret management \ No newline at end of file diff --git a/site/guide/reporting/_view-settings-activity-steps.qmd b/site/guide/reporting/_view-settings-activity-steps.qmd new file mode 100644 index 000000000..868ad90ec --- /dev/null +++ b/site/guide/reporting/_view-settings-activity-steps.qmd @@ -0,0 +1,49 @@ + + +To review settings activity for your organization: + +1. In the left sidebar, click **{{< fa wifi >}} Activity**. + +:::: {.content-visible unless-format="revealjs"} +2. Select the **Settings Activity** tab. + + ::: {.panel-tabset} + + ### Filter settings activity by category + + To narrow down settings activity by category, click: + + - **All** to view all activity. + - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,[^2] severities,[^3] and fields.[^4] + - **Business Units** to view activity related to business unit creation, updates, and deletions.[^5] + - **Documents** to view activity related to creation of, updates to, and deletions of document types,[^6] templates,[^7] and library blocks.[^8] + - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,[^9] as well as risk areas and validation guidelines.[^10] + - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.[^11] + - **Integration Connections** to view activity related to integration connection creation, update, and removal.[^12] + - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. + - **Inventory Records** to view activity related to inventory record type[^13] or record field[^14] creation, updates, and deletions. + - **Organization Settings** to view activity related to authentication configuration, email notifications,[^15] and general settings for documents.[^16] + - **Permissions** to view activity related to permission changes on roles.[^17] + - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.[^18] + - **Users** to view activity related to user information updates and user invitations.[^19] + - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.[^20] + - **Workflows** to view activity related to workflow creation, publications, and deletions.[^21] + + When enough activity categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through the categories. + + ### View more details about specific activity + + To view more details about specific activity, click {{< fa chevron-down >}} when the option is available on an entry. + + ::: + +:::: + + + +:::: {.content-hidden unless-format="revealjs"} + + +:::: diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index 75d47c422..475010d0a 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -10,17 +10,7 @@ Track information events associated with your organization's settings. Audit a h ## What is tracked on settings activity? -The organization **{{< fa bolt >}} Activity** page shows a history of updates to configuration settings for the selected organization, such as activity related to: - -- Organization settings, including management of business units, authentication configuration, email notifications, and general settings for documents -- Management of groups and group membership, users and invitations, permissions and roles -- Inventory record settings, including management of inventory record types and record fields -- Artifact settings, including management of artifact types, severities, and fields -- Document settings, including management of document types, templates, and library blocks -- Governance settings, including management of regulations or polices and related assessments, as well as risk areas and validation guidelines -- Workflow settings, including workflow creations, publications, and deletions -- Integration settings, including integration setup and secret management -- Webhook settings, including webhook secret management +{{< include _view-settings-activity-overview.qmd >}} :::: {.attn} @@ -33,38 +23,14 @@ The organization **{{< fa bolt >}} Activity** page shows a history of updates to ## View settings activity -To review settings activity for your organization: - -1. In the left sidebar, click **{{< fa wifi >}} Activity**. - -2. Select the **Settings Activity** tab. - -3. To narrow down settings activity by category, click: - - - **All** to view all activity. - - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,[^2] severities,[^3] and fields.[^4] - - **Business Units** to view activity related to business unit creation, updates, and deletions.[^5] - - **Documents** to view activity related to creation of, updates to, and deletions of document types,[^6] templates,[^7] and library blocks.[^8] - - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,[^9] as well as risk areas and validation guidelines.[^10] - - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.[^11] - - **Integration Connections** to view activity related to integration connection creation, update, and removal.[^12] - - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - - **Inventory Records** to view activity related to inventory record type[^13] or record field[^14] creation, updates, and deletions. - - **Organization Settings** to view activity related to authentication configuration, email notifications,[^15] and general settings for documents.[^16] - - **Permissions** to view activity related to permission changes on roles.[^17] - - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.[^18] - - **Users** to view activity related to user information updates and user invitations.[^19] - - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.[^20] - - **Workflows** to view activity related to workflow creation, publications, and deletions.[^21] - - To view more details about specific activity, click {{< fa chevron-down >}} when the option is available on an entry. +{{< include _view-settings-activity-steps.qmd >}} [^1]: [Manage permissions](/guide/configuration/manage-permissions.qmd) -[^2]: [Manage artifact types](/guide/validation/manage-artifact-types.qmd) + From fc4590350cb328ae127e8f9ce9b22f9bd6a5067d Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 9 Jun 2026 10:47:33 -0700 Subject: [PATCH 17/41] moving View record activity into reporting --- site/faq/faq-inventory.qmd | 2 +- site/guide/_sidebar.yaml | 2 +- .../customize-your-dashboard.qmd | 2 +- .../documentation/_document-overview.qmd | 2 +- .../work-with-content-blocks.qmd | 2 +- .../configure-analytics-exports.qmd | 2 +- .../guide/inventory/edit-inventory-fields.qmd | 2 +- .../inventory/managing-the-inventory.qmd | 2 +- .../inventory/working-with-the-inventory.qmd | 2 +- .../_view-record-activity-overview.qmd | 0 .../_view-record-activity-steps.qmd | 0 .../_view-settings-activity-steps.qmd | 56 ++++++++++++++----- .../tracking-organization-activity.qmd | 2 +- .../view-record-activity.qmd | 7 ++- .../reporting/view-settings-activity.qmd | 42 -------------- .../preparing-validation-reports.qmd | 2 +- site/guide/validation/update-artifacts.qmd | 2 +- .../guide/workflows/_transition-workflows.qmd | 2 +- .../2023/release-notes-2023-jul-24.qmd | 2 +- .../2024/2024-dec-06/release-notes.qmd | 2 +- .../2024/2024-dec-24/release-notes.qmd | 2 +- site/releases/2024/2024-feb-14/highlights.qmd | 4 +- site/releases/2024/2024-jan-18/highlights.qmd | 2 +- .../2024/2024-oct-22/release-notes.qmd | 2 +- .../common-slides/_track-changes-updates.qmd | 6 +- 25 files changed, 70 insertions(+), 81 deletions(-) rename site/guide/{inventory => reporting}/_view-record-activity-overview.qmd (100%) rename site/guide/{inventory => reporting}/_view-record-activity-steps.qmd (100%) rename site/guide/{inventory => reporting}/view-record-activity.qmd (95%) diff --git a/site/faq/faq-inventory.qmd b/site/faq/faq-inventory.qmd index 3cb987a69..b75fec190 100644 --- a/site/faq/faq-inventory.qmd +++ b/site/faq/faq-inventory.qmd @@ -14,7 +14,7 @@ listing: sort: false fields: [title, description] contents: - - ../guide/inventory/view-record-activity.qmd + - ../guide/reporting/view-record-activity.qmd - ../guide/inventory/working-with-the-inventory.qmd - ../guide/inventory/managing-the-inventory.qmd categories: ["inventory", "inventory registration", "inventory customization", "inventory interdependencies", "activity", "auditing", "exports", "validmind platform"] diff --git a/site/guide/_sidebar.yaml b/site/guide/_sidebar.yaml index af55c8a34..35b1b87c6 100644 --- a/site/guide/_sidebar.yaml +++ b/site/guide/_sidebar.yaml @@ -160,7 +160,7 @@ website: contents: - file: guide/reporting/tracking-organization-activity.qmd contents: - - guide/inventory/view-record-activity.qmd + - guide/reporting/view-record-activity.qmd - guide/reporting/view-settings-activity.qmd - file: guide/reporting/working-with-analytics.qmd contents: diff --git a/site/guide/configuration/customize-your-dashboard.qmd b/site/guide/configuration/customize-your-dashboard.qmd index d86acb88a..56092ff8e 100644 --- a/site/guide/configuration/customize-your-dashboard.qmd +++ b/site/guide/configuration/customize-your-dashboard.qmd @@ -193,7 +193,7 @@ Removing a widget from a dashboard will not delete the linked view or analytics [^5]: [Working with artifacts](/guide/validation/working-with-artifacts.qmd) -[^6]: [View record activity](/guide/inventory/view-record-activity.qmd) +[^6]: [View record activity](/guide/reporting/view-record-activity.qmd) [^7]: [Widget types](#widget-types) diff --git a/site/guide/documentation/_document-overview.qmd b/site/guide/documentation/_document-overview.qmd index 2a62793a5..4b6393e23 100644 --- a/site/guide/documentation/_document-overview.qmd +++ b/site/guide/documentation/_document-overview.qmd @@ -12,7 +12,7 @@ The **Document Overview** shows a section-by-section outline of your record's do - Any unresolved conversations^[[Collaborate with others](/guide/documentation/collaborate-with-others.qmd)] - The number of associated artifacts^[[Working with artifacts](/guide/validation/working-with-artifacts.qmd)] - The completion status for your documentation^[[Assign section statuses](/guide/documentation/assign-development-section-statuses.qmd)] -- Recent activity on your record^[[View record activity](/guide/inventory/view-record-activity.qmd)] +- Recent activity on your record^[[View record activity](/guide/reporting/view-record-activity.qmd)] :::: diff --git a/site/guide/documentation/work-with-content-blocks.qmd b/site/guide/documentation/work-with-content-blocks.qmd index 7d9a0af7d..608cf5b13 100644 --- a/site/guide/documentation/work-with-content-blocks.qmd +++ b/site/guide/documentation/work-with-content-blocks.qmd @@ -236,7 +236,7 @@ Test-driven or metric over time blocks can be re-added later on but **text block [^16]: [Run tests and test suites](/developer/how-to/testing-overview.qmd) -[^17]: [View record activity](/guide/inventory/view-record-activity.qmd) +[^17]: [View record activity](/guide/reporting/view-record-activity.qmd) [^18]: [Export documents](/guide/reporting/export-documents.qmd) diff --git a/site/guide/integrations/configure-analytics-exports.qmd b/site/guide/integrations/configure-analytics-exports.qmd index 24ca31981..1227efcce 100644 --- a/site/guide/integrations/configure-analytics-exports.qmd +++ b/site/guide/integrations/configure-analytics-exports.qmd @@ -169,4 +169,4 @@ Data you exported previously will remain in the cloud storage destination unchan [^3]: [Manage artifact types](/guide/validation/manage-artifact-types.qmd) -[^4]: [View record activity](/guide/inventory/view-record-activity.qmd) +[^4]: [View record activity](/guide/reporting/view-record-activity.qmd) diff --git a/site/guide/inventory/edit-inventory-fields.qmd b/site/guide/inventory/edit-inventory-fields.qmd index ab5194624..eb438af74 100644 --- a/site/guide/inventory/edit-inventory-fields.qmd +++ b/site/guide/inventory/edit-inventory-fields.qmd @@ -136,7 +136,7 @@ In addition to editing individual fields on a per record basis, you can edit fie -[^1]: [View record activity](view-record-activity.qmd) +[^1]: [View record activity](/guide/reporting/view-record-activity.qmd) [^2]: [Register records in the inventory](register-records-in-inventory.qmd) diff --git a/site/guide/inventory/managing-the-inventory.qmd b/site/guide/inventory/managing-the-inventory.qmd index 171dac946..0f9686a34 100644 --- a/site/guide/inventory/managing-the-inventory.qmd +++ b/site/guide/inventory/managing-the-inventory.qmd @@ -25,5 +25,5 @@ Once you're familiar with the {{< var vm.product >}} inventory, dive into more a ## What's next -- [View record activity](view-record-activity.qmd) +- [View record activity](/guide/reporting/view-record-activity.qmd) - [Working with documentation](/guide/documentation/working-with-documentation.qmd) diff --git a/site/guide/inventory/working-with-the-inventory.qmd b/site/guide/inventory/working-with-the-inventory.qmd index 45bf466ba..8fd1ad3c3 100644 --- a/site/guide/inventory/working-with-the-inventory.qmd +++ b/site/guide/inventory/working-with-the-inventory.qmd @@ -68,7 +68,7 @@ For example, if you first search by keyword for `ValidMind` without clearing tha ## What's next - [Managing the inventory](managing-the-inventory.qmd) -- [View record activity](view-record-activity.qmd) +- [View record activity](/guide/reporting/view-record-activity.qmd) diff --git a/site/guide/inventory/_view-record-activity-overview.qmd b/site/guide/reporting/_view-record-activity-overview.qmd similarity index 100% rename from site/guide/inventory/_view-record-activity-overview.qmd rename to site/guide/reporting/_view-record-activity-overview.qmd diff --git a/site/guide/inventory/_view-record-activity-steps.qmd b/site/guide/reporting/_view-record-activity-steps.qmd similarity index 100% rename from site/guide/inventory/_view-record-activity-steps.qmd rename to site/guide/reporting/_view-record-activity-steps.qmd diff --git a/site/guide/reporting/_view-settings-activity-steps.qmd b/site/guide/reporting/_view-settings-activity-steps.qmd index 868ad90ec..af3480ba8 100644 --- a/site/guide/reporting/_view-settings-activity-steps.qmd +++ b/site/guide/reporting/_view-settings-activity-steps.qmd @@ -16,20 +16,20 @@ To review settings activity for your organization: To narrow down settings activity by category, click: - **All** to view all activity. - - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,[^2] severities,[^3] and fields.[^4] - - **Business Units** to view activity related to business unit creation, updates, and deletions.[^5] - - **Documents** to view activity related to creation of, updates to, and deletions of document types,[^6] templates,[^7] and library blocks.[^8] - - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,[^9] as well as risk areas and validation guidelines.[^10] - - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.[^11] - - **Integration Connections** to view activity related to integration connection creation, update, and removal.[^12] + - **Artifacts** to view activity related to creation of, updates to, and deletions of [artifact types](/guide/validation/manage-artifact-types.qmd){target="_blank"}, [severities](/guide/validation/manage-artifact-severities.qmd){target="_blank"}, and [fields](/guide/validation/manage-artifact-fields.qmd){target="_blank"}. + - **Business Units** to view activity related to [business unit creation, updates, and deletions](/guide/configuration/set-up-your-organization.qmd#manage-business-units){target="_blank"}. + - **Documents** to view activity related to creation of, updates to, and deletions of [document types](/guide/templates/manage-document-types.qmd){target="_blank"}, [templates](/guide/templates/manage-document-templates.qmd){target="_blank"}, and [library blocks](/guide/templates/manage-text-block-library.qmd){target="_blank"}. + - **Governance** to view activity related to creation of, updates to, and deletions of [regulations or policies](/guide/templates/customize-document-checker.qmd){target="_blank"}, as well as risk areas and [validation guidelines](/guide/validation/manage-validation-guidelines.qmd){target="_blank"}. + - **Groups** to view activity related to [group creation, updates, deletions, and group membership changes](/guide/configuration/manage-groups.qmd){target="_blank"}. + - **Integration Connections** to view activity related to [integration connection creation, update, and removal](/guide/integrations/managing-integrations.qmd){target="_blank"}. - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - - **Inventory Records** to view activity related to inventory record type[^13] or record field[^14] creation, updates, and deletions. - - **Organization Settings** to view activity related to authentication configuration, email notifications,[^15] and general settings for documents.[^16] - - **Permissions** to view activity related to permission changes on roles.[^17] - - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.[^18] - - **Users** to view activity related to user information updates and user invitations.[^19] - - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.[^20] - - **Workflows** to view activity related to workflow creation, publications, and deletions.[^21] + - **Inventory Records** to view activity related to [inventory record type](/guide/inventory/manage-inventory-record-types.qmd){target="_blank"} or [record field](/guide/inventory/manage-inventory-fields.qmd){target="_blank"} creation, updates, and deletions. + - **Organization Settings** to view activity related to authentication configuration, [email notifications](/guide/configuration/manage-platform-notifications.qmd#customize-email-notifications){target="_blank"}, and [general settings for documents](/guide/templates/managing-documents.qmd#manage-document-defaults){target="_blank"}. + - **Permissions** to view activity related to [permission changes on roles](/guide/configuration/manage-permissions.qmd){target="_blank"}. + - **Roles** to view activity related to [role creation, updates, deletions, and user role assignments](/guide/configuration/manage-roles.qmd){target="_blank"}. + - **Users** to view activity related to [user information updates and user invitations](/guide/configuration/manage-users.qmd){target="_blank"}. + - **Webhook Secrets** to view activity related to [webhook secret creation, revocation, and deletion](/guide/integrations/manage-secrets.qmd#webhook-secrets){target="_blank"}. + - **Workflows** to view activity related to [workflow creation, publications, and deletions](/guide/workflows/setting-up-workflows.qmd){target="_blank"}. When enough activity categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through the categories. @@ -44,6 +44,36 @@ To review settings activity for your organization: :::: {.content-hidden unless-format="revealjs"} +2. Select the **Settings Activity** tab: + ::: {.panel-tabset} + + ### Filter settings activity by category + + To narrow down settings activity by category, click: + + - **All** to view all activity. + - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,^[[Manage artifact types](/guide/validation/manage-artifact-types.qmd)] severities,^[[Manage artifact severities](/guide/validation/manage-artifact-severities.qmd)] and fields.^[[Manage artifact fields](/guide/validation/manage-artifact-fields.qmd)] + - **Business Units** to view activity related to business unit creation, updates, and deletions.^[[Set up your organization](/guide/configuration/set-up-your-organization.qmd#manage-business-units)] + - **Documents** to view activity related to creation of, updates to, and deletions of document types,^[[Manage document types](/guide/templates/manage-document-types.qmd)] templates,^[[Manage document templates](/guide/templates/manage-document-templates.qmd)] and library blocks.^[[Manage text block library](/guide/templates/manage-text-block-library.qmd)] + - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,^[[Customize {{< var validmind.checker >}}](/guide/templates/customize-document-checker.qmd)] as well as risk areas and validation guidelines.^[[Manage validation guidelines](/guide/validation/manage-validation-guidelines.qmd)] + - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.^[[Manage groups](/guide/configuration/manage-groups.qmd)] + - **Integration Connections** to view activity related to integration connection creation, update, and removal.^[[Managing integrations](/guide/integrations/managing-integrations.qmd)] + - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. + - **Inventory Records** to view activity related to inventory record type^[[Manage inventory record types](/guide/inventory/manage-inventory-record-types.qmd)] or record field^[[Manage inventory fields](/guide/inventory/manage-inventory-fields.qmd)] creation, updates, and deletions. + - **Organization Settings** to view activity related to authentication configuration, email notifications,^[[Manage platform notifications](/guide/configuration/manage-platform-notifications.qmd#customize-email-notifications)] and general settings for documents.^[[Managing documents](/guide/templates/managing-documents.qmd#manage-document-defaults)] + - **Permissions** to view activity related to permission changes on roles.^[[Manage permissions](/guide/configuration/manage-permissions.qmd)] + - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.^[[Manage roles](/guide/configuration/manage-roles.qmd)] + - **Users** to view activity related to user information updates and user invitations.^[[Manage users](/guide/configuration/manage-users.qmd)] + - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.^[[Manage secrets](/guide/integrations/manage-secrets.qmd#webhook-secrets)] + - **Workflows** to view activity related to workflow creation, publications, and deletions.^[[Setting up workflows](/guide/workflows/setting-up-workflows.qmd)] + + When enough activity categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through the categories. + + ### View more details about specific activity + + To view more details about specific activity, click {{< fa chevron-down >}} when the option is available on an entry. + + ::: :::: diff --git a/site/guide/reporting/tracking-organization-activity.qmd b/site/guide/reporting/tracking-organization-activity.qmd index 4f3160a54..696555774 100644 --- a/site/guide/reporting/tracking-organization-activity.qmd +++ b/site/guide/reporting/tracking-organization-activity.qmd @@ -12,7 +12,7 @@ listing: grid-columns: 2 fields: [title, description] contents: - - ../inventory/view-record-activity.qmd + - view-record-activity.qmd - view-settings-activity.qmd --- diff --git a/site/guide/inventory/view-record-activity.qmd b/site/guide/reporting/view-record-activity.qmd similarity index 95% rename from site/guide/inventory/view-record-activity.qmd rename to site/guide/reporting/view-record-activity.qmd index 49f336589..f107c10f8 100644 --- a/site/guide/inventory/view-record-activity.qmd +++ b/site/guide/reporting/view-record-activity.qmd @@ -8,6 +8,7 @@ aliases: - /guide/documentation/view-documentation-activity.html - /guide/view-documentation-activity.html - /guide/model-inventory/view-model-activity.html + - /guide/inventory/view-record-activity.html --- Track information events associated with a specific record. Audit a history of updates made to the record, including stage or workflow transitions, updates on associated documents, activity on logged artifacts, and more. @@ -103,7 +104,7 @@ Filters persist in the URL, so you can share or bookmark a filtered view. [^1]: [Register records in the inventory](/guide/inventory/register-records-in-inventory.qmd) -[^2]: [Edit inventory fields](edit-inventory-fields.qmd) +[^2]: [Edit inventory fields](/guide/inventory/edit-inventory-fields.qmd) [^3]: [Manage inventory record types](/guide/inventory/manage-inventory-record-types.qmd) @@ -119,7 +120,7 @@ Filters persist in the URL, so you can share or bookmark a filtered view. [^9]: [Manage record stakeholder types](/guide/configuration/manage-record-stakeholder-types.qmd#manage-stakeholder-types-on-records) -[^10]: [Edit inventory fields](edit-inventory-fields.qmd) +[^10]: [Edit inventory fields](/guide/inventory/edit-inventory-fields.qmd) [^11]: [Working with documentation](/guide/documentation/working-with-documentation.qmd) @@ -133,7 +134,7 @@ Filters persist in the URL, so you can share or bookmark a filtered view. [^16]: For example, selecting **Artifacts: Validation Issue** shows a secondary selection for the names of validation issue artifact type events. -[^17]: [Edit inventory fields](edit-inventory-fields.qmd) +[^17]: [Edit inventory fields](/guide/inventory/edit-inventory-fields.qmd) [^18]: [Working with documentation](/guide/documentation/working-with-documentation.qmd) diff --git a/site/guide/reporting/view-settings-activity.qmd b/site/guide/reporting/view-settings-activity.qmd index 475010d0a..c73a1aa8c 100644 --- a/site/guide/reporting/view-settings-activity.qmd +++ b/site/guide/reporting/view-settings-activity.qmd @@ -29,45 +29,3 @@ Track information events associated with your organization's settings. Audit a h [^1]: [Manage permissions](/guide/configuration/manage-permissions.qmd) - - - - diff --git a/site/guide/validation/preparing-validation-reports.qmd b/site/guide/validation/preparing-validation-reports.qmd index df3c6a6c9..6ac8b0f0c 100644 --- a/site/guide/validation/preparing-validation-reports.qmd +++ b/site/guide/validation/preparing-validation-reports.qmd @@ -166,7 +166,7 @@ Wrap up your report, collaborate with other stakeholders, and submit your work f [^12]: [Manage validation guidelines](/guide/validation/manage-validation-guidelines.qmd) -[^13]: [View record activity](/guide/inventory/view-record-activity.qmd) +[^13]: [View record activity](/guide/reporting/view-record-activity.qmd) [^14]: [Collaborate with others](/guide/documentation/collaborate-with-others.qmd) diff --git a/site/guide/validation/update-artifacts.qmd b/site/guide/validation/update-artifacts.qmd index 34894b1c6..45bad46e1 100644 --- a/site/guide/validation/update-artifacts.qmd +++ b/site/guide/validation/update-artifacts.qmd @@ -136,7 +136,7 @@ If you logged an artifact in error or otherwise no longer need to track that art [^7]: [View and filter artifacts](view-filter-artifacts.qmd) -[^8]: [View record activity](/guide/inventory/view-record-activity.qmd) +[^8]: [View record activity](/guide/reporting/view-record-activity.qmd) [^9]: [Link artifacts to reports](/guide/validation/assess-compliance.qmd#link-artifacts-to-reports) diff --git a/site/guide/workflows/_transition-workflows.qmd b/site/guide/workflows/_transition-workflows.qmd index a94ba254d..870a4d76c 100644 --- a/site/guide/workflows/_transition-workflows.qmd +++ b/site/guide/workflows/_transition-workflows.qmd @@ -47,7 +47,7 @@ To transition a workflow into next steps: [^submission-drafts]: - - Working copies are tracked under record activity: [View record activity](/guide/inventory/view-record-activity.qmd) + - Working copies are tracked under record activity: [View record activity](/guide/reporting/view-record-activity.qmd) - Working copies can be viewed, edited, and submitted by any other users with permissions to transition the workflow into its next state. - Fields values updated via working copies will reflect on the field itself. diff --git a/site/releases/2023/release-notes-2023-jul-24.qmd b/site/releases/2023/release-notes-2023-jul-24.qmd index 5c9bdc6b0..deb842b84 100644 --- a/site/releases/2023/release-notes-2023-jul-24.qmd +++ b/site/releases/2023/release-notes-2023-jul-24.qmd @@ -116,7 +116,7 @@ We've now enhanced the flexibility of documenting models by allowing you to swap ::: ::: {.w-40-ns .tc} -[View record activity](/guide/inventory/view-record-activity.qmd){.button .button-green} +[View record activity](/guide/reporting/view-record-activity.qmd){.button .button-green} [Working with the inventory](/guide/inventory/working-with-the-inventory.qmd){.button .button-green} diff --git a/site/releases/2024/2024-dec-06/release-notes.qmd b/site/releases/2024/2024-dec-06/release-notes.qmd index 6f0b2eb79..aacebdeed 100644 --- a/site/releases/2024/2024-dec-06/release-notes.qmd +++ b/site/releases/2024/2024-dec-06/release-notes.qmd @@ -919,7 +919,7 @@ This new guide features expanded information on the auditing feature, including ::: ::: {.w-30-ns .tc} -[View record activity](/guide/inventory/view-record-activity.qmd){.button} +[View record activity](/guide/reporting/view-record-activity.qmd){.button} ::: diff --git a/site/releases/2024/2024-dec-24/release-notes.qmd b/site/releases/2024/2024-dec-24/release-notes.qmd index 9fefde093..17ee901a8 100644 --- a/site/releases/2024/2024-dec-24/release-notes.qmd +++ b/site/releases/2024/2024-dec-24/release-notes.qmd @@ -436,6 +436,6 @@ The **{{< fa cube >}} Developers** portal also got a makeover, with a {{< var vm [^2]: [Working with analytics](/guide/reporting/working-with-analytics.qmd) -[^3]: [View record activity](/guide/inventory/view-record-activity.qmd) +[^3]: [View record activity](/guide/reporting/view-record-activity.qmd) [^4]: [Manage permissions](/guide/configuration/manage-permissions.qmd) \ No newline at end of file diff --git a/site/releases/2024/2024-feb-14/highlights.qmd b/site/releases/2024/2024-feb-14/highlights.qmd index ca223cc2e..42a50f830 100644 --- a/site/releases/2024/2024-feb-14/highlights.qmd +++ b/site/releases/2024/2024-feb-14/highlights.qmd @@ -285,7 +285,7 @@ We implemented a toggle feature in the **{{< fa wifi >}} Model Activity** and Re ::: {.w-30-ns} -[View record activity](/guide/inventory/view-record-activity.qmd){.button} +[View record activity](/guide/reporting/view-record-activity.qmd){.button} ::: @@ -378,7 +378,7 @@ We implemented a toggle feature in the **{{< fa wifi >}} Model Activity** and Re ::: ::: {.w-30-ns .tc} -[View record activity](/guide/inventory/view-record-activity.qmd){.button} +[View record activity](/guide/reporting/view-record-activity.qmd){.button} ::: diff --git a/site/releases/2024/2024-jan-18/highlights.qmd b/site/releases/2024/2024-jan-18/highlights.qmd index a95046768..df10d2f02 100644 --- a/site/releases/2024/2024-jan-18/highlights.qmd +++ b/site/releases/2024/2024-jan-18/highlights.qmd @@ -237,7 +237,7 @@ You use these new template management features to: ::: ::: {.w-25-ns .tc} -[View record activity](/guide/inventory/view-record-activity.qmd){.button} +[View record activity](/guide/reporting/view-record-activity.qmd){.button} ::: diff --git a/site/releases/2024/2024-oct-22/release-notes.qmd b/site/releases/2024/2024-oct-22/release-notes.qmd index 469e0a772..a7eb8aa28 100644 --- a/site/releases/2024/2024-oct-22/release-notes.qmd +++ b/site/releases/2024/2024-oct-22/release-notes.qmd @@ -441,7 +441,7 @@ The new activity type filters are available on: ::: {.w-40-ns} -[Filter activity](/guide/inventory/view-record-activity.qmd#filter-activity){.button} +[Filter activity](/guide/reporting/view-record-activity.qmd#filter-activity){.button} ::: diff --git a/site/training/common-slides/_track-changes-updates.qmd b/site/training/common-slides/_track-changes-updates.qmd index b7041942a..5c2e1a33d 100644 --- a/site/training/common-slides/_track-changes-updates.qmd +++ b/site/training/common-slides/_track-changes-updates.qmd @@ -9,7 +9,7 @@ SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial --> Track changes & updates ::: {.tc} -[Learn more ...](/guide/inventory/view-record-activity.qmd){.button target="_blank"} +[Learn more ...](/guide/reporting/view-record-activity.qmd){.button target="_blank"} :::
    Try it **live** on the next page. {{< fa hand-point-right >}} @@ -18,9 +18,9 @@ Track changes & updates ::: {.column width="70%" .bl .pl4 .f4} #### View record activity -{{< include /guide/inventory/_view-record-activity-overview.qmd >}} +{{< include /guide/reporting/_view-record-activity-overview.qmd >}} -{{< include /guide/inventory/_view-record-activity-steps.qmd >}} +{{< include /guide/reporting/_view-record-activity-steps.qmd >}} ::: From 75e993c1ba558125b32ddadd2d6284eb261386fa Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 9 Jun 2026 10:48:58 -0700 Subject: [PATCH 18/41] save --- .../_view-settings-activity-steps.qmd | 52 +++++++++---------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/site/guide/reporting/_view-settings-activity-steps.qmd b/site/guide/reporting/_view-settings-activity-steps.qmd index af3480ba8..048c9ee7c 100644 --- a/site/guide/reporting/_view-settings-activity-steps.qmd +++ b/site/guide/reporting/_view-settings-activity-steps.qmd @@ -16,20 +16,20 @@ To review settings activity for your organization: To narrow down settings activity by category, click: - **All** to view all activity. - - **Artifacts** to view activity related to creation of, updates to, and deletions of [artifact types](/guide/validation/manage-artifact-types.qmd){target="_blank"}, [severities](/guide/validation/manage-artifact-severities.qmd){target="_blank"}, and [fields](/guide/validation/manage-artifact-fields.qmd){target="_blank"}. - - **Business Units** to view activity related to [business unit creation, updates, and deletions](/guide/configuration/set-up-your-organization.qmd#manage-business-units){target="_blank"}. - - **Documents** to view activity related to creation of, updates to, and deletions of [document types](/guide/templates/manage-document-types.qmd){target="_blank"}, [templates](/guide/templates/manage-document-templates.qmd){target="_blank"}, and [library blocks](/guide/templates/manage-text-block-library.qmd){target="_blank"}. - - **Governance** to view activity related to creation of, updates to, and deletions of [regulations or policies](/guide/templates/customize-document-checker.qmd){target="_blank"}, as well as risk areas and [validation guidelines](/guide/validation/manage-validation-guidelines.qmd){target="_blank"}. - - **Groups** to view activity related to [group creation, updates, deletions, and group membership changes](/guide/configuration/manage-groups.qmd){target="_blank"}. - - **Integration Connections** to view activity related to [integration connection creation, update, and removal](/guide/integrations/managing-integrations.qmd){target="_blank"}. + - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,^[[Manage artifact types](/guide/validation/manage-artifact-types.qmd)] severities,^[[Manage artifact severities](/guide/validation/manage-artifact-severities.qmd)] and fields.^[[Manage artifact fields](/guide/validation/manage-artifact-fields.qmd)] + - **Business Units** to view activity related to business unit creation, updates, and deletions.^[[Set up your organization](/guide/configuration/set-up-your-organization.qmd#manage-business-units)] + - **Documents** to view activity related to creation of, updates to, and deletions of document types,^[[Manage document types](/guide/templates/manage-document-types.qmd)] templates,^[[Manage document templates](/guide/templates/manage-document-templates.qmd)] and library blocks.^[[Manage text block library](/guide/templates/manage-text-block-library.qmd)] + - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,^[[Customize {{< var validmind.checker >}}](/guide/templates/customize-document-checker.qmd)] as well as risk areas and validation guidelines.^[[Manage validation guidelines](/guide/validation/manage-validation-guidelines.qmd)] + - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.^[[Manage groups](/guide/configuration/manage-groups.qmd)] + - **Integration Connections** to view activity related to integration connection creation, update, and removal.^[[Managing integrations](/guide/integrations/managing-integrations.qmd)] - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - - **Inventory Records** to view activity related to [inventory record type](/guide/inventory/manage-inventory-record-types.qmd){target="_blank"} or [record field](/guide/inventory/manage-inventory-fields.qmd){target="_blank"} creation, updates, and deletions. - - **Organization Settings** to view activity related to authentication configuration, [email notifications](/guide/configuration/manage-platform-notifications.qmd#customize-email-notifications){target="_blank"}, and [general settings for documents](/guide/templates/managing-documents.qmd#manage-document-defaults){target="_blank"}. - - **Permissions** to view activity related to [permission changes on roles](/guide/configuration/manage-permissions.qmd){target="_blank"}. - - **Roles** to view activity related to [role creation, updates, deletions, and user role assignments](/guide/configuration/manage-roles.qmd){target="_blank"}. - - **Users** to view activity related to [user information updates and user invitations](/guide/configuration/manage-users.qmd){target="_blank"}. - - **Webhook Secrets** to view activity related to [webhook secret creation, revocation, and deletion](/guide/integrations/manage-secrets.qmd#webhook-secrets){target="_blank"}. - - **Workflows** to view activity related to [workflow creation, publications, and deletions](/guide/workflows/setting-up-workflows.qmd){target="_blank"}. + - **Inventory Records** to view activity related to inventory record type^[[Manage inventory record types](/guide/inventory/manage-inventory-record-types.qmd)] or record field^[[Manage inventory fields](/guide/inventory/manage-inventory-fields.qmd)] creation, updates, and deletions. + - **Organization Settings** to view activity related to authentication configuration, email notifications,^[[Manage platform notifications](/guide/configuration/manage-platform-notifications.qmd#customize-email-notifications)] and general settings for documents.^[[Managing documents](/guide/templates/managing-documents.qmd#manage-document-defaults)] + - **Permissions** to view activity related to permission changes on roles.^[[Manage permissions](/guide/configuration/manage-permissions.qmd)] + - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.^[[Manage roles](/guide/configuration/manage-roles.qmd)] + - **Users** to view activity related to user information updates and user invitations.^[[Manage users](/guide/configuration/manage-users.qmd)] + - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.^[[Manage secrets](/guide/integrations/manage-secrets.qmd#webhook-secrets)] + - **Workflows** to view activity related to workflow creation, publications, and deletions.^[[Setting up workflows](/guide/workflows/setting-up-workflows.qmd)] When enough activity categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through the categories. @@ -53,20 +53,20 @@ To review settings activity for your organization: To narrow down settings activity by category, click: - **All** to view all activity. - - **Artifacts** to view activity related to creation of, updates to, and deletions of artifact types,^[[Manage artifact types](/guide/validation/manage-artifact-types.qmd)] severities,^[[Manage artifact severities](/guide/validation/manage-artifact-severities.qmd)] and fields.^[[Manage artifact fields](/guide/validation/manage-artifact-fields.qmd)] - - **Business Units** to view activity related to business unit creation, updates, and deletions.^[[Set up your organization](/guide/configuration/set-up-your-organization.qmd#manage-business-units)] - - **Documents** to view activity related to creation of, updates to, and deletions of document types,^[[Manage document types](/guide/templates/manage-document-types.qmd)] templates,^[[Manage document templates](/guide/templates/manage-document-templates.qmd)] and library blocks.^[[Manage text block library](/guide/templates/manage-text-block-library.qmd)] - - **Governance** to view activity related to creation of, updates to, and deletions of regulations or policies,^[[Customize {{< var validmind.checker >}}](/guide/templates/customize-document-checker.qmd)] as well as risk areas and validation guidelines.^[[Manage validation guidelines](/guide/validation/manage-validation-guidelines.qmd)] - - **Groups** to view activity related to group creation, updates, deletions, and group membership changes.^[[Manage groups](/guide/configuration/manage-groups.qmd)] - - **Integration Connections** to view activity related to integration connection creation, update, and removal.^[[Managing integrations](/guide/integrations/managing-integrations.qmd)] + - **Artifacts** to view activity related to creation of, updates to, and deletions of [artifact types](/guide/validation/manage-artifact-types.qmd){target="_blank"}, [severities](/guide/validation/manage-artifact-severities.qmd){target="_blank"}, and [fields](/guide/validation/manage-artifact-fields.qmd){target="_blank"}. + - **Business Units** to view activity related to [business unit creation, updates, and deletions](/guide/configuration/set-up-your-organization.qmd#manage-business-units){target="_blank"}. + - **Documents** to view activity related to creation of, updates to, and deletions of [document types](/guide/templates/manage-document-types.qmd){target="_blank"}, [templates](/guide/templates/manage-document-templates.qmd){target="_blank"}, and [library blocks](/guide/templates/manage-text-block-library.qmd){target="_blank"}. + - **Governance** to view activity related to creation of, updates to, and deletions of [regulations or policies](/guide/templates/customize-document-checker.qmd){target="_blank"}, as well as [risk areas and validation guidelines](/guide/validation/manage-validation-guidelines.qmd){target="_blank"}. + - **Groups** to view activity related to [group creation, updates, deletions, and group membership changes](/guide/configuration/manage-groups.qmd){target="_blank"}. + - **Integration Connections** to view activity related to [integration connection creation, update, and removal](/guide/integrations/managing-integrations.qmd){target="_blank"}. - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - - **Inventory Records** to view activity related to inventory record type^[[Manage inventory record types](/guide/inventory/manage-inventory-record-types.qmd)] or record field^[[Manage inventory fields](/guide/inventory/manage-inventory-fields.qmd)] creation, updates, and deletions. - - **Organization Settings** to view activity related to authentication configuration, email notifications,^[[Manage platform notifications](/guide/configuration/manage-platform-notifications.qmd#customize-email-notifications)] and general settings for documents.^[[Managing documents](/guide/templates/managing-documents.qmd#manage-document-defaults)] - - **Permissions** to view activity related to permission changes on roles.^[[Manage permissions](/guide/configuration/manage-permissions.qmd)] - - **Roles** to view activity related to role creation, updates, deletions, and user role assignments.^[[Manage roles](/guide/configuration/manage-roles.qmd)] - - **Users** to view activity related to user information updates and user invitations.^[[Manage users](/guide/configuration/manage-users.qmd)] - - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.^[[Manage secrets](/guide/integrations/manage-secrets.qmd#webhook-secrets)] - - **Workflows** to view activity related to workflow creation, publications, and deletions.^[[Setting up workflows](/guide/workflows/setting-up-workflows.qmd)] + - **Inventory Records** to view activity related to [inventory record type](/guide/inventory/manage-inventory-record-types.qmd){target="_blank"} or [record field](/guide/inventory/manage-inventory-fields.qmd){target="_blank"} creation, updates, and deletions. + - **Organization Settings** to view activity related to authentication configuration, [email notifications](/guide/configuration/manage-platform-notifications.qmd#customize-email-notifications){target="_blank"}, and [general settings for documents](/guide/templates/managing-documents.qmd#manage-document-defaults){target="_blank"}. + - **Permissions** to view activity related to [permission changes on roles](/guide/configuration/manage-permissions.qmd){target="_blank"}. + - **Roles** to view activity related to [role creation, updates, deletions, and user role assignments](/guide/configuration/manage-roles.qmd){target="_blank"}. + - **Users** to view activity related to [user information updates and user invitations](/guide/configuration/manage-users.qmd){target="_blank"}. + - **Webhook Secrets** to view activity related to [webhook secret creation, revocation, and deletion](/guide/integrations/manage-secrets.qmd#webhook-secrets){target="_blank"}. + - **Workflows** to view activity related to [workflow creation, publications, and deletions](/guide/workflows/setting-up-workflows.qmd){target="_blank"}. When enough activity categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through the categories. From 5af31544c9f73a80e90a581ac02a0c5be9c5bfce Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 9 Jun 2026 11:02:53 -0700 Subject: [PATCH 19/41] training done --- .../_view-settings-activity-steps.qmd | 39 +++------------ .../organizational-oversight-reporting.qmd | 48 +++++++++++++++++-- .../common-slides/_track-changes-updates.qmd | 4 +- .../common-slides/_view-record-activity.qmd | 27 +++++++++++ .../finalizing-documentation.qmd | 2 +- .../finalizing-validation-reports.qmd | 2 +- 6 files changed, 79 insertions(+), 43 deletions(-) create mode 100644 site/training/common-slides/_view-record-activity.qmd diff --git a/site/guide/reporting/_view-settings-activity-steps.qmd b/site/guide/reporting/_view-settings-activity-steps.qmd index 048c9ee7c..b53fb6ed3 100644 --- a/site/guide/reporting/_view-settings-activity-steps.qmd +++ b/site/guide/reporting/_view-settings-activity-steps.qmd @@ -4,10 +4,10 @@ SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial --> To review settings activity for your organization: +:::: {.content-visible unless-format="revealjs"} 1. In the left sidebar, click **{{< fa wifi >}} Activity**. -:::: {.content-visible unless-format="revealjs"} -2. Select the **Settings Activity** tab. +2. Select the **Settings Activity** tab: ::: {.panel-tabset} @@ -31,7 +31,7 @@ To review settings activity for your organization: - **Webhook Secrets** to view activity related to webhook secret creation, revocation, and deletion.^[[Manage secrets](/guide/integrations/manage-secrets.qmd#webhook-secrets)] - **Workflows** to view activity related to workflow creation, publications, and deletions.^[[Setting up workflows](/guide/workflows/setting-up-workflows.qmd)] - When enough activity categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through the categories. + When enough activity categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through left or right respectively through the list. ### View more details about specific activity @@ -42,38 +42,11 @@ To review settings activity for your organization: :::: - :::: {.content-hidden unless-format="revealjs"} +1. In the left sidebar, click **{{< fa wifi >}} Activity**. 2. Select the **Settings Activity** tab: - ::: {.panel-tabset} - - ### Filter settings activity by category - - To narrow down settings activity by category, click: - - - **All** to view all activity. - - **Artifacts** to view activity related to creation of, updates to, and deletions of [artifact types](/guide/validation/manage-artifact-types.qmd){target="_blank"}, [severities](/guide/validation/manage-artifact-severities.qmd){target="_blank"}, and [fields](/guide/validation/manage-artifact-fields.qmd){target="_blank"}. - - **Business Units** to view activity related to [business unit creation, updates, and deletions](/guide/configuration/set-up-your-organization.qmd#manage-business-units){target="_blank"}. - - **Documents** to view activity related to creation of, updates to, and deletions of [document types](/guide/templates/manage-document-types.qmd){target="_blank"}, [templates](/guide/templates/manage-document-templates.qmd){target="_blank"}, and [library blocks](/guide/templates/manage-text-block-library.qmd){target="_blank"}. - - **Governance** to view activity related to creation of, updates to, and deletions of [regulations or policies](/guide/templates/customize-document-checker.qmd){target="_blank"}, as well as [risk areas and validation guidelines](/guide/validation/manage-validation-guidelines.qmd){target="_blank"}. - - **Groups** to view activity related to [group creation, updates, deletions, and group membership changes](/guide/configuration/manage-groups.qmd){target="_blank"}. - - **Integration Connections** to view activity related to [integration connection creation, update, and removal](/guide/integrations/managing-integrations.qmd){target="_blank"}. - - **Integration Secrets** to view activity related to integration secret creation, revocation, and deletion. - - **Inventory Records** to view activity related to [inventory record type](/guide/inventory/manage-inventory-record-types.qmd){target="_blank"} or [record field](/guide/inventory/manage-inventory-fields.qmd){target="_blank"} creation, updates, and deletions. - - **Organization Settings** to view activity related to authentication configuration, [email notifications](/guide/configuration/manage-platform-notifications.qmd#customize-email-notifications){target="_blank"}, and [general settings for documents](/guide/templates/managing-documents.qmd#manage-document-defaults){target="_blank"}. - - **Permissions** to view activity related to [permission changes on roles](/guide/configuration/manage-permissions.qmd){target="_blank"}. - - **Roles** to view activity related to [role creation, updates, deletions, and user role assignments](/guide/configuration/manage-roles.qmd){target="_blank"}. - - **Users** to view activity related to [user information updates and user invitations](/guide/configuration/manage-users.qmd){target="_blank"}. - - **Webhook Secrets** to view activity related to [webhook secret creation, revocation, and deletion](/guide/integrations/manage-secrets.qmd#webhook-secrets){target="_blank"}. - - **Workflows** to view activity related to [workflow creation, publications, and deletions](/guide/workflows/setting-up-workflows.qmd){target="_blank"}. - - When enough activity categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through the categories. - - ### View more details about specific activity - - To view more details about specific activity, click {{< fa chevron-down >}} when the option is available on an entry. - - ::: + - Narrow down settings activity by clicking on the tab for the category you want to view activity for. When enough categories are displayed, click {{< fa chevron-left >}} or {{< fa chevron-right >}} to scroll through left or right respectively through the list. + - View more details about specific activity by clicking {{< fa chevron-down >}} when the option is available on an entry. :::: diff --git a/site/training/administrator-fundamentals/organizational-oversight-reporting.qmd b/site/training/administrator-fundamentals/organizational-oversight-reporting.qmd index 3cc2be264..1a04aff90 100644 --- a/site/training/administrator-fundamentals/organizational-oversight-reporting.qmd +++ b/site/training/administrator-fundamentals/organizational-oversight-reporting.qmd @@ -49,7 +49,7 @@ This final module is part of a four-part series: ## Module 4 — Contents {.center} ::: {.f2} -1. [Audit record activity](#audit-record-activity) +1. [Audit organization activity](#audit-organization-activity) 2. [Audit workflows](#audit-workflows) 3. [Work with analytics](#work-with-analytics) @@ -100,9 +100,47 @@ Already logged in and refreshed this module? Click [{{< fa chevron-right >}}]() ::: -# Audit record activity {background-color="#083E44" background-image="/training/assets/home-hero.svg"} +# Audit organization activity {background-color="#083E44" background-image="/training/assets/home-hero.svg"} -{{< include /training/common-slides/_track-changes-updates.qmd >}} +## {.scrollable} + +:::: {.columns} +::: {.column width="30%" .pr4 .f2} +View settings activity + +::: {.tc} +[Learn more ...](/guide/reporting/view-settings-activity.qmd){.button target="_blank"} +::: + +
    Try it **live** on the next page. {{< fa hand-point-right >}} +::: + +::: {.column width="70%" .bl .pl4 .f4} +{{< include /guide/reporting/_view-settings-activity-overview.qmd >}} + +{{< include /guide/reporting/_view-settings-activity-steps.qmd >}} + +::: + +:::: + +## {background-iframe="https://app.prod.validmind.ai/activity" background-interactive="true" data-preload="yes"} + +:::: {.slideover--b .auto-collapse-10} +::: {.tc} +**Review settings activity** +::: + +1. In the left sidebar, click **{{< fa wifi >}} Activity**. +2. Select the **Settings Activity** tab: + - Narrow down settings activity by clicking on the tab for the category you want to view activity for. + - View more details about specific activity by clicking {{< fa chevron-down >}} when the option is available on an entry. + +When you're done, click [{{< fa chevron-right >}}]() to continue. + +:::: + +{{< include /training/common-slides/_view-record-activity.qmd >}} {{< include /training/common-slides/_review-model-activity.qmd >}} @@ -112,7 +150,7 @@ Already logged in and refreshed this module? Click [{{< fa chevron-right >}}]() :::: {.columns} ::: {.column width="30%" .pr4 .f2} -Review workflow executions +View workflow executions ::: {.f5 .nt2 .pl2 .mb4} (Scroll down for the full instructions.) @@ -153,7 +191,7 @@ When you're done, click [{{< fa chevron-right >}}]() to continue. :::: {.columns} ::: {.column width="30%" .pr4 .f2} -Review workflows on records & artifacts +View record & artifact workflows ::: {.f5 .nt2 .pl2 .mb4} (Scroll down for the full instructions.) diff --git a/site/training/common-slides/_track-changes-updates.qmd b/site/training/common-slides/_track-changes-updates.qmd index 5c2e1a33d..b3e11642b 100644 --- a/site/training/common-slides/_track-changes-updates.qmd +++ b/site/training/common-slides/_track-changes-updates.qmd @@ -6,7 +6,7 @@ SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial --> :::: {.columns} ::: {.column width="30%" .pr4 .f2} -Track changes & updates +View record activity ::: {.tc} [Learn more ...](/guide/reporting/view-record-activity.qmd){.button target="_blank"} @@ -16,8 +16,6 @@ Track changes & updates ::: ::: {.column width="70%" .bl .pl4 .f4} -#### View record activity - {{< include /guide/reporting/_view-record-activity-overview.qmd >}} {{< include /guide/reporting/_view-record-activity-steps.qmd >}} diff --git a/site/training/common-slides/_view-record-activity.qmd b/site/training/common-slides/_view-record-activity.qmd new file mode 100644 index 000000000..b72cddc4a --- /dev/null +++ b/site/training/common-slides/_view-record-activity.qmd @@ -0,0 +1,27 @@ + + +## {.scrollable .center} + +:::: {.columns} +::: {.column width="30%" .pr4 .f2} +View record activity + +::: {.tc} +[Learn more ...](/guide/reporting/view-record-activity.qmd){.button target="_blank"} +::: + +
    Try it **live** on the next page. {{< fa hand-point-right >}} +::: + +::: {.column width="70%" .bl .pl4 .f4} +#### View record activity + +{{< include /guide/reporting/_view-record-activity-overview.qmd >}} + +{{< include /guide/reporting/_view-record-activity-steps.qmd >}} + +::: + +:::: \ No newline at end of file diff --git a/site/training/developer-fundamentals/finalizing-documentation.qmd b/site/training/developer-fundamentals/finalizing-documentation.qmd index ddfc6a166..4ab3f9a62 100644 --- a/site/training/developer-fundamentals/finalizing-documentation.qmd +++ b/site/training/developer-fundamentals/finalizing-documentation.qmd @@ -326,7 +326,7 @@ When you're done, click [{{< fa chevron-right >}}]() to continue. # Track activity {background-color="#083E44" background-image="/training/assets/home-hero.svg"} -{{< include /training/common-slides/_track-changes-updates.qmd >}} +{{< include /training/common-slides/_view-record-activity.qmd >}} {{< include /training/common-slides/_review-model-activity.qmd >}} diff --git a/site/training/validator-fundamentals/finalizing-validation-reports.qmd b/site/training/validator-fundamentals/finalizing-validation-reports.qmd index 5f7ca5093..e0afe4558 100644 --- a/site/training/validator-fundamentals/finalizing-validation-reports.qmd +++ b/site/training/validator-fundamentals/finalizing-validation-reports.qmd @@ -614,7 +614,7 @@ When you're done, click [{{< fa chevron-right >}}]() to continue. # Track activity {background-color="#083E44" background-image="/training/assets/home-hero.svg"} -{{< include /training/common-slides/_track-changes-updates.qmd >}} +{{< include /training/common-slides/_view-record-activity.qmd >}} {{< include /training/common-slides/_review-model-activity.qmd >}} From 117b8695a16e6f6a47756fb82c07cfbe76033e8c Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 9 Jun 2026 11:17:47 -0700 Subject: [PATCH 20/41] regen sitemap --- site/llm/chatbot-product-map.md | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/site/llm/chatbot-product-map.md b/site/llm/chatbot-product-map.md index 0ef651839..581976040 100644 --- a/site/llm/chatbot-product-map.md +++ b/site/llm/chatbot-product-map.md @@ -13,7 +13,7 @@ **Docs (primary):** - `/guide/configuration/managing-users.html` - - Sections: Key concepts; Key terms; Default roles; User management + - Sections: Key concepts; Key terms; Default roles; User management; What's next ### Governance @@ -69,7 +69,7 @@ **Docs (related):** - `/guide/configuration/managing-your-organization.html` - - Sections: Prerequisites; Switch between organizations; Change names of organizations; Manage document defaults; Tracked changes; Numbered table and figure captions; Organization setup + - Sections: Prerequisites; Switch between organizations; Change names of organizations; Organization setup; What's next ### Documents @@ -92,8 +92,8 @@ - Sections: Prerequisites; Add document types; Edit or delete document types; Development, Validation, and Monitoring document types are stock types and cannot be deleted. - `/guide/templates/manage-documents.html` - Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents -- `/guide/templates/working-with-document-templates.html` - - Sections: What's next +- `/guide/templates/managing-documents.html` + - Sections: Manage document defaults; Manage document types and documents; What's next ### Main navigation @@ -125,8 +125,8 @@ - Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents - `/guide/templates/manage-text-block-library.html` - Sections: Prerequisites; Add text blocks; Add existing text blocks to library; Duplicate text blocks; Edit text blocks; Delete text blocks -- `/guide/templates/working-with-document-templates.html` - - Sections: What's next +- `/guide/templates/managing-documents.html` + - Sections: Manage document defaults; Manage document types and documents; What's next ### Organization @@ -135,12 +135,12 @@ **Docs (primary):** - `/guide/configuration/manage-platform-notifications.html` (section: #customize-email-notifications) - - Sections: Prerequisites; View platform notifications; Review updates; Mark updates as read; Dismiss updates; Customize email notifications + - Sections: Prerequisites; View platform notifications; Review updates; Mark updates as read; Dismiss updates; Customize email notifications; What's next **Docs (related):** - `/guide/configuration/managing-your-organization.html` - - Sections: Prerequisites; Switch between organizations; Change names of organizations; Manage document defaults; Tracked changes; Numbered table and figure captions; Organization setup + - Sections: Prerequisites; Switch between organizations; Change names of organizations; Organization setup; What's next ### Artifacts @@ -224,7 +224,7 @@ - `/guide/configuration/manage-users.html` - Sections: Prerequisites; View and search for users; Manage user invitations; Invite new users; Monitor user invitations; Manage user roles - `/guide/configuration/managing-users.html` - - Sections: Key concepts; Key terms; Default roles; User management + - Sections: Key concepts; Key terms; Default roles; User management; What's next ### Integrations @@ -292,7 +292,7 @@ **Docs (primary):** - `/guide/configuration/managing-users.html` - - Sections: Key concepts; Key terms; Default roles; User management + - Sections: Key concepts; Key terms; Default roles; User management; What's next - `/guide/configuration/manage-users.html` (section: #manage-user-invitations) - Sections: Prerequisites; View and search for users; Manage user invitations; Invite new users; Monitor user invitations; Manage user roles @@ -303,7 +303,7 @@ **Docs (primary):** - `/guide/configuration/managing-your-organization.html` - - Sections: Prerequisites; Switch between organizations; Change names of organizations; Manage document defaults; Tracked changes; Numbered table and figure captions; Organization setup + - Sections: Prerequisites; Switch between organizations; Change names of organizations; Organization setup; What's next ### Users & Access @@ -319,7 +319,7 @@ - `/guide/configuration/manage-users.html` - Sections: Prerequisites; View and search for users; Manage user invitations; Invite new users; Monitor user invitations; Manage user roles - `/guide/configuration/managing-users.html` - - Sections: Key concepts; Key terms; Default roles; User management + - Sections: Key concepts; Key terms; Default roles; User management; What's next ### Main navigation @@ -374,7 +374,7 @@ **Docs (primary):** - `/guide/configuration/managing-users.html` - - Sections: Key concepts; Key terms; Default roles; User management + - Sections: Key concepts; Key terms; Default roles; User management; What's next - `/guide/configuration/manage-roles.html` - Sections: Prerequisites; Add or update roles; Two special default roles provided by } have unique characteristics.; Manage role permissions; Manage role users; Rename existing roles @@ -420,8 +420,8 @@ - Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents - `/guide/templates/manage-text-block-library.html` - Sections: Prerequisites; Add text blocks; Add existing text blocks to library; Duplicate text blocks; Edit text blocks; Delete text blocks -- `/guide/templates/working-with-document-templates.html` - - Sections: What's next +- `/guide/templates/managing-documents.html` + - Sections: Manage document defaults; Manage document types and documents; What's next ### Your Account @@ -436,7 +436,7 @@ **Docs (primary):** - `/guide/configuration/managing-users.html` - - Sections: Key concepts; Key terms; Default roles; User management + - Sections: Key concepts; Key terms; Default roles; User management; What's next - `/guide/configuration/manage-users.html` - Sections: Prerequisites; View and search for users; Manage user invitations; Invite new users; Monitor user invitations; Manage user roles From 292f29a43bfc9be7909c07d62f002da3c6c0fb9c Mon Sep 17 00:00:00 2001 From: Beck <164545837+validbeck@users.noreply.github.com> Date: Tue, 9 Jun 2026 11:29:31 -0700 Subject: [PATCH 21/41] oops i somehow committed some rendered files --- site/404.html | 821 -- .../contributing/style-guide/conventions.html | 1863 ---- .../style-guide/style-guide-listing.json | 7 - .../contributing/style-guide/style-guide.html | 1078 --- .../style-guide/voice-and-tone.html | 1216 --- .../contributing/validmind-community.html | 1095 --- .../deployment-options-listing.json | 7 - site/about/deployment/deployment-options.html | 1142 --- .../multi-tenant-cloud-listing.json | 4 - site/about/deployment/multi-tenant-cloud.html | 1121 --- .../system-access-requirements.html | 1025 --- .../virtual-private-validmind-listing.json | 4 - .../deployment/virtual-private-validmind.html | 1110 --- .../data-privacy-policy-listing.json | 4 - .../about/fine-print/data-privacy-policy.html | 1145 --- site/about/fine-print/license-agreement.html | 971 -- site/about/glossary/glossary.html | 1794 ---- 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    At the center of this page, you will see the elements organized from most recent to least recent by default.At the centre of this page, you will see the elements organised from most recent to least recent by default.
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    Register records in the inventoryInventory record registration
    Working with the inventoryUse the inventory
    Developer referenceArticles for developers
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    Get started with ​ValidMindGet Started with ​ValidMind
    In the left sidebar, click Inventory.In the left sidebar, click inventory.
    Honor the Human with ​ValidMindHonor the human with ​ValidMind
    On the landing page of your model, locate the [model status]{.smallcaps} section:On the landing page of your model, locate the MODEL STATUS section:
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    Login to ​ValidMind1. Logging into ​ValidMind.
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    At ​ValidMind, we value transparency and accessibility — we aim to speak simply and effectively. (e.g. Highlighting the important concept within a sentence.)At ​ValidMind, we value transparency and accessibility — we aim to speak simply and effectively. (e.g. Highlighting the entire sentence.)
    In the ValidMind Platform, click Inventory on the left sidebar.In the ValidMind Platform, click “ Inventory” on the left sidebar.
    Uncertainty can be summed up as the difference between reality and the outputs from the model selected to approximate reality.“Uncertainty” can be summed up as the difference between reality and the outputs from the model selected to approximate reality.
    “​ValidMind is the only platform today that is purpose-built for model risk management professionals in the banking industry,” Jacobi says.At ​ValidMind, we value “transparency and accessibility” — we aim to speak simply and effectively.
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    Screenshot of the verification email sent by ValidMind
    ![Verification email sent by ​ValidMind](/guide/configuration/verify-email.png){width=70% fig-alt="Screenshot of the verification email sent by ValidMind" .screenshot}![](/guide/configuration/verify-email.png){width=80%}
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    Learn how to store record identifier credentials in a .env file instead of using inline credentials.Learn how to store record identifier credentials in a “.env” file instead of using inline credentials.
    For example, the classifier_full_suite test suite runs tests from the tabular_dataset and classifier test suites to fully document the data and model sections for binary classification model use cases.For example, the “classifier_full_suite” test suite runs tests from the “tabular_dataset” and “classifier” test suites to fully document the data and model sections for binary classification model use cases.
    Under When these conditions are met, you are able to set both AND and OR conditions.Under When these conditions are met, you are able to set both “AND” and “OR” conditions.
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    SS1/23 – Model risk management principles for banksModel Validation
    validation reportbasel 4
    machine learningFinancial Services industry
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    ValidMind AI Risk Platform

    		{{< var validmind.product >}}Comprehensive suite of tools with a library for documenting and testing records (such as models), alongside a platform hosting cloud-based tools, APIs, databases, and validation engines.

    ValidMind Library

    		{{< var validmind.developer >}}Open-source library that connects to the ValidMind Platform.

    ValidMind Platform

    		{{< var validmind.platform >}}Hosted multi-tenant architecture that includes a cloud-based web interface.

    ValidMind Library Python API

    		{{< var validmind.api >}}Used to make calls to the ValidMind Library.21

    ​ValidMind

    		{{< var vm.product >}}Short form of ValidMind AI Risk Platform.

    library

    		{{< var vm.developer >}}Short form of ValidMind Library.

    platform

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    Python API

    		{{< var validmind.api >}}Short form of ValidMind Library Python API.

    https://app.prod.validmind.ai

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    https://app.ca1.validmind.ai

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-- Onboard new users
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    You’ll need to review the generated content.The generated content will be reviewed by you.
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    Model risk management aims to ensure the accuracy and reliability of models, reduce operational risks, and comply with regulatory requirements such as the Comprehensive Capital Analysis and Review (CCAR) and the Dodd-Frank Act Stress Tests (DFAST).MRM aims to corroborate the accuracy and reliability of models, abate OpRisk, and induce observation with regulatory requirements such as CCAR and DFAS.
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    User acknowledgement: Documenting artifacts can be difficult and tedious for even the most seasoned of validators.User dismissal: For experienced validators, documenting artifacts is a breeze.
    Success toast: Nice work — you’ve successfully registered your first record!Inappropriate humor: We lost your documentation, oops! Here, have a pony! (e.g. error message for serious issue)
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    While the layout of these columns on this page is fixed, you can export the data which will allow you to rearrange the information as desired.You can’t edit this page, or change the layout of these columns.
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    The artifacts are then submitted to the chair of the MRMG (model risk management group), who will then review the recommendations and provide their status assessments.The artifacts are then submitted to the chairman of the MRMG, who will then review the recommendations and provide his status assessments.
    ![Validation report section 2.1.1. that shows a compliance assessment with the option to link to evidence](link-evidence.png){fig-alt="A screenshot of the validation report section 2.1.1. that shows a compliance assessment with the option to link to evidence"}![](link-evidence.png)
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    After completing this quickstart, you will be able to view your test results as part of your documentation right in the ValidMind Platform.After completing this quickstart, the developer will be able to view the test results as part of the documentation right in the ValidMind Platform.
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    Once you’ve registered the record, you can then grab the unique code snippet that will have been generated for you to use in the next step.First, you must register the record as this will generate a unique code snippet that needs to be copied. Then, you need to retrieve the code snippet so that you can make use of it in the following step.
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    What if you were able to more easily, more effortlessly discern uncertainty, unleashing the full potential of your model outputs? That’s the power of ​ValidMind.​ValidMind easily and effortlessly discerns uncertainty, unleashing the full potential of model outputs.
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    Allow list the following domains in your organization’s firewall to ensure you can access the ValidMind Platform, and use a supported web browser.

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    This page outlines ​ValidMind’s data privacy policy, explaining how we protect your personal information.

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    Our goal is to provide transparency about our data handling practices and to demonstrate our commitment to protecting your privacy and data security.

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    commitment to transparency

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    Understanding our policies shouldn’t feel like deciphering code, so we’ve made our legal texts as clear and accessible as possible:

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    What is ​ValidMind’s data privacy policy?

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    The key points of our data privacy policy include:

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    • No personally identifiable information in documentation — When the ValidMind Library generates documentation, it ensures that no personally identifiable information (PII) is included. This practice is a critical part of our commitment to protecting your privacy and maintaining the confidentiality of your data.

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    • No storage of customer data — ​ValidMind does not retain any customer datasets or records (models). This policy is in place in order to protect your data privacy and security. By not storing this information, ​ValidMind minimizes the risk of unauthorized access or data breaches.

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    We believe it is important for users of ​ValidMind’s products to understand these practices as they reflect our dedication to data security and privacy.

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    Do you comply with the SOC 2 security standard?

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    Service Organization Control 2 (SOC 2) is a type of audit report that evaluates the security and privacy controls of a service organization, such as a cloud vendor like ​ValidMind. The report provides assurance to customers that an organization has implemented effective security and privacy controls to protect sensitive data.

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    ​ValidMind’s security and privacy controls are designed to align with the stringent requirements of the SOC 2 standard. This compliance means that ​ValidMind has established and consistently maintains a set of security measures and protocols that meet or exceed the benchmark set by SOC 2. We also regularly review and update these controls to ensure that they stay current with evolving security threats and regulatory requirements.

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    The ValidMind Platform is a secure, multi-tenant solution that can be hosted on Amazon Web Services (AWS), Microsoft Azure Cloud (Azure), or Google Cloud Platform (GCP). For organizations that require a stricter trust model and the highest level of security, such as financial services organizations handling highly sensitive data, ​ValidMind also offers a Virtual Private ValidMind (VPV) option to host our solution in a dedicated single-tenant cloud instance.

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    The Virtual Private ValidMind option provides all our features and services but hosted within a separate environment that is isolated from other ​ValidMind accounts. VPV accounts do not share resources with non-VPV accounts.

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    IMPORTANT: READ THIS SOFTWARE LICENSE AGREEMENT (THIS “AGREEMENT”) CAREFULLY BEFORE USING THE SOFTWARE. BY USING THE SOFTWARE, YOU ARE AGREEING TO BE BOUND BY THE TERMS OF THIS LICENSE AGREEMENT:

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    As between you and Licensor, this software and associated media, printed materials, and “online” or electronic documentation files (collectively, the “Software”), is the proprietary information of ValidMind Inc. (“Licensor”), and is protected under copyright and other intellectual property laws.

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    No part of this Software may be copied, reproduced, distributed, republished, displayed, posted or transmitted in any form or by any means, including, but not limited to, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of Licensor.

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    By installing, copying, or otherwise using the Software, you as a user of the Software (“you”) agree to be bound by the terms of this Agreement. If you do not agree to the terms of this Agreement, do not install or use the Software.

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    1. LICENSE GRANT. Subject to the terms and conditions of this Agreement, Licensor grants you a non-exclusive, non-transferable, limited license (without the right to sublicense) during the term of this Agreement to install and use the Software only in object code or byte code form for the sole purpose of testing its functionality.

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    2. OWNERSHIP. The Software is owned by Licensor and its licensors and is protected by copyright laws and international copyright treaties, as well as other intellectual property laws and treaties. As between you and Licensor, Licensor retains all right, title, and interest in and to the Software, including all intellectual property rights. This Agreement does not transfer any ownership of the Software to you.

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    3. RESTRICTIONS. You will not use the Software for any commercial, production, or operational purposes. You will not (a) modify, adapt, translate, reverse engineer, decompile, disassemble, or otherwise attempt to discover the source code of the Software including (without limitation) for the purpose of obtaining unauthorized access to the Software, (b) modify, translate, copy, or create derivative works based on the Software, (c) use the Software to create or develop a competitive product or service, (d) circumvent, remove, alter, or thwart any technological measure or content protections of the Software, (e) distribute, sublicense, rent, lease, or lend the Software to any third party, (f) remove or alter any copyright, trademark, or proprietary rights notice contained in the Software or (g) use the Software for any purpose except as expressly permitted under this Agreement.

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    4. CONFIDENTIALITY. You agree to safeguard the Software and its related materials with the same care and precaution as you use to protect your own proprietary information and trade secrets, but in no event less than a reasonable degree of care so that no unauthorized use is made of them and no disclosure of any part of their contents is made to anyone other than your employees, agents or consultants whose duties reasonably require such disclosure, or as necessary in the ordinary course of business. You shall make all such persons fully aware of their responsibility to fulfill your obligations under this Agreement and agree to be responsible for any breach of this Agreement by such persons. You agree to promptly notify Licensor if you obtain information as to any unauthorized possession, use or disclosure of the Software by any person or entity, and further agrees to cooperate with Licensor in protecting Licensor’s proprietary rights. Upon the request of Licensor, copies and embodiments of the Software and its related materials shall be promptly returned to Licensor by you or destroyed by you, and you agree to certify such destruction in writing.

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    5. TERM & TERMINATION. This Agreement is effective until terminated. The Licensee may terminate this Agreement at any time by destroying all copies of the Software and its related materials. The Licensor may terminate this License if you fail to comply with any term or condition of this Agreement. Upon termination, you must destroy all copies of the Software and its related materials in your possession.

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    6. DISCLAIMER OF WARRANTY. THE SOFTWARE IS PROVIDED “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. LICENSOR DOES NOT WARRANT THAT THE SOFTWARE WILL MEET YOUR REQUIREMENTS OR THAT OPERATION OF THE SOFTWARE WILL BE UNINTERRUPTED OR ERROR-FREE.

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    7. LIMITATION OF LIABILITY. TO THE EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT SHALL LICENSOR BE LIABLE FOR ANY (A) INDIRECT, PUNITIVE, EXEMPLARY, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES (INCLUDING LOST PROFITS) ARISING OUT OF OR IN CONNECTION WITH THIS AGREEMENT OR THE USE OR INABILITY TO USE THE SOFTWARE, EVEN IF LICENSEE HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES OR (B) ANY AMOUNTS IN EXCESS OF THE AMOUNT PAID BY YOU FOR THE SOFTWARE.

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    8. INDEMNIFICATION. You agree to indemnify and hold Licensor harmless from and against all loss, cost, expense or liability (including reasonable attorney’s fees) (i) arising out of a claim by a third party against Licensor based upon your use of the Software, or (ii) related to, or associated with your customizations, updates and/or corrections to the Software. Licensor shall have no liability to you for any damage sustained by you as a result of your use of the Software, whether such damages would arise as a result of breach of contract, tort or otherwise.

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    9. GOVERNING LAW. This Agreement and any dispute arising hereunder shall be governed by and construed in accordance with the laws of the State of California and the federal laws of the United States of America, without giving effect to any principles of conflicts of law.

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    10. ENTIRE AGREEMENT. This Agreement constitutes the entire agreement between you and Licensor with respect to the Software and supersedes all prior or contemporaneous communications and proposals, whether oral or written, between you and Licensor.

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    11. ASSIGNMENT. You will not assign your rights and obligations under this Agreement without prior written consent of the Licensor. Licensor may freely assign its rights and obligations without your consent.

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    12. MISCELLANEOUS. If any provision of this Agreement is found to be unenforceable or invalid, that provision will be limited or eliminated to the minimum extent necessary so that this Agreement will otherwise remain in full force and effect and enforceable. Without limiting anything herein, and except for your payment obligations, neither party shall have any liability for any failure or delay resulting from any condition beyond the reasonable control of such party, including but not limited to governmental action or acts of terrorism, earthquake or other acts of God, labor conditions, epidemics, pandemics, and power failures. For all purposes under this Agreement each party shall be and act as an independent contractor and shall not bind nor attempt to bind the other to any contract. Any notices in connection with this Agreement will be in writing.

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    By installing or using the Software, you acknowledge that you have read this Agreement, understand it, and agree to be bound by its terms and conditions.

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    Copyright © 2026 ValidMind Inc. All rights reserved.

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    documentation template7
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    8 Refer also to: validation reports

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    Artificial intelligence (AI) governance

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    AI

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    Refer to IBM’s series on artificial intelligence for more in-depth resources.

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    AI governance
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    -The organizational framework for directing and overseeing how AI is designed, deployed, and used. It sets policy, accountability, and decision rights, covering ethics, compliance, risk appetite, lifecycle controls, and ongoing oversight across people, process, and technology.11 -
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    11 Refer also to: AI governance

    AI system
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    -A combination of software, algorithms, and data designed to perform tasks that typically require human intelligence. In AI governance, an AI system is the primary unit of management, distinct from individual records (such as models).12 -
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    12 Refer to:

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    AI use case
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    -A specific application or deployment of AI technology to solve a business problem or achieve an objective. Use cases are often the unit of oversight in AI governance frameworks. -
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    artificial intelligence (AI)
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    -Artificial intelligence is a broad term used to classify machines that mimic human intelligence and human cognitive functions like problem-solving and learning. -
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    deep-learning
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    -A subset of machine learning that uses multi-layered neural networks (deep neural networks) to simulate the complex decision-making power of the human brain. -
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    generative AI (GenAI)
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    -Generative AI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on. -
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    human oversight
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    -Controls and processes ensuring human involvement in AI-driven decisions. Required by regulations like the EU AI Act for high-risk AI systems to enable human intervention and override capabilities.13 -
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    13 Refer also to: EU AI Act

    impact assessment
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    -An evaluation of the potential risks, harms, and consequences associated with deploying an AI system. Impact assessments are a core artifact in AI governance programs. -
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    large language model (LLM)
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    -An advanced type of artificial intelligence model designed to understand, generate, and interact with human language at a sophisticated level, such as ChatGPT.14 -
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    machine learning (ML)
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    -Machine learning is a subset of artificial intelligence that allows for optimization. It helps make predictions that minimize the errors that arise from merely guessing. -
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    risk tier
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    -A classification level assigned to an AI system based on its potential impact and risk. The EU AI Act defines tiers including prohibited, high-risk, limited-risk, and minimal-risk categories.15 -
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    15 Refer also to: EU AI Act

    traditional statistical model
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    -A mathematical framework used to analyze and make inferences from data. Traditional statistical models are foundational in statistics and serve to explain relationships, predict outcomes, and guide decision-making across various fields, such as economics, biology, engineering, and social sciences. -
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    use case owner
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    -The individual accountable for an AI use case within an organization. Responsible for decisions about AI deployment, compliance, and ongoing oversight. -
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    AI governance

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    AI ethics
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    -A set of principles and practices guiding the responsible design, development, and deployment of AI systems. Common tenets include fairness, transparency, accountability, privacy, and human well-being. -
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    AI lifecycle
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    -The end-to-end stages an AI system progresses through, including problem framing, data collection, model development, validation, deployment, monitoring, and retirement. Each stage carries distinct governance requirements. -
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    AI risk
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    -The potential for adverse outcomes — financial, reputational, ethical, regulatory, or societal — arising from the design, deployment, or use of AI systems. AI risk extends beyond traditional model risk to include concerns such as bias, opacity, misuse, and unintended consequences. -
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    algorithmic accountability
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    -The principle that organizations must take responsibility for the outcomes of the AI systems they deploy, including documenting decisions, monitoring performance, and providing mechanisms to identify and remediate harm. -
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    bias, algorithmic bias
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    -Systematic errors or unfair outcomes in AI system results that disproportionately affect specific groups. Sources include unrepresentative training data, flawed assumptions in system design, or feedback loops introduced during deployment. Detecting and mitigating bias is a core AI governance activity. -
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    EU AI Act
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    -A regulatory framework introduced by the European Union that classifies AI systems by risk tier16 — prohibited, high-risk, limited-risk, and minimal-risk — and imposes proportionate obligations such as risk management, data governance, transparency, human oversight, and conformity assessment. -
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    explainability
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    -The degree to which the internal mechanics or outputs of an AI system can be understood by humans. Explainability is a core requirement for high-risk AI systems and supports accountability, debugging, and regulatory review. -
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    fairness
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    -The principle that AI systems should produce equitable outcomes across individuals and groups. Fairness assessments are a routine part of bias evaluation and impact assessment within AI governance programs. -
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    ISO/IEC 42001
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    -An international management system standard for artificial intelligence published by the International Organization for Standardization. Provides requirements for establishing, implementing, maintaining, and continually improving an AI management system within an organization. -
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    model card, system card
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    -A standardized document that summarizes an AI system’s intended use, training data, performance characteristics, limitations, and ethical considerations. Model and system cards support transparency and informed deployment decisions.17 -
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    NIST AI Risk Management Framework (AI RMF)
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    -A voluntary framework published by the U.S. National Institute of Standards and Technology to help organizations manage risks associated with AI. Organized around four core functions: govern, map, measure, and manage. -
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    responsible AI
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    -An umbrella approach to designing, building, and deploying AI systems in ways that are ethical, transparent, accountable, fair, and aligned with human values and societal expectations. -
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    transparency
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    -The disclosure of meaningful information about an AI system’s design, data, capabilities, limitations, and decision-making processes to relevant stakeholders. Transparency supports trust, accountability, and informed oversight. -
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    Model risk management

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    Models

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    documentation, model documentation
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    -A structured and detailed record pertaining to a record (such as a model), encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. -
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    Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record’s application.

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    model
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    -SR 26-218 (which supersedes SR 11-7) defines a model as a “complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates.” Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. -
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    Within ​ValidMind, a model is a type of record tracked in the inventory.19

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    19 Refer also to: record

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    model development
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    -An iterative process in which many models are derived, tested, and built upon until a model fitting the desired criteria is achieved. -
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    model inventory20
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    20 Refer also to: inventory

    -A systematic and organized record of all quantitative and qualitative models used within an organization. This inventory facilitates oversight, tracking, and assessment by listing each model’s purpose, characteristics, owners, validation status, and associated risks. -
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    model lifecycle
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    -Subset of stages defining the lifecycle of a model; encompasses all steps for operating, governing, and maintaining a model until it is decommissioned (model development, model validation, model approval, model implementation, retirement). -
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    model risk
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    -The potential for financial loss, incorrect decisions, or unintended consequences resulting from errors or inaccuracies in AI or machine learning models. Model risk typically arises from incorrect or inappropriate use of models, inaccurate assumptions, or limitations in data quality. -
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    For example, consequences of unmitigated model risk can include adverse outcomes such as financial loss, damage to reputation, and regulatory penalties.

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    model risk management (MRM)
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    -A structured approach to identifying, assessing, mitigating, and monitoring risks arising from the use of quantitative and qualitative models within an organization. Ensures that models are developed, validated, and used appropriately, with robust controls in place. Encompasses practices such as maintaining a model inventory, conducting periodic validations, and ensuring proper documentation. -
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    model vendor, record vendor
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    -A company that develops, documents, and sells models or records to financial institutions. -
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    vendor model, vendor record
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    -A model or record created by an external source, such as a vendor. -
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    Model risk management

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    1st line of defense
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    -Business unit(s) responsible for model development, initial validation, and implementation during the model lifecycle. As the 1st line of defense, model developers must document and test models to ensure that they are accurate, robust, and fit for purpose. -
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    2nd line of defense
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    -An independent oversight function that provides a governance framework for the model lifecycle. As the 2nd line of defense, model validators must independently validate and challenge models created by model developers to ensure that model risk management principles are followed. -
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    3rd line of defense
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    -Typically an internal audit function responsible for providing an independent and comprehensive review of the risk management processes and controls that the first two lines have implemented. -
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    model developer, developer
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    -Responsible for the design, implementation, and maintenance of models to ensure they are fit-for-purpose, accurate, and aligned with business requirements. As subject matter experts, they collaborate with model validators and other business units, ensuring the models are conceptually sound and robust. -
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    model governance, governance
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    -A framework of policies, procedures, and standards established to oversee the lifecycle of models within an organization. Ensures that models are developed, validated, implemented, and retired in a controlled and consistent manner, promoting accountability, transparency, and adherence to regulatory requirements. -
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    model implementation, implementation
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    -A collaborative effort among model developers and model owners. Model implementation includes a formalized implementation plan and associated procedures, a review of results, and a record of model change procedures. -
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    model owner, owner
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    -Responsible for coordinating model development, model implementation, ongoing model monitoring and maintaining the model’s administration, such as model documentation and model risk reporting. -
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    model user, user
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    -Those who rely on the model’s outputs to inform business decisions. -
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    model validation, validation
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    -A systematic process to evaluate and verify that a model is performing as intended, accurately represents the phenomena it is designed to capture, and is appropriate for its specified purpose. This assessment encompasses a review of the model’s conceptual soundness, data integrity, calibration, and performance outcomes, as well as testing against out-of-sample datasets. -
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    Within model risk management, model validation ensures that potential risks associated with model errors, misuse, or misunderstanding are identified and mitigated.

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    model validator, validator
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    -Responsible for conducting independent assessments of models to ensure their accuracy, reliability, and appropriateness for intended purposes. The role involves evaluating a model’s conceptual soundness, data integrity, calibration methods, and overall performance, typically using out-of-sample datasets. -
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    Model validators identify potential risks and weaknesses, ensuring that models within an organization meet established standards and regulatory requirements, and provide recommendations to model developers for improvements or modifications.

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    three lines of defense
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    -A structured approach to model risk management, consisting of three independent functions: -
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    validation report
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    Within model risk management, the validation report is crucial for ensuring transparency, demonstrating regulatory compliance, and offering actionable insights for model refinement or adjustments.

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    Documentation

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    Each section of your development documentation should address critical aspects of the record’s lifecycle, from conceptualization and data preparation through development and ongoing management. This comprehensive documentation approach is essential for ensuring the record’s reliability, relevance, and compliance with business and regulatory standards.

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    For example, documentation for models typically include sections on:

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    conceptual soundness
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    -Establishes the foundation of a selected record (such as a model), covering the overview, intended use and business use case, regulatory requirements, limitations, and the rationale behind selection. It emphasizes purpose, scope, and constraints, which are crucial for stakeholders to understand applicability and limitations. -
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    data preparation
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    -Details the data description, including dataset summary, data quality tests, descriptive statistics, correlations and interactions, and feature selection and engineering. It provides transparency into the data used for training, ensuring that the record (such as a model) is built on a solid and relevant dataset. -
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    model development, development
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    -Discusses the training, evaluation, explainability, interpretability, and diagnosis, including weak spots, overfit regions, and robustness. This section is vital for understanding how the record (such as a model) was developed, how it performs, and its areas of strength and weakness. -
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    monitoring and governance
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    Validation reports

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    A validation report is a comprehensive and structured review evaluating a record’s accuracy, performance, and suitability for its intended purpose. A report follows established validation guidelines to ensure consistency and adherence to internal and regulatory standards — encompassing the process of risk assessment, identifying areas of potential error or risk within the record’s components, supporting transparency, regulatory compliance, and informed decision-making by documenting the validator’s independent review and conclusions.

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    actions
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    artifacts (previously findings)
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    -Observations or issues identified during validation, including any deviations from expected performance or standards. Artifacts are organized by type — default types provided by ​ValidMind include Validation Issue, Policy Exception, and Limitation. Custom artifact types can be created to track other categories relevant to your organization. -
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    evidence
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    -Material provided by the developer and reviewed by the validator, such as documentation, source code, datasets, monitoring reports or previous validation reports. -
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    review
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    • Conceptual soundness — Examines the foundational elements of the record, including its overview, intended business use, regulatory requirements, and limitations. Ensures that the record’s purpose, scope, and constraints are well-defined and understood by stakeholders.

      • -

    • Data preparation — Assesses the quality and preparation of the data used for record training. Includes a detailed look at dataset summaries, data quality tests, descriptive statistics, correlations, interactions, and feature engineering. The aim is to verify that the record is built on a robust and relevant dataset.

      • -

    • Development — Focuses on the record’s development process, including training, evaluation, explainability, interpretability, and diagnosis. Highlights the record’s performance, identifies strengths and weaknesses, and ensures that any potential issues such as overfitting are addressed. Evaluates the assumptions made and examines the qualitative information and judgments to ensure they are conducted appropriately and systematically.

      • -

    • Monitoring and governance — Evaluates the ongoing strategies for monitoring the record’s performance and ensuring compliance with regulatory and ethical standards. Involves checking the implementation of the monitoring plan and governance strategies to maintain the record’s efficacy over time. Covers reporting outputs to ensure transparency and accuracy in the record’s documented results.

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    These elements collectively ensure that the documentation is thorough, transparent, and meets all necessary standards and regulatory requirements.

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    model risk assessment, risk assessment
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    model risk areas, risk areas
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    -Specific components or aspects of a record (such as a model) where risk might be present, such as data inputs, algorithms, or implementation. -
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    validation guidelines
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    Ongoing monitoring

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    backtesting
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    compliance and regulatory adherence
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    model drift, drift
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    ongoing monitoring, ongoing monitoring report, ongoing monitoring plan, monitoring plan
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    -A comprehensive and structured periodic report assessing the record’s performance and compliance over time, ensuring it remains valid under changing conditions. Monitoring includes key elements such as data sources, inputs, performance metrics, and periodic evaluations, ensuring transparency and visibility of the record’s performance in the production environment. -
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    recalibrating models, recalibrating
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    Attestations

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    attestation
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    -A formal process where attestation participants certify key record (model) information at a specific time. Attestation is part of your audit trail and confirms that governance, documentation, and control requirements are met. -
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    attestation instance
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    -The invocation of the attestation process on the ValidMind Platform. Created when the attestation is triggered by the schedule you set up, it includes a snapshot with record activity and artifacts, questionnaire responses and review status, forming a full record of the review and approval process. -
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    attestation participant
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    -A user who participates in the attestation workflow as a submitter, reviewer, or approver. Submitters are assigned from inventory record stakeholders; reviewers and approvers are assigned from organizational roles. -
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    attestation period
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    -The time window during which attestation is active, with fixed start and end dates. Each period creates an unchanging record (model) snapshot. Periods are usually scheduled quarterly or annually and can align with regulatory or internal cycles. -
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    attestation questionnaire
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    -A structured form that submitters use to confirm record (model) status, documentation and compliance. It supports formatted inputs like checkboxes and text fields, serving as both a compliance check and formal review record. -
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    execution schedule
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    group
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    inventory scope
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    -The filter conditions that define which records (models) are included in an attestation. Scope can be set using rules based on fields, stages, or custom attributes. -
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    snapshot
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    -A fixed capture of record (model) data at a specific time. It includes optional custom fields and related artifacts and stays unchanged throughout the attestation, ensuring historical accuracy. -
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    Integrations

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    Integrations

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    integration
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    secret
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    connection
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    -A packaged adapter that knows how to authenticate, map data, and orchestrate workflows with a specific third-party platform. -
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    HTTP request
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    Developer tools

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    Decorators are a simpler way for users to run their own code as a ​ValidMind test.

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    inputs
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    -Objects to be evaluated and documented in the ValidMind Library. They can be any of the following: -
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    • dataset: A single dataset that has been initialized in ​ValidMind.23
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    • models: A list of ​ValidMind records — usually this is used when you want to compare multiple records in your custom tests.
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    22 Refer to: init_model()

    23 Refer to: init_dataset()

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    outputs
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    parameters
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    ​ValidMind uses the pip command to install the Python client library that is part of the ValidMind Library so that developers can make use of its features.

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    JupyterHub
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    ​ValidMind uses JupyterHub to share live code, how-to instructions, and visualizations via notebooks as part of our getting started experience for new users.

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    Jupyter Notebook
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    -Allows users to create and share documents containing live code, data visualizations, and narrative text. Supports various programming languages, most notably Python, and is widely used for data analysis, machine learning, scientific research, and educational purposes. -
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    ​ValidMind uses notebooks to share sample code and how-to instructions with users that you can adapt to your own use case.

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    metrics, custom metrics
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    In the context of ​ValidMind’s Jupyter Notebooks, metrics and tests can be thought of as interchangeable concepts.25

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    25 Refer also to: test

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    GitHub
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    -A cloud-based platform that provides hosting for software development and version control using Git. GitHub26 offers collaboration tools such as bug tracking, feature requests, task management, and continuous integration pipelines. -
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    26 GitHub

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    ​ValidMind uses GitHub to share open-source software27 with you.

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    27 GitHub: validmind

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    -A tool tracked in the ValidMind Platform inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management. -
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    model
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    -SR 26-2 (which supersedes SR 11-7) defines a model as a “complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates.” Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. -
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    Within ​ValidMind, a model is a type of record tracked in the inventory.

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    documentation, model documentation
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    -A structured and detailed record pertaining to a record (such as a model), encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. -
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    validation report
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    The ValidMind Library

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    The ValidMind Library is a Python library and documentation engine designed to streamline the process of documenting various types of records, including traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and more.

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    It offers developers a systematic approach to documenting and testing with repeatability and consistency, ensuring alignment with regulatory and compliance standards.

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    The ValidMind Library consists of a client-side library, a Python API integration for records (models) and testing, and validation tests that streamline the development process. Implemented as a series of independent libraries in Python and R, our library ensures compatibility and flexibility with diverse sets of developer environments and requirements.

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    Install the library with:

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    ​ValidMind is designed to be highly extensible to cater to our customers’ specific requirements. You can expand its functionality in the following ways:

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    ValidMind Library Python API integration

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    ​ValidMind imports the following artifacts into the documentation via our ValidMind Library Python API integration:

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    Artifacts imported into the documentation via our Python API
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    Ready to try out ​ValidMind?

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    Our philosophy

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    At ​ValidMind, our approach to AI features, particularly in testing and risk management, reflects a philosophy of drinking our own champagne or using our own products. This means that while we create risk management tools for our users, we also rigorously apply these tools and practices within our own product development.

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    Our testing methodologies and philosophy around testing are readily available, and we openly share information about our approach. Specific test results are also available on request, allowing us to demonstrate the same standards of robustness and compliance that we promote for our users.

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    Our features

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    ​ValidMind enhances documentation, testing, and compliance workflows, providing your team with tools for effective risk governance.

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    Interprets results from tests run within ​ValidMind. This feature generates a description of the tests, highlights key insights from the outcomes, and provides a summary with actionable takeaways.

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    Leverages metadata from the inventory, test outcomes, and additional data provided to create qualitative sections within documentation.

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    Using data from test results, generates a tailored risk assessment for each section of documentation. This feature aids in identifying potential risks based on the record’s performance and results.

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    A commitment to transparency

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    Understanding our policies shouldn’t feel like deciphering code, so we’ve made our legal texts as clear and accessible as possible.

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    These documents detail our AI usage policy and more:

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    Legal Ease

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    Try it yourself

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    Discover how ​ValidMind’s LLM-powered platform, purpose-built for risk management teams, enables streamlined and confident testing, documentation, validation, and governance of generative AI systems and processes.

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    Request a Demo

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    ​ValidMind is the system of record for AI governance. You use ​ValidMind to model the full lifecycle of AI systems, records (such as models), use cases, and tools, along with their dependencies, and automates the governance and documentation you build on top.

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    Flexible by design, the platform lets you define your own inventory hierarchy, dependencies, and governance rules, powered by ​ValidMind’s documentation automation, workflows, and analytics.

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    AI governance is the organizational framework for directing and overseeing how AI is designed, deployed, and used.

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    AI governance addresses several key areas:

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    Using ​ValidMind for AI governance

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    ​ValidMind supports AI governance workflows through a unified platform:

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    Model your AI inventory

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    Configure primary record types to match how your organization actually manages AI — whether that’s an entire AI systems, the underlying large-language models (LLMs), use cases, tools, applications, data assets, or a combination thereof:

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    Create typed relationships between records to map how AI components connect and depend on each other:

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    Use version-aware workflows that differentiate between major and minor changes, triggering appropriate validation and approvals:

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    OSFI Guideline E-23 (Enterprise-Wide Model Risk Management for Deposit-Taking Institutions)1 contains the most comprehensive treatment of AI among major MRM regulations, reflecting the 2027 effective date and the anticipated evolution of AI. By emphasizing outcomes over prescribed processes, E-23 ensures that institutions retain flexibility in how they achieve regulatory objectives.

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    1 Office of the Superintendent of Financial Institutions:
    Guideline E-23 – Model Risk Management (2027)

    This guide is organized around the expectations for federally regulated deposit-taking institutions in Canada to manage model risk effectively by covering traditional model risk management (MRM)2 requirements while incorporating forward-looking considerations for AI and machine learning models.

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    E-23 aligns with established MRM frameworks, requiring:

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    E-23 introduces enhanced requirements for AI and ML models in addition to traditional MRM requirements. ​ValidMind helps you address these considerations:

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    Enhanced requirements for model interpretability ensure stakeholders can understand AI-driven decisions.

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    Compensating controls for “black box” approaches where full interpretability is not achievable.

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    Evaluation of ethical implications and discrimination risks in AI models.

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    1. Run fairness and bias tests9 during development.

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    2. Document bias assessment methodology.10

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    Governance for self-learning capabilities and automated decisions.

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    Monitoring for performance degradation over time.

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    Management of continuously updating models.

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    3. Configure validation requirements14 for dynamic models.

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    Implement EU AI Act compliance using ​ValidMind.

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    Overview

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    The EU AI Act1 establishes requirements across six key areas for high-risk AI systems. This guide is organized around these requirements to help you set up ​ValidMind for compliance.

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    Quick reference to risk classifications

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    • Prohibited — AI systems that pose unacceptable risks, such as social scoring or real-time biometric identification in public spaces.

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    Harmonization with other standards

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    The EU AI Act requirements overlap with existing model risk management frameworks. Organizations already following SR 26-2, SS1/23, or E-23 can leverage existing documentation and controls but must also comply with the EU AI Act requirements.

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    1. Risk management system implementation (Article 9)

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    Set up a risk management system in the ValidMind Platform to identify, evaluate, and mitigate risks at all stages of the AI system’s lifecycle.

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    1. Complete the risk classification questionnaire:

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    Implement data quality controls and bias detection to meet Article 10 requirements for training, validation, and testing data.

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    1. Initialize the ValidMind Library:4

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    Generate and customize required model documentation to meet Article 11 requirements for technical documentation.

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    5. Add content blocks:8

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    Validate model accuracy and demonstrate robustness to meet Article 15 requirements.

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    1. Prepare validation reports:9

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    Set up transparent reporting and decision explanations to meet Article 13 requirements.

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    1. Work with analytics11 for non-technical stakeholders:

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    2. Set up custom dashboards:12

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    Implement human oversight capabilities to meet Article 14 requirements.

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    Integrate all components into a complete compliance workflow addressing Articles 43, 60, 61, and 62.

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    1. Set up the conformity assessment process (Article 43):

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    Model risk management

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    Model risk management (MRM) is a discipline focused on managing risk from models themselves.

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    By defining how models are developed, validated, approved, monitored, and retired, MRM reduces model errors, supports safe and consistent model use, strengthens decision-making, and ensures that models are fit for purpose throughout their lifecycles.

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    Traditionally, the scope of MRM is narrower and more technical than AI governance, focusing specifically on the models that power decision-making.

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    Focus areas

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    MRM addresses several key areas:

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    Typical artifacts

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    MRM programs produce documentation that demonstrates sound practices:

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    • Model inventories — Centralized registries of all models and their metadata

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    • Validation reports — Independent assessments of model performance and limitations

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    • Findings — Issues identified during validation with remediation tracking

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    Using ​ValidMind for MRM

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    ​ValidMind supports MRM workflows through a unified platform:

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    flowchart TB
-    center((ValidMind<br>Model Risk Management<br>Platform))
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-    inv((Model<br>Inventory))
-    docs((Documentation<br>Automation))
-    val((Validation<br>Workflows))
-    lod((Three Lines<br>of Defense))
-    mon((Ongoing<br>Monitoring))
-    life((Lifecycle))
-    
-    center --- inv
-    center --- docs
-    center --- val
-    center --- lod
-    center --- mon
-    center --- life
-
-    %% styling
-    classDef centerNode fill:#ffffff,stroke:#de257e,stroke-width:2px,color:#de257e,text-align:center
-    class center centerNode
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    Model inventory
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    Regulatory frameworks

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    ​ValidMind is designed to help organizations comply with MRM regulatory requirements:

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    SR 26-2 (United States)

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    Interagency Guidance on Model Risk Management for Banking Organizations was issued jointly by the Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corporation, and the Office of the Comptroller of the Currency. It supersedes SR 11-7 and sets expectations for a risk-based MRM program — including a narrowed model definition, materiality-based tiering, strong ongoing monitoring and outcomes analysis, and a comprehensive enterprise inventory.

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    SR 26-2 implementation guide

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    SS1/23 (United Kingdom)

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    The Model Risk Management Principles for Banks, issued by the Prudential Regulation Authority (PRA), set out comprehensive expectations for managing model risk in UK banks. The principles emphasize governance, strategic planning, model development, validation, and robust technical capabilities.

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    SS1/23 implementation guide

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    E-23 (Canada)

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    The Office of the Superintendent of Financial Institutions (OSFI) issued the Guideline on Enterprise-Wide Model Risk Management (E-23) for Deposit-Taking Institutions in Canada. E-23 sets out prudent practices for model development, validation, approval, use, and ongoing change management, including considerations for AI and machine learning models.

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    E-23 implementation guide

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    Other frameworks

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    ​ValidMind also supports compliance with:

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    Ready to try out ​ValidMind?

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    SR 26-2

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    Implement SR 26-2 compliance using ​ValidMind.

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    Follow this detailed, step-by-step guide which will:

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    1. Walk you through the practical steps required to implement end-to-end compliance using ​ValidMind.

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    Overview

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    SR 26-2 (Interagency Guidance on Model Risk Management for Banking Organizations)1 was issued jointly by the Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corporation, and the Office of the Comptroller of the Currency. It supersedes SR 11-7 and reframes U.S. banking agencies’ expectations for model risk management (MRM).

    -

    1 Board of Governors of the Federal Reserve System, FDIC, and OCC:
    SR 26-2: Interagency Guidance on Model Risk Management for Banking Organizations (April 17, 2026; supersedes SR 11-7)

    SR 26-2 keeps core themes from prior guidance — sound development, independent validation, governance, and effective challenge — while emphasizing a risk-based posture: a narrower definition of what counts as a model, an explicit materiality framework, stronger ongoing monitoring and outcomes analysis, and an enterprise model inventory that supports concentration and dependency visibility.

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    SR 26-2 explicitly excludes generative AI and agentic AI from its scope because these technologies are novel and rapidly evolving. Underlying MRM principles — including materiality, ongoing monitoring, and effective challenge — still apply when you govern those systems alongside traditional models.

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    This guide organizes platform actions around four priorities that align with SR 26-2:

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    1. Distinguish models from non-models — Focus MRM on complex quantitative methods that apply statistical, economic, or financial theory, rather than on simple arithmetic, purely deterministic rules, or software without substantive theoretical underpinning.

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    2. Assess and tier models by materiality — Use model exposure and model purpose so high-materiality models receive commensurate rigor while lower-materiality models can use more automated governance where appropriate.

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    3. Emphasize ongoing monitoring and outcomes analysis — Treat continuous performance tracking, outcomes testing, and escalation as first-class obligations, not only point-in-time validation events.

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    4. Maintain a comprehensive model inventory — Keep enterprise visibility into models, dependencies, concentrations, and aggregate risk.

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    1. Distinguish models from non-models

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    Purpose

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    Apply a clear, documented definition of model versus non-model so MRM effort targets SR 26-2 scope.

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    1. Document inclusion and exclusion criteria:

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      • Define which methods count as models under SR 26-2’s narrowed definition.
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    2. Align inventory registration rules:2

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    Purpose

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    Implement SR 26-2’s materiality lens — combining exposure and purpose — so controls scale with risk.

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    1. Configure materiality and tiering fields:3

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      • Map organizational tiers to exposure and purpose dimensions.
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    2. Document purpose and business use:4

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      • Capture how each model affects decisions, capital, liquidity, or customers.
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    3. Apply tier-aware workflows:5

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    Meet SR 26-2’s heightened expectations for continuous insight into model performance and outcomes, complementing periodic validation.

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    1. Run automated testing in the ValidMind Library:6

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      • Store results as evidence for monitoring and review.
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    2. Configure monitoring and alerts:7

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      • Define metrics, thresholds, and escalation paths by materiality tier.
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    3. Schedule periodic reviews with monitoring context:8

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    Document models so development choices, data, theory, and limitations are transparent and reviewable.

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      • Tier template depth by materiality.
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    2. Document purpose, design, theory, and logic:10

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    Preserve effective challenge for in-scope models — independent validation that identifies limitations and supports sound use.

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    2. Prepare validation reports:11

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    Operate a governance framework — policies, roles, approvals, and auditability — that matches SR 26-2’s risk-based MRM program.

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    1. Configure role-based access:13

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    AccessSigning up for and logging into ​ValidMindRegister, sign in via SSO, recover access
    ConfigurationSetting up your organization and usersAdd users, create groups, assign roles and permissions
    IntegrationsConnecting ​ValidMind to external systemsManage secrets, configure connections, link external records (models)
    WorkflowsAutomating lifecycle processesConfigure workflow steps, manage transitions, set up approvals
    InventoryManaging your records (models) and record inventoryRegister records, edit fields, configure interdependencies
    Documents & templatesCreating and customizing documentationManage document types, customize templates, use the text block library
    DocumentationAuthoring and collaborating on documentsEdit content blocks, add test results, manage versions, submit for approval
    ValidationReviewing and validating records (models)Review documentation, assess compliance, manage findings and artifacts
    Reporting & auditingAnalyzing, exporting, and auditing dataView reports, track organization activity, create custom analytics, export inventory and documents
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    Use the ValidMind R package to document and validate records (models) built in R. The package interfaces with the ValidMind Library through reticulate, giving you access to the full Python API from R.

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    Prerequisites

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    Or download the .pkg installer from CRAN.

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    Download and run the installer from CRAN.

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    Install Python dependencies

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    On macOS, if rpy2 fails to find R libraries, rebuild it from source against your installed R:

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    Install R packages

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    Open R and install the required packages:

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    install.packages(c("reticulate", "dplyr", "caTools", "knitr", "glue", "plotly", "htmltools", "rmarkdown", "DT", "base64enc"))
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    Install the ValidMind R package

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    Clone the repository and run from the r/validmind directory:

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    devtools::install()
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    Initialize ValidMind

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    Connect to your ValidMind profile by providing your credentials. Point python_version to your Python binary — for example, the one in your project's .venv:

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    vm_r <- vm(
-  api_key = "YOUR_API_KEY",
-  api_secret = "YOUR_API_SECRET",
-  model = "YOUR_MODEL_ID",
-  python_version = "path/to/python",
-  api_host = "https://API_HOST.validmind.ai/api/v1/tracking",
-  document = "documentation"
-)
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    The document parameter specifies which document type to associate with the session:

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    Key APIs

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    Since the R package returns the full Python validmind module, you can call any Python API directly via reticulate:

    -
    # Preview the documentation template
-vm_r$preview_template()
-
-# Initialize datasets
-vm_dataset <- vm_r$init_dataset(
-  dataset = df,
-  input_id = "my_dataset",
-  target_column = "target"
-)
-
-# Initialize R models
-model_path <- save_model(model)
-vm_model <- vm_r$init_r_model(model_path = model_path, input_id = "model")
-
-# Assign predictions
-vm_dataset$assign_predictions(model = vm_model)
-
-# Run the full documentation test suite
-vm_r$run_documentation_tests(config = test_config)
-
-# Run individual tests
-vm_r$tests$run_test(
-  "validmind.data_validation.ClassImbalance",
-  inputs = list(dataset = vm_dataset)
-)$log()
-
-# List available tests
-vm_r$tests$list_tests(tags = list("data_quality"), task = "classification")
-vm_r$tests$list_tasks_and_tags()
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    Troubleshooting

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    When calling vm() you may see an error like:

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    This typically means the libc++ library is installed but R cannot find it. Create a symlink:

    -
    # Find the path to libc++.1.dylib
-sudo find / -name "libc++.1.dylib" 2>/dev/null
-
-# Create a symlink (adjust the source path based on the find result)
-sudo ln -s /opt/homebrew/Cellar/llvm/.../libc++.1.dylib /Library/Frameworks/R.framework/Resources/lib/libc++.1.dylib
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    Reinstall Numba and restart the R session:

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    What's next

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    - - - - - - \ No newline at end of file diff --git a/site/developer/quickstart/install-and-initialize-validmind-library-listing.json b/site/developer/quickstart/install-and-initialize-validmind-library-listing.json deleted file mode 100644 index 9210e006c..000000000 --- a/site/developer/quickstart/install-and-initialize-validmind-library-listing.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "listing": "/developer/quickstart/install-and-initialize-validmind-library.html", - "items": [ - "/developer/quickstart/store-credentials-in-env-file.html", - "/developer/how-to/testing-overview.html" - ] -} \ No newline at end of file diff --git a/site/developer/quickstart/install-and-initialize-validmind-library.html b/site/developer/quickstart/install-and-initialize-validmind-library.html deleted file mode 100644 index f7faa0e95..000000000 --- a/site/developer/quickstart/install-and-initialize-validmind-library.html +++ /dev/null @@ -1,1746 +0,0 @@ - - - - - - - - - - -Install and initialize the ValidMind Library – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Install and initialize the ValidMind Library

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    Initialize the ValidMind Library with the code snippet unique to each record (model) per document to connect your environment to the ValidMind Platform, ensuring test results are uploaded to the correct record and automatically populated in the right document.

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    Prerequisites

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    The ValidMind Library requires access to the data sources where relevant datasets and record files are stored in order to help you run tests.

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    Installing the ValidMind Library

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    In order to upload test results from the ValidMind Library to the ValidMind Platform, provide the following information through a code snippet copied from the platform:

    - ---- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    ArgumentDescription
    api_hostThe location of the ​ValidMind API
    api_keyThe account API key
    api_secretThe account secret key
    documentThe record document identifier key
    modelThe record identifier
    -
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    Get your code snippet

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    Retrieve your code snippet for your record's selected document from the ValidMind Platform:

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    1. In the left sidebar, click Inventory.

      2. -

    2. Under the record type drop-down, select the type of record you want to retrieve the code snippet for.4

      3. -

    3. Select a record by clicking on it or find your record by applying a filter or searching for it.5

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    4. In the left sidebar that appears for your record, click Getting Started.

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    5. Select the document you want to automatically upload test results to.6

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    6. Click Copy snippet to clipboard.

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    -NoneSelecting a document to connect to requires ValidMind Library version >=2.12.0.7 -
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    A template must already be applied to your selected document to populate test results in the ValidMind Platform. Attempting to initialize the library with a document that does not have a template applied will result in an error.

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    Install the library

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    To install the library:

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    %pip install validmind
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    Initialize the library

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    To initialize the library, paste the code snippet with the client integration details directly into your development source code, replacing this example with your own:

    -
    import validmind as vm
-
-vm.init(
-  api_host = "https://API_HOST.validmind.ai/api/v1/tracking",
-  api_key = "API_KEY",
-  api_secret = "API_SECRET",
-  document="document-key", # requires library >=2.12.0
-  model = "MODEL_IDENTIFIER"
-)
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    To also enable monitoring, add monitoring=True to the vm.init method in your code snippet. 8

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    Automate with ​ValidMind

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    After you run the code snippet in your environment, the ValidMind Library will connect to your record and selected document in the ValidMind Platform. Automate your workflow by using the library to run tests, then seamlessly upload your test results to the platform.

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    Upgrade the ValidMind Library

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    After installing the ValidMind Library,9 you'll want to periodically make sure you are on the latest version to access any new features and other enhancements:

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    1. In your Jupyter Notebook or developer environment, retrieve the information for the currently installed version of the library:

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    Example output:

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    Name: validmind
-Version: 2.11.0
-...
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    1. If the version returned is lower than the version indicated in our production open-source code,10 run the following command:

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    What's next

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    - - - - - - \ No newline at end of file diff --git a/site/developer/quickstart/store-credentials-in-env-file-listing.json b/site/developer/quickstart/store-credentials-in-env-file-listing.json deleted file mode 100644 index 60b8d6b77..000000000 --- a/site/developer/quickstart/store-credentials-in-env-file-listing.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "listing": "/developer/quickstart/store-credentials-in-env-file.html", - "items": [ - "/developer/how-to/testing-overview.html", - "/developer/how-to/feature-overview.html" - ] -} \ No newline at end of file diff --git a/site/developer/quickstart/store-credentials-in-env-file.html b/site/developer/quickstart/store-credentials-in-env-file.html deleted file mode 100644 index 839abbc0a..000000000 --- a/site/developer/quickstart/store-credentials-in-env-file.html +++ /dev/null @@ -1,1745 +0,0 @@ - - - - - - - - - - -Store record credentials in .env files – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Learn how to store record (model) identifier credentials in an .env file instead of using inline credentials, allowing you to follow best practices for security when running Jupyter Notebooks.

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    Storing credentials in a .env file is considered a best practice for security. Embedding credentials directly within the code makes them more susceptible to accidental exposure when sharing code or collaborating on records.

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    Keeping record credentials in a separate file also allows for precise access control and ensures that sensitive credentials are not publicly accessible.

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    1. Store credentials in an .env file

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    In the ValidMind Platform, retrieve the code snippet for your record:

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    1. In the left sidebar, click Inventory.

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    2. Under the record type drop-down, select the type of record you want to retrieve the code snippet for.3

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    3. Select a record by clicking on it or find your record by applying a filter or searching for it.4

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    4. In the left sidebar that appears for your record, click Getting Started.

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    5. Select the document you want to automatically upload test results to.5

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    6. Click Copy snippet to clipboard.

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    1. Create a new file in the same folder as your notebook and name it .env.

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      This is a hidden file, so you may need to change your settings to view it.

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    2. Use the code snippet from your clipboard to build the credentials in your .env file in the following format:6

      -
      VM_API_HOST=<api_host>
-VM_API_KEY=<api_key>
-VM_API_SECRET=<api_secret>
-VM_API_MODEL=<model>
      3. -
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    6 For example, if your credentials look like this:

    -
    import validmind as vm
-
-vm.init(
-api_host = "https://api.prod.validmind.ai/api/v1/tracking",
-api_key = "key123",
-api_secret = "secret456",
-document = "document-key",
-model = "model789"
-)
    -

    Then your .env file should look like this:

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    VM_API_HOST=https://api.prod.validmind.ai/api/v1/tracking
-VM_API_KEY=key123
-VM_API_SECRET=secret456
-VM_API_MODEL=model789
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    To define the document to connect the ValidMind Library to, or to enable monitoring, include these in your vm.init() snippet within your Jupyter Notebook.7

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    2. Use credentials in your Jupyter Notebook

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    1. After installing the ValidMind Library within your Jupyter Notebook,8 insert this code snippet in a code cell above your record (model) identifier credentials:

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-%dotenv dev.env
      2. -

    2. Remove the inline credentials from vm.init() after importing the library:

      -
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      Example updated code cell:

      -
      %load_ext dotenv
-%dotenv .env
-
-import validmind as vm
-
-vm.init(
-   document="document-key",
-)
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      Example updated code cell including ongoing monitoring:9

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      %load_ext dotenv
-%dotenv .env
-
-import validmind as vm
-
-vm.init(
-   document="monitoring",
-   monitoring=True
-)
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      Selecting a document to connect to requires ValidMind Library version >=2.12.0.10 Attempting to initialize the library with a document that does not have a template applied will result in an error.

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    3. Run the cell.

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      Instead of using inline credentials, this code cell will now load your record credentials from a .env file.

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    What's next

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    - - - - - - \ No newline at end of file diff --git a/site/developer/quickstart/use-http-proxy-with-validmind-library-listing.json b/site/developer/quickstart/use-http-proxy-with-validmind-library-listing.json deleted file mode 100644 index 810db4107..000000000 --- a/site/developer/quickstart/use-http-proxy-with-validmind-library-listing.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "listing": "/developer/quickstart/use-http-proxy-with-validmind-library.html", - "items": [ - "/notebooks/quickstart/quickstart_documentation.html", - "/notebooks/quickstart/quickstart_validation.html" - ] -} \ No newline at end of file diff --git a/site/developer/quickstart/use-http-proxy-with-validmind-library.html b/site/developer/quickstart/use-http-proxy-with-validmind-library.html deleted file mode 100644 index 84ee5c40c..000000000 --- a/site/developer/quickstart/use-http-proxy-with-validmind-library.html +++ /dev/null @@ -1,1700 +0,0 @@ - - - - - - - - - - -Use an HTTP proxy with the ValidMind Library – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Outbound HTTPS traffic to the ValidMind API relies on standard Python HTTP clients that honor proxy-related environment variables, which lets you work behind corporate or regional proxies without custom library configuration.

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    1 If you have not installed the library yet, start with Install and initialize the ValidMind Library.

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    What do I need to know?

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    Underlying behavior

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    Proxy handling is implemented by the underlying HTTP stacks, including aiohttp and requests for ValidMind API traffic, and httpx in some third-party clients. The ValidMind Library itself does not provide a way to set a proxy on vm.init().

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    Precedence between ALL_PROXY and scheme-specific variables, and exact NO_PROXY matching rules, can vary by library version. If results differ from what you expect, refer to the documentation for those libraries or your HTTP client.2

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    Other outbound traffic

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    Features that generate text with OpenAI or Azure OpenAI use the Python SDKs provided by those vendors. These clients usually respect the same HTTP_PROXY, HTTPS_PROXY, and NO_PROXY values, but authentication, regional endpoints, and enterprise policies can add requirements.

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    Consult your provider’s documentation when something works for the ValidMind API but not for LLM calls.3

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    3 For example, refer to OpenAI and Azure OpenAI for service-specific networking guidance.

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    Step 1: Set proxy environment variables

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    Set the variables before you start the Python process or Jupyter kernel, so that the process inherits them when the library opens HTTP connections.

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    VariablePurpose
    HTTP_PROXYProxy URL for http requests.
    HTTPS_PROXYProxy URL for https requests typically used for the ValidMind API.
    ALL_PROXYFallback when a scheme-specific variable is not set. Behavior depends on the client library.
    NO_PROXYComma-separated hostnames, domains, or IP ranges that should NOT use the proxy. For example: localhost, 127.0.0.1, or an internal API hostname.
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    Many tools and Linux environments use lowercase names, such as http_proxy or https_proxy. On Unix-like systems, Python and common HTTP clients often treat uppercase and lowercase as equivalent for these variables, but some scripts or containers only read one convention.

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    Tip: Use the names your organization or image already exports, or standardize on a single case for your team.

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    Example for a POSIX shell

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    export HTTPS_PROXY=http://proxy.example.com:8080
-export HTTP_PROXY=http://proxy.example.com:8080
-export NO_PROXY=localhost,127.0.0.1,.corp.example.com
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    Troubleshooting

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    • If you see SSL or certificate verification errors when calling vm.init() behind a proxy: refer to our troubleshooting guide.4
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      • -
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    Our Jupyter Notebook code samples showcase the capabilities and features of the ValidMind Library, while also providing you with useful examples that you can build on and adapt for your own use cases.

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    Try Notebooks on JupyterHub

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    Learn about the wide range of records (such as models) and frameworks for testing and documentation supported by the ValidMind Library.

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    Understand which frameworks are supported, what your record needs to provide for tests to run, and how to work with records that don't fit standard patterns.

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    What does supported mean?

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    Supported here means the ValidMind Library provides dedicated wrappers, install options, and guidance for these records and frameworks.

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    You can also use other code with the library for records that don't fit standard framework patterns. For example, you can wrap any Python callable with FunctionModel or pass precomputed predictions when you don't have a traditional model object.

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    Any PyTorch neural network architecture, including LSTM, RNN, CNN, transformers, and custom architectures.

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    The ValidMind Library provides wrapper classes for common frameworks. When you call vm.init_model(), the library automatically detects your record's framework from its module, such as sklearn, torch, or transformers, and selects the appropriate wrapper. You don't need to specify the wrapper class manually.

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    flowchart LR
-    subgraph Inputs
-        Model[Model Object]
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-    Model --> predict["predict()"]
-    Model --> predict_proba["predict_proba()"]
-    Precomputed --> Dataset
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-        ModelTests[Model Tests]
-        DatasetTests[Dataset Tests]
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    PyTorch TensorDatasetRequires pip install validmind[pytorch]
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    Use when you have model metadata but no inference capability — for example, when documenting a model deployed in an external system. Calling vm.init_model() with attributes creates a MetadataModel implicitly:

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    This section covers how to configure and test large language models and generative AI systems.

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    huggingfaceTransformers, sentencepiece
    nlplangdetect, nltk, textblob, evaluate, rouge, bert-score
    statsscipy, statsmodels, arch
    explainabilitySHAP
    credit_riskscorecardpy
    datasetsHugging Face datasets
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    What's next

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    The ValidMind Library streamlines development and validation by automating testing. Run tests, log those test results to the ValidMind Platform, and have fully supported drafts of documentation or reporting ready for you to fine-tune.

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    What is the ValidMind Library?

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    The ValidMind Library provides a rich collection of documentation tools and test suites, from documenting descriptions of your datasets to validation testing your records (such as models) for weak spots and overfit areas.

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    ​ValidMind offers two primary methods for automating documentation:

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    • Run validation tests2 — The library provides a suite of validation tests for common financial services use cases. For cases where these tests do not cover everything you need, you can also extend existing test suites with your own proprietary tests or testing providers.

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    The ValidMind Library is designed to be agnostic. For example, if you have a model built with Python, the library provides all the standard functionality you may need without requiring you to rewrite any functions.

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    Within ​ValidMind, a model is a type of record tracked in the inventory.

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    Set up the foundational units for your organization within the ValidMind Platform, prepare your organization for users, then grant access to your first user.

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    2025-06-10 22:06:42,610 - INFO(validmind.api_client): 🎉 Connected to ​ValidMind!
-📊 Model: [ValidMind] Model development quickstart (ID: cmbpnxrv6012o0g9w5049nsct)
-📁 Document Type: model_documentation
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    Registration modal with the options filled out for the validation quickstart
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    Inventory record stakeholder selection step
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    Inventory record stakeholders with only the validators assigned
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    Getting Started page with a sample code snippet
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    2025-06-11 22:06:42,610 - INFO(validmind.api_client): 🎉 Connected to ​ValidMind!
-📊 Model: [ValidMind] Model validation quickstart (ID: cmbs6g0pi02li0g874mq9fek4)
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    Linking the Class Imbalance test results
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    Class Imbalance test results inserted as validator evidence
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    Sign up for ​ValidMind through AWS Marketplace to streamline billing and take advantage of your existing AWS relationship.

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    When you subscribe to ​ValidMind via AWS Marketplace, your usage is billed through your AWS account, simplifying procurement and consolidating your cloud spending.

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    AWS Marketplace logo

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    New to ​ValidMind?

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    Take a guided tour of ​ValidMind and discover how the platform helps you implement governance and oversight through automated documentation, comprehensive testing, and streamlined workflows.

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    What’s next

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    Sign off on reviewed attestation questionnaires to formally approve the accuracy of key record (model) information at a point in time.

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    As an approver, you provide the final sign-off on attestation questionnaires that have been reviewed, completing the attestation process and creating a permanent audit entry in the record’s history.

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    2. Review the questionnaire responses and record context to verify the attestation is ready for approval.

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    Enable attestation participants to step through the attestation process required by your organization.

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    Setting up attestations involves adding an attestation template and completing the required sections: attestation periods, attestation participants, and questionnaire template. Optionally, you can also set the inventory scope, add attestation fields, start attestation schedules to test the process, and edit or cancel attestation periods.

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    1. In the left sidebar, click Settings.

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    2. Under Governance, select Attestation Templates.

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    3. Click Add Attestation Template.

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    4. Enter a name and description for the attestation template.

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    5. Click Add Attestation Template.

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    6. Complete the required sections as described below.

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    2. Under Governance, select Attestation Templates and then click an attestation template to edit.

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    Attestation periods reflect the time zone of the user setting up attestation and they trigger in the time zone of the submitter.

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    Attestation periods are typically scheduled quarterly or annually and should be configured to align with regulatory or internal review cycles:

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    1. Click Add Attestation Period.

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    2. Enter a name for the attestation period, such as Quarterly attestation.

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    3. Set the start date for the attestation period.

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    4. Set the end date when the attestation period ends.

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    5. (Optional) Toggle email reminders to send reminders to Submitters, Reviewers, and Approvers:

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      • days before end — How many days before the end date to send the reminder.
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      Available template variables: {Recipient Name}, {Attestation Name}, {Reminder Reason}, {Start Date}, {End Date}, {Schedule Name}, {Status Text}.

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    6. Click Add Period.

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    When the attestation period begins, the system triggers the process automatically. Attestation participants are then notified via a banner on their dashboard prompting them to complete their attestation tasks.

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    Assign users and roles responsible for this attestation. Each participant type has a specific responsibility in the attestation workflow:

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    3. Select the approver from the roles dropdown.

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    4. Click Save.

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    Define which records are included in the attestation process. By default, all records are included. Use filters to narrow the scope based on inventory record fields, stages, or custom attributes.

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      A new filter row with three components

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    The AND and OR combinators

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      Show items matching Condition A AND Condition B OR Condition C
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    Show records implemented between January 1, 2025 and January 31, 2025:

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    1. On the Inventory page, click Filter.

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    Filtering for user groups
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        • Number
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      • required field — Toggle to require submitters to complete this field before submission
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    3. Click Add Field.

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    4. Repeat to add additional fields as needed.

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    5. (Optional) Reorder fields by dragging them to change the order in which they appear to submitters.

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    Questionnaire fields are available immediately for new attestation instances. Submitters see structured inputs for each field, and responses are collected consistently across all submissions.

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    For testing purposes only, you can trigger the attestation process:

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    1. In the left sidebar, click Settings.

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    2. Under Governance, select Attestation Templates and then click the attestation template to test.

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    3. Under Attestation Periods, hover over an attestation period.

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    4. When the menu appears, select Start Now.

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    5. Click Confirm.

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    After starting the schedule, attestation participants are notified via My Inbox that the attestation is ready to be completed.

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    1. In the left sidebar, click Settings.

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    2. Under Governance, select Attestation Templates and then click the attestation template to edit.

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    3. Under Attestation Periods, hover over an attestation period.

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    4. When the menu appears, select Edit.

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    5. Make your desired changes to the following:

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    Cancel attestation periods

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    1. In the left sidebar, click Settings.

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    3. Under Attestation Periods, hover over an attestation period.

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    4. When the menu appears, select Cancel Period.

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    5. Click Confirm to cancel the period.

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    View attestations dashboard

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    When an attestation period is active or completed, the Attestations item appears in the left sidebar. This dashboard provides a centralized view for tracking, reviewing, and exporting attestation data.

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    2. Use the dropdown at the top to select the attestation period to view. You can choose from active periods or view completed historical periods.

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    The dashboard organizes information across five tabs:

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    Before and after toggle for comparing snapshots

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    Before and after snapshot comparison
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    The Progress tab displays a swimlane view of all attestation submissions organized by status:

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    The Inventory tab shows a snapshot of all records included in the attestation scope at the time the period started. This provides an organization-wide view of the inventory state.

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    Export attestation data in CSV or Excel format for external reporting and compliance documentation.

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    1. Navigate to the Attestations dashboard.

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    2. Select the tab containing the data to export: Progress, Responses, Inventory, Validation Issues, or Artifacts.

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    3. Click Export in the upper right.

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    4. Choose your preferred format and save the file.

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    1. In the left sidebar, click Settings.

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    2. Under Governance, select Attestation Templates and then click the attestation template.

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    3. Under Attestation Periods, hover over an active attestation period.

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    4. When the menu appears, select Complete Period.

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    5. Click Confirm.

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    1. In the left sidebar, click Settings.

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    2. Under Governance, select Attestation Templates.

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    4. When the menu appears, select Delete.

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    5. Click Confirm to delete the template.

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    Review attestation questionnaires to verify the accuracy of key record (model) information at a point in time.

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    As a reviewer, you assess the submitter’s attestation, including ownership, usage, performance, controls, and documentation. You can provide feedback and request changes to support a traceable and accountable audit trail.

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    2. Review the submitter’s questionnaire responses displayed on the right side of the screen.

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    4. (Optional) Click Activity to review the attestation history:

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      If you previously rejected an attestation, review the submitter’s updated responses on resubmission.

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    5. Accept or reject the attestation:

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      If you reject an attestation, the submitter is prompted to revise their responses and resubmit. The platform maintains a history of all status changes.

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    Certify the accuracy of key record (model) information at a point in time by submitting the attestation questionnaire.

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    As a submitter, this process is part of the audit trail for your record’s information, supporting accountability across its lifecycle. Attestation typically includes details about ownership, usage, performance, controls, and documentation.

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    1. Access your attestation task using one of these methods:

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    2. Click Start Attestation.

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      After you start the attestation, the questionnaire fields are available to complete.

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    4. Complete the attestation questionnaire by filling in each field. Required fields are marked with an asterisk (*).

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      Click on any record in the Inventory tab to view its snapshot details. Use the View Live Data link to navigate to the current record if you need additional context.

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    5. Verify that your answers are complete and click Submit for Review.

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    After you submit, the attestation is sent to your reviewer. The platform maintains a history of all status changes and notes. If your reviewer rejects your attestation questionnaire, you will need to revise your responses and resubmit for review.

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    Attestation enables submitters, reviewers, and approvers to formally certify, review, and sign off on record (model) attributes at specific points in time, supporting compliance and governance processes.

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    Key concepts

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    attestation
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    -A formal process where attestation participants certify key record (model) information at a specific time. Attestation is part of your audit trail and confirms that governance, documentation, and control requirements are met. -
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    attestation instance
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    attestation participant
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    -A user who participates in the attestation workflow as a submitter, reviewer, or approver. Submitters are assigned from inventory record stakeholders; reviewers and approvers are assigned from organizational roles. -
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    attestation period
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    attestation questionnaire
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    -A structured form that submitters use to confirm record (model) status, documentation and compliance. It supports formatted inputs like checkboxes and text fields, serving as both a compliance check and formal review record. -
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    execution schedule
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    -An organizational unit that associates records (models) with specific teams or functions. When reviewers or approvers are assigned by role, they can only act on records within groups they belong to — resulting in one attestation submission per owner per group. -
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    inventory scope
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    -The filter conditions that define which records (models) are included in an attestation. Scope can be set using rules based on fields, stages, or custom attributes. -
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    -A fixed capture of record (model) data at a specific time. It includes optional custom fields and related artifacts and stays unchanged throughout the attestation, ensuring historical accuracy. -
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    Where do I access attestations?

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    Attestation progress showing swimlane view

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    Sample attestations in progress
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    When an attestation period is active or has been completed in the past, Attestations appears in the left sidebar. This centralized dashboard provides:

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    Attestation participants can also access their individual tasks via My Inbox in the left sidebar, where tasks appear under the Tasks tab with the current attestation status.

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    The Attestations sidebar item appears only when there is at least one active or completed attestation period. If your organization has not yet configured attestations, this option is not visible. If you have configured attestations but every period has a future start date, the Attestations area stays empty until the first period becomes active.

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    How does the attestation process work?

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    Attestation on the ValidMind Platform begins at the start of the attestation period after the process has been set up initially. The platform captures a snapshot of each record and its artifacts, preserving the state at that point in time. A second snapshot is captured when the attestation period is completed, enabling comparative analysis between the start and end states.

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    Responsibilities for the attestation process are shared:

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    graph LR
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-        C --> D["**Reviewed**"]
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-        D --> E["**Signed Off**"]
-    end
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    The attestation process — Submitter, reviewer, and approver responsibilities

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    The submitter is responsible for completing the questionnaire and addressing change requests. The reviewer is responsible for reviewing the questionnaire and requesting any necessary changes. The approver is responsible for signing off on the questionnaire.

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    Plan your attestation periods ahead of time to ensure they can be completed on time. The start date must be today or a future date, and you cannot set a date in the past to trigger attestation retroactively.

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    How are attestation submissions organized?

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    Attestation submissions are organized by owner AND group. This structure supports organizations where:

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    Steven owns 10 records: 4 in the Fraud group and 6 in the KYC group. Emma is the Governance Lead for Fraud, and Kam is the Governance Lead for KYC.

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    If attestations were organized by owner alone, neither Emma nor Kam would have permissions to see all of Steven’s records. With owner+group organization:

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    • Steven submits Attestation #1 for his 4 Fraud records → Emma approves
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    This approach ensures 100% accountability per group, where Emma approves all Fraud records and Kam approves all KYC records. If Fraud has two co-leads (Emma and Andres), either can approve.

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    How do I create meaningful attestation questionnaires?

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    Attestation questionnaires are typically used in risk management to confirm that key controls, governance processes and documentation requirements have been followed throughout a record’s lifecycle. These questionnaires are part of a broader effort to ensure accountability and regulatory compliance.

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    Questionnaires are composed of structured fields that you define when configuring the attestation template. Each field can be a text input, dropdown, checkbox, or other field type, with optional validation rules. Responses are collected consistently across all submissions and can be viewed in the consolidated Responses tab or exported for external reporting.

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    • Verification of responsibilities — Attests that record owners, developers and validators have performed their roles in accordance with internal policies and procedures.

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    • Documentation of compliance — Confirms that required documentation — such as documentation, validation reports and change logs — has been completed and properly archived.

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    • Evidence of control operation — Serves as formal evidence that key controls (for example: backtesting, performance monitoring, version control) are in place and functioning as intended.

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    • Governance checkpoints — Used as checkpoints at critical stages, such as approval, annual reviews or retirement, to ensure all required steps have been completed before proceeding.

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    • Regulatory reporting — In regulated environments, provides a documented trail that can be reviewed during internal audits or by external regulators.

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    • Risk ownership and accountability — By signing off on questionnaires, individuals acknowledge their responsibilities and support a strong control culture.

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    Work with your risk management team to create a template that accurately reflects your organization’s attestation requirements.

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    The in-app assistant (described on this page) is focused on helping you use the platform. For programmatic access to your inventory, refer to Connect AI assistants via MCP.

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    To keep your network traffic private and minimize its attack surface, configure AWS PrivateLink1 to establish a private connection between ​ValidMind and your company network.

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    AWS PrivateLink establishing a private connection to ​ValidMind
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    AWS PrivateLink is a networking service that allows secure and private communication between Amazon Virtual Private Cloud (VPC) resources and services hosted in other VPCs or in AWS partner services, such as ​ValidMind. With AWS PrivateLink, you can connect to services over the Amazon network, without needing to expose your network traffic to the public internet.

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      The status for the endpoint should show Pending.

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    A graphic showing Azure Private Link establishing a private connection to ValidMind

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    Azure Private Link establishing a private connection to ​ValidMind
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    Azure Private Link is a networking service that allows secure and private communication between Azure Virtual Network (VNet) resources and services hosted in other VNets or in Azure partner services, such as ​ValidMind. With Azure Private Link, you can connect to services over the Azure network without exposing your network traffic to the public internet.

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    Private Link works by creating a private endpoint for a supported Azure service within your virtual private cloud. This endpoint acts as a proxy between your VNet and ​ValidMind, allowing traffic to be routed privately over the Azure network. To make the endpoint easier to use, ​ValidMind provides a private DNS name that developers and validators can connect to in a browser.

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    VNet service information

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    2. Contact ​ValidMind at support@validmind.com to get your new private endpoint connection request accepted. Include the following information:

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    Google Private Service Connect establishing a private connection to ​ValidMind
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    Private Service Connect is a networking service that allows secure and private communication between Google Virtual Private Cloud (VPC) resources and services hosted in other VPCs or Google partner services, such as ​ValidMind. By creating private endpoints within your VPC, Private Service Connect allows you to connect to services over the Google network without needing to expose your network traffic to the public internet.

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    To learn more about Private Service Connect, check the official Google documentation.1

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    Configure your Google Cloud Platform project

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    Enable the APIs for cloud DNS and networking to get ready for the next steps:

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    1. Log into the Google Cloud Console.

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    2. From the Select a project drop-down at the top of the console, select the project where you want to enable the Google APIs.

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    3. In the navigation menu on the left, click APIs & Services and then Library.

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    4. In the API Library, search for “Cloud DNS API” and click the result to open the API page.

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    5. On the API page for Cloud DNS, click Enable to activate the API for your project.

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    6. Repeat these steps for the Networking API:

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    Please provide this information to ​ValidMind at least 24 hours before attempting to connect, so that we can add your project to the allowlist.

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    Prepare your network for connection

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    Create a private subnet in a supported GCP region that can be used to expose ​ValidMind services:

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    1. In Google Cloud Console, create the subnet:

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      1. On the VPC networks page, click Create VPC network.
        2. -
      2. Name your network.
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    2. (Optional) Enable Private Google Access to provide access to Google APIs and services.2

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    3. Provision two IP addresses in this subnet for later use:

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    Create an endpoint to connect to ​ValidMind

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    Create a Private Service Connect endpoint for accessing ​ValidMind services securely and privately, with service discovery managed via Google Cloud’s Service Directory.

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    Screenshot of an endpoint being created with the options specified in the steps

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    Creating an endpoint in the Google Cloud Console
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    2. Create the endpoint:

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      1. Click Create Connection Endpoint.

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      2. Check Published service for the target.

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      3. For Endpoint name, enter private.

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        projects/validmind-us3-prod/regions/us-central1/serviceAttachments/private.

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      5. For IP address, enter validmind.

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      7. For Namespace, enter validmind.

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      8. Click Add Endpoint.

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    Active endpoint being shown in the Google Cloud Console

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    Active private endpoint in the Google Cloud Console
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    Create an endpoint to connect to the ​ValidMind authentication service

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    Repeat the steps to create an endpoint to connect to ​ValidMind3 to add another endpoint for the ​ValidMind authentication service.

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    Creating an endpoint in the Google Cloud Console
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    3. For Endpoint name, enter auth.

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    4. Enable Service Directory for this endpoint

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    5. For Namespace, enter validmind.

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    Active endpoint being shown in the Google Cloud Console

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    Active auth endpoint in the Google Cloud Console
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    2. Test your connection to the following hosts:

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    3. Click Add Stakeholder Type.

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    4. Enter the unique name and description for the stakeholder type.2

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    1. In the left sidebar, click Settings.

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    3. Click on the stakeholder type you want to edit.

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    1. Under inventory record types, select the inventory record types the stakeholder type is available on.

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    3. Click Save Inventory Record Type Links to apply your changes.

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    1. Toggle available on record registration9 to display user selection for that stakeholder type.

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    3. Toggle specific permissions for that stakeholder type by checking or unchecking the boxes next to the permission.

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    4. Click Save Changes to apply your changes.

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    1. In the left sidebar, click Inventory.

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    2. Select a record or find your record by applying a filter or searching for it.15

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    3. Click Manage Stakeholders.

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    4. Toggle the stakeholder types you would like to display for that record.

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    5. Add at least one user to each of your desired custom stakeholder types to save the display settings for that record:

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    3. Click Add Role.

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    4. Enter the unique name and description for the role.3

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    1. Click on the role whose user permissions you want to change.

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    -A default ​ValidMind document type that serves as a standardized framework for conducting and documenting validation, including sections designated for attaching test results, evidence, or artifacts (findings). By outlining required documentation, recommended analyses, and expected validation tests, validation report templates ensure consistency and completeness across validation reports and help guide validators through a systematic review process while promoting comparability and traceability of validation outcomes. -
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    4. From the datasets dropdown, choose which analytics datasets to export:

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    Learn more: View record activity

    4 Any custom inventory record types defined for your organization also appear in the datasets dropdown and can be exported.

    Learn more: Manage inventory record types

    5 Any custom artifact types defined for your organization also appear in the datasets dropdown and can be exported.

    Learn more: Manage artifact types

    The export is created and runs on the schedule you set. Data is written to the configured path in your cloud storage so BI tools can read it.

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    Connections link ​ValidMind with the external systems your teams already use.

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    A project tracking tool that teams use to plan work, track progress, and manage issues across a project lifecycle.

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    An IT service management platform that helps teams handle requests, incidents, assets, and other operational workflows.

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    An ML development service that lets teams build, train, and deploy models at scale.

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    A managed service that provides access to foundation models and tools for building generative AI applications, including agents, flows, and AgentCore runtimes.

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    An open-source toolset that tracks experiments, packages models, and manages their deployment.

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    Synchronize your GitLab model registry with the ​ValidMind inventory for comprehensive record tracking.

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    A unified analytics platform for data engineering, data science, and machine learning, including Unity Catalog for centralized metadata management.

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    A user-defined connection to a third-party system that implements the ​ValidMind reference API.1

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    2. Under Integrations, select Connections.

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    3. Click Add Connection.

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    4. In the modal that opens, select one of the supported connections, such as MLflow or Jira.

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    3. These metrics can be added to monitoring documents for records linked to this connection.5

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    Integrations connect the ValidMind Platform with external systems or services, enabling bidirectional data synchronization, workflow automation, and API access.

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    Access built-in analytics and dashboards. Or, import analytics data into external reporting tools to build unified reports that combine ​ValidMind insights with other sources. Schedule exports of analytics datasets to cloud storage for BI tools like Tableau, Snowflake, and Looker.12

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    Link to models in external registries and development platforms and keep inventories synced with versions, inventory fields, and other metadata.3

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    A workflow showing an automation sequence. A green start node runs an HTTP request that sends a POST to https://some.example.com/request. The workflow then moves to a webhook step that pauses until a webhook call is received. Once the webhook arrives, the flow continues to a red end node.

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    Integrate the ValidMind Library with other development platforms to run tests and test suites, generate documentation, and support ongoing monitoring. See the results locally or upload them to the ValidMind Platform.6

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    Link to tickets in external systems and keep them synchronized. Attach tickets to ​ValidMind artifacts, such as validation issues, to support coordinated tracking across your validation workflow and external project or issue management tools.

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    View interdependencies

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    1. In the left sidebar, click Inventory.

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    2. Under the record type drop-down, select the type of record you want edit interdependencies for.6

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    3. Select a record or find your record by applying a filter or searching for it.7

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    4. On the record details page, click the Interdependencies tab.

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    5. Click Manage Dependencies to open up the interdependencies detail menu.

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        • To hide fields or field groups from view: Hover over an inventory field, then click .
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    Manage the fields that appear on records in your inventory across your organization’s record types. Choose from an array of field types with different properties and use cases.

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    Prerequisites

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    Inventory field types

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    1. Select the type of artifacts to aggregate from.3

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    -Define a formula(params) function that reads field values from the params dictionary and returns a read-only value. Formulas are written in Starlark6, a small, sandboxed scripting language with a Python-style syntax and a set of built-in helper functions for working with dates, numbers, and lists. -
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    ​ValidMind runs calculated field formulas on the Starlark formula engine. Formulas authored on earlier releases were written in Python and have been migrated to Starlark automatically. When creating or editing formulas, you now must use the Starlark syntax.

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    1. Select from the drop-down of available record fields to allow your formula access to the field’s values.7
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    4. Enter sample values in the testing area then click Test Calculation to validate your formula.

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    7 Fields are grouped by field type.

    8 Reference fields with dictionary-style access — params["fieldKey"] — and use the available helpers for date and number work.

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    You have inventory fields named materiality and complexity that classify a record’s risk.

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    A calculated field that returns a combined risk tier based on materiality and complexity:

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-    if params["materiality"] == "High Risk":
-        return "High Risk"
-    if params["materiality"] == "Low Risk" and params["complexity"] == "High Risk":
-        return "Medium Risk"
-    return "Low Risk"
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    ParamApplies toAvailabilityDescription
    params["<fieldKey>"]Inventory records & artifactsAdd the field from the available fields drop-downCurrent value of another custom field on the same record or artifact.
    params["model_stage"]Inventory recordsAdd Model Stage from the available fields drop-downCurrent model stage name as a string (for example, "Production"). Returns an empty string when no stage is assigned. Compare directly: params["model_stage"] == "Production".
    params["stakeholders"]Inventory recordsAdd a stakeholder role from the available fields drop-down (for example, Stakeholders — Owners)Assigned users by stakeholder role. Default role keys are owners, developers, and validators. Custom stakeholder types use custom_stakeholder_<cuid>. Each entry includes name, email, and title.
    params["integrations"]Inventory records & artifactsReference params["integrations"] in the formula sourceLinked external integration data keyed by service (for example, params["integrations"]["mlflow"]). Empty when integrations are not configured or not referenced.
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    Reference these helpers in your formulas — they cover the date, number, and list operations the engine does not expose directly:

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    HelperReturnsDescription
    today()"YYYY-MM-DD"Current date as an ISO string.
    safe_parse_date(value, default=None)"YYYY-MM-DD" or defaultParses ISO dates/datetimes or millisecond/second epoch timestamps.
    to_iso(value)"YYYY-MM-DD" or NoneAlias for safe_parse_date with no default.
    add_days(date, n)"YYYY-MM-DD" or NoneAdds n days to an ISO date.
    add_months(date, n)"YYYY-MM-DD" or NoneAdds n months to an ISO date.
    days_between(a, b)int or NoneDays between two ISO dates (a − b).
    days_since(date)int or NoneDays from date to today.
    months_between(a, b)int or NoneFull-month difference between two ISO dates.
    get_year(date) / get_month(date) / get_day(date)int or NoneComponents of an ISO date.
    safe_int(value, default=0)intTolerates strings and missing values.
    safe_float(value, default=0.0)floatTolerates strings and missing values.
    list_count(list, value)intNumber of times value appears in list.
    zfill(value, width)stringZero-pads value to width characters.
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    A screenshot showing a simple number field type

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    Simple number field type
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    A screenshot showing a currency number field type

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    1. In the left sidebar, click Settings.

      2. -

    2. Under Inventory, select Inventory Record Fields.

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      Displayed in groups12 as lists are the inventory fields available to records in your organization with a quick view of:13

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    3. Click Search to narrow down your results by your entered keywords.

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      • workflow triggered by field — What workflows are initiated when this field is edited.17
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      • write permissions — Which inventory record stakeholder types have permissions to edit this field.18
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    2. Click Apply Filters to narrow down your results by your configured criteria.

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    1. Inventory fields require you to enter a title (name), select a field type,19 and indicate what inventory record types20 the field is available on.

      2. -

    2. Include optional information for your field:

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    3. When you are satisfied with the setup of your inventory field, click Save.

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    21 If left blank, a key will be automatically generated in camelCase format.

    22 Ungrouped fields will be listed under No Group: Add inventory field groups

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    Combining different inventory field types provides a flexible way to automatically track record review schedules. This example creates fields that calculate the next review date based on approval date and validation frequency, adjusted by risk tier, and then computes the days remaining until that review.

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    Common date and time field types available in your formulas include:

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      • -
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      • -
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    Here, we show you how to use date, relativedelta, and vm_today.

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    Calculate the next review date

    -

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    -
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    1. Create an Approved Date date field.

      2. -

    2. Create a Frequency of Validation string field with the following options:

      3. -
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      • -
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      -
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-    if params.frequencyOfValidation == "Weekly":
-        return "Tier 1"
-    elif params.frequencyOfValidation == "Monthly":
-        return "Tier 2"
-    elif params.frequencyOfValidation == "Yearly":
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-        return "N/A"
      2. -

    2. Create a Next Review Date calculation field that depends on the Approved Date and Risk Tier fields:

      -
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-    """
-    Calculate the next review date based on the approval date and review frequency.
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-    Args:
-        params.dmApprovedDate (str): The approval date in 'YYYY-MM-DD' format.
-        params.dmRiskTier (string): The review tier (Tier 1, Tier 2, or Tier 3).
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-        datetime: The next review date.
-    """
-    # Guard against empty dates
-    if params.dmApprovedDate == "":
-        return "N/A"
-
-    # Convert the approved_on date string to a datetime object
-    approved_date = date.fromtimestamp((int(params.dmApprovedDate))/1000)
-
-    # Define the review frequency mapping
-    review_frequency = {
-        "Tier 1": relativedelta(months=3),  # Quarterly
-        "Tier 2": relativedelta(months=6),  # Semi-annually
-        "Tier 3": relativedelta(years=1),   # Annually
-    }
-
-    # Get the appropriate time delta based on the tier
-    frequency = review_frequency.get(params.dmRiskTier)
-    if not frequency:
-        # "Invalid tier. Must be Tier 1, Tier 2, or Tier 3."
-        return "N/A"
-
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-    next_review_date = approved_date + frequency
-    return next_review_date.isoformat()
      3. -
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    A screenshot showing the screen for adding a calculation type field that automatically calculates the next review date

    -
    Adding a calculation type field that automatically calculates the next review date
    -
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    You can now determine the next review date in workflows by making a workflow depend on Approved Date. To test, change the Approved Date after the fact and see how Next Review Date changes.

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    -
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    1. Create a Days Remaining calculation field that depends on the Next Review Date field:

      -
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-    """
-    Calculate days remaining until the next review.
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-        params.nextReviewDate (str): The next review date in ISO format.
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-        str: Days remaining until review (example:"45 days remaining").
-    """
-    # Get next review date (stored as ISO format string)
-    next_review_date = getattr(params, "nextReviewDate", "")
-    if not next_review_date:
-        return "Not applicable"
-    next_review = date.fromisoformat(next_review_date)
-
-    # Calculate days until review using built-in vm_today
-    difference = next_review - vm_today
-    return f"{difference.days} days remaining"
      2. -
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    A screenshot showing the screen for adding a calculation type field that automatically calculates the days remaining until the next review

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    Adding a calculation type field that automatically calculates the days remaining until the next review
    -
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    You can now check the number of days remaining until the next review.

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    Default Fields are fields created by default by ​ValidMind.24 To modify a default field:

    -

    24 ​ValidMind default fields:

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    1. Under the Default Fields grouping, click on the inventory field you want to edit.

      -

      Expand any inventory field group to see more fields.25

      2. -

    2. Toggle the following options on or off:

      -
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    3. Click Save Configuration to apply your changes.

      4. -
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    Edit custom inventory fields

    -

    Custom inventory fields are fields created by your organization. Expand any inventory field group to see more fields.28

    -

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    -
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    1. Click on the custom inventory field you want to edit.

      2. -

    2. Make your desired changes to your inventory field, then click Save.

      -

      Field keys for custom inventory field can be renamed under the actions column.29

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    -

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    1. Hover over the actions column on the far-right for the field whose key you want to rename.

      2. -

    2. When the appears, click on it and select Rename Key.

      3. -

    3. Enter the new key and click Check Availability.

      4. -

    4. If the key is not already in use, you’ll be presented with a list of dependencies to review.

      -

      Verify that these dependencies are correct and as expected.

      5. -

    5. Click Rename Key to apply your changes to the key.

      -

      Confirm that the dependencies updated are corrected and as expected.

      6. -
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    -

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    -
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    1. In the left sidebar, click Settings.

      2. -

    2. Under Inventory, select Inventory Record Fields.

      3. -

    3. Click Add Group and enter a name and an optional description for the group.

      4. -

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    The ​ValidMind MCP (Model Context Protocol) server enables AI assistants to query and manage your model inventory through natural language, providing seamless access to models, artifacts, templates, and custom fields.

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-        MCPServer[ValidMind MCP Server]
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-    subgraph vm [ValidMind Platform]
-        Models[Models]
-        Artifacts[Artifacts]
-        Templates[Templates]
-        CustomFields[Custom fields]
-    end
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    Your AI assistant sends natural language queries through the MCP protocol. The ​ValidMind MCP server translates these into API calls, authenticates using your API key, and returns data from the ValidMind Platform.

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    1. In Cursor, open Settings > Cursor Settings and navigate to the MCP section.

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    2. Click Add new global MCP server to open your ~/.cursor/mcp.json file.

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    3. Add the ​ValidMind MCP server configuration:

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      {
-  "mcpServers": {
-    "validmind": {
-      "url": "https://api.prod.validmind.ai/mcp",
-      "headers": {
-        "x-api-key": "YOUR_API_KEY",
-        "x-api-secret": "YOUR_API_SECRET"
-      }
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-  }
-}
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    4. Replace YOUR_API_KEY and YOUR_API_SECRET with your ValidMind credentials.4

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    5. Save the file and reload Cursor with the Command/Ctrl + Shift + P > Developer: Reload Window shortcut.

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    6. Return to Cursor Settings > MCP and verify that the ValidMind server appears. If it shows as disabled, click the toggle to enable it.

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    7. Try asking Cursor questions like:

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-  --header "x-api-key: YOUR_API_KEY" \
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    2. Replace YOUR_API_KEY and YOUR_API_SECRET with your ValidMind credentials.5

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    3. Verify the connection:

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    Monitoring of record (model) performance in risk management involves regularly assessing a record’s accuracy, stability, and robustness to ensure it remains reliable after deployment.

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    Monitoring is a critical component of risk management, as emphasized in regulations such as SR 26-2, SS1/23, and E-24,1 and includes:

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    Monitoring scenarios

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    Scenarios where ongoing monitoring is warranted:

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      Trial phases where a record is subject to ongoing monitoring are typically fairly short, ranging from a few days to several weeks.

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    • Monitoring during significant updates — When a record undergoes a significant update, ongoing monitoring should compare the updated record’s performance to the original. This process, called parallel runs, involves running both versions of records such as AI models simultaneously for a set period.

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      Record outputs should be closely monitored to assess whether the update improves performance or introduces new risks. The results help determine if the updated record should replace the original or if further adjustments are needed. Parallel runs are especially important for regulatory or critical records, ensuring changes don’t harm performance.

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      The record’s output should be assessed regularly against predefined performance benchmarks to ensure it meets the required standards. Any deviations from the expected performance should be quickly identified, allowing for timely intervention.

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    A visualisation of prediction probabilities histogram drift for a binary classification model. It compares the distribution of predicted probabilities between two datasets: a reference dataset (baseline) and a monitoring dataset (current data).

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    Histogram of prediction probabilities to indicate model drift
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    Comparing predicted probabilities between reference and monitoring datasets
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    A robust ongoing monitoring plan is crucial for maintaining record accuracy and reliability. Developers should start regular monitoring from the outset, ideally during the pre-approval phase of development, refining the plan as new insights are gained. This plan should be included in the initial documentation, with clear instructions for execution and use of results.2

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    As monitoring continues, it’s important to report record status to key stakeholders, such as your risk management committee. Regular updates with summary metrics will keep stakeholder informed of artifacts and emerging risks, highlighting significant trends or issues that may need action.

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    The design of your ongoing monitoring plan overall should be a collaborative effort between the first line of defense, typically business units or record (model) owners, and the second line of defense, namely your risk management team. Together, they should select the performance metrics, determine monitoring frequency, and tailor the ongoing monitoring plan to the record’s specific use case and risk profile.

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    This entails that the ongoing monitoring plan is primarily designed and implemented by the developers involved in the record’s development and deployment. Their work is then reviewed during validation to ensure the robustness of the ongoing monitoring plan and alignment with risk management goals.

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    The implementation of the ongoing monitoring plan typically also falls on developers. This effort includes executing the monitoring activities, collecting performance data, and generating reports. The developers also ensure that the monitoring is carried out according to the established schedule and that any anomalies or deviations are promptly identified and addressed.

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    These thresholds and alerts apply to metrics over time blocks1, helping you track record (model) performance and identify issues more easily. Thresholds help you flag values that suggest drift, underperformance, or other types of risk—for example, by signaling low, medium, or high risk based on how a metric evolves over time.

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-    "high_risk": 0.5,
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-}
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-    return value >= thresholds["low_risk"]
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-    key="GINI Score",
-    value=gini, 
-    recorded_at=datetime(2025, 6, 10),
-    thresholds=thresholds,
-    passed=passed_fn(gini)
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    Here, the GINI score fluctuates over time but stays above the medium risk threshold. It ends on an upward trend, with the latest value of 0.75 classified as satisfactory, as indicated in the upper-right corner.

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    A metric that is satisfactory
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    The passed boolean is determined by you, either through a custom function, such as passed_fn in the earlier example, or by assigning a value directly. This approach allows you to interpret the configured thresholds according to your own logic, rather than relying on automatic evaluation.

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    A metric is considered to have breached a threshold when its value falls within the high or medium risk range, that is, when it is below the low risk threshold (in this example, below 0.8). Breaching a threshold affects visual risk indicators and triggers alert notifications, independently of whether the passed flag is True or False.

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    An email is sent to inventory record stakeholders notifying them that the record has a metric that did not pass an ongoing monitoring threshold and requires attention.

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    Automated email alert from ValidMind notifying that the GINI Score for the [Test] Customer Churn model is 0.75, which falls between the medium (0.6) and low (0.8) risk thresholds, with a button to view the model.

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    Sample alert notification email
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    Responding to these notifications involves prioritizing the alerts and taking appropriate action, ideally as part of your documented ongoing monitoring plan.4

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    View, manage, and export analytics that allow you to gain quick insights into your risk governance efforts across your organization.

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    2. Select a record or find your record by applying a filter or searching for it.9

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    1. In the left sidebar, click Inventory.

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    2. Select a record or find your record by applying a filter or searching for it.14

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    2. Select a record or find your record by applying a filter or searching for it.5

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    1. In the left sidebar, click Inventory.

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    1. In the left sidebar, click Inventory.

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    1. In the Artifacts section under your evidence, click Suggest Validation Issues.

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    4. Make your desired changes to the risk area’s title and description.

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    4. Click Yes, Delete Guideline to confirm permanent deletion of that guideline.

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    1. In the left sidebar, click Settings.

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    2. Under Documents, select Templates.5

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    3. Select the Validation tab, then click on the template you want to edit.

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    4. On the template details page, click Edit Outline.

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    5. For the section you’d like to add a guideline to,6 click and select Guideline Block under content.

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    6. Select the guidelines you’d like to add to that section, then click Add # Guideline(s) to save your changes.

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    7. When you’re done, click Create New Version to view a side-by-side comparison of your changes with the previous version:

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      Once saved, your new template version with the modified guidelines becomes available for use with your validation reports.

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    Map evidence to guidelines

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    Map Evidence panel showing evidence type toggles for Developer Evidence and Validator Evidence, and a Relevance Threshold slider set to 0.7.

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    Map Evidence uses AI to suggest relevant evidence for each validation guideline, helping you find and link supporting documentation from both developers and validators.

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    1. In the left sidebar, click Inventory.

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    2. Select a record or find your record by applying a filter or searching for it.5

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    3. In the left sidebar that appears for your record, click Documents and select the Latest tab.6

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    4. Click on a Validation type file.7

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    5. You can map evidence at three levels:

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    1. Configure the mapping options:

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    2. Click Map Evidence to start the mapping.

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      If this is your first time running Map Evidence, review the information presented to you explaining the mapping process, then click Map Evidence to run the mapping.9

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    When mapping is complete, the # of evidence items to review is displayed. Review the evidence items and approve or reject the suggestions as needed.

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    Review and approve mapped evidence

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    3. Review individual evidence suggestions on the Developer Evidence or Validator Evidence tabs:

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    4. Confirm that any approved evidence appears in the Evidence panel for that guideline as expected.

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    1. In the left sidebar, click Inventory.

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    2. Select a record or find your record by applying a filter or searching for it.10

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    3. In the left sidebar that appears for your record, click Documents and select the Latest tab.11

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    5. You can assess evidence for compliance at three levels:

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    1. Click Assess Evidence to run an analysis on the linked evidence and generate an Evidence Assessment containing:

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      If this is your first time running Assess Evidence, review the information presented to you explaining the assessment process, then click Assess Evidence to run the assessment.13

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    Filtering supports simple and complex conditions using AND or OR logic with nested groups. You can filter on both the default fields created by ​ValidMind and custom fields created by your organization.

    -
    -

    Rules

    -

    Rules define individual conditions to filter by, such as the business unit or owner.

    -

    To add a rule:

    -
      -

    1. Click click Add Rule to add a filter condition.

      -

      A filter rule contains three components:

      -

      A new filter row with three components

      2. -
    -
    -
    -
    -
    Field selector
    -
    -Dropdown to choose the field type to filter by. -
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    Operator selector
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    -Dropdown to select the comparison operator. -
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    Value editor
    -
    -Input area that varies based on the selected field type. -
    -
    -
    -
    -
      -
    1. From the field selector dropdown, select one of:
      2. -
    -
      -
    • artifact information — One of the artifact fields always created by ​ValidMind, such as the Business Unit.
      • -
    • assignees — User assigned to the Validation Issue type artifact.
      • -
    • artifact fields — One of the organization-specific artifact fields you added for your own use.
      • -
    -
      -
    1. From the operator dropdown, select one of:
      Available operators update based on the field selector
      2. -
    -
    - - ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Operator selector options
    Text fieldsDate fieldsNumber fieldsBoolean fieldsMulti-select fields
    equalsequalsequalsequalsin
    not equalsnot equalsnot equalsnot equalsnot in
    containsbeforegreater thanis emptycontains
    begins withafterless thanis not emptyis empty
    ends withbetweenbetweenis not empty
    is emptyis emptyis empty
    is not emptyis not emptyis not empty
    -
    -
      -
    1. From the value editor, enter or select a value based on the field type:
      Value input changes based on field selector
      2. -
    -
    -
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    -
    Text
    -
    -Text input or text area for longer content -
    -
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    Number
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    Select/multi-select
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    Entity fields
    -
    -Dropdown with search for users or records -
    -
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      -

    1. (Optional) Add more rules by following the same steps.

      2. -

    2. Click Apply Filters.

      3. -
    -
    -
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    Groups

    -

    Groups combine multiple rules that are evaluated together and can be nested one level deep. You use groups for more complex filters, such as Condition A AND (Condition B OR Condition C).

    -

    To add a group:

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    1. Click Add Group to create a nested group of conditions.

      2. -

    2. Follow the same steps as in Rules to add one or several conditions to the group.

      3. -

    3. Click Apply Filters.

      4. -
    -
    -
    -
    -

    A rule AND a group of OR rules

    -
    Example: Use case is Attrition/Churn Management AND (Business Unit is Commerical Banking OR Business Unit is Retail Banking)
    -
    -
    -
    -
    -
    -

    Combinators

    -

    Combinators define how multiple rules or groups are logically combined when matching conditions. By default, multiple rules are combined with AND logic, meaning all conditions must match.

    -

    Supported combinators:

    -
    -
    -
      -
    • AND — All conditions must match
      • -
    • OR — At least one condition must match
      • -
    -

    To change the combinator:

    -
      -

    1. Click the AND or OR button between rules or groups to change how conditions are combined.

      2. -

    2. Click Apply Filters.

      3. -
    -
    -

    The AND and OR combinators

    -
    -
    -
    -

    Why can’t I use both AND and OR top-level combinators?

    -

    Combinators must evaluate logically for the filter to be applied:

    -

      Show items matching Condition A AND Condition B OR Condition C
      Show items matching Condition A AND (Condition B OR Condition C)

    -

    To achive more complex filters, use groups.

    -
    -
    -
    -

    View active filters

    -

    After applying filters, active filter conditions appear as individual chips next to the label Filtered by:.

    -
      -
    • Field names
      • -
    • Operators
      • -
    • Values
      • -
    -
    -
    -

    Remove active filters

    -

    Click the icon on any filter chip to remove that specific filter condition. The remaining filters are automatically reapplied.

    -
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    Examples

    -

    Available values depend on your organization’s custom configuration

    -
    - -
    -
    -

    Show records where Record Field: Risk Level is High (requires that you have a custom select field called Risk Level):

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Click Add Rule:

      -
        -
      • Field = Record Field: Risk Level
        • -
      • Operator = equals
        • -
      • Value = High
        • -
      3. -

    3. Click Apply Filters.

      4. -
    -
    -
    -

    Show records that are Stage: In Production AND in Business Unit: Risk Management:

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. On the Select Your Filters dialog that opens, click Add Rule:

      -
        -
      • Field = Stage
        • -
      • Operator = in
        • -
      • Value = In Production
        • -
      3. -

    3. Add another rule:

      -
        -
      • Field = Business Unit
        • -
      • Operator = in
        • -
      • Value = Risk Management
        • -
      4. -

    4. Click Apply Filters.

      5. -
    -
    -
    -

    Show records that are (Stage: In Production AND in Business Unit: Risk Management) OR (Stage: In Production AND in Use: Attrition/Churn Management):

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Click Add Rule to add your rules:

      -
        -
      1. First rule: -
          -
        • Field = Stage
          • -
        • Operator = in
          • -
        • Value = In Production
          • -
        2. -
      2. Second rule: -
          -
        • Field = Business Unit
          • -
        • Operator = in
          • -
        • Value = Risk Management
          • -
        3. -
      3. -

    3. Click Add Group to create a new group:

      -
        -

      1. Change combinator to OR (between the two groups).

        2. -

      2. In the new group, click Add Rule to add:

        -
          -
        1. First rule: -
            -
          • Field = Stage
            • -
          • Operator = in
            • -
          • Value = In Production
            • -
          2. -
        2. Second rule: -
            -
          • Field = Use
            • -
          • Operator = in
            • -
          • Value = Attrition/Churn Management
            • -
          3. -
        3. -
      4. -

    4. Click Apply Filters.

      5. -
    -
    -
    -

    Show records where (Stage: Active OR In Periodic Review) AND (Risk Level: High OR Medium) AND Owner: John Doe:

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Click Add Group to add your groups:

      -
        -
      1. First group: -
          -
        • Field = Stage
          • -
        • Operator = in
          • -
        • Value = In Production, In Periodic Review
          • -
        2. -
      2. Second group: -
          -
        • Field = Record Field: Risk Level
          • -
        • Operator = equals
          • -
        • Value = High, Medium
          • -
        3. -
      3. -

    3. Add Rule to add a rule:

      -
        -
      • Field = Owner
        • -
      • Operator = in
        • -
      • Value = John Doe
        • -
      4. -

    4. Ensure all groups and rules use AND combinator.

      5. -

    5. Click Apply Filters.

      6. -
    -
    -
    -

    Show records implemented between January 1, 2025 and January 31, 2025:

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Add Rule:

      -
        -
      • Field = Implementation Date
        • -
      • Operator = between.
        • -
      -
        -
      1. Enter start date — January 1, 2025
        2. -
      2. Enter end date — January 31, 2025
        3. -
      3. -

    3. Click Apply Filters.

      4. -
    -
    -
    -

    Show records owned by a specific user that have .pdf attachments:

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Click Add Rule:

      -
        -
      • Field = Owner
        • -
      • Operator = in
        • -
      • Value = Select the user from dropdown.
        • -
      3. -

    3. Add another rule:

      -
        -
      • Field = Attachments
        • -
      • Operator = file name contains
        • -
      • Value = .pdf.
        • -
      4. -

    4. Ensure the combinator between rules is set to AND.

      5. -

    5. Click Apply Filters.

      6. -
    -
    -
    -
    -
    -
    -
    -
    -
    -
      -
    1. Click Apply Filters.
      2. -
    - -

    Changes are automatically saved to your account and do not affect other users.

    -
    -
    -
      -

    1. In the left sidebar, click Artifacts to view lists of other artifact types across all records.

      2. -

    2. Select an artifact type14 to view from the tabs:

      -
        -
      • Policy Exception
        • -
      • Limitation
        • -
      3. -

    3. Click Filter.

      4. -

    4. On the Select Your Filters dialog that opens, enter your filtering criteria.

      5. -
    -
    -
    - -
    -
    - -

    Filtering supports simple and complex conditions using AND or OR logic with nested groups. You can filter on both the default fields created by ​ValidMind and custom fields created by your organization.

    -
    -

    Rules

    -

    Rules define individual conditions to filter by, such as the business unit or owner.

    -

    To add a rule:

    -
      -

    1. Click click Add Rule to add a filter condition.

      -

      A filter rule contains three components:

      -

      A new filter row with three components

      2. -
    -
    -
    -
    -
    Field selector
    -
    -Dropdown to choose the field type to filter by. -
    -
    -
    -
    -
    Operator selector
    -
    -Dropdown to select the comparison operator. -
    -
    -
    -
    -
    Value editor
    -
    -Input area that varies based on the selected field type. -
    -
    -
    -
    -
      -
    1. From the field selector dropdown, select one of:
      2. -
    -
      -
    • artifact information — One of the artifact fields always created by ​ValidMind, such as the Business Unit.
      • -
    • assignees — User assigned to the Validation Issue type artifact.
      • -
    • artifact fields — One of the organization-specific artifact fields you added for your own use.
      • -
    -
      -
    1. From the operator dropdown, select one of:
      Available operators update based on the field selector
      2. -
    -
    - - ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Operator selector options
    Text fieldsDate fieldsNumber fieldsBoolean fieldsMulti-select fields
    equalsequalsequalsequalsin
    not equalsnot equalsnot equalsnot equalsnot in
    containsbeforegreater thanis emptycontains
    begins withafterless thanis not emptyis empty
    ends withbetweenbetweenis not empty
    is emptyis emptyis empty
    is not emptyis not emptyis not empty
    -
    -
      -
    1. From the value editor, enter or select a value based on the field type:
      Value input changes based on field selector
      2. -
    -
    -
    -
    -
    Text
    -
    -Text input or text area for longer content -
    -
    -
    -
    -
    Date
    -
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    -
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    -
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    -
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    -
    Boolean
    -
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    -
    -
    -
    -
    Select/multi-select
    -
    -Dropdown with search -
    -
    -
    -
    -
    Entity fields
    -
    -Dropdown with search for users or records -
    -
    -
    -
    -
      -

    1. (Optional) Add more rules by following the same steps.

      2. -

    2. Click Apply Filters.

      3. -
    -
    -
    -

    Groups

    -

    Groups combine multiple rules that are evaluated together and can be nested one level deep. You use groups for more complex filters, such as Condition A AND (Condition B OR Condition C).

    -

    To add a group:

    -
      -

    1. Click Add Group to create a nested group of conditions.

      2. -

    2. Follow the same steps as in Rules to add one or several conditions to the group.

      3. -

    3. Click Apply Filters.

      4. -
    -
    -
    -
    -

    A rule AND a group of OR rules

    -
    Example: Use case is Attrition/Churn Management AND (Business Unit is Commerical Banking OR Business Unit is Retail Banking)
    -
    -
    -
    -
    -
    -

    Combinators

    -

    Combinators define how multiple rules or groups are logically combined when matching conditions. By default, multiple rules are combined with AND logic, meaning all conditions must match.

    -

    Supported combinators:

    -
    -
    -
      -
    • AND — All conditions must match
      • -
    • OR — At least one condition must match
      • -
    -

    To change the combinator:

    -
      -

    1. Click the AND or OR button between rules or groups to change how conditions are combined.

      2. -

    2. Click Apply Filters.

      3. -
    -
    -

    The AND and OR combinators

    -
    -
    -
    -

    Why can’t I use both AND and OR top-level combinators?

    -

    Combinators must evaluate logically for the filter to be applied:

    -

      Show items matching Condition A AND Condition B OR Condition C
      Show items matching Condition A AND (Condition B OR Condition C)

    -

    To achive more complex filters, use groups.

    -
    -
    -
    -

    View active filters

    -

    After applying filters, active filter conditions appear as individual chips next to the label Filtered by:.

    -
      -
    • Field names
      • -
    • Operators
      • -
    • Values
      • -
    -
    -
    -

    Remove active filters

    -

    Click the icon on any filter chip to remove that specific filter condition. The remaining filters are automatically reapplied.

    -
    -
    -

    Examples

    -

    Available values depend on your organization’s custom configuration

    -
    - -
    -
    -

    Show records where Record Field: Risk Level is High (requires that you have a custom select field called Risk Level):

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Click Add Rule:

      -
        -
      • Field = Record Field: Risk Level
        • -
      • Operator = equals
        • -
      • Value = High
        • -
      3. -

    3. Click Apply Filters.

      4. -
    -
    -
    -

    Show records that are Stage: In Production AND in Business Unit: Risk Management:

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. On the Select Your Filters dialog that opens, click Add Rule:

      -
        -
      • Field = Stage
        • -
      • Operator = in
        • -
      • Value = In Production
        • -
      3. -

    3. Add another rule:

      -
        -
      • Field = Business Unit
        • -
      • Operator = in
        • -
      • Value = Risk Management
        • -
      4. -

    4. Click Apply Filters.

      5. -
    -
    -
    -

    Show records that are (Stage: In Production AND in Business Unit: Risk Management) OR (Stage: In Production AND in Use: Attrition/Churn Management):

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Click Add Rule to add your rules:

      -
        -
      1. First rule: -
          -
        • Field = Stage
          • -
        • Operator = in
          • -
        • Value = In Production
          • -
        2. -
      2. Second rule: -
          -
        • Field = Business Unit
          • -
        • Operator = in
          • -
        • Value = Risk Management
          • -
        3. -
      3. -

    3. Click Add Group to create a new group:

      -
        -

      1. Change combinator to OR (between the two groups).

        2. -

      2. In the new group, click Add Rule to add:

        -
          -
        1. First rule: -
            -
          • Field = Stage
            • -
          • Operator = in
            • -
          • Value = In Production
            • -
          2. -
        2. Second rule: -
            -
          • Field = Use
            • -
          • Operator = in
            • -
          • Value = Attrition/Churn Management
            • -
          3. -
        3. -
      4. -

    4. Click Apply Filters.

      5. -
    -
    -
    -

    Show records where (Stage: Active OR In Periodic Review) AND (Risk Level: High OR Medium) AND Owner: John Doe:

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Click Add Group to add your groups:

      -
        -
      1. First group: -
          -
        • Field = Stage
          • -
        • Operator = in
          • -
        • Value = In Production, In Periodic Review
          • -
        2. -
      2. Second group: -
          -
        • Field = Record Field: Risk Level
          • -
        • Operator = equals
          • -
        • Value = High, Medium
          • -
        3. -
      3. -

    3. Add Rule to add a rule:

      -
        -
      • Field = Owner
        • -
      • Operator = in
        • -
      • Value = John Doe
        • -
      4. -

    4. Ensure all groups and rules use AND combinator.

      5. -

    5. Click Apply Filters.

      6. -
    -
    -
    -

    Show records implemented between January 1, 2025 and January 31, 2025:

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Add Rule:

      -
        -
      • Field = Implementation Date
        • -
      • Operator = between.
        • -
      -
        -
      1. Enter start date — January 1, 2025
        2. -
      2. Enter end date — January 31, 2025
        3. -
      3. -

    3. Click Apply Filters.

      4. -
    -
    -
    -

    Show records owned by a specific user that have .pdf attachments:

    -
      -

    1. On the Inventory page, click Filter.

      2. -

    2. Click Add Rule:

      -
        -
      • Field = Owner
        • -
      • Operator = in
        • -
      • Value = Select the user from dropdown.
        • -
      3. -

    3. Add another rule:

      -
        -
      • Field = Attachments
        • -
      • Operator = file name contains
        • -
      • Value = .pdf.
        • -
      4. -

    4. Ensure the combinator between rules is set to AND.

      5. -

    5. Click Apply Filters.

      6. -
    -
    -
    -
    -
    -
    -
    -
    -
    -
      -
    1. Click Apply Filters.
      2. -
    -
    -
    -
    - - - -
    - - -
    - - -
    - - - - - - - \ No newline at end of file diff --git a/site/guide/validation/working-with-artifacts-listing.json b/site/guide/validation/working-with-artifacts-listing.json deleted file mode 100644 index 9c3a3961a..000000000 --- a/site/guide/validation/working-with-artifacts-listing.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "listing": "/guide/validation/working-with-artifacts.html", - "items": [ - "/guide/validation/view-filter-artifacts.html", - "/guide/validation/customize-artifact-layouts.html", - "/guide/validation/log-artifacts.html", - "/guide/validation/update-artifacts.html", - "/guide/validation/customize-artifact-overview-page.html" - ] -} \ No newline at end of file diff --git a/site/guide/workflows/conditional-step-requirements.html b/site/guide/workflows/conditional-step-requirements.html deleted file mode 100644 index 5e8fda375..000000000 --- a/site/guide/workflows/conditional-step-requirements.html +++ /dev/null @@ -1,1940 +0,0 @@ - - - - - - - - - - -Conditional step requirements – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Conditional step requirements

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    June 1, 2026

    -
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    Configure conditional requirements on certain workflow step types.

    -
    -

    Prerequisites

    -
      -
      • -
      • -
      • -
    -
    -
    -

    Configure conditional requirements

    - -

    Conditional requirements are required or optional for the following step types:3

    -
    -

    Inventory Record Stage Change optional

    -

    -
      -

    1. Under when these conditions are met, set your AND and OR conditions.

      2. -

    2. Add a single independent rule or a linked condition group. These rules and groups can be nested if desired:

      -
        -
      • Click Add Rule to add an independent rule.
        • -
      • Click Add Group to add a linked group of rules that all must be true to qualify.
        • -
      3. -
    -
    -
    -

    Artifact Status Change

    -

    -
      -

    1. Under when these conditions are met, set your AND and OR conditions.

      2. -

    2. Add a single independent rule or a linked condition group. These rules and groups can be nested if desired:

      -
        -
      • Click Add Rule to add an independent rule.
        • -
      • Click Add Group to add a linked group of rules that all must be true to qualify.
        • -
      3. -
    -
    -
    -

    User Action optional

    -

    -
    - -
    -
    -
      -

    1. Under display action button when, set your AND and OR conditions.

      2. -

    2. Add a single independent rule or a linked condition group. These rules and groups can be nested if desired:

      -
        -
      • Click Add Rule to add an independent rule.
        • -
      • Click Add Group to add a linked group of rules that all must be true to qualify.
        • -
      3. -
    -
    -
    -
      -

    • Select any requested fields4 to be entered by users during the action.

      -

      Toggle Optional to ON if the field should be optional and not required.

      • -

    • (Optional) Present these requested fields to users in steps:

      -
        -
      1. Click Add Step to add a step.
        2. -
      2. On the Add Step modal that appears, enter the step title and provide an optional description.
        3. -
      3. Click Save to insert the step.
        4. -
      4. Drag request fields into a step to add them to that step.
        5. -
      • -
    -
    -
    -
      -

    1. Toggle Enable deadline on to configure a user action deadline for when users should complete this action by.

      2. -

    2. Then, under end action, select the type of deadline:

      -
        -
      • After Time Interval — Set the amount and unit of the deadline.
        • -
      • At Specified Date — Set the date and time the action should wait until.
        • -
      • At Specified Date on Custom Field — Select a date, date time, or calculated record field that defines the deadline.
        • -
      • Time Delta from Specified Date on Custom Field — Select a date, date time, or calculated record field to calculate a deadline from, then set a time delta direction and wait duration.
        • -
      3. -
    -
    -
    -
    -
    -
    -

    Approval required

    -

    -
      -

    1. Under approval group, click Add Individual Approver to set an individual approver selected from User Roles,5 Inventory Record Stakeholder Types,6 or Inventory Record Fields — User:7

      -
        -
      • Click Add Rule to add an individual rule.
        • -
      • Click Add Group to add a group of rules.
        • -
      -

      One approver per rule or rule group must provide approval. If you’ve set multiple rules or rule groups, select whether they are AND or OR conditions.

      2. -

    2. (Optional) Add more individual rule or rule group approvers as required.

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    At Specified Datewait untilWait until a specific timestamp. Applies to all records or artifacts under the workflow. If the milestone date inputted is in the past, the next workflow step will display immediately.
    At Specified Date on Custom Fieldcustom field19Wait until a specific timestamp as defined by a record or artifact field on a per record or per artifact basis. If the selected field is empty20 or the milestone date inputted is in the past, the next workflow step will display immediately.
    Time Delta from Specified Date on Custom Fieldcustom field, time delta direction, wait durationWait for a calculated time relative to a date in a record or artifact field on a per record or per artifact basis. If the selected field is empty21 or the milestone date inputted is in the past, the next workflow step will display immediately.
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    21 Make record or artifact fields required on registration to ensure necessary fields have valid values.

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    allow invalid certificates (optional)Toggle whether or not the request will be allowed to use invalid certificates.
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    -Jan 23, 2026 - -Model inventory management: AWS Bedrock support, inventory integrations, and external fields -
    -Jan 23, 2026 - -Reporting & analytics: Model activity page filtering -
    -Jan 23, 2026 - -Workflows: Field grouping, webhook integrations -
    -Jan 22, 2026 - -Upgrade of AI features to OpenAI GPT-4.1 -
    -Dec 19, 2025 - -Helper text for custom model and artifact fields -
    -Dec 19, 2025 - -Attestations: Stakeholder roles, model scoping, and notifications -
    -Dec 19, 2025 - -Model inventory: Date entry and use case management -
    -Dec 19, 2025 - -Manage platform notifications -
    -Dec 19, 2025 - -Testing & automated documentation: Document versioning and PDF export -
    -Dec 19, 2025 - -AI governance overview, use case guides, and governance training -
    -Dec 10, 2025 - -Use shared library blocks in templates -
    -Dec 10, 2025 - -Artifacts (formerly findings): terminology updates and configurable fields -
    -Dec 10, 2025 - -Model inventory: Field keys and configurable fields -
    -Dec 10, 2025 - -Public API enhancements: Model stages, findings, custom fields, and more -
    -Dec 10, 2025 - -Workflows: Approval enhancements and duplication, and more -
    -Nov 1, 2025 - -Configure finding severities with custom levels -
    -Nov 1, 2025 - -Workflow scheduling with date-based triggers -
    -Nov 1, 2025 - -Assign default templates to document types -
    -Nov 1, 2025 - -Public REST API: Bypass restrictions, download attachments, and more -
    -Oct 7, 2025 - -Document checker: Bulk uploads, audit trails & more -
    -Oct 7, 2025 - -Findings: Formula capabilities, CSV exports & more -
    -Oct 7, 2025 - -Public REST API: New endpoints for findings, inventory model schema & more -
    -Oct 7, 2025 - -Workflows: Findings support, editor improvements & more -
    -Oct 7, 2025 - -Add support for the DeepEval dataset in LLM tests -
    -Sep 3, 2025 - -Document checker now supports the EU AI Act -
    -Sep 3, 2025 - -Implement finding types for enhanced management -
    -Sep 3, 2025 - -New ValidMind public REST API reference -
    -Jul 31, 2025 - -Support for new document types -
    -Jul 31, 2025 - -Enhanced Document Checker for regulatory compliance -
    -Jul 31, 2025 - -Add copy node ID feature to NodeBox component -
    -Jul 31, 2025 - -Add email alerts for metric threshold breaches -
    -Jul 31, 2025 - -Add support for PDF documents in Document Checker -
    -Jul 31, 2025 - -Revamped Administrator Fundamentals course with new modules -
    -Jun 27, 2025 - -UI allows customization of finding statuses -
    -Jun 27, 2025 - -Updated workflow documentation and configuration -
    -Jun 26, 2025 - -Interfaces to support code explainer feature in ValidMind -
    -Jun 26, 2025 - -Add demo notebook for code explainer -
    -Jun 26, 2025 - -Quickstart guide for model validation with ValidMind Library -
    -May 30, 2025 - -Revamped Developer Fundamentals training into four modules -
    -May 30, 2025 - -Updated Validator Fundamentals courses -
    -Apr 28, 2025 - -User guide updates for attestation management -
    -Apr 16, 2025 - -New notebook series for model validation with ValidMind -
    -Apr 16, 2025 - -Add text support for custom test descriptions -
    -Apr 10, 2025 - -Add date and datetime support for analytics filters -
    -Apr 3, 2025 - -Expose log text interface for qualitative text sections -
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    Settings

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    Governance

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    /settings/attestation-templates — Attestation Templates

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    Organization

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    /settings/authentication — Authentication

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      • Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents
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    Documents

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    /settings/document-types — Document Types

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      • Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents
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    Organization

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    /settings/email-notifications — Email Notifications

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    Artifacts

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    /settings/finding-custom-fields — Artifact Fields

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      • Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents
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    • /guide/templates/manage-document-templates.html -
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      • Sections: Prerequisites; View document templates; Edit document template outlines; Swap document templates; View currently applied templates; Swap between templates; Duplicate document templates; Delete document templates
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    • /guide/templates/manage-document-types.html -
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      • Sections: Prerequisites; Add document types; Edit or delete document types; Development, Validation, and Monitoring document types are stock types and cannot be deleted.
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    • /guide/templates/manage-documents.html -
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      • Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents
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      • Sections: Prerequisites; Add text blocks; Add existing text blocks to library; Duplicate text blocks; Edit text blocks; Delete text blocks
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    /settings/finding-types — Artifact Types

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      • Sections: Prerequisites; Edit template outlines; Configure assessment options[^4]; Edit YAML templates; Template schema; Troubleshooting YAML templates; Add text blocks to templates; Add text blocks via template outlines
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    • /guide/templates/manage-document-templates.html -
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      • Sections: Prerequisites; View document templates; Edit document template outlines; Swap document templates; View currently applied templates; Swap between templates; Duplicate document templates; Delete document templates
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      • Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents
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    Users & Access

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    /settings/groups — Groups

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    Integrations

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    /settings/integrations/connections — Connections

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    /settings/integrations/data-exports — Analytics Exports

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    /settings/integrations/secrets — Secrets

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    Users & Access

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    /settings/invitation — Invite New Users

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    Organization

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    Users & Access

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    /settings/permissions — Permissions

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    Your Account

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    /settings/profile — Profile

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    Governance

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    /settings/regulations — Regulations & Policies

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    /settings/risk-areas — Risk Areas & Validation Guidelines

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    Users & Access

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    /settings/roles — Roles

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    Documents

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    /settings/templates — Templates

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      • Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents
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    Your Account

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    /settings/theme-customization — Theme Customization

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    Users & Access

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    /settings/user-directory — User Directory

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    Governance

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    /settings/workflow-states — Workflow States

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      • Sections: Prerequisites; Start a workflow via webhook; 1. Configure workflow in }; 2. Start workflow from external system; Trigger a paused workflow to continue; 1. Configure workflow in }; 2. Trigger workflow to continue from external system
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      • Sections: Prerequisites; Create custom workflows; 1. Add new workflows; 2. Configure workflow steps; 3. Link workflow together; Workflow steps relationship unclear on your canvas?; 4. Publish workflow; Clone existing workflows
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    Main application

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    /analytics — sidebar.analytics

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      • Sections: Prerequisites; Use a custom function; Set the passed parameter; Output examples; Alert notifications
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      • Sections: Log metrics over time }; Prerequisites; Add metrics over time; Add integration metrics; Use the global time range; View metric over time metadata
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    • No direct help link in frontend; related docs inferred from keywords.
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    /artifacts — Artifacts

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    • /guide/templates/customize-document-templates.html -
        -
      • Sections: Prerequisites; Edit template outlines; Configure assessment options[^4]; Edit YAML templates; Template schema; Troubleshooting YAML templates; Add text blocks to templates; Add text blocks via template outlines
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      • Sections: Prerequisites; View document templates; Edit document template outlines; Swap document templates; View currently applied templates; Swap between templates; Duplicate document templates; Delete document templates
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    • /guide/templates/manage-document-types.html -
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      • Sections: Prerequisites; Add document types; Edit or delete document types; Development, Validation, and Monitoring document types are stock types and cannot be deleted.
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        -
      • Sections: Prerequisites; Add record documents; How do I get the best results when converting PDFs into editable documents?; How can I trust that the conversion is accurate?; Troubleshooting; My PDF conversion is stuck. What can I do?; Edit record documents; Delete record documents
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      • Sections: Prerequisites; Add text blocks; Add existing text blocks to library; Duplicate text blocks; Edit text blocks; Delete text blocks
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    /attestations — Attestations

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      • Sections: Prerequisites; Add attestation templates; Test attestation schedules; Edit attestation periods; Cancel attestation periods; View attestations dashboard; Progress; Responses
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      • Sections: Prerequisites; Steps
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      • Sections: Prerequisites; Steps
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    /validation-issues — Validation Issues

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    /workflows — Workflows

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    Docs (related):

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        -
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        • -
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    • /guide/integrations/integrations-examples/use-webhooks-with-workflows.html -
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      • Sections: Prerequisites; Start a workflow via webhook; 1. Configure workflow in }; 2. Start workflow from external system; Trigger a paused workflow to continue; 1. Configure workflow in }; 2. Trigger workflow to continue from external system
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    • /guide/workflows/conditional-step-requirements.html -
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      • Sections: Prerequisites; Configure conditional requirements
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      • -
    • /guide/workflows/configure-workflows.html -
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      • Sections: Prerequisites; Create custom workflows; 1. Add new workflows; 2. Configure workflow steps; 3. Link workflow together; Workflow steps relationship unclear on your canvas?; 4. Publish workflow; Clone existing workflows
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    • /guide/workflows/introduction-to-workflows.html -
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      • Sections: Workflow elements; What’s next
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      • Sections: Prerequisites; Add record stages; Edit or delete record stages
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    Documentation index (human-oriented)

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    See AGENTS.md and about/using-the-documentation.md in the LLM corpus for guides organized by feature area (Configuration, Workflows, Inventory, etc.).

    - - -
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    - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/EXECUTED/development/2-start_development_process.html b/site/notebooks/EXECUTED/development/2-start_development_process.html deleted file mode 100644 index b1a5951e7..000000000 --- a/site/notebooks/EXECUTED/development/2-start_development_process.html +++ /dev/null @@ -1,1342 +0,0 @@ - - - - - - - - - -ValidMind for development 2 — Start the development process – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    ValidMind for development 2 — Start the development process

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    - - - -
    - - - -

    Learn how to use ValidMind for your end-to-end documentation process with our series of four introductory notebooks. In this second notebook, you'll run tests and investigate results, then add the results or evidence to your documentation.

    -

    You'll become familiar with the individual tests available in ValidMind, as well as how to run them and change parameters as necessary. Using ValidMind's repository of individual tests as building blocks helps you ensure that a record (model) is being built appropriately.

    -

    For a full list of out-of-the-box tests and descriptions, use the interactive ValidMind test sandbox.

    -
    -Learn by doing

    Our course tailor-made for developers new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Developer Fundamentals -
    - - -

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    Prerequisites

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    In order to log test results or evidence to your documentation with this notebook, you'll need to first have:

    -
      -
      • -
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    -Need help with the above steps?

    Refer to the first notebook in this series: 1 — Set up the ValidMind Library -
    -

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    Setting up

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    Initialize the ValidMind Library

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    First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

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      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Make sure the ValidMind Library is installed
-
-%pip install -q validmind
-
-# Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
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    Import sample dataset

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    Then, let's import the public Bank Customer Churn Prediction dataset from Kaggle.

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    In our below example, note that:

    -
      -
    • The target column, Exited has a value of 1 when a customer has churned and 0 otherwise.
      • -
    • The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns.
      • -
    -
    -
    from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
-raw_df.head()
    -
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    Identify qualitative tests

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    Next, let's say we want to do some data quality assessments by running a few individual tests.

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    Use the vm.tests.list_tests() function introduced by the first notebook in this series in combination with vm.tests.list_tags() and vm.tests.list_tasks() to find which prebuilt tests are relevant for data quality assessment:

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    • tasks represent the kind of modeling task associated with a test. Here we'll focus on classification tasks.
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    • tags are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the data_quality tag.
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    # Get the list of available task types
-sorted(vm.tests.list_tasks())
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    # Get the list of available tags
-sorted(vm.tests.list_tags())
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    You can pass tags and tasks as parameters to the vm.tests.list_tests() function to filter the tests based on the tags and task types.

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    For example, to find tests related to tabular data quality for classification models, you can call list_tests() like this:

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    vm.tests.list_tests(task="classification", tags=["tabular_data", "data_quality"])
    -
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    -Want to learn more about navigating ValidMind tests?

    Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests -
    -

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    Initialize the ValidMind dataset

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    With the individual tests we want to run identified, the next step is to connect your data with a ValidMind Dataset object. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind, but you only need to do it once per dataset.

    -

    Initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module. For this example, we'll pass in the following arguments:

    -
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    • dataset — The raw dataset that you want to provide as input to tests.
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    • input_id — A unique identifier that allows tracking what inputs are used when running each individual test.
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    • target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    -
    -
    # vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column="Exited",
-)
    -
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    Running tests on datasets

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    Now that we know how to initialize a ValidMind dataset object, we're ready to run some tests!

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    You run individual tests by calling the run_test function provided by the validmind.tests module. For the examples below, we'll pass in the following arguments:

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    • test_id — The ID of the test to run, as seen in the ID column when you run list_tests.
      • -
    • params — A dictionary of parameters for the test. These will override any default_params set in the test definition.
      • -
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    Run tabular data tests

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    The inputs expected by a test can also be found in the test definition — let's take validmind.data_validation.DescriptiveStatistics as an example.

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    Note that the output of the describe_test() function below shows that this test expects a dataset as input:

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    vm.tests.describe_test("validmind.data_validation.DescriptiveStatistics")
    -
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    Now, let's run a few tests to assess the quality of the dataset:

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    result = vm.tests.run_test(
-    test_id="validmind.data_validation.DescriptiveStatistics",
-    inputs={"dataset": vm_raw_dataset},
-)
    -
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    result2 = vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={"dataset": vm_raw_dataset},
-    params={"min_percent_threshold": 30},
-)
    -
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    The output above shows that the validmind.data_validation.ClassImbalance test did not pass according to the value we set for min_percent_threshold.

    -

    To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:

    -
    -
    import pandas as pd
-
-raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset
-
-# Create a balanced dataset with the same number of exited and not exited customers
-exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
-not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])
-
-balanced_raw_df = pd.concat([exited_df, not_exited_df])
-balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)
    -
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    With this new balanced dataset, you can re-run the individual test to see if it now passes the class imbalance test requirement.

    -

    As this is technically a different dataset, remember to first initialize a new ValidMind Dataset object to pass in as input as required by run_test():

    -
    -
    # Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
-vm_balanced_raw_dataset = vm.init_dataset(
-    dataset=balanced_raw_df,
-    input_id="balanced_raw_dataset",
-    target_column="Exited",
-)
    -
    -
    -
    # Pass the initialized `balanced_raw_dataset` as input into the test run
-result = vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={"dataset": vm_balanced_raw_dataset},
-    params={"min_percent_threshold": 30},
-)
    -
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    Utilize test output

    -

    You can utilize the output from a ValidMind test for further use, for example, if you want to remove highly correlated features. Removing highly correlated features helps make the model simpler, more stable, and easier to understand.

    -

    Below we demonstrate how to retrieve the list of features with the highest correlation coefficients and use them to reduce the final list of features for modeling.

    -

    First, we'll run validmind.data_validation.HighPearsonCorrelation with the balanced_raw_dataset we initialized previously as input as is for comparison with later runs:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
    -
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    The output above shows that the test did not pass according to the value we set for max_threshold.

    -

    corr_result is an object of type TestResult. We can inspect the result object to see what the test has produced:

    -
    -
    print(type(corr_result))
-print("Result ID: ", corr_result.result_id)
-print("Params: ", corr_result.params)
-print("Passed: ", corr_result.passed)
-print("Tables: ", corr_result.tables)
    -
    -

    Let's remove the highly correlated features and create a new VM dataset object.

    -

    We'll begin by checking out the table in the result and extracting a list of features that failed the test:

    -
    -
    # Extract table from `corr_result.tables`
-features_df = corr_result.tables[0].data
-features_df
    -
    -
    -
    # Extract list of features that failed the test
-high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
-high_correlation_features
    -
    -

    Next, extract the feature names from the list of strings (example: (Age, Exited) > Age):

    -
    -
    high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
-high_correlation_features
    -
    -

    Now, it's time to re-initialize the dataset with the highly correlated features removed.

    -

    Note the use of a different input_id. This allows tracking the inputs used when running each individual test.

    -
    -
    # Remove the highly correlated features from the dataset
-balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)
-
-# Re-initialize the dataset object
-vm_raw_dataset_preprocessed = vm.init_dataset(
-    dataset=balanced_raw_no_age_df,
-    input_id="raw_dataset_preprocessed",
-    target_column="Exited",
-)
    -
    -

    Re-running the test with the reduced feature set should pass the test:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    You can also plot the correlation matrix to visualize the new correlation between features:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.PearsonCorrelationMatrix",
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    -
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    Documenting test results

    -

    Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it.

    -

    Every test result returned by the run_test() function has a .log() method that can be used to send the test results to the ValidMind Platform:

    -
      -
    • When using run_documentation_tests(), documentation sections will be automatically populated with the results of all tests registered in the documentation template.
      • -
    • When logging individual test results to the platform, you'll need to manually add those results to the desired section of the documentation.
      • -
    -

    To demonstrate how to add test results to your documentation, we'll populate the entire Data Preparation section of the documentation using the clean vm_raw_dataset_preprocessed dataset as input, and then document an additional individual result for the highly correlated dataset vm_balanced_raw_dataset.

    -

    -
    -

    Run and log multiple tests

    -

    run_documentation_tests() allows you to run multiple tests at once and automatically log the results to your documentation. Below, we'll run the tests using the previously initialized vm_raw_dataset_preprocessed as input — this will populate the entire Data Preparation section for every test that is part of the documentation template.

    -

    For this example, we'll pass in the following arguments:

    -
      -
    • inputs: Any inputs to be passed to the tests.
      • -
    • config: A dictionary <test_id>:<test_config> that allows configuring each test individually. Each test config requires the following: -
        -
      • params: Individual test parameters.
        • -
      • inputs: Individual test inputs. This overrides any inputs passed from the run_documentation_tests() function.
        • -
      • -
    -

    When including explicit configuration for individual tests, you'll need to specify the inputs even if they mirror what is included in your global configuration.

    -
    -
    # Individual test config with inputs specified
-test_config = {
-    "validmind.data_validation.ClassImbalance": {
-        "params": {"min_percent_threshold": 30},
-        "inputs": {"dataset": vm_raw_dataset_preprocessed},
-    },
-    "validmind.data_validation.HighPearsonCorrelation": {
-        "params": {"max_threshold": 0.3},
-        "inputs": {"dataset": vm_raw_dataset_preprocessed},
-    },
-}
-
-# Global test config
-tests_suite = vm.run_documentation_tests(
-    inputs={
-        "dataset": vm_raw_dataset_preprocessed,
-    },
-    config=test_config,
-    section=["data_preparation"],
-)
    -
    -

    -
    -
    -

    Run and log an individual test

    -

    Next, we'll use the previously initialized vm_balanced_raw_dataset (that still has a highly correlated Age column) as input to run an individual test, then log the result to the ValidMind Platform.

    -

    When running individual tests, you can use a custom result_id to tag the individual result with a unique identifier:

    -
      -
    • This result_id can be appended to test_id with a : separator.
      • -
    • The balanced_raw_dataset result identifier will correspond to the balanced_raw_dataset input, the dataset that still has the Age column.
      • -
    -
    -
    result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation:balanced_raw_dataset",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
-result.log()
    -
    -
    -Note the output returned indicating that a test-driven block doesn't currently exist in your documentation for this particular test ID.

    That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. -
    -

    -
    -

    Add individual test results to documentation

    -

    With the test results logged, let's head to the model we connected to at the beginning of this notebook and insert our test results into the documentation (Learn more: Work with test results):

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you connected to earlier.

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -

    3. Locate the Data Preparation section and click on 2.3. Correlations and Interactions to expand that section.

      4. -

    4. Hover under the Pearson Correlation Matrix content block until a horizontal dashed line with a + button appears, indicating that you can insert a new block.

      -

      Screenshot showing insert block button in model documentation

      5. -

    5. Click + and then select Test-Driven Block under FROM LIBRARY:

      -
        -
      • Click on VM Library under TEST-DRIVEN in the left sidebar.
        • -
      • In the search bar, type in HighPearsonCorrelation.
        • -
      • Select HighPearsonCorrelation:balanced_raw_dataset as the test.
        • -
      -

      A preview of the test gets shown:

      -

      Screenshot showing the HighPearsonCorrelation test selected

      6. -

    6. Finally, click Insert 1 Test Result to Document to add the test result to the documentation.

      -

      Confirm that the individual results for the high correlation test has been correctly inserted into section 2.3. Correlations and Interactions of the documentation.

      7. -

    7. Finalize the documentation by editing the test result's description block to explain the changes you made to the raw data and the reasons behind them as shown in the screenshot below:

      -

      Screenshot showing the inserted High Pearson Correlation block

      8. -
    -

    -
    -
    -
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    -

    Running model evaluation tests

    -

    So far, we've focused on the data assessment and pre-processing that usually occurs prior to any models being built. Now, let's instead assume we have already built a model and we want to incorporate some model results into our documentation.

    -

    -
    -

    Train simple logistic regression model

    -

    Using ValidMind tests, we'll train a simple logistic regression model on our dataset and evaluate its performance by using the LogisticRegression class from the sklearn.linear_model.

    -

    To start, let's grab the first few rows from the balanced_raw_no_age_df dataset with the highly correlated features removed we initialized earlier:

    -
    -
    balanced_raw_no_age_df.head()
    -
    -

    Before training the model, we need to encode the categorical features in the dataset:

    -
      -
    • Use the OneHotEncoder class from the sklearn.preprocessing module to encode the categorical features.
      • -
    • The categorical features in the dataset are Geography and Gender.
      • -
    -
    -
    balanced_raw_no_age_df = pd.get_dummies(
-    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
-)
-balanced_raw_no_age_df.head()
    -
    -

    We'll split our preprocessed dataset into training and testing, to help assess how well the model generalizes to unseen data:

    -
      -
    • We start by dividing our balanced_raw_no_age_df dataset into training and test subsets using train_test_split, with 80% of the data allocated to training (train_df) and 20% to testing (test_df).
      • -
    • From each subset, we separate the features (all columns except "Exited") into X_train and X_test, and the target column ("Exited") into y_train and y_test.
      • -
    -
    -
    from sklearn.model_selection import train_test_split
-
-train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)
-
-X_train = train_df.drop("Exited", axis=1)
-y_train = train_df["Exited"]
-X_test = test_df.drop("Exited", axis=1)
-y_test = test_df["Exited"]
    -
    -

    Then using GridSearchCV, we'll find the best-performing hyperparameters or settings and save them:

    -
    -
    from sklearn.linear_model import LogisticRegression
-
-# Logistic Regression grid params
-log_reg_params = {
-    "penalty": ["l1", "l2"],
-    "C": [0.001, 0.01, 0.1, 1, 10, 100, 1000],
-    "solver": ["liblinear"],
-}
-
-# Grid search for Logistic Regression
-from sklearn.model_selection import GridSearchCV
-
-grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)
-grid_log_reg.fit(X_train, y_train)
-
-# Logistic Regression best estimator
-log_reg = grid_log_reg.best_estimator_
    -
    -

    -
    -
    -

    Initialize ValidMind datasets

    -

    The last step for evaluating the model's performance is to initialize the ValidMind Dataset and Model objects in preparation for assigning model predictions to each dataset.

    -
    -
    # Initialize the datasets into their own dataset objects
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset_final",
-    dataset=train_df,
-    target_column="Exited",
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset_final",
-    dataset=test_df,
-    target_column="Exited",
-)
    -
    -

    -
    -
    -

    Initialize a ValidMind model

    -

    You'll also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data for each of our three models.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
    -
    # Register the model
-vm_model = vm.init_model(log_reg, input_id="log_reg_model_v1")
    -
    -

    -
    -

    Assign predictions

    -

    Once the model has been registered you can assign predictions to the training and testing datasets.

    -
      -
    • The assign_predictions() method from the Dataset object can link existing predictions to any number of models.
      • -
    • This method links the model's class prediction values and probabilities to our vm_train_ds and vm_test_ds datasets.
      • -
    -

    If no prediction values are passed, the method will compute predictions automatically:

    -
    -
    vm_train_ds.assign_predictions(model=vm_model)
-vm_test_ds.assign_predictions(model=vm_model)
    -
    -

    -
    -
    -
    -

    Run the model evaluation tests

    -

    In this next example, we'll focus on running the tests within the Model Development section of the documentation. Only tests associated with this section will be executed, and the corresponding results will be updated in the documentation.

    -
      -
    • Note the additional config that is passed to run_documentation_tests() — this allows you to override inputs or params in certain tests.
      • -
    • In our case, we want to explicitly use the vm_train_ds for the validmind.model_validation.sklearn.ClassifierPerformance:in_sample test, since it's supposed to run on the training dataset and not the test dataset.
      • -
    -
    -
    test_config = {
-    "validmind.model_validation.sklearn.ClassifierPerformance:in_sample": {
-        "inputs": {
-            "dataset": vm_train_ds,
-            "model": vm_model,
-        },
-    }
-}
-results = vm.run_documentation_tests(
-    section=["model_development"],
-    inputs={
-        "dataset": vm_test_ds,  # Any test that requires a single dataset will use vm_test_ds
-        "model": vm_model,
-        "datasets": (
-            vm_train_ds,
-            vm_test_ds,
-        ),  # Any test that requires multiple datasets will use vm_train_ds and vm_test_ds
-    },
-    config=test_config,
-)
    -
    -

    -
    -
    -
    -

    In summary

    -

    In this second notebook, you learned how to:

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    Next steps

    -

    -
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    Integrate custom tests

    -

    Now that you're familiar with the basics of using the ValidMind Library to run and log tests to provide evidence for your documentation, let's learn how to incorporate your own custom tests into ValidMind: 3 — Integrate custom tests

    - -

    -
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    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
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    - - - - - - \ No newline at end of file diff --git a/site/notebooks/EXECUTED/development/4-finalize_testing_documentation.html b/site/notebooks/EXECUTED/development/4-finalize_testing_documentation.html deleted file mode 100644 index 2e8e22bc9..000000000 --- a/site/notebooks/EXECUTED/development/4-finalize_testing_documentation.html +++ /dev/null @@ -1,1414 +0,0 @@ - - - - - - - - - -ValidMind for development 4 — Finalize testing and documentation – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    ValidMind for development 4 — Finalize testing and documentation

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    Learn how to use ValidMind for your end-to-end documentation process with our introductory notebook series. In this last notebook, finalize the testing and documentation of your model and have a fully documented sample model ready for review.

    -

    We'll first use run_documentation_tests() previously covered in 2 — Start the development process to ensure that your custom test results generated in 3 — Integrate custom tests are included in your documentation. Then, we'll view and update the configuration for the entire documentation template to suit your needs.

    -
    -Learn by doing

    Our course tailor-made for developers new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Developer Fundamentals -
    - - -

    -
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    Prerequisites

    -

    In order to finalize the testing and documentation for your sample model, you'll need to first have:

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    Need help with the above steps?

    Refer to the first three notebooks in this series:

    - -
    -

    -
    -
    -

    Setting up

    -

    This section should be very familiar to you now — as we performed the same actions in the previous two notebooks in this series.

    -

    -
    -

    Initialize the ValidMind Library

    -

    As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Make sure the ValidMind Library is installed
-
-%pip install -q validmind
-
-# Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -

    Import sample dataset

    -

    Next, we'll import the same public Bank Customer Churn Prediction dataset from Kaggle we used in the last notebooks so that we have something to work with:

    -
    -
    from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
    -
    -

    We'll apply a simple rebalancing technique to the dataset before continuing:

    -
    -
    import pandas as pd
-
-raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset
-
-# Create a balanced dataset with the same number of exited and not exited customers
-exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
-not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])
-
-balanced_raw_df = pd.concat([exited_df, not_exited_df])
-balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)
    -
    -

    -
    -

    Remove highly correlated features

    -

    Let's also quickly remove highly correlated features from the dataset using the output from a ValidMind test.

    -

    As you learned previously, before we can run tests you'll need to initialize a ValidMind dataset object:

    -
    -
    # Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
-vm_balanced_raw_dataset = vm.init_dataset(
-    dataset=balanced_raw_df,
-    input_id="balanced_raw_dataset",
-    target_column="Exited",
-)
    -
    -

    With our balanced dataset initialized, we can then run our test and utilize the output to help us identify the features we want to remove:

    -
    -
    # Run HighPearsonCorrelation test with our balanced dataset as input and return a result object
-corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
    -
    -
    -
    # From result object, extract table from `corr_result.tables`
-features_df = corr_result.tables[0].data
-features_df
    -
    -
    -
    # Extract list of features that failed the test
-high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
-high_correlation_features
    -
    -
    -
    # Extract feature names from the list of strings
-high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
-high_correlation_features
    -
    -

    We can then re-initialize the dataset with a different input_id and the highly correlated features removed and re-run the test for confirmation:

    -
    -
    # Remove the highly correlated features from the dataset
-balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)
-
-# Re-initialize the dataset object
-vm_raw_dataset_preprocessed = vm.init_dataset(
-    dataset=balanced_raw_no_age_df,
-    input_id="raw_dataset_preprocessed",
-    target_column="Exited",
-)
    -
    -
    -
    # Re-run the test with the reduced feature set
-corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    -
    -
    -
    -

    Train the model

    -

    We'll then use ValidMind tests to train a simple logistic regression model on our prepared dataset:

    -
    -
    # First encode the categorical features in our dataset with the highly correlated features removed
-balanced_raw_no_age_df = pd.get_dummies(
-    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
-)
-balanced_raw_no_age_df.head()
    -
    -
    -
    # Split the processed dataset into train and test
-from sklearn.model_selection import train_test_split
-
-train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)
-
-X_train = train_df.drop("Exited", axis=1)
-y_train = train_df["Exited"]
-X_test = test_df.drop("Exited", axis=1)
-y_test = test_df["Exited"]
    -
    -
    -
    from sklearn.linear_model import LogisticRegression
-
-# Logistic Regression grid params
-log_reg_params = {
-    "penalty": ["l1", "l2"],
-    "C": [0.001, 0.01, 0.1, 1, 10, 100, 1000],
-    "solver": ["liblinear"],
-}
-
-# Grid search for Logistic Regression
-from sklearn.model_selection import GridSearchCV
-
-grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)
-grid_log_reg.fit(X_train, y_train)
-
-# Logistic Regression best estimator
-log_reg = grid_log_reg.best_estimator_
    -
    -

    -
    -

    Initialize the ValidMind objects

    -

    Let's initialize the ValidMind Dataset and Model objects in preparation for assigning model predictions to each dataset:

    -
    -
    # Initialize the datasets into their own ValidMind dataset objects
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset_final",
-    dataset=train_df,
-    target_column="Exited",
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset_final",
-    dataset=test_df,
-    target_column="Exited",
-)
-
-# Initialize the ValidMind model object
-vm_model = vm.init_model(log_reg, input_id="log_reg_model_v1")
    -
    -

    -
    -
    -

    Assign predictions

    -

    Once the model is registered, we'll assign predictions to the training and test datasets:

    -
    -
    vm_train_ds.assign_predictions(model=vm_model)
-vm_test_ds.assign_predictions(model=vm_model)
    -
    -

    -
    -
    -
    -

    Add custom tests

    -

    We'll also add the same custom tests we implemented in the previous notebook so that this session has access to the same custom inline test and local test provider.

    -

    -
    -

    Implement custom inline test

    -

    Let's set up a custom inline test that calculates the confusion matrix for a binary classification model:

    -
    -
    # First create a confusion matrix plot
-import matplotlib.pyplot as plt
-from sklearn import metrics
-
-# Get the predicted classes
-y_pred = log_reg.predict(vm_test_ds.x)
-
-confusion_matrix = metrics.confusion_matrix(y_test, y_pred)
-
-cm_display = metrics.ConfusionMatrixDisplay(
-    confusion_matrix=confusion_matrix, display_labels=[False, True]
-)
-cm_display.plot()
    -
    -
    -
    # Create the reusable ConfusionMatrix inline test with normalized matrix
-@vm.test("my_custom_tests.ConfusionMatrix")
-def confusion_matrix(dataset, model, normalize=False):
-    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.
-
-    The confusion matrix is a 2x2 table that contains 4 values:
-
-    - True Positive (TP): the number of correct positive predictions
-    - True Negative (TN): the number of correct negative predictions
-    - False Positive (FP): the number of incorrect positive predictions
-    - False Negative (FN): the number of incorrect negative predictions
-
-    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
-    """
-    y_true = dataset.y
-    y_pred = dataset.y_pred(model=model)
-
-    if normalize:
-        confusion_matrix = metrics.confusion_matrix(y_true, y_pred, normalize="all")
-    else:
-        confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
-
-    cm_display = metrics.ConfusionMatrixDisplay(
-        confusion_matrix=confusion_matrix, display_labels=[False, True]
-    )
-    cm_display.plot()
-
-    plt.close()  # close the plot to avoid displaying it
-
-    return cm_display.figure_  # return the figure object itself
    -
    -
    -
    # Test dataset with normalize=True
-result = vm.tests.run_test(
-    "my_custom_tests.ConfusionMatrix:test_dataset_normalized",
-    inputs={"model": vm_model, "dataset": vm_test_ds},
-    params={"normalize": True},
-)
    -
    -

    -
    -
    -

    Add a local test provider

    -

    Finally, let's save our custom inline test to our local test provider:

    -
    -
    # Create custom tests folder
-tests_folder = "my_tests"
-
-import os
-
-# create tests folder
-os.makedirs(tests_folder, exist_ok=True)
-
-# remove existing tests
-for f in os.listdir(tests_folder):
-    # remove files and pycache
-    if f.endswith(".py") or f == "__pycache__":
-        os.system(f"rm -rf {tests_folder}/{f}")
    -
    -
    -
    # Save custom inline test to custom tests folder
-confusion_matrix.save(
-    tests_folder,
-    imports=["import matplotlib.pyplot as plt", "from sklearn import metrics"],
-)
    -
    -
    -
    # Register local test provider
-from validmind.tests import LocalTestProvider
-
-# initialize the test provider with the tests folder we created earlier
-my_test_provider = LocalTestProvider(tests_folder)
-
-vm.tests.register_test_provider(
-    namespace="my_test_provider",
-    test_provider=my_test_provider,
-)
    -
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    -
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    Reconnect to ValidMind

    -

    After you insert test-driven blocks into your model documentation, changes should persist and become available every time you call vm.preview_template().

    -

    However, you'll need to reload the connection to the ValidMind Platform if you have added test-driven blocks when the connection was already established using reload():

    -
    -
    vm.reload()
    -
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    Now, when you run preview_template() again, the three test-driven blocks you added to your documentation in the last two notebooks in should show up in the template in sections 2.3 Correlations and Interactions and 3.2 Model Evaluation:

    -
    -
    vm.preview_template()
    -
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    -
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    Include custom test results

    -

    Since your custom test IDs are now part of your documentation template, you can now run tests for an entire section and all additional custom tests should be loaded without any issues.

    -

    Let's run all tests in the Model Evaluation section of the documentation. Note that we have been running the sample custom confusion matrix with normalize=True to demonstrate the ability to provide custom parameters.

    -

    In the Run the model evaluation tests section of 2 — Start the development process, you learned how to assign inputs to individual tests with run_documentation_tests(). Assigning parameters is similar, you only need to provide assign a params dictionary to a given test ID, my_test_provider.ConfusionMatrix in this case.

    -
    -
    test_config = {
-    "validmind.model_validation.sklearn.ClassifierPerformance:in_sample": {
-        "inputs": {
-            "dataset": vm_train_ds,
-            "model": vm_model,
-        },
-    },
-    "my_test_provider.ConfusionMatrix": {
-        "params": {"normalize": True},
-        "inputs": {"dataset": vm_test_ds, "model": vm_model},
-    },
-}
-results = vm.run_documentation_tests(
-    section=["model_evaluation"],
-    inputs={
-        "dataset": vm_test_ds,  # Any test that requires a single dataset will use vm_test_ds
-        "model": vm_model,
-        "datasets": (
-            vm_train_ds,
-            vm_test_ds,
-        ),  # Any test that requires multiple datasets will use vm_train_ds and vm_test_ds
-    },
-    config=test_config,
-)
    -
    -

    -
    -
    -

    Configuring documentation template tests

    -

    Let's call the utility function vm.get_test_suite().get_default_config() which will return the default configuration for the entire documentation template as a dictionary:

    -
      -
    • This configuration will contain all the test IDs and their default parameters.
      • -
    • You can then modify this configuration as needed and pass it to run_documentation_tests() to run all tests in the documentation template if needed.
      • -
    • You still have the option to continue running tests for one section at a time; get_default_config() simply provides a useful reference for providing default parameters to every test.
      • -
    -
    -
    import json
-
-model_test_suite = vm.get_test_suite()
-config = model_test_suite.get_default_config()
-print("Suite Config: \n", json.dumps(config, indent=2))
    -
    -

    -
    -

    Preview test configuration

    -

    The default config does not assign any inputs to a test, but you can assign inputs to individual tests as needed depending on the datasets and records (models) you want to pass to individual tests.

    -

    For this particular documentation template (binary classification), the ValidMind Library provides a sample configuration that can be used to populate the entire documentation using the following inputs as placeholders:

    -
      -
    • A raw_dataset raw dataset
      • -
    • A train_dataset training dataset
      • -
    • A test_dataset test dataset
      • -
    • A trained model instance
      • -
    -

    As part of updating the config you will need to ensure the correct input_ids are used in the final config passed to run_documentation_tests().

    -
    -
    from validmind.datasets.classification import customer_churn
-from validmind.utils import preview_test_config
-
-test_config = customer_churn.get_demo_test_config()
-preview_test_config(test_config)
    -
    -

    -
    -
    -

    Run updated documentation section tests

    -

    Using this sample configuration, let's finish populating documentation by running all tests for the Model Development section of the documentation.

    -

    Recall that the training and test datasets in our exercise have the following input_id values:

    -
      -
    • train_dataset_final for the training dataset
      • -
    • test_dataset_final for the test dataset
      • -
    -
    -
    config = {
-    "validmind.model_validation.ModelMetadata": {
-        "inputs": {"model": "log_reg_model_v1"},
-    },
-    "validmind.data_validation.DatasetSplit": {
-        "inputs": {"datasets": ["train_dataset_final", "test_dataset_final"]},
-    },
-    "validmind.model_validation.sklearn.PopulationStabilityIndex": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {"num_bins": 10, "mode": "fixed"},
-    },
-    "validmind.model_validation.sklearn.ConfusionMatrix": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-    },
-    "my_test_provider.ConfusionMatrix": {
-        "inputs": {"dataset": "test_dataset_final", "model": "log_reg_model_v1"},
-    },
-    "my_custom_tests.ConfusionMatrix:test_dataset_normalized": {
-        "inputs": {"dataset": "test_dataset_final", "model": "log_reg_model_v1"},
-    },
-    "validmind.model_validation.sklearn.ClassifierPerformance:in_sample": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "train_dataset_final"}
-    },
-    "validmind.model_validation.sklearn.ClassifierPerformance:out_of_sample": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"}
-    },
-    "validmind.model_validation.sklearn.PrecisionRecallCurve": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-    },
-    "validmind.model_validation.sklearn.ROCCurve": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-    },
-    "validmind.model_validation.sklearn.TrainingTestDegradation": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {
-            "metrics": ["accuracy", "precision", "recall", "f1"],
-            "max_threshold": 0.1,
-        },
-    },
-    "validmind.model_validation.sklearn.MinimumAccuracy": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-        "params": {"min_threshold": 0.7},
-    },
-    "validmind.model_validation.sklearn.MinimumF1Score": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-        "params": {"min_threshold": 0.5},
-    },
-    "validmind.model_validation.sklearn.MinimumROCAUCScore": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-        "params": {"min_threshold": 0.5},
-    },
-    "validmind.model_validation.sklearn.PermutationFeatureImportance": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-    },
-    "validmind.model_validation.sklearn.SHAPGlobalImportance": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-        "params": {"kernel_explainer_samples": 10},
-    },
-    "validmind.model_validation.sklearn.WeakspotsDiagnosis": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {
-            "thresholds": {"accuracy": 0.75, "precision": 0.5, "recall": 0.5, "f1": 0.7}
-        },
-    },
-    "validmind.model_validation.sklearn.OverfitDiagnosis": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {"cut_off_percentage": 4},
-    },
-    "validmind.model_validation.sklearn.RobustnessDiagnosis": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {
-            "scaling_factor_std_dev_list": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5],
-            "accuracy_decay_threshold": 4,
-        },
-    },
-}
-
-
-full_suite = vm.run_documentation_tests(
-    section="model_development",
-    config=config,
-)
    -
    -

    -
    -
    -
    -

    In summary

    -

    In this final notebook, you learned how to:

    -
      -
      • -
      • -
      • -
    -

    With our ValidMind for development series of notebooks, you learned how to document a record (model) end-to-end with the ValidMind Library by running through some common scenarios in a typical development setting:

    -
      -
    • Running out-of-the-box tests
      • -
    • Documenting your record (model) by adding evidence to documentation
      • -
    • Extending the capabilities of the ValidMind Library by implementing custom tests
      • -
    • Ensuring that the documentation is complete by running all tests in the documentation template
      • -
    -

    -
    -
    -

    Next steps

    -

    -
    -

    Work with your documentation

    -

    Now that you've logged all your test results and generated a draft for your documentation, head to the ValidMind Platform to wrap up your documentation. Continue to work on your documentation by:

    -
      -

    • Run and log more tests: Use the skills you learned in this series of notebooks to run and log more individual tests, including custom tests, then insert them into your documentation as supplementary evidence. (Learn more: validmind.tests)

      • -

    • Inserting additional test results: Add Test-Driven Blocks under any relevant section of your documentation. (Learn more: Work with test results)

      • -

    • Making qualitative edits to your test descriptions: Click on the description of any inserted test results to review and edit the ValidMind-generated test descriptions for quality and accuracy. (Learn more: Working with documentation)

      • -

    • View guidelines: In any section of your documentation, click ValidMind Insights in the top right corner to reveal the Documentation Guidelines for each section to help guide the contents of your documentation. (Learn more: View development guidelines)

      • -

    • Collaborate with other stakeholders: Use the ValidMind Platform's real-time collaborative features to work seamlessly together with the rest of your organization, including validators. Review suggested changes in your content blocks, work with versioned history, and use comments to discuss specific portions of your documentation. (Learn more: Collaborate with others)

      • -
    -

    When your documentation is complete and ready for review, submit it for approval from the same ValidMind Platform where you made your edits and collaborated with the rest of your organization, ensuring transparency and a thorough development history. (Learn more: Submit documents)

    -

    -
    -
    -

    Learn more

    -

    Now that you're familiar with the basics, you can explore the following notebooks to get a deeper understanding on how the ValidMind Library allows you generate documentation for any use case:

    -

    -
    -

    Use cases

    - -

    -
    -
    -

    Discover more learning resources

    -

    Learn more about the ValidMind Library tools we used in this notebook:

    - -

    We offer many interactive notebooks to help you automate testing, documenting, validating, and more:

    - -

    Or, visit our documentation to learn more about ValidMind.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    -
    -
    - -
    - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/EXECUTED/validation/2-start_validation_process.html b/site/notebooks/EXECUTED/validation/2-start_validation_process.html deleted file mode 100644 index a1b471fb3..000000000 --- a/site/notebooks/EXECUTED/validation/2-start_validation_process.html +++ /dev/null @@ -1,1268 +0,0 @@ - - - - - - - - - -ValidMind for validation 2 — Start the validation process – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - -
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    ValidMind for validation 2 — Start the validation process

    -
    - - - -
    - - - - -
    - - - -
    - - - -

    Learn how to use ValidMind for your end-to-end validation process with our series of four introductory notebooks. In this second notebook, independently verify the data quality tests performed on the dataset used to train the champion.

    -

    You'll learn how to run relevant validation tests with ValidMind, log the results of those tests to the ValidMind Platform, and insert your logged test results as evidence into your validation report. You'll become familiar with the tests available in ValidMind, as well as how to run them. Running tests during validation is crucial to the effective challenge process, as we want to independently evaluate the evidence and assessments provided by the development team.

    -

    While running our tests in this notebook, we'll focus on:

    - -

    For a full list of out-of-the-box tests and descriptions, use the interactive ValidMind test sandbox.

    -
    -Learn by doing

    Our course tailor-made for validators new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Validator Fundamentals -
    - - -

    -
    -

    Prerequisites

    -

    In order to independently assess the quality of your datasets with notebook, you'll need to first have:

    -
      -
      • -
      • -
    -
    -Need help with the above steps?

    Refer to the first notebook in this series: 1 — Set up the ValidMind Library for validation -
    -

    -
    -
    -

    Setting up

    -

    -
    -

    Initialize the ValidMind Library

    -

    First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Validation from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Make sure the ValidMind Library is installed
-
-%pip install -q validmind
-
-# Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="validation-report",
-)
    -
    -

    -
    -
    -
    -

    Load the sample dataset

    -

    Let's first import the public Bank Customer Churn Prediction dataset from Kaggle, which was used to develop the dummy champion.

    -

    We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the champion to ensure that the model was built correctly. By independently performing steps taken by the development team, we can confirm whether the model was built using appropriate and properly processed data.

    -

    In our below example, note that:

    -
      -
    • The target column, Exited has a value of 1 when a customer has churned and 0 otherwise.
      • -
    • The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns.
      • -
    -
    -
    from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Verifying data quality adjustments

    -

    Let's say that thanks to the documentation submitted by the development team (Learn more: ValidMind for development), we know that the sample dataset was first modified before being used to train the champion. After performing some data quality assessments on the raw dataset, it was determined that the dataset required rebalancing, and highly correlated features were also removed.

    -

    -
    -

    Identify qualitative tests

    -

    During validation, we use the same data processing logic and training procedure to confirm that the model's results can be reproduced independently, so let's start by doing some data quality assessments by running a few individual tests just like the development team did.

    -

    Use the vm.tests.list_tests() function introduced by the first notebook in this series in combination with vm.tests.list_tags() and vm.tests.list_tasks() to find which prebuilt tests are relevant for data quality assessment:

    -
      -
    • tasks represent the kind of modeling task associated with a test. Here we'll focus on classification tasks.
      • -
    • tags are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the data_quality tag.
      • -
    -
    -
    # Get the list of available task types
-sorted(vm.tests.list_tasks())
    -
    -
    -
    # Get the list of available tags
-sorted(vm.tests.list_tags())
    -
    -

    You can pass tags and tasks as parameters to the vm.tests.list_tests() function to filter the tests based on the tags and task types.

    -

    For example, to find tests related to tabular data quality for classification models, you can call list_tests() like this:

    -
    -
    vm.tests.list_tests(task="classification", tags=["tabular_data", "data_quality"])
    -
    -
    -Want to learn more about navigating ValidMind tests?

    Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests -
    -

    -
    -
    -

    Initialize the ValidMind dataset

    -

    With the individual tests we want to run identified, the next step is to connect your data with a ValidMind Dataset object. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind, but you only need to do it once per dataset.

    -

    Initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module. For this example, we'll pass in the following arguments:

    -
      -
    • dataset — The raw dataset that you want to provide as input to tests.
      • -
    • input_id — A unique identifier that allows tracking what inputs are used when running each individual test.
      • -
    • target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    -
    -
    # vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column="Exited",
-)
    -
    -

    -
    -
    -

    Run data quality tests

    -

    Now that we know how to initialize a ValidMind dataset object, we're ready to run some tests!

    -

    You run individual tests by calling the run_test function provided by the validmind.tests module. For the examples below, we'll pass in the following arguments:

    -
      -
    • test_id — The ID of the test to run, as seen in the ID column when you run list_tests.
      • -
    • params — A dictionary of parameters for the test. These will override any default_params set in the test definition.
      • -
    -

    -
    -

    Run tabular data tests

    -

    The inputs expected by a test can also be found in the test definition — let's take validmind.data_validation.DescriptiveStatistics as an example.

    -

    Note that the output of the describe_test() function below shows that this test expects a dataset as input:

    -
    -
    vm.tests.describe_test("validmind.data_validation.DescriptiveStatistics")
    -
    -

    Now, let's run a few tests to assess the quality of the dataset:

    -
    -
    result2 = vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={"dataset": vm_raw_dataset},
-    params={"min_percent_threshold": 30},
-)
    -
    -

    The output above shows that the validmind.data_validation.ClassImbalance test did not pass according to the value we set for min_percent_threshold — great, this matches what was reported by the development team.

    -

    To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:

    -
    -
    import pandas as pd
-
-raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset
-
-# Create a balanced dataset with the same number of exited and not exited customers
-exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
-not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])
-
-balanced_raw_df = pd.concat([exited_df, not_exited_df])
-balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)
    -
    -

    With this new balanced dataset, you can re-run the individual test to see if it now passes the class imbalance test requirement.

    -

    As this is technically a different dataset, remember to first initialize a new ValidMind Dataset object to pass in as input as required by run_test():

    -
    -
    # Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
-vm_balanced_raw_dataset = vm.init_dataset(
-    dataset=balanced_raw_df,
-    input_id="balanced_raw_dataset",
-    target_column="Exited",
-)
    -
    -
    -
    # Pass the initialized `balanced_raw_dataset` as input into the test run
-result = vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={"dataset": vm_balanced_raw_dataset},
-    params={"min_percent_threshold": 30},
-)
    -
    -

    -
    -
    -
    -

    Remove highly correlated features

    -

    Next, let's also remove highly correlated features from our dataset as outlined by the development team. Removing highly correlated features helps make the model simpler, more stable, and easier to understand.

    -

    You can utilize the output from a ValidMind test for further use — in this below example, to retrieve the list of features with the highest correlation coefficients and use them to reduce the final list of features for modeling.

    -

    First, we'll run validmind.data_validation.HighPearsonCorrelation with the balanced_raw_dataset we initialized previously as input as is for comparison with later runs:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
    -
    -

    The output above shows that the test did not pass according to the value we set for max_threshold — as reported and expected.

    -

    corr_result is an object of type TestResult. We can inspect the result object to see what the test has produced:

    -
    -
    print(type(corr_result))
-print("Result ID: ", corr_result.result_id)
-print("Params: ", corr_result.params)
-print("Passed: ", corr_result.passed)
-print("Tables: ", corr_result.tables)
    -
    -

    Let's remove the highly correlated features and create a new VM dataset object.

    -

    We'll begin by checking out the table in the result and extracting a list of features that failed the test:

    -
    -
    # Extract table from `corr_result.tables`
-features_df = corr_result.tables[0].data
-features_df
    -
    -
    -
    # Extract list of features that failed the test
-high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
-high_correlation_features
    -
    -

    Next, extract the feature names from the list of strings (example: (Age, Exited) > Age):

    -
    -
    high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
-high_correlation_features
    -
    -

    Now, it's time to re-initialize the dataset with the highly correlated features removed.

    -

    Note the use of a different input_id. This allows tracking the inputs used when running each individual test.

    -
    -
    # Remove the highly correlated features from the dataset
-balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)
-
-# Re-initialize the dataset object
-vm_raw_dataset_preprocessed = vm.init_dataset(
-    dataset=balanced_raw_no_age_df,
-    input_id="raw_dataset_preprocessed",
-    target_column="Exited",
-)
    -
    -

    Re-running the test with the reduced feature set should pass the test:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    You can also plot the correlation matrix to visualize the new correlation between features:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.PearsonCorrelationMatrix",
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    -
    -
    -
    -

    Documenting test results

    -

    Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it. Every test result returned by the run_test() function has a .log() method that can be used to send the test results to the ValidMind Platform.

    -

    When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report. To demonstrate how to add test results to your validation report, we'll log our data quality tests and insert the results via the ValidMind Platform.

    -

    -
    -

    Configure and run comparison tests

    -

    Below, we'll perform comparison tests between the original raw dataset (raw_dataset) and the final preprocessed (raw_dataset_preprocessed) dataset, again logging the results to the ValidMind Platform.

    -

    We can specify all the tests we'd ike to run in a dictionary called test_config, and we'll pass in the following arguments for each test:

    -
      -
    • params: Individual test parameters.
      • -
    • input_grid: Individual test inputs to compare. In this case, we'll input our two datasets for comparison.
      • -
    -

    Note here that the input_grid expects the input_id of the dataset as the value rather than the variable name we specified:

    -
    -
    # Individual test config with inputs specified
-test_config = {
-    "validmind.data_validation.ClassImbalance": {
-        "input_grid": {"dataset": ["raw_dataset", "raw_dataset_preprocessed"]},
-        "params": {"min_percent_threshold": 30}
-    },
-    "validmind.data_validation.HighPearsonCorrelation": {
-        "input_grid": {"dataset": ["raw_dataset", "raw_dataset_preprocessed"]},
-        "params": {"max_threshold": 0.3}
-    },
-}
    -
    -

    Then batch run and log our tests in test_config:

    -
    -
    for t in test_config:
-    print(t)
-    try:
-        # Check if test has input_grid
-        if 'input_grid' in test_config[t]:
-            # For tests with input_grid, pass the input_grid configuration
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()
-        else:
-            # Original logic for regular inputs
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()
-    except Exception as e:
-        print(f"Error running test {t}: {str(e)}")
    -
    -
    -Note the output returned indicating that a test-driven block doesn't currently exist in your documentation for some test IDs.

    That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform. -
    -

    -
    -
    -

    Log tests with unique identifiers

    -

    Next, we'll use the previously initialized vm_balanced_raw_dataset (that still has a highly correlated Age column) as input to run an individual test, then log the result to the ValidMind Platform.

    -

    When running individual tests, you can use a custom result_id to tag the individual result with a unique identifier:

    -
      -
    • This result_id can be appended to test_id with a : separator.
      • -
    • The balanced_raw_dataset result identifier will correspond to the balanced_raw_dataset input, the dataset that still has the Age column.
      • -
    -
    -
    result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation:balanced_raw_dataset",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
-result.log()
    -
    -

    -
    -
    -

    Add test results to reporting

    -

    With some test results logged, let's head to the model we connected to at the beginning of this notebook and learn how to insert a test result into our validation report. (Learn more: Assess compliance)

    -

    While the example below focuses on a specific test result, you can follow the same general procedure for your other results:

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you connected to earlier.

      2. -

    2. In the left sidebar that appears for your model, click Validation under Documents.

      3. -

    3. Click on 2.2.1. Data Quality to expand that section.

      4. -

    4. Under the Class Imbalance Assessment guideline, click Evidence to expand the evidence panel.

      5. -

    5. Click Link Evidence, then select Validator Evidence.

      6. -

    6. Select the Class Imbalance test results we logged: ValidMind Data Validation Class Imbalance

      -

      Screenshot showing the ClassImbalance test selected

      7. -

    7. Click Update Linked Evidence to add the test results to the validation report.

      8. -

    8. Confirm that the results for the Class Imbalance test you inserted has been correctly inserted into section 2.2.1. Data Quality of the report.

      -
        -
      • Note that these test results are flagged as Requires Attention — as they include comparative results from our initial raw dataset.
        • -
      • Click See evidence details to review the LLM-generated description that summarizes the test results, that confirm that our final preprocessed dataset actually passes our test:
        • -
      -

      Screenshot showing the ClassImbalance test generated description in the text editor

      9. -
    -
    - Here in this text editor, you can make qualitative edits to the draft that ValidMind generated to finalize the test results.

    Learn more: Work with content blocks -
    -

    -
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    Preparing the preprocessed dataset

    -

    -
    -

    Split the preprocessed dataset

    -

    With our raw dataset rebalanced with highly correlated features removed, let's now spilt our dataset into train and test in preparation for model evaluation testing.

    -

    To start, let's grab the first few rows from the balanced_raw_no_age_df dataset we initialized earlier:

    -
    -
    balanced_raw_no_age_df.head()
    -
    -

    Before training the model, we need to encode the categorical features in the dataset:

    -
      -
    • Use the OneHotEncoder class from the sklearn.preprocessing module to encode the categorical features.
      • -
    • The categorical features in the dataset are Geography and Gender.
      • -
    -
    -
    balanced_raw_no_age_df = pd.get_dummies(
-    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
-)
-balanced_raw_no_age_df.head()
    -
    -

    Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:

    -
      -
    • We start by dividing our balanced_raw_no_age_df dataset into training and test subsets using train_test_split, with 80% of the data allocated to training (train_df) and 20% to testing (test_df).
      • -
    • From each subset, we separate the features (all columns except "Exited") into X_train and X_test, and the target column ("Exited") into y_train and y_test.
      • -
    -
    -
    from sklearn.model_selection import train_test_split
-
-train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)
-
-X_train = train_df.drop("Exited", axis=1)
-y_train = train_df["Exited"]
-X_test = test_df.drop("Exited", axis=1)
-y_test = test_df["Exited"]
    -
    -

    -
    -
    -

    Initialize the split datasets

    -

    Next, let's initialize the training and testing datasets so they are available for use:

    -
    -
    vm_train_ds = vm.init_dataset(
-    input_id="train_dataset_final",
-    dataset=train_df,
-    target_column="Exited",
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset_final",
-    dataset=test_df,
-    target_column="Exited",
-)
    -
    -

    -
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    In summary

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    In this second notebook, you learned how to:

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    Next steps

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    Develop potential challenger models

    -

    Now that you're familiar with the basics of using the ValidMind Library, let's use it to develop a challenger model: 3 — Developing a potential challenger

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
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    - -
    - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/EXECUTED/validation/4-finalize_validation_reporting.html b/site/notebooks/EXECUTED/validation/4-finalize_validation_reporting.html deleted file mode 100644 index d2122a18d..000000000 --- a/site/notebooks/EXECUTED/validation/4-finalize_validation_reporting.html +++ /dev/null @@ -1,1570 +0,0 @@ - - - - - - - - - -ValidMind for validation 4 — Finalize testing and reporting – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    ValidMind for validation 4 — Finalize testing and reporting

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    - - - -

    Learn how to use ValidMind for your end-to-end validation process with our series of four introductory notebooks. In this last notebook, finalize the compliance assessment process and have a complete validation report ready for review.

    -

    This notebook will walk you through how to supplement ValidMind tests with your own custom tests and include them as additional evidence in your validation report. A custom test is any function that takes a set of inputs and parameters as arguments and returns one or more outputs:

    - -

    For a more in-depth introduction to custom tests, refer to our Implement custom tests notebook.

    -
    -Learn by doing

    Our course tailor-made for validators new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Validator Fundamentals -
    - - -

    -
    -

    Prerequisites

    -

    In order to finalize validation and reporting, you'll need to first have:

    -
      -
      • -
      • -
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      • -
      • -
      • -
      • -
    -
    -

    Need help with the above steps?

    Refer to the first three notebooks in this series:

    - -
    -

    -
    -
    -

    Setting up

    -

    This section should be very familiar to you now — as we performed the same actions in the previous two notebooks in this series.

    -

    -
    -

    Initialize the ValidMind Library

    -

    As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Validation from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Make sure the ValidMind Library is installed
-
-%pip install -q validmind
-
-# Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="validation-report",
-)
    -
    -

    -
    -
    -

    Import the sample dataset

    -

    Next, we'll load in the same sample Bank Customer Churn Prediction dataset used to develop the champion that we will independently preprocess:

    -
    -
    # Load the sample dataset
-from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
    -
    -
    -
    # Initialize the raw dataset for use in ValidMind tests
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column="Exited",
-)
    -
    -
    -
    import pandas as pd
-
-raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset
-
-# Create a balanced dataset with the same number of exited and not exited customers
-exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
-not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])
-
-balanced_raw_df = pd.concat([exited_df, not_exited_df])
-balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)
    -
    -

    Let’s also quickly remove highly correlated features from the dataset using the output from a ValidMind test:

    -
    -
    # Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
-vm_balanced_raw_dataset = vm.init_dataset(
-    dataset=balanced_raw_df,
-    input_id="balanced_raw_dataset",
-    target_column="Exited",
-)
    -
    -
    -
    # Run HighPearsonCorrelation test with our balanced dataset as input and return a result object
-corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
    -
    -
    -
    # From result object, extract table from `corr_result.tables`
-features_df = corr_result.tables[0].data
-features_df
    -
    -
    -
    # Extract list of features that failed the test
-high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
-high_correlation_features
    -
    -
    -
    # Extract feature names from the list of strings
-high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
-high_correlation_features
    -
    -
    -
    # Remove the highly correlated features from the dataset
-balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)
-
-# Re-initialize the dataset object
-vm_raw_dataset_preprocessed = vm.init_dataset(
-    dataset=balanced_raw_no_age_df,
-    input_id="raw_dataset_preprocessed",
-    target_column="Exited",
-)
    -
    -
    -
    # Re-run the test with the reduced feature set
-corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    -
    -
    -

    Split the preprocessed dataset

    -

    With our raw dataset rebalanced with highly correlated features removed, let's now spilt our dataset into train and test in preparation for model evaluation testing:

    -
    -
    # Encode categorical features in the dataset
-balanced_raw_no_age_df = pd.get_dummies(
-    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
-)
-balanced_raw_no_age_df.head()
    -
    -
    -
    from sklearn.model_selection import train_test_split
-
-# Split the dataset into train and test
-train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)
-
-X_train = train_df.drop("Exited", axis=1)
-y_train = train_df["Exited"]
-X_test = test_df.drop("Exited", axis=1)
-y_test = test_df["Exited"]
    -
    -
    -
    # Initialize the split datasets
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset_final",
-    dataset=train_df,
-    target_column="Exited",
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset_final",
-    dataset=test_df,
-    target_column="Exited",
-)
    -
    -

    -
    -
    -

    Import the champion model

    -

    With our raw dataset assessed and preprocessed, let's go ahead and import the champion submitted by the development team in the format of a .pkl file: lr_model_champion.pkl

    -
    -
    # Import the champion model
-import pickle as pkl
-
-with open("lr_model_champion.pkl", "rb") as f:
-    log_reg = pkl.load(f)
    -
    -

    -
    -
    -

    Train potential challenger model

    -

    We'll also train our random forest classification challenger to see how it compares:

    -
    -
    # Import the Random Forest Classification model
-from sklearn.ensemble import RandomForestClassifier
-
-# Create the model instance with 50 decision trees
-rf_model = RandomForestClassifier(
-    n_estimators=50,
-    random_state=42,
-)
-
-# Train the model
-rf_model.fit(X_train, y_train)
    -
    -

    -
    -
    -

    Initialize the ValidMind models

    -

    In addition to the initialized datasets, you'll also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data for each of our two models:

    -
    -
    # Initialize the champion logistic regression model
-vm_log_model = vm.init_model(
-    log_reg,
-    input_id="log_model_champion",
-)
-
-# Initialize the challenger random forest classification model
-vm_rf_model = vm.init_model(
-    rf_model,
-    input_id="rf_model",
-)
    -
    -
    -
    # Assign predictions to Champion — Logistic regression model
-vm_train_ds.assign_predictions(model=vm_log_model)
-vm_test_ds.assign_predictions(model=vm_log_model)
-
-# Assign predictions to Challenger — Random forest classification model
-vm_train_ds.assign_predictions(model=vm_rf_model)
-vm_test_ds.assign_predictions(model=vm_rf_model)
    -
    -

    -
    -
    -
    -

    Implementing custom tests

    -

    Thanks to the documentation (Learn more: ValidMind for development), we know that the development team implemented a custom test to further evaluate the performance of the champion.

    -

    In a usual validation situation, you would load a saved custom test provided by the development team. In the following section, we'll have you implement the same custom test and make it available for reuse, to familiarize you with the processes.

    -
    -Want to learn more about custom tests?

    Refer to our in-depth introduction to custom tests: Implement custom tests -
    -

    -
    -

    Implement a custom inline test

    -

    Let's implement the same custom inline test that calculates the confusion matrix for a binary classification model that the development team used in their performance evaluations.

    -
      -
    • An inline test refers to a test written and executed within the same environment as the code being tested — in this case, right in this Jupyter Notebook — without requiring a separate test file or framework.
      • -
    • You'll note that the custom test function is just a regular Python function that can include and require any Python library as you see fit.
      • -
    -

    -
    -

    Create a confusion matrix plot

    -

    Let's first create a confusion matrix plot using the confusion_matrix function from the sklearn.metrics module:

    -
    -
    import matplotlib.pyplot as plt
-from sklearn import metrics
-
-# Get the predicted classes
-y_pred = log_reg.predict(vm_test_ds.x)
-
-confusion_matrix = metrics.confusion_matrix(y_test, y_pred)
-
-cm_display = metrics.ConfusionMatrixDisplay(
-    confusion_matrix=confusion_matrix, display_labels=[False, True]
-)
-cm_display.plot()
    -
    -

    Next, create a @vm.test wrapper that will allow you to create a reusable test. Note the following changes in the code below:

    -
      -
    • The function confusion_matrix takes two arguments dataset and model. This is a VMDataset and VMModel object respectively. -
        -
      • VMDataset objects allow you to access the dataset's true (target) values by accessing the .y attribute.
        • -
      • VMDataset objects allow you to access the predictions for a given record (model) by accessing the .y_pred() method.
        • -
      • -
    • The function docstring provides a description of what the test does. This will be displayed along with the result in this notebook as well as in the ValidMind Platform.
      • -
    • The function body calculates the confusion matrix using the sklearn.metrics.confusion_matrix function as we just did above.
      • -
    • The function then returns the ConfusionMatrixDisplay.figure_ object — this is important as the ValidMind Library expects the output of the custom test to be a plot or a table.
      • -
    • The @vm.test decorator is doing the work of creating a wrapper around the function that will allow it to be run by the ValidMind Library. It also registers the test so it can be found by the ID my_custom_tests.ConfusionMatrix.
      • -
    -
    -
    @vm.test("my_custom_tests.ConfusionMatrix")
-def confusion_matrix(dataset, model):
-    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.
-
-    The confusion matrix is a 2x2 table that contains 4 values:
-
-    - True Positive (TP): the number of correct positive predictions
-    - True Negative (TN): the number of correct negative predictions
-    - False Positive (FP): the number of incorrect positive predictions
-    - False Negative (FN): the number of incorrect negative predictions
-
-    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
-    """
-    y_true = dataset.y
-    y_pred = dataset.y_pred(model=model)
-
-    confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
-
-    cm_display = metrics.ConfusionMatrixDisplay(
-        confusion_matrix=confusion_matrix, display_labels=[False, True]
-    )
-    cm_display.plot()
-
-    plt.close()  # close the plot to avoid displaying it
-
-    return cm_display.figure_  # return the figure object itself
    -
    -

    You can now run the newly created custom test on both the training and test datasets for both models using the run_test() function:

    -
    -
    # Champion train and test
-vm.tests.run_test(
-    test_id="my_custom_tests.ConfusionMatrix:champion",
-    input_grid={
-        "dataset": [vm_train_ds,vm_test_ds],
-        "model" : [vm_log_model]
-    }
-).log()
    -
    -
    -
    # Challenger train and test
-vm.tests.run_test(
-    test_id="my_custom_tests.ConfusionMatrix:challenger",
-    input_grid={
-        "dataset": [vm_train_ds,vm_test_ds],
-        "model" : [vm_rf_model]
-    }
-).log()
    -
    -
    -Note the output returned indicating that a test-driven block doesn't currently exist in your documentation for some test IDs.

    That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform. -
    -

    -
    -
    -

    Add parameters to custom tests

    -

    Custom tests can take parameters just like any other function. To demonstrate, let's modify the confusion_matrix function to take an additional parameter normalize that will allow you to normalize the confusion matrix:

    -
    -
    @vm.test("my_custom_tests.ConfusionMatrix")
-def confusion_matrix(dataset, model, normalize=False):
-    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.
-
-    The confusion matrix is a 2x2 table that contains 4 values:
-
-    - True Positive (TP): the number of correct positive predictions
-    - True Negative (TN): the number of correct negative predictions
-    - False Positive (FP): the number of incorrect positive predictions
-    - False Negative (FN): the number of incorrect negative predictions
-
-    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
-    """
-    y_true = dataset.y
-    y_pred = dataset.y_pred(model=model)
-
-    if normalize:
-        confusion_matrix = metrics.confusion_matrix(y_true, y_pred, normalize="all")
-    else:
-        confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
-
-    cm_display = metrics.ConfusionMatrixDisplay(
-        confusion_matrix=confusion_matrix, display_labels=[False, True]
-    )
-    cm_display.plot()
-
-    plt.close()  # close the plot to avoid displaying it
-
-    return cm_display.figure_  # return the figure object itself
    -
    -

    -
    -
    -

    Pass parameters to custom tests

    -

    You can pass parameters to custom tests by providing a dictionary of parameters to the run_test() function.

    -
      -
    • The parameters will override any default parameters set in the custom test definition. Note that dataset and model are still passed as inputs.
      • -
    • Since these are VMDataset or VMModel inputs, they have a special meaning.
      • -
    -

    Re-running and logging the custom confusion matrix with normalize=True for both models and our testing dataset looks like this:

    -
    -
    # Champion with test dataset and normalize=True
-vm.tests.run_test(
-    test_id="my_custom_tests.ConfusionMatrix:test_normalized_champion",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model" : [vm_log_model]
-    },
-    params={"normalize": True}
-).log()
    -
    -
    -
    # Challenger with test dataset and normalize=True
-vm.tests.run_test(
-    test_id="my_custom_tests.ConfusionMatrix:test_normalized_challenger",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model" : [vm_rf_model]
-    },
-    params={"normalize": True}
-).log()
    -
    -

    -
    -
    -
    -

    Use external test providers

    -

    Sometimes you may want to reuse the same set of custom tests across multiple records (models) and share them with others in your organization, like the development team would have done with you in this example workflow featured in this series of notebooks. In this case, you can create an external custom test provider that will allow you to load custom tests from a local folder or a Git repository.

    -

    In this section you will learn how to declare a local filesystem test provider that allows loading tests from a local folder following these high level steps:

    -
      -
    1. Create a folder of custom tests from existing inline tests (tests that exist in your active Jupyter Notebook)
      2. -
    2. Save an inline test to a file
      3. -
    3. Define and register a LocalTestProvider that points to that folder
      4. -
    4. Run test provider tests
      5. -
    5. Add the test results to your documentation
      6. -
    -

    -
    -

    Create custom tests folder

    -

    Let's start by creating a new folder that will contain reusable custom tests from your existing inline tests.

    -

    The following code snippet will create a new my_tests directory in the current working directory if it doesn't exist:

    -
    -
    tests_folder = "my_tests"
-
-import os
-
-# create tests folder
-os.makedirs(tests_folder, exist_ok=True)
-
-# remove existing tests
-for f in os.listdir(tests_folder):
-    # remove files and pycache
-    if f.endswith(".py") or f == "__pycache__":
-        os.system(f"rm -rf {tests_folder}/{f}")
    -
    -

    After running the command above, confirm that a new my_tests directory was created successfully. For example:

    -
    ~/notebooks/tutorials/validation/my_tests/
    -

    -
    -
    -

    Save an inline test

    -

    The @vm.test decorator we used in Implement a custom inline test above to register one-off custom tests also includes a convenience method on the function object that allows you to simply call <func_name>.save() to save the test to a Python file at a specified path.

    -

    While save() will get you started by creating the file and saving the function code with the correct name, it won't automatically include any imports, or other functions or variables, outside of the functions that are needed for the test to run. To solve this, pass in an optional imports argument ensuring necessary imports are added to the file.

    -

    The confusion_matrix test requires the following additional imports:

    -
    import matplotlib.pyplot as plt
-from sklearn import metrics
    -

    Let's pass these imports to the save() method to ensure they are included in the file with the following command:

    -
    -
    confusion_matrix.save(
-    # Save it to the custom tests folder we created
-    tests_folder,
-    imports=["import matplotlib.pyplot as plt", "from sklearn import metrics"],
-)
    -
    -
      -

      • -

    • -
      # Saved from __main__.confusion_matrix
-# Original Test ID: my_custom_tests.ConfusionMatrix
-# New Test ID: <test_provider_namespace>.ConfusionMatrix
      • -

    • -
      def ConfusionMatrix(dataset, model, normalize=False):
      • -
    -

    -
    -
    -

    Register a local test provider

    -

    Now that your my_tests folder has a sample custom test, let's initialize a test provider that will tell the ValidMind Library where to find your custom tests:

    -
      -
    • ValidMind offers out-of-the-box test providers for local tests (tests in a folder) or a Github provider for tests in a Github repository.
      • -
    • You can also create your own test provider by creating a class that has a load_test method that takes a test ID and returns the test function matching that ID.
      • -
    -
    -Want to learn more about test providers?

    An extended introduction to test providers can be found in: Integrate external test providers -
    -
    -
    Initialize a local test provider
    -

    For most use cases, using a LocalTestProvider that allows you to load custom tests from a designated directory should be sufficient.

    -

    The most important attribute for a test provider is its namespace. This is a string that will be used to prefix test IDs in documentation. This allows you to have multiple test providers with tests that can even share the same ID, but are distinguished by their namespace.

    -

    Let's go ahead and load the custom tests from our my_tests directory:

    -
    -
    from validmind.tests import LocalTestProvider
-
-# initialize the test provider with the tests folder we created earlier
-my_test_provider = LocalTestProvider(tests_folder)
-
-vm.tests.register_test_provider(
-    namespace="my_test_provider",
-    test_provider=my_test_provider,
-)
-# `my_test_provider.load_test()` will be called for any test ID that starts with `my_test_provider`
-# e.g. `my_test_provider.ConfusionMatrix` will look for a function named `ConfusionMatrix` in `my_tests/ConfusionMatrix.py` file
    -
    -
    -
    -
    Run test provider tests
    -

    Now that we've set up the test provider, we can run any test that's located in the tests folder by using the run_test() method as with any other test:

    -
      -
    • For tests that reside in a test provider directory, the test ID will be the namespace specified when registering the provider, followed by the path to the test file relative to the tests folder.
      • -
    • For example, the Confusion Matrix test we created earlier will have the test ID my_test_provider.ConfusionMatrix. You could organize the tests in subfolders, say classification and regression, and the test ID for the Confusion Matrix test would then be my_test_provider.classification.ConfusionMatrix.
      • -
    -

    Let's go ahead and re-run the confusion matrix test with our testing dataset for our two models by using the test ID my_test_provider.ConfusionMatrix. This should load the test from the test provider and run it as before.

    -
    -
    # Champion with test dataset and test provider custom test
-vm.tests.run_test(
-    test_id="my_test_provider.ConfusionMatrix:champion",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model" : [vm_log_model]
-    }
-).log()
    -
    -
    -
    # Challenger with test dataset  and test provider custom test
-vm.tests.run_test(
-    test_id="my_test_provider.ConfusionMatrix:challenger",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model" : [vm_rf_model]
-    }
-).log()
    -
    -

    -
    -
    -
    -
    -
    -

    Verify test runs

    -

    Our final task is to verify that all the tests provided by the development team were run and reported accurately. Note the appended result_ids to delineate which dataset we ran the test with for the relevant tests.

    -

    Here, we'll specify all the tests we'd like to independently rerun in a dictionary called test_config. Note here that inputs and input_grid expect the input_id of the dataset or model as the value rather than the variable name we specified:

    -
    -
    test_config = {
-    # Run with the raw dataset
-    'validmind.data_validation.DatasetDescription:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'}
-    },
-    'validmind.data_validation.DescriptiveStatistics:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'}
-    },
-    'validmind.data_validation.MissingValues:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'min_percentage_threshold': 1}
-    },
-    'validmind.data_validation.ClassImbalance:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'min_percent_threshold': 10}
-    },
-    'validmind.data_validation.Duplicates:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'min_threshold': 1}
-    },
-    'validmind.data_validation.HighCardinality:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {
-            'num_threshold': 100,
-            'percent_threshold': 0.1,
-            'threshold_type': 'percent'
-        }
-    },
-    'validmind.data_validation.Skewness:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'max_threshold': 1}
-    },
-    'validmind.data_validation.UniqueRows:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'min_percent_threshold': 1}
-    },
-    'validmind.data_validation.TooManyZeroValues:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'max_percent_threshold': 0.03}
-    },
-    'validmind.data_validation.IQROutliersTable:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'threshold': 5}
-    },
-    # Run with the preprocessed dataset
-    'validmind.data_validation.DescriptiveStatistics:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'}
-    },
-    'validmind.data_validation.TabularDescriptionTables:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'}
-    },
-    'validmind.data_validation.MissingValues:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'},
-        'params': {'min_percentage_threshold': 1}
-    },
-    'validmind.data_validation.TabularNumericalHistograms:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'}
-    },
-    'validmind.data_validation.TabularCategoricalBarPlots:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'}
-    },
-    'validmind.data_validation.TargetRateBarPlots:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'},
-        'params': {'default_column': 'loan_status'}
-    },
-    # Run with the training and test datasets
-    'validmind.data_validation.DescriptiveStatistics:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}
-    },
-    'validmind.data_validation.TabularDescriptionTables:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}
-    },
-    'validmind.data_validation.ClassImbalance:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},
-        'params': {'min_percent_threshold': 10}
-    },
-    'validmind.data_validation.UniqueRows:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},
-        'params': {'min_percent_threshold': 1}
-    },
-    'validmind.data_validation.TabularNumericalHistograms:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}
-    },
-    'validmind.data_validation.MutualInformation:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},
-        'params': {'min_threshold': 0.01}
-    },
-    'validmind.data_validation.PearsonCorrelationMatrix:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}
-    },
-    'validmind.data_validation.HighPearsonCorrelation:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},
-        'params': {'max_threshold': 0.3, 'top_n_correlations': 10}
-    },
-    'validmind.model_validation.ModelMetadata': {
-        'input_grid': {'model': ['log_model_champion', 'rf_model']}
-    },
-    'validmind.model_validation.sklearn.ModelParameters': {
-        'input_grid': {'model': ['log_model_champion', 'rf_model']}
-    },
-    'validmind.model_validation.sklearn.ROCCurve': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final'], 'model': ['log_model_champion']}
-    },
-    'validmind.model_validation.sklearn.MinimumROCAUCScore': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final'], 'model': ['log_model_champion']},
-        'params': {'min_threshold': 0.5}
-    }
-}
    -
    -

    Then batch run and log our tests in test_config:

    -
    -
    for t in test_config:
-    print(t)
-    try:
-        # Check if test has input_grid
-        if 'input_grid' in test_config[t]:
-            # For tests with input_grid, pass the input_grid configuration
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()
-        else:
-            # Original logic for regular inputs
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()
-    except Exception as e:
-        print(f"Error running test {t}: {str(e)}")
    -
    -

    -
    -
    -

    In summary

    -

    In this final notebook, you learned how to:

    -
      -
      • -
      • -
      • -
      • -
      • -
    -

    With our ValidMind for validation series of notebooks, you learned how to validate a record (model) end-to-end with the ValidMind Library by running through some common scenarios in a typical validation setting:

    -
      -
    • Verifying the data quality steps performed by the development team
      • -
    • Independently replicating the champion's results and conducting additional tests to assess performance, stability, and robustness
      • -
    • Setting up test inputs and a challenger for comparative analysis
      • -
    • Running validation tests, analyzing results, and logging artifacts to ValidMind
      • -
    -

    -
    -
    -

    Next steps

    -

    -
    -

    Work with your validation report

    -

    Now that you've logged all your test results and verified the work done by the development team, head to the ValidMind Platform to wrap up your validation report. Continue to work on your validation report by:

    -
      -

    • Inserting additional test results: Click Link Evidence under any Evidence panel of 2. Validation in your validation report. (Learn more: Link evidence to reports)

      • -

    • Making qualitative edits to your test descriptions: Expand any linked evidence under Validator Evidence and click See evidence details to review and edit the ValidMind-generated test descriptions for quality and accuracy. (Learn more: Preparing validation reports)

      • -

    • Adding more findings: Click Link Finding to Report in any validation report section, then click + Create New Finding. (Learn more: Add and manage artifacts)

      • -

    • Adding risk assessment notes: Click under Risk Assessment Notes in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. Once generated, edit your ValidMind-generated test descriptions to adhere to your organization's requirements. (Learn more: Work with content blocks)

      • -

    • Assessing compliance: Under the Guideline for any validation report section, click ASSESSMENT and select the compliance status from the drop-down menu. (Learn more: Assign compliance assessments)

      • -

    • Collaborate with other stakeholders: Use the ValidMind Platform's real-time collaborative features to work seamlessly together with the rest of your organization, including developers. Propose suggested changes in the documentation, work with versioned history, and use comments to discuss specific portions of the documentation. (Learn more: Collaborate with others)

      • -
    -

    When your validation report is complete and ready for review, submit it for approval from the same ValidMind Platform where you made your edits and collaborated with the rest of your organization, ensuring transparency and a thorough validation history. (Learn more: Submit documents)

    -

    -
    -
    -

    Learn more

    -

    Now that you're familiar with the basics, you can explore the following notebooks to get a deeper understanding on how the ValidMind Library assists you in streamlining validation:

    -

    -
    -

    Use cases

    - -

    -
    -
    -

    Discover more learning resources

    -

    Learn more about the ValidMind Library tools we used in this notebook:

    - -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    -
    -
    - -
    - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/data_and_datasets/dataset_inputs/configure_dataset_features.html b/site/notebooks/how_to/data_and_datasets/dataset_inputs/configure_dataset_features.html deleted file mode 100644 index 5e3a7ec9e..000000000 --- a/site/notebooks/how_to/data_and_datasets/dataset_inputs/configure_dataset_features.html +++ /dev/null @@ -1,1772 +0,0 @@ - - - - - - - - - -Configure dataset features – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - -
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    - - - -
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    Configure dataset features

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    - - - - -
    - - - -
    - - - -

    When initializing a ValidMind dataset object, you can pass in a list of features to use instead of utilizing all dataset columns when running tests.

    -

    This notebook shows how to use custom feature columns with init_dataset. The default behavior of init_dataset is to utilize all dataset columns when running tests. It is also possible to pass in a list of features to use and thus restrict computations to only those features.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

    -

    -
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    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
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    -
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    Load the sample dataset

    -
    -
    %matplotlib inline
-
-# Import the sample dataset from the library
-
-from validmind.datasets.classification import customer_churn as demo_dataset
-
-# You can also try a different dataset with:
-# from validmind.datasets.classification import taiwan_credit as demo_dataset
-
-df = demo_dataset.load_data()
    -
    -

    -
    -

    Initialize the training and test datasets

    -

    Before you can run a test suite, which are just a collection of tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -

    This function takes a number of arguments:

    -
      -
    • dataset — the raw dataset that you want to analyze
      • -
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      • -
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      • -
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      • -
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    -
    feature_columns = [
-    "CreditScore",
-    "Age",
-    "Tenure",
-    "Balance",
-    "NumOfProducts",
-    "HasCrCard",
-    "IsActiveMember",
-    "EstimatedSalary",
-]
-
-vm_dataset = vm.init_dataset(
-    dataset=df,
-    input_id="raw_dataset",
-    target_column=demo_dataset.target_column,
-    feature_columns=feature_columns,
-)
    -
    -

    -
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    Defining custom features

    -

    This section shows how we can define a subset of features to use when running dataset tests. Any feature that is not included in the feature_columns argument is omitted from the computation of the DescriptiveStatistics test in the examples below.

    -

    In the following example we use the DescriptiveStatistics test to show how the output changes when customizing features.

    -
      -
    1. Running a test with all the features.
      2. -
    -
    -
    vm_dataset = vm.init_dataset(
-    dataset=df,
-    input_id="raw_dataset_all_features",
-    target_column=demo_dataset.target_column,
-)
-
-test = vm.tests.run_test(
-    test_id="validmind.data_validation.DescriptiveStatistics",
-    inputs={"dataset": vm_dataset},
-)
    -
    -
      -
    1. Running a test with a subset of features.
      2. -
    -
    -
    vm_dataset = vm.init_dataset(
-    dataset=df,
-    input_id="raw_dataset_subset",
-    target_column=demo_dataset.target_column,
-    feature_columns=["CreditScore", "Age", "Balance", "Geography"],
-)
-
-test = vm.tests.run_test(
-    test_id="validmind.data_validation.DescriptiveStatistics",
-    inputs={"dataset": vm_dataset},
-)
    -
    -

    -
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    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

    -

    -
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    Work with your documentation

    -
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    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

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    -
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    Discover more learning resources

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    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

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    Or, visit our documentation to learn more about ValidMind.

    -

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    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

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    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

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    - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/data_and_datasets/use_dataset_model_objects.html b/site/notebooks/how_to/data_and_datasets/use_dataset_model_objects.html deleted file mode 100644 index d8ef52c1f..000000000 --- a/site/notebooks/how_to/data_and_datasets/use_dataset_model_objects.html +++ /dev/null @@ -1,2018 +0,0 @@ - - - - - - - - - -Introduction to ValidMind Dataset and Model Objects – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Introduction to ValidMind Dataset and Model Objects

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    When writing custom tests, it is essential to be aware of the interfaces of the ValidMind Dataset and ValidMind Model, which are used as input arguments.

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    As a model developer, writing custom tests is beneficial when the ValidMind library lacks a built-in test for your specific needs. For example, a model might require new tests to evaluate specific aspects of the model or dataset based on a particular use case.

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    This interactive notebook offers a detailed understanding of ValidMind objects and their use in writing custom tests. It introduces various interfaces provided by these objects and demonstrates how they can be leveraged to implement tests effortlessly.

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    About ValidMind

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    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models. You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

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    Before you begin

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    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

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    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

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    New to ValidMind?

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    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

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    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
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    Key concepts

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    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

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    • dataset-based test

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      Dataset based test architecture Dataset-based tests take VM dataset objects as inputs, can be configured with values passed in as parameters, and return outputs such as tables, plots, or images.

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    • model-based test:

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      Model based test architecture Similar to dataset-based tests, model-based tests take additional VM model objects as inputs alongside VM dataset objects. The VM model object can wrap any type of record and is used to obtain prediction values for entries in the dataset.

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    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

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    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

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    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

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    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

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    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
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    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
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    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
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    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
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    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

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    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

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    Setting up

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    Install the ValidMind Library

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    Please note the following recommended Python versions to use:

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    • Python 3.7 > x <= 3.11
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    To install the library:

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    %pip install -q validmind
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    Initialize the ValidMind Library

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    Register sample model

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    Let's first register a sample record (model) for use with this notebook:

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    1. In a browser, log in to ValidMind.

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    2. In the left sidebar, select Inventory.

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    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

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    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

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    5. Select your own name under the RECORD OWNER drop-down.

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    6. Click Register Model to add the model to your inventory.

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    Apply documentation template

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    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

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    1. In the left sidebar that appears for your model, click Documents and select Development.

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      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

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    2. Under TEMPLATE, select Binary classification.

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    3. Click Use Template to apply the template.

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    Get your code snippet

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    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

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    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

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    2. Click Copy snippet to clipboard.

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    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

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    # Load your model identifier credentials from an `.env` file
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-%load_ext dotenv
-%dotenv .env
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-# Or replace with your code snippet
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-import validmind as vm
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-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
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    %matplotlib inline
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-import xgboost as xgb
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    Load the demo dataset

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    from validmind.datasets.classification import customer_churn as demo_dataset
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-raw_df = demo_dataset.load_data()
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    Prepocess the raw dataset

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    train_df, validation_df, test_df = demo_dataset.preprocess(raw_df)
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    Train a model for testing

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    We train a simple customer churn model for our test.

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    x_train = train_df.drop(demo_dataset.target_column, axis=1)
-y_train = train_df[demo_dataset.target_column]
-x_val = validation_df.drop(demo_dataset.target_column, axis=1)
-y_val = validation_df[demo_dataset.target_column]
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-model = xgb.XGBClassifier(early_stopping_rounds=10)
-model.set_params(
-    eval_metric=["error", "logloss", "auc"],
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-model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
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    Explore basic components of the ValidMind library

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    In this section, you will learn about the basic objects of the ValidMind library that are necessary to implement both custom and built-in tests. As explained above, these objects are: * VMDataset: The high level APIs can be found here * VMModel: The high level APIs can be found here

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    Let's understand these objects and their interfaces step by step:

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    VMDataset Object

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    Initialize the ValidMind datasets

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    You can initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

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    The function wraps the dataset to create a ValidMind Dataset object so that you can write tests effectively using the common interface provided by the VM objects. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind. You only need to do it one time per dataset.

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    This function takes a number of arguments. Some of the arguments are:

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    • dataset — the raw dataset that you want to provide as input to tests
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    • input_id - a unique identifier that allows tracking what inputs are used when running each individual test
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    • target_column — a required argument if tests require access to true values. This is the name of the target column in the dataset
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    The detailed list of the arguments can be found here

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    # vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column="Exited",
-)
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    Once you have a ValidMind dataset object (VMDataset), you can inspect its attributes and methods using the inspect_obj utility module. This module provides a list of available attributes and interfaces for use in tests. Understanding how to use VMDatasets is crucial for comprehending how a custom test functions.

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-inspect_obj(vm_raw_dataset)
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    Interfaces of the dataset object

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    DataFrame

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    vm_raw_dataset.df
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    Feature columns

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    vm_raw_dataset.feature_columns
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    Target column

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    vm_raw_dataset.target_column
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    Features values

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    vm_raw_dataset.x_df()
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    Target value

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    vm_raw_dataset.y_df()
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    Numeric feature columns

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    vm_raw_dataset.feature_columns_numeric
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    Categorical feature columns

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    vm_raw_dataset.feature_columns_categorical
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    Similarly, you can use all other interfaces of the VMDataset objects

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    Using VM Dataset object as arguments in custom tests

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    A custom test is simply a Python function that takes two types of arguments: inputs and params. The inputs are ValidMind objects (VMDataset, VMModel), and the params are additional parameters required for the underlying computation of the test. We will discuss both types of arguments in the following sections.

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    Let's start with a custom test that requires only a ValidMind dataset object. In this example, we will check the balance of classes in the target column of the dataset:

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    • The custom test below requires a single argument of type VMDataset (dataset).
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    • The my_custom_tests.ClassImbalance is a unique test identifier that can be assigned using the vm.test decorator functionality. This unique test ID will be used in the platform to load test results in the documentation.
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    • The dataset.target_column and dataset.df attributes of the VMDataset object are used in the test.
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    Other high-level APIs (attributes and methods) of the dataset object are listed here.

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    If you've gone through the Implement custom tests notebook, you should have a good understanding of how custom tests are implemented in details. If you haven't, we recommend going through that notebook first.

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    from validmind.vm_models.dataset.dataset import VMDataset
-import pandas as pd
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-@vm.test("my_custom_tests.ClassImbalance")
-def class_imbalance(dataset):
-    # Can only run this test if we have a Dataset object
-    if not isinstance(dataset, VMDataset):
-        raise ValueError("ClassImbalance requires a validmind Dataset object")
-
-    if dataset.target_column is None:
-        print("Skipping class_imbalance test because no target column is defined")
-        return
-
-    # VMDataset object provides target_column attribute
-    target_column = dataset.target_column
-    # we can access pandas DataFrame using df attribute
-    imbalance_percentages = dataset.df[target_column].value_counts(
-        normalize=True
-    )
-    classes = list(imbalance_percentages.index) 
-    percentages = list(imbalance_percentages.values * 100)
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-    return pd.DataFrame({"Classes":classes, "Percentage": percentages})
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    Run the test

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    Let's run the test using the run_test method, which is part of the validmind.tests module. Here, we pass the dataset through the inputs. Similarly, you can pass datasets, model, or models as inputs if your custom test requires them. In this example below, we run the custom test my_custom_tests.ClassImbalance by passing the dataset through the inputs.

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    from validmind.tests import run_test
-result = run_test(
-    test_id="my_custom_tests.ClassImbalance",
-    inputs={
-        "dataset": vm_raw_dataset
-    }
-)
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    You can move custom tests into separate modules in a folder. It allows you to take one-off tests and move them into an organized structure that makes it easier to manage, maintain and share them. We have provided a seperate notebook with detailed explaination here

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    Using VM Dataset object and parameters as arguments in custom tests

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    Simlilar to inputs, you can pass params to a custom test by providing a dictionary of parameters to the run_test() function. The parameters will override any default parameters set in the custom test definition. Note that the dataset is still passed as inputs. Let's modify the class imbalance test so that it provides flexibility to normalize the results.

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    from validmind.vm_models.dataset.dataset import VMDataset
-import pandas as pd
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-@vm.test("my_custom_tests.ClassImbalance")
-def class_imbalance(dataset, normalize=True):
-    # Can only run this test if we have a Dataset object
-    if not isinstance(dataset, VMDataset):
-        raise ValueError("ClassImbalance requires a validmind Dataset object")
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-    if dataset.target_column is None:
-        print("Skipping class_imbalance test because no target column is defined")
-        return
-
-    # VMDataset object provides target_column attribute
-    target_column = dataset.target_column
-    # we can access pandas DataFrame using df attribute
-    imbalance_percentages = dataset.df[target_column].value_counts(
-        normalize=normalize
-    )
-    classes = list(imbalance_percentages.index) 
-    if normalize:  
-        result = pd.DataFrame({"Classes":classes, "Percentage": list(imbalance_percentages.values*100)})
-    else:
-        result = pd.DataFrame({"Classes":classes, "Count": list(imbalance_percentages.values)})
-    return result
    -
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    In this example, the normalize parameter is set to False, so the class counts will not be normalized. You can change the value to True if you want the counts to be normalized. The results of the test will reflect this flexibility, allowing for different outputs based on the parameter passed.

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    Here, we have passed the dataset through the inputs and the normalize parameter using the params.

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    from validmind.tests import run_test
-result = run_test(
-    test_id = "my_custom_tests.ClassImbalance",
-    inputs={"dataset": vm_raw_dataset},
-    params={"normalize": True},
-)
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    VMModel Object

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    Initialize ValidMind model object

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    Similar to ValidMind Dataset object, you can initialize a ValidMind Model object using the init_model function from the ValidMind (vm) module.

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    This function takes a number of arguments. Some of the arguments are:

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    The detailed list of the arguments can be found here

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-vm_model = vm.init_model(
-    model=model,
-    input_id="xgb_model",
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    Let's inspect the methods and attributes of the model now:

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    inspect_obj(vm_model)
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    Assign predictions to the datasets

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    We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:

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    vm_train_ds = vm.init_dataset(
-    input_id="train_dataset",
-    dataset=train_df,
-    type="generic",
-    target_column=demo_dataset.target_column,
-)
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-vm_train_ds.assign_predictions(model=vm_model)
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    You can see below, the extra prediction column (xgb_model_prediction) for the model (xgb_model) has been added in the dataset.

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    print(vm_train_ds)
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    Using VM Model and Dataset objects as arguments in Custom tests

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    We will now create a @vm.test wrapper that will allow you to create a reusable test. Note the following changes in the code below:

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    • The function confusion_matrix takes two arguments dataset and model. This is a VMDataset and VMModel object respectively. -
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      • VMDataset objects allow you to access the dataset's true (target) values by accessing the .y attribute.
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      • VMDataset objects allow you to access the predictions for a given record (model) by accessing the .y_pred() method.
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    • The function docstring provides a description of what the test does. This will be displayed along with the result in this notebook as well as in the ValidMind Platform.
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    • The function body calculates the confusion matrix using the sklearn.tests.confusion_matrix function as we just did above.
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    • The function then returns the ConfusionMatrixDisplay.figure_ object - this is important as the ValidMind Library expects the output of the custom test to be a plot or a table.
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    • The @vm.test decorator is doing the work of creating a wrapper around the function that will allow it to be run by the ValidMind Library. It also registers the test so it can be found by the ID my_custom_tests.ConfusionMatrix (see the section below on how test IDs work in ValidMind and why this format is important)
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    Similarly, you can use the functinality provided by VMDataset and VMModel objects. You can refer our documentation page for all the avalialble APIs here

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    from sklearn import metrics
-import matplotlib.pyplot as plt
-@vm.test("my_custom_tests.ConfusionMatrix")
-def confusion_matrix(dataset, model):
-    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.
-
-    The confusion matrix is a 2x2 table that contains 4 values:
-
-    - True Positive (TP): the number of correct positive predictions
-    - True Negative (TN): the number of correct negative predictions
-    - False Positive (FP): the number of incorrect positive predictions
-    - False Negative (FN): the number of incorrect negative predictions
-
-    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
-    """
-    # we can retrieve traget value from dataset which is y attribute
-    y_true = dataset.y
-    # The prediction value of a specific model using y_pred method 
-    y_pred = dataset.y_pred(model=model)
-
-    confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
-
-    cm_display = metrics.ConfusionMatrixDisplay(
-        confusion_matrix=confusion_matrix, display_labels=[False, True]
-    )
-    cm_display.plot()
-    plt.close()
-
-    return cm_display.figure_  # return the figure object itself
    -
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    Here, we run test using two inputs; dataset and model.

    -
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    from validmind.tests import run_test
-result = run_test(
-    test_id = "my_custom_tests.ConfusionMatrix",
-    inputs={
-        "dataset": vm_train_ds,
-        "model": vm_model,
-    }
-)
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    Log the test results

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    You can log any test result to the ValidMind Platform with the .log() method of the result object. This will allow you to add the result to the documentation.

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    You can now do the same for the confusion matrix results.

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    result.log()
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    In summary

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    In this notebook you have learned the end-to-end process to document a model with the ValidMind Library, running through some very common scenarios in a typical model development setting:

    -
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    • Running out-of-the-box tests
      • -
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    • Extending the capabilities of the ValidMind Library by implementing custom tests
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    • Ensuring that the documentation is complete by running all tests in the documentation template
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    Discover more learning resources

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    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

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    Or, visit our documentation to learn more about ValidMind.

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    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
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    -
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    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

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    To turn complex evidence into actionable insights, you can run a unit metric as a single-value measure to quantify and monitor risks throughout a model's lifecycle.

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    In this interactive notebook, we introduce the concept of unit metric and provide a step-by-step guide on how to define, execute and extract results from these measures. As an example, we use data from a customer churn use case to fit a binary classification model. To illustrate the application of these measures, we show you how to run sklearn classification metrics as unit metrics, demonstrating their utility in quantifying model performance and risk.

    -

    In Model Risk Management (MRM), the primary objective is to identify, assess, and mitigate the risks associated with the development, implementation, and ongoing use of quantitative models. The process of measuring risk involves the understanding and assessment of evidence generated throw multiple tests across all the model development lifecycle stages, from data collection and data quality to model performance and explainability.

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    Evidence versus risk

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    The distinction between evidence and quantifiable risk measures is a critical aspect of MRM. Evidence, in this context, refers to the outputs from various tests conducted throughout the model lifecycle. For instance, a table displaying the number of missing values per feature in a dataset is a form of evidence. It shows where data might be incomplete, which can affect the model's performance and reliability. Similarly, a Receiver Operating Characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The curve is evidence of the model's classification performance.

    -

    However, these pieces of evidence do not offer a direct measure of risk. To quantify risk, one must derive metrics from this evidence that reflect the potential impact on the model's performance and the decisions it informs. For example, the missing data rate, calculated as the percentage of missing values in the dataset, is a quantifiable risk measure that indicates the risk associated with data quality. Similarly, the accuracy score, which measures the proportion of correctly classified labels, acts as an indicator of performance risk in a classification model.

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    Unit metric

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    A Unit metric is a single value measure that is used to identify and monitor risks arising from the development of Machine Learning or AI models. This metric simplifies evidence into a single actionable number, that can be monitored and compared over time or across different models or datasets.

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    Properties:

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    Incorporating unit metrics into your ML workflow streamlines risk assessment, turning complex analyses into clear, actionable insights.

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    About ValidMind

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    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

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    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

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    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

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    To install the library:

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    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

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    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -
    -

    Load the demo dataset

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    In this example, we load a demo dataset to fit a customer churn model.

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    -
    from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Train a model for testing

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    We train a simple customer churn model for our test.

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    -
    import xgboost as xgb
-
-train_df, validation_df, test_df = demo_dataset.preprocess(raw_df)
-
-x_train = train_df.drop(demo_dataset.target_column, axis=1)
-y_train = train_df[demo_dataset.target_column]
-x_val = validation_df.drop(demo_dataset.target_column, axis=1)
-y_val = validation_df[demo_dataset.target_column]
-
-model = xgb.XGBClassifier(early_stopping_rounds=10)
-model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
-)
    -
    -

    -
    -
    -

    Initialize ValidMind objects

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    Once the datasets and model are prepared for validation, we initialize ValidMind dataset and model, specifying features and targets columns. The property input_id allows users to uniquely identify each dataset and model. This allows for the creation of multiple versions of datasets and models, enabling us to compute metrics by specifying which versions we want to use as inputs.

    -
    -
    import validmind as vm
-
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset",
-    dataset=train_df,
-    target_column=demo_dataset.target_column,
-)
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset",
-    dataset=test_df,
-    target_column=demo_dataset.target_column,
-)
-
-vm_model = vm.init_model(model=model, input_id="xbg_model")
    -
    -

    -
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    Assign predictions

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    We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:

    -
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    vm_train_ds.assign_predictions(model=vm_model)
-vm_test_ds.assign_predictions(model=vm_model)
    -
    -

    -
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    Running Unit Metrics

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    List Available Metrics

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    We can list all available unit metrics by using the list_metrics() function from the unit_metrics module. This function returns a list of all available metric functions.

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    from validmind.unit_metrics import list_metrics
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-metrics = list_metrics()
-metrics
    -
    -

    -
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    Learn More About a Metric

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    We can use the describe_metric() function to learn more about a metric, specifically its required inputs and optional parameters. Let's check out the details for the ROC AUC metric.

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    from validmind.unit_metrics import describe_metric
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-describe_metric("validmind.unit_metrics.classification.ROC_AUC")
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    -
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    Run ROC AUC Metric

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    The following snippet shows how to set up and execute a unit metric implementation of the ROC AUC score. We are going to use the run_metric() function from the validmind.unit_metrics module. This function has a signature very similar to run_test() from the validmind.tests module, but it is specifically designed to compute unit metrics. It takes the following arguments:

    -
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    • metric_id: The unique identifier for the metric, in this case validmind.unit_metrics.classification.ROC_AUC
      • -
    • inputs: A dictionary containing the input dataset and model or their respective input IDs
      • -
    • params: A dictionary containing the keyword arguments for the unit metric (in this case, none are required, but we can pass any kwargs that the underlying sklearn implementation accepts)
      • -
    -

    The function returns and displays a result object like a normal ValidMind test, accept that it simply shows the unit metrics value. This result object has a .log() method that will log the result to the ValidMind Platform where it can be used for monitoring and model documentation.

    -
    -
    from validmind.unit_metrics import run_metric
-
-result = run_metric(
-    "validmind.unit_metrics.classification.ROC_AUC",
-    inputs={
-        "model": vm_model,
-        "dataset": vm_test_ds,
-    },
-)
-
-# Log the result to the ValidMind Platform
-result.log()
    -
    -

    -
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    Pass parameters

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    Unit Metrics can accept parameters as mentioned above. In the case of the ROC AUC and many of the other unit metrics that use an underlying sklearn implementation, these metrics support all parameters of the associated sklearn metric functions. Let's see how we can do this in the case of the above ROC AUC metric.

    -

    Available parameters for the ROC AUC metric include:

    -
      -
    • average: This parameter is used to compute the average of the ROC AUC score across all classes. The options include micro, macro, weighted, and samples.
      • -
    • sample_weight: This parameter is used to pass sample weights to the metric.
      • -
    • max_fpr: This parameter is used to compute the partial AUC ROC over the range [0, max_fpr].
      • -
    -
    -

    See the sklearn documentation for more details on these parameters.

    -
    -
    -
    result = run_metric(
-    "validmind.unit_metrics.classification.ROC_AUC",
-    inputs={
-        "model": vm_model,
-        "dataset": vm_test_ds,
-    },
-    params={
-        "average": "weighted",
-        "max_fpr": 1.0,
-    },
-)
-
-# Log the result to the ValidMind Platform
-result.log()
    -
    -

    -
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    Other Model Performance Unit Metrics

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    In addition to the ROC AUC score, you can compute other classification metrics, such as accuracy score, precision, recall etc. Let's list out all metrics and filter down to classification metrics. Then we can run and log them in a loop.

    -
    -
    metrics = [metric for metric in list_metrics() if "classification" in metric]
-
-for metric_id in metrics:
-    describe_metric(metric_id)
    -
    -
    -
    for metric_id in metrics:
-    result = run_metric(
-        metric_id,
-        inputs={
-            "model": vm_model,
-            "dataset": vm_test_ds,
-        },
-        show=False, # set show to False to avoid showing an ipywidget output
-    )
-    result.log()
-
-    # instead of showing the widget, we can print the result value
-    print(f"{metric_id}: {result.metric}")
    -
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    Comparing Unit Metrics for Different Inputs

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    -
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    Running the ROC AUC Metric across Multiple Datasets

    -

    We can use the run_test() function from the validmind.tests module and pass in a unit metric ID to run the metric as a test. In practice this is useful for composing and comparing one or more metrics across one or more inptu combinations. If you have followed along some of the other instructional notebooks, you'll remember that run_test() accepts an input_grid parameter that will allow you to pass multiple values for each input. This will result in the metric being computed for each combination of input values and then combined into a single test result object. For instance, we can compute the ROC AUC score for the train and test datasets of our model. Let's do that now.

    -
    -
    from validmind.tests import run_test
-
-result = run_test(
-    "validmind.unit_metrics.classification.ROC_AUC",
-    input_grid={
-        "model": [vm_model],
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-)
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    result.log()
    -
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    -
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    Composing Test Results from Individual Unit Metrics

    -

    -
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    Run multiple unit metrics as a single test

    -

    Another higher-level way to use unit metrics is to compose multiple metrics into a single test result. This is useful, for instance, if you want to create a single test result for your documentation that contains a table of model performance metrics. In the case of our customer churn model, we can compute the F1, Precision, Recall, Accuracy and ROC AUC scores for the test dataset and combine them into a single test result. Let's do that now.

    -
    -
    from validmind.tests import run_test
-
-result = run_test(
-    name="Model Performance",
-    unit_metrics=[
-        "validmind.unit_metrics.classification.F1",
-        "validmind.unit_metrics.classification.Precision",
-        "validmind.unit_metrics.classification.Recall",
-        "validmind.unit_metrics.classification.Accuracy",
-        "validmind.unit_metrics.classification.ROC_AUC",
-    ],
-    inputs={
-        "model": vm_model,
-        "dataset": vm_test_ds,
-    },
-)
    -
    -

    If we take a look at the result_id for the result, we'll see that it is a unique identifier that starts with validmind.composite_metric.<user-supplied-metric-name>. This will be used to identify this result as coming from a composite metric and is used to rebuild the composite metric as we will see in the next section.

    -
    -
    result.result_id
    -
    -

    Let's go ahead and log the result to save it to the ValidMind Platform.

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    result.log()
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    Bringing It All Together

    -

    -
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    Comparing Composite Metrics for Multiple Input Combinations

    -

    We can bring all of the above concepts together to create a very sophisticated test result that compares multiple metrics across multiple inputs. This is a very modular and flexible way to create complex test results for your model documentation. Let's go ahead and compare the training and test performance of our model across Accuracy, Precision, Recall, F1 and ROC AUC scores.

    -
    -
    result = run_test(
-    name="Model Performance Comparison Train vs Test",
-    unit_metrics=[
-        "validmind.unit_metrics.classification.F1",
-        "validmind.unit_metrics.classification.Precision",
-        "validmind.unit_metrics.classification.Recall",
-        "validmind.unit_metrics.classification.Accuracy",
-        "validmind.unit_metrics.classification.ROC_AUC",
-    ],
-    input_grid={
-        "model": [vm_model],
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-)
-
-result.log()
    -
    -

    -
    -
    -

    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/scoring/assign_scores_complete_tutorial.html b/site/notebooks/how_to/scoring/assign_scores_complete_tutorial.html deleted file mode 100644 index b02596b12..000000000 --- a/site/notebooks/how_to/scoring/assign_scores_complete_tutorial.html +++ /dev/null @@ -1,1992 +0,0 @@ - - - - - - - - - -Intro to Assign Scores – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Intro to Assign Scores

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    - - - -
    - - - -

    The assign_scores() method is a powerful feature that allows you to compute and add scorer scores as new columns in your dataset. This method takes a model and metric(s) as input, computes the specified metrics from the ValidMind scorer library, and adds them as new columns. The computed metrics provide per-row values, giving you granular insights into model performance at the individual prediction level.

    -

    In this interactive notebook, we demonstrate how to use the assign_scores() method effectively. We'll walk through a complete example using a customer churn dataset, showing how to compute and assign row-level metrics (like Brier Score and Log Loss) that provide detailed performance insights for each prediction. You'll learn how to work with single and multiple scorers, pass custom parameters, and handle different metric types - all while maintaining a clean, organized dataset structure. Currently, assign_scores() supports all metrics available in the validmind.scorer module.

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    The Power of Row-Level Scoring

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    Traditional model evaluation workflows often focus on aggregate metrics that provide overall performance summaries. The assign_scores() method complements this by providing granular, row-level insights that help you:

    - -

    Understanding assign_scores()

    -

    The assign_scores() method computes row metrics for a given model-dataset combination and adds the results as new columns to your dataset. Each new column follows the naming convention: {model.input_id}_{metric_name}, ensuring clear identification of which model and metric combination generated each score.

    -

    Key features:

    - -

    This approach provides detailed insights into your model's performance at the individual prediction level, enabling more sophisticated analysis and debugging workflows.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
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    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    api_host="...",
-    api_key="...",
-    api_secret="...",
-    model="...",
-    # document="documentation",
-)
    -
    -

    -
    -
    -
    -
    -

    Load the demo dataset

    -

    In this example, we load a demo dataset to demonstrate the assign_scores functionality with customer churn prediction models.

    -
    -
    from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Train models for testing

    -

    We'll train two different customer churn models to demonstrate the assign_scores functionality with multiple models.

    -
    -
    import xgboost as xgb
-from sklearn.ensemble import RandomForestClassifier
-
-# Preprocess the data
-train_df, validation_df, test_df = demo_dataset.preprocess(raw_df)
-
-# Prepare training data
-x_train = train_df.drop(demo_dataset.target_column, axis=1)
-y_train = train_df[demo_dataset.target_column]
-x_val = validation_df.drop(demo_dataset.target_column, axis=1)
-y_val = validation_df[demo_dataset.target_column]
-
-# Train XGBoost model
-xgb_model = xgb.XGBClassifier(early_stopping_rounds=10, random_state=42)
-xgb_model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-xgb_model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
-)
-
-# Train Random Forest model
-rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
-rf_model.fit(x_train, y_train)
-
-print("Models trained successfully!")
-print(f"XGBoost training accuracy: {xgb_model.score(x_train, y_train):.3f}")
-print(f"Random Forest training accuracy: {rf_model.score(x_train, y_train):.3f}")
    -
    -

    -
    -
    -

    Initialize ValidMind objects

    -

    We initialize ValidMind dataset and model objects. The input_id parameter is crucial for the assign_scores functionality as it determines the column naming convention for assigned scores.

    -
    -
    # Initialize datasets
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset",
-    dataset=train_df,
-    target_column=demo_dataset.target_column,
-)
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset",
-    dataset=test_df,
-    target_column=demo_dataset.target_column,
-)
-
-# Initialize models with descriptive input_ids
-vm_xgb_model = vm.init_model(model=xgb_model, input_id="xgboost_model")
-vm_rf_model = vm.init_model(model=rf_model, input_id="random_forest_model")
-
-print("ValidMind objects initialized successfully!")
-print(f"XGBoost model ID: {vm_xgb_model.input_id}")
-print(f"Random Forest model ID: {vm_rf_model.input_id}")
    -
    -

    -
    -
    -

    Assign predictions

    -

    Before we can use assign_scores(), we need to assign predictions to our datasets. This step is essential as many unit metrics require both actual and predicted values.

    -
    -
    # Assign predictions for both models to both datasets
-vm_train_ds.assign_predictions(model=vm_xgb_model)
-vm_train_ds.assign_predictions(model=vm_rf_model)
-
-vm_test_ds.assign_predictions(model=vm_xgb_model)
-vm_test_ds.assign_predictions(model=vm_rf_model)
-
-print("Predictions assigned successfully!")
-print(f"Test dataset now has {len(vm_test_ds.df.columns)} columns")
    -
    -

    -
    -
    -

    Using assign_scores()

    -

    Now we'll explore the various ways to use the assign_scores() method to integrate performance metrics directly into your dataset.

    -

    -
    -

    Basic Usage

    -

    The assign_scores() method has a simple interface:

    -
    dataset.assign_scores(model, metrics, **kwargs)
    -
      -
    • model: A ValidMind model object
      • -
    • metrics: Single metric ID or list of metric IDs (can use short names or full IDs)
      • -
    • kwargs: Additional parameters passed to the underlying metric implementations
      • -
    -

    Let's first check what columns we currently have in our test dataset:

    -
    -
    print("Current columns in test dataset:")
-for i, col in enumerate(vm_test_ds.df.columns, 1):
-    print(f"{i:2d}. {col}")
-
-print(f"\nDataset shape: {vm_test_ds.df.shape}")
    -
    -

    -
    -
    -

    Single Scorer Assignment

    -

    Let's start by assigning a single Scorer - the Brier Score - for our XGBoost model on the test dataset.

    -
    -
    # Assign Brier Score for XGBoost model
-vm_test_ds.assign_scores(metrics = "validmind.scorers.classification.BrierScore", model = vm_xgb_model)
-
-print("After assigning Brier Score:")
-print(f"New column added: {vm_test_ds.df.columns}")
-# Display the metric values
-vm_test_ds.df.head()
    -
    -

    -
    -
    -

    A Scorer returns complex object

    -

    The OutlierScore scorer demonstrates how scorers can return complex objects. It returns a dictionary containing per-row outlier detection results. For each row, it includes: - is_outlier: Boolean indicating if the row is an outlier - anomaly_score: Numerical score indicating degree of outlierness - isolation_path: Length of isolation path in the tree

    -

    When assigned to a dataset, these dictionary values are automatically unpacked into separate columns with appropriate prefixes.

    -
    -
    # Assign Brier Score for XGBoost model
-vm_test_ds.assign_scores(metrics = "validmind.scorers.classification.OutlierScore", model = vm_xgb_model)
-
-print("After assigning Score With Confidence:")
-print(f"New column added: {vm_test_ds.df.columns}")
-# Display the metric values
-vm_test_ds.df.head()
    -
    -
    -
    # Assign Brier Score for XGBoost model
-vm_test_ds.assign_scores("validmind.scorers.classification.OutlierScore")
-
-print("After assigning Score With Confidence:")
-print(f"New column added: {vm_test_ds.df.columns}")
-# Display the metric values
-vm_test_ds.df.head()
    -
    -

    -
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    -

    Multiple Scorers Assignment

    -

    We can assign multiple metrics at once by passing a list of Scorer names. This is more efficient than calling assign_scores() multiple times.

    -
    -
    # Assign multiple classification metrics for the Random Forest model
-scorer = [
-    "validmind.scorers.classification.BrierScore",
-    "validmind.scorers.classification.LogLoss",
-    "validmind.scorers.classification.Confidence"
-]
-
-vm_test_ds.assign_scores(metrics = scorer, model = vm_rf_model)
-
-print("After assigning multiple row metrics for Random Forest:")
-rf_columns = [col for col in vm_test_ds.df.columns if 'random_forest_model' in col]
-print(f"Random Forest columns: {rf_columns}")
-
-# Display the metric values
-vm_test_ds.df[rf_columns].head()
    -
    -

    -
    -
    -

    Passing Parameters to Scorer

    -

    Many row metrics accept additional parameters that are passed through to the underlying implementations. Let's demonstrate this with the LogLoss metric.

    -
    -
    # Assign LogLoss
-vm_test_ds.assign_scores(metrics = "validmind.scorers.classification.LogLoss", model = vm_xgb_model, eps = 1e-16)
-
-# We can also assign with different parameters by calling assign_scores again
-# Note: This will overwrite the previous column with the same name
-print("LogLoss assigned successfully")
-
-# Let's also assign BrierScore and Confidence
-vm_test_ds.assign_scores(metrics = ["validmind.scorers.classification.BrierScore","validmind.scorers.classification.Confidence"], model = vm_xgb_model)
-
-print("BrierScore and Confidence assigned successfully")
-
-# Display current XGBoost metric columns
-xgb_columns = [col for col in vm_test_ds.df.columns if 'xgboost_model' in col]
-print(f"\nXGBoost model columns: {xgb_columns}")
-
-vm_test_ds.df[xgb_columns].head()
    -
    -

    -
    -
    -

    Multi-Model scorers

    -

    One of the powerful features of assign_scores() is the ability to assign scores from multiple models to the same dataset, enabling detailed model comparison at the prediction level.

    -
    -
    # Let's assign a comprehensive set of metrics for both models
-comprehensive_metrics = [
-    "validmind.scorers.classification.BrierScore",
-    "validmind.scorers.classification.LogLoss",
-    "validmind.scorers.classification.Confidence",
-    "validmind.scorers.classification.Correctness"
-]
-
-# Assign for XGBoost model
-vm_test_ds.assign_scores(metrics = comprehensive_metrics, model = vm_xgb_model)
-
-# Assign for Random Forest model}
-vm_test_ds.assign_scores(metrics = comprehensive_metrics, model = vm_rf_model)
-
-print("Row-level metrics assigned for both models!")
    -
    -

    -
    -
    -

    Scorer Metrics

    -

    The next section demonstrates how to assign individual metrics that compute scores per row, rather than aggregate metrics. We'll use several important row metrics:

    -
      -
    • Brier Score: Measures how well calibrated the model's probability predictions are for each individual prediction
      • -
    • Log Loss: Evaluates how well the predicted probabilities match the true labels on a per-prediction basis
      • -
    • Confidence: Measures the model's confidence in its predictions for each row
      • -
    • Correctness: Indicates whether each prediction is correct (1) or incorrect (0)
      • -
    -

    All these metrics provide granular insights into model performance at the individual prediction level.

    -
    -
    # Let's add some individual metrics that compute per-row scores
-print("Adding individual metrics...")
-
-# Add Brier Score - measures accuracy of probabilistic predictions per row
-vm_test_ds.assign_scores(metrics = "validmind.scorers.classification.BrierScore", model = vm_xgb_model)
-print("Added Brier Score - lower values indicate better calibrated probabilities")
-
-# Add Log Loss - measures how well the predicted probabilities match true labels per row
-vm_test_ds.assign_scores(metrics = "validmind.scorers.classification.LogLoss", model = vm_xgb_model)
-print("Added Log Loss - lower values indicate better probability estimates")
-
-# Create a comparison summary showing first few rows of individual metrics
-print("\nFirst few rows of individual metrics:")
-individual_metrics = [col for col in vm_test_ds.df.columns if any(m in col for m in ['BrierScore', 'LogLoss', 'Confidence', 'Correctness'])]
-print(vm_test_ds.df[individual_metrics].head())
    -
    -
    -
    vm_test_ds._df.head()
    -
    -

    -
    -
    -

    Custom Scorer

    -

    Let's see how to create your own custom scorers using the @scorer decorator.

    -

    The example below demonstrates a scorer that looks at the class balance in the neighborhood around each data point. For each row, it will give you a score from 0 to 1, where a score closer to 1 means there's a nice even balance of classes in that area of your data. This can help you identify regions where your classes are well-mixed vs regions dominated by a single class.

    -
    -
    from validmind.scorer import scorer
-import numpy as np
-
-@scorer("my_scorers.TestScorer") 
-def test_scorer(model, dataset):
-    """Custom scorer that calculates class balance ratio.
-    
-    Args:
-        model: Not used in this scorer
-        dataset: The dataset to analyze
-        
-    Returns:
-        numpy.ndarray: Array of class balance ratios between 0 and 1,
-        where values closer to 1 indicate better class balance in the local neighborhood
-    """
-    # Get target values
-    y = dataset.df[dataset.target_column].values
-    
-    # Calculate local class balance in sliding windows
-    window_size = 100
-    balance_scores = []
-    
-    for i in range(len(y)):
-        start_idx = max(0, i - window_size//2)
-        end_idx = min(len(y), i + window_size//2)
-        window = y[start_idx:end_idx]
-        
-        # Calculate ratio of minority class
-        class_ratio = np.mean(window)
-        # Adjust to be symmetric around 0.5
-        balance_score = 1 - abs(0.5 - class_ratio) * 2
-        
-        balance_scores.append(balance_score)
-        
-    return np.array(balance_scores)
-
-# Assign the class balance scores to the dataset
-vm_test_ds.assign_scores(metrics = "my_scorers.TestScorer", model = vm_xgb_model)
-
    -
    -

    -
    -
    -
    -

    Next steps

    -

    You can explore the assigned scores right in the notebook as demonstrated above. However, there's even more value in using the ValidMind Platform to work with your model documentation and monitoring.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    We offer many interactive notebooks to help you work with model scoring and evaluation:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you'll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -

    %pip show validmind

    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/tests/custom_tests/integrate_external_test_providers.html b/site/notebooks/how_to/tests/custom_tests/integrate_external_test_providers.html deleted file mode 100644 index 61c2761b9..000000000 --- a/site/notebooks/how_to/tests/custom_tests/integrate_external_test_providers.html +++ /dev/null @@ -1,2142 +0,0 @@ - - - - - - - - - -Integrate external test providers – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Integrate external test providers

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    - - - -
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    - - - -
    - - - -

    Register a custom test provider with the ValidMind Library to run your own tests.

    -

    The ValidMind Library offers the ability to extend the built-in library of tests with custom tests. If you've followed along in the Implement custom tests notebook, you will be familiar with the process of creating custom tests and running them for use in your model documentation. In that notebook, the tests were defined inline, as a notebook cell, using the validmind.test decorator. This works very well when you just want to create one-off, ad-hoc tests or for experimenting. But for more complex, reusable and shareable tests, it is recommended to use a more structured approach.

    -

    This is where the concept of External Test Providers come in. A test "Provider" is a Python class that gets registered with the ValidMind Library and loads tests based on a test ID, for example my_test_provider.my_test_id. The built-in suite of tests that ValidMind offers is technically its own test provider. You can use one the built-in test provider offered by ValidMind (validmind.tests.test_providers.LocalTestProvider) or you can create your own. More than likely, you'll want to use the LocalTestProvider to add a directory of custom tests but there's flexibility to be able to load tests from any source.

    -

    In this notebook, we'll take you through the process of creating a folder of custom tests from existing, inline tests. We'll then show you how to defined and register a LocalTestProvider that points to that folder. Finally, we'll show how the test ID works when using test providers and how you can run and use custom tests from your provider.

    - - -

    -
    -

    Prerequisites

    -

    As mentioned above, its recommended that you have gone through the Implement custom tests notebook to understand the basics of creating custom tests. We will be using the same tests defined in that notebook.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    Key concepts

    -
      -
    • Test Provider: A Python class that gets registered with the ValidMind Library and loads tests based on a test ID e.g. my_test_provider.my_test_id.
      • -
    • LocalTestProvider: A built-in test provider that loads tests from a local directory.
      • -
    • Test ID: A unique identifier for a test that is used to load the test from a test provider. Test IDs in ValidMind have multiple parts, separated by dots (.), that have special meaning/functions - namespace.path.test_name:result_id (e.g. validmind.data_validation.ClassImbalance:1): -
        -
      • namespace: The namespace of the test - built-in tests use the validmind namespace, custom test providers specify a custom namespace when they are registered
        • -
      • path: The path to the test within the test provider. For internal tests, this is relative to the validmind/tests directory. When using a LocalTestProvider this is relative to the root directory you specify when creating the provider class instance. Note: This path-based approach is a convention and not a strict requirement when implementing your own test provider.
        • -
      • test_name: The name of the test module (file) as well as the name of the function or class that is the "test" itself. Again, this is a convention for internal and LocalTestProvider tests, but test files should be named the same as the test function or class they contain.
        • -
      • result_id: An optional identifier for the test. Especially useful, and required, for running tests multiple times in one section. This is separated from the previous parts by a colon : and can be effectively ignored when loading the test itself as its only used to uniquely identify the test result.
        • -
      • -
    -

    -
    -
    -

    High-level steps

    -
      -
    1. Create a folder of custom tests from existing, inline tests.
      2. -
    2. Define and register a LocalTestProvider that points to that folder.
      3. -
    3. Use the test ID to run, view and log custom tests from your provider.
      4. -
    4. Add the test results to your documentation
      5. -
    5. run_documentation_tests() to run custom tests as part of your documentation template suite
      6. -
    -

    -
    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    If this is your first time trying out ValidMind, we recommend going through the following resources first:

    -
      -
    • Get started — The basics, including key concepts, and how our products work
      • -
    • ValidMind Library — The path for developers, more code samples, and our developer reference
      • -
    -

    -
    -

    Before you begin

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -
    -

    Set up custom tests

    -

    If you've gone through the Implement custom tests notebook, you should have a good understanding of how custom tests are implemented. If you haven't, we recommend going through that notebook first. In this notebook, we will take those custom tests and move them into separate modules in a folder. This is the logical progression from the previous notebook, as it allows you to take one-off tests and move them into an organized structure that makes it easier to manage, maintain and share them.

    -

    First let's set the path to our custom tests folder.

    -
    -
    tests_folder = "my_tests"
    -
    -

    Now let's create this folder if it doesn't exist. If it does, let's empty it to start fresh.

    -
    -
    import os
-
-# create tests folder
-os.makedirs(tests_folder, exist_ok=True)
-
-# remove existing tests
-for f in os.listdir(tests_folder):
-    # remove files and pycache
-    if f.endswith(".py") or f == "__pycache__":
-        os.system(f"rm -rf {tests_folder}/{f}")
    -
    -

    Let's go ahead and redefine the custom tests we created in the previous notebook.

    -
    -
    import matplotlib.pyplot as plt
-from sklearn import metrics
-from validmind import test
-
-
-@test("my_custom_tests.ConfusionMatrix")
-def confusion_matrix(dataset, model):
-    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.
-
-    The confusion matrix is a 2x2 table that contains 4 values:
-
-    - True Positive (TP): the number of correct positive predictions
-    - True Negative (TN): the number of correct negative predictions
-    - False Positive (FP): the number of incorrect positive predictions
-    - False Negative (FN): the number of incorrect negative predictions
-
-    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
-    """
-    confusion_matrix = metrics.confusion_matrix(dataset.y, dataset.y_pred(model))
-
-    cm_display = metrics.ConfusionMatrixDisplay(
-        confusion_matrix=confusion_matrix, display_labels=[False, True]
-    )
-    cm_display.plot()
-
-    plt.close()  # close the plot to avoid displaying it
-
-    return cm_display.figure_  # return the figure object itself
    -
    -

    The decorator thats used to register these as one-off custom tests adds a convenience method to the function object that allows us to simply call <func_name>.save() to save it to a file. This will save the function to a Python file in the current directory. You can also pass a path to a folder to save it to a different location. In our case, we can pass the variable tests_folder to save it to the custom tests folder we created.

    -

    Normally, this will get you started by creating the file and saving the function code with the correct name. But it won't automatically add any import or other functions/variables outside of the function that are needed for the test to run. In that case, you would need to manually add those to the file.

    -

    But for the sake of this demo, we will pass a list of imports into the save method to automatically add them to the file.

    -
    -
    confusion_matrix.save(
-    tests_folder,
-    imports=["import matplotlib.pyplot as plt", "from sklearn import metrics"],
-)
    -
    -

    Let's go ahead and do this for the rest of the tests.

    -
    -
    @test("my_custom_tests.Hyperparameters")
-def hyperparameters(model):
-    """The hyperparameters of a machine learning model are the settings that control the learning process.
-    These settings are specified before the learning process begins and can have a significant impact on the
-    performance of the model.
-
-    The hyperparameters of a model can be used to tune the model to achieve the best possible performance
-    on a given dataset. By examining the hyperparameters of a model, you can gain insight into how the model
-    was trained and how it might be improved.
-    """
-    hyperparameters = model.model.get_xgb_params()  # dictionary of hyperparameters
-
-    # turn the dictionary into a table where each row contains a hyperparameter and its value
-    return [{"Hyperparam": k, "Value": v} for k, v in hyperparameters.items() if v]
-
-
-hyperparameters.save(tests_folder)
    -
    -
    -
    import requests
-import random
-
-
-@test("my_custom_tests.ExternalAPI")
-def external_api():
-    """This test calls an external API to get a list of fake users. It then creates
-    a table with the relevant data so it can be displayed in the documentation.
-
-    The purpose of this test is to demonstrate how to call an external API and use the
-    data in a test. A test like this could even be setup to run in a scheduled
-    pipeline to keep your documentation in-sync with an external data source.
-    """
-    url = "https://jsonplaceholder.typicode.com/users"
-    response = requests.get(url)
-    data = response.json()
-
-    # extract the time and the current BTC price in USD
-    return {
-        "Model Owners/Stakeholders": [
-            {
-                "Name": user["name"],
-                "Role": random.choice(["Owner", "Stakeholder"]),
-                "Email": user["email"],
-                "Phone": user["phone"],
-                "Slack Handle": f"@{user['name'].lower().replace(' ', '.')}",
-            }
-            for user in data[:3]
-        ],
-        "Model Developers": [
-            {
-                "Name": user["name"],
-                "Role": "Developer",
-                "Email": user["email"],
-            }
-            for user in data[3:7]
-        ],
-        "Model Validators": [
-            {
-                "Name": user["name"],
-                "Role": "Validator",
-                "Email": user["email"],
-            }
-            for user in data[7:]
-        ],
-    }
-
-
-external_api.save(tests_folder, imports=["import requests", "import random"])
    -
    -
    -
    import plotly.express as px
-
-
-@test("my_custom_tests.ParameterExample")
-def parameter_example(
-    plot_title="Default Plot Title", x_col="sepal_width", y_col="sepal_length"
-):
-    """This test takes two parameters and creates a scatter plot based on them.
-
-    The purpose of this test is to demonstrate how to create a test that takes
-    parameters and uses them to generate a plot. This can be useful for creating
-    tests that are more flexible and can be used in a variety of scenarios.
-    """
-    # return px.scatter(px.data.iris(), x=x_col, y=y_col, color="species")
-    return px.scatter(
-        px.data.iris(), x=x_col, y=y_col, color="species", title=plot_title
-    )
-
-
-parameter_example.save(tests_folder, imports=["import plotly.express as px"])
    -
    -
    -
    import numpy as np
-import plotly.express as px
-
-
-@test("my_custom_tests.ComplexOutput")
-def complex_output():
-    """This test demonstrates how to return many tables and figures in a single test"""
-    # create a couple tables
-    table = [{"A": 1, "B": 2}, {"A": 3, "B": 4}]
-    table2 = [{"C": 5, "D": 6}, {"C": 7, "D": 8}]
-
-    # create a few figures showing some random data
-    fig1 = px.line(x=np.arange(10), y=np.random.rand(10), title="Random Line Plot")
-    fig2 = px.bar(x=["A", "B", "C"], y=np.random.rand(3), title="Random Bar Plot")
-    fig3 = px.scatter(
-        x=np.random.rand(10), y=np.random.rand(10), title="Random Scatter Plot"
-    )
-
-    return (
-        {
-            "My Cool Table": table,
-            "Another Table": table2,
-        },
-        fig1,
-        fig2,
-        fig3,
-    )
-
-
-complex_output.save(
-    tests_folder, imports=["import numpy as np", "import plotly.express as px"]
-)
    -
    -
    -
    import io
-import matplotlib.pyplot as plt
-
-
-@test("my_custom_tests.Image")
-def image():
-    """This test demonstrates how to return an image in a test"""
-
-    # create a simple plot
-    fig, ax = plt.subplots()
-    ax.plot([1, 2, 3, 4])
-    ax.set_title("Simple Line Plot")
-
-    # save the plot as a PNG image (in-memory buffer)
-    img_data = io.BytesIO()
-    fig.savefig(img_data, format="png")
-    img_data.seek(0)
-
-    plt.close()  # close the plot to avoid displaying it
-
-    return img_data.read()
-
-
-image.save(tests_folder, imports=["import io", "import matplotlib.pyplot as plt"])
    -
    -

    -
    -
    -

    Registering the test provider

    -

    -
    -

    Test providers overview

    -

    Now that we have a folder with our custom tests, we can create a test provider that will tell the ValidMind Library where to find these tests. ValidMind offers out-of-the-box test providers for local tests (i.e. tests in a folder) or a Github provider for tests in a Github repository. You can also create your own test provider by creating a class that has a load_test method that takes a test ID and returns the test function matching that ID. The protocol for test providers is below:

    -
    class ExternalTestProvider(Protocol):
-    """Protocol for user-defined test providers"""
-
-    def load_test(self, test_id: str) -> callable:
-        """Load the test by test ID
-
-        Args:
-            test_id (str): The test ID (does not contain the namespace under which
-                the test is registered)
-
-        Returns:
-            callable: The test object
-
-        Raises:
-            FileNotFoundError: If the test is not found
-        """
-        ...
    -

    -
    -
    -

    Local test provider

    -

    For most use-cases, the local test provider should be sufficient. Let's go ahead and see how we can do this with our custom tests.

    -
    -
    from validmind.tests import LocalTestProvider
-
-# initialize the test provider with the tests folder we created earlier
-my_test_provider = LocalTestProvider(tests_folder)
-
-vm.tests.register_test_provider(
-    namespace="my_test_provider",
-    test_provider=my_test_provider,
-)
-# `my_test_provider.load_test()` will be called for any test ID that starts with `my_test_provider`
-# e.g. `my_test_provider.ConfusionMatrix` will look for a function named `ConfusionMatrix` in `my_tests/ConfusionMatrix.py` file
    -
    -

    -
    -
    -

    Running test provider tests

    -

    Now that we have our test provider set up, we can run any test that's located in our tests folder by using the run_test() method. This function is your entry point to running single tests in the ValidMind Library. It takes a test ID and runs the test associated with that ID. For our custom tests, the test ID will be the namespace specified when registering the provider, followed by the path to the test file relative to the tests folder. For example, the Confusion Matrix test we created earlier will have the test ID my_test_provider.ConfusionMatrix. You could organize the tests in subfolders, say classification and regression, and the test ID for the Confusion Matrix test would then be my_test_provider.classification.ConfusionMatrix.

    -

    Let's go ahead and run some of our tests. But first, let's setup a dataset and model to run against.

    -

    -
    -

    Set up the model and dataset

    -

    First let's setup a an example model and dataset to run our custom metic against. Since this is a Confusion Matrix, we will use the Customer Churn dataset that ValidMind provides and train a simple XGBoost model.

    -
    -
    import xgboost as xgb
-from validmind.datasets.classification import customer_churn
-
-raw_df = customer_churn.load_data()
-train_df, validation_df, test_df = customer_churn.preprocess(raw_df)
-
-x_train = train_df.drop(customer_churn.target_column, axis=1)
-y_train = train_df[customer_churn.target_column]
-x_val = validation_df.drop(customer_churn.target_column, axis=1)
-y_val = validation_df[customer_churn.target_column]
-
-model = xgb.XGBClassifier(early_stopping_rounds=10)
-model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
-)
    -
    -

    Easy enough! Now we have a model and dataset setup and trained. One last thing to do is bring the dataset and model into the ValidMind Library:

    -
    -
    # for now, we'll just use the test dataset
-vm_raw_ds = vm.init_dataset(
-    dataset=raw_df,
-    target_column=customer_churn.target_column,
-    input_id="raw_dataset",
-)
-vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    target_column=customer_churn.target_column,
-    input_id="train_dataset",
-)
-vm_test_ds = vm.init_dataset(
-    dataset=test_df,
-    target_column=customer_churn.target_column,
-    input_id="test_dataset",
-)
-
-vm_model = vm.init_model(model, input_id="model")
-# link the model to the datasets
-vm_train_ds.assign_predictions(model=vm_model)
-vm_test_ds.assign_predictions(model=vm_model)
    -
    -

    -
    -
    -

    Run the tests

    -

    Now that we have our model and dataset setup, we can run our custom tests against them. Let's go ahead and run the Confusion Matrix test.

    -
    -
    from validmind.tests import run_test
-
-result = run_test(
-    "my_test_provider.ConfusionMatrix",
-    inputs={"dataset": vm_test_ds, "model": vm_model},
-)
    -
    -

    You should see the output of the test above. If you want to learn more about how test functions are run and get turned into test results, check out the Implement custom tests notebook.

    -

    Let's go ahead and log the result to the ValidMind Platform.

    -
    -
    result.log()
    -
    -

    -
    -
    -

    Add custom tests to model documentation

    -

    Now that the result has been logged to the ValidMind Platform, you can add it to your model documentation. This will add the result where you specify but it also will add the test to the template so it gets run anytime you run_documentation_tests(). To do this, go to the documentation page of the model you connected to above and navigate to the Model Development -> Model Evaluation section. Then hover between any existing content block to reveal the + button as shown in the screenshot below.

    -
    -
    -

    screenshot showing insert button for test-driven blocks

    -
    screenshot showing insert button for test-driven blocks
    -
    -
    -

    Now click on the + button and select the Test-Driven Block option. This will open a dialog where you can select My Test Provider Confusion Matrix from the list of available tests. You can preview the result and then click Insert Block to add it to the documentation.

    -
    -
    -

    screenshot showing how to insert a test-driven block

    -
    screenshot showing how to insert a test-driven block
    -
    -
    -

    The test should match the result you see above.

    -

    -
    -
    -

    Run, save, and add other tests

    -

    Let's take the same steps for all of our other custom tests.

    -
    -
    run_test(
-    "my_test_provider.ComplexOutput", inputs={"dataset": vm_test_ds, "model": vm_model}
-).log()
-run_test("my_test_provider.ExternalAPI").log()
-run_test("my_test_provider.Hyperparameters", inputs={"model": vm_model}).log()
-run_test("my_test_provider.Image").log()
-run_test(
-    "my_test_provider.ParameterExample",
-    params={
-        "plot_title": "Test Provider Plot",
-        "x_col": "petal_width",
-        "y_col": "petal_length",
-    },
-).log()
    -
    -

    Now that they are all saved, follow the same steps above to add them to the model documentation. Once thats done, we will run the documentation tests to see all of them run automatically.

    -

    -
    -
    -
    -

    Verify that preview_template() now loads the tests from the test providers

    -

    Now that we have added the tests to the model documentation, we can run the preview_template() method to see the tests run automatically.

    -

    First, let's reload the connection to the model to get the updated documentation template.

    -
    -
    vm.reload()
    -
    -

    Now, run preview_template() and verify that the tests you added are included in the proper section.

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -

    Run the documentation tests

    -

    Now we can run the documentation tests as normal. This should include all of our custom tests which will be loaded from our test provider folder.

    -
    -
    test_config = customer_churn.get_demo_test_config()
-suite_results = vm.run_documentation_tests(config=test_config)
    -
    -

    -
    -
    -
    -

    Conclusion

    -

    In this notebook, we showed how to integrate custom tests into the ValidMind Library. We created a custom test provider that loads tests from a folder and then ran the tests against a model and dataset. We then added the tests to the model documentation and ran the documentation tests to see all of the tests run automatically. This is a powerful concept that allows you to create, organize and, most importantly, share custom tests with other model developers.

    -

    -
    -
    -

    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/tests/explore_tests/explore_tests.html b/site/notebooks/how_to/tests/explore_tests/explore_tests.html deleted file mode 100644 index d82821bc3..000000000 --- a/site/notebooks/how_to/tests/explore_tests/explore_tests.html +++ /dev/null @@ -1,5644 +0,0 @@ - - - - - - - - - -Explore tests – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - -
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    Explore tests

    -
    - - - -
    - - - - -
    - - - -
    - - - -

    Explore the individual out-the-box tests available in the ValidMind Library, and identify which tests to run to evaluate different aspects of your model. Browse available tests, view their descriptions, and filter by tags or task type to find tests relevant to your use case.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

    -

    -
    -
    -
    -

    Install the ValidMind Library

    -
    -Recommended Python versions

    Python 3.8 <= x <= 3.14 -
    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    List all available tests

    -

    Start by importing the functions from the validmind.tests module for listing tests, listing tasks, listing tags, and listing tasks and tags to access these functions in the rest of this notebook:

    -
    -
    from validmind.tests import (
-    list_tests,
-    list_tasks,
-    list_tags,
-    list_tasks_and_tags,
-)
    -
    -

    Use list_tests() to retrieve all available ValidMind tests, which returns a DataFrame with the following columns:

    -
      -
    • ID – A unique identifier for each test.
      • -
    • Name – The test’s name.
      • -
    • Description – A short summary of what the test evaluates.
      • -
    • Tags – Keywords that describe what the test does or applies to.
      • -
    • Tasks – The type of modeling task the test supports.
      • -
    -
    -
    list_tests()
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    IDNameDescriptionHas FigureHas TableRequired InputsParamsTagsTasks
    validmind.data_validation.ACFandPACFPlotAC Fand PACF PlotAnalyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to...TrueFalse['dataset']{}['time_series_data', 'forecasting', 'statistical_test', 'visualization']['regression']
    validmind.data_validation.ADFADFAssesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test....FalseTrue['dataset']{}['time_series_data', 'statsmodels', 'forecasting', 'statistical_test', 'stationarity']['regression']
    validmind.data_validation.AutoARAuto ARAutomatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria....FalseTrue['dataset']{'max_ar_order': {'type': 'int', 'default': 3}}['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']['regression']
    validmind.data_validation.AutoMAAuto MAAutomatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on...FalseTrue['dataset']{'max_ma_order': {'type': 'int', 'default': 3}}['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']['regression']
    validmind.data_validation.AutoStationarityAuto StationarityAutomates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame....FalseTrue['dataset']{'max_order': {'type': 'int', 'default': 5}, 'threshold': {'type': 'float', 'default': 0.05}}['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']['regression']
    validmind.data_validation.BivariateScatterPlotsBivariate Scatter PlotsGenerates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...TrueFalse['dataset']{}['tabular_data', 'numerical_data', 'visualization']['classification']
    validmind.data_validation.BoxPierceBox PierceDetects autocorrelation in time-series data through the Box-Pierce test to validate model performance....FalseTrue['dataset']{}['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']['regression']
    validmind.data_validation.ChiSquaredFeaturesTableChi Squared Features TableAssesses the statistical association between categorical features and a target variable using the Chi-Squared test....FalseTrue['dataset']{'p_threshold': {'type': '_empty', 'default': 0.05}}['tabular_data', 'categorical_data', 'statistical_test']['classification']
    validmind.data_validation.ClassImbalanceClass ImbalanceEvaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....TrueTrue['dataset']{'min_percent_threshold': {'type': 'int', 'default': 10}}['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']['classification']
    validmind.data_validation.DatasetDescriptionDataset DescriptionProvides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....FalseTrue['dataset']{}['tabular_data', 'time_series_data', 'text_data']['classification', 'regression', 'text_classification', 'text_summarization']
    validmind.data_validation.DatasetSplitDataset SplitEvaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...FalseTrue['datasets']{}['tabular_data', 'time_series_data', 'text_data']['classification', 'regression', 'text_classification', 'text_summarization']
    validmind.data_validation.DescriptiveStatisticsDescriptive StatisticsPerforms a detailed descriptive statistical analysis of both numerical and categorical data within a model's...FalseTrue['dataset']{}['tabular_data', 'time_series_data', 'data_quality']['classification', 'regression']
    validmind.data_validation.DickeyFullerGLSDickey Fuller GLSAssesses stationarity in time series data using the Dickey-Fuller GLS test to determine the order of integration....FalseTrue['dataset']{}['time_series_data', 'forecasting', 'unit_root_test']['regression']
    validmind.data_validation.DuplicatesDuplicatesTests dataset for duplicate entries, ensuring model reliability via data quality verification....FalseTrue['dataset']{'min_threshold': {'type': '_empty', 'default': 1}}['tabular_data', 'data_quality', 'text_data']['classification', 'regression']
    validmind.data_validation.EngleGrangerCointEngle Granger CointAssesses the degree of co-movement between pairs of time series data using the Engle-Granger cointegration test....FalseTrue['dataset']{'threshold': {'type': 'float', 'default': 0.05}}['time_series_data', 'statistical_test', 'forecasting']['regression']
    validmind.data_validation.FeatureTargetCorrelationPlotFeature Target Correlation PlotVisualizes the correlation between input features and the model's target output in a color-coded horizontal bar...TrueFalse['dataset']{'fig_height': {'type': '_empty', 'default': 600}}['tabular_data', 'visualization', 'correlation']['classification', 'regression']
    validmind.data_validation.HighCardinalityHigh CardinalityAssesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....FalseTrue['dataset']{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}['tabular_data', 'data_quality', 'categorical_data']['classification', 'regression']
    validmind.data_validation.HighPearsonCorrelationHigh Pearson CorrelationIdentifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....FalseTrue['dataset']{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}['tabular_data', 'data_quality', 'correlation']['classification', 'regression']
    validmind.data_validation.IQROutliersBarPlotIQR Outliers Bar PlotVisualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....TrueFalse['dataset']{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}['tabular_data', 'visualization', 'numerical_data']['classification', 'regression']
    validmind.data_validation.IQROutliersTableIQR Outliers TableDetermines and summarizes outliers in numerical features using the Interquartile Range method....FalseTrue['dataset']{'threshold': {'type': 'float', 'default': 1.5}}['tabular_data', 'numerical_data']['classification', 'regression']
    validmind.data_validation.IsolationForestOutliersIsolation Forest OutliersDetects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....TrueFalse['dataset']{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}['tabular_data', 'anomaly_detection']['classification']
    validmind.data_validation.JarqueBeraJarque BeraAssesses normality of dataset features in an ML model using the Jarque-Bera test....FalseTrue['dataset']{}['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']['classification', 'regression']
    validmind.data_validation.KPSSKPSSAssesses the stationarity of time-series data in a machine learning model using the KPSS unit root test....FalseTrue['dataset']{}['time_series_data', 'stationarity', 'unit_root_test', 'statsmodels']['data_validation']
    validmind.data_validation.LJungBoxL Jung BoxAssesses autocorrelations in dataset features by performing a Ljung-Box test on each feature....FalseTrue['dataset']{}['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']['regression']
    validmind.data_validation.LaggedCorrelationHeatmapLagged Correlation HeatmapAssesses and visualizes correlation between target variable and lagged independent variables in a time-series...TrueFalse['dataset']{'num_lags': {'type': 'int', 'default': 10}}['time_series_data', 'visualization']['regression']
    validmind.data_validation.MissingValuesMissing ValuesEvaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....FalseTrue['dataset']{'min_threshold': {'type': 'int', 'default': 1}}['tabular_data', 'data_quality']['classification', 'regression']
    validmind.data_validation.MissingValuesBarPlotMissing Values Bar PlotAssesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...TrueFalse['dataset']{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}['tabular_data', 'data_quality', 'visualization']['classification', 'regression']
    validmind.data_validation.MutualInformationMutual InformationCalculates mutual information scores between features and target variable to evaluate feature relevance....TrueFalse['dataset']{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}['feature_selection', 'data_analysis']['classification', 'regression']
    validmind.data_validation.PearsonCorrelationMatrixPearson Correlation MatrixEvaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....TrueFalse['dataset']{}['tabular_data', 'numerical_data', 'correlation']['classification', 'regression']
    validmind.data_validation.PhillipsPerronArchPhillips Perron ArchAssesses the stationarity of time series data in each feature of the ML model using the Phillips-Perron test....FalseTrue['dataset']{}['time_series_data', 'forecasting', 'statistical_test', 'unit_root_test']['regression']
    validmind.data_validation.ProtectedClassesDescriptionProtected Classes DescriptionVisualizes the distribution of protected classes in the dataset relative to the target variable...TrueTrue['dataset']{'protected_classes': {'type': '_empty', 'default': None}}['bias_and_fairness', 'descriptive_statistics']['classification', 'regression']
    validmind.data_validation.RollingStatsPlotRolling Stats PlotEvaluates the stationarity of time series data by plotting its rolling mean and standard deviation over a specified...TrueFalse['dataset']{'window_size': {'type': 'int', 'default': 12}}['time_series_data', 'visualization', 'stationarity']['regression']
    validmind.data_validation.RunsTestRuns TestExecutes Runs Test on ML model to detect non-random patterns in output data sequence....FalseTrue['dataset']{}['tabular_data', 'statistical_test', 'statsmodels']['classification', 'regression']
    validmind.data_validation.ScatterPlotScatter PlotAssesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....TrueFalse['dataset']{}['tabular_data', 'visualization']['classification', 'regression']
    validmind.data_validation.ScoreBandDefaultRatesScore Band Default RatesAnalyzes default rates and population distribution across credit score bands....FalseTrue['dataset', 'model']{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}['visualization', 'credit_risk', 'scorecard']['classification']
    validmind.data_validation.SeasonalDecomposeSeasonal DecomposeAssesses patterns and seasonality in a time series dataset by decomposing its features into foundational components....TrueFalse['dataset']{'seasonal_model': {'type': 'str', 'default': 'additive'}}['time_series_data', 'seasonality', 'statsmodels']['regression']
    validmind.data_validation.ShapiroWilkShapiro WilkEvaluates feature-wise normality of training data using the Shapiro-Wilk test....FalseTrue['dataset']{}['tabular_data', 'data_distribution', 'statistical_test']['classification', 'regression']
    validmind.data_validation.SkewnessSkewnessEvaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...FalseTrue['dataset']{'max_threshold': {'type': '_empty', 'default': 1}}['data_quality', 'tabular_data']['classification', 'regression']
    validmind.data_validation.SpreadPlotSpread PlotAssesses potential correlations between pairs of time series variables through visualization to enhance...TrueFalse['dataset']{}['time_series_data', 'visualization']['regression']
    validmind.data_validation.TabularCategoricalBarPlotsTabular Categorical Bar PlotsGenerates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....TrueFalse['dataset']{}['tabular_data', 'visualization']['classification', 'regression']
    validmind.data_validation.TabularDateTimeHistogramsTabular Date Time HistogramsGenerates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...TrueFalse['dataset']{}['time_series_data', 'visualization']['classification', 'regression']
    validmind.data_validation.TabularDescriptionTablesTabular Description TablesSummarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....FalseTrue['dataset']{}['tabular_data']['classification', 'regression']
    validmind.data_validation.TabularNumericalHistogramsTabular Numerical HistogramsGenerates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...TrueFalse['dataset']{}['tabular_data', 'visualization']['classification', 'regression']
    validmind.data_validation.TargetRateBarPlotsTarget Rate Bar PlotsGenerates bar plots visualizing the default rates of categorical features for a classification machine learning...TrueFalse['dataset']{}['tabular_data', 'visualization', 'categorical_data']['classification']
    validmind.data_validation.TimeSeriesDescriptionTime Series DescriptionGenerates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends,...FalseTrue['dataset']{}['time_series_data', 'analysis']['regression']
    validmind.data_validation.TimeSeriesDescriptiveStatisticsTime Series Descriptive StatisticsEvaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues....FalseTrue['dataset']{}['time_series_data', 'analysis']['regression']
    validmind.data_validation.TimeSeriesFrequencyTime Series FrequencyEvaluates consistency of time series data frequency and generates a frequency plot....TrueTrue['dataset']{}['time_series_data']['regression']
    validmind.data_validation.TimeSeriesHistogramTime Series HistogramVisualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines....TrueFalse['dataset']{'nbins': {'type': '_empty', 'default': 30}}['data_validation', 'visualization', 'time_series_data']['regression', 'time_series_forecasting']
    validmind.data_validation.TimeSeriesLinePlotTime Series Line PlotGenerates and analyses time-series data through line plots revealing trends, patterns, anomalies over time....TrueFalse['dataset']{}['time_series_data', 'visualization']['regression']
    validmind.data_validation.TimeSeriesMissingValuesTime Series Missing ValuesValidates time-series data quality by confirming the count of missing values is below a certain threshold....TrueTrue['dataset']{'min_threshold': {'type': 'int', 'default': 1}}['time_series_data']['regression']
    validmind.data_validation.TimeSeriesOutliersTime Series OutliersIdentifies and visualizes outliers in time-series data using the z-score method....FalseTrue['dataset']{'zscore_threshold': {'type': 'int', 'default': 3}}['time_series_data']['regression']
    validmind.data_validation.TooManyZeroValuesToo Many Zero ValuesIdentifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...FalseTrue['dataset']{'max_percent_threshold': {'type': 'float', 'default': 0.03}}['tabular_data']['regression', 'classification']
    validmind.data_validation.UniqueRowsUnique RowsVerifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....FalseTrue['dataset']{'min_percent_threshold': {'type': 'float', 'default': 1}}['tabular_data']['regression', 'classification']
    validmind.data_validation.WOEBinPlotsWOE Bin PlotsGenerates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...TrueFalse['dataset']{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}['tabular_data', 'visualization', 'categorical_data']['classification']
    validmind.data_validation.WOEBinTableWOE Bin TableAssesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...FalseTrue['dataset']{'breaks_adj': {'type': 'list', 'default': None}}['tabular_data', 'categorical_data']['classification']
    validmind.data_validation.ZivotAndrewsArchZivot Andrews ArchEvaluates the order of integration and stationarity of time series data using the Zivot-Andrews unit root test....FalseTrue['dataset']{}['time_series_data', 'stationarity', 'unit_root_test']['regression']
    validmind.data_validation.nlp.CommonWordsCommon WordsAssesses the most frequent non-stopwords in a text column for identifying prevalent language patterns....TrueFalse['dataset']{}['nlp', 'text_data', 'visualization', 'frequency_analysis']['text_classification', 'text_summarization']
    validmind.data_validation.nlp.HashtagsHashtagsAssesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam....TrueFalse['dataset']{'top_hashtags': {'type': 'int', 'default': 25}}['nlp', 'text_data', 'visualization', 'frequency_analysis']['text_classification', 'text_summarization']
    validmind.data_validation.nlp.LanguageDetectionLanguage DetectionAssesses the diversity of languages in a textual dataset by detecting and visualizing the distribution of languages....TrueFalse['dataset']{}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.data_validation.nlp.MentionsMentionsCalculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis....TrueFalse['dataset']{'top_mentions': {'type': 'int', 'default': 25}}['nlp', 'text_data', 'visualization', 'frequency_analysis']['text_classification', 'text_summarization']
    validmind.data_validation.nlp.PolarityAndSubjectivityPolarity And SubjectivityAnalyzes the polarity and subjectivity of text data within a given dataset to visualize the sentiment distribution....TrueTrue['dataset']{'threshold_subjectivity': {'type': '_empty', 'default': 0.5}, 'threshold_polarity': {'type': '_empty', 'default': 0}}['nlp', 'text_data', 'data_validation']['nlp']
    validmind.data_validation.nlp.PunctuationsPunctuationsAnalyzes and visualizes the frequency distribution of punctuation usage in a given text dataset....TrueFalse['dataset']{'count_mode': {'type': '_empty', 'default': 'token'}}['nlp', 'text_data', 'visualization', 'frequency_analysis']['text_classification', 'text_summarization', 'nlp']
    validmind.data_validation.nlp.SentimentSentimentAnalyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool....TrueFalse['dataset']{}['nlp', 'text_data', 'data_validation']['nlp']
    validmind.data_validation.nlp.StopWordsStop WordsEvaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold....TrueTrue['dataset']{'min_percent_threshold': {'type': 'float', 'default': 0.5}, 'num_words': {'type': 'int', 'default': 25}}['nlp', 'text_data', 'frequency_analysis', 'visualization']['text_classification', 'text_summarization']
    validmind.data_validation.nlp.TextDescriptionText DescriptionConducts comprehensive textual analysis on a dataset using NLTK to evaluate various parameters and generate...TrueFalse['dataset']{'unwanted_tokens': {'type': 'set', 'default': {'s', 'mrs', 'us', "''", ' ', 'ms', 'dr', 'dollar', '``', 'mr', "'s", "s'"}}, 'lang': {'type': 'str', 'default': 'english'}}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.data_validation.nlp.ToxicityToxicityAssesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores....TrueFalse['dataset']{}['nlp', 'text_data', 'data_validation']['nlp']
    validmind.model_validation.BertScoreBert ScoreAssesses the quality of machine-generated text using BERTScore metrics and visualizes results through histograms...TrueTrue['dataset', 'model']{'evaluation_model': {'type': '_empty', 'default': 'distilbert-base-uncased'}}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.model_validation.BleuScoreBleu ScoreEvaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms...TrueTrue['dataset', 'model']{}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.model_validation.ClusterSizeDistributionCluster Size DistributionAssesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...TrueFalse['dataset', 'model']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.ContextualRecallContextual RecallEvaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct...TrueTrue['dataset', 'model']{}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.model_validation.FeaturesAUCFeatures AUCEvaluates the discriminatory power of each individual feature within a binary classification model by calculating...TrueFalse['dataset']{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}['feature_importance', 'AUC', 'visualization']['classification']
    validmind.model_validation.MeteorScoreMeteor ScoreAssesses the quality of machine-generated translations by comparing them to human-produced references using the...TrueTrue['dataset', 'model']{}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.model_validation.ModelMetadataModel MetadataCompare metadata of different models and generate a summary table with the results....FalseTrue['model']{}['model_training', 'metadata']['regression', 'time_series_forecasting']
    validmind.model_validation.ModelPredictionResidualsModel Prediction ResidualsAssesses normality and behavior of residuals in regression models through visualization and statistical tests....TrueTrue['dataset', 'model']{'nbins': {'type': 'int', 'default': 100}, 'p_value_threshold': {'type': 'float', 'default': 0.05}, 'start_date': {'type': None, 'default': None}, 'end_date': {'type': None, 'default': None}}['regression']['residual_analysis', 'visualization']
    validmind.model_validation.RegardScoreRegard ScoreAssesses the sentiment and potential biases in text generated by NLP models by computing and visualizing regard...TrueTrue['dataset', 'model']{}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.model_validation.RegressionResidualsPlotRegression Residuals PlotEvaluates regression model performance using residual distribution and actual vs. predicted plots....TrueFalse['model', 'dataset']{'bin_size': {'type': 'float', 'default': 0.1}}['model_performance', 'visualization']['regression']
    validmind.model_validation.RougeScoreRouge ScoreAssesses the quality of machine-generated text using ROUGE metrics and visualizes the results to provide...TrueTrue['dataset', 'model']{'metric': {'type': 'str', 'default': 'rouge-1'}}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.model_validation.TimeSeriesPredictionWithCITime Series Prediction With CIAssesses predictive accuracy and uncertainty in time series models, highlighting breaches beyond confidence...TrueTrue['dataset', 'model']{'confidence': {'type': 'float', 'default': 0.95}}['model_predictions', 'visualization']['regression', 'time_series_forecasting']
    validmind.model_validation.TimeSeriesPredictionsPlotTime Series Predictions PlotPlot actual vs predicted values for time series data and generate a visual comparison for the model....TrueFalse['dataset', 'model']{}['model_predictions', 'visualization']['regression', 'time_series_forecasting']
    validmind.model_validation.TimeSeriesR2SquareBySegmentsTime Series R2 Square By SegmentsEvaluates the R-Squared values of regression models over specified time segments in time series data to assess...TrueTrue['dataset', 'model']{'segments': {'type': None, 'default': None}}['model_performance', 'sklearn']['regression', 'time_series_forecasting']
    validmind.model_validation.TokenDisparityToken DisparityEvaluates the token disparity between reference and generated texts, visualizing the results through histograms and...TrueTrue['dataset', 'model']{}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.model_validation.ToxicityScoreToxicity ScoreAssesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content....TrueTrue['dataset', 'model']{}['nlp', 'text_data', 'visualization']['text_classification', 'text_summarization']
    validmind.model_validation.embeddings.ClusterDistributionCluster DistributionAssesses the distribution of text embeddings across clusters produced by a model using KMeans clustering....TrueFalse['model', 'dataset']{'num_clusters': {'type': 'int', 'default': 5}}['llm', 'text_data', 'embeddings', 'visualization']['feature_extraction']
    validmind.model_validation.embeddings.CosineSimilarityComparisonCosine Similarity ComparisonAssesses the similarity between embeddings generated by different models using Cosine Similarity, providing both...TrueTrue['dataset', 'models']{}['visualization', 'dimensionality_reduction', 'embeddings']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.embeddings.CosineSimilarityDistributionCosine Similarity DistributionAssesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution...TrueFalse['dataset', 'model']{}['llm', 'text_data', 'embeddings', 'visualization']['feature_extraction']
    validmind.model_validation.embeddings.CosineSimilarityHeatmapCosine Similarity HeatmapGenerates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model....TrueFalse['dataset', 'model']{'title': {'type': '_empty', 'default': 'Cosine Similarity Matrix'}, 'color': {'type': '_empty', 'default': 'Cosine Similarity'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}['visualization', 'dimensionality_reduction', 'embeddings']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.embeddings.DescriptiveAnalyticsDescriptive AnalyticsEvaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation...TrueFalse['dataset', 'model']{}['llm', 'text_data', 'embeddings', 'visualization']['feature_extraction']
    validmind.model_validation.embeddings.EmbeddingsVisualization2DEmbeddings Visualization2 DVisualizes 2D representation of text embeddings generated by a model using t-SNE technique....TrueFalse['dataset', 'model']{'cluster_column': {'type': None, 'default': None}, 'perplexity': {'type': 'int', 'default': 30}}['llm', 'text_data', 'embeddings', 'visualization']['feature_extraction']
    validmind.model_validation.embeddings.EuclideanDistanceComparisonEuclidean Distance ComparisonAssesses and visualizes the dissimilarity between model embeddings using Euclidean distance, providing insights...TrueTrue['dataset', 'models']{}['visualization', 'dimensionality_reduction', 'embeddings']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.embeddings.EuclideanDistanceHeatmapEuclidean Distance HeatmapGenerates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model....TrueFalse['dataset', 'model']{'title': {'type': '_empty', 'default': 'Euclidean Distance Matrix'}, 'color': {'type': '_empty', 'default': 'Euclidean Distance'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}['visualization', 'dimensionality_reduction', 'embeddings']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.embeddings.PCAComponentsPairwisePlotsPCA Components Pairwise PlotsGenerates scatter plots for pairwise combinations of principal component analysis (PCA) components of model...TrueFalse['dataset', 'model']{'n_components': {'type': 'int', 'default': 3}}['visualization', 'dimensionality_reduction', 'embeddings']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.embeddings.StabilityAnalysisKeywordStability Analysis KeywordEvaluates robustness of embedding models to keyword swaps in the test dataset....TrueTrue['dataset', 'model']{'keyword_dict': {'type': None, 'default': None}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}['llm', 'text_data', 'embeddings', 'visualization']['feature_extraction']
    validmind.model_validation.embeddings.StabilityAnalysisRandomNoiseStability Analysis Random NoiseAssesses the robustness of text embeddings models to random noise introduced via text perturbations....TrueTrue['dataset', 'model']{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}['llm', 'text_data', 'embeddings', 'visualization']['feature_extraction']
    validmind.model_validation.embeddings.StabilityAnalysisSynonymsStability Analysis SynonymsEvaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly....TrueTrue['dataset', 'model']{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}['llm', 'text_data', 'embeddings', 'visualization']['feature_extraction']
    validmind.model_validation.embeddings.StabilityAnalysisTranslationStability Analysis TranslationEvaluates robustness of text embeddings models to noise introduced by translating the original text to another...TrueTrue['dataset', 'model']{'source_lang': {'type': 'str', 'default': 'en'}, 'target_lang': {'type': 'str', 'default': 'fr'}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}['llm', 'text_data', 'embeddings', 'visualization']['feature_extraction']
    validmind.model_validation.embeddings.TSNEComponentsPairwisePlotsTSNE Components Pairwise PlotsCreates scatter plots for pairwise combinations of t-SNE components to visualize embeddings and highlight potential...TrueFalse['dataset', 'model']{'n_components': {'type': 'int', 'default': 2}, 'perplexity': {'type': 'int', 'default': 30}, 'title': {'type': 'str', 'default': 't-SNE'}}['visualization', 'dimensionality_reduction', 'embeddings']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.ragas.AnswerCorrectnessAnswer CorrectnessEvaluates the correctness of answers in a dataset with respect to the provided ground...TrueTrue['dataset']{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.ragas.AspectCriticAspect CriticEvaluates generations against the following aspects: harmfulness, maliciousness,...TrueTrue['dataset']{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': None, 'default': None}, 'aspects': {'type': None, 'default': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness']}, 'additional_aspects': {'type': None, 'default': None}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm', 'qualitative']['text_summarization', 'text_generation', 'text_qa']
    validmind.model_validation.ragas.ContextEntityRecallContext Entity RecallEvaluates the context entity recall for dataset entries and visualizes the results....TrueTrue['dataset']{'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm', 'retrieval_performance']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.ragas.ContextPrecisionContext PrecisionContext Precision is a metric that evaluates whether all of the ground-truth...TrueTrue['dataset']{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm', 'retrieval_performance']['text_qa', 'text_generation', 'text_summarization', 'text_classification']
    validmind.model_validation.ragas.ContextPrecisionWithoutReferenceContext Precision Without ReferenceContext Precision Without Reference is a metric used to evaluate the relevance of...TrueTrue['dataset']{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'response_column': {'type': 'str', 'default': 'response'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm', 'retrieval_performance']['text_qa', 'text_generation', 'text_summarization', 'text_classification']
    validmind.model_validation.ragas.ContextRecallContext RecallContext recall measures the extent to which the retrieved context aligns with the...TrueTrue['dataset']{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm', 'retrieval_performance']['text_qa', 'text_generation', 'text_summarization', 'text_classification']
    validmind.model_validation.ragas.FaithfulnessFaithfulnessEvaluates the faithfulness of the generated answers with respect to retrieved contexts....TrueTrue['dataset']{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm', 'rag_performance']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.ragas.NoiseSensitivityNoise SensitivityAssesses the sensitivity of a Large Language Model (LLM) to noise in retrieved context by measuring how often it...TrueTrue['dataset']{'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'focus': {'type': 'str', 'default': 'relevant'}, 'user_input_column': {'type': 'str', 'default': 'user_input'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm', 'rag_performance']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.ragas.ResponseRelevancyResponse RelevancyAssesses how pertinent the generated answer is to the given prompt....TrueTrue['dataset']{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': None}, 'response_column': {'type': 'str', 'default': 'response'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm', 'rag_performance']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.ragas.SemanticSimilaritySemantic SimilarityCalculates the semantic similarity between generated responses and ground truths...TrueTrue['dataset']{'response_column': {'type': 'str', 'default': 'response'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}['ragas', 'llm']['text_qa', 'text_generation', 'text_summarization']
    validmind.model_validation.sklearn.AdjustedMutualInformationAdjusted Mutual InformationEvaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.AdjustedRandIndexAdjusted Rand IndexMeasures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.CalibrationCurveCalibration CurveEvaluates the calibration of probability estimates by comparing predicted probabilities against observed...TrueFalse['model', 'dataset']{'n_bins': {'type': 'int', 'default': 10}}['sklearn', 'model_performance', 'classification']['classification']
    validmind.model_validation.sklearn.ClassifierPerformanceClassifier PerformanceEvaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...FalseTrue['dataset', 'model']{'average': {'type': 'str', 'default': 'macro'}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.ClassifierThresholdOptimizationClassifier Threshold OptimizationAnalyzes and visualizes different threshold optimization methods for binary classification models....FalseTrue['dataset', 'model']{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}['model_validation', 'threshold_optimization', 'classification_metrics']['classification']
    validmind.model_validation.sklearn.ClusterCosineSimilarityCluster Cosine SimilarityMeasures the intra-cluster similarity of a clustering model using cosine similarity....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.ClusterPerformanceMetricsCluster Performance MetricsEvaluates the performance of clustering machine learning models using multiple established metrics....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.CompletenessScoreCompleteness ScoreEvaluates a clustering model's capacity to categorize instances from a single class into the same cluster....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.ConfusionMatrixConfusion MatrixEvaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...TrueFalse['dataset', 'model']{'threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.FeatureImportanceFeature ImportanceCompute feature importance scores for a given model and generate a summary table...FalseTrue['dataset', 'model']{'num_features': {'type': 'int', 'default': 3}}['model_explainability', 'sklearn']['regression', 'time_series_forecasting']
    validmind.model_validation.sklearn.FowlkesMallowsScoreFowlkes Mallows ScoreEvaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...FalseTrue['dataset', 'model']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.sklearn.HomogeneityScoreHomogeneity ScoreAssesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...FalseTrue['dataset', 'model']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.sklearn.HyperParametersTuningHyper Parameters TuningPerforms exhaustive grid search over specified parameter ranges to find optimal model configurations...FalseTrue['model', 'dataset']{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}['sklearn', 'model_performance']['clustering', 'classification']
    validmind.model_validation.sklearn.KMeansClustersOptimizationK Means Clusters OptimizationOptimizes the number of clusters in K-means models using Elbow and Silhouette methods....TrueFalse['model', 'dataset']{'n_clusters': {'type': None, 'default': None}}['sklearn', 'model_performance', 'kmeans']['clustering']
    validmind.model_validation.sklearn.MinimumAccuracyMinimum AccuracyChecks if the model's prediction accuracy meets or surpasses a specified threshold....FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.7}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.MinimumF1ScoreMinimum F1 ScoreAssesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.MinimumROCAUCScoreMinimum ROCAUC ScoreValidates model by checking if the ROC AUC score meets or surpasses a specified threshold....FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.ModelParametersModel ParametersExtracts and displays model parameters in a structured format for transparency and reproducibility....FalseTrue['model']{'model_params': {'type': None, 'default': None}}['model_training', 'metadata']['classification', 'regression']
    validmind.model_validation.sklearn.ModelsPerformanceComparisonModels Performance ComparisonEvaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...FalseTrue['dataset', 'models']{}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']['classification', 'text_classification']
    validmind.model_validation.sklearn.OverfitDiagnosisOverfit DiagnosisAssesses potential overfitting in a model's predictions, identifying regions where performance between training and...TrueTrue['model', 'datasets']{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']['classification', 'regression']
    validmind.model_validation.sklearn.PermutationFeatureImportancePermutation Feature ImportanceAssesses the significance of each feature in a model by evaluating the impact on model performance when feature...TrueFalse['model', 'dataset']{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.PopulationStabilityIndexPopulation Stability IndexAssesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...TrueTrue['datasets', 'model']{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.PrecisionRecallCurvePrecision Recall CurveEvaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.ROCCurveROC CurveEvaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.RegressionErrorsRegression ErrorsAssesses the performance and error distribution of a regression model using various error metrics....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance']['regression', 'classification']
    validmind.model_validation.sklearn.RegressionErrorsComparisonRegression Errors ComparisonAssesses multiple regression error metrics to compare model performance across different datasets, emphasizing...FalseTrue['datasets', 'models']{}['model_performance', 'sklearn']['regression', 'time_series_forecasting']
    validmind.model_validation.sklearn.RegressionPerformanceRegression PerformanceEvaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance']['regression']
    validmind.model_validation.sklearn.RegressionR2SquareRegression R2 SquareAssesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...FalseTrue['dataset', 'model']{}['sklearn', 'model_performance']['regression']
    validmind.model_validation.sklearn.RegressionR2SquareComparisonRegression R2 Square ComparisonCompares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...FalseTrue['datasets', 'models']{}['model_performance', 'sklearn']['regression', 'time_series_forecasting']
    validmind.model_validation.sklearn.RobustnessDiagnosisRobustness DiagnosisAssesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....TrueTrue['datasets', 'model']{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}['sklearn', 'model_diagnosis', 'visualization']['classification', 'regression']
    validmind.model_validation.sklearn.SHAPGlobalImportanceSHAP Global ImportanceEvaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....FalseTrue['model', 'dataset']{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.ScoreProbabilityAlignmentScore Probability AlignmentAnalyzes the alignment between credit scores and predicted probabilities....TrueTrue['model', 'dataset']{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}['visualization', 'credit_risk', 'calibration']['classification']
    validmind.model_validation.sklearn.SilhouettePlotSilhouette PlotCalculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...TrueTrue['model', 'dataset']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.sklearn.TrainingTestDegradationTraining Test DegradationTests if model performance degradation between training and test datasets exceeds a predefined threshold....FalseTrue['datasets', 'model']{'max_threshold': {'type': 'float', 'default': 0.1}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.VMeasureV MeasureEvaluates homogeneity and completeness of a clustering model using the V Measure Score....FalseTrue['dataset', 'model']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.sklearn.WeakspotsDiagnosisWeakspots DiagnosisIdentifies and visualizes weak spots in a machine learning model's performance across various sections of the...TrueTrue['datasets', 'model']{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']['classification', 'text_classification']
    validmind.model_validation.statsmodels.AutoARIMAAuto ARIMAEvaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria....FalseTrue['model', 'dataset']{}['time_series_data', 'forecasting', 'model_selection', 'statsmodels']['regression']
    validmind.model_validation.statsmodels.CumulativePredictionProbabilitiesCumulative Prediction ProbabilitiesVisualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....TrueFalse['dataset', 'model']{'title': {'type': 'str', 'default': 'Cumulative Probabilities'}}['visualization', 'credit_risk']['classification']
    validmind.model_validation.statsmodels.DurbinWatsonTestDurbin Watson TestAssesses autocorrelation in time series data features using the Durbin-Watson statistic....FalseTrue['dataset', 'model']{'threshold': {'type': None, 'default': [1.5, 2.5]}}['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']['regression']
    validmind.model_validation.statsmodels.GINITableGINI TableEvaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....FalseTrue['dataset', 'model']{}['model_performance']['classification']
    validmind.model_validation.statsmodels.KolmogorovSmirnovKolmogorov SmirnovAssesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....FalseTrue['model', 'dataset']{'dist': {'type': 'str', 'default': 'norm'}}['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']['classification', 'regression']
    validmind.model_validation.statsmodels.LillieforsLillieforsAssesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....FalseTrue['dataset']{}['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']['classification', 'regression']
    validmind.model_validation.statsmodels.PredictionProbabilitiesHistogramPrediction Probabilities HistogramAssesses the predictive probability distribution for binary classification to evaluate model performance and...TrueFalse['dataset', 'model']{'title': {'type': 'str', 'default': 'Histogram of Predictive Probabilities'}}['visualization', 'credit_risk']['classification']
    validmind.model_validation.statsmodels.RegressionCoeffsRegression CoeffsAssesses the significance and uncertainty of predictor variables in a regression model through visualization of...TrueTrue['model']{}['tabular_data', 'visualization', 'model_training']['regression']
    validmind.model_validation.statsmodels.RegressionFeatureSignificanceRegression Feature SignificanceAssesses and visualizes the statistical significance of features in a regression model....TrueFalse['model']{'fontsize': {'type': 'int', 'default': 10}, 'p_threshold': {'type': 'float', 'default': 0.05}}['statistical_test', 'model_interpretation', 'visualization', 'feature_importance']['regression']
    validmind.model_validation.statsmodels.RegressionModelForecastPlotRegression Model Forecast PlotGenerates plots to visually compare the forecasted outcomes of a regression model against actual observed values over...TrueFalse['model', 'dataset']{'start_date': {'type': None, 'default': None}, 'end_date': {'type': None, 'default': None}}['time_series_data', 'forecasting', 'visualization']['regression']
    validmind.model_validation.statsmodels.RegressionModelForecastPlotLevelsRegression Model Forecast Plot LevelsAssesses the alignment between forecasted and observed values in regression models through visual plots...TrueFalse['model', 'dataset']{}['time_series_data', 'forecasting', 'visualization']['regression']
    validmind.model_validation.statsmodels.RegressionModelSensitivityPlotRegression Model Sensitivity PlotAssesses the sensitivity of a regression model to changes in independent variables by applying shocks and...TrueFalse['dataset', 'model']{'shocks': {'type': None, 'default': [0.1]}, 'transformation': {'type': None, 'default': None}}['senstivity_analysis', 'visualization']['regression']
    validmind.model_validation.statsmodels.RegressionModelSummaryRegression Model SummaryEvaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE....FalseTrue['dataset', 'model']{}['model_performance', 'regression']['regression']
    validmind.model_validation.statsmodels.RegressionPermutationFeatureImportanceRegression Permutation Feature ImportanceAssesses the significance of each feature in a model by evaluating the impact on model performance when feature...TrueFalse['dataset', 'model']{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}['statsmodels', 'feature_importance', 'visualization']['regression']
    validmind.model_validation.statsmodels.ScorecardHistogramScorecard HistogramThe Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...TrueFalse['dataset']{'title': {'type': 'str', 'default': 'Histogram of Scores'}, 'score_column': {'type': 'str', 'default': 'score'}}['visualization', 'credit_risk', 'logistic_regression']['classification']
    validmind.ongoing_monitoring.CalibrationCurveDriftCalibration Curve DriftEvaluates changes in probability calibration between reference and monitoring datasets....TrueTrue['datasets', 'model']{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.ClassDiscriminationDriftClass Discrimination DriftCompares classification discrimination metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.ClassImbalanceDriftClass Imbalance DriftEvaluates drift in class distribution between reference and monitoring datasets....TrueTrue['datasets']{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}['tabular_data', 'binary_classification', 'multiclass_classification']['classification']
    validmind.ongoing_monitoring.ClassificationAccuracyDriftClassification Accuracy DriftCompares classification accuracy metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.ConfusionMatrixDriftConfusion Matrix DriftCompares confusion matrix metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDriftCumulative Prediction Probabilities DriftCompares cumulative prediction probability distributions between reference and monitoring datasets....TrueFalse['datasets', 'model']{}['visualization', 'credit_risk']['classification']
    validmind.ongoing_monitoring.FeatureDriftFeature DriftEvaluates changes in feature distribution over time to identify potential model drift....TrueTrue['datasets']{'bins': {'type': '_empty', 'default': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}, 'feature_columns': {'type': '_empty', 'default': None}, 'psi_threshold': {'type': '_empty', 'default': 0.2}}['visualization']['monitoring']
    validmind.ongoing_monitoring.PredictionAcrossEachFeaturePrediction Across Each FeatureAssesses differences in model predictions across individual features between reference and monitoring datasets...TrueFalse['datasets', 'model']{}['visualization']['monitoring']
    validmind.ongoing_monitoring.PredictionCorrelationPrediction CorrelationAssesses correlation changes between model predictions from reference and monitoring datasets to detect potential...TrueTrue['datasets', 'model']{'drift_pct_threshold': {'type': 'float', 'default': 20}}['visualization']['monitoring']
    validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDriftPrediction Probabilities Histogram DriftCompares prediction probability distributions between reference and monitoring datasets....TrueTrue['datasets', 'model']{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}['visualization', 'credit_risk']['classification']
    validmind.ongoing_monitoring.PredictionQuantilesAcrossFeaturesPrediction Quantiles Across FeaturesAssesses differences in model prediction distributions across individual features between reference...TrueFalse['datasets', 'model']{}['visualization']['monitoring']
    validmind.ongoing_monitoring.ROCCurveDriftROC Curve DriftCompares ROC curves between reference and monitoring datasets....TrueFalse['datasets', 'model']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.ScoreBandsDriftScore Bands DriftAnalyzes drift in population distribution and default rates across score bands....FalseTrue['datasets', 'model']{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}['visualization', 'credit_risk', 'scorecard']['classification']
    validmind.ongoing_monitoring.ScorecardHistogramDriftScorecard Histogram DriftCompares score distributions between reference and monitoring datasets for each class....TrueTrue['datasets']{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}['visualization', 'credit_risk', 'logistic_regression']['classification']
    validmind.ongoing_monitoring.TargetPredictionDistributionPlotTarget Prediction Distribution PlotAssesses differences in prediction distributions between a reference dataset and a monitoring dataset to identify...TrueTrue['datasets', 'model']{'drift_pct_threshold': {'type': 'float', 'default': 20}}['visualization']['monitoring']
    validmind.prompt_validation.BiasBiasAssesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the...FalseTrue['model']{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}['llm', 'few_shot']['text_classification', 'text_summarization']
    validmind.prompt_validation.ClarityClarityEvaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines....FalseTrue['model']{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}['llm', 'zero_shot', 'few_shot']['text_classification', 'text_summarization']
    validmind.prompt_validation.ConcisenessConcisenessAnalyzes and grades the conciseness of prompts provided to a Large Language Model....FalseTrue['model']{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}['llm', 'zero_shot', 'few_shot']['text_classification', 'text_summarization']
    validmind.prompt_validation.DelimitationDelimitationEvaluates the proper use of delimiters in prompts provided to Large Language Models....FalseTrue['model']{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}['llm', 'zero_shot', 'few_shot']['text_classification', 'text_summarization']
    validmind.prompt_validation.NegativeInstructionNegative InstructionEvaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts....FalseTrue['model']{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}['llm', 'zero_shot', 'few_shot']['text_classification', 'text_summarization']
    validmind.prompt_validation.RobustnessRobustnessAssesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts. This test...FalseTrue['model', 'dataset']{'num_tests': {'type': '_empty', 'default': 10}, 'judge_llm': {'type': '_empty', 'default': None}}['llm', 'zero_shot', 'few_shot']['text_classification', 'text_summarization']
    validmind.prompt_validation.SpecificitySpecificityEvaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity, detail,...FalseTrue['model']{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}['llm', 'zero_shot', 'few_shot']['text_classification', 'text_summarization']
    validmind.unit_metrics.classification.AccuracyAccuracyCalculates the accuracy of a modelFalseFalse['dataset', 'model']{}['classification']['classification']
    validmind.unit_metrics.classification.F1F1Calculates the F1 score for a classification model.FalseFalse['model', 'dataset']{}['classification']['classification']
    validmind.unit_metrics.classification.PrecisionPrecisionCalculates the precision for a classification model.FalseFalse['model', 'dataset']{}['classification']['classification']
    validmind.unit_metrics.classification.ROC_AUCROC AUCCalculates the ROC AUC for a classification model.FalseFalse['model', 'dataset']{}['classification']['classification']
    validmind.unit_metrics.classification.RecallRecallCalculates the recall for a classification model.FalseFalse['model', 'dataset']{}['classification']['classification']
    validmind.unit_metrics.regression.AdjustedRSquaredScoreAdjusted R Squared ScoreCalculates the adjusted R-squared score for a regression model.FalseFalse['model', 'dataset']{}['regression']['regression']
    validmind.unit_metrics.regression.GiniCoefficientGini CoefficientCalculates the Gini coefficient for a regression model.FalseFalse['dataset', 'model']{}['regression']['regression']
    validmind.unit_metrics.regression.HuberLossHuber LossCalculates the Huber loss for a regression model.FalseFalse['model', 'dataset']{}['regression']['regression']
    validmind.unit_metrics.regression.KolmogorovSmirnovStatisticKolmogorov Smirnov StatisticCalculates the Kolmogorov-Smirnov statistic for a regression model.FalseFalse['dataset', 'model']{}['regression']['regression']
    validmind.unit_metrics.regression.MeanAbsoluteErrorMean Absolute ErrorCalculates the mean absolute error for a regression model.FalseFalse['model', 'dataset']{}['regression']['regression']
    validmind.unit_metrics.regression.MeanAbsolutePercentageErrorMean Absolute Percentage ErrorCalculates the mean absolute percentage error for a regression model.FalseFalse['model', 'dataset']{}['regression']['regression']
    validmind.unit_metrics.regression.MeanBiasDeviationMean Bias DeviationCalculates the mean bias deviation for a regression model.FalseFalse['model', 'dataset']{}['regression']['regression']
    validmind.unit_metrics.regression.MeanSquaredErrorMean Squared ErrorCalculates the mean squared error for a regression model.FalseFalse['model', 'dataset']{}['regression']['regression']
    validmind.unit_metrics.regression.QuantileLossQuantile LossCalculates the quantile loss for a regression model.FalseFalse['model', 'dataset']{'quantile': {'type': '_empty', 'default': 0.5}}['regression']['regression']
    validmind.unit_metrics.regression.RSquaredScoreR Squared ScoreCalculates the R-squared score for a regression model.FalseFalse['model', 'dataset']{}['regression']['regression']
    validmind.unit_metrics.regression.RootMeanSquaredErrorRoot Mean Squared ErrorCalculates the root mean squared error for a regression model.FalseFalse['model', 'dataset']{}['regression']['regression']
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    Understand tags and task types

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    Use list_tasks() to view all unique task types used to classify tests in the ValidMind Library.

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    Understanding task types helps you filter tests that match your record's objective. For example:

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    • classification: Works with Classification Models and Datasets.
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    • regression: Works with Regression Models and Datasets.
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    • text classification: Works with Text Classification Models and Datasets.
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    • text summarization: Works with Text Summarization Models and Datasets.
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    list_tasks()
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    ['text_qa',
- 'classification',
- 'data_validation',
- 'text_classification',
- 'feature_extraction',
- 'regression',
- 'visualization',
- 'clustering',
- 'time_series_forecasting',
- 'text_summarization',
- 'nlp',
- 'residual_analysis',
- 'monitoring',
- 'text_generation']
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    Use list_tags() to view all unique tags used to describe tests in the ValidMind Library.

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    Tags describe what a test applies to and help you filter tests for your use case. Examples include:

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    • llm: Tests that work with Large Language Models.
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    • nlp: Tests relevant for natural language processing.
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    • binary_classification: Tests for binary classification tasks.
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    • forecasting: Tests for forecasting and time-series analysis.
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    • tabular_data: Tests for tabular data like CSVs and Excel spreadsheets.
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    list_tags()
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    ['senstivity_analysis',
- 'calibration',
- 'clustering',
- 'anomaly_detection',
- 'nlp',
- 'classification_metrics',
- 'dimensionality_reduction',
- 'tabular_data',
- 'time_series_data',
- 'model_predictions',
- 'feature_selection',
- 'correlation',
- 'frequency_analysis',
- 'embeddings',
- 'regression',
- 'llm',
- 'statsmodels',
- 'ragas',
- 'model_performance',
- 'model_validation',
- 'rag_performance',
- 'model_training',
- 'qualitative',
- 'classification',
- 'kmeans',
- 'multiclass_classification',
- 'linear_regression',
- 'data_quality',
- 'text_data',
- 'binary_classification',
- 'threshold_optimization',
- 'stationarity',
- 'bias_and_fairness',
- 'scorecard',
- 'model_explainability',
- 'model_comparison',
- 'numerical_data',
- 'sklearn',
- 'model_selection',
- 'retrieval_performance',
- 'zero_shot',
- 'statistical_test',
- 'descriptive_statistics',
- 'seasonality',
- 'analysis',
- 'data_validation',
- 'data_distribution',
- 'feature_importance',
- 'metadata',
- 'few_shot',
- 'visualization',
- 'credit_risk',
- 'forecasting',
- 'AUC',
- 'logistic_regression',
- 'model_diagnosis',
- 'model_interpretation',
- 'unit_root_test',
- 'categorical_data',
- 'data_analysis']
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    Finally, to match each task type with its related tags, use the list_tasks_and_tags() function:

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    list_tasks_and_tags()
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    TaskTags
    regressionsenstivity_analysis, tabular_data, time_series_data, model_predictions, feature_selection, correlation, regression, statsmodels, model_performance, model_training, multiclass_classification, linear_regression, data_quality, text_data, model_explainability, binary_classification, stationarity, bias_and_fairness, numerical_data, sklearn, model_selection, statistical_test, descriptive_statistics, seasonality, analysis, data_validation, data_distribution, metadata, feature_importance, visualization, forecasting, model_diagnosis, model_interpretation, unit_root_test, categorical_data, data_analysis
    classificationcalibration, anomaly_detection, classification_metrics, tabular_data, time_series_data, feature_selection, correlation, statsmodels, model_performance, model_validation, model_training, classification, multiclass_classification, linear_regression, data_quality, text_data, binary_classification, threshold_optimization, bias_and_fairness, scorecard, model_comparison, numerical_data, sklearn, statistical_test, descriptive_statistics, feature_importance, data_distribution, metadata, visualization, credit_risk, AUC, logistic_regression, model_diagnosis, categorical_data, data_analysis
    text_classificationmodel_performance, feature_importance, multiclass_classification, few_shot, frequency_analysis, zero_shot, text_data, visualization, llm, binary_classification, ragas, model_diagnosis, model_comparison, sklearn, nlp, retrieval_performance, tabular_data, time_series_data
    text_summarizationqualitative, few_shot, frequency_analysis, embeddings, zero_shot, text_data, visualization, llm, rag_performance, ragas, retrieval_performance, nlp, dimensionality_reduction, tabular_data, time_series_data
    data_validationstationarity, statsmodels, unit_root_test, time_series_data
    time_series_forecastingmodel_training, data_validation, metadata, visualization, model_explainability, sklearn, model_performance, model_predictions, time_series_data
    nlpdata_validation, frequency_analysis, text_data, visualization, nlp
    clusteringclustering, model_performance, kmeans, sklearn
    residual_analysisregression
    visualizationregression
    feature_extractionembeddings, text_data, visualization, llm
    text_qaqualitative, embeddings, visualization, llm, rag_performance, ragas, dimensionality_reduction, retrieval_performance
    text_generationqualitative, embeddings, visualization, llm, rag_performance, ragas, dimensionality_reduction, retrieval_performance
    monitoringvisualization
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    Filter tests by tags and task types

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    While listing all tests is useful, you’ll often want to narrow your search. The list_tests() function supports filter, task, and tags parameters to assist in refining your results.

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    Use the filter parameter to find tests that match a specific keyword, such as sklearn:

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    list_tests(filter="sklearn")
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    IDNameDescriptionHas FigureHas TableRequired InputsParamsTagsTasks
    validmind.model_validation.ClusterSizeDistributionCluster Size DistributionAssesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...TrueFalse['dataset', 'model']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.TimeSeriesR2SquareBySegmentsTime Series R2 Square By SegmentsEvaluates the R-Squared values of regression models over specified time segments in time series data to assess...TrueTrue['dataset', 'model']{'segments': {'type': None, 'default': None}}['model_performance', 'sklearn']['regression', 'time_series_forecasting']
    validmind.model_validation.sklearn.AdjustedMutualInformationAdjusted Mutual InformationEvaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.AdjustedRandIndexAdjusted Rand IndexMeasures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.CalibrationCurveCalibration CurveEvaluates the calibration of probability estimates by comparing predicted probabilities against observed...TrueFalse['model', 'dataset']{'n_bins': {'type': 'int', 'default': 10}}['sklearn', 'model_performance', 'classification']['classification']
    validmind.model_validation.sklearn.ClassifierPerformanceClassifier PerformanceEvaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...FalseTrue['dataset', 'model']{'average': {'type': 'str', 'default': 'macro'}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.ClassifierThresholdOptimizationClassifier Threshold OptimizationAnalyzes and visualizes different threshold optimization methods for binary classification models....FalseTrue['dataset', 'model']{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}['model_validation', 'threshold_optimization', 'classification_metrics']['classification']
    validmind.model_validation.sklearn.ClusterCosineSimilarityCluster Cosine SimilarityMeasures the intra-cluster similarity of a clustering model using cosine similarity....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.ClusterPerformanceMetricsCluster Performance MetricsEvaluates the performance of clustering machine learning models using multiple established metrics....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.CompletenessScoreCompleteness ScoreEvaluates a clustering model's capacity to categorize instances from a single class into the same cluster....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance', 'clustering']['clustering']
    validmind.model_validation.sklearn.ConfusionMatrixConfusion MatrixEvaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...TrueFalse['dataset', 'model']{'threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.FeatureImportanceFeature ImportanceCompute feature importance scores for a given model and generate a summary table...FalseTrue['dataset', 'model']{'num_features': {'type': 'int', 'default': 3}}['model_explainability', 'sklearn']['regression', 'time_series_forecasting']
    validmind.model_validation.sklearn.FowlkesMallowsScoreFowlkes Mallows ScoreEvaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...FalseTrue['dataset', 'model']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.sklearn.HomogeneityScoreHomogeneity ScoreAssesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...FalseTrue['dataset', 'model']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.sklearn.HyperParametersTuningHyper Parameters TuningPerforms exhaustive grid search over specified parameter ranges to find optimal model configurations...FalseTrue['model', 'dataset']{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}['sklearn', 'model_performance']['clustering', 'classification']
    validmind.model_validation.sklearn.KMeansClustersOptimizationK Means Clusters OptimizationOptimizes the number of clusters in K-means models using Elbow and Silhouette methods....TrueFalse['model', 'dataset']{'n_clusters': {'type': None, 'default': None}}['sklearn', 'model_performance', 'kmeans']['clustering']
    validmind.model_validation.sklearn.MinimumAccuracyMinimum AccuracyChecks if the model's prediction accuracy meets or surpasses a specified threshold....FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.7}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.MinimumF1ScoreMinimum F1 ScoreAssesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.MinimumROCAUCScoreMinimum ROCAUC ScoreValidates model by checking if the ROC AUC score meets or surpasses a specified threshold....FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.ModelParametersModel ParametersExtracts and displays model parameters in a structured format for transparency and reproducibility....FalseTrue['model']{'model_params': {'type': None, 'default': None}}['model_training', 'metadata']['classification', 'regression']
    validmind.model_validation.sklearn.ModelsPerformanceComparisonModels Performance ComparisonEvaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...FalseTrue['dataset', 'models']{}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']['classification', 'text_classification']
    validmind.model_validation.sklearn.OverfitDiagnosisOverfit DiagnosisAssesses potential overfitting in a model's predictions, identifying regions where performance between training and...TrueTrue['model', 'datasets']{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']['classification', 'regression']
    validmind.model_validation.sklearn.PermutationFeatureImportancePermutation Feature ImportanceAssesses the significance of each feature in a model by evaluating the impact on model performance when feature...TrueFalse['model', 'dataset']{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.PopulationStabilityIndexPopulation Stability IndexAssesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...TrueTrue['datasets', 'model']{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.PrecisionRecallCurvePrecision Recall CurveEvaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.ROCCurveROC CurveEvaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.RegressionErrorsRegression ErrorsAssesses the performance and error distribution of a regression model using various error metrics....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance']['regression', 'classification']
    validmind.model_validation.sklearn.RegressionErrorsComparisonRegression Errors ComparisonAssesses multiple regression error metrics to compare model performance across different datasets, emphasizing...FalseTrue['datasets', 'models']{}['model_performance', 'sklearn']['regression', 'time_series_forecasting']
    validmind.model_validation.sklearn.RegressionPerformanceRegression PerformanceEvaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance']['regression']
    validmind.model_validation.sklearn.RegressionR2SquareRegression R2 SquareAssesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...FalseTrue['dataset', 'model']{}['sklearn', 'model_performance']['regression']
    validmind.model_validation.sklearn.RegressionR2SquareComparisonRegression R2 Square ComparisonCompares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...FalseTrue['datasets', 'models']{}['model_performance', 'sklearn']['regression', 'time_series_forecasting']
    validmind.model_validation.sklearn.RobustnessDiagnosisRobustness DiagnosisAssesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....TrueTrue['datasets', 'model']{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}['sklearn', 'model_diagnosis', 'visualization']['classification', 'regression']
    validmind.model_validation.sklearn.SHAPGlobalImportanceSHAP Global ImportanceEvaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....FalseTrue['model', 'dataset']{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.ScoreProbabilityAlignmentScore Probability AlignmentAnalyzes the alignment between credit scores and predicted probabilities....TrueTrue['model', 'dataset']{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}['visualization', 'credit_risk', 'calibration']['classification']
    validmind.model_validation.sklearn.SilhouettePlotSilhouette PlotCalculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...TrueTrue['model', 'dataset']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.sklearn.TrainingTestDegradationTraining Test DegradationTests if model performance degradation between training and test datasets exceeds a predefined threshold....FalseTrue['datasets', 'model']{'max_threshold': {'type': 'float', 'default': 0.1}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.VMeasureV MeasureEvaluates homogeneity and completeness of a clustering model using the V Measure Score....FalseTrue['dataset', 'model']{}['sklearn', 'model_performance']['clustering']
    validmind.model_validation.sklearn.WeakspotsDiagnosisWeakspots DiagnosisIdentifies and visualizes weak spots in a machine learning model's performance across various sections of the...TrueTrue['datasets', 'model']{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.CalibrationCurveDriftCalibration Curve DriftEvaluates changes in probability calibration between reference and monitoring datasets....TrueTrue['datasets', 'model']{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.ClassDiscriminationDriftClass Discrimination DriftCompares classification discrimination metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.ClassificationAccuracyDriftClassification Accuracy DriftCompares classification accuracy metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.ConfusionMatrixDriftConfusion Matrix DriftCompares confusion matrix metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.ROCCurveDriftROC Curve DriftCompares ROC curves between reference and monitoring datasets....TrueFalse['datasets', 'model']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
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    Use the task parameter to find tests that match a specific task type, such as classification:

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    list_tests(task="classification")
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    IDNameDescriptionHas FigureHas TableRequired InputsParamsTagsTasks
    validmind.data_validation.BivariateScatterPlotsBivariate Scatter PlotsGenerates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...TrueFalse['dataset']{}['tabular_data', 'numerical_data', 'visualization']['classification']
    validmind.data_validation.ChiSquaredFeaturesTableChi Squared Features TableAssesses the statistical association between categorical features and a target variable using the Chi-Squared test....FalseTrue['dataset']{'p_threshold': {'type': '_empty', 'default': 0.05}}['tabular_data', 'categorical_data', 'statistical_test']['classification']
    validmind.data_validation.ClassImbalanceClass ImbalanceEvaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....TrueTrue['dataset']{'min_percent_threshold': {'type': 'int', 'default': 10}}['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']['classification']
    validmind.data_validation.DatasetDescriptionDataset DescriptionProvides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....FalseTrue['dataset']{}['tabular_data', 'time_series_data', 'text_data']['classification', 'regression', 'text_classification', 'text_summarization']
    validmind.data_validation.DatasetSplitDataset SplitEvaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...FalseTrue['datasets']{}['tabular_data', 'time_series_data', 'text_data']['classification', 'regression', 'text_classification', 'text_summarization']
    validmind.data_validation.DescriptiveStatisticsDescriptive StatisticsPerforms a detailed descriptive statistical analysis of both numerical and categorical data within a model's...FalseTrue['dataset']{}['tabular_data', 'time_series_data', 'data_quality']['classification', 'regression']
    validmind.data_validation.DuplicatesDuplicatesTests dataset for duplicate entries, ensuring model reliability via data quality verification....FalseTrue['dataset']{'min_threshold': {'type': '_empty', 'default': 1}}['tabular_data', 'data_quality', 'text_data']['classification', 'regression']
    validmind.data_validation.FeatureTargetCorrelationPlotFeature Target Correlation PlotVisualizes the correlation between input features and the model's target output in a color-coded horizontal bar...TrueFalse['dataset']{'fig_height': {'type': '_empty', 'default': 600}}['tabular_data', 'visualization', 'correlation']['classification', 'regression']
    validmind.data_validation.HighCardinalityHigh CardinalityAssesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....FalseTrue['dataset']{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}['tabular_data', 'data_quality', 'categorical_data']['classification', 'regression']
    validmind.data_validation.HighPearsonCorrelationHigh Pearson CorrelationIdentifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....FalseTrue['dataset']{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}['tabular_data', 'data_quality', 'correlation']['classification', 'regression']
    validmind.data_validation.IQROutliersBarPlotIQR Outliers Bar PlotVisualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....TrueFalse['dataset']{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}['tabular_data', 'visualization', 'numerical_data']['classification', 'regression']
    validmind.data_validation.IQROutliersTableIQR Outliers TableDetermines and summarizes outliers in numerical features using the Interquartile Range method....FalseTrue['dataset']{'threshold': {'type': 'float', 'default': 1.5}}['tabular_data', 'numerical_data']['classification', 'regression']
    validmind.data_validation.IsolationForestOutliersIsolation Forest OutliersDetects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....TrueFalse['dataset']{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}['tabular_data', 'anomaly_detection']['classification']
    validmind.data_validation.JarqueBeraJarque BeraAssesses normality of dataset features in an ML model using the Jarque-Bera test....FalseTrue['dataset']{}['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']['classification', 'regression']
    validmind.data_validation.MissingValuesMissing ValuesEvaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....FalseTrue['dataset']{'min_threshold': {'type': 'int', 'default': 1}}['tabular_data', 'data_quality']['classification', 'regression']
    validmind.data_validation.MissingValuesBarPlotMissing Values Bar PlotAssesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...TrueFalse['dataset']{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}['tabular_data', 'data_quality', 'visualization']['classification', 'regression']
    validmind.data_validation.MutualInformationMutual InformationCalculates mutual information scores between features and target variable to evaluate feature relevance....TrueFalse['dataset']{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}['feature_selection', 'data_analysis']['classification', 'regression']
    validmind.data_validation.PearsonCorrelationMatrixPearson Correlation MatrixEvaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....TrueFalse['dataset']{}['tabular_data', 'numerical_data', 'correlation']['classification', 'regression']
    validmind.data_validation.ProtectedClassesDescriptionProtected Classes DescriptionVisualizes the distribution of protected classes in the dataset relative to the target variable...TrueTrue['dataset']{'protected_classes': {'type': '_empty', 'default': None}}['bias_and_fairness', 'descriptive_statistics']['classification', 'regression']
    validmind.data_validation.RunsTestRuns TestExecutes Runs Test on ML model to detect non-random patterns in output data sequence....FalseTrue['dataset']{}['tabular_data', 'statistical_test', 'statsmodels']['classification', 'regression']
    validmind.data_validation.ScatterPlotScatter PlotAssesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....TrueFalse['dataset']{}['tabular_data', 'visualization']['classification', 'regression']
    validmind.data_validation.ScoreBandDefaultRatesScore Band Default RatesAnalyzes default rates and population distribution across credit score bands....FalseTrue['dataset', 'model']{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}['visualization', 'credit_risk', 'scorecard']['classification']
    validmind.data_validation.ShapiroWilkShapiro WilkEvaluates feature-wise normality of training data using the Shapiro-Wilk test....FalseTrue['dataset']{}['tabular_data', 'data_distribution', 'statistical_test']['classification', 'regression']
    validmind.data_validation.SkewnessSkewnessEvaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...FalseTrue['dataset']{'max_threshold': {'type': '_empty', 'default': 1}}['data_quality', 'tabular_data']['classification', 'regression']
    validmind.data_validation.TabularCategoricalBarPlotsTabular Categorical Bar PlotsGenerates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....TrueFalse['dataset']{}['tabular_data', 'visualization']['classification', 'regression']
    validmind.data_validation.TabularDateTimeHistogramsTabular Date Time HistogramsGenerates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...TrueFalse['dataset']{}['time_series_data', 'visualization']['classification', 'regression']
    validmind.data_validation.TabularDescriptionTablesTabular Description TablesSummarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....FalseTrue['dataset']{}['tabular_data']['classification', 'regression']
    validmind.data_validation.TabularNumericalHistogramsTabular Numerical HistogramsGenerates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...TrueFalse['dataset']{}['tabular_data', 'visualization']['classification', 'regression']
    validmind.data_validation.TargetRateBarPlotsTarget Rate Bar PlotsGenerates bar plots visualizing the default rates of categorical features for a classification machine learning...TrueFalse['dataset']{}['tabular_data', 'visualization', 'categorical_data']['classification']
    validmind.data_validation.TooManyZeroValuesToo Many Zero ValuesIdentifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...FalseTrue['dataset']{'max_percent_threshold': {'type': 'float', 'default': 0.03}}['tabular_data']['regression', 'classification']
    validmind.data_validation.UniqueRowsUnique RowsVerifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....FalseTrue['dataset']{'min_percent_threshold': {'type': 'float', 'default': 1}}['tabular_data']['regression', 'classification']
    validmind.data_validation.WOEBinPlotsWOE Bin PlotsGenerates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...TrueFalse['dataset']{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}['tabular_data', 'visualization', 'categorical_data']['classification']
    validmind.data_validation.WOEBinTableWOE Bin TableAssesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...FalseTrue['dataset']{'breaks_adj': {'type': 'list', 'default': None}}['tabular_data', 'categorical_data']['classification']
    validmind.model_validation.FeaturesAUCFeatures AUCEvaluates the discriminatory power of each individual feature within a binary classification model by calculating...TrueFalse['dataset']{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}['feature_importance', 'AUC', 'visualization']['classification']
    validmind.model_validation.sklearn.CalibrationCurveCalibration CurveEvaluates the calibration of probability estimates by comparing predicted probabilities against observed...TrueFalse['model', 'dataset']{'n_bins': {'type': 'int', 'default': 10}}['sklearn', 'model_performance', 'classification']['classification']
    validmind.model_validation.sklearn.ClassifierPerformanceClassifier PerformanceEvaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...FalseTrue['dataset', 'model']{'average': {'type': 'str', 'default': 'macro'}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.ClassifierThresholdOptimizationClassifier Threshold OptimizationAnalyzes and visualizes different threshold optimization methods for binary classification models....FalseTrue['dataset', 'model']{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}['model_validation', 'threshold_optimization', 'classification_metrics']['classification']
    validmind.model_validation.sklearn.ConfusionMatrixConfusion MatrixEvaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...TrueFalse['dataset', 'model']{'threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.HyperParametersTuningHyper Parameters TuningPerforms exhaustive grid search over specified parameter ranges to find optimal model configurations...FalseTrue['model', 'dataset']{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}['sklearn', 'model_performance']['clustering', 'classification']
    validmind.model_validation.sklearn.MinimumAccuracyMinimum AccuracyChecks if the model's prediction accuracy meets or surpasses a specified threshold....FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.7}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.MinimumF1ScoreMinimum F1 ScoreAssesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.MinimumROCAUCScoreMinimum ROCAUC ScoreValidates model by checking if the ROC AUC score meets or surpasses a specified threshold....FalseTrue['dataset', 'model']{'min_threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.ModelParametersModel ParametersExtracts and displays model parameters in a structured format for transparency and reproducibility....FalseTrue['model']{'model_params': {'type': None, 'default': None}}['model_training', 'metadata']['classification', 'regression']
    validmind.model_validation.sklearn.ModelsPerformanceComparisonModels Performance ComparisonEvaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...FalseTrue['dataset', 'models']{}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']['classification', 'text_classification']
    validmind.model_validation.sklearn.OverfitDiagnosisOverfit DiagnosisAssesses potential overfitting in a model's predictions, identifying regions where performance between training and...TrueTrue['model', 'datasets']{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']['classification', 'regression']
    validmind.model_validation.sklearn.PermutationFeatureImportancePermutation Feature ImportanceAssesses the significance of each feature in a model by evaluating the impact on model performance when feature...TrueFalse['model', 'dataset']{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.PopulationStabilityIndexPopulation Stability IndexAssesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...TrueTrue['datasets', 'model']{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.model_validation.sklearn.PrecisionRecallCurvePrecision Recall CurveEvaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.ROCCurveROC CurveEvaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.RegressionErrorsRegression ErrorsAssesses the performance and error distribution of a regression model using various error metrics....FalseTrue['model', 'dataset']{}['sklearn', 'model_performance']['regression', 'classification']
    validmind.model_validation.sklearn.RobustnessDiagnosisRobustness DiagnosisAssesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....TrueTrue['datasets', 'model']{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}['sklearn', 'model_diagnosis', 'visualization']['classification', 'regression']
    validmind.model_validation.sklearn.SHAPGlobalImportanceSHAP Global ImportanceEvaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....FalseTrue['model', 'dataset']{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.ScoreProbabilityAlignmentScore Probability AlignmentAnalyzes the alignment between credit scores and predicted probabilities....TrueTrue['model', 'dataset']{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}['visualization', 'credit_risk', 'calibration']['classification']
    validmind.model_validation.sklearn.TrainingTestDegradationTraining Test DegradationTests if model performance degradation between training and test datasets exceeds a predefined threshold....FalseTrue['datasets', 'model']{'max_threshold': {'type': 'float', 'default': 0.1}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.WeakspotsDiagnosisWeakspots DiagnosisIdentifies and visualizes weak spots in a machine learning model's performance across various sections of the...TrueTrue['datasets', 'model']{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']['classification', 'text_classification']
    validmind.model_validation.statsmodels.CumulativePredictionProbabilitiesCumulative Prediction ProbabilitiesVisualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....TrueFalse['dataset', 'model']{'title': {'type': 'str', 'default': 'Cumulative Probabilities'}}['visualization', 'credit_risk']['classification']
    validmind.model_validation.statsmodels.GINITableGINI TableEvaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....FalseTrue['dataset', 'model']{}['model_performance']['classification']
    validmind.model_validation.statsmodels.KolmogorovSmirnovKolmogorov SmirnovAssesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....FalseTrue['model', 'dataset']{'dist': {'type': 'str', 'default': 'norm'}}['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']['classification', 'regression']
    validmind.model_validation.statsmodels.LillieforsLillieforsAssesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....FalseTrue['dataset']{}['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']['classification', 'regression']
    validmind.model_validation.statsmodels.PredictionProbabilitiesHistogramPrediction Probabilities HistogramAssesses the predictive probability distribution for binary classification to evaluate model performance and...TrueFalse['dataset', 'model']{'title': {'type': 'str', 'default': 'Histogram of Predictive Probabilities'}}['visualization', 'credit_risk']['classification']
    validmind.model_validation.statsmodels.ScorecardHistogramScorecard HistogramThe Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...TrueFalse['dataset']{'title': {'type': 'str', 'default': 'Histogram of Scores'}, 'score_column': {'type': 'str', 'default': 'score'}}['visualization', 'credit_risk', 'logistic_regression']['classification']
    validmind.ongoing_monitoring.CalibrationCurveDriftCalibration Curve DriftEvaluates changes in probability calibration between reference and monitoring datasets....TrueTrue['datasets', 'model']{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.ClassDiscriminationDriftClass Discrimination DriftCompares classification discrimination metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.ClassImbalanceDriftClass Imbalance DriftEvaluates drift in class distribution between reference and monitoring datasets....TrueTrue['datasets']{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}['tabular_data', 'binary_classification', 'multiclass_classification']['classification']
    validmind.ongoing_monitoring.ClassificationAccuracyDriftClassification Accuracy DriftCompares classification accuracy metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.ConfusionMatrixDriftConfusion Matrix DriftCompares confusion matrix metrics between reference and monitoring datasets....FalseTrue['datasets', 'model']{'drift_pct_threshold': {'type': '_empty', 'default': 20}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']['classification', 'text_classification']
    validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDriftCumulative Prediction Probabilities DriftCompares cumulative prediction probability distributions between reference and monitoring datasets....TrueFalse['datasets', 'model']{}['visualization', 'credit_risk']['classification']
    validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDriftPrediction Probabilities Histogram DriftCompares prediction probability distributions between reference and monitoring datasets....TrueTrue['datasets', 'model']{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}['visualization', 'credit_risk']['classification']
    validmind.ongoing_monitoring.ROCCurveDriftROC Curve DriftCompares ROC curves between reference and monitoring datasets....TrueFalse['datasets', 'model']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.ScoreBandsDriftScore Bands DriftAnalyzes drift in population distribution and default rates across score bands....FalseTrue['datasets', 'model']{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}['visualization', 'credit_risk', 'scorecard']['classification']
    validmind.ongoing_monitoring.ScorecardHistogramDriftScorecard Histogram DriftCompares score distributions between reference and monitoring datasets for each class....TrueTrue['datasets']{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}['visualization', 'credit_risk', 'logistic_regression']['classification']
    validmind.unit_metrics.classification.AccuracyAccuracyCalculates the accuracy of a modelFalseFalse['dataset', 'model']{}['classification']['classification']
    validmind.unit_metrics.classification.F1F1Calculates the F1 score for a classification model.FalseFalse['model', 'dataset']{}['classification']['classification']
    validmind.unit_metrics.classification.PrecisionPrecisionCalculates the precision for a classification model.FalseFalse['model', 'dataset']{}['classification']['classification']
    validmind.unit_metrics.classification.ROC_AUCROC AUCCalculates the ROC AUC for a classification model.FalseFalse['model', 'dataset']{}['classification']['classification']
    validmind.unit_metrics.classification.RecallRecallCalculates the recall for a classification model.FalseFalse['model', 'dataset']{}['classification']['classification']
    -
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    Use the tags parameter to find tests based on their tags, such as model_performance or visualization:

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    list_tests(tags=["model_performance", "visualization"])
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    IDNameDescriptionHas FigureHas TableRequired InputsParamsTagsTasks
    validmind.model_validation.RegressionResidualsPlotRegression Residuals PlotEvaluates regression model performance using residual distribution and actual vs. predicted plots....TrueFalse['model', 'dataset']{'bin_size': {'type': 'float', 'default': 0.1}}['model_performance', 'visualization']['regression']
    validmind.model_validation.sklearn.ConfusionMatrixConfusion MatrixEvaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...TrueFalse['dataset', 'model']{'threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.PrecisionRecallCurvePrecision Recall CurveEvaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.ROCCurveROC CurveEvaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.TrainingTestDegradationTraining Test DegradationTests if model performance degradation between training and test datasets exceeds a predefined threshold....FalseTrue['datasets', 'model']{'max_threshold': {'type': 'float', 'default': 0.1}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.CalibrationCurveDriftCalibration Curve DriftEvaluates changes in probability calibration between reference and monitoring datasets....TrueTrue['datasets', 'model']{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.ROCCurveDriftROC Curve DriftCompares ROC curves between reference and monitoring datasets....TrueFalse['datasets', 'model']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
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    Use filter, task, and tags together to create more specific queries.

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    For example, apply all three to find tests compatible with sklearn models, designed for classification tasks:

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    list_tests(filter="sklearn",
-    tags=["model_performance", "visualization"], task="classification"
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    IDNameDescriptionHas FigureHas TableRequired InputsParamsTagsTasks
    validmind.model_validation.sklearn.ConfusionMatrixConfusion MatrixEvaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...TrueFalse['dataset', 'model']{'threshold': {'type': 'float', 'default': 0.5}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.PrecisionRecallCurvePrecision Recall CurveEvaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.ROCCurveROC CurveEvaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...TrueFalse['model', 'dataset']{}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.model_validation.sklearn.TrainingTestDegradationTraining Test DegradationTests if model performance degradation between training and test datasets exceeds a predefined threshold....FalseTrue['datasets', 'model']{'max_threshold': {'type': 'float', 'default': 0.1}}['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.CalibrationCurveDriftCalibration Curve DriftEvaluates changes in probability calibration between reference and monitoring datasets....TrueTrue['datasets', 'model']{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
    validmind.ongoing_monitoring.ROCCurveDriftROC Curve DriftCompares ROC curves between reference and monitoring datasets....TrueFalse['datasets', 'model']{}['sklearn', 'binary_classification', 'model_performance', 'visualization']['classification', 'text_classification']
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    Store test sets for use

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    Once you've identified specific sets of tests you'd like to run, you can store the tests in variables, enabling you to easily reuse those tests in later steps.

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    For example, if you're validating a summarization model, use list_tests() to retrieve all tests tagged for text summarization and save them to text_summarization_tests for later use:

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    text_summarization_tests = list_tests(task="text_summarization", pretty=False)
-text_summarization_tests
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    ['validmind.data_validation.DatasetDescription',
- 'validmind.data_validation.DatasetSplit',
- 'validmind.data_validation.nlp.CommonWords',
- 'validmind.data_validation.nlp.Hashtags',
- 'validmind.data_validation.nlp.LanguageDetection',
- 'validmind.data_validation.nlp.Mentions',
- 'validmind.data_validation.nlp.Punctuations',
- 'validmind.data_validation.nlp.StopWords',
- 'validmind.data_validation.nlp.TextDescription',
- 'validmind.model_validation.BertScore',
- 'validmind.model_validation.BleuScore',
- 'validmind.model_validation.ContextualRecall',
- 'validmind.model_validation.MeteorScore',
- 'validmind.model_validation.RegardScore',
- 'validmind.model_validation.RougeScore',
- 'validmind.model_validation.TokenDisparity',
- 'validmind.model_validation.ToxicityScore',
- 'validmind.model_validation.embeddings.CosineSimilarityComparison',
- 'validmind.model_validation.embeddings.CosineSimilarityHeatmap',
- 'validmind.model_validation.embeddings.EuclideanDistanceComparison',
- 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap',
- 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots',
- 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots',
- 'validmind.model_validation.ragas.AnswerCorrectness',
- 'validmind.model_validation.ragas.AspectCritic',
- 'validmind.model_validation.ragas.ContextEntityRecall',
- 'validmind.model_validation.ragas.ContextPrecision',
- 'validmind.model_validation.ragas.ContextPrecisionWithoutReference',
- 'validmind.model_validation.ragas.ContextRecall',
- 'validmind.model_validation.ragas.Faithfulness',
- 'validmind.model_validation.ragas.NoiseSensitivity',
- 'validmind.model_validation.ragas.ResponseRelevancy',
- 'validmind.model_validation.ragas.SemanticSimilarity',
- 'validmind.prompt_validation.Bias',
- 'validmind.prompt_validation.Clarity',
- 'validmind.prompt_validation.Conciseness',
- 'validmind.prompt_validation.Delimitation',
- 'validmind.prompt_validation.NegativeInstruction',
- 'validmind.prompt_validation.Robustness',
- 'validmind.prompt_validation.Specificity']
    -
    -
    -

    -
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    -

    Next steps

    -

    Now that you know how to browse and filter tests in the ValidMind Library, you’re ready to take the next step. Use the test IDs you’ve selected to either run individual tests or batch run them with custom test suites.

    -
    -Learn about the tests suites available in the ValidMind Library.

    Check out our Explore test suites notebook for more code examples and usage of key functions. -
    -

    -
    -

    Discover more learning resources

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    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you'll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/tests/run_tests/2-run_comparison_tests.html b/site/notebooks/how_to/tests/run_tests/2-run_comparison_tests.html deleted file mode 100644 index 89f4ff926..000000000 --- a/site/notebooks/how_to/tests/run_tests/2-run_comparison_tests.html +++ /dev/null @@ -1,2049 +0,0 @@ - - - - - - - - - -Run comparison tests – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Run comparison tests

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    - - - -

    Learn how to use the ValidMind Library to run comparison tests that take any datasets or records (models) as inputs. Identify comparison tests to run, initialize ValidMind dataset and model objects in preparation for passing them to tests, and then run tests — generating outputs automatically logged to your documentation in the ValidMind Platform.

    -
    -We recommend that you first complete our introductory notebook on running tests.

    Run dataset-based tests -
    - - -

    -
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    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

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    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
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    New to ValidMind?

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    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
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    Key concepts

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    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

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    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

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    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

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    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

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    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

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    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

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    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

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    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

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      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
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    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
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    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
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    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

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    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

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    Setting up

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    Install the ValidMind Library

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    -Recommended Python versions

    Python 3.8 <= x <= 3.14 -
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    To install the library:

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    %pip install -q validmind
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    Initialize the ValidMind Library

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    Register sample model

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    Let's first register a sample record (model) for use with this notebook.

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      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
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    Apply documentation template

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    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
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    Get your code snippet

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    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

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      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
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    -

    -
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    Preview the documentation template

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    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

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    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

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    vm.preview_template()
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    Initialize the Python environment

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    Next, let's import the necessary libraries and set up your Python environment for data analysis:

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    import xgboost as xgb
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-%matplotlib inline
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    Explore a ValidMind test

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    Before we run a test, use the vm.tests.list_tests() function to return information on out-of-the-box tests available in the ValidMind Library.

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    Let's assume you want to evaluate classifier performance for a model. Classifier performance measures how well a classification model correctly predicts outcomes, using metrics like precision, recall, and F1 score.

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    We'll pass in a filter to the list_tests function to find the test ID for classifier performance:

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    vm.tests.list_tests(filter="ClassifierPerformance")
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    -

    We've identified from the output that the test ID for the classifier performance test is validmind.model_validation.ClassifierPerformance.

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    Use this ID combined with the describe_test() function to retrieve more information about the test, including its Required Inputs:

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    test_id = "validmind.model_validation.sklearn.ClassifierPerformance"
-vm.tests.describe_test(test_id)
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    Since this test requires both a dataset object and a model object, you can expect it to throw an error when we run it without passing in either as input:

    -
    -
    try:
-    vm.tests.run_test(test_id)
-except Exception as e:
-    print(e)
    -
    -
    -Learn more about the individual tests available in the ValidMind Library

    Check out our Explore tests notebook for more code examples and usage of key functions. -
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    Working with ValidMind datasets

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    Prepare the sample dataset

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    Import the sample dataset

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    Since we need a dataset to run tests, let's import the public Bank Customer Churn Prediction dataset from Kaggle so that we have something to work with.

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    In our below example, note that:

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      -
    • The target column, Exited has a value of 1 when a customer has churned and 0 otherwise.
      • -
    • The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns.
      • -
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    -
    # Import the sample dataset from the library
-
-from validmind.datasets.classification import customer_churn
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{customer_churn.target_column}' \n\t• Class labels: {customer_churn.class_labels}"
-)
-
-raw_df = customer_churn.load_data()
-raw_df.head()
    -
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    -
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    Split the dataset

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    Let's first split our dataset to help assess how well the model generalizes to unseen data.

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    Use preprocess() to split our dataset into three subsets:

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      -
    1. train_df — Used to train the model.
      2. -
    2. validation_df — Used to evaluate the model's performance during training.
      3. -
    3. test_df — Used later on to asses the model's performance on new, unseen data.
      4. -
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    train_df, validation_df, test_df = customer_churn.preprocess(raw_df)
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    Initialize the ValidMind dataset

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    The next step is to connect your data with a ValidMind Dataset object. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind, but you only need to do it once per dataset.

    -

    ValidMind dataset objects provide a wrapper to any type of dataset (NumPy, Pandas, Polars, etc.) so that tests can run transparently regardless of the underlying library.

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    Initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module. For this example, we'll pass in the following arguments:

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    • dataset — The raw dataset that you want to provide as input to tests.
      • -
    • input_id — A unique identifier that allows tracking what inputs are used when running each individual test.
      • -
    • target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
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    -
    vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="train_dataset",
-    target_column=customer_churn.target_column,
-)
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-vm_test_ds = vm.init_dataset(
-    dataset=test_df,
-    input_id="test_dataset",
-    target_column=customer_churn.target_column,
-)
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    Working with ValidMind models

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    Train a sample model

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    To train the model, we need to provide it with:

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    1. Inputs — Features such as customer age, usage, etc.
      2. -
    2. Outputs (Expected answers/labels) — in our case, we would like to know whether the customer churned or not.
      3. -
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    Here, we'll use x_train and x_val to hold the input data (features), and y_train and y_val to hold the answers (the target we want to predict):

    -
    -
    x_train = train_df.drop(customer_churn.target_column, axis=1)
-y_train = train_df[customer_churn.target_column]
-x_val = validation_df.drop(customer_churn.target_column, axis=1)
-y_val = validation_df[customer_churn.target_column]
    -
    -

    Next, let's create an XGBoost classifier model that will automatically stop training if it doesn't improve after 10 tries. XGBoost is a gradient-boosted tree ensemble that builds trees sequentially, with each tree correcting the errors of the previous ones — typically known for strong predictive performance and built-in regularization to reduce overfitting.

    -

    Setting an explicit threshold avoids wasting time and helps prevent further overfitting by stopping training when further improvement isn't happening. We'll also set three evaluation metrics to get a more complete picture of model performance:

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    1. error — Measures how often the model makes incorrect predictions.
      2. -
    2. logloss — Indicates how confident the predictions are.
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    3. auc — Evaluates how well the model distinguishes between churn and not churn.
      4. -
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    model = xgb.XGBClassifier(early_stopping_rounds=10)
-model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
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    Finally, our actual training step — where the model learns patterns from the data, so it can make predictions later:

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    • The model is trained on x_train and y_train, and evaluates its performance using x_val and y_val to check if it’s learning well.
      • -
    • To turn off printed output while training, we'll set verbose to False.
      • -
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    model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
-)
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    Initialize the ValidMind model

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    You'll also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data for our model.

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    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
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    Initialize your model object with vm.init_model():

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    vm_model_xgb = vm.init_model(
-    model,
-    input_id="xgboost",
-)
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    -
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    Assign predictions

    -

    Once the model has been registered, you can assign model predictions to the training and testing datasets.

    -
      -
    • The assign_predictions() method from the Dataset object can link existing predictions to any number of models.
      • -
    • This method links the model's class prediction values and probabilities to our vm_train_ds and vm_test_ds datasets.
      • -
    -

    If no prediction values are passed, the method will compute predictions automatically:

    -
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    vm_train_ds.assign_predictions(model=vm_model_xgb)
-vm_test_ds.assign_predictions(model=vm_model_xgb)
    -
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    Running ValidMind tests

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    Now that we know how to initialize ValidMind dataset and model objects, we're ready to run some tests!

    -

    You run individual tests by calling the run_test function provided by the validmind.tests module. For the examples below, we'll pass in the following arguments:

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      -
    • test_id — The ID of the test to run, as seen in the ID column when you run list_tests.
      • -
    • inputs — A dictionary of test inputs, such as dataset, model, datasets, or models. These are ValidMind objects initialized with vm.init_dataset() or vm.init_model().
      • -
    -

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    Run classifier performance test with one model

    -

    Run validmind.data_validation.ClassifierPerformance test with the testing dataset (vm_test_ds) and model (vm_model_xgb) as inputs:

    -
    -
    result = vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model_xgb,
-    },
-)
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    Run comparison tests

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    To evaluate which models might be a better fit for a use case based on their performance on selected criteria, we can run the same test with multiple models. We'll train three additional models and run the classifier performance test with for all four models using a single run_test() call.

    -
    -

    ValidMind helps streamline your documentation and testing.

    You could call run_test() multiple times passing in different inputs, but you can also pass an input_grid object — a dictionary of test input keys and values that allow you to run a single test for a combination of models and datasets.

    With input_grid, run comparison tests for multiple datasets, or even multiple datasets and models simultaneously — input_grid can be used with run_test() for all possible combinations of inputs, generating a cohesive and comprehensive single output.

    -
    -

    Random forest classifier models use an ensemble method that builds multiple decision trees and averages their predictions. Random forest is robust to overfitting and handles non-linear relations well, but is typically less interpretable than simpler models:

    -
    -
    from sklearn.ensemble import RandomForestClassifier
-
-# Train the random forest classifer model
-model_rf = RandomForestClassifier()
-model_rf.fit(x_train, y_train)
-
-# Initialize the ValidMind model object for the random forest classifer model
-vm_model_rf = vm.init_model(
-    model_rf,
-    input_id="random_forest",
-)
-
-# Assign predictions to the test dataset for the random forest classifer model
-vm_test_ds.assign_predictions(model=vm_model_rf)
    -
    -

    Logistic regression models are linear models that estimate class probabilities via a logistic (sigmoid) function. Logistic regression is highly interpretable with fast training, establishing a strong baseline — however, they struggle when relationships are non-linear as real-world relationships often are:

    -
    -
    from sklearn.linear_model import LogisticRegression
-from sklearn.preprocessing import StandardScaler
-from sklearn.pipeline import Pipeline
-
-# Scaling features ensures the lbfgs solver converges reliably
-model_lr = Pipeline([
-    ("scaler", StandardScaler()),
-    ("lr", LogisticRegression()),
-])
-model_lr.fit(x_train, y_train)
-
-# Initialize the ValidMind model object for the logistic regression model
-vm_model_lr = vm.init_model(
-    model_lr,
-    input_id="logistic_regression",
-)
-
-# Assign predictions to the test dataset for the logistic regression model
-vm_test_ds.assign_predictions(model=vm_model_lr)
    -
    -

    Decision tree classifier models are a single tree with data split on feature thresholds. Useful as an explanability benchmark, decision trees are easy to visualize and interpret — but are prone to overfitting without pruning or ensemble techniques:

    -
    -
    from sklearn.tree import DecisionTreeClassifier
-
-# Train the decision tree classifer model
-model_dt = DecisionTreeClassifier()
-model_dt.fit(x_train, y_train)
-
-# Initialize the ValidMind model object for the decision tree classifier model
-vm_model_dt = vm.init_model(
-    model_dt,
-    input_id="decision_tree",
-)
-
-# Assign predictions to the test dataset for the decision tree classifiermodel
-vm_test_ds.assign_predictions(model=vm_model_dt)
    -
    -

    -
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    Run classifier performance test with multiple models

    -

    Now, we'll use the input_grid to run the model_validation.sklearn.ClassifierPerformance test on all four models using the testing dataset (vm_test_ds).

    -

    When running individual tests, you can use a custom result_id to tag the individual result with a unique identifier by appending this result_id to the test_id with a : separator. We'll append an identifier to signify that this test was run on all_models to differentiate this test run from other runs:

    -
    -
    perf_comparison_result = vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance:all_models",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_model_xgb, vm_model_rf, vm_model_lr, vm_model_dt],
-    },
-)
    -
    -

    Our output indicates that the XGBoost and random forest classification models provide the strongest overall classification performance, so we'll continue our testing with those two models as input only.

    -

    -
    -
    -

    Run classifier performance test with multiple parameter values

    -

    Next, let's run the classifier performance test with the param_grid object, which runs the same test multiple times with different parameter values. We'll append an identifier to signify that this test was run with our parameter_grid configuration:

    -
    -
    parameter_comparison_result = vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance:parameter_grid",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_model_xgb,vm_model_rf]
-    },
-    param_grid={
-        "average": ["macro", "micro"]
-    },
-)
    -
    -

    -
    -
    -

    Run comparison test with multiple datasets

    -

    Let's also run the model_validation.sklearn.ROCCurve test using input_grid to iterate through multiple datasets, which plots the ROC curves for the training (vm_train_ds) and test (vm_test_ds) datasets side by side — a common scenario when you want to compare the performance of a model on the training and test datasets and visually assess how much performance is lost in the test dataset.

    -

    We'll also need to assign predictions to the training dataset for the random forest classifier model, since we didn't do that in our earlier setup:

    -
    -
    vm_train_ds.assign_predictions(model=vm_model_rf)
    -
    -

    We'll append an identifier to signify that this test was run with our train_vs_test dataset comparison configuration:

    -
    -
    roc_curve_result = vm.tests.run_test(
-    "validmind.model_validation.sklearn.ROCCurve:train_vs_test",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_model_xgb,vm_model_rf],
-    },
-)
    -
    -

    -
    -
    -
    -
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    Work with test results

    -

    Every test result returned by the run_test() function has a .log() method that can be used to send the test results to the ValidMind Platform. When logging individual test results to the platform, you'll need to manually add those results to the desired section of the documentation.

    -

    You can do this through the ValidMind Platform interface after logging your test results (Learn more: Work with test results), or directly via the ValidMind Library when calling .log() by providing an optional section_id. The section_id should be a string that matches the title of a section in the documentation template in snake_case.

    -

    Let's log the results of the classifier performance test (perf_comparison_result) and the ROCCurve (roc_curve_result) test in the model_evaluation section of the documentation — present in the template we previewed in the beginning of this notebook:

    -
    -
    perf_comparison_result.log(section_id="model_evaluation")
-roc_curve_result.log(section_id="model_evaluation")
    -
    -

    Finally, let's head to the model we connected to at the beginning of this notebook and view our inserted test results in the updated documentation (Learn more: Working with documentation):

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you connected to earlier.

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -

    3. Expand the 3.2. Model Evaluation section.

      4. -

    4. Confirm that perf_comparison_result and roc_curve_result display in this section as expected.

      5. -
    -

    -
    -
    -

    Next steps

    -

    Now that you know how to run comparison tests with the ValidMind Library, you’re ready to take the next step. Extend the functionality of run_test() with your own custom test functions that can be incorporated into documentation templates just like any default out-of-the-box ValidMind test.

    -
    -Learn how to implement custom tests with the ValidMind Library.

    Check out our Implement comparison tests notebook for code examples and usage of key functions. -
    -

    -
    -

    Discover more learning resources

    -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/tests/run_tests/configure_tests/customize_test_result_descriptions.html b/site/notebooks/how_to/tests/run_tests/configure_tests/customize_test_result_descriptions.html deleted file mode 100644 index cad7a03e9..000000000 --- a/site/notebooks/how_to/tests/run_tests/configure_tests/customize_test_result_descriptions.html +++ /dev/null @@ -1,2169 +0,0 @@ - - - - - - - - - -Customize test result descriptions – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Customize test result descriptions

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    - - - -
    - - - - -
    - - - -
    - - - -

    When you run ValidMind tests, test descriptions are automatically generated with LLM using the test results, the test name, and the static test definitions provided in the test's docstring. While this metadata offers valuable high-level overviews of tests, insights produced by the LLM-based descriptions may not always align with your specific use cases or incorporate organizational policy requirements.

    -

    In this notebook, you'll learn how to take complete control over the context that drives test description generation. ValidMind provides a context parameter in run_test that accepts a dictionary with three complementary keys for comprehensive context management:

    - -

    Together, these context parameters allow you to manage every aspect of how the LLM interprets and presents your test results. Whether you need to align descriptions with regulatory requirements, target specific audiences, incorporate organizational policies, or ensure consistent reporting standards, this context management approach gives you the flexibility to generate descriptions that perfectly match your needs while still leveraging the analytical power of AI-generated insights.

    - - -

    -
    -

    Setting up

    -

    This section covers the basic setup required to run the examples in this notebook. We'll install ValidMind, connect to the platform, and create a customer churn model that we'll use to demonstrate the instructions and knowledge parameters throughout the examples.

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

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    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Initialize the Python environment

    -

    After you've connected to your model registered in the ValidMind Platform, let's import the necessary libraries and set up your Python environment for data analysis:

    -
    -
    import xgboost as xgb
-import os
-
-%matplotlib inline
    -
    -

    -
    -
    -
    -

    Model development

    -

    Now we'll build the customer churn model using XGBoost and ValidMind's sample dataset. This trained model will generate the test results we'll use to demonstrate the instructions and knowledge parameters.

    -

    -
    -

    Load data

    -

    First, we'll import a sample ValidMind dataset and load it into a pandas dataframe:

    -
    -
    # Import the sample dataset from the library
-
-from validmind.datasets.classification import customer_churn
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{customer_churn.target_column}' \n\t• Class labels: {customer_churn.class_labels}"
-)
-
-raw_df = customer_churn.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Fit the model

    -

    Then, we prepare the data and model by first splitting the DataFrame into training, validation, and test sets, then separating features from targets. An XGBoost classifier is initialized with early stopping, evaluation metrics (error, logloss, and auc) are defined, and the model is trained on the training data with validation monitoring.

    -
    -
    train_df, validation_df, test_df = customer_churn.preprocess(raw_df)
-
-x_train = train_df.drop(customer_churn.target_column, axis=1)
-y_train = train_df[customer_churn.target_column]
-x_val = validation_df.drop(customer_churn.target_column, axis=1)
-y_val = validation_df[customer_churn.target_column]
-
-model = xgb.XGBClassifier(early_stopping_rounds=10)
-model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
-)
    -
    -

    -
    -
    -

    Initialize the ValidMind objects

    -

    Before you can run tests, you'll need to initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -

    We'll include the following arguments:

    -
      -
    • dataset — the raw dataset that you want to provide as input to tests
      • -
    • input_id - a unique identifier that allows tracking what inputs are used when running each individual test
      • -
    • target_column — a required argument if tests require access to true values. This is the name of the target column in the dataset
      • -
    • class_labels — an optional value to map predicted classes to class labels
      • -
    -

    With all datasets ready, you can now initialize the raw, training, and test datasets (raw_df, train_df and test_df) created earlier into their own dataset objects using vm.init_dataset():

    -
    -
    vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column=customer_churn.target_column,
-    class_labels=customer_churn.class_labels,
-)
-
-vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="train_dataset",
-    target_column=customer_churn.target_column,
-)
-
-vm_test_ds = vm.init_dataset(
-    dataset=test_df, input_id="test_dataset", target_column=customer_churn.target_column
-)
    -
    -

    You'll also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data for our model.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
    -
    vm_model = vm.init_model(
-    model,
-    input_id="model",
-)
    -
    -

    We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model.

    -

    If no prediction values are passed, the method will compute predictions automatically:

    -
    -
    vm_train_ds.assign_predictions(
-    model=vm_model,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_model,
-)
    -
    -

    -
    -
    -
    -

    Understanding test result descriptions

    -

    Before diving into custom instructions, let's understand how ValidMind generates test descriptions by default.

    -

    -
    -

    Default LLM-generated descriptions

    -

    When you run a test without custom instructions, ValidMind's LLM analyzes: - The test results (tables, figures) - The test's built-in documentation (docstring)

    -

    When ValidMind generates test descriptions automatically (without custom instructions), the LLM follows a series of standardized sections designed to provide comprehensive, objective analysis of test results:

    -
      -

    • Test purpose: This section opens with a clear explanation of what the test does and why it exists. It draws from the test’s documentation and presents the purpose in accessible, straightforward language.

      • -

    • Test mechanism: Here the description outlines how the test works, including its methodology, what it measures, and how those measurements are derived. For statistical tests, it also explains the meaning of each metric, how values are typically interpreted, and what ranges are expected.

      • -

    • Test strengths: This part highlights the value of the test by pointing out its key strengths and the scenarios where it is most useful. It also notes the kinds of insights it can provide that other tests may not capture.

      • -

    • Test limitations: Limitations focus on both technical constraints and interpretation challenges. The text notes when results should be treated with caution and highlights specific risk indicators tied to the test type.

      • -

    • Results interpretation: The results section explains how to read the outputs, whether tables or figures, and clarifies what each column, axis, or metric means. It also points out key data points, units of measurement, and any notable observations that help frame interpretation.

      • -

    • Key insights: Insights are listed in bullet points, moving from broad to specific. Each one has a clear title, includes relevant numbers or ranges, and ensures that all important aspects of the results are addressed.

      • -

    • Conclusions: The conclusion ties the insights together into a coherent narrative. It synthesizes the findings into objective technical takeaways and emphasizes what the results reveal about the model or data.

      • -
    -

    Let's see a default description:

    -
    -
    vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
    -
    -

    -
    -
    -
    -

    Customizing results structure with instructions

    -

    While the default descriptions are designed to be comprehensive, there are many cases where you might want to tailor them for your specific context. Customizing test results allows you to shape descriptions to fit your organization’s standards and practical needs. This can involve adjusting report formats, applying specific risk rating scales, adding mandatory disclaimer text, or emphasizing particular metrics.

    -

    The instructions parameter is what enables this flexibility by adapting the generated descriptions to different audiences and test types. Executives often need concise summaries that emphasize overall risk, data scientists look for detailed explanations of the methodology behind tests, and compliance teams require precise language that aligns with regulatory expectations. Different test types also demand different emphases: performance metrics may benefit from technical breakdowns, while validation checks might require risk-focused narratives.

    -

    -
    -

    Simple instruction example

    -

    Let's start with simple examples of the instructions parameter. Here's how to provide basic guidance to the LLM-generated descriptions:

    -
    -
    simple_instructions = """
-Please focus on business impact and provide a concise summary. 
-Include specific actionable recommendations.
-"""
-
-vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-    context={
-        "instructions": simple_instructions,
-    },
-)
    -
    -

    -
    -
    -

    Structured format instructions

    -

    You can request specific formatting and structure:

    -
    -
    structured_instructions = """
-Please structure your analysis using the following format:
-
-### Executive Summary
-- One sentence overview of the test results
-
-### Key Findings
-- Bullet points with the most important insights
-- Include specific percentages and thresholds
-
-### Risk Assessment
-- Classify risk level as Low/Medium/High
-- Explain reasoning for the risk classification
-
-### Recommendations
-- Specific actionable next steps
-- Priority level for each recommendation
-
-"""
-
-vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-    context={
-        "instructions": structured_instructions,
-    },
-)
    -
    -

    -
    -
    -

    Template with LLM fill-ins

    -

    One of the most powerful features is combining hardcoded text with LLM-generated content using placeholders. This allows you to ensure specific information is always included while still getting intelligent analysis of the results.

    -

    Create a template where specific sections are filled by the LLM:

    -
    -
    template_instructions = """
-Please generate the description using this exact template. 
-Fill in the [PLACEHOLDER] sections with your analysis:
-
----
-**VALIDATION REPORT: CLASSIFIER PERFORMANCE ASSESSMENT**
-
-**Dataset ID:** test_dataset
-**Validation Type:** Classification Performance Analysis
-**Reviewer:** ValidMind AI Analysis
-
-**EXECUTIVE SUMMARY:**
-[PROVIDE_2_SENTENCE_SUMMARY_OF_RESULTS]
-
-**KEY FINDINGS:**
-[ANALYZE_AND_LIST_TOP_3_MOST_IMPORTANT_FINDINGS_WITH_VALUES]
-
-**CLASSIFICATION PERFORMANCE ASSESSMENT:**
-[DETAILED_ANALYSIS_OF_CLASSIFICATION_PERFORMANCE_PATTERNS_AND_IMPACT]
-
-**RISK RATING:** [ASSIGN_LOW_MEDIUM_HIGH_RISK_WITH_JUSTIFICATION]
-
-**RECOMMENDATIONS:**
-[PROVIDE_SPECIFIC_ACTIONABLE_RECOMMENDATIONS_NUMBERED_LIST]
-
-**VALIDATION STATUS:** [PASS_CONDITIONAL_PASS_OR_FAIL_WITH_REASONING]
-
----
-*This report was generated using ValidMind's automated validation platform.*
-*For questions about this analysis, contact the Data Science team.*
----
-
-Important: Use the exact template structure above and fill in each [PLACEHOLDER] section.
-"""
-
-vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-    context={
-        "instructions": template_instructions,
-    },
-)
    -
    -

    -
    -
    -

    Mixed static and dynamic content

    -

    Combine mandatory text with intelligent analysis:

    -
    -
    # Mixed static and dynamic content
-mixed_content_instructions ="""
-Return ONLY the assembled content in plain Markdown paragraphs and lists.
-Do NOT include any headings or titles (no lines starting with '#'), labels,
-XML-like tags (<MANDATORY>, <PLACEHOLDER>), variable names, or code fences.
-Do NOT repeat or paraphrase these instructions. Start the first line with the
-first mandatory sentence below—no preface.
-
-You MUST include all MANDATORY blocks verbatim (exact characters, spacing, and punctuation).
-You MUST replace PLACEHOLDER blocks with the requested content.
-Between blocks, include exactly ONE blank line.
-
-MANDATORY BLOCK A (include verbatim):
-This data validation assessment was conducted in accordance with the 
-XYZ Bank Model Risk Management Policy (Document ID: MRM-2024-001). 
-All findings must be reviewed by the Model Validation Team before 
-model deployment.
-
-PLACEHOLDER BLOCK B (replace with prose paragraphs; no headings):
-[Provide detailed analysis of the test results, including specific values, 
-interpretations, and implications for model quality. Focus on classification performance quality 
-aspects and potential issues that could affect model performance.]
-
-MANDATORY BLOCK C (include verbatim):
-IMPORTANT: This automated analysis is supplementary to human expert review. 
-All high-risk findings require immediate escalation to the Chief Risk Officer. 
-Model deployment is prohibited until all Medium and High risk items are resolved.
-
-PLACEHOLDER BLOCK D (replace with a numbered list only):
-[Create a numbered list of specific action items with responsible parties 
-and suggested timelines for resolution.]
-
-MANDATORY BLOCK E (include verbatim):
-Validation performed using ValidMind Platform v2.0 | 
-Next review required: [30 days from test date] | 
-Contact: model-risk@xyzbank.com
-
-Compliance checks BEFORE you finalize your answer:
-- No headings or titles present (no '#' anywhere).
-- No tags (<MANDATORY>, <PLACEHOLDER>) or labels (e.g., "BLOCK A") in the output.
-- All three MANDATORY blocks included exactly as written.
-- PLACEHOLDER B replaced with prose; PLACEHOLDER D replaced with a numbered list.
-- Exactly one blank line between each block.
-"""
-
-
-vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-    context={
-        "instructions": mixed_content_instructions,
-    },
-)
    -
    -

    -
    -
    -
    -

    Enriching results with additional context

    -

    While the instructions parameter controls how your test descriptions are formatted and structured, the additional_context parameter provides background information about what the results mean for your specific business situation. Think of instructions as the "presentation guide" and additional_context as the "business background" that helps the LLM understand what matters most in your organization and how to interpret the results in your specific context.

    -

    -
    -

    Understanding the additional context parameter

    -

    The additional_context parameter can be used to add any background information that helps put the test results into context. For example, you might include business priorities and constraints that shape how results are interpreted, risk tolerance levels or acceptance criteria specific to your organization, regulatory requirements that influence what counts as acceptable performance, or details about the intended use case of the record (model) in production. These are just examples—the parameter is flexible and can capture whatever context is most relevant to your needs.

    -

    Key difference: - instructions: "Write a 3-paragraph executive summary"

    -
      -
    • additional_context: "If Accuracy is above 0.85 but Class 1 Recall falls below 0.60, the model should be considered high risk"
      • -
    -

    When used together, these parameters create descriptions that don’t just report the Recall or Accuracy measures for Class 1, but explain that because Accuracy is above 0.85 while Recall falls below 0.60, the model should be treated as high risk for your business.

    -

    -
    -
    -

    Basic additional context usage

    -

    Here's how business context transforms the interpretation of our classifier results:

    -
    -
    simple_context = """
-MODEL CONTEXT:
-- Class 0 = Customer stays (retains banking relationship)
-- Class 1 = Customer churns (closes accounts, leaves bank)
-
-DECISION RULES:
-- ROC AUC >0.9: APPROVE deployment
-- ROC AUC <0.9: REJECT model
-
-CHURN DETECTION RULES:
-- Recall >50% for churning customers: Good - use high-touch retention  
-- Recall <50% for churning customers: Poor - retention program will fail
-"""
-
-vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-    context={
-        "additional_context": simple_context,
-    },
-)
    -
    -

    -
    -
    -

    Combining instructions and additional context

    -

    Here's how combining both parameters creates targeted analysis of our churn model performance, using additional_context to pass both static business rules and dynamic real-time information like analysis dates:

    -
    -
    from datetime import datetime
-
-# Get today's date
-today = datetime.now().strftime("%B %d, %Y")
-
-# Executive decision instructions with date placeholder
-executive_instructions = """
-Create a GO/NO-GO decision memo following this template:
-
-<TEMPLATE>
-**DATE:** [Use analysis date from context]
-**THRESHOLD ANALYSIS:** [Pass/Fail against specific thresholds]
-**BUSINESS IMPACT:** [Revenue impact of current performance]  
-**DEPLOYMENT DECISION:** [APPROVE/CONDITIONAL/REJECT]
-**REQUIRED ACTIONS:** [Specific next steps with timelines]
-</TEMPLATE>
-
-Be definitive - use the thresholds to make clear recommendations.
-"""
-
-# Retail banking with hard thresholds including date
-retail_thresholds = f"""
-RETAIL BANKING CONTEXT (Analysis Date: {today}):
-- Class 0 = Customer retention (keeps checking/savings accounts)
-- Class 1 = Customer churn (closes accounts, switches banks)
-
-REGULATORY THRESHOLDS:
-- AUC >0.80: Meets regulatory model standards
-- Churn Recall >55%: Adequate churn detection 
-- Churn Precision >65%: Cost-effective targeting 
-
-DEPLOYMENT CRITERIA:
-- All 3 Pass: FULL DEPLOYMENT
-- 2 Pass: CONDITIONAL DEPLOYMENT
-- <2 Pass: REJECT MODEL
-
-"""
-
-vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-    context={
-        "instructions": executive_instructions,
-        "additional_context": retail_thresholds,
-    },
-)
    -
    -

    -
    -
    -
    -

    Overriding test documentation with test description parameter

    -

    Each test, whether built-in or customized, includes a built-in docstring that serves as its default documentation. This docstring usually explains what the test does and what it outputs. In many cases, especially for specialized tests with well-defined purposes—the default docstring is already useful and sufficient.

    -

    -
    -

    Structure of ValidMind built-in test docstrings

    -

    Every ValidMind built-in test includes a docstring that serves as its default documentation. This docstring follows a consistent structure so that both users and the LLM can rely on a predictable format. While the content varies depending on the type of test—for example, highly specific tests like SHAP values or PSI provide technical detail, whereas generic tests like descriptive statistics or histograms are more general—the overall layout remains the same.

    -

    A typical docstring contains the following sections:

    -
      -

    • Overview: A short description of what the test does and what kind of output it generates.

      • -

    • Purpose: Explains why the test exists and what it is designed to evaluate. This section provides the context for the test’s role in documentation, often describing the intended use cases or the kind of insights it supports.

      • -

    • Test mechanism: Describes how the test works internally. This includes the approach or methodology, what inputs are used, how results are calculated or visualized, and the logic behind the test’s implementation.

      • -

    • Signs of high risks: Outlines risk indicators that are specific to the test. These highlight situations where results should be interpreted with caution—for example, imbalances in distributions or errors in processing steps.

      • -

    • Strengths: Highlights the capabilities and benefits of the test, explaining what makes it particularly useful and what kinds of insights it provides that may not be captured elsewhere.

      • -

    • Limitations: Discusses the constraints of the test, including technical shortcomings, interpretive challenges, and situations where the results might be misleading or incomplete.

      • -
    -

    This structure ensures that all built-in tests provide a comprehensive explanation of their purpose, mechanics, strengths, and limitations. For more generic tests, the docstring may read as boilerplate information about the test’s mechanics. In these cases, the doc parameter can be used to override the docstring with context that is more relevant to the dataset, feature, or business use case under analysis.

    -

    -
    -
    -

    Understanding the test description parameter

    -

    Overriding the docstring with the test_description parameter is particularly valuable for more generic tests, where the default text often focuses on the mechanics of producing an output rather than the data or variable being analyzed. For example, instead of including documentation about the details about the methodology used to compute an histogram, you may want to document the business meaning of the feature being visualized, its expected distribution, or what to pay attention to. Similarly, when generating a descriptive statistics table, you may prefer documentation that describes the dataset under review.

    -

    Customizing the doc, allows you to shift the focus of the explanation from the test machinery to the aspects of the data that matter most for your audience, while still relying on the built-in docstring for cases where the default detail is already fit for purpose.

    -

    When to override

    -

    For tests like histograms or descriptive statistics where the statistical methodology is standard and uninteresting, replace the generic documentation with meaningful descriptions of the variables being analyzed. Also use this to customize ValidMind's built-in test documentation when you want different terminology, structure, or emphasis than what's provided by default.

    -

    -
    -
    -

    Basic test description usage

    -
    -
    custom_description = """
-This test evaluates customer churn prediction model performance specifically 
-for retail banking applications. The analysis focuses on classification 
-metrics relevant to customer retention programs and regulatory compliance 
-requirements under our internal Model Risk Management framework.
-
-Key metrics analyzed:
-- Precision: Accuracy of churn predictions to minimize wasted retention costs
-- Recall: Coverage of actual churners to maximize retention program effectiveness  
-- F1-Score: Balanced measure considering both precision and recall
-- ROC AUC: Overall discriminatory power for regulatory model approval
-
-Results inform deployment decisions for automated retention campaigns.
-"""
-
-result = vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "model": vm_model,
-        "dataset": vm_test_ds
-    },
-    context={
-        "test_description": custom_description,
-    },
-)
    -
    -

    -
    -
    -

    Combining test description with instructions and additional context

    -
    -
    # All three parameters working together
-banking_test_description = """
-Customer Churn Risk Assessment Test for Retail Banking.
-Evaluates model's ability to identify customers likely to close accounts 
-and switch to competitor banks within 12 months.
-- Class 0 = Customer retention (maintains banking relationship)
-- Class 1 = Customer churn (closes primary accounts)
-"""
-
-executive_instructions = """
-Format as a risk committee briefing:
-**TEST DESCRIPTION:** [Test description]
-**RISK ASSESSMENT:** [Model risk level]
-**REGULATORY STATUS:** [Compliance with banking regulations]
-**BUSINESS RECOMMENDATION:** [Deploy/Hold/Reject with rationale]
-"""
-
-banking_contetx = """
-REGULATORY CONTEXT:
-- OCC guidance requires AUC >0.80 for model approval
-- Our threshold: Churn recall >50% for retention program viability
-"""
-
-result = vm.tests.run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    inputs={
-        "model": vm_model,
-        "dataset": vm_test_ds
-    },
-    context={
-        "test_description": banking_test_description,
-        "instructions": executive_instructions,
-        "additional_context": banking_contetx,
-    },
-)
    -
    -

    -
    -
    -
    -

    Best practices for managing context

    -

    When using instructions, additional_context, and test_description parameters together, follow these guidelines to create effective, consistent, and maintainable test descriptions.

    -

    Choose the right parameter for each need:

    -
      -

    • Use test_description for technical corrections when you need to fix or clarify test methodology, override ValidMind's built-in documentation with your preferred structure or terminology, replace generic test mechanics with meaningful descriptions of variables and features being analyzed, or provide domain-specific context for regulatory compliance.

      • -

    • Apply additional_context for business rules like performance thresholds and decision criteria, business context such as customer economics and operational constraints, threshold-driven decision logic, regulatory requirements, real-time information like dates or risk indicators, stakeholder priorities, or any background information that helps the LLM interpret results in your specific context

      • -

    • Leverage instructions for audience targeting to control format and presentation style, create structured templates with specific sections and placeholders for LLM fill-ins, combine hardcoded mandatory text with dynamic analysis, and ensure consistent organizational reporting standards across different stakeholder groups.

      • -
    -

    Avoid redundancy:

    -

    Don't repeat the same information across multiple parameters, as each parameter should add unique value to the description generation. If content overlaps, choose the most appropriate parameter for that information to maintain clarity and prevent conflicting or duplicate guidance in your test descriptions.

    -

    Increasing consistency and grounding:

    -

    Since LLMs can produce variable responses, use hardcoded sections in your instructions for content that requires no variability, combined with specific placeholders for data you trust the LLM to generate. For example, include mandatory disclaimers, policy references, and fixed formatting exactly as written, while using placeholders like [ANALYZE_PERFORMANCE_METRICS] for dynamic content. This approach ensures critical information appears consistently while still leveraging the LLM's analytical capabilities.

    -

    Use test_description and additional_context parameters to anchor test results descriptions in your specific domain and business context, preventing the LLM from generating generic or inappropriate interpretations. Then use instructions to explicitly direct the LLM to ground its analysis in this provided context, such as "Base all recommendations on the thresholds specified in the additional context section" or "Interpret all metrics according to the test description provided."

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
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    - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/tests/run_tests/configure_tests/filter_input_columns.html b/site/notebooks/how_to/tests/run_tests/configure_tests/filter_input_columns.html deleted file mode 100644 index b3030456f..000000000 --- a/site/notebooks/how_to/tests/run_tests/configure_tests/filter_input_columns.html +++ /dev/null @@ -1,1538 +0,0 @@ - - - - - - - - - -Dataset Column Filters when Running Tests – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    To run a test on a dataset but only include certain columns from that dataset, you can use the new columns option. This is done by passing a dictionary for the dataset input for the test instead of the dataset object or dataset input ID directly. This dictionary should have the following keys:

    - -

    This mechanism is intended to be general enough to support many other types of options that users may want to set on datasets or models that apply to a specific test or set of tests. These can be implemented in the with_options() method of the VMDataset or VMModel classes which gets called when the user passes the above described dictionary for the dataset or model input. The with_options() method should return a new instance of the class with the specified options set.

    -
    -
    from validmind import init
-
-init()
    -
    -
    -
    import pandas as pd
-
-from validmind import init_dataset
-
-my_df = pd.DataFrame(
-    {
-        "col1": [1, 1, 3],
-        "col2": [4, 5, 6],
-        "target": [0, 1, 0],
-    }
-)
-
-my_dataset = init_dataset(my_df, target_column="target", input_id="my_dataset")
    -
    -
    -
    from validmind.tests import run_test
-
-result = run_test(
-    test_id="validmind.data_validation.DatasetDescription",
-    inputs={"dataset": {"input_id": "my_dataset", "columns": ["col1"]}},
-)
-
-# notice that only the column "col1" is shown in the test result
    -
    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - - -
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    - - - - - - \ No newline at end of file diff --git a/site/notebooks/how_to/tests/run_tests/configure_tests/understand_utilize_rawdata.html b/site/notebooks/how_to/tests/run_tests/configure_tests/understand_utilize_rawdata.html deleted file mode 100644 index 6483cf9af..000000000 --- a/site/notebooks/how_to/tests/run_tests/configure_tests/understand_utilize_rawdata.html +++ /dev/null @@ -1,1944 +0,0 @@ - - - - - - - - - -Understand and utilize RawData in ValidMind tests – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Understand and utilize RawData in ValidMind tests

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    - - - - -
    - - - -
    - - - -

    Test functions in ValidMind can return a special object called RawData, which holds intermediate or unprocessed data produced somewhere in the test logic but not returned as part of the test's visible output, such as in tables or figures.

    - -

    In this notebook, you'll learn how to access, inspect, and utilize RawData from ValidMind tests.

    - - -

    -
    -

    Setup

    -

    Before we can run our examples, we'll need to set the stage to enable running tests with the ValidMind Library. Since the focus of this notebook is on the RawData object, this section will merely summarize the steps instead of going into greater detail.

    -

    To learn more about running tests with ValidMind: Run tests and test suites

    -

    -
    -

    Installation and intialization

    -

    First, let's make sure that the ValidMind Library is installed and ready to go, and our Python environment set up for data analysis:

    -
    -
    # Install the ValidMind Library
-%pip install -q validmind
-
-# Initialize the ValidMind Library
-import validmind as vm
-
-# Import the `xgboost` library with an alias
-import xgboost as xgb
    -
    -

    -
    -
    -

    Load the sample dataset

    -

    Then, we'll import a sample ValidMind dataset and preprocess it:

    -
    -
    # Import the `customer_churn` sample dataset
-from validmind.datasets.classification import customer_churn
-raw_df = customer_churn.load_data()
-
-# Preprocess the raw dataset
-train_df, validation_df, test_df = customer_churn.preprocess(raw_df)
-
-# Separate features and targets
-x_train = train_df.drop(customer_churn.target_column, axis=1)
-y_train = train_df[customer_churn.target_column]
-x_val = validation_df.drop(customer_churn.target_column, axis=1)
-y_val = validation_df[customer_churn.target_column]
-
-# Create an `XGBClassifier` object
-model = xgb.XGBClassifier(early_stopping_rounds=10)
-model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-
-# Train the model using the validation set
-model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
-)
    -
    -

    -
    -
    -

    Initialize the ValidMind objects

    -

    Before you can run tests, you'll need to initialize a ValidMind dataset object, as well as a ValidMind model object that can be passed to other functions for analysis and tests on the data:

    -
    -
    # Initialize the dataset object
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column=customer_churn.target_column,
-    class_labels=customer_churn.class_labels,
-    __log=False,
-)
-
-# Initialize the datasets into their own ValidMind dataset objects
-vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="train_dataset",
-    target_column=customer_churn.target_column,
-    __log=False,
-)
-vm_test_ds = vm.init_dataset(
-    dataset=test_df,
-    input_id="test_dataset",
-    target_column=customer_churn.target_column,
-    __log=False,
-)
-
-# Initialize the ValidMind model object wrapper so that it can be passed as input to tests or test suites
-# ValidMind model objects can be any type of record you want to test, document, validate, or monitor
-vm_model = vm.init_model(
-    model,
-    input_id="model",
-    __log=False,
-)
-
-# Assign predictions to the datasets
-vm_train_ds.assign_predictions(
-    model=vm_model,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_model,
-)
    -
    -

    -
    -
    -
    -

    RawData usage examples

    -

    Once you're set up to run tests, you can then try out the following examples:

    - -

    -
    -

    Using RawData from the ROC Curve Test

    -

    In this introductory example, we run the model_validation.sklearn.ROCCurve test, inspect its RawData output, and then create a custom ROC curve using the raw data values.

    -

    First, let's run the default ROC Curve test for comparison with later iterations:

    -
    -
    from validmind.tests import run_test
-
-# Run the ROC Curve test normally
-result_roc = run_test(
-    "validmind.model_validation.sklearn.ROCCurve",
-    inputs={"dataset": vm_test_ds, "model": vm_model},
-    generate_description=False,
-)
    -
    -

    Now let's assume we want to create a custom version of the above figure. First, let's inspect the raw data that this test produces so we can see what we have to work with.

    -

    RawData objects have a inspect() method that will pretty print the attributes of the object to be able to quickly see the data and its types:

    -
    -
    # Inspect the RawData output from the ROC test
-print("RawData from ROC Curve Test:")
-result_roc.raw_data.inspect()
    -
    -

    As we can see, the ROC Curve test returns a RawData object with the following attributes: - fpr: A list of false positive rates - tpr: A list of true positive rates - auc: The area under the curve

    -

    This should be enough to create our own custom ROC curve via a post-processing function without having to create a whole new test from scratch and without having to recompute any of the data:

    -
    -
    import matplotlib.pyplot as plt
-
-from validmind.vm_models.result import TestResult
-
-
-def custom_roc_curve(result: TestResult):
-    # Extract raw data from the test result
-    fpr = result.raw_data.fpr
-    tpr = result.raw_data.tpr
-    auc = result.raw_data.auc
-
-    # Create a custom ROC curve plot
-    fig = plt.figure()
-    plt.plot(fpr, tpr, label=f"Custom ROC (AUC = {auc:.2f})", color="blue")
-    plt.plot([0, 1], [0, 1], linestyle="--", color="gray", label="Random Guess")
-    plt.xlabel("False Positive Rate")
-    plt.ylabel("True Positive Rate")
-    plt.title("Custom ROC Curve from RawData")
-    plt.legend()
-
-    # close the plot to avoid it automatically being shown in the notebook
-    plt.close()
-
-    # remove existing figure
-    result.remove_figure(0)
-
-    # add new figure
-    result.add_figure(fig)
-
-    return result
-
-# test it on the existing result
-modified_result = custom_roc_curve(result_roc)
-
-# show the modified result
-modified_result.show()
    -
    -

    Now that we have created a post-processing function and verified that it works on our existing test result, we can use it directly in run_test() from now on:

    -
    -
    result = run_test(
-    "validmind.model_validation.sklearn.ROCCurve",
-    inputs={"dataset": vm_test_ds, "model": vm_model},
-    post_process_fn=custom_roc_curve,
-    generate_description=False,
-)
    -
    -

    -
    -
    -

    Pearson Correlation Matrix

    -

    In this next example, try commenting out the post_process_fn argument in the following cell and see what happens between different runs:

    -
    -
    import plotly.graph_objects as go
-
-
-def custom_heatmap(result: TestResult):
-    corr_matrix = result.raw_data.correlation_matrix
-
-    heatmap = go.Heatmap(
-        z=corr_matrix.values,
-        x=list(corr_matrix.columns),
-        y=list(corr_matrix.index),
-        colorscale="Viridis",
-    )
-    fig = go.Figure(data=[heatmap])
-    fig.update_layout(title="Custom Heatmap from RawData")
-
-    plt.close()
-
-    result.remove_figure(0)
-    result.add_figure(fig)
-
-    return result
-
-
-result_corr = run_test(
-    "validmind.data_validation.PearsonCorrelationMatrix",
-    inputs={"dataset": vm_test_ds},
-    generate_description=False,
-    # COMMENT OUT `post_process_fn`
-    post_process_fn=custom_heatmap,
-)
    -
    -

    -
    -
    -

    Precision-Recall Curve

    -

    Then, let's try the same thing with the model_validation.sklearn.PrecisionRecallCurve test:

    -
    -
    def custom_pr_curve(result: TestResult):
-    precision = result.raw_data.precision
-    recall = result.raw_data.recall
-
-    fig = plt.figure()
-    plt.plot(recall, precision, label="Precision-Recall Curve")
-    plt.xlabel("Recall")
-    plt.ylabel("Precision")
-    plt.title("Custom Precision-Recall Curve from RawData")
-    plt.legend()
-
-    plt.close()
-    result.remove_figure(0)
-    result.add_figure(fig)
-
-    return result
-
-result_pr = run_test(
-    "validmind.model_validation.sklearn.PrecisionRecallCurve",
-    inputs={"dataset": vm_test_ds, "model": vm_model},
-    generate_description=False,
-    # COMMENT OUT `post_process_fn`
-    post_process_fn=custom_pr_curve,
-)
    -
    -

    -
    -
    -

    Using RawData in custom tests

    -

    These examples demonstrate some very simple ways to use the RawData feature of ValidMind tests. The majority of ValidMind-developed tests return some form of raw data that can be used to customize the output of the test, but you can also create your own tests that return RawData objects and use them in the same way.

    -

    Let's take a look at how this can be done in custom tests. To start, define and run your custom test:

    -
    -
    import pandas as pd
-
-from validmind import test, RawData
-from validmind.vm_models import VMDataset, VMModel
-
-
-@test("custom.MyCustomTest")
-def MyCustomTest(dataset: VMDataset, model: VMModel) -> tuple[go.Figure, RawData]:
-    """Custom test that produces a figure and a RawData object"""
-    # pretend we are using the dataset and model to compute some data
-    # ...
-
-    # create some fake data that will be used to generate a figure
-    data = pd.DataFrame({"x": [10, 20, 30, 40, 50], "y": [10, 20, 30, 40, 50]})
-
-    # create the figure (scatter plot)
-    fig = go.Figure(data=go.Scatter(x=data["x"], y=data["y"]))
-
-    # now let's create a RawData object that holds the "computed" data
-    raw_data = RawData(scatter_data_df=data)
-
-    # finally, return both the figure and the raw data
-    return fig, raw_data
-
-
-my_result = run_test(
-    "custom.MyCustomTest",
-    inputs={"dataset": vm_test_ds, "model": vm_model},
-    generate_description=False,
-)
    -
    -

    We can see that the test result shows the figure. But since we returned a RawData object, we can also inspect the contents and see how we could use it to customize or regenerate the figure in the post-processing function:

    -
    -
    my_result.raw_data.inspect()
    -
    -

    We can see that we get a nicely-formatted preview of the dataframe we stored in the raw data object. Let's go ahead and use it to re-plot our data:

    -
    -
    def custom_plot(result: TestResult):
-    data = result.raw_data.scatter_data_df
-
-    # use something other than a scatter plot
-    fig = go.Figure(data=go.Bar(x=data["x"], y=data["y"]))
-    fig.update_layout(title="Custom Bar Chart from RawData")
-    fig.update_xaxes(title="X Axis")
-    fig.update_yaxes(title="Y Axis")
-
-    result.remove_figure(0)
-    result.add_figure(fig)
-
-    return result
-
-result = run_test(
-    "custom.MyCustomTest",
-    inputs={"dataset": vm_test_ds, "model": vm_model},
-    post_process_fn=custom_plot,
-    generate_description=False,
-)
    -
    -

    -
    -
    -

    Using RawData in comparison tests

    -

    When running comparison tests, the RawData object will contain the raw data for each individual test result as well as the comparison results between the test results. To support this, the RawData object contains the model and dataset input_ids for each of the datasets and models in the test, so that the post-processing function can use them to customize the output. The example below shows how to use the RawData object to customize the output of a comparison test and add a table to the test result that shows the confusion matrix for each individual test result as well as the comparison results between the test results.

    -

    When designing post-processing functions that need to handle both individual and comparison test results, you can check the structure of the raw data to determine which case you're dealing with. In the example below, we check if confusion_matrix is a list (comparison test with multiple matrices) or a single matrix (individual test). For comparison tests, the function creates two tables: one showing the confusion matrices for each test case, and another showing the percentage drift between them. For individual tests, it creates a single table with the confusion matrix values. This pattern of checking the raw data structure can be applied to other tests to create versatile post-processing functions that work in both scenarios.

    -
    -
    def cm_table(result: TestResult):
-    # For individual results
-    if not isinstance(result.raw_data.confusion_matrix, list):
-        # Extract values from single confusion matrix
-        cm = result.raw_data.confusion_matrix
-        tn, fp = cm[0, 0], cm[0, 1]
-        fn, tp = cm[1, 0], cm[1, 1]
-        
-        # Create DataFrame for individual matrix
-        cm_df = pd.DataFrame({
-            'TN': [tn],
-            'FP': [fp],
-            'FN': [fn],
-            'TP': [tp]
-        })
-        
-        # Add individual table
-        result.add_table(cm_df, title="Confusion Matrix")
-        
-    # For comparison results
-    else:
-        cms = result.raw_data.confusion_matrix
-        cm1, cm2 = cms[0], cms[1]
-        
-        # Create individual results table
-        rows = []
-        for i, cm in enumerate(cms):
-            rows.append({
-                'dataset': result.raw_data.dataset[i],
-                'model': result.raw_data.model[i],
-                'TN': cm[0, 0],
-                'FP': cm[0, 1],
-                'FN': cm[1, 0],
-                'TP': cm[1, 1]
-            })
-        individual_df = pd.DataFrame(rows)
-        
-        # Calculate percentage differences
-        diff_df = pd.DataFrame({
-            'TN_drift (%)': [(cm2[0, 0] - cm1[0, 0]) / cm1[0, 0] * 100],
-            'FP_drift (%)': [(cm2[0, 1] - cm1[0, 1]) / cm1[0, 1] * 100],
-            'FN_drift (%)': [(cm2[1, 0] - cm1[1, 0]) / cm1[1, 0] * 100],
-            'TP_drift (%)': [(cm2[1, 1] - cm1[1, 1]) / cm1[1, 1] * 100]
-        }).round(2)
-        
-        # Add both tables
-        result.add_table(individual_df, title="Individual Confusion Matrices")
-        result.add_table(diff_df, title="Confusion Matrix Drift")
-        
-    return result
    -
    -

    Let's first run the confusion matrix test on a single dataset-model pair to see how our post-processing function handles individual results:

    -
    -
    from validmind.tests import run_test
-
-result_cm = run_test(
-    "validmind.model_validation.sklearn.ConfusionMatrix",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-    post_process_fn=cm_table,
-    generate_description=False,
-)
    -
    -

    Now let's run a comparison test between test and train datasets to see how the function handles multiple results:

    -
    -
    result_cm = run_test(
-    "validmind.model_validation.sklearn.ConfusionMatrix",
-    input_grid={
-        "dataset": [vm_test_ds, vm_train_ds],
-        "model": [vm_model]
-    },
-    post_process_fn=cm_table,
-    generate_description=False,
-)
    -
    -

    Let's inspect the raw data to see how comparison tests structure their data - notice how the RawData object contains not just the confusion matrices for both datasets, but also tracks which dataset and model each result came from:

    -
    -
    result_cm.raw_data.inspect()
    -
    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

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    For targeted testing, you can run tests on individual sections or specific groups of sections in your model documentation.

    -

    As a model developer, running individual documentation sections is useful in various development scenarios. For instance, when updates are made to a model, often only certain parts of the documentation require revision. The run_documentation_tests() function allows you to directly test only these affected sections, thus saving you time and ensuring that the documentation accurately reflects the latest changes.

    -

    This interactive notebook includes the code required to load the demo dataset, preprocess the raw dataset, train a model for testing, initialize ValidMind objects, and run the data preparation, model development, and multiple documentation sections.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

    -

    -
    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -
    -
    %matplotlib inline
-
-import xgboost as xgb
    -
    -

    -
    -
    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -
    -

    Load the Demo Dataset

    -
    -
    # You can also import taiwan_credit like this:
-# from validmind.datasets.classification import taiwan_credit as demo_dataset
-from validmind.datasets.classification import customer_churn as demo_dataset
-
-df = demo_dataset.load_data()
    -
    -

    -
    -

    Prepocess the raw dataset

    -
    -
    train_df, validation_df, test_df = demo_dataset.preprocess(df)
    -
    -

    -
    -
    -
    -

    Train a model for testing

    -

    We train a simple customer churn model for our test.

    -
    -
    x_train = train_df.drop(demo_dataset.target_column, axis=1)
-y_train = train_df[demo_dataset.target_column]
-x_val = validation_df.drop(demo_dataset.target_column, axis=1)
-y_val = validation_df[demo_dataset.target_column]
-
-model = xgb.XGBClassifier(early_stopping_rounds=10)
-model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
-)
    -
    -

    -
    -
    -

    Initialize ValidMind objects

    -

    We initize the objects required to run test suites using the ValidMind Library.

    -
    -
    vm_dataset = vm.init_dataset(
-    input_id="raw_dataset",
-    dataset=df,
-    target_column=demo_dataset.target_column,
-    class_labels=demo_dataset.class_labels,
-)
-
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset",
-    dataset=train_df,
-    type="generic",
-    target_column=demo_dataset.target_column,
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset",
-    dataset=test_df,
-    type="generic",
-    target_column=demo_dataset.target_column,
-)
-
-vm_model = vm.init_model(model, input_id="model")
    -
    -

    -
    -

    Assign predictions to the datasets

    -

    We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:

    -
    -
    vm_train_ds.assign_predictions(
-    model=vm_model,
-)
-vm_test_ds.assign_predictions(
-    model=vm_model,
-)
    -
    -

    -
    -
    -
    -

    Run the data preparation section

    -

    In this section, we focus on running the tests within the data preparation section of the model documentation. After running this function, only the tests associated with this section will be executed, and the corresponding section in the model documentation will be updated.

    -
    -
    results = vm.run_documentation_tests(
-    section="data_preparation",
-    inputs={
-        "dataset": vm_dataset,
-    },
-)
    -
    -

    -
    -
    -

    Run the model development section

    -

    In this section, we focus on running the tests within the model development section of the model documentation. After running this function, only the tests associated with this section will be executed, and the corresponding section in the model documentation will be updated.

    -
    -
    results = vm.run_documentation_tests(
-    section="model_development",
-    inputs={
-        "dataset": vm_train_ds,
-        "model": vm_model,
-        "datasets": (vm_train_ds, vm_test_ds),
-    },
-)
    -
    -

    -
    -
    -

    Run multiple model documentation sections

    -

    This section demonstrates how you can execute both the data preparation and model development sections using run_documentation_tests(). After running this function, the tests associated with both sections will be executed, and their corresponding model documentation sections updated.

    -
    -
    results = vm.run_documentation_tests(
-    section=["model_development", "model_diagnosis"],
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-        "datasets": (vm_train_ds, vm_test_ds),
-    },
-)
    -
    -

    -
    -
    -

    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/quickstart/quickstart_documentation.html b/site/notebooks/quickstart/quickstart_documentation.html deleted file mode 100644 index 9fd41fb90..000000000 --- a/site/notebooks/quickstart/quickstart_documentation.html +++ /dev/null @@ -1,1990 +0,0 @@ - - - - - - - - - -Quickstart for documentation – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
    - -
    - - -
    - - - -
    - -
    -
    -

    Quickstart for documentation

    -
    - - - -
    - - - - -
    - - - -
    - - - -

    Learn the basics of using ValidMind to document records as part of a development workflow. Set up the ValidMind Library in your environment, and generate a draft of documentation using ValidMind tests for a binary classification model.

    -

    To document our model with the ValidMind Library, we'll:

    -
      -
    1. Import a sample dataset and preprocess it
      2. -
    2. Split the datasets and initialize them for use with ValidMind
      3. -
    3. Initialize a ValidMind model object for use with testing
      4. -
    4. Run a full suite of tests as defined by our documentation template, which will send the results of those tests to the ValidMind Platform
      5. -
    - - -

    -
    -

    Introduction

    -

    Development aims to produce a fit-for-purpose champion by conducting thorough testing and analysis, supporting the capabilities of the champion with evidence in the form of documentation and test results. Documentation should be clear and comprehensive, ideally following a structure or template covering all aspects of compliance with risk regulation.

    -

    A binary classification model is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.

    -
      -
    • This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.
      • -
    • Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss.
      • -
    -

    -
    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

    -

    -
    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -
    -Recommended Python versions

    Python 3.8 <= x <= 3.14 -
    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Initialize the Python environment

    -

    Then, let's import the necessary libraries and set up your Python environment for data analysis:

    -
      -
    • Import Extreme Gradient Boosting (XGBoost) with an alias so that we can reference its functions in later calls. XGBoost is a powerful machine learning library designed for speed and performance, especially in handling structured or tabular data.
      • -
    • Enable matplotlib, a plotting library used for visualizing data. Ensures that any plots you generate will render inline in our notebook output rather than opening in a separate window.
      • -
    -
    -
    import xgboost as xgb
-
-%matplotlib inline
    -
    -

    -
    -
    -
    -

    Getting to know ValidMind

    -

    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -

    View documentation in the ValidMind Platform

    -

    Next, let's head to the ValidMind Platform to see the template in action:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, navigate to Inventory and select the model you registered for this notebook.

      3. -

    3. Click Development under Documents for your model and note how the structure of the documentation matches our preview above.

      4. -
    -

    -
    -
    -
    -

    Working with ValidMind datasets

    -

    -
    -

    Prepare the sample dataset

    -

    -
    -

    Import the sample dataset

    -

    First, let's import the public Bank Customer Churn Prediction dataset from Kaggle so that we have something to work with.

    -

    In our below example, note that:

    -
      -
    • The target column, Exited has a value of 1 when a customer has churned and 0 otherwise.
      • -
    • The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns.
      • -
    -
    -
    from validmind.datasets.classification import customer_churn
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{customer_churn.target_column}' \n\t• Class labels: {customer_churn.class_labels}"
-)
-
-raw_df = customer_churn.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Preprocess the raw dataset

    -

    Before running tests with ValidMind, we'll need to preprocess our imported dataset. This involves splitting the data and separating the features (inputs) from the targets (outputs).

    -

    -
    -
    -

    Split the dataset

    -

    Splitting our dataset helps assess how well the model generalizes to unseen data.

    -

    Use preprocess() to split our dataset into three subsets:

    -
      -
    1. train_df — Used to train the model.
      2. -
    2. validation_df — Used to evaluate the model's performance during training.
      3. -
    3. test_df — Used later on to asses the model's performance on new, unseen data.
      4. -
    -
    -
    train_df, validation_df, test_df = customer_churn.preprocess(raw_df)
    -
    -

    -
    -
    -

    Separate features and targets

    -

    To train the model, we need to provide it with:

    -
      -
    1. Inputs — Features such as customer age, usage, etc.
      2. -
    2. Outputs (Expected answers/labels) — in our case, we would like to know whether the customer churned or not.
      3. -
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    Here, we'll use x_train and x_val to hold the input data (features), and y_train and y_val to hold the answers (the target we want to predict):

    -
    -
    x_train = train_df.drop(customer_churn.target_column, axis=1)
-y_train = train_df[customer_churn.target_column]
-x_val = validation_df.drop(customer_churn.target_column, axis=1)
-y_val = validation_df[customer_churn.target_column]
    -
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    Initialize the ValidMind datasets

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    Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind Dataset object using the init_dataset function from the ValidMind (vm) module. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind, but you only need to do it once per dataset.

    -

    For this example, we'll pass in the following arguments:

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    • dataset — The raw dataset that you want to provide as input to tests.
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    • input_id — A unique identifier that allows tracking what inputs are used when running each individual test.
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    • target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    • class_labels — An optional value to map predicted classes to class labels.
      • -
    -
    -
    # Initialize the raw dataset
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column=customer_churn.target_column,
-    class_labels=customer_churn.class_labels,
-)
-
-# Initialize the training dataset
-vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="train_dataset",
-    target_column=customer_churn.target_column,
-)
-
-# Initialize the testing dataset
-vm_test_ds = vm.init_dataset(
-    dataset=test_df,
-    input_id="test_dataset",
-    target_column=customer_churn.target_column
-)
    -
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    Working with ValidMind models

    -

    -
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    Train an XGBoost classifier model

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    Next, let's create an XGBoost classifier model that will automatically stop training if it doesn’t improve after 10 tries.

    -

    Setting a threshold avoids wasting time and helps prevent overfitting by stopping training when further improvement isn’t happening.

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    model = xgb.XGBClassifier(early_stopping_rounds=10)
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    Set evaluation metrics

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    Then, we'll set the evaluation metrics, which tells the model to use three different ways to measure its performance:

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    1. error — Measures how often the model makes incorrect predictions.
      2. -
    2. logloss — Indicates how confident the predictions are.
      3. -
    3. auc — Evaluates how well the model distinguishes between churn and not churn.
      4. -
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    Using multiple metrics gives a more complete picture of how good (or bad) the model is.

    -
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    model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
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    Fit the model

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    Finally, our actual training step — where the model learns patterns from the data, so it can make predictions later:

    -
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    • The model is trained on x_train and y_train, and evaluates its performance using x_val and y_val to check if it’s learning well.
      • -
    • To turn off printed output while training, we'll set verbose to False.
      • -
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    model.fit(
-    x_train,
-    y_train,
-    eval_set=[(x_val, y_val)],
-    verbose=False,
-)
    -
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    -
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    Initialize the ValidMind model

    -

    You'll also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data for our model.

    -
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    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
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    vm_model = vm.init_model(
-    model,
-    input_id="model",
-)
    -
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    -
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    Assign predictions

    -

    Once the model has been registered, you can assign model predictions to the training and testing datasets.

    -
      -
    • The assign_predictions() method from the Dataset object can link existing predictions to any number of models.
      • -
    • This method links the model's class prediction values and probabilities to our vm_train_ds and vm_test_ds datasets.
      • -
    -

    If no prediction values are passed, the method will compute predictions automatically:

    -
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    vm_train_ds.assign_predictions(
-    model=vm_model,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_model,
-)
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    -
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    Run a ValidMind test suite

    -

    This is where it all comes together — you are now ready to run the documentation tests for the model as defined by the documentation template you looked at earlier.

    -

    The vm.run_documentation_tests function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform:

    -
      -

    • The function requires information about the inputs to use on every test. These inputs can be passed as an inputs argument if we want to use the same inputs for all tests.

      • -

    • It's also possible to pass a config argument that has information about the params and inputs that each test requires. The config parameter is a dictionary with the following structure:

      -
      config = {
-    "<test-id>": {
-        "params": {
-            "param1": "value1",
-            "param2": "value2",
-            ...
-        },
-        "inputs": {
-            "input1": "value1",
-            "input2": "value2",
-            ...
-        }
-    },
-    ...
-}
      -

      Each <test-id> above corresponds to the test driven block identifiers shown by vm.preview_template(). For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method get_demo_test_config() below constructs the default input configuration for our demo.

      • -
    -
    -
    from validmind.utils import preview_test_config
-
-test_config = customer_churn.get_demo_test_config()
-preview_test_config(test_config)
    -
    -

    Now we can pass the input configuration to vm.run_documentation_tests() and run the full suite of tests.

    -

    The variable full_suite then holds the result of these tests:

    -
    -
    full_suite = vm.run_documentation_tests(config=test_config)
    -
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    -
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    In summary

    -

    In this notebook, you learned how to:

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    Next steps

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    You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    For a more in-depth introduction to using the ValidMind Library for development, check out our introductory development series and the accompanying interactive training:

    - -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/quickstart/quickstart_validation.html b/site/notebooks/quickstart/quickstart_validation.html deleted file mode 100644 index d4e924bd6..000000000 --- a/site/notebooks/quickstart/quickstart_validation.html +++ /dev/null @@ -1,2169 +0,0 @@ - - - - - - - - - -Quickstart for validation – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Quickstart for validation

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    - - - -
    - - - -

    Learn the basics of using ValidMind to validate records as part of a validation workflow. Set up the ValidMind Library in your environment, and generate a draft of a validation report using ValidMind tests for a binary classification model.

    -

    To validate our model with the ValidMind Library, we'll:

    -
      -
    1. Import a sample dataset and preprocess it, then split the datasets and initialize them for use with ValidMind
      2. -
    2. Independently verify data quality tests performed on datasets by model development
      3. -
    3. Import a champion model for evaluation
      4. -
    4. Run model evaluation tests with the ValidMind Library, which will send the results of those tests to the ValidMind Platform
      5. -
    - - -

    -
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    Introduction

    -

    Validation aims to independently assess the compliance of champions created by developers with regulatory guidance by conducting thorough testing and analysis, potentially including the use of challengers to benchmark performance. Assessments, presented in the form of a validation report, typically include artifacts (findings) and recommendations to address those issues.

    -

    A binary classification model is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.

    -
      -
    • This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.
      • -
    • Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss.
      • -
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    -
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    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate comparison and other validation tests, and then use the ValidMind Platform to submit compliance assessments of champions via comprehensive validation reports. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and developers.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about validating records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    validation report: A validation report is a comprehensive and structured review evaluating a record's accuracy, performance, and suitability for its intended purpose. A report follows established validation guidelines to ensure consistency and adherence to internal and regulatory standards — encompassing the process of risk assessment, identifying areas of potential error or risk within the record's components, supporting transparency, regulatory compliance, and informed decision-making by documenting the validator’s independent review and conclusions.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    validation report template: A default ValidMind document template that serves as a standardized framework for conducting and documenting validation, including sections designated for attaching test results, evidence, or artifacts (findings). By outlining required documentation, recommended analyses, and expected validation tests, validation report templates ensure consistency and completeness across validation reports and help guide validators through a systematic review process while promoting comparability and traceability of validation outcomes.

    -

    artifacts (findings): Observations or issues identified during validation, including any deviations from expected performance or standards. Artifacts are organized by type — default types provided by ValidMind include Validation Issue, Policy Exception, and Limitation. Custom artifact types can be created to track other categories relevant to your organization.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

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    Setting up

    -

    -
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    Register a sample model

    -

    In a usual lifecycle, a champion will have been independently registered in your inventory and submitted to you for validation by your development team as part of the effective challenge process. (Learn more: Submit documents)

    -

    For this notebook, we'll have you register a dummy record (model) in the ValidMind Platform inventory and assign yourself as the validator to familiarize you with the ValidMind interface and circumvent the need for an existing model:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down — don’t worry, we’ll adjust these permissions next for validation.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
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    -
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    Assign validator credentials

    -

    In order to log tests as a validator instead of as a developer, on the details page that appears after you've successfully registered your sample model:

    -
      -

    1. Remove yourself as an owner:

      -
        -
      • Click on the OWNERS tile.
        • -
      • Click the x next to your name to remove yourself from that model's role.
        • -
      • Click Save to apply your changes to that role.
        • -
      2. -

    2. Remove yourself as a developer:

      -
        -
      • Click on the DEVELOPERS tile.
        • -
      • Click the x next to your name to remove yourself from that model's role.
        • -
      • Click Save to apply your changes to that role.
        • -
      3. -

    3. Add yourself as a validator:

      -
        -
      • Click on the VALIDATORS tile.
        • -
      • Select your name from the drop-down menu.
        • -
      • Click Save to apply your changes to that role.
        • -
      4. -
    -

    -
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    Apply validation report template

    -

    Next, let's select a validation report template. A template predefines sections for your report and provides a general outline to follow, making the validation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Validation.

      -

      If you cannot locate your Validation document, make sure Validation type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Generic Validation Report.

      3. -

    3. Click Use Template to apply the template.

      4. -
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    -
    -
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    Install the ValidMind Library

    -
    -Recommended Python versions

    Python 3.8 <= x <= 3.14 -
    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
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    -
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    Initialize the ValidMind Library

    -

    -
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    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Validation from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="validation-report",
-)
    -
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    -
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    Initialize the Python environment

    -

    Then, let's import the necessary libraries and set up your Python environment for data analysis by enabling matplotlib, a plotting library used for visualizing data.

    -

    This ensures that any plots you generate will render inline in our notebook output rather than opening in a separate window:

    -
    -
    
-%matplotlib inline
    -
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    -
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    Getting to know ValidMind

    -

    -
    -

    Preview the validation report template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for validation. A template predefines sections for your validation report and provides a general outline to follow, making the validation process much easier.

    -

    You will attach evidence to this template in the form of risk assessment notes, artifacts, and test results later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library:

    -
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    vm.preview_template()
    -
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    -
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    View validation report in the ValidMind Platform

    -

    Next, let's head to the ValidMind Platform to see the template in action:

    -
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    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, navigate to Inventory and select the model you registered for this notebook.

      3. -

    3. Click Validation under Documents for your model and note:

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      Screenshot showing the risk assessment compliance summary

      4. -
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    Working with ValidMind datasets

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    Prepare the sample dataset

    -

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    Load the sample dataset

    -

    First, let's import the public Bank Customer Churn Prediction dataset from Kaggle, which was used to develop the dummy champion.

    -

    We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the champion to ensure that the model was built correctly. By independently performing steps taken by the development team, we can confirm whether the model was built using appropriate and properly processed data.

    -

    In our below example, note that:

    -
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    • The target column, Exited has a value of 1 when a customer has churned and 0 otherwise.
      • -
    • The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns.
      • -
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    -
    from validmind.datasets.classification import customer_churn
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{customer_churn.target_column}' \n\t• Class labels: {customer_churn.class_labels}"
-)
-
-raw_df = customer_churn.load_data()
-raw_df.head()
    -
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    Preprocess the raw dataset

    -

    Let's say that thanks to the documentation submitted by the development team (Learn more: Quickstart for documentation), we know that the sample dataset was first preprocessed before being used to train the champion.

    -

    During validation, we use the same data processing logic and training procedure to confirm that the champion's results can be reproduced independently, so let's also start by preprocessing our imported dataset to verify that preprocessing was done correctly. This involves splitting the data and separating the features (inputs) from the targets (outputs).

    -

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    Split the dataset

    -

    Splitting our dataset helps assess how well the model generalizes to unseen data.

    -

    Use preprocess() to split our dataset into three subsets:

    -
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    1. train_df — Used to train the model.
      2. -
    2. validation_df — Used to evaluate the model's performance during training.
      3. -
    3. test_df — Used later on to asses the model's performance on new, unseen data.
      4. -
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    -
    train_df, validation_df, test_df = customer_churn.preprocess(raw_df)
    -
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    -
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    Separate features and targets

    -

    To train the model, we need to provide it with:

    -
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    1. Inputs — Features such as customer age, usage, etc.
      2. -
    2. Outputs (Expected answers/labels) — in our case, we would like to know whether the customer churned or not.
      3. -
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    Here, we'll use x_train to hold the input features, and y_train to hold the target variable — the values we want the model to predict:

    -
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    x_train = train_df.drop(customer_churn.target_column, axis=1)
-y_train = train_df[customer_churn.target_column]
    -
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    Initialize the ValidMind datasets

    -

    Before you can run tests with your preprocessed datasets, you must first initialize a ValidMind Dataset object using the init_dataset function from the ValidMind (vm) module. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind, but you only need to do it once per dataset.

    -

    For this example, we'll pass in the following arguments:

    -
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    • dataset — The raw dataset that you want to provide as input to tests.
      • -
    • input_id — A unique identifier that allows tracking what inputs are used when running each individual test.
      • -
    • target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    • class_labels — An optional value to map predicted classes to class labels.
      • -
    -
    -
    # Initialize the raw dataset
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column=customer_churn.target_column,
-    class_labels=customer_churn.class_labels,
-)
-
-# Initialize the training dataset
-vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="train_dataset",
-    target_column=customer_churn.target_column,
-)
-
-# Initialize the validation dataset
-vm_validation_ds = vm.init_dataset(
-    dataset=validation_df,
-    input_id="validation_dataset",
-    target_column=customer_churn.target_column,
-)
-
-# Initialize the testing dataset
-vm_test_ds = vm.init_dataset(
-    dataset=test_df,
-    input_id="test_dataset",
-    target_column=customer_churn.target_column
-)
    -
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    -
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    Running data quality tests

    -

    With everything ready to go, let's explore some of ValidMind's available tests to help us assess the quality of our datasets. Using ValidMind’s repository of tests streamlines your validation testing, and helps you ensure that your records are being validated appropriately.

    -

    -
    -

    Identify qualitative tests

    -

    We want to narrow down the tests we want to run from the selection provided by ValidMind, so we'll use the vm.tests.list_tasks_and_tags() function to list which tags are associated with each task type:

    -
      -
    • tasks represent the kind of modeling task associated with a test. Here we'll focus on classification tasks.
      • -
    • tags are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the data_quality tag.
      • -
    -
    -
    vm.tests.list_tasks_and_tags()
    -
    -

    Then we'll call the vm.tests.list_tests() function to list all the data quality tests for classification:

    -
    -
    vm.tests.list_tests(
-    tags=["data_quality"], task="classification"
-)
    -
    -

    -
    -
    -

    Run an individual data quality test

    -

    Next, we'll use our previously initialized raw dataset (vm_raw_dataset) as input to run an individual test, then log the result to the ValidMind Platform.

    -
      -
    • You run validation tests by calling the run_test function provided by the validmind.tests module.
      • -
    • Every test result returned by the run_test() function has a .log() method that can be used to send the test results to the ValidMind Platform.
      • -
    -

    Here, we'll use the data_validation.ClassImbalance test as an example:

    -
    -
    vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={
-        "dataset": vm_raw_dataset
-    }
-).log()
    -
    -
    -Note the output returned indicating that a test-driven block doesn't currently exist in your documentation for some test IDs.

    That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform. You'll continue to see this message throughout this notebook as we run and log more tests. -
    -

    -
    -
    -

    Run data comparison tests

    -

    -

    We can also use ValidMind to perform comparison tests between our datasets, again logging the results to the ValidMind Platform. Below, we'll perform two sets of comparison tests with a mix of our datasets and the same class imbalance test:

    -
      -
    • When running individual tests, you can use a custom result_id to tag the individual result with a unique identifier, appended to the test_id with a : separator.
      • -
    • We can specify all the tests we'd ike to run in a dictionary called test_config, and we'll pass in an input_grid of individual test inputs to compare. In this case, we'll input our two datasets for comparison. Note here that the input_grid expects the input_id of the dataset as the value rather than the variable name we specified.
      • -
    -
    -
    # Individual test config with inputs specified
-test_config = {
-    # Comparison between training and testing datasets to check if class balance is the same in both sets
-    "validmind.data_validation.ClassImbalance:train_vs_validation": {
-        "input_grid": {"dataset": ["train_dataset", "validation_dataset"]}
-    },
-    # Comparison between training and testing datasets to confirm that both sets have similar class distributions
-    "validmind.data_validation.ClassImbalance:train_vs_test": {
-        "input_grid": {"dataset": ["train_dataset", "test_dataset"]},
-    },
-}
    -
    -

    Then batch run and log our tests in test_config:

    -
    -
    for t in test_config:
-    print(t)
-    try:
-        # Check if test has input_grid
-        if 'input_grid' in test_config[t]:
-            # For tests with input_grid, pass the input_grid configuration
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()
-        else:
-            # Original logic for regular inputs
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()
-    except Exception as e:
-        print(f"Error running test {t}: {str(e)}")
    -
    -

    -
    -
    -
    -

    Working with ValidMind models

    -

    -
    -

    Import the champion model

    -

    With our raw dataset preprocessed, let's go ahead and import the champion submitted by the development team in the format of a .pkl file: xgboost_model_champion.pkl

    -
    -
    # Import the champion model
-import joblib
-
-xgboost = joblib.load("xgboost_model_champion.pkl")
    -
    -

    -
    -
    -

    Initialize the ValidMind model

    -

    In addition to the initialized datasets, you'll also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data for our champion.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
    -
    # Initialize the champion XGBoost model
-vm_xgboost = vm.init_model(
-    xgboost,
-    input_id="xgboost_champion",
-)
    -
    -

    -
    -
    -

    Assign predictions

    -

    Once the model has been registered, you can assign model predictions to the training and testing datasets.

    -
      -
    • The assign_predictions() method from the Dataset object can link existing predictions to any number of models.
      • -
    • This method links the model's class prediction values and probabilities to our vm_train_ds and vm_test_ds datasets.
      • -
    -

    If no prediction values are passed, the method will compute predictions automatically:

    -
    -
    vm_train_ds.assign_predictions(
-    model=vm_xgboost,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_xgboost,
-)
    -
    -

    -
    -
    -
    -

    Running model evaluation tests

    -

    With our setup complete, let's run the rest of our validation tests. Since we have already verified the data quality of the dataset used to train our champion, we will now focus on evaluating the model's performance.

    -

    -
    -

    Run model performance tests

    -

    First, let's run some performance tests. Use vm.tests.list_tests() to identify all the model performance tests for classification:

    -
    -
    vm.tests.list_tests(tags=["model_performance"], task="classification")
    -
    -

    We'll isolate the specific tests we want to run in mpt, and append an identifier for our champion model here to the result_id with a : separator like we did above in another test:

    -
    -
    mpt = [
-    "validmind.model_validation.sklearn.ClassifierPerformance:xgboost_champion",
-    "validmind.model_validation.sklearn.ConfusionMatrix:xgboost_champion",
-    "validmind.model_validation.sklearn.ROCCurve:xgboost_champion"
-]
    -
    -

    Now, let's run and log our batch of model performance tests using our testing dataset (vm_test_ds) for our champion model:

    -
      -
    • The test set serves as a proxy for real-world data, providing an unbiased estimate of model performance since it was not used during training or tuning.
      • -
    • The test set also acts as protection against selection bias and model tweaking, giving a final, more unbiased checkpoint.
      • -
    -
    -
    for test in mpt:
-    vm.tests.run_test(
-        test,
-        inputs={
-            "dataset": vm_test_ds, "model" : vm_xgboost,
-        },
-    ).log()
    -
    -

    -
    -
    -

    Run diagnostic tests

    -

    Next, we want to inspect the robustness and stability of our champion. Use list_tests() to list all available diagnosis tests applicable to classification tasks:

    -
    -
    vm.tests.list_tests(tags=["model_diagnosis"], task="classification")
    -
    -

    Let’s now assess the model for potential signs of overfitting and identify any sub-segments where performance may inconsistent.

    -

    Overfitting occurs when a model learns the training data too well, capturing not only the true pattern but noise and random fluctuations resulting in excellent performance on the training dataset but poor generalization to new, unseen data:

    -
      -
    • Since the training dataset (vm_train_ds) was used to fit the model, we use this set to establish a baseline performance for how well the model performs on data it has already seen.
      • -
    • The testing dataset (vm_test_ds) was never seen during training, and here simulates real-world generalization, or how well the model performs on new, unseen data.
      • -
    -
    -
    vm.tests.run_test(
-    test_id="validmind.model_validation.sklearn.OverfitDiagnosis:xgboost_champion",
-    input_grid={
-        "datasets": [[vm_train_ds,vm_test_ds]],
-        "model" : [vm_xgboost]
-    }
-).log()
    -
    -

    Let's also conduct robustness and stability tests.

    -
      -
    • Robustness evaluates the model’s ability to maintain consistent performance under varying input conditions.
      • -
    • Stability assesses whether the model produces consistent outputs across different data subsets or over time.
      • -
    -

    Again, we'll use both the training and testing datasets to establish baseline performance and to simulate real-world generalization:

    -
    -
    vm.tests.run_test(
-    test_id="validmind.model_validation.sklearn.RobustnessDiagnosis:xgboost_champion",
-    input_grid={
-        "datasets": [[vm_train_ds,vm_test_ds]],
-        "model" : [vm_xgboost]
-    },
-).log()
    -
    -

    -
    -
    -

    Run feature importance tests

    -

    We also want to verify the relative influence of different input features on our model's predictions. Use list_tests() to identify all the feature importance tests for classification and store them in FI:

    -
    -
    # Store the feature importance tests
-FI = vm.tests.list_tests(tags=["feature_importance"], task="classification",pretty=False)
-FI
    -
    -

    We'll only use our testing dataset (vm_test_ds) here, to provide a realistic, unseen sample that mimic future or production data, as the training dataset has already influenced our model during learning:

    -
    -
    # Run and log our feature importance tests with the testing dataset
-for test in FI:
-    vm.tests.run_test(
-        "".join((test,':xgboost_champion')),
-        inputs={
-            "dataset": vm_test_ds, "model": vm_xgboost
-        },
-    ).log()
    -
    -

    -
    -
    -
    -

    In summary

    -

    In this notebook, you learned how to:

    -
      -
      • -
      • -
      • -
      • -
      • -
      • -
      • -
    -

    In a usual validation workflow, you would wrap up your validation testing by verifying that all the tests provided by the development team were run and reported accurately, and perhaps even propose a challenger, comparing the performance of the challenger with the running champion.

    -
    -With ValidMind, you can easily: - -
    -

    -
    -
    -

    Next steps

    -

    You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your validation report.

    -

    -
    -

    Work with your validation report

    -

    Now that you've logged all your test results and verified the work done by the development team, head to the ValidMind Platform to wrap up your validation report:

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you connected to earlier.

      2. -

    2. In the left sidebar that appears for your model, click Validation under Documents.

      3. -
    -

    Include your logged test results as evidence, create risk assessment notes, add artifacts, and assess compliance, then submit your report for review when it's ready. (Learn more: Preparing validation reports)

    -

    -
    -
    -

    Discover more learning resources

    -

    For a more in-depth introduction to using the ValidMind Library for validation, check out our introductory validation series and the accompanying interactive training:

    - -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:q

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/tutorials/development/2-start_development_process.html b/site/notebooks/tutorials/development/2-start_development_process.html deleted file mode 100644 index b553f08bd..000000000 --- a/site/notebooks/tutorials/development/2-start_development_process.html +++ /dev/null @@ -1,2052 +0,0 @@ - - - - - - - - - -ValidMind for development 2 — Start the development process – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
    - -
    - - -
    - - - -
    - -
    -
    -

    ValidMind for development 2 — Start the development process

    -
    - - - -
    - - - - -
    - - - -
    - - - -

    Learn how to use ValidMind for your end-to-end documentation process with our series of four introductory notebooks. In this second notebook, you'll run tests and investigate results, then add the results or evidence to your documentation.

    -

    You'll become familiar with the individual tests available in ValidMind, as well as how to run them and change parameters as necessary. Using ValidMind's repository of individual tests as building blocks helps you ensure that a record (model) is being built appropriately.

    -

    For a full list of out-of-the-box tests and descriptions, use the interactive ValidMind test sandbox.

    -
    -Learn by doing

    Our course tailor-made for developers new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Developer Fundamentals -
    - - -

    -
    -

    Prerequisites

    -

    In order to log test results or evidence to your documentation with this notebook, you'll need to first have:

    -
      -
      • -
      • -
    -
    -Need help with the above steps?

    Refer to the first notebook in this series: 1 — Set up the ValidMind Library -
    -

    -
    -
    -

    Setting up

    -

    -
    -

    Initialize the ValidMind Library

    -

    First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Make sure the ValidMind Library is installed
-
-%pip install -q validmind
-
-# Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -

    Import sample dataset

    -

    Then, let's import the public Bank Customer Churn Prediction dataset from Kaggle.

    -

    In our below example, note that:

    -
      -
    • The target column, Exited has a value of 1 when a customer has churned and 0 otherwise.
      • -
    • The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns.
      • -
    -
    -
    from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Identify qualitative tests

    -

    Next, let's say we want to do some data quality assessments by running a few individual tests.

    -

    Use the vm.tests.list_tests() function introduced by the first notebook in this series in combination with vm.tests.list_tags() and vm.tests.list_tasks() to find which prebuilt tests are relevant for data quality assessment:

    -
      -
    • tasks represent the kind of modeling task associated with a test. Here we'll focus on classification tasks.
      • -
    • tags are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the data_quality tag.
      • -
    -
    -
    # Get the list of available task types
-sorted(vm.tests.list_tasks())
    -
    -
    -
    # Get the list of available tags
-sorted(vm.tests.list_tags())
    -
    -

    You can pass tags and tasks as parameters to the vm.tests.list_tests() function to filter the tests based on the tags and task types.

    -

    For example, to find tests related to tabular data quality for classification models, you can call list_tests() like this:

    -
    -
    vm.tests.list_tests(task="classification", tags=["tabular_data", "data_quality"])
    -
    -
    -Want to learn more about navigating ValidMind tests?

    Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests -
    -

    -
    -
    -

    Initialize the ValidMind dataset

    -

    With the individual tests we want to run identified, the next step is to connect your data with a ValidMind Dataset object. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind, but you only need to do it once per dataset.

    -

    Initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module. For this example, we'll pass in the following arguments:

    -
      -
    • dataset — The raw dataset that you want to provide as input to tests.
      • -
    • input_id — A unique identifier that allows tracking what inputs are used when running each individual test.
      • -
    • target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    -
    -
    # vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column="Exited",
-)
    -
    -

    -
    -
    -
    -

    Running tests on datasets

    -

    Now that we know how to initialize a ValidMind dataset object, we're ready to run some tests!

    -

    You run individual tests by calling the run_test function provided by the validmind.tests module. For the examples below, we'll pass in the following arguments:

    -
      -
    • test_id — The ID of the test to run, as seen in the ID column when you run list_tests.
      • -
    • params — A dictionary of parameters for the test. These will override any default_params set in the test definition.
      • -
    -

    -
    -

    Run tabular data tests

    -

    The inputs expected by a test can also be found in the test definition — let's take validmind.data_validation.DescriptiveStatistics as an example.

    -

    Note that the output of the describe_test() function below shows that this test expects a dataset as input:

    -
    -
    vm.tests.describe_test("validmind.data_validation.DescriptiveStatistics")
    -
    -

    Now, let's run a few tests to assess the quality of the dataset:

    -
    -
    result = vm.tests.run_test(
-    test_id="validmind.data_validation.DescriptiveStatistics",
-    inputs={"dataset": vm_raw_dataset},
-)
    -
    -
    -
    result2 = vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={"dataset": vm_raw_dataset},
-    params={"min_percent_threshold": 30},
-)
    -
    -

    The output above shows that the validmind.data_validation.ClassImbalance test did not pass according to the value we set for min_percent_threshold.

    -

    To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:

    -
    -
    import pandas as pd
-
-raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset
-
-# Create a balanced dataset with the same number of exited and not exited customers
-exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
-not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])
-
-balanced_raw_df = pd.concat([exited_df, not_exited_df])
-balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)
    -
    -

    With this new balanced dataset, you can re-run the individual test to see if it now passes the class imbalance test requirement.

    -

    As this is technically a different dataset, remember to first initialize a new ValidMind Dataset object to pass in as input as required by run_test():

    -
    -
    # Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
-vm_balanced_raw_dataset = vm.init_dataset(
-    dataset=balanced_raw_df,
-    input_id="balanced_raw_dataset",
-    target_column="Exited",
-)
    -
    -
    -
    # Pass the initialized `balanced_raw_dataset` as input into the test run
-result = vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={"dataset": vm_balanced_raw_dataset},
-    params={"min_percent_threshold": 30},
-)
    -
    -

    -
    -
    -

    Utilize test output

    -

    You can utilize the output from a ValidMind test for further use, for example, if you want to remove highly correlated features. Removing highly correlated features helps make the model simpler, more stable, and easier to understand.

    -

    Below we demonstrate how to retrieve the list of features with the highest correlation coefficients and use them to reduce the final list of features for modeling.

    -

    First, we'll run validmind.data_validation.HighPearsonCorrelation with the balanced_raw_dataset we initialized previously as input as is for comparison with later runs:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
    -
    -

    The output above shows that the test did not pass according to the value we set for max_threshold.

    -

    corr_result is an object of type TestResult. We can inspect the result object to see what the test has produced:

    -
    -
    print(type(corr_result))
-print("Result ID: ", corr_result.result_id)
-print("Params: ", corr_result.params)
-print("Passed: ", corr_result.passed)
-print("Tables: ", corr_result.tables)
    -
    -

    Let's remove the highly correlated features and create a new VM dataset object.

    -

    We'll begin by checking out the table in the result and extracting a list of features that failed the test:

    -
    -
    # Extract table from `corr_result.tables`
-features_df = corr_result.tables[0].data
-features_df
    -
    -
    -
    # Extract list of features that failed the test
-high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
-high_correlation_features
    -
    -

    Next, extract the feature names from the list of strings (example: (Age, Exited) > Age):

    -
    -
    high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
-high_correlation_features
    -
    -

    Now, it's time to re-initialize the dataset with the highly correlated features removed.

    -

    Note the use of a different input_id. This allows tracking the inputs used when running each individual test.

    -
    -
    # Remove the highly correlated features from the dataset
-balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)
-
-# Re-initialize the dataset object
-vm_raw_dataset_preprocessed = vm.init_dataset(
-    dataset=balanced_raw_no_age_df,
-    input_id="raw_dataset_preprocessed",
-    target_column="Exited",
-)
    -
    -

    Re-running the test with the reduced feature set should pass the test:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    You can also plot the correlation matrix to visualize the new correlation between features:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.PearsonCorrelationMatrix",
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    -
    -
    -
    -

    Documenting test results

    -

    Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it.

    -

    Every test result returned by the run_test() function has a .log() method that can be used to send the test results to the ValidMind Platform:

    -
      -
    • When using run_documentation_tests(), documentation sections will be automatically populated with the results of all tests registered in the documentation template.
      • -
    • When logging individual test results to the platform, you'll need to manually add those results to the desired section of the documentation.
      • -
    -

    To demonstrate how to add test results to your documentation, we'll populate the entire Data Preparation section of the documentation using the clean vm_raw_dataset_preprocessed dataset as input, and then document an additional individual result for the highly correlated dataset vm_balanced_raw_dataset.

    -

    -
    -

    Run and log multiple tests

    -

    run_documentation_tests() allows you to run multiple tests at once and automatically log the results to your documentation. Below, we'll run the tests using the previously initialized vm_raw_dataset_preprocessed as input — this will populate the entire Data Preparation section for every test that is part of the documentation template.

    -

    For this example, we'll pass in the following arguments:

    -
      -
    • inputs: Any inputs to be passed to the tests.
      • -
    • config: A dictionary <test_id>:<test_config> that allows configuring each test individually. Each test config requires the following: -
        -
      • params: Individual test parameters.
        • -
      • inputs: Individual test inputs. This overrides any inputs passed from the run_documentation_tests() function.
        • -
      • -
    -

    When including explicit configuration for individual tests, you'll need to specify the inputs even if they mirror what is included in your global configuration.

    -
    -
    # Individual test config with inputs specified
-test_config = {
-    "validmind.data_validation.ClassImbalance": {
-        "params": {"min_percent_threshold": 30},
-        "inputs": {"dataset": vm_raw_dataset_preprocessed},
-    },
-    "validmind.data_validation.HighPearsonCorrelation": {
-        "params": {"max_threshold": 0.3},
-        "inputs": {"dataset": vm_raw_dataset_preprocessed},
-    },
-}
-
-# Global test config
-tests_suite = vm.run_documentation_tests(
-    inputs={
-        "dataset": vm_raw_dataset_preprocessed,
-    },
-    config=test_config,
-    section=["data_preparation"],
-)
    -
    -

    -
    -
    -

    Run and log an individual test

    -

    Next, we'll use the previously initialized vm_balanced_raw_dataset (that still has a highly correlated Age column) as input to run an individual test, then log the result to the ValidMind Platform.

    -

    When running individual tests, you can use a custom result_id to tag the individual result with a unique identifier:

    -
      -
    • This result_id can be appended to test_id with a : separator.
      • -
    • The balanced_raw_dataset result identifier will correspond to the balanced_raw_dataset input, the dataset that still has the Age column.
      • -
    -
    -
    result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation:balanced_raw_dataset",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
-result.log()
    -
    -
    -Note the output returned indicating that a test-driven block doesn't currently exist in your documentation for this particular test ID.

    That's expected, as when we run individual tests the results logged need to be manually added to your documentation within the ValidMind Platform. -
    -

    -
    -

    Add individual test results to documentation

    -

    With the test results logged, let's head to the model we connected to at the beginning of this notebook and insert our test results into the documentation (Learn more: Work with test results):

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you connected to earlier.

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -

    3. Locate the Data Preparation section and click on 2.3. Correlations and Interactions to expand that section.

      4. -

    4. Hover under the Pearson Correlation Matrix content block until a horizontal dashed line with a + button appears, indicating that you can insert a new block.

      -

      Screenshot showing insert block button in model documentation

      5. -

    5. Click + and then select Test-Driven Block under FROM LIBRARY:

      -
        -
      • Click on VM Library under TEST-DRIVEN in the left sidebar.
        • -
      • In the search bar, type in HighPearsonCorrelation.
        • -
      • Select HighPearsonCorrelation:balanced_raw_dataset as the test.
        • -
      -

      A preview of the test gets shown:

      -

      Screenshot showing the HighPearsonCorrelation test selected

      6. -

    6. Finally, click Insert 1 Test Result to Document to add the test result to the documentation.

      -

      Confirm that the individual results for the high correlation test has been correctly inserted into section 2.3. Correlations and Interactions of the documentation.

      7. -

    7. Finalize the documentation by editing the test result's description block to explain the changes you made to the raw data and the reasons behind them as shown in the screenshot below:

      -

      Screenshot showing the inserted High Pearson Correlation block

      8. -
    -

    -
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    Running model evaluation tests

    -

    So far, we've focused on the data assessment and pre-processing that usually occurs prior to any models being built. Now, let's instead assume we have already built a model and we want to incorporate some model results into our documentation.

    -

    -
    -

    Train simple logistic regression model

    -

    Using ValidMind tests, we'll train a simple logistic regression model on our dataset and evaluate its performance by using the LogisticRegression class from the sklearn.linear_model.

    -

    To start, let's grab the first few rows from the balanced_raw_no_age_df dataset with the highly correlated features removed we initialized earlier:

    -
    -
    balanced_raw_no_age_df.head()
    -
    -

    Before training the model, we need to encode the categorical features in the dataset:

    -
      -
    • Use the OneHotEncoder class from the sklearn.preprocessing module to encode the categorical features.
      • -
    • The categorical features in the dataset are Geography and Gender.
      • -
    -
    -
    balanced_raw_no_age_df = pd.get_dummies(
-    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
-)
-balanced_raw_no_age_df.head()
    -
    -

    We'll split our preprocessed dataset into training and testing, to help assess how well the model generalizes to unseen data:

    -
      -
    • We start by dividing our balanced_raw_no_age_df dataset into training and test subsets using train_test_split, with 80% of the data allocated to training (train_df) and 20% to testing (test_df).
      • -
    • From each subset, we separate the features (all columns except "Exited") into X_train and X_test, and the target column ("Exited") into y_train and y_test.
      • -
    -
    -
    from sklearn.model_selection import train_test_split
-
-train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)
-
-X_train = train_df.drop("Exited", axis=1)
-y_train = train_df["Exited"]
-X_test = test_df.drop("Exited", axis=1)
-y_test = test_df["Exited"]
    -
    -

    Then using GridSearchCV, we'll find the best-performing hyperparameters or settings and save them:

    -
    -
    from sklearn.linear_model import LogisticRegression
-
-# Logistic Regression grid params
-log_reg_params = {
-    "penalty": ["l1", "l2"],
-    "C": [0.001, 0.01, 0.1, 1, 10, 100, 1000],
-    "solver": ["liblinear"],
-}
-
-# Grid search for Logistic Regression
-from sklearn.model_selection import GridSearchCV
-
-grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)
-grid_log_reg.fit(X_train, y_train)
-
-# Logistic Regression best estimator
-log_reg = grid_log_reg.best_estimator_
    -
    -

    -
    -
    -

    Initialize ValidMind datasets

    -

    The last step for evaluating the model's performance is to initialize the ValidMind Dataset and Model objects in preparation for assigning model predictions to each dataset.

    -
    -
    # Initialize the datasets into their own dataset objects
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset_final",
-    dataset=train_df,
-    target_column="Exited",
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset_final",
-    dataset=test_df,
-    target_column="Exited",
-)
    -
    -

    -
    -
    -

    Initialize a ValidMind model

    -

    You'll also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data for each of our three models.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
    -
    # Register the model
-vm_model = vm.init_model(log_reg, input_id="log_reg_model_v1")
    -
    -

    -
    -

    Assign predictions

    -

    Once the model has been registered you can assign predictions to the training and testing datasets.

    -
      -
    • The assign_predictions() method from the Dataset object can link existing predictions to any number of models.
      • -
    • This method links the model's class prediction values and probabilities to our vm_train_ds and vm_test_ds datasets.
      • -
    -

    If no prediction values are passed, the method will compute predictions automatically:

    -
    -
    vm_train_ds.assign_predictions(model=vm_model)
-vm_test_ds.assign_predictions(model=vm_model)
    -
    -

    -
    -
    -
    -

    Run the model evaluation tests

    -

    In this next example, we'll focus on running the tests within the Model Development section of the documentation. Only tests associated with this section will be executed, and the corresponding results will be updated in the documentation.

    -
      -
    • Note the additional config that is passed to run_documentation_tests() — this allows you to override inputs or params in certain tests.
      • -
    • In our case, we want to explicitly use the vm_train_ds for the validmind.model_validation.sklearn.ClassifierPerformance:in_sample test, since it's supposed to run on the training dataset and not the test dataset.
      • -
    -
    -
    test_config = {
-    "validmind.model_validation.sklearn.ClassifierPerformance:in_sample": {
-        "inputs": {
-            "dataset": vm_train_ds,
-            "model": vm_model,
-        },
-    }
-}
-results = vm.run_documentation_tests(
-    section=["model_development"],
-    inputs={
-        "dataset": vm_test_ds,  # Any test that requires a single dataset will use vm_test_ds
-        "model": vm_model,
-        "datasets": (
-            vm_train_ds,
-            vm_test_ds,
-        ),  # Any test that requires multiple datasets will use vm_train_ds and vm_test_ds
-    },
-    config=test_config,
-)
    -
    -

    -
    -
    -
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    In summary

    -

    In this second notebook, you learned how to:

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    Next steps

    -

    -
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    Integrate custom tests

    -

    Now that you're familiar with the basics of using the ValidMind Library to run and log tests to provide evidence for your documentation, let's learn how to incorporate your own custom tests into ValidMind: 3 — Integrate custom tests

    - -

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    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
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    - - - - - - \ No newline at end of file diff --git a/site/notebooks/tutorials/development/4-finalize_testing_documentation.html b/site/notebooks/tutorials/development/4-finalize_testing_documentation.html deleted file mode 100644 index 9c3772fa0..000000000 --- a/site/notebooks/tutorials/development/4-finalize_testing_documentation.html +++ /dev/null @@ -1,2124 +0,0 @@ - - - - - - - - - -ValidMind for development 4 — Finalize testing and documentation – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    ValidMind for development 4 — Finalize testing and documentation

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    Learn how to use ValidMind for your end-to-end documentation process with our introductory notebook series. In this last notebook, finalize the testing and documentation of your model and have a fully documented sample model ready for review.

    -

    We'll first use run_documentation_tests() previously covered in 2 — Start the development process to ensure that your custom test results generated in 3 — Integrate custom tests are included in your documentation. Then, we'll view and update the configuration for the entire documentation template to suit your needs.

    -
    -Learn by doing

    Our course tailor-made for developers new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Developer Fundamentals -
    - - -

    -
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    Prerequisites

    -

    In order to finalize the testing and documentation for your sample model, you'll need to first have:

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    Need help with the above steps?

    Refer to the first three notebooks in this series:

    - -
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    -
    -
    -

    Setting up

    -

    This section should be very familiar to you now — as we performed the same actions in the previous two notebooks in this series.

    -

    -
    -

    Initialize the ValidMind Library

    -

    As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Make sure the ValidMind Library is installed
-
-%pip install -q validmind
-
-# Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -

    Import sample dataset

    -

    Next, we'll import the same public Bank Customer Churn Prediction dataset from Kaggle we used in the last notebooks so that we have something to work with:

    -
    -
    from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
    -
    -

    We'll apply a simple rebalancing technique to the dataset before continuing:

    -
    -
    import pandas as pd
-
-raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset
-
-# Create a balanced dataset with the same number of exited and not exited customers
-exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
-not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])
-
-balanced_raw_df = pd.concat([exited_df, not_exited_df])
-balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)
    -
    -

    -
    -

    Remove highly correlated features

    -

    Let's also quickly remove highly correlated features from the dataset using the output from a ValidMind test.

    -

    As you learned previously, before we can run tests you'll need to initialize a ValidMind dataset object:

    -
    -
    # Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
-vm_balanced_raw_dataset = vm.init_dataset(
-    dataset=balanced_raw_df,
-    input_id="balanced_raw_dataset",
-    target_column="Exited",
-)
    -
    -

    With our balanced dataset initialized, we can then run our test and utilize the output to help us identify the features we want to remove:

    -
    -
    # Run HighPearsonCorrelation test with our balanced dataset as input and return a result object
-corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
    -
    -
    -
    # From result object, extract table from `corr_result.tables`
-features_df = corr_result.tables[0].data
-features_df
    -
    -
    -
    # Extract list of features that failed the test
-high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
-high_correlation_features
    -
    -
    -
    # Extract feature names from the list of strings
-high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
-high_correlation_features
    -
    -

    We can then re-initialize the dataset with a different input_id and the highly correlated features removed and re-run the test for confirmation:

    -
    -
    # Remove the highly correlated features from the dataset
-balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)
-
-# Re-initialize the dataset object
-vm_raw_dataset_preprocessed = vm.init_dataset(
-    dataset=balanced_raw_no_age_df,
-    input_id="raw_dataset_preprocessed",
-    target_column="Exited",
-)
    -
    -
    -
    # Re-run the test with the reduced feature set
-corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    -
    -
    -
    -

    Train the model

    -

    We'll then use ValidMind tests to train a simple logistic regression model on our prepared dataset:

    -
    -
    # First encode the categorical features in our dataset with the highly correlated features removed
-balanced_raw_no_age_df = pd.get_dummies(
-    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
-)
-balanced_raw_no_age_df.head()
    -
    -
    -
    # Split the processed dataset into train and test
-from sklearn.model_selection import train_test_split
-
-train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)
-
-X_train = train_df.drop("Exited", axis=1)
-y_train = train_df["Exited"]
-X_test = test_df.drop("Exited", axis=1)
-y_test = test_df["Exited"]
    -
    -
    -
    from sklearn.linear_model import LogisticRegression
-
-# Logistic Regression grid params
-log_reg_params = {
-    "penalty": ["l1", "l2"],
-    "C": [0.001, 0.01, 0.1, 1, 10, 100, 1000],
-    "solver": ["liblinear"],
-}
-
-# Grid search for Logistic Regression
-from sklearn.model_selection import GridSearchCV
-
-grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)
-grid_log_reg.fit(X_train, y_train)
-
-# Logistic Regression best estimator
-log_reg = grid_log_reg.best_estimator_
    -
    -

    -
    -

    Initialize the ValidMind objects

    -

    Let's initialize the ValidMind Dataset and Model objects in preparation for assigning model predictions to each dataset:

    -
    -
    # Initialize the datasets into their own ValidMind dataset objects
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset_final",
-    dataset=train_df,
-    target_column="Exited",
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset_final",
-    dataset=test_df,
-    target_column="Exited",
-)
-
-# Initialize the ValidMind model object
-vm_model = vm.init_model(log_reg, input_id="log_reg_model_v1")
    -
    -

    -
    -
    -

    Assign predictions

    -

    Once the model is registered, we'll assign predictions to the training and test datasets:

    -
    -
    vm_train_ds.assign_predictions(model=vm_model)
-vm_test_ds.assign_predictions(model=vm_model)
    -
    -

    -
    -
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    -

    Add custom tests

    -

    We'll also add the same custom tests we implemented in the previous notebook so that this session has access to the same custom inline test and local test provider.

    -

    -
    -

    Implement custom inline test

    -

    Let's set up a custom inline test that calculates the confusion matrix for a binary classification model:

    -
    -
    # First create a confusion matrix plot
-import matplotlib.pyplot as plt
-from sklearn import metrics
-
-# Get the predicted classes
-y_pred = log_reg.predict(vm_test_ds.x)
-
-confusion_matrix = metrics.confusion_matrix(y_test, y_pred)
-
-cm_display = metrics.ConfusionMatrixDisplay(
-    confusion_matrix=confusion_matrix, display_labels=[False, True]
-)
-cm_display.plot()
    -
    -
    -
    # Create the reusable ConfusionMatrix inline test with normalized matrix
-@vm.test("my_custom_tests.ConfusionMatrix")
-def confusion_matrix(dataset, model, normalize=False):
-    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.
-
-    The confusion matrix is a 2x2 table that contains 4 values:
-
-    - True Positive (TP): the number of correct positive predictions
-    - True Negative (TN): the number of correct negative predictions
-    - False Positive (FP): the number of incorrect positive predictions
-    - False Negative (FN): the number of incorrect negative predictions
-
-    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
-    """
-    y_true = dataset.y
-    y_pred = dataset.y_pred(model=model)
-
-    if normalize:
-        confusion_matrix = metrics.confusion_matrix(y_true, y_pred, normalize="all")
-    else:
-        confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
-
-    cm_display = metrics.ConfusionMatrixDisplay(
-        confusion_matrix=confusion_matrix, display_labels=[False, True]
-    )
-    cm_display.plot()
-
-    plt.close()  # close the plot to avoid displaying it
-
-    return cm_display.figure_  # return the figure object itself
    -
    -
    -
    # Test dataset with normalize=True
-result = vm.tests.run_test(
-    "my_custom_tests.ConfusionMatrix:test_dataset_normalized",
-    inputs={"model": vm_model, "dataset": vm_test_ds},
-    params={"normalize": True},
-)
    -
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    -
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    -

    Add a local test provider

    -

    Finally, let's save our custom inline test to our local test provider:

    -
    -
    # Create custom tests folder
-tests_folder = "my_tests"
-
-import os
-
-# create tests folder
-os.makedirs(tests_folder, exist_ok=True)
-
-# remove existing tests
-for f in os.listdir(tests_folder):
-    # remove files and pycache
-    if f.endswith(".py") or f == "__pycache__":
-        os.system(f"rm -rf {tests_folder}/{f}")
    -
    -
    -
    # Save custom inline test to custom tests folder
-confusion_matrix.save(
-    tests_folder,
-    imports=["import matplotlib.pyplot as plt", "from sklearn import metrics"],
-)
    -
    -
    -
    # Register local test provider
-from validmind.tests import LocalTestProvider
-
-# initialize the test provider with the tests folder we created earlier
-my_test_provider = LocalTestProvider(tests_folder)
-
-vm.tests.register_test_provider(
-    namespace="my_test_provider",
-    test_provider=my_test_provider,
-)
    -
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    Reconnect to ValidMind

    -

    After you insert test-driven blocks into your model documentation, changes should persist and become available every time you call vm.preview_template().

    -

    However, you'll need to reload the connection to the ValidMind Platform if you have added test-driven blocks when the connection was already established using reload():

    -
    -
    vm.reload()
    -
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    Now, when you run preview_template() again, the three test-driven blocks you added to your documentation in the last two notebooks in should show up in the template in sections 2.3 Correlations and Interactions and 3.2 Model Evaluation:

    -
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    vm.preview_template()
    -
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    -
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    Include custom test results

    -

    Since your custom test IDs are now part of your documentation template, you can now run tests for an entire section and all additional custom tests should be loaded without any issues.

    -

    Let's run all tests in the Model Evaluation section of the documentation. Note that we have been running the sample custom confusion matrix with normalize=True to demonstrate the ability to provide custom parameters.

    -

    In the Run the model evaluation tests section of 2 — Start the development process, you learned how to assign inputs to individual tests with run_documentation_tests(). Assigning parameters is similar, you only need to provide assign a params dictionary to a given test ID, my_test_provider.ConfusionMatrix in this case.

    -
    -
    test_config = {
-    "validmind.model_validation.sklearn.ClassifierPerformance:in_sample": {
-        "inputs": {
-            "dataset": vm_train_ds,
-            "model": vm_model,
-        },
-    },
-    "my_test_provider.ConfusionMatrix": {
-        "params": {"normalize": True},
-        "inputs": {"dataset": vm_test_ds, "model": vm_model},
-    },
-}
-results = vm.run_documentation_tests(
-    section=["model_evaluation"],
-    inputs={
-        "dataset": vm_test_ds,  # Any test that requires a single dataset will use vm_test_ds
-        "model": vm_model,
-        "datasets": (
-            vm_train_ds,
-            vm_test_ds,
-        ),  # Any test that requires multiple datasets will use vm_train_ds and vm_test_ds
-    },
-    config=test_config,
-)
    -
    -

    -
    -
    -

    Configuring documentation template tests

    -

    Let's call the utility function vm.get_test_suite().get_default_config() which will return the default configuration for the entire documentation template as a dictionary:

    -
      -
    • This configuration will contain all the test IDs and their default parameters.
      • -
    • You can then modify this configuration as needed and pass it to run_documentation_tests() to run all tests in the documentation template if needed.
      • -
    • You still have the option to continue running tests for one section at a time; get_default_config() simply provides a useful reference for providing default parameters to every test.
      • -
    -
    -
    import json
-
-model_test_suite = vm.get_test_suite()
-config = model_test_suite.get_default_config()
-print("Suite Config: \n", json.dumps(config, indent=2))
    -
    -

    -
    -

    Preview test configuration

    -

    The default config does not assign any inputs to a test, but you can assign inputs to individual tests as needed depending on the datasets and records (models) you want to pass to individual tests.

    -

    For this particular documentation template (binary classification), the ValidMind Library provides a sample configuration that can be used to populate the entire documentation using the following inputs as placeholders:

    -
      -
    • A raw_dataset raw dataset
      • -
    • A train_dataset training dataset
      • -
    • A test_dataset test dataset
      • -
    • A trained model instance
      • -
    -

    As part of updating the config you will need to ensure the correct input_ids are used in the final config passed to run_documentation_tests().

    -
    -
    from validmind.datasets.classification import customer_churn
-from validmind.utils import preview_test_config
-
-test_config = customer_churn.get_demo_test_config()
-preview_test_config(test_config)
    -
    -

    -
    -
    -

    Run updated documentation section tests

    -

    Using this sample configuration, let's finish populating documentation by running all tests for the Model Development section of the documentation.

    -

    Recall that the training and test datasets in our exercise have the following input_id values:

    -
      -
    • train_dataset_final for the training dataset
      • -
    • test_dataset_final for the test dataset
      • -
    -
    -
    config = {
-    "validmind.model_validation.ModelMetadata": {
-        "inputs": {"model": "log_reg_model_v1"},
-    },
-    "validmind.data_validation.DatasetSplit": {
-        "inputs": {"datasets": ["train_dataset_final", "test_dataset_final"]},
-    },
-    "validmind.model_validation.sklearn.PopulationStabilityIndex": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {"num_bins": 10, "mode": "fixed"},
-    },
-    "validmind.model_validation.sklearn.ConfusionMatrix": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-    },
-    "my_test_provider.ConfusionMatrix": {
-        "inputs": {"dataset": "test_dataset_final", "model": "log_reg_model_v1"},
-    },
-    "my_custom_tests.ConfusionMatrix:test_dataset_normalized": {
-        "inputs": {"dataset": "test_dataset_final", "model": "log_reg_model_v1"},
-    },
-    "validmind.model_validation.sklearn.ClassifierPerformance:in_sample": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "train_dataset_final"}
-    },
-    "validmind.model_validation.sklearn.ClassifierPerformance:out_of_sample": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"}
-    },
-    "validmind.model_validation.sklearn.PrecisionRecallCurve": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-    },
-    "validmind.model_validation.sklearn.ROCCurve": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-    },
-    "validmind.model_validation.sklearn.TrainingTestDegradation": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {
-            "metrics": ["accuracy", "precision", "recall", "f1"],
-            "max_threshold": 0.1,
-        },
-    },
-    "validmind.model_validation.sklearn.MinimumAccuracy": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-        "params": {"min_threshold": 0.7},
-    },
-    "validmind.model_validation.sklearn.MinimumF1Score": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-        "params": {"min_threshold": 0.5},
-    },
-    "validmind.model_validation.sklearn.MinimumROCAUCScore": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-        "params": {"min_threshold": 0.5},
-    },
-    "validmind.model_validation.sklearn.PermutationFeatureImportance": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-    },
-    "validmind.model_validation.sklearn.SHAPGlobalImportance": {
-        "inputs": {"model": "log_reg_model_v1", "dataset": "test_dataset_final"},
-        "params": {"kernel_explainer_samples": 10},
-    },
-    "validmind.model_validation.sklearn.WeakspotsDiagnosis": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {
-            "thresholds": {"accuracy": 0.75, "precision": 0.5, "recall": 0.5, "f1": 0.7}
-        },
-    },
-    "validmind.model_validation.sklearn.OverfitDiagnosis": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {"cut_off_percentage": 4},
-    },
-    "validmind.model_validation.sklearn.RobustnessDiagnosis": {
-        "inputs": {
-            "model": "log_reg_model_v1",
-            "datasets": ["train_dataset_final", "test_dataset_final"],
-        },
-        "params": {
-            "scaling_factor_std_dev_list": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5],
-            "accuracy_decay_threshold": 4,
-        },
-    },
-}
-
-
-full_suite = vm.run_documentation_tests(
-    section="model_development",
-    config=config,
-)
    -
    -

    -
    -
    -
    -

    In summary

    -

    In this final notebook, you learned how to:

    -
      -
      • -
      • -
      • -
    -

    With our ValidMind for development series of notebooks, you learned how to document a record (model) end-to-end with the ValidMind Library by running through some common scenarios in a typical development setting:

    -
      -
    • Running out-of-the-box tests
      • -
    • Documenting your record (model) by adding evidence to documentation
      • -
    • Extending the capabilities of the ValidMind Library by implementing custom tests
      • -
    • Ensuring that the documentation is complete by running all tests in the documentation template
      • -
    -

    -
    -
    -

    Next steps

    -

    -
    -

    Work with your documentation

    -

    Now that you've logged all your test results and generated a draft for your documentation, head to the ValidMind Platform to wrap up your documentation. Continue to work on your documentation by:

    -
      -

    • Run and log more tests: Use the skills you learned in this series of notebooks to run and log more individual tests, including custom tests, then insert them into your documentation as supplementary evidence. (Learn more: validmind.tests)

      • -

    • Inserting additional test results: Add Test-Driven Blocks under any relevant section of your documentation. (Learn more: Work with test results)

      • -

    • Making qualitative edits to your test descriptions: Click on the description of any inserted test results to review and edit the ValidMind-generated test descriptions for quality and accuracy. (Learn more: Working with documentation)

      • -

    • View guidelines: In any section of your documentation, click ValidMind Insights in the top right corner to reveal the Documentation Guidelines for each section to help guide the contents of your documentation. (Learn more: View development guidelines)

      • -

    • Collaborate with other stakeholders: Use the ValidMind Platform's real-time collaborative features to work seamlessly together with the rest of your organization, including validators. Review suggested changes in your content blocks, work with versioned history, and use comments to discuss specific portions of your documentation. (Learn more: Collaborate with others)

      • -
    -

    When your documentation is complete and ready for review, submit it for approval from the same ValidMind Platform where you made your edits and collaborated with the rest of your organization, ensuring transparency and a thorough development history. (Learn more: Submit documents)

    -

    -
    -
    -

    Learn more

    -

    Now that you're familiar with the basics, you can explore the following notebooks to get a deeper understanding on how the ValidMind Library allows you generate documentation for any use case:

    -

    -
    -

    Use cases

    - -

    -
    -
    -

    Discover more learning resources

    -

    Learn more about the ValidMind Library tools we used in this notebook:

    - -

    We offer many interactive notebooks to help you automate testing, documenting, validating, and more:

    - -

    Or, visit our documentation to learn more about ValidMind.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
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    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/tutorials/validation/2-start_validation_process.html b/site/notebooks/tutorials/validation/2-start_validation_process.html deleted file mode 100644 index fb96c2a82..000000000 --- a/site/notebooks/tutorials/validation/2-start_validation_process.html +++ /dev/null @@ -1,1978 +0,0 @@ - - - - - - - - - -ValidMind for validation 2 — Start the validation process – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    ValidMind for validation 2 — Start the validation process

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    - - - -
    - - - - -
    - - - -
    - - - -

    Learn how to use ValidMind for your end-to-end validation process with our series of four introductory notebooks. In this second notebook, independently verify the data quality tests performed on the dataset used to train the champion.

    -

    You'll learn how to run relevant validation tests with ValidMind, log the results of those tests to the ValidMind Platform, and insert your logged test results as evidence into your validation report. You'll become familiar with the tests available in ValidMind, as well as how to run them. Running tests during validation is crucial to the effective challenge process, as we want to independently evaluate the evidence and assessments provided by the development team.

    -

    While running our tests in this notebook, we'll focus on:

    - -

    For a full list of out-of-the-box tests and descriptions, use the interactive ValidMind test sandbox.

    -
    -Learn by doing

    Our course tailor-made for validators new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Validator Fundamentals -
    - - -

    -
    -

    Prerequisites

    -

    In order to independently assess the quality of your datasets with notebook, you'll need to first have:

    -
      -
      • -
      • -
    -
    -Need help with the above steps?

    Refer to the first notebook in this series: 1 — Set up the ValidMind Library for validation -
    -

    -
    -
    -

    Setting up

    -

    -
    -

    Initialize the ValidMind Library

    -

    First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Validation from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Make sure the ValidMind Library is installed
-
-%pip install -q validmind
-
-# Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="validation-report",
-)
    -
    -

    -
    -
    -
    -

    Load the sample dataset

    -

    Let's first import the public Bank Customer Churn Prediction dataset from Kaggle, which was used to develop the dummy champion.

    -

    We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the champion to ensure that the model was built correctly. By independently performing steps taken by the development team, we can confirm whether the model was built using appropriate and properly processed data.

    -

    In our below example, note that:

    -
      -
    • The target column, Exited has a value of 1 when a customer has churned and 0 otherwise.
      • -
    • The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns.
      • -
    -
    -
    from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Verifying data quality adjustments

    -

    Let's say that thanks to the documentation submitted by the development team (Learn more: ValidMind for development), we know that the sample dataset was first modified before being used to train the champion. After performing some data quality assessments on the raw dataset, it was determined that the dataset required rebalancing, and highly correlated features were also removed.

    -

    -
    -

    Identify qualitative tests

    -

    During validation, we use the same data processing logic and training procedure to confirm that the model's results can be reproduced independently, so let's start by doing some data quality assessments by running a few individual tests just like the development team did.

    -

    Use the vm.tests.list_tests() function introduced by the first notebook in this series in combination with vm.tests.list_tags() and vm.tests.list_tasks() to find which prebuilt tests are relevant for data quality assessment:

    -
      -
    • tasks represent the kind of modeling task associated with a test. Here we'll focus on classification tasks.
      • -
    • tags are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the data_quality tag.
      • -
    -
    -
    # Get the list of available task types
-sorted(vm.tests.list_tasks())
    -
    -
    -
    # Get the list of available tags
-sorted(vm.tests.list_tags())
    -
    -

    You can pass tags and tasks as parameters to the vm.tests.list_tests() function to filter the tests based on the tags and task types.

    -

    For example, to find tests related to tabular data quality for classification models, you can call list_tests() like this:

    -
    -
    vm.tests.list_tests(task="classification", tags=["tabular_data", "data_quality"])
    -
    -
    -Want to learn more about navigating ValidMind tests?

    Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests -
    -

    -
    -
    -

    Initialize the ValidMind dataset

    -

    With the individual tests we want to run identified, the next step is to connect your data with a ValidMind Dataset object. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind, but you only need to do it once per dataset.

    -

    Initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module. For this example, we'll pass in the following arguments:

    -
      -
    • dataset — The raw dataset that you want to provide as input to tests.
      • -
    • input_id — A unique identifier that allows tracking what inputs are used when running each individual test.
      • -
    • target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    -
    -
    # vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column="Exited",
-)
    -
    -

    -
    -
    -

    Run data quality tests

    -

    Now that we know how to initialize a ValidMind dataset object, we're ready to run some tests!

    -

    You run individual tests by calling the run_test function provided by the validmind.tests module. For the examples below, we'll pass in the following arguments:

    -
      -
    • test_id — The ID of the test to run, as seen in the ID column when you run list_tests.
      • -
    • params — A dictionary of parameters for the test. These will override any default_params set in the test definition.
      • -
    -

    -
    -

    Run tabular data tests

    -

    The inputs expected by a test can also be found in the test definition — let's take validmind.data_validation.DescriptiveStatistics as an example.

    -

    Note that the output of the describe_test() function below shows that this test expects a dataset as input:

    -
    -
    vm.tests.describe_test("validmind.data_validation.DescriptiveStatistics")
    -
    -

    Now, let's run a few tests to assess the quality of the dataset:

    -
    -
    result2 = vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={"dataset": vm_raw_dataset},
-    params={"min_percent_threshold": 30},
-)
    -
    -

    The output above shows that the validmind.data_validation.ClassImbalance test did not pass according to the value we set for min_percent_threshold — great, this matches what was reported by the development team.

    -

    To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:

    -
    -
    import pandas as pd
-
-raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset
-
-# Create a balanced dataset with the same number of exited and not exited customers
-exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
-not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])
-
-balanced_raw_df = pd.concat([exited_df, not_exited_df])
-balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)
    -
    -

    With this new balanced dataset, you can re-run the individual test to see if it now passes the class imbalance test requirement.

    -

    As this is technically a different dataset, remember to first initialize a new ValidMind Dataset object to pass in as input as required by run_test():

    -
    -
    # Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
-vm_balanced_raw_dataset = vm.init_dataset(
-    dataset=balanced_raw_df,
-    input_id="balanced_raw_dataset",
-    target_column="Exited",
-)
    -
    -
    -
    # Pass the initialized `balanced_raw_dataset` as input into the test run
-result = vm.tests.run_test(
-    test_id="validmind.data_validation.ClassImbalance",
-    inputs={"dataset": vm_balanced_raw_dataset},
-    params={"min_percent_threshold": 30},
-)
    -
    -

    -
    -
    -
    -

    Remove highly correlated features

    -

    Next, let's also remove highly correlated features from our dataset as outlined by the development team. Removing highly correlated features helps make the model simpler, more stable, and easier to understand.

    -

    You can utilize the output from a ValidMind test for further use — in this below example, to retrieve the list of features with the highest correlation coefficients and use them to reduce the final list of features for modeling.

    -

    First, we'll run validmind.data_validation.HighPearsonCorrelation with the balanced_raw_dataset we initialized previously as input as is for comparison with later runs:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
    -
    -

    The output above shows that the test did not pass according to the value we set for max_threshold — as reported and expected.

    -

    corr_result is an object of type TestResult. We can inspect the result object to see what the test has produced:

    -
    -
    print(type(corr_result))
-print("Result ID: ", corr_result.result_id)
-print("Params: ", corr_result.params)
-print("Passed: ", corr_result.passed)
-print("Tables: ", corr_result.tables)
    -
    -

    Let's remove the highly correlated features and create a new VM dataset object.

    -

    We'll begin by checking out the table in the result and extracting a list of features that failed the test:

    -
    -
    # Extract table from `corr_result.tables`
-features_df = corr_result.tables[0].data
-features_df
    -
    -
    -
    # Extract list of features that failed the test
-high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
-high_correlation_features
    -
    -

    Next, extract the feature names from the list of strings (example: (Age, Exited) > Age):

    -
    -
    high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
-high_correlation_features
    -
    -

    Now, it's time to re-initialize the dataset with the highly correlated features removed.

    -

    Note the use of a different input_id. This allows tracking the inputs used when running each individual test.

    -
    -
    # Remove the highly correlated features from the dataset
-balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)
-
-# Re-initialize the dataset object
-vm_raw_dataset_preprocessed = vm.init_dataset(
-    dataset=balanced_raw_no_age_df,
-    input_id="raw_dataset_preprocessed",
-    target_column="Exited",
-)
    -
    -

    Re-running the test with the reduced feature set should pass the test:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    You can also plot the correlation matrix to visualize the new correlation between features:

    -
    -
    corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.PearsonCorrelationMatrix",
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    -
    -
    -
    -

    Documenting test results

    -

    Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it. Every test result returned by the run_test() function has a .log() method that can be used to send the test results to the ValidMind Platform.

    -

    When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report. To demonstrate how to add test results to your validation report, we'll log our data quality tests and insert the results via the ValidMind Platform.

    -

    -
    -

    Configure and run comparison tests

    -

    Below, we'll perform comparison tests between the original raw dataset (raw_dataset) and the final preprocessed (raw_dataset_preprocessed) dataset, again logging the results to the ValidMind Platform.

    -

    We can specify all the tests we'd ike to run in a dictionary called test_config, and we'll pass in the following arguments for each test:

    -
      -
    • params: Individual test parameters.
      • -
    • input_grid: Individual test inputs to compare. In this case, we'll input our two datasets for comparison.
      • -
    -

    Note here that the input_grid expects the input_id of the dataset as the value rather than the variable name we specified:

    -
    -
    # Individual test config with inputs specified
-test_config = {
-    "validmind.data_validation.ClassImbalance": {
-        "input_grid": {"dataset": ["raw_dataset", "raw_dataset_preprocessed"]},
-        "params": {"min_percent_threshold": 30}
-    },
-    "validmind.data_validation.HighPearsonCorrelation": {
-        "input_grid": {"dataset": ["raw_dataset", "raw_dataset_preprocessed"]},
-        "params": {"max_threshold": 0.3}
-    },
-}
    -
    -

    Then batch run and log our tests in test_config:

    -
    -
    for t in test_config:
-    print(t)
-    try:
-        # Check if test has input_grid
-        if 'input_grid' in test_config[t]:
-            # For tests with input_grid, pass the input_grid configuration
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()
-        else:
-            # Original logic for regular inputs
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()
-    except Exception as e:
-        print(f"Error running test {t}: {str(e)}")
    -
    -
    -Note the output returned indicating that a test-driven block doesn't currently exist in your documentation for some test IDs.

    That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform. -
    -

    -
    -
    -

    Log tests with unique identifiers

    -

    Next, we'll use the previously initialized vm_balanced_raw_dataset (that still has a highly correlated Age column) as input to run an individual test, then log the result to the ValidMind Platform.

    -

    When running individual tests, you can use a custom result_id to tag the individual result with a unique identifier:

    -
      -
    • This result_id can be appended to test_id with a : separator.
      • -
    • The balanced_raw_dataset result identifier will correspond to the balanced_raw_dataset input, the dataset that still has the Age column.
      • -
    -
    -
    result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation:balanced_raw_dataset",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
-result.log()
    -
    -

    -
    -
    -

    Add test results to reporting

    -

    With some test results logged, let's head to the model we connected to at the beginning of this notebook and learn how to insert a test result into our validation report. (Learn more: Assess compliance)

    -

    While the example below focuses on a specific test result, you can follow the same general procedure for your other results:

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you connected to earlier.

      2. -

    2. In the left sidebar that appears for your model, click Validation under Documents.

      3. -

    3. Click on 2.2.1. Data Quality to expand that section.

      4. -

    4. Under the Class Imbalance Assessment guideline, click Evidence to expand the evidence panel.

      5. -

    5. Click Link Evidence, then select Validator Evidence.

      6. -

    6. Select the Class Imbalance test results we logged: ValidMind Data Validation Class Imbalance

      -

      Screenshot showing the ClassImbalance test selected

      7. -

    7. Click Update Linked Evidence to add the test results to the validation report.

      8. -

    8. Confirm that the results for the Class Imbalance test you inserted has been correctly inserted into section 2.2.1. Data Quality of the report.

      -
        -
      • Note that these test results are flagged as Requires Attention — as they include comparative results from our initial raw dataset.
        • -
      • Click See evidence details to review the LLM-generated description that summarizes the test results, that confirm that our final preprocessed dataset actually passes our test:
        • -
      -

      Screenshot showing the ClassImbalance test generated description in the text editor

      9. -
    -
    - Here in this text editor, you can make qualitative edits to the draft that ValidMind generated to finalize the test results.

    Learn more: Work with content blocks -
    -

    -
    -
    -
    -

    Preparing the preprocessed dataset

    -

    -
    -

    Split the preprocessed dataset

    -

    With our raw dataset rebalanced with highly correlated features removed, let's now spilt our dataset into train and test in preparation for model evaluation testing.

    -

    To start, let's grab the first few rows from the balanced_raw_no_age_df dataset we initialized earlier:

    -
    -
    balanced_raw_no_age_df.head()
    -
    -

    Before training the model, we need to encode the categorical features in the dataset:

    -
      -
    • Use the OneHotEncoder class from the sklearn.preprocessing module to encode the categorical features.
      • -
    • The categorical features in the dataset are Geography and Gender.
      • -
    -
    -
    balanced_raw_no_age_df = pd.get_dummies(
-    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
-)
-balanced_raw_no_age_df.head()
    -
    -

    Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:

    -
      -
    • We start by dividing our balanced_raw_no_age_df dataset into training and test subsets using train_test_split, with 80% of the data allocated to training (train_df) and 20% to testing (test_df).
      • -
    • From each subset, we separate the features (all columns except "Exited") into X_train and X_test, and the target column ("Exited") into y_train and y_test.
      • -
    -
    -
    from sklearn.model_selection import train_test_split
-
-train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)
-
-X_train = train_df.drop("Exited", axis=1)
-y_train = train_df["Exited"]
-X_test = test_df.drop("Exited", axis=1)
-y_test = test_df["Exited"]
    -
    -

    -
    -
    -

    Initialize the split datasets

    -

    Next, let's initialize the training and testing datasets so they are available for use:

    -
    -
    vm_train_ds = vm.init_dataset(
-    input_id="train_dataset_final",
-    dataset=train_df,
-    target_column="Exited",
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset_final",
-    dataset=test_df,
-    target_column="Exited",
-)
    -
    -

    -
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    -
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    In summary

    -

    In this second notebook, you learned how to:

    -
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    Next steps

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    Develop potential challenger models

    -

    Now that you're familiar with the basics of using the ValidMind Library, let's use it to develop a challenger model: 3 — Developing a potential challenger

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
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    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/tutorials/validation/4-finalize_validation_reporting.html b/site/notebooks/tutorials/validation/4-finalize_validation_reporting.html deleted file mode 100644 index ac5ebad4a..000000000 --- a/site/notebooks/tutorials/validation/4-finalize_validation_reporting.html +++ /dev/null @@ -1,2276 +0,0 @@ - - - - - - - - - -ValidMind for validation 4 — Finalize testing and reporting – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    ValidMind for validation 4 — Finalize testing and reporting

    -
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    - - - -

    Learn how to use ValidMind for your end-to-end validation process with our series of four introductory notebooks. In this last notebook, finalize the compliance assessment process and have a complete validation report ready for review.

    -

    This notebook will walk you through how to supplement ValidMind tests with your own custom tests and include them as additional evidence in your validation report. A custom test is any function that takes a set of inputs and parameters as arguments and returns one or more outputs:

    - -

    For a more in-depth introduction to custom tests, refer to our Implement custom tests notebook.

    -
    -Learn by doing

    Our course tailor-made for validators new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Validator Fundamentals -
    - - -

    -
    -

    Prerequisites

    -

    In order to finalize validation and reporting, you'll need to first have:

    -
      -
      • -
      • -
      • -
      • -
      • -
      • -
      • -
    -
    -

    Need help with the above steps?

    Refer to the first three notebooks in this series:

    - -
    -

    -
    -
    -

    Setting up

    -

    This section should be very familiar to you now — as we performed the same actions in the previous two notebooks in this series.

    -

    -
    -

    Initialize the ValidMind Library

    -

    As usual, let's first connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Validation from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Make sure the ValidMind Library is installed
-
-%pip install -q validmind
-
-# Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="validation-report",
-)
    -
    -

    -
    -
    -

    Import the sample dataset

    -

    Next, we'll load in the same sample Bank Customer Churn Prediction dataset used to develop the champion that we will independently preprocess:

    -
    -
    # Load the sample dataset
-from validmind.datasets.classification import customer_churn as demo_dataset
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
-)
-
-raw_df = demo_dataset.load_data()
    -
    -
    -
    # Initialize the raw dataset for use in ValidMind tests
-vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column="Exited",
-)
    -
    -
    -
    import pandas as pd
-
-raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset
-
-# Create a balanced dataset with the same number of exited and not exited customers
-exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
-not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])
-
-balanced_raw_df = pd.concat([exited_df, not_exited_df])
-balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)
    -
    -

    Let’s also quickly remove highly correlated features from the dataset using the output from a ValidMind test:

    -
    -
    # Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
-vm_balanced_raw_dataset = vm.init_dataset(
-    dataset=balanced_raw_df,
-    input_id="balanced_raw_dataset",
-    target_column="Exited",
-)
    -
    -
    -
    # Run HighPearsonCorrelation test with our balanced dataset as input and return a result object
-corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_balanced_raw_dataset},
-)
    -
    -
    -
    # From result object, extract table from `corr_result.tables`
-features_df = corr_result.tables[0].data
-features_df
    -
    -
    -
    # Extract list of features that failed the test
-high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
-high_correlation_features
    -
    -
    -
    # Extract feature names from the list of strings
-high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
-high_correlation_features
    -
    -
    -
    # Remove the highly correlated features from the dataset
-balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)
-
-# Re-initialize the dataset object
-vm_raw_dataset_preprocessed = vm.init_dataset(
-    dataset=balanced_raw_no_age_df,
-    input_id="raw_dataset_preprocessed",
-    target_column="Exited",
-)
    -
    -
    -
    # Re-run the test with the reduced feature set
-corr_result = vm.tests.run_test(
-    test_id="validmind.data_validation.HighPearsonCorrelation",
-    params={"max_threshold": 0.3},
-    inputs={"dataset": vm_raw_dataset_preprocessed},
-)
    -
    -

    -
    -
    -

    Split the preprocessed dataset

    -

    With our raw dataset rebalanced with highly correlated features removed, let's now spilt our dataset into train and test in preparation for model evaluation testing:

    -
    -
    # Encode categorical features in the dataset
-balanced_raw_no_age_df = pd.get_dummies(
-    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
-)
-balanced_raw_no_age_df.head()
    -
    -
    -
    from sklearn.model_selection import train_test_split
-
-# Split the dataset into train and test
-train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)
-
-X_train = train_df.drop("Exited", axis=1)
-y_train = train_df["Exited"]
-X_test = test_df.drop("Exited", axis=1)
-y_test = test_df["Exited"]
    -
    -
    -
    # Initialize the split datasets
-vm_train_ds = vm.init_dataset(
-    input_id="train_dataset_final",
-    dataset=train_df,
-    target_column="Exited",
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_dataset_final",
-    dataset=test_df,
-    target_column="Exited",
-)
    -
    -

    -
    -
    -

    Import the champion model

    -

    With our raw dataset assessed and preprocessed, let's go ahead and import the champion submitted by the development team in the format of a .pkl file: lr_model_champion.pkl

    -
    -
    # Import the champion model
-import pickle as pkl
-
-with open("lr_model_champion.pkl", "rb") as f:
-    log_reg = pkl.load(f)
    -
    -

    -
    -
    -

    Train potential challenger model

    -

    We'll also train our random forest classification challenger to see how it compares:

    -
    -
    # Import the Random Forest Classification model
-from sklearn.ensemble import RandomForestClassifier
-
-# Create the model instance with 50 decision trees
-rf_model = RandomForestClassifier(
-    n_estimators=50,
-    random_state=42,
-)
-
-# Train the model
-rf_model.fit(X_train, y_train)
    -
    -

    -
    -
    -

    Initialize the ValidMind models

    -

    In addition to the initialized datasets, you'll also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data for each of our two models:

    -
    -
    # Initialize the champion logistic regression model
-vm_log_model = vm.init_model(
-    log_reg,
-    input_id="log_model_champion",
-)
-
-# Initialize the challenger random forest classification model
-vm_rf_model = vm.init_model(
-    rf_model,
-    input_id="rf_model",
-)
    -
    -
    -
    # Assign predictions to Champion — Logistic regression model
-vm_train_ds.assign_predictions(model=vm_log_model)
-vm_test_ds.assign_predictions(model=vm_log_model)
-
-# Assign predictions to Challenger — Random forest classification model
-vm_train_ds.assign_predictions(model=vm_rf_model)
-vm_test_ds.assign_predictions(model=vm_rf_model)
    -
    -

    -
    -
    -
    -

    Implementing custom tests

    -

    Thanks to the documentation (Learn more: ValidMind for development), we know that the development team implemented a custom test to further evaluate the performance of the champion.

    -

    In a usual validation situation, you would load a saved custom test provided by the development team. In the following section, we'll have you implement the same custom test and make it available for reuse, to familiarize you with the processes.

    -
    -Want to learn more about custom tests?

    Refer to our in-depth introduction to custom tests: Implement custom tests -
    -

    -
    -

    Implement a custom inline test

    -

    Let's implement the same custom inline test that calculates the confusion matrix for a binary classification model that the development team used in their performance evaluations.

    -
      -
    • An inline test refers to a test written and executed within the same environment as the code being tested — in this case, right in this Jupyter Notebook — without requiring a separate test file or framework.
      • -
    • You'll note that the custom test function is just a regular Python function that can include and require any Python library as you see fit.
      • -
    -

    -
    -

    Create a confusion matrix plot

    -

    Let's first create a confusion matrix plot using the confusion_matrix function from the sklearn.metrics module:

    -
    -
    import matplotlib.pyplot as plt
-from sklearn import metrics
-
-# Get the predicted classes
-y_pred = log_reg.predict(vm_test_ds.x)
-
-confusion_matrix = metrics.confusion_matrix(y_test, y_pred)
-
-cm_display = metrics.ConfusionMatrixDisplay(
-    confusion_matrix=confusion_matrix, display_labels=[False, True]
-)
-cm_display.plot()
    -
    -

    Next, create a @vm.test wrapper that will allow you to create a reusable test. Note the following changes in the code below:

    -
      -
    • The function confusion_matrix takes two arguments dataset and model. This is a VMDataset and VMModel object respectively. -
        -
      • VMDataset objects allow you to access the dataset's true (target) values by accessing the .y attribute.
        • -
      • VMDataset objects allow you to access the predictions for a given record (model) by accessing the .y_pred() method.
        • -
      • -
    • The function docstring provides a description of what the test does. This will be displayed along with the result in this notebook as well as in the ValidMind Platform.
      • -
    • The function body calculates the confusion matrix using the sklearn.metrics.confusion_matrix function as we just did above.
      • -
    • The function then returns the ConfusionMatrixDisplay.figure_ object — this is important as the ValidMind Library expects the output of the custom test to be a plot or a table.
      • -
    • The @vm.test decorator is doing the work of creating a wrapper around the function that will allow it to be run by the ValidMind Library. It also registers the test so it can be found by the ID my_custom_tests.ConfusionMatrix.
      • -
    -
    -
    @vm.test("my_custom_tests.ConfusionMatrix")
-def confusion_matrix(dataset, model):
-    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.
-
-    The confusion matrix is a 2x2 table that contains 4 values:
-
-    - True Positive (TP): the number of correct positive predictions
-    - True Negative (TN): the number of correct negative predictions
-    - False Positive (FP): the number of incorrect positive predictions
-    - False Negative (FN): the number of incorrect negative predictions
-
-    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
-    """
-    y_true = dataset.y
-    y_pred = dataset.y_pred(model=model)
-
-    confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
-
-    cm_display = metrics.ConfusionMatrixDisplay(
-        confusion_matrix=confusion_matrix, display_labels=[False, True]
-    )
-    cm_display.plot()
-
-    plt.close()  # close the plot to avoid displaying it
-
-    return cm_display.figure_  # return the figure object itself
    -
    -

    You can now run the newly created custom test on both the training and test datasets for both models using the run_test() function:

    -
    -
    # Champion train and test
-vm.tests.run_test(
-    test_id="my_custom_tests.ConfusionMatrix:champion",
-    input_grid={
-        "dataset": [vm_train_ds,vm_test_ds],
-        "model" : [vm_log_model]
-    }
-).log()
    -
    -
    -
    # Challenger train and test
-vm.tests.run_test(
-    test_id="my_custom_tests.ConfusionMatrix:challenger",
-    input_grid={
-        "dataset": [vm_train_ds,vm_test_ds],
-        "model" : [vm_rf_model]
-    }
-).log()
    -
    -
    -Note the output returned indicating that a test-driven block doesn't currently exist in your documentation for some test IDs.

    That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform. -
    -

    -
    -
    -

    Add parameters to custom tests

    -

    Custom tests can take parameters just like any other function. To demonstrate, let's modify the confusion_matrix function to take an additional parameter normalize that will allow you to normalize the confusion matrix:

    -
    -
    @vm.test("my_custom_tests.ConfusionMatrix")
-def confusion_matrix(dataset, model, normalize=False):
-    """The confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known.
-
-    The confusion matrix is a 2x2 table that contains 4 values:
-
-    - True Positive (TP): the number of correct positive predictions
-    - True Negative (TN): the number of correct negative predictions
-    - False Positive (FP): the number of incorrect positive predictions
-    - False Negative (FN): the number of incorrect negative predictions
-
-    The confusion matrix can be used to assess the holistic performance of a classification model by showing the accuracy, precision, recall, and F1 score of the model on a single figure.
-    """
-    y_true = dataset.y
-    y_pred = dataset.y_pred(model=model)
-
-    if normalize:
-        confusion_matrix = metrics.confusion_matrix(y_true, y_pred, normalize="all")
-    else:
-        confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
-
-    cm_display = metrics.ConfusionMatrixDisplay(
-        confusion_matrix=confusion_matrix, display_labels=[False, True]
-    )
-    cm_display.plot()
-
-    plt.close()  # close the plot to avoid displaying it
-
-    return cm_display.figure_  # return the figure object itself
    -
    -

    -
    -
    -

    Pass parameters to custom tests

    -

    You can pass parameters to custom tests by providing a dictionary of parameters to the run_test() function.

    -
      -
    • The parameters will override any default parameters set in the custom test definition. Note that dataset and model are still passed as inputs.
      • -
    • Since these are VMDataset or VMModel inputs, they have a special meaning.
      • -
    -

    Re-running and logging the custom confusion matrix with normalize=True for both models and our testing dataset looks like this:

    -
    -
    # Champion with test dataset and normalize=True
-vm.tests.run_test(
-    test_id="my_custom_tests.ConfusionMatrix:test_normalized_champion",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model" : [vm_log_model]
-    },
-    params={"normalize": True}
-).log()
    -
    -
    -
    # Challenger with test dataset and normalize=True
-vm.tests.run_test(
-    test_id="my_custom_tests.ConfusionMatrix:test_normalized_challenger",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model" : [vm_rf_model]
-    },
-    params={"normalize": True}
-).log()
    -
    -

    -
    -
    -
    -

    Use external test providers

    -

    Sometimes you may want to reuse the same set of custom tests across multiple records (models) and share them with others in your organization, like the development team would have done with you in this example workflow featured in this series of notebooks. In this case, you can create an external custom test provider that will allow you to load custom tests from a local folder or a Git repository.

    -

    In this section you will learn how to declare a local filesystem test provider that allows loading tests from a local folder following these high level steps:

    -
      -
    1. Create a folder of custom tests from existing inline tests (tests that exist in your active Jupyter Notebook)
      2. -
    2. Save an inline test to a file
      3. -
    3. Define and register a LocalTestProvider that points to that folder
      4. -
    4. Run test provider tests
      5. -
    5. Add the test results to your documentation
      6. -
    -

    -
    -

    Create custom tests folder

    -

    Let's start by creating a new folder that will contain reusable custom tests from your existing inline tests.

    -

    The following code snippet will create a new my_tests directory in the current working directory if it doesn't exist:

    -
    -
    tests_folder = "my_tests"
-
-import os
-
-# create tests folder
-os.makedirs(tests_folder, exist_ok=True)
-
-# remove existing tests
-for f in os.listdir(tests_folder):
-    # remove files and pycache
-    if f.endswith(".py") or f == "__pycache__":
-        os.system(f"rm -rf {tests_folder}/{f}")
    -
    -

    After running the command above, confirm that a new my_tests directory was created successfully. For example:

    -
    ~/notebooks/tutorials/validation/my_tests/
    -

    -
    -
    -

    Save an inline test

    -

    The @vm.test decorator we used in Implement a custom inline test above to register one-off custom tests also includes a convenience method on the function object that allows you to simply call <func_name>.save() to save the test to a Python file at a specified path.

    -

    While save() will get you started by creating the file and saving the function code with the correct name, it won't automatically include any imports, or other functions or variables, outside of the functions that are needed for the test to run. To solve this, pass in an optional imports argument ensuring necessary imports are added to the file.

    -

    The confusion_matrix test requires the following additional imports:

    -
    import matplotlib.pyplot as plt
-from sklearn import metrics
    -

    Let's pass these imports to the save() method to ensure they are included in the file with the following command:

    -
    -
    confusion_matrix.save(
-    # Save it to the custom tests folder we created
-    tests_folder,
-    imports=["import matplotlib.pyplot as plt", "from sklearn import metrics"],
-)
    -
    -
      -

      • -

    • -
      # Saved from __main__.confusion_matrix
-# Original Test ID: my_custom_tests.ConfusionMatrix
-# New Test ID: <test_provider_namespace>.ConfusionMatrix
      • -

    • -
      def ConfusionMatrix(dataset, model, normalize=False):
      • -
    -

    -
    -
    -

    Register a local test provider

    -

    Now that your my_tests folder has a sample custom test, let's initialize a test provider that will tell the ValidMind Library where to find your custom tests:

    -
      -
    • ValidMind offers out-of-the-box test providers for local tests (tests in a folder) or a Github provider for tests in a Github repository.
      • -
    • You can also create your own test provider by creating a class that has a load_test method that takes a test ID and returns the test function matching that ID.
      • -
    -
    -Want to learn more about test providers?

    An extended introduction to test providers can be found in: Integrate external test providers -
    -
    -
    Initialize a local test provider
    -

    For most use cases, using a LocalTestProvider that allows you to load custom tests from a designated directory should be sufficient.

    -

    The most important attribute for a test provider is its namespace. This is a string that will be used to prefix test IDs in documentation. This allows you to have multiple test providers with tests that can even share the same ID, but are distinguished by their namespace.

    -

    Let's go ahead and load the custom tests from our my_tests directory:

    -
    -
    from validmind.tests import LocalTestProvider
-
-# initialize the test provider with the tests folder we created earlier
-my_test_provider = LocalTestProvider(tests_folder)
-
-vm.tests.register_test_provider(
-    namespace="my_test_provider",
-    test_provider=my_test_provider,
-)
-# `my_test_provider.load_test()` will be called for any test ID that starts with `my_test_provider`
-# e.g. `my_test_provider.ConfusionMatrix` will look for a function named `ConfusionMatrix` in `my_tests/ConfusionMatrix.py` file
    -
    -
    -
    -
    Run test provider tests
    -

    Now that we've set up the test provider, we can run any test that's located in the tests folder by using the run_test() method as with any other test:

    -
      -
    • For tests that reside in a test provider directory, the test ID will be the namespace specified when registering the provider, followed by the path to the test file relative to the tests folder.
      • -
    • For example, the Confusion Matrix test we created earlier will have the test ID my_test_provider.ConfusionMatrix. You could organize the tests in subfolders, say classification and regression, and the test ID for the Confusion Matrix test would then be my_test_provider.classification.ConfusionMatrix.
      • -
    -

    Let's go ahead and re-run the confusion matrix test with our testing dataset for our two models by using the test ID my_test_provider.ConfusionMatrix. This should load the test from the test provider and run it as before.

    -
    -
    # Champion with test dataset and test provider custom test
-vm.tests.run_test(
-    test_id="my_test_provider.ConfusionMatrix:champion",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model" : [vm_log_model]
-    }
-).log()
    -
    -
    -
    # Challenger with test dataset  and test provider custom test
-vm.tests.run_test(
-    test_id="my_test_provider.ConfusionMatrix:challenger",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model" : [vm_rf_model]
-    }
-).log()
    -
    -

    -
    -
    -
    -
    -
    -

    Verify test runs

    -

    Our final task is to verify that all the tests provided by the development team were run and reported accurately. Note the appended result_ids to delineate which dataset we ran the test with for the relevant tests.

    -

    Here, we'll specify all the tests we'd like to independently rerun in a dictionary called test_config. Note here that inputs and input_grid expect the input_id of the dataset or model as the value rather than the variable name we specified:

    -
    -
    test_config = {
-    # Run with the raw dataset
-    'validmind.data_validation.DatasetDescription:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'}
-    },
-    'validmind.data_validation.DescriptiveStatistics:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'}
-    },
-    'validmind.data_validation.MissingValues:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'min_percentage_threshold': 1}
-    },
-    'validmind.data_validation.ClassImbalance:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'min_percent_threshold': 10}
-    },
-    'validmind.data_validation.Duplicates:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'min_threshold': 1}
-    },
-    'validmind.data_validation.HighCardinality:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {
-            'num_threshold': 100,
-            'percent_threshold': 0.1,
-            'threshold_type': 'percent'
-        }
-    },
-    'validmind.data_validation.Skewness:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'max_threshold': 1}
-    },
-    'validmind.data_validation.UniqueRows:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'min_percent_threshold': 1}
-    },
-    'validmind.data_validation.TooManyZeroValues:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'max_percent_threshold': 0.03}
-    },
-    'validmind.data_validation.IQROutliersTable:raw_data': {
-        'inputs': {'dataset': 'raw_dataset'},
-        'params': {'threshold': 5}
-    },
-    # Run with the preprocessed dataset
-    'validmind.data_validation.DescriptiveStatistics:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'}
-    },
-    'validmind.data_validation.TabularDescriptionTables:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'}
-    },
-    'validmind.data_validation.MissingValues:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'},
-        'params': {'min_percentage_threshold': 1}
-    },
-    'validmind.data_validation.TabularNumericalHistograms:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'}
-    },
-    'validmind.data_validation.TabularCategoricalBarPlots:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'}
-    },
-    'validmind.data_validation.TargetRateBarPlots:preprocessed_data': {
-        'inputs': {'dataset': 'raw_dataset_preprocessed'},
-        'params': {'default_column': 'loan_status'}
-    },
-    # Run with the training and test datasets
-    'validmind.data_validation.DescriptiveStatistics:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}
-    },
-    'validmind.data_validation.TabularDescriptionTables:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}
-    },
-    'validmind.data_validation.ClassImbalance:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},
-        'params': {'min_percent_threshold': 10}
-    },
-    'validmind.data_validation.UniqueRows:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},
-        'params': {'min_percent_threshold': 1}
-    },
-    'validmind.data_validation.TabularNumericalHistograms:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}
-    },
-    'validmind.data_validation.MutualInformation:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},
-        'params': {'min_threshold': 0.01}
-    },
-    'validmind.data_validation.PearsonCorrelationMatrix:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']}
-    },
-    'validmind.data_validation.HighPearsonCorrelation:development_data': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final']},
-        'params': {'max_threshold': 0.3, 'top_n_correlations': 10}
-    },
-    'validmind.model_validation.ModelMetadata': {
-        'input_grid': {'model': ['log_model_champion', 'rf_model']}
-    },
-    'validmind.model_validation.sklearn.ModelParameters': {
-        'input_grid': {'model': ['log_model_champion', 'rf_model']}
-    },
-    'validmind.model_validation.sklearn.ROCCurve': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final'], 'model': ['log_model_champion']}
-    },
-    'validmind.model_validation.sklearn.MinimumROCAUCScore': {
-        'input_grid': {'dataset': ['train_dataset_final', 'test_dataset_final'], 'model': ['log_model_champion']},
-        'params': {'min_threshold': 0.5}
-    }
-}
    -
    -

    Then batch run and log our tests in test_config:

    -
    -
    for t in test_config:
-    print(t)
-    try:
-        # Check if test has input_grid
-        if 'input_grid' in test_config[t]:
-            # For tests with input_grid, pass the input_grid configuration
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()
-        else:
-            # Original logic for regular inputs
-            if 'params' in test_config[t]:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()
-            else:
-                vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()
-    except Exception as e:
-        print(f"Error running test {t}: {str(e)}")
    -
    -

    -
    -
    -

    In summary

    -

    In this final notebook, you learned how to:

    -
      -
      • -
      • -
      • -
      • -
      • -
    -

    With our ValidMind for validation series of notebooks, you learned how to validate a record (model) end-to-end with the ValidMind Library by running through some common scenarios in a typical validation setting:

    -
      -
    • Verifying the data quality steps performed by the development team
      • -
    • Independently replicating the champion's results and conducting additional tests to assess performance, stability, and robustness
      • -
    • Setting up test inputs and a challenger for comparative analysis
      • -
    • Running validation tests, analyzing results, and logging artifacts to ValidMind
      • -
    -

    -
    -
    -

    Next steps

    -

    -
    -

    Work with your validation report

    -

    Now that you've logged all your test results and verified the work done by the development team, head to the ValidMind Platform to wrap up your validation report. Continue to work on your validation report by:

    -
      -

    • Inserting additional test results: Click Link Evidence under any Evidence panel of 2. Validation in your validation report. (Learn more: Link evidence to reports)

      • -

    • Making qualitative edits to your test descriptions: Expand any linked evidence under Validator Evidence and click See evidence details to review and edit the ValidMind-generated test descriptions for quality and accuracy. (Learn more: Preparing validation reports)

      • -

    • Adding more findings: Click Link Finding to Report in any validation report section, then click + Create New Finding. (Learn more: Add and manage artifacts)

      • -

    • Adding risk assessment notes: Click under Risk Assessment Notes in any validation report section to access the text editor and content editing toolbar, including an option to generate a draft with AI. Once generated, edit your ValidMind-generated test descriptions to adhere to your organization's requirements. (Learn more: Work with content blocks)

      • -

    • Assessing compliance: Under the Guideline for any validation report section, click ASSESSMENT and select the compliance status from the drop-down menu. (Learn more: Assign compliance assessments)

      • -

    • Collaborate with other stakeholders: Use the ValidMind Platform's real-time collaborative features to work seamlessly together with the rest of your organization, including developers. Propose suggested changes in the documentation, work with versioned history, and use comments to discuss specific portions of the documentation. (Learn more: Collaborate with others)

      • -
    -

    When your validation report is complete and ready for review, submit it for approval from the same ValidMind Platform where you made your edits and collaborated with the rest of your organization, ensuring transparency and a thorough validation history. (Learn more: Submit documents)

    -

    -
    -
    -

    Learn more

    -

    Now that you're familiar with the basics, you can explore the following notebooks to get a deeper understanding on how the ValidMind Library assists you in streamlining validation:

    -

    -
    -

    Use cases

    - -

    -
    -
    -

    Discover more learning resources

    -

    Learn more about the ValidMind Library tools we used in this notebook:

    - -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    -
    -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/capital_markets/quickstart_option_pricing_models.html b/site/notebooks/use_cases/capital_markets/quickstart_option_pricing_models.html deleted file mode 100644 index 23fe51c1a..000000000 --- a/site/notebooks/use_cases/capital_markets/quickstart_option_pricing_models.html +++ /dev/null @@ -1,2969 +0,0 @@ - - - - - - - - - -Quickstart for knockout option pricing model documentation – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
    - -
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    - - - -
    - -
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    Quickstart for knockout option pricing model documentation

    -
    - - - -
    - - - - -
    - - - -
    - - - -

    Welcome! Let's get you started with the basic process of documenting models with ValidMind.

    -

    A knockout option is a barrier option that ceases to exist if the underlying asset hits a predetermined price, known as the "barrier." This barrier level, set above or below the current market price, determines whether the option will "knock out" before its expiration date. There are two types: "up-and-out" and "down-and-out." In an up-and-out knockout option, the option expires if the asset price rises above the barrier, while in a down-and-out, it expires if the asset price falls below. Knockout options generally offer a lower premium than standard options since there is a chance they will expire worthless if the barrier is reached.

    -

    Pricing knockout options involves accounting for the proximity of the asset's price to the barrier, as well as market volatility and the option’s time to expiration. High volatility and longer expiry increase the likelihood of the barrier being triggered, which reduces the option’s value. Models like modified Black-Scholes are used for simpler cases, while Monte Carlo simulations or binomial trees handle complex scenarios. Knockout options are useful for hedging or cost-effective investment strategies, allowing investors to save on premiums but with the risk of losing the option entirely if the barrier is hit.

    -

    You will learn how to initialize the ValidMind Library, develop a option pricing model, and then write custom tests that can be used for sensitivity and stress testing to quickly generate documentation about model.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

    -

    -
    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Capital markets.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Initialize the Python environment

    -

    Next, let's import the necessary libraries and set up your Python environment for data analysis:

    -
    -
    %matplotlib inline
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.optimize import minimize
-
-from validmind.tests import run_test
    -
    -

    -
    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -
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    Model development

    -
    -
    class OptionPricing:
-    def __init__(self, S0, K, T, r):
-        self.S0 = S0
-        self.K = K
-        self.T = T
-        self.r = r
-
-    def monte_carlo_simulation(self, N, M):
-        raise NotImplementedError("Must be implemented by subclasses")
-
-    def price_option(self, N, M):
-        raise NotImplementedError("Must be implemented by subclasses")
    -
    -
    -
    
-class BlackScholesModel(OptionPricing):
-    def __init__(self, S0, K, T, r, sigma):
-        super().__init__(S0, K, T, r)
-        self.sigma = sigma
-    def monte_carlo_simulation(self, N, M):
-        dt = self.T / M
-        price_paths = np.zeros((N, M + 1))
-        price_paths[:, 0] = self.S0
-        for t in range(1, M + 1):
-            Z = np.random.standard_normal(N)
-            price_paths[:, t] = price_paths[:, t - 1] * np.exp((self.r - 0.5 * self.sigma**2) * dt + self.sigma * np.sqrt(dt) * Z)
-        return price_paths
-
-    def price_option(self, N, M):
-        price_paths = self.monte_carlo_simulation(N, M)
-        payoffs = np.maximum(price_paths[:, -1] - self.K, 0)
-        return np.exp(-self.r * self.T) * np.mean(payoffs)
-    
-    def calibrate(self, market_prices, strikes, maturities):
-        def objective_function(params):
-            self.sigma = params[0]
-            for K, T in zip(strikes, maturities):
-                self.K = K
-                self.T = T
-                model_prices.append(self.price_option(10000, 100))
-            return np.sum((np.array(market_prices) - np.array(model_prices))**2)
-        result = minimize(objective_function, [self.sigma], bounds=[(0.01, 1.0)])
-        self.sigma = result.x[0]
-
-class StochasticVolatilityModel(OptionPricing):
-    def __init__(self, S0, K, T, r, v0, kappa, theta, xi, rho):
-        super().__init__(S0, K, T, r)
-        self.v0 = v0
-        self.kappa = kappa
-        self.theta = theta
-        self.xi = xi
-        self.rho = rho
-    def monte_carlo_simulation(self, N, M):
-        dt = self.T / M
-        price_paths = np.zeros((N, M + 1))
-        vol_paths = np.zeros((N, M + 1))
-        price_paths[:, 0] = self.S0
-        vol_paths[:, 0] = self.v0
-        for t in range(1, M + 1):
-            Z1 = np.random.standard_normal(N)
-            Z2 = np.random.standard_normal(N)
-            W1 = Z1
-            W2 = self.rho * Z1 + np.sqrt(1 - self.rho**2) * Z2
-            vol_paths[:, t] = np.abs(vol_paths[:, t - 1] + self.kappa * (self.theta - vol_paths[:, t - 1]) * dt + self.xi * np.sqrt(vol_paths[:, t - 1] * dt) * W1)
-            price_paths[:, t] = price_paths[:, t - 1] * np.exp((self.r - 0.5 * vol_paths[:, t - 1]) * dt + np.sqrt(vol_paths[:, t - 1] * dt) * W2)
-        return price_paths
-
-    def price_option(self, N, M):
-        price_paths = self.monte_carlo_simulation(N, M)
-        payoffs = np.maximum(price_paths[:, -1] - self.K, 0)
-        return np.exp(-self.r * self.T) * np.mean(payoffs)
-    
-    def calibrate(self, market_prices, strikes, maturities):
-        def objective_function(params):
-            self.v0, self.kappa, self.theta, self.xi, self.rho = params
-            model_prices = []
-            for K, T in zip(strikes, maturities):
-                self.K = K
-                self.T = T
-                model_prices.append(self.price_option(10000, 100))
-
-            return np.sum((np.array(market_prices) - np.array(model_prices))**2)
-        
-        initial_guess = [self.v0, self.kappa, self.theta, self.xi, self.rho]
-        bounds = [(0.01, 1.0), (0.01, 5.0), (0.01, 1.0), (0.01, 1.0), (-1.0, 1.0)]
-        result = minimize(objective_function, initial_guess, bounds=bounds)
-        self.v0, self.kappa, self.theta, self.xi, self.rho = result.x
-
-
-class KnockoutOption:
-    def __init__(self, model, S0, K, T, r, barrier):
-        self.model = model
-        self.S0 = S0
-        self.K = K
-        self.T = T
-        self.r = r
-        self.barrier = barrier
-
-    def price_knockout_option(self, N, M):
-        dt = self.T / M
-        price_paths = np.zeros((N, M + 1))
-        vol_paths = np.zeros((N, M + 1)) if isinstance(self.model, StochasticVolatilityModel) else None
-        price_paths[:, 0] = self.S0
-        if vol_paths is not None:
-            vol_paths[:, 0] = self.model.v0
-        
-        for t in range(1, M + 1):
-            Z1 = np.random.standard_normal(N)
-            if vol_paths is None:
-                # Black-Scholes Model
-                price_paths[:, t] = price_paths[:, t - 1] * np.exp(
-                    (self.r - 0.5 * self.model.sigma**2) * dt + self.model.sigma * np.sqrt(dt) * Z1
-                )
-            else:
-                # Stochastic Volatility Model
-                Z2 = np.random.standard_normal(N)
-                W1 = Z1
-                W2 = self.model.rho * Z1 + np.sqrt(1 - self.model.rho**2) * Z2
-                vol_paths[:, t] = np.abs(vol_paths[:, t - 1] + self.model.kappa * (self.model.theta - vol_paths[:, t - 1]) * dt + self.model.xi * np.sqrt(vol_paths[:, t - 1] * dt) * W1)
-                price_paths[:, t] = price_paths[:, t - 1] * np.exp(
-                    (self.r - 0.5 * vol_paths[:, t - 1]) * dt + np.sqrt(vol_paths[:, t - 1] * dt) * W2
-                )
-            
-            # Knockout condition
-            price_paths[:, t][price_paths[:, t] >= self.barrier] = 0
-        payoffs = np.maximum(price_paths[:, -1] - self.K, 0)
-        return np.exp(-self.r * self.T) * np.mean(payoffs)
    -
    -

    -
    -
    -

    Data Preparation

    -

    -
    -

    Synthetic data generation

    -
    -
    def generate_synthetic_market_data(model, strikes, maturities):
-    market_prices = []
-    market_data = []
-    for K, T in zip(strikes, maturities):
-        model.K = K
-        model.T = T
-        market_prices.append(model.price_option(10000, 100))
-        market_data.append({"strike": K, "option_price": model.price_option(10000, 100)})
-    return market_prices, market_data
    -
    -
    -
    N = 10000
-M = 100
-
-# Parameters for synthetic data
-S0 = 100
-K = 100
-T = 1
-r = 0.05
-# BlackSholes
-true_sigma = 0.2
-
-# Stochastic Volatility
-true_v0 = 0.2
-true_kappa = 2.0
-true_theta = 0.2
-true_xi = 0.1
-true_rho = -0.5
-
-# Synthetic data generation parameters
-strikes = list(np.linspace(75, 130, 25))
-maturities = list(np.linspace(0.2, 3.0, 25))
-
-# Generate synthetic market data using the true parameters
-bs_model = BlackScholesModel(S0, K, T, r, true_sigma)
-bs_market_prices, bs_market_data = generate_synthetic_market_data(bs_model, strikes, maturities)
-
-
-sv_model = StochasticVolatilityModel(S0, K, T, r, true_v0, true_kappa, true_theta, true_xi, true_rho)
-sv_market_prices, sv_market_data = generate_synthetic_market_data(sv_model, strikes, maturities)
    -
    -

    -
    -
    -

    Initialize the ValidMind datasets

    -

    Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -
    -
    bs_market_data_df = pd.DataFrame(bs_market_data)
-vm_bs_market_data = vm.init_dataset(
-    dataset=bs_market_data_df,
-    input_id="sv_market_data",
-)
-
-sv_market_data_df = pd.DataFrame(sv_market_data)
-vm_sv_market_data = vm.init_dataset(
-    dataset=sv_market_data_df,
-    input_id="sv_market_data",
-)
    -
    -

    -
    -
    -

    Data Quality

    -

    Let's check quality of the data using outliers and missing data tests.

    -

    -
    -

    Outliers detection using IQR method

    -

    Let's visualizes the distribution of outliers in the option_price feature using the Interquartile Range (IQR) method.

    -
    -
    result = run_test(
-    "validmind.data_validation.IQROutliersBarPlot:BlackScholes",
-    inputs={
-        "dataset": vm_bs_market_data,
-    },
-    title="Outliers detection using IQR method for BlackScholes",
-)
-result.log()
    -
    -
    -
    result = run_test(
-    "validmind.data_validation.IQROutliersTable:BlackScholes",
-    inputs={
-        "dataset": vm_bs_market_data,
-    },
-    title="Outliers table using IQR method for BlackScholes",
-)
-result.log()
    -
    -
    -
    result = run_test(
-    "validmind.data_validation.IQROutliersBarPlot:StochasticVolatility",
-    inputs={
-        "dataset": vm_sv_market_data,
-    },
-    title="Outliers detection using IQR method for StochasticVolatility",
-)
-result.log()
    -
    -
    -
    result = run_test(
-    "validmind.data_validation.IQROutliersTable:StochasticVolatility",
-    inputs={
-        "dataset": vm_sv_market_data,
-    },
-    title="Outliers table using IQR method for StochasticVolatility",
-)
-result.log()
    -
    -

    -
    -
    -

    Isolation Forest Outliers Test

    -

    Let's detects anomalies in the dataset using the Isolation Forest algorithm, visualized through scatter plots.

    -
    -
    result = run_test(
-    "validmind.data_validation.IsolationForestOutliers:BlackScholes",
-    inputs={
-        "dataset": vm_bs_market_data,
-    },
-    title="Outliers detection using Isolation Forest for BlackScholes",
-)
-result.log()
    -
    -
    -
    result = run_test(
-    "validmind.data_validation.IsolationForestOutliers:StochasticVolatility",
-    inputs={
-        "dataset": vm_sv_market_data,
-    },
-    title="Outliers detection using Isolation Forest for StochasticVolatility",
-)
-result.log()
    -
    -
    -
    Missing Values Test
    -

    Let's evaluates dataset quality by ensuring the missing value ratio across all features does not exceed a set threshold.

    -
    -
    result = run_test(
-    "validmind.data_validation.MissingValues:BlackScholes",
-    inputs={
-        "dataset": vm_bs_market_data,
-    },
-    title="Missing Values detection for BlackScholes",
-)
-result.log()
    -
    -
    -
    
-result = run_test(
-    "validmind.data_validation.MissingValues:StochasticVolatility",
-    inputs={
-        "dataset": vm_sv_market_data,
-    },
-    title="MissingValues detection for StochasticVolatility",
-)
-result.log()
    -
    -

    -
    -
    -
    -
    -

    Model Calibration

    -
      -
    • Clearly state the purpose of the calibration process. For example, in the context of an option pricing model, calibration aims to adjust model parameters to fit market data (e.g., market option prices, volatility surfaces).
      • -
    • Specify whether the calibration is to historical data, current market data, or a blend of both.
      • -
    -
    -
    import pandas as pd
-
-@vm.test("my_custom_tests.SyntheticDataCalibrationTest")
-def generate_synthetic_data_summary(option_pricing_model, strikes, maturities, synthetic_prices):
-    """
-    This function will use synthetic prices to calibrate each model
-    and then generate derived prices based on the calibrated parameters.
-    It will output a DataFrame summarizing the strikes, maturities,
-    synthetic and derived prices, and the model parameters.
-
-    """
-    derived_prices = []
-    for K, T in zip(strikes, maturities):
-        option_pricing_model.K = K
-        option_pricing_model.T = T
-        derived_prices.append(option_pricing_model.price_option(10000, 100))
-    
-    model_type = type(option_pricing_model).__name__
-    data = {
-        "Strike": strikes,
-        "Maturity": maturities,
-        "Synthetic_Price": synthetic_prices,
-        "Derived_Price": derived_prices,
-        "Model_Type": model_type,
-        "S0": [option_pricing_model.S0] * len(strikes),
-        "K": [option_pricing_model.K] * len(strikes),
-        "T": [option_pricing_model.T] * len(strikes),
-        "r": [option_pricing_model.r] * len(strikes)
-    }
-    
-    if model_type == "BlackScholesModel":
-        data["sigma"] = [option_pricing_model.sigma] * len(strikes)
-    elif model_type == "StochasticVolatilityModel":
-        data["v0"] = [option_pricing_model.v0] * len(strikes)
-        data["kappa"] = [option_pricing_model.kappa] * len(strikes)
-        data["theta"] = [option_pricing_model.theta] * len(strikes)
-        data["xi"] = [option_pricing_model.xi] * len(strikes)
-        data["rho"] = [option_pricing_model.rho] * len(strikes)
-    
-    df = pd.DataFrame(data)
-    return df
    -
    -

    -
    -
    -

    Synthetic Data Calibration Test

    -

    Let's evaluates the accuracy of a stochastic volatility model by comparing synthetic prices with derived prices after model calibration.

    -
    -
    result = run_test(
-    "my_custom_tests.SyntheticDataCalibrationTest",
-    params={
-        "option_pricing_model": sv_model,
-        "strikes": strikes,
-        "maturities": maturities,
-        "synthetic_prices": sv_market_prices
-    },
-)
-result.log()
    -
    -

    -
    -
    -

    Model Evaluation

    -

    -
    -

    Benchmark Testing

    -
      -
    • Compare the model’s performance with alternative models or industry-standard models to assess its relative effectiveness.
      • -
    • Ensure that the model is competitive in pricing, accuracy, and computational efficiency.
      • -
    -
    -
    @vm.test("my_custom_tests.BenchmarkTest")
-def benchmark_test(bs_model, sv_model, strikes, maturities):
-    """
-    Comparison between Black Scholes and stochastic volatility model
-
-    """
-    bs_model_type = type(bs_model).__name__
-    sv_model_type = type(sv_model).__name__
-
-    bs_derived_prices = []
-    sv_derived_prices = []
-    for K in strikes:
-        bs_model.K = K
-        bs_derived_prices.append(bs_model.price_option(10000, 100))
-        sv_model.K = K
-        sv_derived_prices.append(sv_model.price_option(10000, 100))
-
-    data = {
-        "Strike": strikes,
-        "Maturities": [sv_model.T] * len(strikes),
-        "bs_model_price": bs_derived_prices,
-        "sv_model_price": sv_derived_prices,
-
-    }
-    df1 = pd.DataFrame(data)
-
-    bs_derived_prices = []
-    sv_derived_prices = []
-    for T in maturities:
-        bs_model.T = T
-        bs_derived_prices.append(bs_model.price_option(10000, 100))
-        sv_model.T = T
-        sv_derived_prices.append(sv_model.price_option(10000, 100))
-
-    data = {
-        "Strike": [sv_model.K] * len(maturities),
-        "Maturities": maturities,
-        "bs_model_price": bs_derived_prices,
-        "sv_model_price": sv_derived_prices,
-    }
-
-    df2 = pd.DataFrame(data)
-
-    return {"strikes variation benchmarking": df1}, {"maturities variation benchmarking": df2}
    -
    -
    -
    result = run_test(
-    "my_custom_tests.BenchmarkTest",
-    params={
-        "sv_model": sv_model,
-        "bs_model": bs_model,
-        "strikes": strikes,
-        "maturities": maturities,
-    },
-)
-result.log()
    -
    -
    -
    Surface Volatility Test
    -

    Let's calculates the implied volatility across different strikes and maturities based on market prices

    -
    -
    import numpy as np
-import pandas as pd
-from scipy.optimize import minimize
-import plotly.graph_objects as go
-
-@vm.test("my_custom_tests.ImpliedVolSurface")
-def implied_vol_surface(market_prices, strikes, maturities, S0, r, barrier, N=10000, M=100):
-    """
-    This is a test to compute the implied volatility surface for a given set of market prices,
-    strikes, and maturities.
-    """
-    def implied_volatility(market_price, N, M, initial_guess=0.2):
-        def objective_function(sigma):
-            model.sigma = sigma
-            model_price = model.price_option(N, M)
-            return (model_price - market_price) ** 2
-
-        result = minimize(objective_function, initial_guess, bounds=[(0.01, 1.0)])
-        return result.x[0]
-    
-    implied_vols = np.zeros((len(strikes), len(maturities)))
-
-    for i, K in enumerate(strikes):
-        for j, T in enumerate(maturities):
-            market_price = market_prices[i]
-            model = BlackScholesModel(S0, K, T, r, sigma=0.2)
-
-            implied_vol = implied_volatility(market_price, N, M)
-            implied_vols[i, j] = implied_vol
-
-    # Create the 3D surface plot
-    X, Y = np.meshgrid(strikes, maturities)
-    Z = implied_vols.T  # Transpose to match the meshgrid orientation
-
-    fig = go.Figure(data=[go.Surface(x=X, y=Y, z=Z)])
-    
-    # Update the layout
-    fig.update_layout(
-        title=f'3D Surface Plot of Implied Volatility',
-        scene=dict(
-            xaxis_title='Strike',
-            yaxis_title='Maturity',
-            zaxis_title='Implied Volatility',
-            camera=dict(
-                up=dict(x=0, y=0, z=1),
-                center=dict(x=0, y=0, z=0),
-                eye=dict(x=1.5, y=1.5, z=1.5)
-            )
-        ),
-        width=900,
-        height=700,
-        margin=dict(l=65, r=50, b=65, t=90)
-    )
-
-    return fig
    -
    -
    -
    result = run_test(
-    "my_custom_tests.ImpliedVolSurface",
-    params={
-        "market_prices": sv_market_prices,
-        "strikes": strikes,
-        "maturities": maturities,
-        "S0": S0,
-        "r": r,
-        "barrier": 120
-    }
-)
-result.log()
    -
    -

    -
    -
    -
    -

    Sensitivity Testing

    -
    -
    
-@vm.test("my_custom_tests.Sensitivity")
-def sensitivity_test(model_type, S0, T, r, N, M, strike=None, barrier=None, sigma=None, v0=None, kappa=None,theta=None, xi=None, rho=None):
-    """
-    This is sensitivity test
-"""
-    if model_type == 'BS':
-        model = BlackScholesModel(S0, strike, T, r, sigma)
-    else:
-        model = StochasticVolatilityModel(S0, strike, T, r, v0, kappa, theta, xi, rho)
-    
-    knockout_option = KnockoutOption(model, S0, strike, T, r, barrier)
-    price = knockout_option.price_knockout_option(N, M)
-
-    return pd.DataFrame({"Option price": [price]})
    -
    -
    -
    Initialise parameters
    -
    -
    
-strike_range = (min(strikes), max(strikes))
-barrier_range = (100, 120)
    -
    -
    -
    -
    Common plot function
    -

    Let's create a line plot using the default result output data and log it by passing the function through the post_process_fn parameter in the run_test() method.

    -
    -
    from plotly.express import bar
-from validmind.vm_models.figure import Figure
-from validmind.vm_models.result import TestResult
-import plotly.graph_objects as go
-import random
-
-def process_results(result: TestResult):
-
-    # Convert to DataFrame
-    df = pd.DataFrame(result.tables[0].data)
-    
-    # Get the first two column names
-    x_col = df.columns[0]
-    y_col = df.columns[1]
-    
-    # Create figure
-    fig = go.Figure()
-    fig.add_trace(
-        go.Scatter(
-            x=df[x_col],
-            y=df[y_col],
-            mode='lines',
-            name=y_col  # Use y-axis column name as trace name
-        )
-    )
-    
-    fig.update_layout(
-        xaxis_title=x_col,
-        yaxis_title=y_col,
-        showlegend=True,
-        template="plotly_white"
-    )
-
-    result.add_figure(
-        Figure(
-            figure=fig,
-            key="sensitivity_plot_" + str(random.randint(0, 1000000)),
-            ref_id=result.ref_id,
-        )
-    )
-
-    return result
    -
    -
    -
    -
    Strike sensitivity Test
    -

    Let's evaluates the sensitivity of a model's output value to changes in the strike price, while keeping other parameters constant. This test is crucial for understanding how variations in strike prices affect the valuation of financial derivatives, particularly options.

    -
    -
    result = run_test(
-    "my_custom_tests.Sensitivity:S0",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike":[strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": list(np.linspace(S0-20, S0+20, 20)),
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-    },
-    post_process_fn= process_results
-)
-result.log()
    -
    -
    -
    result = run_test(
-    "my_custom_tests.Sensitivity:ToStrike",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike": list(np.linspace(strike_range[0], strike_range[1], 20)),
-        "barrier": [barrier_range[0]],
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-    },
-    post_process_fn= process_results
-)
-result.log()
    -
    -
    -
    -
    Barrier Sensitivity Test
    -

    Let's evaluates the sensitivity of a model's output to changes in the barrier level of a financial derivative, specifically a barrier option. This test is crucial for understanding how small changes in the barrier can impact the option's valuation, which is essential for risk management and pricing strategies.

    -
    -
    result = run_test(
-    "my_custom_tests.Sensitivity:ToBarrier",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike": [strike_range[0]],
-        "barrier": list(np.linspace(barrier_range[0], barrier_range[1], 20)),
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-    },
-    post_process_fn=process_results
-
-)
-result.log()
    -
    -

    -
    -
    -
    -

    Greeks

    -

    These Greeks are crucial for traders and risk managers as they provide insights into the risk and potential price movements of options and derivatives, allowing for more informed decision-making and risk management strategies.

    -

    -
    -
    -
    -

    Delta

    -

    Let's measures the sensitivity of the option's price to a change in the price of the underlying asset. It indicates how much the price of an option is expected to move per $1 change in the underlying asset's price.

    -
    -
    @vm.test("my_custom_tests.GreeksDelta")
-def calculate_delta(model_type, S0, T, r, N, M, strike=None, barrier=None, 
-                   sigma=None, v0=None, kappa=None, theta=None, xi=None, rho=None, 
-                   h=0.001):  # h is the step size for finite difference
-    """
-    Calculate delta using finite difference method.
-    Delta = (V(S0 + h) - V(S0 - h)) / (2h)
-    where V is the option price and h is a small increment
-    """
-    # Initialize the model with S0 + h
-    if model_type == 'BS':
-        model_up = BlackScholesModel(S0 + h, strike, T, r, sigma)
-        model_down = BlackScholesModel(S0 - h, strike, T, r, sigma)
-    else:
-        model_up = StochasticVolatilityModel(S0 + h, strike, T, r, v0, kappa, theta, xi, rho)
-        model_down = StochasticVolatilityModel(S0 - h, strike, T, r, v0, kappa, theta, xi, rho)
-    
-
-    # Calculate option prices for up and down moves
-    knockout_up = KnockoutOption(model_up, S0 + h, strike, T, r, barrier)
-    knockout_down = KnockoutOption(model_down, S0 - h, strike, T, r, barrier)
-    
-    price_up = knockout_up.price_knockout_option(N, M)
-    price_down = knockout_down.price_knockout_option(N, M)
-    
-    # Calculate delta using central difference
-    delta = (price_up - price_down) / (2 * h)
-    df = pd.DataFrame({"Delta": [delta], "Price_Up": [price_up], "Price_Down": [price_down], "h": [h]})
-    return df
-
    -
    -
    -
    # To analyze delta sensitivity to underlying price changes
-result = run_test(
-    "my_custom_tests.GreeksDelta",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [1000000],
-        "M": [M],
-        "strike":[strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": list(np.linspace(S0-20, S0+20, 20)),
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-        "h": [0.001]
-    },
-post_process_fn=process_results  # Using the plotting function defined earlier
-)
-result.log()
    -
    -

    -
    -
    -

    Gamma

    -

    Let's measures the rate of change of Delta with respect to changes in the underlying asset's price. It indicates the curvature of the option's price relative to the underlying asset's price.

    -
    -
    @vm.test("my_custom_tests.GreeksGamma")
-def calculate_gamma(model_type, S0, T, r, N, M, strike=None, barrier=None, 
-                   sigma=None, v0=None, kappa=None, theta=None, xi=None, rho=None, 
-                   h=0.01):  # h is the step size for finite difference
-    """
-    Calculate gamma using finite difference method.
-    Gamma = (V(S0 + h) - 2V(S0) + V(S0 - h)) / h^2
-    where V is the option price and h is a small increment
-    """
-    # Initialize the models with S0 + h, S0, and S0 - h
-    if model_type == 'BS':
-        model_up = BlackScholesModel(S0 + h, strike, T, r, sigma)
-        model_center = BlackScholesModel(S0, strike, T, r, sigma)
-        model_down = BlackScholesModel(S0 - h, strike, T, r, sigma)
-    else:
-        model_up = StochasticVolatilityModel(S0 + h, strike, T, r, v0, kappa, theta, xi, rho)
-        model_center = StochasticVolatilityModel(S0, strike, T, r, v0, kappa, theta, xi, rho)
-        model_down = StochasticVolatilityModel(S0 - h, strike, T, r, v0, kappa, theta, xi, rho)
-    
-    # Calculate option prices for up, center, and down moves
-    knockout_up = KnockoutOption(model_up, S0 + h, strike, T, r, barrier)
-    knockout_center = KnockoutOption(model_center, S0, strike, T, r, barrier)
-    knockout_down = KnockoutOption(model_down, S0 - h, strike, T, r, barrier)
-    
-    price_up = knockout_up.price_knockout_option(N, M)
-    price_center = knockout_center.price_knockout_option(N, M)
-    price_down = knockout_down.price_knockout_option(N, M)
-    
-    # Calculate gamma using second-order central difference
-    gamma = (price_up - 2*price_center + price_down) / (h * h)
-    
-    df = pd.DataFrame({
-        "Gamma": [gamma], 
-        "Price_Up": [price_up], 
-        "Price_Center": [price_center],
-        "Price_Down": [price_down], 
-        "h": [h]
-    })
-    return df
-
-# To analyze gamma sensitivity to underlying price changes
-result = run_test(
-    "my_custom_tests.GreeksGamma",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [1000000],
-        "M": [M],
-        "strike":[strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": list(np.linspace(S0-20, S0+20, 20)),
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-        "h": [0.1]
-    },
-    post_process_fn=process_results  # Using the plotting function defined earlier
-)
-result.log()
    -
    -

    -
    -
    -

    Theta

    -

    Let's measures the sensitivity of the option's price to the passage of time, also known as time decay. It indicates how much the price of an option is expected to decrease as the option approaches its expiration date.

    -
    -
    @vm.test("my_custom_tests.GreeksTheta")
-def calculate_theta(model_type, S0, T, r, N, M, strike=None, barrier=None, 
-                   sigma=None, v0=None, kappa=None, theta=None, xi=None, rho=None, 
-                   dt=1/365):  # dt is typically one day
-    """
-    Calculate theta using finite difference method.
-    Theta = (V(t + dt) - V(t)) / dt
-    where V is the option price and dt is a small time increment (typically 1 day)
-    """
-    # Initialize the models with T and T + dt
-    if model_type == 'BS':
-        model_current = BlackScholesModel(S0, strike, T, r, sigma)
-        model_future = BlackScholesModel(S0, strike, T + dt, r, sigma)
-    else:
-        model_current = StochasticVolatilityModel(S0, strike, T, r, v0, kappa, theta, xi, rho)
-        model_future = StochasticVolatilityModel(S0, strike, T + dt, r, v0, kappa, theta, xi, rho)
-    
-    # Calculate option prices for current and future time
-    knockout_current = KnockoutOption(model_current, S0, strike, T, r, barrier)
-    knockout_future = KnockoutOption(model_future, S0, strike, T + dt, r, barrier)
-    
-    price_current = knockout_current.price_knockout_option(N, M)
-    price_future = knockout_future.price_knockout_option(N, M)
-    
-    # Calculate theta using forward difference
-    # Note: We divide by dt and multiply by -1 since theta represents the negative rate of change
-    theta_value = -1 * (price_future - price_current) / dt
-    
-    df = pd.DataFrame({
-        "Theta": [theta_value], 
-        "Price_Current": [price_current],
-        "Price_Future": [price_future],
-        "dt": [dt]
-    })
-    return df
-
-# Example usage to analyze theta sensitivity across different underlying prices
-result = run_test(
-    "my_custom_tests.GreeksTheta",
-    param_grid={
-               "model_type": ['SV'],
-        "N": [1000000],
-        "M": [M],
-        "strike":[strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": list(np.linspace(S0-20, S0+20, 20)),
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-        "dt": [1/365]  # One day time step
-    },
-    post_process_fn=process_results  # Using the plotting function defined earlier
-)
-result.log()
    -
    -

    -
    -
    -

    Vega

    -

    Let's measures the sensitivity of the option's price to changes in the volatility of the underlying asset. It indicates how much the price of an option is expected to change with a 1% change in the underlying asset's volatility.

    -
    -
    @vm.test("my_custom_tests.GreeksVega")
-def calculate_vega(model_type, S0, T, r, N, M, strike=None, barrier=None, 
-                  sigma=None, v0=None, kappa=None, theta=None, xi=None, rho=None, 
-                  h=0.001):  # h is the step size for finite difference
-    """
-    Calculate vega using finite difference method.
-    For Black-Scholes: Vega = (V(σ + h) - V(σ - h)) / (2h)
-    For Stochastic Vol: Vega = (V(v0 + h) - V(v0 - h)) / (2h)
-    where V is the option price and h is a small increment in volatility
-    """
-    if model_type == 'BS':
-        # For Black-Scholes, perturb sigma
-        model_up = BlackScholesModel(S0, strike, T, r, sigma + h)
-        model_down = BlackScholesModel(S0, strike, T, r, sigma - h)
-    else:
-        # For Stochastic Volatility, perturb v0
-        model_up = StochasticVolatilityModel(S0, strike, T, r, v0 + h, kappa, theta, xi, rho)
-        model_down = StochasticVolatilityModel(S0, strike, T, r, v0 - h, kappa, theta, xi, rho)
-    
-    # Calculate option prices for up and down moves in volatility
-    knockout_up = KnockoutOption(model_up, S0, strike, T, r, barrier)
-    knockout_down = KnockoutOption(model_down, S0, strike, T, r, barrier)
-    
-    price_up = knockout_up.price_knockout_option(N, M)
-    price_down = knockout_down.price_knockout_option(N, M)
-    
-    # Calculate vega using central difference
-    vega = (price_up - price_down) / (2 * h)
-    
-    df = pd.DataFrame({
-        "Vega": [vega], 
-        "Price_Up": [price_up], 
-        "Price_Down": [price_down], 
-        "h": [h]
-    })
-    return df
-
-# Example usage to analyze vega sensitivity across different underlying prices
-result = run_test(
-    "my_custom_tests.GreeksVega",
-    param_grid={
-                "model_type": ['SV'],
-        "N": [1000000],
-        "M": [M],
-        "strike":[strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": list(np.linspace(S0-20, S0+20, 20)),
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-        "h": [0.0001]  # Small step size for better accuracy
-    },
-    post_process_fn=process_results  # Using the plotting function defined earlier
-)
-result.log()
    -
    -

    -
    -
    -

    Rho

    -

    Let's measures the sensitivity of the option's price to changes in the interest rate. It indicates how much the price of an option is expected to change with a 1% change in interest rates.

    -
    -
    @vm.test("my_custom_tests.GreeksRho")
-def calculate_rho(model_type, S0, T, r, N, M, strike=None, barrier=None, 
-                 sigma=None, v0=None, kappa=None, theta=None, xi=None, rho=None, 
-                 h=0.0001):  # h is the step size for finite difference
-    """
-    Calculate rho using finite difference method.
-    Rho = (V(r + h) - V(r - h)) / (2h)
-    where V is the option price and h is a small increment in interest rate
-    """
-    # Initialize the models with r + h and r - h
-    if model_type == 'BS':
-        model_up = BlackScholesModel(S0, strike, T, r + h, sigma)
-        model_down = BlackScholesModel(S0, strike, T, r - h, sigma)
-    else:
-        model_up = StochasticVolatilityModel(S0, strike, T, r + h, v0, kappa, theta, xi, rho)
-        model_down = StochasticVolatilityModel(S0, strike, T, r - h, v0, kappa, theta, xi, rho)
-    
-    # Calculate option prices for up and down moves in interest rate
-    knockout_up = KnockoutOption(model_up, S0, strike, T, r + h, barrier)
-    knockout_down = KnockoutOption(model_down, S0, strike, T, r - h, barrier)
-    
-    price_up = knockout_up.price_knockout_option(N, M)
-    price_down = knockout_down.price_knockout_option(N, M)
-    
-    # Calculate rho using central difference
-    rho_value = (price_up - price_down) / (2 * h)
-    
-    df = pd.DataFrame({
-        "Rho": [rho_value], 
-        "Price_Up": [price_up], 
-        "Price_Down": [price_down], 
-        "h": [h]
-    })
-    return df
-
-# Example usage to analyze rho sensitivity across different underlying prices
-result = run_test(
-    "my_custom_tests.GreeksRho",
-    param_grid={
-               "model_type": ['SV'],
-        "N": [1000000],
-        "M": [M],
-        "strike":[strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": list(np.linspace(S0-20, S0+20, 20)),
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-        "h": [0.0001]  # Small step size for better accuracy
-    },
-    post_process_fn=process_results  # Using the plotting function defined earlier
-)
-result.log()
    -
    -

    -
    -

    Stress Testing

    -
    -
    @vm.test("my_custom_tests.Stressing")
-def sensitivity_test(model_type, S0, T, r, N, M, strike=None, barrier=None, sigma=None, v0=None, kappa=None,theta=None, xi=None, rho=None):
-    """
-    This is stress test
-    """
-    if model_type == 'BS':
-        model = BlackScholesModel(S0, strike, T, r, sigma)
-    else:
-        model = StochasticVolatilityModel(S0, strike, T, r, v0, kappa, theta, xi, rho)
-    
-    knockout_option = KnockoutOption(model, S0, strike, T, r, barrier)
-    price = knockout_option.price_knockout_option(N, M)
-
-    return pd.DataFrame({"Option price": [price]})
    -
    -
    -
    Rho (correlation) and Theta (long term vol) stress test
    -

    First, we create a surface plot to visualize the option price with respect to two variables.

    -
    -
    def two_parameters_stress_surface_plot(result: TestResult):
-    import plotly.graph_objects as go
-    import numpy as np
-    import pandas as pd
-    # Convert to DataFrame
-    data = pd.DataFrame(result.tables[0].data)
-    
-    # Get column names (assuming first column is x, next two are y1 and y2)
-    z_col = data.columns[2]
-    x_col = data.columns[0]
-    y_col = data.columns[1]
-    
-    # Get unique values for x and y
-    x_unique = np.sort(data[x_col].unique())
-    y_unique = np.sort(data[y_col].unique())
-    
-    # Create meshgrid
-    X, Y = np.meshgrid(x_unique, y_unique)
-    
-    # Create Z matrix
-    Z = np.zeros_like(X)
-    for i, x_val in enumerate(x_unique):
-        for j, y_val in enumerate(y_unique):
-            mask = (data[x_col] == x_val) & (data[y_col] == y_val)
-            if mask.any():
-                Z[j, i] = data.loc[mask, z_col].iloc[0]
-    
-    # Create the 3D surface plot
-    fig = go.Figure(data=[go.Surface(x=X, y=Y, z=Z)])
-    
-    # Update the layout
-    fig.update_layout(
-        title=f'3D Surface Plot of {z_col}',
-        scene=dict(
-            xaxis_title=x_col,
-            yaxis_title=y_col,
-            zaxis_title=z_col,
-            camera=dict(
-                up=dict(x=0, y=0, z=1),
-                center=dict(x=0, y=0, z=0),
-                eye=dict(x=1.5, y=1.5, z=1.5)
-            )
-        ),
-        width=900,
-        height=700,
-        margin=dict(l=65, r=50, b=65, t=90)
-    )
-
-    result.add_figure(
-        Figure(
-            figure=fig,
-            key="sensitivity_plot_" + str(random.randint(0, 1000000)),
-            ref_id=result.ref_id,
-        )
-    )
-
-    return result
    -
    -

    Let's evaluates the sensitivity of a model's output to changes in the correlation parameter (rho) and the long-term variance parameter (theta) within a stochastic volatility framework.

    -

    This test is useful for understanding how variations in these parameters affect the model's valuation, which is crucial for risk management and model validation.

    -
    -
    
-
-result = run_test(
-    "my_custom_tests.Stressing:TheRhoAndThetaParameters",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike": [strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": list(np.linspace(0,0.8, 10)),
-        "xi": [0.1],
-        "rho": list(np.linspace(-1,0.8, 10)),
-    },
-    post_process_fn=two_parameters_stress_surface_plot
-)
-result.log()
    -
    -
    -
    -
    Rho (correlation) and Xi (vol of vol) stress test
    -
    -
    
-
-result = run_test(
-    "my_custom_tests.Stressing:TheRhoAndXiParameters",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike": [strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": list(np.linspace(0,0.8, 10)),
-        "rho": list(np.linspace(-1,0.8, 10)),
-    },
-    post_process_fn=two_parameters_stress_surface_plot
-)
-result.log()
    -
    -
    -
    -
    Sigma stress test
    -

    evaluates the sensitivity of a model's output to changes in the volatility parameter, sigma. This test is crucial for understanding how variations in market volatility impact the model's valuation of financial instruments, particularly options.

    -

    This test is useful for risk management and model validation, as it helps identify the robustness of the model under different market conditions. By analyzing the changes in the model's output as sigma varies, stakeholders can assess the model's stability and reliability.

    -
    -
    result = run_test(
-    "my_custom_tests.Stressing:TheSigmaParameter",
-    param_grid={
-        "model_type": ['BS'],
-        "N": [N],
-        "M": [M],
-        "strike": [strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "sigma": list(np.linspace(0.2, 0.8, 10)),
-    },
-    post_process_fn=process_results
-)
-result.log()
    -
    -
    -
    -
    Stress kappa
    -

    Let's evaluates the sensitivity of a model's output to changes in the kappa parameter, which is a mean reversion rate in stochastic volatility models.

    -
    -
    result = run_test(
-    "my_custom_tests.Stressing:TheKappaParameter",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike": [strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": list(np.linspace(0, 8, 10)),
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": [-0.5],
-    },
-    post_process_fn=process_results
-)
-result.log()
    -
    -
    -
    -
    Stress theta
    -

    Stress Theta evaluates the sensitivity of a model's output to changes in the parameter theta, which represents the long-term variance in a stochastic volatility model

    -
    -
    result = run_test(
-    "my_custom_tests.Stressing:TheThetaParameter",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike": [strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": list(np.linspace(0, 0.8, 10)),
-        "xi": [0.1],
-        "rho": [-0.5],
-    },
-    post_process_fn=process_results
-)
-result.log()
    -
    -
    -
    -
    Stress xi
    -

    Stress Xi evaluates the sensitivity of a model's output to changes in the parameter xi, which represents the volatility of volatility in a stochastic volatility model. This test is crucial for understanding how variations in xi impact the model's valuation, particularly in financial derivatives pricing.

    -
    -
    result = run_test(
-    "my_custom_tests.Stressing:TheXiParameter",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike": [strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": list(np.linspace(0.05, 0.95, 10)),
-        "rho": [-0.5],
-    },
-    post_process_fn=process_results
-)
-result.log()
    -
    -
    -
    -
    Stress rho
    -

    Stress rho test evaluates the sensitivity of a model's output to changes in the correlation parameter, rho, within a stochastic volatility (SV) model framework. This test is crucial for understanding how variations in rho, which represents the correlation between the asset price and its volatility, impact the model's valuation output.

    -
    -
    result = run_test(
-    "my_custom_tests.Stressing:TheRhoParameter",
-    param_grid={
-        "model_type": ['SV'],
-        "N": [N],
-        "M": [M],
-        "strike": [strike_range[0]],
-        "barrier": [barrier_range[0]],
-        "S0": [S0],
-        "T": [T],
-        "r": [r],
-        "v0": [0.2],
-        "kappa": [2],
-        "theta": [0.2],
-        "xi": [0.1],
-        "rho": list(np.linspace(-1.0, 1.0, 20)),
-    },
-    post_process_fn=process_results
-)
-result.log()
    -
    -

    -
    -
    -
    -
    -
    -

    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

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    -
    - - - -
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    Welcome! This notebook demonstrates how to use the ValidMind code explainer to automatically generate comprehensive documentation for your codebase. The code explainer analyzes your source code and provides detailed explanations across various aspects of your implementation.

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    About Code Explainer

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    The ValidMind code explainer is a powerful tool that automatically analyzes your source code and generates comprehensive documentation. It helps you:

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    • Understand the structure and organization of your codebase
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    • Document dependencies and environment setup
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    • Explain data processing and model implementation details
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    • Document training, evaluation, and inference pipelines
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    • Track configuration, testing, and security measures
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    This tool is particularly useful for: - Onboarding new team members - Maintaining up-to-date documentation - Ensuring code quality and best practices - Facilitating code reviews and audits

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    About ValidMind

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    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

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    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

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    Before you begin

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    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

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    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

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    New to ValidMind?

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    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

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    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
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    Key concepts

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    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

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    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

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    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

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    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

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    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

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    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

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    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

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    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

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    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

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    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

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    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
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    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
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    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
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    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
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    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

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    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

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    Setting up

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    Install the ValidMind Library

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    To install the library:

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    %pip install -q validmind
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    Initialize the ValidMind Library

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    Register sample model

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    Let's first register a sample record (model) for use with this notebook:

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    1. In a browser, log in to ValidMind.

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    2. In the left sidebar, select Inventory.

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    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

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    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

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    5. Select your own name under the RECORD OWNER drop-down.

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    6. Click Register Model to add the model to your inventory.

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    Apply documentation template

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    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

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    1. In the left sidebar that appears for your model, click Documents and select Development.

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      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

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    2. Under TEMPLATE, select Model Source Code Documentation.

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    3. Click Use Template to apply the template.

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    -
    -Can't select this template?

    Your organization administrators may need to add it to your template library: - -
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    Get your code snippet

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    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

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    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

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    2. Click Copy snippet to clipboard.

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    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

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    # Load your model identifier credentials from an `.env` file
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-%load_ext dotenv
-%dotenv .env
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-# Or replace with your code snippet
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-import validmind as vm
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-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
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    Preview the documentation template

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    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

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    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

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    vm.preview_template()
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    Common function

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    The code above defines two key functions: 1. A function to read source code from 'customer_churn_full_suite.py' file 2. An 'explain_code' function that uses ValidMind's experimental agents to analyze and explain code.

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    source_code=""
-with open("customer_churn_full_suite.py", "r") as f:
-    source_code = f.read()
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    The vm.experimental.agents.run_task function is used to execute AI agent tasks.

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    It requires: - task: The type of task to run (e.g. code_explainer) - input: A dictionary containing task-specific parameters - For code_explainer, this includes: - source_code (str): The code to be analyzed - user_instructions (str): Instructions for how to analyze the code

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    def explain_code(content_id: str, user_instructions: str):
-    """Run code explanation task and log the results.
-    By default, the code explainer includes sections for:
-    - Main Purpose and Overall Functionality
-    - Breakdown of Key Functions or Components
-    - Potential Risks or Failure Points  
-    - Assumptions or Limitations
-    If you want default sections, specify user_instructions as an empty string.
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-    Args:
-        user_instructions (str): Instructions for how to analyze the code
-        content_id (str): ID to use when logging the results
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-    Returns:
-        The result object from running the code explanation task
-    """
-    result = vm.experimental.agents.run_task(
-        task="code_explainer",
-        input={
-            "source_code": source_code,
-            "user_instructions": user_instructions
-        }
-    )
-    result.log(content_id=content_id)
-    return result
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    Default Behavior

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    By default, the code explainer includes sections for: - Main Purpose and Overall Functionality - Breakdown of Key Functions or Components - Potential Risks or Failure Points
    -- Assumptions or Limitations

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    If you want default sections, specify user_instructions as an empty string. For example:

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    result = vm.experimental.agents.run_task(
-    task="code_explainer",
-    input={
-        "source_code": source_code,
-        "user_instructions": ""
-    }
-)
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    Codebase Overview

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    Let's analyze your codebase structure to understand the main modules, components, entry points and their relationships. We'll also examine the technology stack and frameworks that are being utilized in the implementation.

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    result = explain_code(
-    user_instructions="""
-        Please provide a summary of the following bullet points only.
-        - Describe the overall structure of the source code repository.
-        - Identify main modules, folders, and scripts.
-        - Highlight entry points for training, inference, and evaluation.
-        - State the main programming languages and frameworks used.
-        """,
-    content_id="code_structure_summary"
-)
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    result = explain_code(
-    user_instructions="",
-    content_id="code_structure_summary"
-)
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    Environment and Dependencies ('environment_setup')

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    Let's document the technical requirements and setup needed to run your code, including Python packages, system dependencies, and environment configuration files. Understanding these requirements is essential for proper development environment setup and consistent deployments across different environments.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - List Python packages and system dependencies (OS, compilers, etc.).
-    - Reference environment files (requirements.txt, environment.yml, Dockerfile).
-    - Include setup instructions using Conda, virtualenv, or containers.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="setup_instructions"
-)
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    Data Ingestion and Preprocessing

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    Let's document how your code handles data, including data sources, validation procedures, and preprocessing steps. We'll examine the data pipeline architecture, covering everything from initial data loading through feature engineering and quality checks.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - Specify data input formats and sources.
-    - Document ingestion, validation, and transformation logic.
-    - Explain how raw data is preprocessed and features are generated.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.    """,
-    content_id="data_handling_notes"
-)
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    Model Implementation Details

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    Let's document the core implementation details of your model, including its architecture, components, and key algorithms. Understanding the technical implementation is crucial for maintenance, debugging, and future improvements to the codebase. We'll examine how theoretical concepts are translated into working code.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - Describe the core model code structure (classes, functions).
-    - Link code to theoretical models or equations when applicable.
-    - Note custom components like loss functions or feature selectors.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="model_code_description"
-)
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    Model Training Pipeline

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    Let's document the training pipeline implementation, including how models are trained, optimized and evaluated. We'll examine the training process workflow, hyperparameter tuning approach, and model checkpointing mechanisms. This section provides insights into how the model learns from data and achieves optimal performance.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - Explain the training process, optimization strategy, and hyperparameters.
-    - Describe logging, checkpointing, and early stopping mechanisms.
-    - Include references to training config files or tuning logic.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="training_logic_details"
-)
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    Evaluation and Validation Code

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    Let's examine how the model's validation and evaluation code is implemented, including the metrics calculation and validation processes. We'll explore the diagnostic tools and visualization methods used to assess model performance. This section will also cover how validation results are logged and stored for future reference.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - Describe how validation is implemented and metrics are calculated.
-    - Include plots and diagnostic tools (e.g., ROC, SHAP, confusion matrix).
-    - State how outputs are logged and persisted.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="evaluation_logic_notes"
-)
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    Inference and Scoring Logic

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    Let's examine how the model performs inference and scoring on new data. This section will cover the implementation details of loading trained models, making predictions, and any required pre/post-processing steps. We'll also look at the APIs and interfaces available for both real-time serving and batch scoring scenarios.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - Detail how the trained model is loaded and used for predictions.
-    - Explain I/O formats and APIs for serving or batch scoring.
-    - Include any preprocessing/postprocessing logic required.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="inference_mechanism"
-)
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    Configuration and Parameters

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    Let's explore how configuration and parameters are managed in the codebase. We'll examine the configuration files, command-line arguments, environment variables, and other mechanisms used to control model behavior. This section will also cover parameter versioning and how different configurations are tracked across model iterations.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - Describe configuration management (files, CLI args, env vars).
-    - Highlight default parameters and override mechanisms.
-    - Reference versioning practices for config files.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="config_control_notes"
-)
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    Unit and Integration Testing

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    Let's examine the testing strategy and implementation in the codebase. We'll analyze the unit tests, integration tests, and testing frameworks used to ensure code quality and reliability. This section will also cover test coverage metrics and continuous integration practices.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - List unit and integration tests and what they cover.
-    - Mention testing frameworks and coverage tools used.
-    - Explain testing strategy for production-readiness.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="test_strategy_overview"
-)
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    Logging and Monitoring Hooks

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    Let's analyze how logging and monitoring are implemented in the codebase. We'll examine the logging configuration, monitoring hooks, and key metrics being tracked. This section will also cover any real-time observability integrations and alerting mechanisms in place.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-    - Describe logging configuration and structure.
-    - Highlight real-time monitoring or observability integrations.
-    - List key events, metrics, or alerts tracked.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="logging_monitoring_notes"
-)
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    Code and Model Versioning

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    Let's examine how code and model versioning is managed in the codebase. This section will cover version control practices, including Git workflows and model artifact versioning tools like DVC or MLflow. We'll also look at how versioning integrates with the CI/CD pipeline.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-      - Describe Git usage, branching, tagging, and commit standards.
-      - Include model artifact versioning practices (e.g., DVC, MLflow).
-      - Reference any automation in CI/CD.
-    Please remove the following sections: 
-      - Potential Risks or Failure Points
-      - Assumptions or Limitations
-      - Breakdown of Key Functions or Components
-    Please don't add any other sections.
-    """,
-    content_id="version_tracking_description"
-)
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    Security and Access Control

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    Let's analyze the security and access control measures implemented in the codebase. We'll examine how sensitive data and code are protected through access controls, encryption, and compliance measures. Additionally, we'll review secure deployment practices and any specific handling of PII data.

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    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-      - Document access controls for source code and data.
-      - Include any encryption, PII handling, or compliance measures.
-      - Mention secure deployment practices.
-    Please remove the following sections: 
-      - Potential Risks or Failure Points
-      - Assumptions or Limitations
-      - Breakdown of Key Functions or Components
-    Please don't add any other sections.
-    """,
-    content_id="security_policies_notes"
-)
    -
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    Example Runs and Scripts

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    Let's explore example runs and scripts that demonstrate how to use this codebase in practice. We'll look at working examples, command-line usage, and sample notebooks that showcase the core functionality. This section will also point to demo datasets and test scenarios that can help new users get started quickly.

    -
    -
    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-      - Provide working script examples.
-      - Include CLI usage instructions or sample notebooks.
-      - Link to demo datasets or test scenarios.
-    Please remove the following sections: 
-      - Potential Risks or Failure Points
-      - Assumptions or Limitations
-      - Breakdown of Key Functions or Components
-    Please don't add any other sections.
-    """,
-    content_id="runnable_examples"
-)
    -
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    -

    -
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    Known Issues and Future Improvements

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    Let's examine the current limitations and areas for improvement in the codebase. This section will document known technical debt, bugs, and feature gaps that need to be addressed. We'll also outline proposed enhancements and reference any existing tickets or GitHub issues tracking these improvements.

    -
    -
    result = explain_code(
-    user_instructions="""
-    Please provide a summary of the following bullet points only.
-      - List current limitations or technical debt.
-      - Outline proposed enhancements or refactors.
-      - Reference relevant tickets, GitHub issues, or roadmap items.
-    Please remove Potential Risks or Failure Points and Assumptions or Limitations sections. Please don't add any other sections.
-    """,
-    content_id="issues_and_improvements_log"
-)
    -
    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/credit_risk/application_scorecard_full_suite.html b/site/notebooks/use_cases/credit_risk/application_scorecard_full_suite.html deleted file mode 100644 index 634dbbf93..000000000 --- a/site/notebooks/use_cases/credit_risk/application_scorecard_full_suite.html +++ /dev/null @@ -1,2047 +0,0 @@ - - - - - - - - - -Document an application scorecard model – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Document an application scorecard model

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    Build and document an application scorecard model with the ValidMind Library by using Kaggle's Lending Club sample dataset to build a simple application scorecard.

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    An application scorecard model is a type of statistical model used in credit scoring to evaluate the creditworthiness of potential borrowers by generating a score based on various characteristics of an applicant — such as credit history, income, employment status, and other relevant financial data.

    - -

    This interactive notebook provides a step-by-step guide for loading a demo dataset, preprocessing the raw data, training a model for testing, setting up test inputs, initializing the required ValidMind objects, running the test, and then logging the results to ValidMind.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

    -

    -
    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Credit Risk Scorecard.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-  # api_host = "...",
-  # api_key = "...",
-  # api_secret = "...",
-  # model = "...",
-  document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Initialize the Python environment

    -

    Next, let's import the necessary libraries and set up your Python environment for data analysis:

    -
    -
    import xgboost as xgb
-from sklearn.ensemble import RandomForestClassifier
-
-from validmind.datasets.credit_risk import lending_club
-
-%matplotlib inline
    -
    -

    -
    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -
    -

    Load the sample dataset

    -

    The sample dataset used here is provided by the ValidMind library. To be able to use it, you'll need to import the dataset and load it into a pandas DataFrame, a two-dimensional tabular data structure that makes use of rows and columns:

    -
    -
    df = lending_club.load_data(source="offline")
-
-df.head()
    -
    -

    -
    -

    Prepocess the dataset

    -

    In the preprocessing step we perform a number of operations to get ready for building our application scorecard.

    -

    We use the lending_club.preprocess to simplify preprocessing. This function performs the following operations: - Filters the dataset to include only loans for debt consolidation or credit card purposes - Removes loans classified under the riskier grades "F" and "G" - Excludes uncommon home ownership types and standardizes employment length and loan terms into numerical formats - Discards unnecessary fields and any entries with missing information to maintain a clean and robust dataset for modeling

    -
    -
    preprocess_df = lending_club.preprocess(df)
-preprocess_df.head()
    -
    -

    -
    -
    -

    Feature engineering

    -

    In the feature engineering phase, we apply specific transformations to optimize the dataset for predictive modeling in our application scorecard.

    -

    Using the ending_club.feature_engineering() function, we conduct the following operations: - WoE encoding: Converts both numerical and categorical features into Weight of Evidence (WoE) values. WoE is a statistical measure used in scorecard modeling that quantifies the relationship between a predictor variable and the binary target variable. It calculates the ratio of the distribution of good outcomes to the distribution of bad outcomes for each category or bin of a feature. This transformation helps to ensure that the features are predictive and consistent in their contribution to the model. - Integration of WoE bins: Ensures that the WoE transformed values are integrated throughout the dataset, replacing the original feature values while excluding the target variable from this transformation. This transformation is used to maintain a consistent scale and impact of each variable within the model, which helps make the predictions more stable and accurate.

    -
    -
    fe_df = lending_club.feature_engineering(preprocess_df)
-fe_df.head()
    -
    -

    -
    -
    -
    -

    Train the model

    -

    In this section, we focus on constructing and refining our predictive model. - We begin by dividing our data, which is based on Weight of Evidence (WoE) features, into training and testing sets (train_df, test_df). - With lending_club.split, we employ a simple random split, randomly allocating data points to each set to ensure a mix of examples in both.

    -
    -
    # Split the data
-train_df, test_df = lending_club.split(fe_df, test_size=0.2)
-
-x_train = train_df.drop(lending_club.target_column, axis=1)
-y_train = train_df[lending_club.target_column]
-
-x_test = test_df.drop(lending_club.target_column, axis=1)
-y_test = test_df[lending_club.target_column]
    -
    -
    -
    # Define the XGBoost model
-xgb_model = xgb.XGBClassifier(
-    n_estimators=50, 
-    random_state=42, 
-    early_stopping_rounds=10
-)
-xgb_model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-
-# Fit the model
-xgb_model.fit(
-    x_train, 
-    y_train,
-    eval_set=[(x_test, y_test)],
-    verbose=False
-)
    -
    -
    -
    # Define the Random Forest model
-rf_model = RandomForestClassifier(
-    n_estimators=50, 
-    random_state=42,
-)
-
-# Fit the model
-rf_model.fit(x_train, y_train)
    -
    -

    -
    -

    Compute probabilities

    -
    -
    train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]
-test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]
-
-train_rf_prob = rf_model.predict_proba(x_train)[:, 1]
-test_rf_prob = rf_model.predict_proba(x_test)[:, 1]
    -
    -

    -
    -
    -

    Compute binary predictions

    -
    -
    cut_off_threshold = 0.3
-
-train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)
-test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)
-
-train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)
-test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)
    -
    -

    -
    -
    -
    -

    Document the model

    -

    To document the model with the ValidMind Library, you'll need to: 1. Preprocess the raw dataset 2. Initialize some training and test datasets 3. Initialize a model object you can use for testing 4. Run the full suite of tests

    -

    -
    -

    Initialize the ValidMind datasets

    -

    Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -

    This function takes a number of arguments:

    -
      -
    • dataset: The dataset that you want to provide as input to tests.
      • -
    • input_id: A unique identifier that allows tracking what inputs are used when running each individual test.
      • -
    • target_column: A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    -

    With all datasets ready, you can now initialize the raw, processed, training and test datasets (raw_df, preprocessed_df, fe_df, train_df and test_df) created earlier into their own dataset objects using vm.init_dataset():

    -
    -
    vm_raw_dataset = vm.init_dataset(
-    dataset=df,
-    input_id="raw_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_preprocess_dataset = vm.init_dataset(
-    dataset=preprocess_df,
-    input_id="preprocess_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_fe_dataset = vm.init_dataset(
-    dataset=fe_df,
-    input_id="fe_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="train_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_test_ds = vm.init_dataset(
-    dataset=test_df,
-    input_id="test_dataset",
-    target_column=lending_club.target_column,
-)
    -
    -

    -
    -
    -

    Initialize ValidMind models

    -

    You'll also need to initialize ValidMind model objects (vm_model) that can be passed to other functions for analysis and tests on the data for our modelS.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model objects with vm.init_model():

    -
    -
    vm_xgb_model = vm.init_model(
-    xgb_model,
-    input_id="xgb_model",
-)
-
-vm_rf_model = vm.init_model(
-    rf_model,
-    input_id="rf_model",
-)
    -
    -

    -
    -
    -

    Assign prediction values and probabilities to the datasets

    -

    With our model now trained, we'll move on to assigning both the predictive probabilities coming directly from the model's predictions, and the binary prediction after applying the cutoff threshold described in the previous steps. - These tasks are achieved through the use of the assign_predictions() method associated with the VM dataset object. - This method links the model's class prediction values and probabilities to our VM train and test datasets.

    -
    -
    # XGBoost
-vm_train_ds.assign_predictions(
-    model=vm_xgb_model,
-    prediction_values=train_xgb_binary_predictions,
-    prediction_probabilities=train_xgb_prob,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_xgb_model,
-    prediction_values=test_xgb_binary_predictions,
-    prediction_probabilities=test_xgb_prob,
-)
-
-# Random Forest
-vm_train_ds.assign_predictions(
-    model=vm_rf_model,
-    prediction_values=train_rf_binary_predictions,
-    prediction_probabilities=train_rf_prob,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_rf_model,
-    prediction_values=test_rf_binary_predictions,
-    prediction_probabilities=test_rf_prob,
-)
    -
    -

    -
    -
    -

    Compute credit risk scores

    -

    In this phase, we translate model predictions into actionable scores using probability estimates generated by our trained model.

    -
    -
    train_xgb_scores = lending_club.compute_scores(train_xgb_prob)
-test_xgb_scores = lending_club.compute_scores(test_xgb_prob)
-
-# Assign scores to the datasets
-vm_train_ds.add_extra_column("xgb_scores", train_xgb_scores)
-vm_test_ds.add_extra_column("xgb_scores", test_xgb_scores)
    -
    -

    -
    -
    -

    Adding custom context to the LLM descriptions

    -

    To enable the LLM descriptions context, you need to set the VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED environment variable to 1. This will enable the LLM descriptions context, which will be used to provide additional context to the LLM descriptions. This is a global setting that will affect all tests.

    -
    -
    import os
-os.environ["VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED"] = "1"
-
-context = """
-FORMAT FOR THE LLM DESCRIPTIONS: 
-    **<Test Name>** is designed to <begin with a concise overview of what the test does and its primary purpose, 
-    extracted from the test description>.
-
-    The test operates by <write a paragraph about the test mechanism, explaining how it works and what it measures. 
-    Include any relevant formulas or methodologies mentioned in the test description.>
-
-    The primary advantages of this test include <write a paragraph about the test's strengths and capabilities, 
-    highlighting what makes it particularly useful for specific scenarios.>
-
-    Users should be aware that <write a paragraph about the test's limitations and potential risks. 
-    Include both technical limitations and interpretation challenges. 
-    If the test description includes specific signs of high risk, incorporate these here.>
-
-    **Key Insights:**
-
-    The test results reveal:
-
-    - **<insight title>**: <comprehensive description of one aspect of the results>
-    - **<insight title>**: <comprehensive description of another aspect>
-    ...
-
-    Based on these results, <conclude with a brief paragraph that ties together the test results with the test's 
-    purpose and provides any final recommendations or considerations.>
-
-ADDITIONAL INSTRUCTIONS:
-    Present insights in order from general to specific, with each insight as a single bullet point with bold title.
-
-    For each metric in the test results, include in the test overview:
-    - The metric's purpose and what it measures
-    - Its mathematical formula
-    - The range of possible values
-    - What constitutes good/bad performance
-    - How to interpret different values
-
-    Each insight should progressively cover:
-    1. Overall scope and distribution
-    2. Complete breakdown of all elements with specific values
-    3. Natural groupings and patterns
-    4. Comparative analysis between datasets/categories
-    5. Stability and variations
-    6. Notable relationships or dependencies
-
-    Remember:
-    - Keep all insights at the same level (no sub-bullets or nested structures)
-    - Make each insight complete and self-contained
-    - Include specific numerical values and ranges
-    - Cover all elements in the results comprehensively
-    - Maintain clear, concise language
-    - Use only "- **Title**: Description" format for insights
-    - Progress naturally from general to specific observations
-
-""".strip()
-
-os.environ["VALIDMIND_LLM_DESCRIPTIONS_CONTEXT"] = context
    -
    -

    -
    -
    -

    Run the full suite of tests

    -

    This is where it all comes together: you are now ready to run the documentation tests for the model as defined by the documentation template you looked at earlier.

    -

    The vm.run_documentation_tests function finds and runs every test specified in the template and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform.

    -

    The function requires information about the inputs to use on every test. These inputs can be passed as an inputs argument if we want to use the same inputs for all tests. It's also possible to pass a config argument that has information about the params and inputs that each test requires. The config parameter is a dictionary with the following structure:

    -
    config = {
-    "<test-id>": {
-        "params": {
-            "param1": "value1",
-            "param2": "value2",
-            ...
-        },
-        "inputs": {
-            "input1": "value1",
-            "input2": "value2",
-            ...
-        }
-    },
-    ...
-}
    -

    Each <test-id> above corresponds to the test driven block identifiers shown by vm.preview_template(). For this model, we will use the default parameters for all tests, but we'll need to specify the input configuration for each one. The method get_demo_test_config() below constructs the default input configuration for our demo.

    -
    -
    from validmind.utils import preview_test_config
-
-test_config = lending_club.get_demo_test_config(x_test, y_test)
-preview_test_config(test_config)
    -
    -

    Now we can pass the input configuration to vm.run_documentation_tests() and run the full suite of tests. The variable full_suite then holds the result of these tests.

    -
    -
    full_suite = vm.run_documentation_tests(config=test_config)
    -
    -

    -
    -
    -
    -

    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -

    3. Expand the following sections and take a look around:

      -
        -
      • 2. Data Preparation
        • -
      • 3. Model Development
        • -
      4. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation (hint: some of the tests in 2.3. Feature Selection and Engineering look like they need some attention), view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/credit_risk/application_scorecard_with_ml.html b/site/notebooks/use_cases/credit_risk/application_scorecard_with_ml.html deleted file mode 100644 index 14c516511..000000000 --- a/site/notebooks/use_cases/credit_risk/application_scorecard_with_ml.html +++ /dev/null @@ -1,2746 +0,0 @@ - - - - - - - - - -Document an application scorecard model – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
    - -
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    - - - -
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    Document an application scorecard model

    -
    - - - -
    - - - - -
    - - - -
    - - - -

    Build and document an application scorecard model with the ValidMind Library by using Kaggle's Lending Club sample dataset to build a simple application scorecard.

    -

    An application scorecard model is a type of statistical model used in credit scoring to evaluate the creditworthiness of potential borrowers by generating a score based on various characteristics of an applicant — such as credit history, income, employment status, and other relevant financial data.

    - -

    This interactive notebook provides a step-by-step guide for loading a demo dataset, preprocessing the raw data, training a model for testing, setting up test inputs, initializing the required ValidMind objects, running the test, and then logging the results to ValidMind.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

    -

    -
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    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Credit Risk Scorecard.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-  # api_host = "...",
-  # api_key = "...",
-  # api_secret = "...",
-  # model = "...",
-  document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Initialize the Python environment

    -

    Next, let's import the necessary libraries and set up your Python environment for data analysis:

    -
    -
    import xgboost as xgb
-from sklearn.ensemble import RandomForestClassifier
-
-from validmind.tests import run_test
-from validmind.datasets.credit_risk import lending_club
-
-%matplotlib inline
    -
    -

    -
    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -
    -

    Load the sample dataset

    -

    The sample dataset used here is provided by the ValidMind library. To be able to use it, you'll need to import the dataset and load it into a pandas DataFrame, a two-dimensional tabular data structure that makes use of rows and columns:

    -
    -
    df = lending_club.load_data(source="offline")
-
-df.head()
    -
    -

    -
    -

    Prepocess the dataset

    -

    In the preprocessing step we perform a number of operations to get ready for building our application scorecard.

    -

    We use the lending_club.preprocess to simplify preprocessing. This function performs the following operations: - Filters the dataset to include only loans for debt consolidation or credit card purposes - Removes loans classified under the riskier grades "F" and "G" - Excludes uncommon home ownership types and standardizes employment length and loan terms into numerical formats - Discards unnecessary fields and any entries with missing information to maintain a clean and robust dataset for modeling

    -
    -
    preprocess_df = lending_club.preprocess(df)
-preprocess_df.head()
    -
    -

    -
    -
    -

    Feature engineering

    -

    In the feature engineering phase, we apply specific transformations to optimize the dataset for predictive modeling in our application scorecard.

    -

    Using the ending_club.feature_engineering() function, we conduct the following operations: - WoE encoding: Converts both numerical and categorical features into Weight of Evidence (WoE) values. WoE is a statistical measure used in scorecard modeling that quantifies the relationship between a predictor variable and the binary target variable. It calculates the ratio of the distribution of good outcomes to the distribution of bad outcomes for each category or bin of a feature. This transformation helps to ensure that the features are predictive and consistent in their contribution to the model. - Integration of WoE bins: Ensures that the WoE transformed values are integrated throughout the dataset, replacing the original feature values while excluding the target variable from this transformation. This transformation is used to maintain a consistent scale and impact of each variable within the model, which helps make the predictions more stable and accurate.

    -
    -
    fe_df = lending_club.feature_engineering(preprocess_df)
-fe_df.head()
    -
    -

    -
    -
    -
    -

    Train the model

    -

    In this section, we focus on constructing and refining our predictive model. - We begin by dividing our data, which is based on Weight of Evidence (WoE) features, into training and testing sets (train_df, test_df). - With lending_club.split, we employ a simple random split, randomly allocating data points to each set to ensure a mix of examples in both.

    -
    -
    # Split the data
-train_df, test_df = lending_club.split(fe_df, test_size=0.2)
-
-x_train = train_df.drop(lending_club.target_column, axis=1)
-y_train = train_df[lending_club.target_column]
-
-x_test = test_df.drop(lending_club.target_column, axis=1)
-y_test = test_df[lending_club.target_column]
    -
    -
    -
    # Define the XGBoost model
-xgb_model = xgb.XGBClassifier(
-    n_estimators=50, 
-    random_state=42, 
-    early_stopping_rounds=10
-)
-xgb_model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-
-# Fit the model
-xgb_model.fit(
-    x_train, 
-    y_train,
-    eval_set=[(x_test, y_test)],
-    verbose=False
-)
    -
    -
    -
    # Define the Random Forest model
-rf_model = RandomForestClassifier(
-    n_estimators=50, 
-    random_state=42,
-)
-
-# Fit the model
-rf_model.fit(x_train, y_train)
    -
    -

    -
    -

    Compute probabilities

    -
    -
    train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]
-test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]
-
-train_rf_prob = rf_model.predict_proba(x_train)[:, 1]
-test_rf_prob = rf_model.predict_proba(x_test)[:, 1]
    -
    -

    -
    -
    -

    Compute binary predictions

    -
    -
    cut_off_threshold = 0.3
-
-train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)
-test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)
-
-train_rf_binary_predictions = (train_rf_prob > cut_off_threshold).astype(int)
-test_rf_binary_predictions = (test_rf_prob > cut_off_threshold).astype(int)
    -
    -

    -
    -
    -
    -

    Document the model

    -

    To document the model with the ValidMind Library, you'll need to: 1. Preprocess the raw dataset 2. Initialize some training and test datasets 3. Initialize a ValidMind model object for use with testing 4. Run the full suite of tests

    -

    -
    -

    Initialize the ValidMind datasets

    -

    Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -

    This function takes a number of arguments:

    -
      -
    • dataset: The dataset that you want to provide as input to tests.
      • -
    • input_id: A unique identifier that allows tracking what inputs are used when running each individual test.
      • -
    • target_column: A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    -

    With all datasets ready, you can now initialize the raw, processed, training and test datasets (raw_df, preprocessed_df, fe_df, train_df and test_df) created earlier into their own dataset objects using vm.init_dataset():

    -
    -
    vm_raw_dataset = vm.init_dataset(
-    dataset=df,
-    input_id="raw_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_preprocess_dataset = vm.init_dataset(
-    dataset=preprocess_df,
-    input_id="preprocess_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_fe_dataset = vm.init_dataset(
-    dataset=fe_df,
-    input_id="fe_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="train_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_test_ds = vm.init_dataset(
-    dataset=test_df,
-    input_id="test_dataset",
-    target_column=lending_club.target_column,
-)
    -
    -

    -
    -
    -

    Initialize the ValidMind models

    -

    You'll also need to initialize ValidMind model objects (vm_model) that can be passed to other functions for analysis and tests on the data for our modelS.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
    -
    vm_xgb_model = vm.init_model(
-    xgb_model,
-    input_id="xgb_model",
-)
-
-vm_rf_model = vm.init_model(
-    rf_model,
-    input_id="rf_model",
-)
    -
    -

    -
    -
    -

    Assign prediction values and probabilities to the datasets

    -

    With our model now trained, we'll move on to assigning both the predictive probabilities coming directly from the model's predictions, and the binary prediction after applying the cutoff threshold described in the previous steps. - These tasks are achieved through the use of the assign_predictions() method associated with the VM dataset object. - This method links the model's class prediction values and probabilities to our VM train and test datasets.

    -
    -
    # XGBoost
-vm_train_ds.assign_predictions(
-    model=vm_xgb_model,
-    prediction_values=train_xgb_binary_predictions,
-    prediction_probabilities=train_xgb_prob,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_xgb_model,
-    prediction_values=test_xgb_binary_predictions,
-    prediction_probabilities=test_xgb_prob,
-)
-
-# Random Forest
-vm_train_ds.assign_predictions(
-    model=vm_rf_model,
-    prediction_values=train_rf_binary_predictions,
-    prediction_probabilities=train_rf_prob,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_rf_model,
-    prediction_values=test_rf_binary_predictions,
-    prediction_probabilities=test_rf_prob,
-)
    -
    -

    -
    -
    -

    Compute credit risk scores

    -

    In this phase, we translate model predictions into actionable scores using probability estimates generated by our trained model.

    -
    -
    train_xgb_scores = lending_club.compute_scores(train_xgb_prob)
-test_xgb_scores = lending_club.compute_scores(test_xgb_prob)
-
-# Assign scores to the datasets
-vm_train_ds.add_extra_column("xgb_scores", train_xgb_scores)
-vm_test_ds.add_extra_column("xgb_scores", test_xgb_scores)
    -
    -

    -
    -
    -

    Adding custom context to the LLM descriptions

    -

    To enable the LLM descriptions context, you need to set the VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED environment variable to 1. This will enable the LLM descriptions context, which will be used to provide additional context to the LLM descriptions. This is a global setting that will affect all tests.

    -
    -
    import os
-os.environ["VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED"] = "1"
-
-context = """
-FORMAT FOR THE LLM DESCRIPTIONS: 
-    **<Test Name>** is designed to <begin with a concise overview of what the test does and its primary purpose, 
-    extracted from the test description>.
-
-    The test operates by <write a paragraph about the test mechanism, explaining how it works and what it measures. 
-    Include any relevant formulas or methodologies mentioned in the test description.>
-
-    The primary advantages of this test include <write a paragraph about the test's strengths and capabilities, 
-    highlighting what makes it particularly useful for specific scenarios.>
-
-    Users should be aware that <write a paragraph about the test's limitations and potential risks. 
-    Include both technical limitations and interpretation challenges. 
-    If the test description includes specific signs of high risk, incorporate these here.>
-
-    **Key Insights:**
-
-    The test results reveal:
-
-    - **<insight title>**: <comprehensive description of one aspect of the results>
-    - **<insight title>**: <comprehensive description of another aspect>
-    ...
-
-    Based on these results, <conclude with a brief paragraph that ties together the test results with the test's 
-    purpose and provides any final recommendations or considerations.>
-
-ADDITIONAL INSTRUCTIONS:
-    Present insights in order from general to specific, with each insight as a single bullet point with bold title.
-
-    For each metric in the test results, include in the test overview:
-    - The metric's purpose and what it measures
-    - Its mathematical formula
-    - The range of possible values
-    - What constitutes good/bad performance
-    - How to interpret different values
-
-    Each insight should progressively cover:
-    1. Overall scope and distribution
-    2. Complete breakdown of all elements with specific values
-    3. Natural groupings and patterns
-    4. Comparative analysis between datasets/categories
-    5. Stability and variations
-    6. Notable relationships or dependencies
-
-    Remember:
-    - Keep all insights at the same level (no sub-bullets or nested structures)
-    - Make each insight complete and self-contained
-    - Include specific numerical values and ranges
-    - Cover all elements in the results comprehensively
-    - Maintain clear, concise language
-    - Use only "- **Title**: Description" format for insights
-    - Progress naturally from general to specific observations
-
-""".strip()
-
-os.environ["VALIDMIND_LLM_DESCRIPTIONS_CONTEXT"] = context
    -
    -

    -
    -
    -

    Raw data

    -
    -
    run_test(
-    "validmind.data_validation.DatasetDescription:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.DescriptiveStatistics:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.MissingValues:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-    params={
-        "min_percentage_threshold": 1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.ClassImbalance:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-    params={
-        "min_percent_threshold": 10
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.Duplicates:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-    params={
-        "min_percentage_threshold": 1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.HighCardinality:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-    params={
-        "num_threshold": 100,
-        "percent_threshold": 0.1,
-        "threshold_type": "percent"
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.Skewness:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-    params={
-        "max_threshold": 1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.UniqueRows:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-    params={
-        "min_percent_threshold": 1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.TooManyZeroValues:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-    params={
-        "max_percent_threshold": 0.03
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.IQROutliersTable:raw_data",
-    inputs={
-        "dataset": vm_raw_dataset,
-    },
-    params={
-        "threshold": 5
-    }
-).log()
    -
    -

    -
    -
    -

    Pre-processed data

    -
    -
    run_test(
-    "validmind.data_validation.DescriptiveStatistics:preprocessed_data",
-    inputs={
-        "dataset": vm_preprocess_dataset,
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.TabularDescriptionTables:preprocessed_data",
-    inputs={
-        "dataset": vm_preprocess_dataset
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.MissingValues:preprocessed_data",
-    inputs={
-        "dataset": vm_preprocess_dataset,
-    },
-    params={
-        "min_threshold": 1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.TabularNumericalHistograms:preprocessed_data",
-    inputs={
-        "dataset": vm_preprocess_dataset
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.TabularCategoricalBarPlots:preprocessed_data",
-    inputs={
-        "dataset": vm_preprocess_dataset
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.TargetRateBarPlots:preprocessed_data",
-    inputs={
-        "dataset": vm_preprocess_dataset
-    },
-    params={
-        "default_column": lending_club.target_column,
-    },
-).log()
    -
    -

    -
    -
    -

    Development data

    -
    -
    run_test(
-    "validmind.data_validation.DescriptiveStatistics:development_data",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.TabularDescriptionTables:development_data",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.ClassImbalance:development_data",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-    params={
-        "min_percent_threshold": 10
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.UniqueRows:development_data",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-    params={
-        "min_percent_threshold": 1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.TabularNumericalHistograms:development_data",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-).log()
    -
    -

    -
    -
    -

    Feature selection

    -
    -
    run_test(
-    "validmind.data_validation.MutualInformation:development_data",
-    input_grid  ={
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-    params={
-        "min_threshold": 0.01,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.PearsonCorrelationMatrix:development_data",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.HighPearsonCorrelation:development_data",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-    params={
-        "max_threshold": 0.3,
-        "top_n_correlations": 10
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.WOEBinTable",
-    input_grid={
-        "dataset": [vm_preprocess_dataset]
-    },
-    params={
-        "breaks_adj": lending_club.breaks_adj,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.WOEBinPlots",
-    input_grid={
-        "dataset": [vm_preprocess_dataset]
-    },
-    params={
-        "breaks_adj": lending_club.breaks_adj,
-    },
-).log()
    -
    -

    -
    -
    -

    Model training

    -
    -
    run_test(
-    "validmind.data_validation.DatasetSplit",
-    inputs={
-        "datasets": [vm_train_ds, vm_test_ds],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.ModelMetadata",
-    input_grid={
-        "model": [vm_xgb_model, vm_rf_model],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.ModelParameters",
-    input_grid={
-        "model": [vm_xgb_model, vm_rf_model],
-    },
-).log()
    -
    -

    -
    -
    -

    Model selection

    -
    -
    run_test(
-    "validmind.model_validation.statsmodels.GINITable",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model, vm_rf_model],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.ClassifierPerformance",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model, vm_rf_model],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.TrainingTestDegradation:XGBoost",
-    inputs={
-        "datasets": [vm_train_ds, vm_test_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "max_threshold": 0.1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.TrainingTestDegradation:RandomForest",
-    inputs={
-        "datasets": [vm_train_ds, vm_test_ds],
-        "model": vm_rf_model,
-    },
-    params={
-        "max_threshold": 0.1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.HyperParametersTuning",
-    inputs={
-        "model": vm_xgb_model,
-        "dataset": vm_train_ds,
-    },
-    params={
-        "param_grid": {'n_estimators': [50, 100]},
-        "scoring": ['roc_auc', 'recall'],
-        "fit_params": {'eval_set': [(x_test, y_test)], 'verbose': False},
-        "thresholds": [0.3, 0.5],
-    }
-).log()
    -
    -

    -
    -
    -

    Class discrimination

    -
    -
    run_test(
-    "validmind.model_validation.sklearn.ROCCurve",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.MinimumROCAUCScore",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model],
-    },
-    params={
-        "min_threshold": 0.5
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.statsmodels.CumulativePredictionProbabilities",
-    input_grid={
-        "model": [vm_xgb_model],
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.PopulationStabilityIndex",
-    inputs={
-        "datasets": [vm_train_ds, vm_test_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "num_bins": 10,
-        "mode": "fixed"
-    }
-).log()
    -
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    -
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    Classification accuracy

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    run_test(
-    "validmind.model_validation.sklearn.ClassifierThresholdOptimization",
-    inputs={
-        "dataset": vm_train_ds,
-        "model": vm_xgb_model
-    },
-    params={
-        "target_recall": 0.8 # Find a threshold that achieves a recall of 80%
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.CalibrationCurve",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.ConfusionMatrix",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.MinimumAccuracy",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model],
-    },
-    params={
-        "min_threshold": 0.7
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.MinimumF1Score",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model],
-    },
-    params={
-        "min_threshold": 0.5
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.PrecisionRecallCurve",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model]
-    },
-).log()
    -
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    -
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    -

    Model diagnosis

    -
    -
    run_test(
-    "validmind.model_validation.sklearn.WeakspotsDiagnosis",
-    inputs={
-        "datasets": [vm_train_ds, vm_test_ds],
-        "model": vm_xgb_model,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.OverfitDiagnosis",
-    inputs={
-        "model": vm_xgb_model,
-        "datasets": [vm_train_ds, vm_test_ds],
-    },
-    params={
-        "cut_off_threshold": 0.04
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.RobustnessDiagnosis",
-    inputs={
-        "datasets": [vm_train_ds, vm_test_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "scaling_factor_std_dev_list": [
-            0.1,
-            0.2,
-            0.3,
-            0.4,
-            0.5
-        ],
-        "performance_decay_threshold": 0.05
-    }
-).log()
    -
    -

    -
    -
    -

    Model explainability

    -
    -
    run_test(
-    "validmind.model_validation.sklearn.PermutationFeatureImportance",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-        "model": [vm_xgb_model]
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.FeaturesAUC",
-    input_grid={
-        "model": [vm_xgb_model],
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.SHAPGlobalImportance",
-    input_grid={
-        "model": [vm_xgb_model],
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-    params={
-        "kernel_explainer_samples": 10,
-        "tree_or_linear_explainer_samples": 200,
-    }
-).log()
    -
    -

    -
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    Scoring evaluation

    -
    -
    run_test(
-    "validmind.model_validation.statsmodels.ScorecardHistogram",
-    input_grid={
-        "dataset": [vm_train_ds, vm_test_ds],
-    },
-    params={
-        "score_column": "xgb_scores",
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.ScoreBandDefaultRates",
-    input_grid={
-        "dataset": [vm_train_ds],
-        "model": [vm_xgb_model],
-    },
-    params = {
-        "score_column": "xgb_scores",
-        "score_bands": [500, 540, 570]
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.ScoreProbabilityAlignment",
-    input_grid={
-        "dataset": [vm_train_ds],
-        "model": [vm_xgb_model],
-    },
-    params={
-        "score_column": "xgb_scores",
-    },
-).log()
    -
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    -
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    -
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    Custom tests

    -

    Custom tests extend the functionality of ValidMind, allowing you to document any model or use case with added flexibility.

    -

    ValidMind provides a comprehensive set of tests out-of-the-box to evaluate and document your models and datasets. We recognize there will be cases where the default tests do not support a model or dataset, or specific documentation is needed. In these cases, you can create and use your own custom code to accomplish what you need. To streamline custom code integration, we support the creation of custom test functions.

    -

    -
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    In-line custom tests

    -

    The @vm.test decorator is doing the work of creating a wrapper around the function that will allow it to be run by the ValidMind Library. It also registers the test so it can be found by the ID my_custom_tests.ScoreToOdds". The function score_to_odds_analysis takes three arguments dataset, score_column, and score_bands. This is a VMDataset and the rest are parameters that can be passed in.

    -
    -
    import numpy as np
-import pandas as pd
-import plotly.graph_objects as go
-
-
-@vm.test("my_custom_tests.ScoreToOdds")
-def score_to_odds_analysis(dataset, score_column='score', score_bands=[410, 440, 470]):
-    """
-    Analyzes the relationship between score bands and odds (good:bad ratio).
-    Good odds = (1 - default_rate) / default_rate
-    
-    Higher scores should correspond to higher odds of being good.
-    """
-    df = dataset.df
-    
-    # Create score bands
-    df['score_band'] = pd.cut(
-        df[score_column],
-        bins=[-np.inf] + score_bands + [np.inf],
-        labels=[f'<{score_bands[0]}'] + 
-               [f'{score_bands[i]}-{score_bands[i+1]}' for i in range(len(score_bands)-1)] +
-               [f'>{score_bands[-1]}']
-    )
-    
-    # Calculate metrics per band
-    results = df.groupby('score_band').agg({
-        dataset.target_column: ['mean', 'count']
-    })
-    
-    results.columns = ['Default Rate', 'Total']
-    results['Good Count'] = results['Total'] - (results['Default Rate'] * results['Total'])
-    results['Bad Count'] = results['Default Rate'] * results['Total']
-    results['Odds'] = results['Good Count'] / results['Bad Count']
-    
-    # Create visualization
-    fig = go.Figure()
-    
-    # Add odds bars
-    fig.add_trace(go.Bar(
-        name='Odds (Good:Bad)',
-        x=results.index,
-        y=results['Odds'],
-        marker_color='blue'
-    ))
-    
-    fig.update_layout(
-        title='Score-to-Odds Analysis',
-        yaxis=dict(title='Odds Ratio (Good:Bad)'),
-        showlegend=False
-    )
-    
-    return fig
    -
    -
    -
    run_test(
-    "my_custom_tests.ScoreToOdds",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-    params={
-        "score_column": "xgb_scores",
-        "score_bands": [500, 540, 570],
-    },
-).log()
    -
    -

    -
    -
    -

    Local test provider

    -

    The ValidMind Library offers the ability to extend the built-in library of tests with custom tests. A test "Provider" is a Python class that gets registered with the ValidMind Library and loads tests based on a test ID, for example my_test_provider.my_test_id. The built-in suite of tests that ValidMind offers is technically its own test provider. You can use one the built-in test provider offered by ValidMind (validmind.tests.test_providers.LocalTestProvider) or you can create your own. More than likely, you'll want to use the LocalTestProvider to add a directory of custom tests but there's flexibility to be able to load tests from any source.

    -
    -
    from validmind.tests import LocalTestProvider
-
-# Define the folder where your tests are located
-tests_folder = "custom_tests"
-
-# initialize the test provider with the tests folder we created earlier
-my_test_provider = LocalTestProvider(tests_folder)
-
-vm.tests.register_test_provider(
-    namespace="my_test_provider",
-    test_provider=my_test_provider,
-)
    -
    -

    Now that we have our test provider set up, we can run any test that's located in our tests folder by using the run_test() method. This function is your entry point to running single tests in the ValidMind Library. It takes a test ID and runs the test associated with that ID. For our custom tests, the test ID will be the namespace specified when registering the provider, followed by the path to the test file relative to the tests folder. For example, the Confusion Matrix test we created earlier will have the test ID my_test_provider.ConfusionMatrix. You could organize the tests in subfolders, say classification and regression, and the test ID for the Confusion Matrix test would then be my_test_provider.classification.ConfusionMatrix.

    -
    -
    run_test(
-    "my_test_provider.ScoreBandDiscriminationMetrics",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_xgb_model,
-    },
-    params={
-        "score_column": "xgb_scores",
-        "score_bands": [500, 540, 570],
-    }
-).log(section_id="interpretability_insights")
    -
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    -
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    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -

    3. Expand the following sections and take a look around:

      -
        -
      • 2. Data Preparation
        • -
      • 3. Model Development
        • -
      4. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation (hint: some of the tests in 2.3. Feature Selection and Engineering look like they need some attention), view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
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    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/nlp_and_llm/foundation_models_integration_demo.html b/site/notebooks/use_cases/nlp_and_llm/foundation_models_integration_demo.html deleted file mode 100644 index 74e7f111c..000000000 --- a/site/notebooks/use_cases/nlp_and_llm/foundation_models_integration_demo.html +++ /dev/null @@ -1,1757 +0,0 @@ - - - - - - - - - -Sentiment analysis of financial data using a large language model (LLM) – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Sentiment analysis of financial data using a large language model (LLM)

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    Document a large language model (LLM) specialized in sentiment analysis for financial news using the ValidMind Library.

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    This interactive notebook shows you how to set up the ValidMind Library, initializes the library, and uses a specific prompt template for analyzing the sentiment of sentences in a dataset. The notebook also includes example data to test the model's ability to correctly identify sentiment as positive, negative, or neutral.

    - - -

    -
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    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    If this is your first time trying out ValidMind, we recommend going through the following resources first:

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      • -
    • ValidMind Library — The path for developers, more code samples, and our developer reference
      • -
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    -
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    Before you begin

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
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    This notebook requires an OpenAI API secret key to run. If you don't have one, visit API keys on OpenAI's site to create a new key for yourself. Note that API usage charges may apply.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

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    -
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    Setting up

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    Install the ValidMind Library

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    To install the library:

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    Initialize the ValidMind Library

    -

    -
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    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
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    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select LLM-based Text Classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
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    -
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    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
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    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
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    -
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    Get ready to run the analysis

    -

    Import the ValidMind FoundationModel and Prompt classes needed for the sentiment analysis later on:

    -
    -
    from validmind.models import FoundationModel, Prompt
    -
    -

    Check your access to the OpenAI API:

    -
    -
    import os
-
-import dotenv
-
-dotenv.load_dotenv()
-
-if os.getenv("OPENAI_API_KEY") is None:
-    raise Exception("OPENAI_API_KEY not found")
    -
    -
    -
    from openai import OpenAI
-
-model = OpenAI()
-
-
-def call_model(prompt):
-    return (
-        model.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {"role": "user", "content": prompt},
-            ],
-        )
-        .choices[0]
-        .message.content
-    )
    -
    -

    Set the prompt guidelines for the sentiment analysis:

    -
    -
    prompt_template = """
-You are an AI with expertise in sentiment analysis, particularly in the context of financial news.
-Your task is to analyze the sentiment of a specific sentence provided below.
-Before proceeding, take a moment to understand the context and nuances of the financial terminology used in the sentence.
-
-Sentence to Analyze:
-```
-{Sentence}
-```
-
-Please respond with the sentiment of the sentence denoted by one of either 'positive', 'negative', or 'neutral'.
-Please respond only with the sentiment enum value. Do not include any other text in your response.
-
-Note: Ensure that your analysis is based on the content of the sentence and not on external information or assumptions.
-""".strip()
-
-prompt_variables = ["Sentence"]
    -
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    -
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    Get your sample dataset ready for analysis

    -

    To perform the sentiment analysis for financial news we're going to load a local copy of this dataset: https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-for-financial-news.

    -

    This dataset contains two columns, Sentiment and Sentence. The sentiment can be negative, neutral or positive.

    -
    -
    import pandas as pd
-
-df = pd.read_csv("./datasets/sentiments_with_predictions.csv")
    -
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    -
    -
    -

    Run the model documentation tests

    -

    First, use the ValidMind Library to initialize the dataset and model objects necessary for documentation. The ValidMind predict_fn function allows the model to be tested and evaluated in a standardized manner:

    -
    -
    vm_test_ds = vm.init_dataset(
-    dataset=df,
-    input_id="test_dataset",
-    text_column="Sentence",
-    target_column="Sentiment",
-)
-
-vm_model = vm.init_model(
-    model=FoundationModel(
-        predict_fn=call_model,
-        prompt=Prompt(
-            template=prompt_template,
-            variables=prompt_variables,
-        ),
-    ),
-    input_id="gpt_35_model",
-)
-
-# Assign model predictions to the test dataset
-vm_test_ds.assign_predictions(vm_model, prediction_column="gpt_35_prediction")
    -
    -

    Next, use the ValidMind Library to run validation tests on the model. The vm.run_documentation_tests function analyzes the current model's documentation template and collects all the tests associated with it into a test suite.

    -

    The function then runs the test suite, logs the results to the ValidMind API and displays them to you.

    -
    -
    test_suite = vm.run_documentation_tests(
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    }
-)
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    -
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    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way: view the prompt validation test results as part of your model documentation in the ValidMind Platform:

    -
      -

    1. In the ValidMind Platform, click Development under Documents for the model you registered earlier. (Learn more: Working with documentation)

      2. -

    2. Expand 2. Data Preparation or 3. Model Development to review all test results.

      3. -
    -

    What you can see now is a more easily consumable version of the prompt validation testing you just performed, along with other parts of your model documentation that still need to be completed.

    -

    If you want to learn more about where you are in the model documentation process, take a look our documentation on the ValidMind Library.

    -

    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

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    Document a natural language processing (NLP) model using the ValidMind Library after performing a sentiment analysis of financial news data using several different Hugging Face transformers.

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    This notebook provides an introduction for model developers on how to document a natural language processing (NLP) model using the ValidMind Library. It shows you how to set up the ValidMind Library, initialize the library, and load the dataset, followed by performing a sentiment analysis of financial news data using several different Hugging Face transformers. As part of the process, the notebook runs various tests to quickly generate documentation about the data and model.

    - - -

    -
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    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    If this is your first time trying out ValidMind, we recommend going through the following resources first:

    -
      -
    • Get started — The basics, including key concepts, and how our products work
      • -
    • ValidMind Library — The path for developers, more code samples, and our developer reference
      • -
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    Before you begin

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select NLP-based Text Classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -

    Get your sample dataset ready for analysis

    -

    To perform the sentiment analysis for financial news we're going to load a local copy of this dataset: https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-for-financial-news.

    -

    This dataset contains two columns, Sentiment and Sentence. The sentiment can be negative, neutral or positive.

    -
    -
    import pandas as pd
-
-df = pd.read_csv("./datasets/sentiments_with_predictions.csv")
    -
    -

    -
    -
    -
    -

    NLP data quality tests

    -

    Before we proceed with the analysis, it's crucial to ensure the quality of our NLP data. We can run the "data preparation" section of the template to validate the raw dataset's integrity and suitability.

    -
    -
    vm_raw_ds = vm.init_dataset(
-    dataset=df,
-    input_id="raw_dataset",
-    text_column="Sentence",
-    target_column="Sentiment",
-)
-
-text_data_test_plan = vm.run_documentation_tests(
-    section="data_preparation", inputs={"dataset": vm_raw_ds}
-)
    -
    -

    -
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    -

    Hugging Face transformers

    -

    -
    -

    Hugging Face: FinancialBERT for Sentiment Analysis

    -

    Let's now explore integrating and testing FinancialBERT (https://huggingface.co/ahmedrachid/FinancialBERT-Sentiment-Analysis ), a model designed specifically for sentiment analysis in the financial domain:

    -
    -
    from transformers import BertTokenizer, BertForSequenceClassification
-from transformers import pipeline
-
-model = BertForSequenceClassification.from_pretrained(
-    "ahmedrachid/FinancialBERT-Sentiment-Analysis", num_labels=3
-)
-tokenizer = BertTokenizer.from_pretrained(
-    "ahmedrachid/FinancialBERT-Sentiment-Analysis"
-)
-hfmodel = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
    -
    -

    -
    -

    Initialize the ValidMind dataset

    -
    -
    # Load a test dataset with 100 rows only
-vm_test_ds = vm.init_dataset(
-    dataset=df,
-    input_id="test_dataset",
-    text_column="Sentence",
-    target_column="Sentiment",
-)
    -
    -

    -
    -
    -

    Initialize the ValidMind model

    -

    When initializing a ValidMind model, we pre-calculate predictions on the test dataset. This operation can take a long time for large datasets.

    -
    -
    vm_model = vm.init_model(
-    hfmodel,
-)
-
-# Assign model predictions to the test dataset
-vm_test_ds.assign_predictions(vm_model, prediction_column="finbert_prediction")
    -
    -

    -
    -
    -

    Run model validation tests

    -

    It's possible to run a subset of tests on the documentation template by passing a section parameter to run_documentation_tests(). Let's run the tests that correspond to model validation only:

    -
    -
    full_suite = vm.run_documentation_tests(
-    section="model_development",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
    -
    -

    -
    -
    -
    -
    -

    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way: view the prompt validation test results as part of your model documentation in the ValidMind Platform:

    -
      -

    1. In the ValidMind Platform, click Development under Documents for the model you registered earlier. (Learn more: Working with documentation)

      2. -

    2. Expand 2. Data Preparation or 3. Model Development to review all test results.

      3. -
    -

    What you can see now is a more easily consumable version of the prompt validation testing you just performed, along with other parts of your model documentation that still need to be completed.

    -

    If you want to learn more about where you are in the model documentation process, take a look our documentation on the ValidMind Library.

    -

    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/nlp_and_llm/llm_summarization_demo.html b/site/notebooks/use_cases/nlp_and_llm/llm_summarization_demo.html deleted file mode 100644 index 98d9749ce..000000000 --- a/site/notebooks/use_cases/nlp_and_llm/llm_summarization_demo.html +++ /dev/null @@ -1,2101 +0,0 @@ - - - - - - - - - -Automate news summarization using LLMs – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Automate news summarization using LLMs

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    - - - -
    - - - -

    Document a LLM-based text summarization model of news using the CNN DailyMail sample dataset from HuggingFace with the ValidMind Library.

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    As part of the notebook, you will learn how to develop an text summarization model while exploring how the documentation process works:

    - - - -

    -
    -

    Use case

    -

    The purpose of this notebook is to showcase how to document an automated news summarization system using a Large Language Model (LLM). This AI system leverages a large language model (LLM) to process and condense web-based news articles into concise summaries.

    -

    Data Sources

    -

    The CNN/DailyMail Dataset is a collection tailored for text summarization, containing over 300,000 news articles from two significant English-speaking regions, the US and the UK. Each row comprises an article, a highlight section, and a unique ID. The highlights in the dataset are summaries that have been written by the original journalists. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015.

    -

    The original dataset includes pre-divided splits: train, validation, and test. In this demo, as we are not training a LLM in the traditional machine learning sense but rather using prompt engineering to guide the LLM to function as a text summarizer, we do not adhere to the conventional distinction between training and test datasets. Therefore, we exclusively utilize the test dataset, applying it as a validation or "gold" standard to evaluate the effectiveness of our summarization through prompt engineering.

    -

    Workflow

    -

    The workflow comprises four primary stages, starting with article selection, where articles from the test dataset are chosen. This is followed by prompt engineering, where a prompt is crafted to communicate the summarization task to the LLM. In the summarization stage, the prompt is input into the LLM, which then produces summaries based on the article content. The final stage involves LLM response evaluation, where the summaries generated by the LLM are measured against the original journalist-authored highlights to evaluate the summarization quality.

    -

    -
    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    If this is your first time trying out ValidMind, you can make use of the following resources alongside this notebook:

    -
      -
    • Get started — The basics, including key concepts, and how our products work
      • -
    • ValidMind Library — The path for developers, more code samples, and our developer reference
      • -
    -

    -
    -
    -

    Before you begin

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select LLM-based Text Summarization.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Initialize the Python environment

    -

    Next, let's import the necessary libraries and set up your Python environment for data analysis:

    -
    -
    # Install the `datasets` library from huggingface
-%pip install -q datasets
-%matplotlib inline
    -
    -

    -
    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -
    -

    Load the sample dataset

    -

    The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas DataFrame, a two-dimensional tabular data structure that makes use of rows and columns:

    -
    -
    # Import the sample dataset from the library
-from validmind.datasets.nlp import cnn_dailymail
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{cnn_dailymail.target_column}' "
-    f"\n\t• Input text column: {cnn_dailymail.text_column} "
-    f"\n\t• Prediction columns: '{cnn_dailymail.t5_prediction}', '{cnn_dailymail.gpt_35_prediction_column}'"
-)
-
-
-train_df, test_df = cnn_dailymail.load_data(source="offline", dataset_size="100")
-
-# Display the first few rows of the dataframe to check the loaded data. Ignore the "bert_embedding_model_prediction" column
-cnn_dailymail.display_nice(train_df.drop("bert_embedding_model_prediction", axis=1).head())
    -
    -

    -
    -
    -

    Document the model

    -

    As part of documenting the model with the ValidMind Library, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.

    -

    -
    -

    Setup the Large Language Model (LLM)

    -

    This section prepares our environment to use OpenAI's Large Language Model by setting up the API key and defining a function to call the model.

    -
    -
    import os
-
-import dotenv
-import nltk
-
-dotenv.load_dotenv()
-nltk.download('stopwords')
-
-# os.environ["OPENAI_API_KEY"] = "sk-..."
-
-if os.getenv("OPENAI_API_KEY") is None:
-    raise Exception("OPENAI_API_KEY not found")
    -
    -
    -
    from openai import OpenAI
-
-model = OpenAI()
-
-
-def call_model(prompt):
-    return (
-        model.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=[
-                {"role": "user", "content": prompt},
-            ],
-        )
-        .choices[0]
-        .message.content
-    )
    -
    -

    -
    -
    -

    Setup up the Prompt

    -

    In this section, we construct a structured prompt template designed to guide the AI in summarizing the CNN Daily news. The template emphasizes the AI's role as an expert in parsing and condensing news information. It instructs the AI to focus on the article's core content, avoiding assumptions or external data.

    -
    -
    prompt_template = """
-You are an AI with expertise in summarizing financial news.
-Your task is to provide a concise summary of the specific news article provided below.
-Before proceeding, take a moment to understand the context and nuances of the financial terminology used in the article.
-
-Article to Summarize:
-
-```
-{article}
-```
-
-Please respond with a concise summary of the article's main points.
-Ensure that your summary is based on the content of the article and not on external information or assumptions.
-""".strip()
-
-prompt_variables = ["article"]
    -
    -

    -
    -
    -

    Initialize the ValidMind datasets

    -

    Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -

    This function takes a number of arguments:

    -
      -
    • dataset — the raw dataset that you want to provide as input to tests
      • -
    • input_id - a unique identifier that allows tracking what inputs are used when running each individual test
      • -
    • target_column — a required argument if tests require access to true values. This is the name of the target column in the dataset
      • -
    -

    With all datasets ready, you can now initialize training and test datasets (train_df and test_df) created earlier into their own dataset objects using vm.init_dataset():

    -
    -
    from validmind.models import FoundationModel, Prompt
-
-vm_test_ds = vm.init_dataset(
-    dataset=test_df,
-    input_id="test_dataset",
-    text_column="article",
-    target_column="highlights",
-)
-
-vm_model = vm.init_model(
-    model=FoundationModel(
-        predict_fn=call_model,
-        prompt=Prompt(
-            template=prompt_template,
-            variables=prompt_variables,
-        ),
-    ),
-    input_id="gpt_35",
-)
    -
    -

    -
    -
    -

    Assign predictions to the datasets

    -

    We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:

    -
    -
    # Assign pre-computed model predictions to the test dataset
-vm_test_ds.assign_predictions(vm_model, prediction_column="gpt_35_prediction")
-
-print(vm_test_ds)
    -
    -

    -
    -
    -

    Data validation

    -

    This section focuses on performing a series of data description tests to gain insights into the basic characteristics of our text data. The goal of data description in this use case is verifying that the data meets certain standards and criteria before it is used for text summarization tasks. We conduct the follwoing NLP data quality tests:

    -
      -
    • Duplicates: Check for duplicate articles in the dataset.
      • -
    • Text Description: Assess the general context and provide a summary of the dataset.
      • -
    • Common Words: Determine the most frequently occurring words that could indicate key themes.
      • -
    • Punctuations: Analyze punctuation patterns to understand sentence structures and emphases.
      • -
    • Stop Words: Identify and remove common stopwords to clarify the significant textual elements.
      • -
    • Language Detection: Verify the language of the dataset to ensure it is consistent.
      • -
    • Toxicity: Evaluate the presence of toxic language in the dataset.
      • -
    • Polarity and Subjectivity: Measure the sentiment of the dataset to understand the overall tone.
      • -
    • Sentiment: Analyze the sentiment of the dataset to determine the overall mood.
      • -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.Duplicates",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.nlp.TextDescription",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.nlp.CommonWords",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.nlp.Punctuations",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-    params = {
-        "count_mode": "word"
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.nlp.StopWords",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.nlp.LanguageDetection",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.nlp.Toxicity",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.nlp.PolarityAndSubjectivity",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.nlp.Sentiment",
-    inputs={
-        "dataset": vm_test_ds,
-    },
-)
-test.log()
    -
    -

    -
    -
    -

    Prompt Validation

    -

    This section conducts a critical analysis of prompts to ensure their effectiveness when interacting with AI models. It involves systematic checks across several dimensions to enhance the quality of the interaction between the user and the AI:

    -
      -
    • Bias: Evaluate prompts for impartiality.
      • -
    • Clarity: Confirm the prompts are clearly understood.
      • -
    • Conciseness: Verify that the prompts are brief and concise.
      • -
    • Delimitation: Check the boundaries and extent of prompts.
      • -
    • Negative Instruction: Review prompts for any negative phrasing that could be misconstrued.
      • -
    • Specificity: Assess prompts for detailed and precise instructions.
      • -
    -
    -
    test = vm.tests.run_test(
-    "validmind.prompt_validation.Bias",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.prompt_validation.Clarity",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.prompt_validation.Conciseness",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.prompt_validation.Delimitation",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.prompt_validation.NegativeInstruction",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.prompt_validation.Specificity",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -

    -
    -
    -

    Model Validation

    -

    This section is dedicated to the assessment of the AI model's understanding and processing of language data. It involves running various model performance tests, ensuring the model's output is as expected and reliable.

    -

    -
    -

    Run model performance tests

    -

    Here we measure the model's linguistic performance across various metrics, including:

    -
      -
    • Token Disparity: Examine the distribution of token usage.
      • -
    • Rouge Metrics: Use Recall-Oriented Understudy for Gisting Evaluation to assess the summary quality.
      • -
    • Bert Score: Implement BERT-based evaluations of token similarity.
      • -
    • Contextual Recall: Test the model's ability to recall contextual information.
      • -
    • Bleu Score: Evaluate the quality of machine translation.
      • -
    • Meteor Score: Measure translation hypothesis against reference translations.
      • -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.TokenDisparity",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": "gpt_35",
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.RougeScore",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-    params={
-        "metric": "rouge-1",
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.BertScore",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.ContextualRecall",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.BleuScore",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.MeteorScore",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -

    -
    -
    -

    Run bias and toxicity tests

    -

    The focus of this subsection is on identifying any potential bias or toxicity in the model's language processing. We conduct:

    -
      -
    • Toxicity Score: Quantify the degree of toxicity in content generated by the model.
      • -
    • Toxicity Histogram: Visualize the distribution of toxicity scores.
      • -
    • Regard Score: Assess the model's language for indications of respect or disrespect.
      • -
    • Regard Histogram: Plot the frequencies of different levels of regard to identify patterns.
      • -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.ToxicityScore",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.RegardScore",
-    inputs={
-        "dataset": vm_test_ds,
-        "model": vm_model,
-    },
-)
-test.log()
    -
    -

    -
    -
    -
    -
    -

    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way: view the prompt validation test results as part of your model documentation right in the ValidMind Platform:

    -
      -

    1. In the ValidMind Platform, click Development under Documents for the model you registered earlier. (Learn more: Working with documentation)

      2. -

    2. Expand 2. Data Preparation or 3. Model Development to review all test results.

      3. -
    -

    What you can see now is a more easily consumable version of the prompt validation testing you just performed, along with other parts of your model documentation that still need to be completed.

    -

    If you want to learn more about where you are in the model documentation process, take a look at our documentation on the ValidMind Library.

    -

    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/nlp_and_llm/rag_benchmark_demo.html b/site/notebooks/use_cases/nlp_and_llm/rag_benchmark_demo.html deleted file mode 100644 index 42364ef4e..000000000 --- a/site/notebooks/use_cases/nlp_and_llm/rag_benchmark_demo.html +++ /dev/null @@ -1,2603 +0,0 @@ - - - - - - - - - -RAG Model Benchmarking Demo – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
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    RAG Model Benchmarking Demo

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    - - - -
    - - - - -
    - - - -
    - - - -

    In this notebook, we are going to implement a simple RAG Model for automating the process of answering RFP questions using GenAI. We will see how we can initialize an embedding model, a retrieval model and a generator model with LangChain components and use them within the ValidMind Library to run tests against them. We'll demonstrate how to set up multiple models for benchmarking at each stage of the RAG pipeline - specifically two embedding models, two retrieval models with different parameters, and two LLM models (GPT-3.5 and GPT-4o) - allowing for comparison of performance across different configurations. Finally, we will see how we can put them together in a Pipeline and run that to get e2e results and run tests against that.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    -
    -

    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -
      -
    • FunctionModels: ValidMind offers support for creating VMModel instances from Python functions. This enables us to support any "model" by simply using the provided function as the model's predict method.
      • -
    • PipelineModels: ValidMind models (VMModel instances) of any type can be piped together to create a model pipeline. This allows model components to be created and tested/documented independently, and then combined into a single model for end-to-end testing and documentation. We use the | operator to pipe models together.
      • -
    • RAG: RAG stands for Retrieval Augmented Generation and refers to a wide range of GenAI applications where some form of retrieval is used to add context to the prompt so that the LLM that generates content can refer to it when creating its output. In this notebook, we are going to implement a simple RAG setup using LangChain components.
      • -
    -
    -
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    Prerequisites

    -

    Let's go ahead and install the validmind library if its not already installed... Then we can install the qdrant-client library for our vector store and langchain for everything else:

    -
    -
    %pip install -q "validmind[llm]"
    -
    -
    -
    %pip install -q "qdrant-client<1.10" langchain langchain-openai sentencepiece
    -
    -

    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -
    -Recommended Python versions

    Python 3.8 <= x <= 3.14 -
    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Gen AI RAG.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -
    -Can't select this template?

    Your organization administrators may need to add it to your template library: - -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-  # api_host = "...",
-  # api_key = "...",
-  # api_secret = "...",
-  # model = "...",
-  document="documentation",
-)
    -
    -

    -
    -
    -
    -
    -

    Read Open AI API Key

    -

    We will need to have an OpenAI API key to be able to use their text-embedding-3-small and text-embedding-3-large models for our embeddings, gpt-3.5-turbo and gpt-4o models for our generator and gpt-4o model for our LLM-as-Judge tests. If you don't have an OpenAI API key, you can get one by signing up at OpenAI. Then you can create a .env file in the root of your project and the following cell will load it from there. Alternatively, you can just uncomment the line below to directly set the key (not recommended for security reasons).

    -
    -
    # load openai api key
-import os
-
-import dotenv
-import nltk
-
-dotenv.load_dotenv()
-nltk.download('stopwords')
-nltk.download('punkt_tab')
-
-# os.environ["OPENAI_API_KEY"] = "sk-..."
-
-if not "OPENAI_API_KEY" in os.environ:
-    raise ValueError("OPENAI_API_KEY is not set")
    -
    -

    -
    -
    -

    Dataset Loader

    -

    Great, now that we have all of our dependencies installed, the ValidMind Library initialized and connected to our model and our OpenAI API key setup, we can go ahead and load our datasets. We will use the synthetic RFP dataset included with ValidMind for this notebook. This dataset contains a variety of RFP questions and ground truth answers that we can use both as the source where our Retriever will search for similar question-answer pairs as well as our test set for evaluating the performance of our RAG model. To do this, we just have to load it and call the preprocess function to get a split of the data into train and test sets.

    -
    -
    # Import the sample dataset from the library
-from validmind.datasets.llm.rag import rfp
-
-raw_df = rfp.load_data()
-train_df, test_df = rfp.preprocess(raw_df)
    -
    -
    -
    vm_train_ds = vm.init_dataset(
-    train_df,
-    text_column="question",
-    target_column="ground_truth",
-)
-
-vm_test_ds = vm.init_dataset(
-    test_df,
-    text_column="question",
-    target_column="ground_truth",
-)
-
-vm_test_ds.df.head()
    -
    -

    -
    -
    -

    Data validation

    -

    Now that we have loaded our dataset, we can go ahead and run some data validation tests right away to start assessing and documenting the quality of our data. Since we are using a text dataset, we can use ValidMind's built-in array of text data quality tests to check that things like number of duplicates, missing values, and other common text data issues are not present in our dataset. We can also run some tests to check the sentiment and toxicity of our data.

    -

    -
    -

    Duplicates

    -

    First, let's check for duplicates in our dataset. We can use the validmind.data_validation.Duplicates test and pass our dataset:

    -
    -
    from validmind.tests import run_test
-
-run_test(
-    test_id="validmind.data_validation.Duplicates",
-    inputs={"dataset": vm_train_ds},
-).log()
    -
    -

    -
    -
    -

    Stop Words

    -

    Next, let's check for stop words in our dataset. We can use the validmind.data_validation.StopWords test and pass our dataset:

    -
    -
    run_test(
-    test_id="validmind.data_validation.nlp.StopWords",
-    inputs={
-        "dataset": vm_train_ds,
-    },
-).log()
    -
    -

    -
    -
    -

    Punctuations

    -

    Next, let's check for punctuations in our dataset. We can use the validmind.data_validation.Punctuations test:

    -
    -
    run_test(
-    test_id="validmind.data_validation.nlp.Punctuations",
-    inputs={
-        "dataset": vm_train_ds,
-    },
-).log()
    -
    -

    -
    -
    -

    Common Words

    -

    Next, let's check for common words in our dataset. We can use the validmind.data_validation.CommonWord test:

    -
    -
    run_test(
-    test_id="validmind.data_validation.nlp.CommonWords",
-    inputs={
-        "dataset": vm_train_ds,
-    },
-).log()
    -
    -

    -
    -
    -

    Language Detection

    -

    For documentation purposes, we can detect and log the languages used in the dataset with the validmind.data_validation.LanguageDetection test:

    -
    -
    run_test(
-    test_id="validmind.data_validation.nlp.LanguageDetection",
-    inputs={
-        "dataset": vm_train_ds,
-    },
-).log()
    -
    -

    -
    -
    -

    Toxicity Score

    -

    Now, let's go ahead and run the validmind.data_validation.nlp.Toxicity test to compute a toxicity score for our dataset:

    -
    -
    run_test(
-    "validmind.data_validation.nlp.Toxicity",
-    inputs={
-        "dataset": vm_train_ds,
-    },
-).log()
    -
    -

    -
    -
    -

    Polarity and Subjectivity

    -

    We can also run the validmind.data_validation.nlp.PolarityAndSubjectivity test to compute the polarity and subjectivity of our dataset:

    -
    -
    run_test(
-    "validmind.data_validation.nlp.PolarityAndSubjectivity",
-    inputs={
-        "dataset": vm_train_ds,
-    },
-).log()
    -
    -

    -
    -
    -

    Sentiment

    -

    Finally, we can run the validmind.data_validation.nlp.Sentiment test to plot the sentiment of our dataset:

    -
    -
    run_test(
-    "validmind.data_validation.nlp.Sentiment",
-    inputs={
-        "dataset": vm_train_ds,
-    },
-).log()
    -
    -
    -
    -
    -
    -

    Embedding Model

    -

    Now that we have our dataset loaded and have run some data validation tests to assess and document the quality of our data, we can go ahead and initialize our embedding model. We will use text-embedding-3-small and text-embedding-3-large models from OpenAI for this purpose wrapped in the OpenAIEmbeddings class from LangChain. This model will be used to "embed" our questions both for inserting the question-answer pairs from the "train" set into the vector store and for embedding the question from inputs when making predictions with our RAG model.

    -
    -
    from langchain_openai import OpenAIEmbeddings
-
-embedding_small_client = OpenAIEmbeddings(model="text-embedding-3-small")
-
-
-def embed_small(input):
-    """Returns a text embedding for the given text"""
-    return embedding_small_client.embed_query(input["question"])
-
-
-vm_embedder_small = vm.init_model(input_id="embedding_small_model", predict_fn=embed_small)
    -
    -
    -
    embedding_large_client = OpenAIEmbeddings(model="text-embedding-3-large")
-
-
-def embed_large(input):
-    """Returns a text embedding for the given text"""
-    return embedding_large_client.embed_query(input["question"])
-
-
-vm_embedder_large = vm.init_model(input_id="embedding_large_model", predict_fn=embed_large)
    -
    -

    What we have done here is to initialize the OpenAIEmbeddings class so it uses OpenAI's text-embedding-3-small and text-embedding-3-large models. We then created an embed function that takes in an input dictionary and uses the embed_query method of the embedding client to compute the embeddings of the question. We use an embed function since that is how ValidMind supports any custom model. We will use this strategy for the retrieval and generator models as well but you could also use, say, a HuggingFace model directly. See the documentation for more information on which model types are directly supported - ValidMind Documentation... Finally, we use the init_model function from the ValidMind Library to create a VMModel object that can be used in ValidMind tests. This also logs the model to our model documentation and any test that uses the model will be linked to the logged model and its metadata.

    -

    -
    -

    Assign Predictions

    -

    To precompute the embeddings for our test set, we can call the assign_predictions method of our vm_test_ds object we created above. This will compute the embeddings for each question in the test set and store them in the a special prediction column of the test set thats linked to our vm_embedder model. This will allow us to use these embeddings later when we run tests against our embedding model.

    -
    -
    vm_test_ds.assign_predictions(vm_embedder_small)
-vm_test_ds.assign_predictions(vm_embedder_large)
-print(vm_test_ds)
    -
    -

    -
    -
    -

    Run tests

    -

    Now that everything is setup for the embedding model, we can go ahead and run some tests to assess and document the quality of our embeddings. We will use the validmind.model_validation.embeddings.* tests to compute a variety of metrics against our model.

    -
    -
    run_test(
-    "validmind.model_validation.embeddings.StabilityAnalysisRandomNoise",
-    input_grid={
-        "model": [vm_embedder_small, vm_embedder_large],
-        "dataset": [vm_test_ds],
-    },
-    params={
-        "probability": 0.3,
-        "mean_similarity_threshold": 0.7,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.embeddings.StabilityAnalysisSynonyms",
-    input_grid={
-        "model": [vm_embedder_small, vm_embedder_large],
-        "dataset": [vm_test_ds],
-    },
-    params={
-        "probability": 0.3,
-        "mean_similarity_threshold": 0.7,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.embeddings.StabilityAnalysisTranslation",
-    input_grid={
-        "model": [vm_embedder_small, vm_embedder_large],
-        "dataset": [vm_test_ds],
-    },
-    params={
-        "source_lang": "en",
-        "target_lang": "fr",
-        "mean_similarity_threshold": 0.7,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.embeddings.CosineSimilarityHeatmap",
-    input_grid={
-        "model": [vm_embedder_small, vm_embedder_large],
-        "dataset": [vm_test_ds],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.embeddings.CosineSimilarityDistribution",
-    input_grid={
-        "model": [vm_embedder_small, vm_embedder_large],
-        "dataset": [vm_test_ds],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.embeddings.PCAComponentsPairwisePlots",
-    input_grid={
-        "model": [vm_embedder_small, vm_embedder_large],
-        "dataset": [vm_test_ds],
-    },
-    params={
-        "n_components": 3,
-    },
-).log()
    -
    -
    -
    -
    -

    Setup Vector Store

    -

    Great, so now that we have assessed our embedding model and verified that it is performing well, we can go ahead and use it to compute embeddings for our question-answer pairs in the "train" set. We will then use these embeddings to insert the question-answer pairs into a vector store. We will use an in-memory qdrant vector database for demo purposes but any option would work just as well here. We will use the QdrantClient class from LangChain to interact with the vector store. This class will allow us to insert and search for embeddings in the vector store.

    -

    -
    -

    Generate embeddings for the Train Set

    -

    We can use the same assign_predictions method from earlier except this time we will use the vm_train_ds object to compute the embeddings for the question-answer pairs in the "train" set.

    -
    -
    vm_train_ds.assign_predictions(vm_embedder_small)
-print(vm_train_ds)
    -
    -

    -
    -
    -

    Insert embeddings and questions into Vector DB

    -

    Now that we have computed the embeddings for our question-answer pairs in the "train" set, we can go ahead and insert them into the vector store:

    -
    -
    from langchain_community.vectorstores import Qdrant
-from langchain_community.document_loaders import DataFrameLoader
-
-# load documents from dataframe
-loader = DataFrameLoader(train_df, page_content_column="question")
-docs = loader.load()
-
-# setup vector datastore
-qdrant = Qdrant.from_documents(
-    docs,
-    embedding_small_client,
-    location=":memory:",  # Local mode with in-memory storage only
-    collection_name="rfp_rag_collection",
-)
    -
    -
    -
    -
    -

    Retrieval Model

    -

    Now that we have an embedding model and a vector database setup and loaded with our data, we need a Retrieval model that can search for similar question-answer pairs for a given input question. Once created, we can initialize this as a ValidMind model and assign_predictions to it just like our embedding model. In this example, we'll create two retrieval models with different k parameters (the number of documents retrieved) to benchmark and compare their performance. This approach allows us to evaluate how retrieval depth affects the overall system quality.

    -
    -
    def retrieve(input):
-    contexts = []
-
-    for result in qdrant.similarity_search_with_score(input["question"], k=5):
-        document, score = result
-        context = f"Q: {document.page_content}\n"
-        context += f"A: {document.metadata['ground_truth']}\n"
-
-        contexts.append(context)
-
-    return contexts
-
-
-vm_retriever_k5 = vm.init_model(input_id="retrieval_k5_model", predict_fn=retrieve)
    -
    -
    -
    def retrieve(input):
-    contexts = []
-
-    for result in qdrant.similarity_search_with_score(input["question"], k=10):
-        document, score = result
-        context = f"Q: {document.page_content}\n"
-        context += f"A: {document.metadata['ground_truth']}\n"
-
-        contexts.append(context)
-
-    return contexts
-
-
-vm_retriever_k10 = vm.init_model(input_id="retrieval_k10_model", predict_fn=retrieve)
    -
    -
    -
    vm_test_ds.assign_predictions(model=vm_retriever_k5)
-vm_test_ds.assign_predictions(model=vm_retriever_k10)
-print(vm_test_ds)
    -
    -
    -
    vm_test_ds._df.head()
    -
    -
    -
    -

    Generation Model

    -

    As the final piece of this simple RAG pipeline, we can create and initialize a generation model that will use the retrieved context to generate an answer to the input question. We will use the gpt-3.5-turbo and gpt-4o models from OpenAI. Since we have two retrieval models (with different k values) and want to test two different LLMs, we'll create a total of four generator models - pairing each retrieval configuration with each LLM to comprehensively evaluate how both retrieval depth and model capability affect response quality.

    -
    -
    from openai import OpenAI
-
-from validmind.models import Prompt
-
-
-system_prompt = """
-You are an expert RFP AI assistant.
-You are tasked with answering new RFP questions based on existing RFP questions and answers.
-You will be provided with the existing RFP questions and answer pairs that are the most relevant to the new RFP question.
-After that you will be provided with a new RFP question.
-You will generate an answer and respond only with the answer.
-Ignore your pre-existing knowledge and answer the question based on the provided context.
-""".strip()
-
-openai_client = OpenAI()
    -
    -
    -
    def generate(input):
-    
-    response = openai_client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": "\n\n".join(input["retrieval_k5_model"])},
-            {"role": "user", "content": input["question"]},
-        ],
-    )
-    
-    return response.choices[0].message.content
-
-
-vm_generator_k5_gpt35 = vm.init_model(
-    input_id="generation_k5_gpt35_model",
-    predict_fn=generate,
-    prompt=Prompt(template=system_prompt),
-)
    -
    -
    -
    def generate(input):
-    response = openai_client.chat.completions.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": "\n\n".join(input["retrieval_k10_model"])},
-            {"role": "user", "content": input["question"]},
-        ],
-    )
-
-    return response.choices[0].message.content
-
-
-vm_generator_k10_gpt35 = vm.init_model(
-    input_id="generation_k10_gpt35_model",
-    predict_fn=generate,
-    prompt=Prompt(template=system_prompt),
-)
    -
    -
    -
    def generate(input):
-    
-    response = openai_client.chat.completions.create(
-        model="gpt-4o",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": "\n\n".join(input["retrieval_k5_model"])},
-            {"role": "user", "content": input["question"]},
-        ],
-    )
-    
-    return response.choices[0].message.content
-
-
-vm_generator_k5_gpt4o = vm.init_model(
-    input_id="generation_k5_gpt4o_model",
-    predict_fn=generate,
-    prompt=Prompt(template=system_prompt),
-)
    -
    -
    -
    def generate(input):
-    response = openai_client.chat.completions.create(
-        model="gpt-4o",
-        messages=[
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": "\n\n".join(input["retrieval_k10_model"])},
-            {"role": "user", "content": input["question"]},
-        ],
-    )
-
-    return response.choices[0].message.content
-
-
-vm_generator_k10_gpt4o = vm.init_model(
-    input_id="generation_k10_gpt4o_model",
-    predict_fn=generate,
-    prompt=Prompt(template=system_prompt),
-)
    -
    -

    Let's test it out real quick:

    -
    -
    import pandas as pd
-
-vm_generator_k5_gpt35.predict(
-    pd.DataFrame(
-        {"retrieval_k5_model": [["My name is anil"]], "question": ["what is my name"]}
-    )
-)
    -
    -
    -
    vm_generator_k5_gpt4o.predict(
-    pd.DataFrame(
-        {"retrieval_k5_model": [["My name is anil"]], "question": ["what is my name"]}
-    )
-)
    -
    -

    -
    -

    Prompt Evaluation

    -

    Now that we have our generator model initialized, we can run some LLM-as-Judge tests to evaluate the system prompt. This will allow us to get an initial sense of how well the prompt meets a few best practices for prompt engineering. These tests use an LLM to rate the prompt on a scale of 1-10 against the following criteria:

    -
      -
    • Examplar Bias: When using multi-shot prompting, does the prompt contain an unbiased distribution of examples?
      • -
    • Delimitation: When using complex prompts containing examples, contextual information, or other elements, is the prompt formatted in such a way that each element is clearly separated?
      • -
    • Clarity: How clearly the prompt states the task.
      • -
    • Conciseness: How succinctly the prompt states the task.
      • -
    • Instruction Framing: Whether the prompt contains negative instructions.
      • -
    • Specificity: How specific the prompt defines the task.
      • -
    -
    -
    run_test(
-    "validmind.prompt_validation.Bias",
-    inputs={
-        "model": vm_generator_k5_gpt4o,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.prompt_validation.Clarity",
-    inputs={
-        "model": vm_generator_k5_gpt4o,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.prompt_validation.Conciseness",
-    inputs={
-        "model": vm_generator_k5_gpt4o,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.prompt_validation.Delimitation",
-    inputs={
-        "model": vm_generator_k5_gpt4o,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.prompt_validation.NegativeInstruction",
-    inputs={
-        "model": vm_generator_k5_gpt4o,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.prompt_validation.Specificity",
-    inputs={
-        "model": vm_generator_k5_gpt4o,
-    },
-).log()
    -
    -
    -
    -
    -

    Setup RAG Pipeline Model

    -

    Now that we have all of our individual "component" models setup and initialized we need some way to put them all together in a single "pipeline". We can use the PipelineModel class to do this. This ValidMind model type simply wraps any number of other ValidMind models and runs them in sequence. We can use a pipe(|) operator - in Python this is normally an or operator but we have overloaded it for easy pipeline creation - to chain together our models. We can then initialize this pipeline model and assign predictions to it just like any other model.

    -
    -
    vm_rag_k5_gpt35_model = vm.init_model(vm_retriever_k5 | vm_generator_k5_gpt35, input_id="rag_k5_gpt35_model")
-vm_rag_k10_gpt35_model = vm.init_model(vm_retriever_k10 | vm_generator_k10_gpt35, input_id="rag_k10_gpt35_model")
-vm_rag_k5_gpt4o_model = vm.init_model(vm_retriever_k5 | vm_generator_k5_gpt4o, input_id="rag_k5_gpt4o_model")
-vm_rag_k10_gpt4o_model = vm.init_model(vm_retriever_k10 | vm_generator_k10_gpt4o, input_id="rag_k10_gpt4o_model")
    -
    -

    We can assign_predictions to the pipeline model just like we did with the individual models. This will run the pipeline on the test set and store the results in the test set for later use.

    -
    -
    vm_test_ds.assign_predictions(model=vm_rag_k5_gpt35_model)
-vm_test_ds.assign_predictions(model=vm_rag_k10_gpt35_model)
-vm_test_ds.assign_predictions(model=vm_rag_k5_gpt4o_model)
-vm_test_ds.assign_predictions(model=vm_rag_k10_gpt4o_model)
-print(vm_test_ds)
    -
    -
    -
    vm_test_ds._df.head(5)
    -
    -
    -
    -

    Run tests

    -

    -
    -

    RAGAS evaluation

    -

    Let's go ahead and run some of our new RAG tests against our model...

    -
    -

    Note: these tests are still being developed and are not yet in a stable state. We are using advanced tests here that use LLM-as-Judge and other strategies to assess things like the relevancy of the retrieved context to the input question and the correctness of the generated answer when compared to the ground truth. There is more to come in this area so stay tuned!

    -
    -
    -
    import warnings
-
-warnings.filterwarnings("ignore")
    -
    -

    -
    -

    Semantic Similarity

    -

    The concept of Answer Semantic Similarity pertains to the assessment of the semantic resemblance between the generated answer and the ground truth. This evaluation is based on the ground truth and the answer, with values falling within the range of 0 to 1. A higher score signifies a better alignment between the generated answer and the ground truth.

    -

    Measuring the semantic similarity between answers can offer valuable insights into the quality of the generated response. This evaluation utilizes a cross-encoder model to calculate the semantic similarity score.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.SemanticSimilarity",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "response_column": ["rag_k5_gpt35_model_prediction", "rag_k5_gpt4o_model_prediction"],
-        "reference_column": ["ground_truth"],
-    },
-).log()
    -
    -

    -
    -
    -

    Context Entity Recall

    -

    This test gives the measure of recall of the retrieved context, based on the number of entities present in both ground_truths and contexts relative to the number of entities present in the ground_truths alone. Simply put, it is a measure of what fraction of entities are recalled from ground_truths. This test is useful in fact-based use cases like tourism help desk, historical QA, etc. This test can help evaluate the retrieval mechanism for entities, based on comparison with entities present in ground_truths, because in cases where entities matter, we need the contexts which cover them.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.ContextEntityRecall",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "reference_column": ["ground_truth"],
-        "retrieved_contexts_column": ["retrieval_k5_model_prediction", "retrieval_k10_model_prediction"],
-    },
-).log()
    -
    -

    -
    -
    -

    Context Precision

    -

    Context Precision is a test that evaluates whether all of the ground-truth relevant items present in the contexts are ranked higher or not. Ideally all the relevant chunks must appear at the top ranks. This test is computed using the question, ground_truth and the contexts, with values ranging between 0 and 1, where higher scores indicate better precision.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.ContextPrecision",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "user_input_column": ["question"],
-        "retrieved_contexts_column": ["retrieval_k5_model_prediction", "retrieval_k10_model_prediction"],
-        "reference_column": ["ground_truth"],
-    },
-).log()
    -
    -

    -
    -
    -

    Context Precision Without Reference

    -

    This test evaluates whether retrieved contexts align well with the expected response for a given user input, without requiring a ground-truth reference. This test assesses the relevance of each retrieved context chunk by comparing it directly to the response.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.ContextPrecisionWithoutReference",
-    inputs={"dataset": vm_test_ds},
-    param_grid=[
-        {"user_input_column": "question",
-        "retrieved_contexts_column": "retrieval_k5_model_prediction",
-        "response_column": "rag_k5_gpt4o_model_prediction"
-        },
-        {"user_input_column": "question",
-        "retrieved_contexts_column": "retrieval_k10_model_prediction",
-        "response_column": "rag_k10_gpt4o_model_prediction"
-        },
-    ],
-)
    -
    -
    -
    run_test(
-    "validmind.model_validation.ragas.ContextPrecisionWithoutReference",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "user_input_column": ["question"],
-        "retrieved_contexts_column": ["retrieval_k5_model_prediction"],
-        "response_column": ["rag_k5_gpt35_model_prediction", "rag_k5_gpt4o_model_prediction"],
-    },
-).log()
    -
    -

    -
    -
    -

    Faithfulness

    -

    This measures the factual consistency of the generated answer against the given context. It is calculated from answer and retrieved context. The answer is scaled to (0,1) range. Higher the better.

    -

    The generated answer is regarded as faithful if all the claims that are made in the answer can be inferred from the given context. To calculate this a set of claims from the generated answer is first identified. Then each one of these claims are cross checked with given context to determine if it can be inferred from given context or not.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.Faithfulness",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "user_input_column": ["question"],
-        "response_column": ["rag_k5_gpt35_model_prediction", "rag_k5_gpt4o_model_prediction"],
-        "retrieved_contexts_column": ["retrieval_k5_model_prediction"],
-    },
-).log()
    -
    -

    -
    -
    -

    Response Relevancy

    -

    The Response Relevancy test, focuses on assessing how pertinent the generated answer is to the given prompt. A lower score is assigned to answers that are incomplete or contain redundant information and higher scores indicate better relevancy. This test is computed using the question, the context and the answer.

    -

    The Response Relevancy is defined as the mean cosine similartiy of the original question to a number of artifical questions, which where generated (reverse engineered) based on the answer.

    -

    Please note, that eventhough in practice the score will range between 0 and 1 most of the time, this is not mathematically guranteed, due to the nature of the cosine similarity ranging from -1 to 1.

    -
    -

    Note: This is a reference free test. If you’re looking to compare ground truth answer with generated answer refer to Answer Correctness.

    -
    -

    An answer is deemed relevant when it directly and appropriately addresses the original question. Importantly, our assessment of answer relevance does not consider factuality but instead penalizes cases where the answer lacks completeness or contains redundant details. To calculate this score, the LLM is prompted to generate an appropriate question for the generated answer multiple times, and the mean cosine similarity between these generated questions and the original question is measured. The underlying idea is that if the generated answer accurately addresses the initial question, the LLM should be able to generate questions from the answer that align with the original question.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.ResponseRelevancy",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "user_input_column": ["question"],
-        "response_column": ["rag_k5_gpt35_model_prediction", "rag_k5_gpt4o_model_prediction"],
-        "retrieved_contexts_column": ["retrieval_k5_model_prediction"],
-    },
-).log()
    -
    -

    -
    -
    -

    Context Recall

    -

    Context recall measures the extent to which the retrieved context aligns with the annotated answer, treated as the ground truth. It is computed based on the ground truth and the retrieved context, and the values range between 0 and 1, with higher values indicating better performance.

    -

    To estimate context recall from the ground truth answer, each sentence in the ground truth answer is analyzed to determine whether it can be attributed to the retrieved context or not. In an ideal scenario, all sentences in the ground truth answer should be attributable to the retrieved context.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.ContextRecall",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "user_input_column": ["question"],
-        "retrieved_contexts_column": ["retrieval_k5_model_prediction", "retrieval_k10_model_prediction"],
-        "reference_column": ["ground_truth"],
-    },
-).log()
    -
    -

    -
    -
    -

    Answer Correctness

    -

    The assessment of Answer Correctness involves gauging the accuracy of the generated answer when compared to the ground truth. This evaluation relies on the ground truth and the answer, with scores ranging from 0 to 1. A higher score indicates a closer alignment between the generated answer and the ground truth, signifying better correctness.

    -

    Answer correctness encompasses two critical aspects: semantic similarity between the generated answer and the ground truth, as well as factual similarity. These aspects are combined using a weighted scheme to formulate the answer correctness score.

    -

    Factual correctness quantifies the factual overlap between the generated answer and the ground truth answer. This is done using the concepts of:

    -
      -
    • TP (True Positive): Facts or statements that are present in both the ground truth and the generated answer.
      • -
    • FP (False Positive): Facts or statements that are present in the generated answer but not in the ground truth.
      • -
    • FN (False Negative): Facts or statements that are present in the ground truth but not in the generated answer.
      • -
    -
    -
    run_test(
-    "validmind.model_validation.ragas.AnswerCorrectness",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "user_input_column": ["question"],
-        "response_column": ["rag_k5_gpt35_model_prediction", "rag_k5_gpt4o_model_prediction"],
-        "reference_column": ["ground_truth"],
-    },
-).log()
    -
    -

    -
    -
    -

    Aspect Critic

    -

    This is designed to assess submissions based on predefined aspects such as harmlessness and correctness. Additionally, users have the flexibility to define their own aspects for evaluating submissions according to their specific criteria. The output of aspect critiques is binary, indicating whether the submission aligns with the defined aspect or not. This evaluation is performed using the ‘answer’ as input.

    -

    Critiques within the LLM evaluators evaluate submissions based on the provided aspect. Ragas Critiques offers a range of predefined aspects like correctness, harmfulness, etc. Users can also define their own aspects for evaluating submissions based on their specific criteria. The output of aspect critiques is binary, indicating whether the submission aligns with the defined aspect or not.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.AspectCritic",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "user_input_column": ["question"],
-        "response_column": ["rag_k5_gpt35_model_prediction", "rag_k5_gpt4o_model_prediction"],
-        "retrieved_contexts_column": ["retrieval_k5_model_prediction"],
-    },
-).log()
    -
    -

    -
    -
    -

    Noise Sensitivity

    -

    This test is designed to evaluate the robustness of the RAG pipeline model against noise in the retrieved context. It works by checking how well the "claims" in the generated answer match up with the "claims" in the ground truth answer. If the generated answer contains "claims" from the contexts that the ground truth answer does not contain, those claims are considered incorrect. The score for each answer is the number of incorrect claims divided by the total number of claims. This can be interpreted as a measure of how sensitive the LLM is to "noise" in the context where "noise" is information that is relevant but should not be included in the answer since the ground truth answer does not contain it.

    -
    -
    run_test(
-    "validmind.model_validation.ragas.NoiseSensitivity",
-    inputs={"dataset": vm_test_ds},
-    param_grid={
-        "user_input_column": ["question"],
-        "response_column": ["rag_k5_gpt35_model_prediction", "rag_k5_gpt4o_model_prediction"],
-        "reference_column": ["ground_truth"],
-        "retrieved_contexts_column": ["retrieval_k5_model_prediction"],
-    },
-).log()
    -
    -

    -
    -
    -
    -

    Generation quality

    -

    In this section, we evaluate the alignment and relevance of generated responses to reference outputs within our retrieval-augmented generation (RAG) application. We use metrics that assess various quality dimensions of the generated responses, including semantic similarity, structural alignment, and phrasing overlap. Semantic similarity metrics compare embeddings of generated and reference text to capture deeper contextual alignment, while overlap and alignment measures quantify how well the phrasing and structure of generated responses match the intended outputs.

    -

    -
    -

    Token Disparity

    -

    This test assesses the difference in token counts between the reference texts (ground truth) and the answers generated by the RAG model. It helps evaluate how well the model's outputs align with the expected length and level of detail in the reference texts. A significant disparity in token counts could signal issues with generation quality, such as excessive verbosity or insufficient detail. Consistently low token counts in generated answers compared to references might suggest that the model’s outputs are incomplete or overly concise, missing important contextual information.

    -
    -
    run_test(
-    "validmind.model_validation.TokenDisparity",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_rag_k5_gpt35_model, vm_rag_k5_gpt4o_model],
-    },
-).log()
    -
    -

    -
    -
    -

    ROUGE Score

    -

    This test evaluates the quality of answers generated by the RAG model by measuring overlaps in n-grams, word sequences, and word pairs between the model output and the reference (ground truth) text. ROUGE, short for Recall-Oriented Understudy for Gisting Evaluation, assesses both precision and recall, providing a balanced view of how well the generated response captures the reference content. ROUGE precision measures the proportion of n-grams in the generated text that match the reference, highlighting relevance and conciseness, while ROUGE recall assesses the proportion of reference n-grams present in the generated text, indicating completeness and thoroughness.

    -

    Low precision scores might reveal that the generated text includes redundant or irrelevant information, while low recall scores suggest omissions of essential details from the reference. Consistently low ROUGE scores could indicate poor overall alignment with the ground truth, suggesting the model may be missing key content or failing to capture the intended meaning.

    -
    -
    run_test(
-    "validmind.model_validation.RougeScore",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_rag_k5_gpt35_model, vm_rag_k5_gpt4o_model],
-    },
-    params={
-        "metric": "rouge-1",
-    },
-).log()
    -
    -

    -
    -
    -

    BLEU Score

    -

    The BLEU Score test evaluates the quality of answers generated by the RAG model by measuring n-gram overlap between the generated text and the reference (ground truth) text, with a specific focus on exact precision in phrasing. While ROUGE precision also assesses overlap, BLEU differs in two main ways: first, it applies a geometric average across multiple n-gram levels, capturing precise phrase alignment, and second, it includes a brevity penalty to prevent overly short outputs from inflating scores artificially. This added precision focus is valuable in RAG applications where strict adherence to reference language is essential, as BLEU emphasizes the match to exact phrasing. In contrast, ROUGE precision evaluates general content overlap without penalizing brevity, offering a broader sense of content alignment.

    -
    -
    run_test(
-    "validmind.model_validation.BleuScore",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_rag_k5_gpt35_model, vm_rag_k5_gpt4o_model],
-    },
-).log()
    -
    -

    -
    -
    -

    BERT Score

    -

    This test evaluates the quality of the RAG generated answers using BERT embeddings to measure precision, recall, and F1 scores based on semantic similarity, rather than exact n-gram matches as in BLEU and ROUGE. This approach captures contextual meaning, making it valuable when wording differs but the intended message closely aligns with the reference. In RAG applications, the BERT score is especially useful for ensuring that generated answers convey the reference text’s meaning, even if phrasing varies. Consistently low scores indicate a lack of semantic alignment, suggesting the model may miss or misrepresent key content. Low precision may reflect irrelevant or redundant details, while low recall can indicate omissions.

    -
    -
    run_test(
-    "validmind.model_validation.BertScore",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_rag_k5_gpt35_model, vm_rag_k5_gpt4o_model],
-    },
-).log()
    -
    -

    -
    -
    -

    METEOR Score

    -

    This test evaluates the quality of the generated answers by measuring alignment with the ground truth, emphasizing both accuracy and fluency. Unlike BLEU and ROUGE, which focus on n-gram matches, METEOR combines precision, recall, synonym matching, and word order, focusing at how well the generated text conveys meaning and reads naturally. This metric is especially useful for RAG applications where sentence structure and natural flow are crucial for clear communication. Lower scores may suggest alignment issues, indicating that the answers may lack fluency or key content. Discrepancies in word order or high fragmentation penalties can reveal problems with how the model constructs sentences, potentially affecting readability.

    -
    -
    run_test(
-    "validmind.model_validation.MeteorScore",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_rag_k5_gpt35_model, vm_rag_k5_gpt4o_model],
-    },
-).log()
    -
    -

    -
    -
    -
    -

    Bias and Toxicity

    -

    In this section, we use metrics like Toxicity Score and Regard Score to evaluate both the generated responses and the ground truth. These tests helps us detect any harmful, offensive, or inappropriate language and evaluate the level of bias and neutrality enabling us to assess and mitigate potential biases in both the model's responses and the original dataset.

    -

    -
    -

    Toxicity Score

    -

    This test measures the level of harmful or offensive content in the generated answers. The test uses a preloaded toxicity detection tool from Hugging Face, which identifies language that may be inappropriate, aggressive, or derogatory. High toxicity scores indicate potentially toxic content, while consistently elevated scores across multiple outputs may signal underlying issues in the model’s generation process that require attention to prevent the spread of harmful language.

    -
    -
    run_test(
-    "validmind.model_validation.ToxicityScore",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_rag_k5_gpt35_model, vm_rag_k5_gpt4o_model],
-    },
-).log()
    -
    -

    -
    -
    -

    Regard Score

    -

    This test evaluates the sentiment and perceived regard—categorized as positive, negative, neutral, or other—in answers generated by the RAG model. This is important for identifying any biases or sentiment tendencies in responses, ensuring that generated answers are balanced and appropriate for the context. The uses a preloaded regard evaluation tool from Hugging Face to compute scores for each response. High skewness in regard scores, especially if the generated responses consistently diverge from expected sentiments in the reference texts, may reveal biases in the model’s generation, such as overly positive or negative tones where neutrality is expected.

    -
    -
    run_test(
-    "validmind.model_validation.RegardScore",
-    input_grid={
-        "dataset": [vm_test_ds],
-        "model": [vm_rag_k5_gpt35_model, vm_rag_k5_gpt4o_model],
-    },
-).log()
    -
    -
    -
    -
    -
    -

    Conclusion

    -

    In this notebook, we have seen how we can use LangChain and ValidMind together to build, evaluate and document a simple RAG Model as its developed. This is a great example of the interactive development experience that ValidMind is designed to support. We can quickly iterate on our model and document as we go... We have seen how ValidMind supports non-traditional "models" using a functional interface and how we can build pipelines of many models to support complex GenAI workflows.

    -

    This is still a work in progress and we are actively developing new tests to support more advanced GenAI workflows. We are also keeping an eye on the most popular GenAI models and libraries to explore direct integrations. Stay tuned for more updates and new features in this area!

    -

    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
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    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/ongoing_monitoring/application_scorecard_ongoing_monitoring.html b/site/notebooks/use_cases/ongoing_monitoring/application_scorecard_ongoing_monitoring.html deleted file mode 100644 index dac5b2927..000000000 --- a/site/notebooks/use_cases/ongoing_monitoring/application_scorecard_ongoing_monitoring.html +++ /dev/null @@ -1,2346 +0,0 @@ - - - - - - - - - -Ongoing Monitoring for Application Scorecard – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Ongoing Monitoring for Application Scorecard

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    - - - -
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    - - - -
    - - - -

    In this notebook, you'll learn how to seamlessly monitor your production models using the ValidMind Platform.

    -

    We'll walk you through the process of initializing the ValidMind Library, loading a sample dataset and model, and running a monitoring test suite to quickly generate documentation about your new data and model.

    - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation, validation, and monitoring tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

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    -
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    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    ongoing monitoring report: A comprehensive and structured periodic report assessing the record's performance and compliance over time, ensuring it remains valid under changing conditions. Monitoring includes key elements such as data sources, inputs, performance metrics, and periodic evaluations, ensuring transparency and visibility of the record's performance in the production environment.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    monitoring template, monitoring report template: A default ValidMind document template that serves as a standardized framework for ongoing monitoring, including sections designated for test results, performance metrics, and drift analyses. By outlining required monitoring checks and expected routine tests, monitoring templates ensure consistency and completeness across monitoring reports and help guide owners through a systematic monitoring process while promoting early detection of performance degradation.

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    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

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    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

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    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

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    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
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    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
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    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

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    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

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    Setting up

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    Install the ValidMind Library

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    To install the library:

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    -
    %pip install -q validmind
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    -
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    Initialize the ValidMind Library

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    -
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    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
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    -

    Apply monitoring report template

    -

    Once you've registered your model, let's select a monitoring report template. A template predefines sections for your monitoring report and provides a general outline to follow, making the monitoring process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Monitoring.

      -

      If you cannot locate your Monitoring document, make sure Monitoring type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Ongoing Monitoring for Classification Models.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Monitoring from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="monitoring",
-    monitoring = True,
-)
    -
    -

    -
    -
    -
    -

    Preview the monitoring report template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

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    -
    vm.preview_template()
    -
    -

    -
    -
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    Initialize the Python environment

    -

    Next, let's import the necessary libraries and set up your Python environment for data analysis:

    -
    -
    import xgboost as xgb
-import numpy as np
-
-from datetime import datetime, timedelta
-
-from validmind.tests import run_test
-from validmind.datasets.credit_risk import lending_club
-from validmind.unit_metrics import list_metrics
-from validmind.unit_metrics import describe_metric
-from validmind.unit_metrics import run_metric
-from validmind.api_client import log_metric
-
-%matplotlib inline
    -
    -

    -
    -
    -

    Preview the monitoring template

    -

    A template predefines sections for your monitoring documentation and provides a general outline to follow, making the documentation process much easier.

    -

    You will upload documentation and test results into this template later on. For now, take a look at the structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -
    -

    Load the reference and monitoring datasets

    -

    The sample dataset used here is provided by the ValidMind library. For demonstration purposes we'll use the training, test dataset splits as reference and monitoring datasets.

    -
    -
    df = lending_club.load_data(source="offline")
-df.head()
    -
    -
    -
    preprocess_df = lending_club.preprocess(df)
-preprocess_df.head()
    -
    -
    -
    fe_df = lending_club.feature_engineering(preprocess_df)
-fe_df.head()
    -
    -

    -
    -
    -

    Train the model

    -

    In this section, we focus on constructing and refining our predictive model. - We begin by dividing our data, which is based on Weight of Evidence (WoE) features, into training and testing sets (train_df, test_df). - With lending_club.split, we employ a simple random split, randomly allocating data points to each set to ensure a mix of examples in both.

    -
    -
    # Split the data
-train_df, test_df = lending_club.split(fe_df, test_size=0.2)
-
-x_train = train_df.drop(lending_club.target_column, axis=1)
-y_train = train_df[lending_club.target_column]
-
-x_test = test_df.drop(lending_club.target_column, axis=1)
-y_test = test_df[lending_club.target_column]
    -
    -
    -
    # Define the XGBoost model
-xgb_model = xgb.XGBClassifier(
-    n_estimators=50, 
-    random_state=42, 
-    early_stopping_rounds=10
-)
-xgb_model.set_params(
-    eval_metric=["error", "logloss", "auc"],
-)
-
-# Fit the model
-xgb_model.fit(
-    x_train, 
-    y_train,
-    eval_set=[(x_test, y_test)],
-    verbose=False
-)
-
-# Compute probabilities
-train_xgb_prob = xgb_model.predict_proba(x_train)[:, 1]
-test_xgb_prob = xgb_model.predict_proba(x_test)[:, 1]
-
-# Compute binary predictions
-cut_off_threshold = 0.3
-train_xgb_binary_predictions = (train_xgb_prob > cut_off_threshold).astype(int)
-test_xgb_binary_predictions = (test_xgb_prob > cut_off_threshold).astype(int)
    -
    -

    -
    -

    Initialize the ValidMind datasets

    -

    Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -

    This function takes a number of arguments:

    -
      -
    • dataset — The raw dataset that you want to provide as input to tests.
      • -
    • input_id - A unique identifier that allows tracking what inputs are used when running each individual test.
      • -
    • target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.
      • -
    -

    With all datasets ready, you can now initialize training, reference(test) and monitor datasets (reference_df and monitor_df) created earlier into their own dataset objects using vm.init_dataset():

    -
    -
    vm_reference_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="reference_dataset",
-    target_column=lending_club.target_column,
-)
-
-vm_monitoring_ds = vm.init_dataset(
-    dataset=test_df,
-    input_id="monitoring_dataset",
-    target_column=lending_club.target_column,
-)
    -
    -

    -
    -
    -

    Initialize the ValidMind model

    -

    You will also need to initialize a ValidMind model object (vm_model) that can be passed to other functions for analysis and tests on the data.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
    -
    vm_xgb_model = vm.init_model(
-    xgb_model,
-    input_id="xgb_model",
-)
    -
    -

    -
    -
    -

    Assign prediction values and probabilities to the datasets

    -

    With our model now trained, we'll move on to assigning both the predictive probabilities coming directly from the model's predictions, and the binary prediction after applying the cutoff threshold described in the previous steps. - These tasks are achieved through the use of the assign_predictions() method associated with the VM dataset object. - This method links the model's class prediction values and probabilities to our VM train and test datasets.

    -
    -
    vm_reference_ds.assign_predictions(
-    model=vm_xgb_model,
-    prediction_values=train_xgb_binary_predictions,
-    prediction_probabilities=train_xgb_prob,
-)
-
-vm_monitoring_ds.assign_predictions(
-    model=vm_xgb_model,
-    prediction_values=test_xgb_binary_predictions,
-    prediction_probabilities=test_xgb_prob,
-)
    -
    -

    -
    -
    -

    Compute credit risk scores

    -

    In this phase, we translate model predictions into actionable scores using probability estimates generated by our trained model.

    -
    -
    train_xgb_scores = lending_club.compute_scores(train_xgb_prob)
-test_xgb_scores = lending_club.compute_scores(test_xgb_prob)
-
-# Assign scores to the datasets
-vm_reference_ds.add_extra_column("xgb_scores", train_xgb_scores)
-vm_monitoring_ds.add_extra_column("xgb_scores", test_xgb_scores)
    -
    -

    -
    -
    -

    Adding custom context to the LLM descriptions

    -

    To enable the LLM descriptions context, you need to set the VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED environment variable to 1. This will enable the LLM descriptions context, which will be used to provide additional context to the LLM descriptions. This is a global setting that will affect all tests.

    -
    -
    import os
-os.environ["VALIDMIND_LLM_DESCRIPTIONS_CONTEXT_ENABLED"] = "1"
-
-context = """
-FORMAT FOR THE LLM DESCRIPTIONS: 
-    **<Test Name>** is designed to <begin with a concise overview of what the test does and its primary purpose, 
-    extracted from the test description>.
-
-    The test operates by <write a paragraph about the test mechanism, explaining how it works and what it measures. 
-    Include any relevant formulas or methodologies mentioned in the test description.>
-
-    The primary advantages of this test include <write a paragraph about the test's strengths and capabilities, 
-    highlighting what makes it particularly useful for specific scenarios.>
-
-    Users should be aware that <write a paragraph about the test's limitations and potential risks. 
-    Include both technical limitations and interpretation challenges. 
-    If the test description includes specific signs of high risk, incorporate these here.>
-
-    **Key Insights:**
-
-    The test results reveal:
-
-    - **<insight title>**: <comprehensive description of one aspect of the results>
-    - **<insight title>**: <comprehensive description of another aspect>
-    ...
-
-    Based on these results, <conclude with a brief paragraph that ties together the test results with the test's 
-    purpose and provides any final recommendations or considerations.>
-
-ADDITIONAL INSTRUCTIONS:
-    Present insights in order from general to specific, with each insight as a single bullet point with bold title.
-
-    For each metric in the test results, include in the test overview:
-    - The metric's purpose and what it measures
-    - Its mathematical formula
-    - The range of possible values
-    - What constitutes good/bad performance
-    - How to interpret different values
-
-    Each insight should progressively cover:
-    1. Overall scope and distribution
-    2. Complete breakdown of all elements with specific values
-    3. Natural groupings and patterns
-    4. Comparative analysis between datasets/categories
-    5. Stability and variations
-    6. Notable relationships or dependencies
-
-    Remember:
-    - Keep all insights at the same level (no sub-bullets or nested structures)
-    - Make each insight complete and self-contained
-    - Include specific numerical values and ranges
-    - Cover all elements in the results comprehensively
-    - Maintain clear, concise language
-    - Use only "- **Title**: Description" format for insights
-    - Progress naturally from general to specific observations
-
-""".strip()
-
-os.environ["VALIDMIND_LLM_DESCRIPTIONS_CONTEXT"] = context
    -
    -

    -
    -
    -

    Monitoring data description

    -

    The Monitoring Data Description tests aim to provide a comprehensive statistical analysis of the monitoring dataset's characteristics. These tests examine the basic statistical properties, identify any missing data patterns, assess data uniqueness, visualize numerical feature distributions, and evaluate feature relationships through correlation analysis.

    -

    The primary objective is to establish a baseline understanding of the monitoring data's structure and quality, enabling the detection of any significant deviations from expected patterns that could impact model performance. Each test is designed to capture different aspects of the data, from univariate statistics to multivariate relationships, providing a foundation for ongoing data quality assessment in the production environment.

    -
    -
    run_test(
-    "validmind.data_validation.DescriptiveStatistics:monitoring_data",
-    inputs={
-        "dataset": vm_monitoring_ds,
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.MissingValues:monitoring_data",
-    inputs={
-        "dataset": vm_monitoring_ds,
-    },
-    params={
-        "min_percentage_threshold": 1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.UniqueRows:monitoring_data",
-    inputs={
-        "dataset": vm_monitoring_ds,
-    },
-    params={
-        "min_percent_threshold": 1
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.TabularNumericalHistograms:monitoring_data",
-    inputs={
-        "dataset": vm_monitoring_ds,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.PearsonCorrelationMatrix:monitoring_data",
-    inputs={
-        "dataset": vm_monitoring_ds,
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.data_validation.HighPearsonCorrelation:monitoring_data",
-    inputs={
-        "dataset": vm_monitoring_ds,
-    },
-    params={
-        "feature_columns": vm_monitoring_ds.feature_columns,
-        "max_threshold": 0.5,
-        "top_n_correlations": 10
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.ongoing_monitoring.ClassImbalanceDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-    },
-    params={
-        "drift_pct_threshold": 1
-    },
-).log()
    -
    -

    -
    -
    -

    Target and feature drift

    -

    Next, the goal is to investigate the distributional characteristics of predictions and features to determine if the underlying data has changed. These tests are crucial for assessing the expected accuracy of the model.

    -
      -
    1. Target drift: We compare the dataset used for testing (reference data) with the monitoring data. This helps to identify any shifts in the target variable distribution.
      2. -
    2. Feature drift: We compare the training dataset with the monitoring data. Since features were used to train the model, any drift in these features could indicate potential issues, as the underlying patterns that the model was trained on may have changed.
      3. -
    -

    Next, we can examine the correlation between features and predictions. Significant changes in these correlations may trigger a deeper assessment.

    -
    -
    run_test(
-    "validmind.model_validation.sklearn.PopulationStabilityIndex",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.ongoing_monitoring.TargetPredictionDistributionPlot",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "drift_pct_threshold": 5
-    },
-).log()
    -
    -

    Now we want see difference in correlation pairs between model prediction and features.

    -
    -
    run_test(
-    "validmind.ongoing_monitoring.PredictionCorrelation",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "drift_pct_threshold": 5
-    },
-).log()
    -
    -

    Finally for target drift, let's plot each prediction value and feature grid side by side.

    -
    -
    run_test(
-    "validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-).log()
    -
    -

    Next, let's add run a test to investigate how or if the features have drifted. In this instance we want to compare the training data with prediction data.

    -
    -
    run_test(
-    "validmind.ongoing_monitoring.FeatureDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "psi_threshold": 0.2,
-    },
-).log()
    -
    -

    -
    -
    -

    Classification accuracy

    -

    We now evaluate the model's predictive performance by comparing its behavior between reference and monitoring datasets. These tests analyze shifts in overall accuracy metrics, examine changes in the confusion matrix to identify specific classification pattern changes, and assess the model's probability calibration across different prediction thresholds.

    -

    The primary objective is to detect any degradation in the model's classification performance that might indicate reliability issues in production. The tests provide both aggregate performance metrics and detailed breakdowns of prediction patterns, enabling the identification of specific areas where the model's accuracy might be deteriorating.

    -
    -
    run_test(
-    "validmind.ongoing_monitoring.ClassificationAccuracyDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "drift_pct_threshold": 5,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.ongoing_monitoring.ConfusionMatrixDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "drift_pct_threshold": 5,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.ongoing_monitoring.CalibrationCurveDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "n_bins": 10,
-        "drift_pct_threshold": 10,
-    },
-).log()
    -
    -

    -
    -
    -

    Class discrimination

    -

    The following tests assess the model's ability to effectively separate different classes in both reference and monitoring datasets. These tests analyze the model's discriminative power by examining the separation between class distributions, evaluating changes in the ROC curve characteristics, comparing probability distribution patterns, and assessing cumulative prediction trends.

    -

    The primary objective is to identify any deterioration in the model's ability to distinguish between classes, which could indicate a decline in model effectiveness. The tests examine both the overall discriminative capability and the granular patterns in prediction distributions, providing insights into whether the model maintains its ability to effectively differentiate between classes in the production environment.

    -
    -
    run_test(
-    "validmind.ongoing_monitoring.ClassDiscriminationDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "drift_pct_threshold": 5,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.ongoing_monitoring.ROCCurveDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "drift_pct_threshold": 10,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    }
-).log()
    -
    -

    -
    -
    -

    Scoring

    -

    Next we analyze the distribution and stability of credit scores across reference and monitoring datasets. These tests evaluate shifts in score distributions, examine changes in score band populations, and assess the relationship between scores and default rates.

    -

    The primary objective is to identify any significant changes in how the model assigns credit scores, which could indicate drift in risk assessment capabilities. The tests examine both the overall score distribution patterns and the specific performance within defined score bands, providing insights into whether the model maintains consistent and reliable risk segmentation.

    -
    -
    run_test(
-    "validmind.ongoing_monitoring.ScorecardHistogramDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-    },
-    params={
-        "score_column": "xgb_scores",
-        "drift_pct_threshold": 20,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.ongoing_monitoring.ScoreBandsDrift",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "score_column": "xgb_scores",
-        "score_bands": [500, 540, 570],
-        "drift_pct_threshold": 20,
-    },
-).log()
    -
    -

    -
    -
    -

    Model insights

    -
    -
    run_test(
-    "validmind.model_validation.sklearn.PermutationFeatureImportance",
-    input_grid={
-        "dataset": [vm_reference_ds, vm_monitoring_ds],
-        "model": [vm_xgb_model]
-    }
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.FeaturesAUC",
-    input_grid={
-        "model": [vm_xgb_model],
-        "dataset": [vm_reference_ds, vm_monitoring_ds],
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.SHAPGlobalImportance",
-    input_grid={
-        "model": [vm_xgb_model],
-        "dataset": [vm_reference_ds, vm_monitoring_ds],
-    },
-    params={
-        "kernel_explainer_samples": 10,
-        "tree_or_linear_explainer_samples": 200,
-    }
-).log()
    -
    -

    -
    -
    -

    Diagnostic monitoring

    -
    -
    run_test(
-    "validmind.model_validation.sklearn.WeakspotsDiagnosis",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-).log()
    -
    -
    -
    run_test(
-    "validmind.model_validation.sklearn.OverfitDiagnosis",
-    inputs={
-        "model": vm_xgb_model,
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-    },
-    params={
-        "cut_off_threshold": 0.04
-    }
-).log()
    -
    -

    -
    -
    -

    Robustness monitoring

    -
    -
    run_test(
-    "validmind.model_validation.sklearn.RobustnessDiagnosis",
-    inputs={
-        "datasets": [vm_reference_ds, vm_monitoring_ds],
-        "model": vm_xgb_model,
-    },
-    params={
-        "scaling_factor_std_dev_list": [
-            0.1,
-            0.2,
-            0.3,
-            0.4,
-            0.5
-        ],
-        "performance_decay_threshold": 0.05
-    }
-).log()
    -
    -

    -
    -
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    Performance history

    -

    In this section we showcase how to track and visualize the temporal evolution of key model performance metrics, including AUC, F1 score, precision, recall, and accuracy. For demonstration purposes, the section simulates historical performance data by introducing a gradual downward trend and random noise to these metrics over a specified time period. These tests are useful for analyzing the stability and trends in model performance indicators, helping to identify potential degradation or unexpected fluctuations in model behavior over time.

    -

    The main goal is to maintain a continuous record of model performance that can be used to detect gradual drift, sudden changes, or cyclical patterns in model effectiveness. This temporal monitoring approach provides early warning signals of potential issues and helps establish whether the model maintains consistent performance within acceptable boundaries throughout its deployment period.

    -
    -
    metrics = [metric for metric in list_metrics() if "classification" in metric]
-
-for metric_id in metrics:
-    describe_metric(metric_id)
    -
    -
    -
    result = run_metric(
-    "validmind.unit_metrics.classification.ROC_AUC",
-    inputs={
-        "model": vm_xgb_model,
-        "dataset": vm_monitoring_ds,
-    },
-)
-auc = result.metric
    -
    -
    -
    result = run_metric(
-    "validmind.unit_metrics.classification.Accuracy",
-    inputs={
-        "model": vm_xgb_model,
-        "dataset": vm_monitoring_ds,
-    },
-)
-accuracy = result.metric
    -
    -
    -
    result = run_metric(
-    "validmind.unit_metrics.classification.Recall",
-    inputs={
-        "model": vm_xgb_model,
-        "dataset": vm_monitoring_ds,
-    },
-)
-recall = result.metric
    -
    -
    -
    f1 = run_metric(
-    "validmind.unit_metrics.classification.F1",
-    inputs={
-        "model": vm_xgb_model,
-        "dataset": vm_monitoring_ds,
-    },
-)
-f1 = result.metric
    -
    -
    -
    precision = run_metric(
-    "validmind.unit_metrics.classification.Precision",
-    inputs={
-        "model": vm_xgb_model,
-        "dataset": vm_monitoring_ds,
-    },
-)
-precision = result.metric
    -
    -
    -
    NUM_DAYS = 10
-REFERENCE_DATE = datetime(2024, 1, 1)  # Fixed date: January 1st, 2024
-base_date = REFERENCE_DATE - timedelta(days=NUM_DAYS)
-
-
-# Initial values
-performance_metrics = {
-    "AUC Score": auc,
-    "F1 Score": f1,
-    "Precision Score": precision,
-    "Recall Score": recall,
-    "Accuracy Score": accuracy
-}
-
-# Trend parameters
-trend_factor = 0.98  # Slight downward trend (multiply by 0.98 each step)
-noise_scale = 0.02   # Random fluctuation of ±2%
-
-
-for i in range(NUM_DAYS):
-    recorded_at = base_date + timedelta(days=i)
-    print(f"\nrecorded_at: {recorded_at}")
-
-    # Log each metric with trend and noise
-    for metric_name, base_value in performance_metrics.items():
-        # Apply trend and add random noise
-        trend = base_value * (trend_factor ** i)
-        noise = np.random.normal(0, noise_scale * base_value)
-        value = max(0, min(1, trend + noise))  # Ensure value stays between 0 and 1
-        
-        log_metric(
-            key=metric_name,
-            value=value,
-            recorded_at=recorded_at.isoformat()
-        )
-        
-        print(f"{metric_name:<15}: {value:.4f}")
    -
    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/regression/quickstart_regression_full_suite.html b/site/notebooks/use_cases/regression/quickstart_regression_full_suite.html deleted file mode 100644 index ba172cab9..000000000 --- a/site/notebooks/use_cases/regression/quickstart_regression_full_suite.html +++ /dev/null @@ -1,1851 +0,0 @@ - - - - - - - - - -Document a California Housing Price Prediction regression model – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Document a California Housing Price Prediction regression model

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    Use the California Housing Price Prediction sample dataset from Sklearn to train a simple regression model and document that model with the ValidMind Library.

    -

    As part of the notebook, you will learn how to train a sample model while exploring how the documentation process works:

    - - - -

    -
    -

    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

    If this is your first time trying out ValidMind, you can make use of the following resources alongside this notebook:

    -
      -
    • Get started — The basics, including key concepts, and how our products work
      • -
    • ValidMind Library — The path for developers, more code samples, and our developer reference
      • -
    -

    -
    -
    -

    Before you begin

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
    -
    -

    Setting up

    -

    -
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    Install the ValidMind Library

    -

    To install the library:

    -
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    %pip install -q validmind
    -
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    -
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    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Binary classification.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Initialize the Python environment

    -

    Next, let's import the necessary libraries and set up your Python environment for data analysis:

    -
    -
    from sklearn.preprocessing import StandardScaler
-from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
-
-%matplotlib inline
    -
    -

    -
    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -
    -

    Load the sample dataset

    -

    The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas DataFrame, a two-dimensional tabular data structure that makes use of rows and columns:

    -
    -
    # Import the sample dataset from the library
-
-from validmind.datasets.regression import california_housing as demo_dataset
-
-print(f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}")
-
-raw_df = demo_dataset.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Document the model

    -

    As part of documenting the model with the ValidMind Library, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.

    -

    -
    -

    Prepocess the raw dataset

    -

    Preprocessing performs a number of operations to get ready for the subsequent steps:

    -
      -
    • Preprocess the data: Splits the DataFrame (df) into multiple datasets (train_df, validation_df, and test_df) using demo_dataset.preprocess to simplify preprocessing.
      • -
    • Separate features and targets: Drops the target column to create feature sets (x_train, x_val) and target sets (y_train, y_val).
      • -
    • Initialize RandomForestRegressor regressor: Creates an RandomForestRegressor object with random state set to 0.
      • -
    • Set evaluation metrics: Specifies metrics for model evaluation as "errors" and "r2".
      • -
    • Fit the model: Trains the model on x_train and y_train using the validation set (x_val, y_val). Verbose output is disabled.
      • -
    -
    -
    train_df, validation_df, test_df = demo_dataset.preprocess(raw_df)
-
-x_train = train_df.drop(demo_dataset.target_column, axis=1)
-y_train = train_df[demo_dataset.target_column]
-x_val = validation_df.drop(demo_dataset.target_column, axis=1)
-y_val = validation_df[demo_dataset.target_column]
    -
    -

    Here we create two regression models so that the performance of the model can be compared through ValidMind test suite.

    -
    -
    scale = False
-if scale:
-    scaler = StandardScaler()
-    x_train = scaler.fit_transform(x_train)
-    x_val = scaler.fit_transform(x_val)
-
-model = RandomForestRegressor(random_state=0)
-model.fit(x_train, y_train)
-s1 = model.score(x_train, y_train)
-s2 = model.score(x_val, y_val)
-print("R² of Support Vector Regressor on training set: {:.3f}".format(s1))
-print("R² of Support Vector Regressor on test set: {:.3f}".format(s2))
-
-model_1 = GradientBoostingRegressor(random_state=0, max_depth=4)
-model_1.fit(x_train, y_train)
-model1_s1 = model_1.score(x_train, y_train)
-model1_s2 = model_1.score(x_val, y_val)
-print(
-    "R² of Support Gradient Boosting Regressor on training set: {:.3f}".format(
-        model1_s1
-    )
-)
-print("R² of Support Gradient Boosting Regressor on test set: {:.3f}".format(model1_s2))
    -
    -

    -
    -
    -

    Initialize the ValidMind datasets

    -

    Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -

    This function takes a number of arguments:

    -
      -
    • dataset — the raw dataset that you want to provide as input to tests
      • -
    • input_id - a unique identifier that allows tracking what inputs are used when running each individual test
      • -
    • target_column — a required argument if tests require access to true values. This is the name of the target column in the dataset
      • -
    -

    With all datasets ready, you can now initialize the raw, training and test datasets (raw_df, train_df and test_df) created earlier into their own dataset objects using vm.init_dataset():

    -
    -
    vm_raw_dataset = vm.init_dataset(
-    dataset=raw_df,
-    input_id="raw_dataset",
-    target_column=demo_dataset.target_column,
-)
-
-vm_train_ds = vm.init_dataset(
-    dataset=train_df, input_id="train_dataset", target_column=demo_dataset.target_column
-)
-
-vm_test_ds = vm.init_dataset(
-    dataset=test_df, input_id="test_dataset", target_column=demo_dataset.target_column
-)
    -
    -

    -
    -
    -

    Initialize the ValidMind models

    -

    You'll also need to initialize ValidMind model objects (vm_model) that can be passed to other functions for analysis and tests on the data for our models.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
    -
    vm_model = vm.init_model(
-    model,
-    input_id="random_forest_regressor",
-)
-vm_model_1 = vm.init_model(
-    model_1,
-    input_id="gradient_boosting_regressor",
-)
    -
    -

    -
    -
    -

    Assign predictions to the datasets

    -

    We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:

    -
    -
    vm_train_ds.assign_predictions(
-    model=vm_model,
-)
-vm_train_ds.assign_predictions(
-    model=vm_model_1,
-)
-vm_test_ds.assign_predictions(
-    model=vm_model,
-)
-vm_test_ds.assign_predictions(
-    model=vm_model_1,
-)
    -
    -

    -
    -
    -

    Run the full suite of tests

    -

    This is where it all comes together: you are now ready to run the documentation tests for the model as defined by the documentation template you looked at earlier.

    -

    The vm.run_documentation_tests function finds and runs every tests specified in the test suites and then uploads all the documentation and test artifacts that get generated to the ValidMind Platform.

    -

    The function takes two arguments:

    -
      -
    • dataset: The data to be tested, specified as vm_dataset.
      • -
    • model: The candidate model to be used for testing, specified as vm_model. -models: The list of models that can be compare with candidate model.
      • -
    -

    The variable full_suite then holds the result of these tests.

    -
    -
    full_suite = vm.run_documentation_tests(
-    inputs={
-        "dataset": vm_train_ds,
-        "datasets": (vm_train_ds, vm_test_ds),
-        "model": vm_model,
-        "models":[vm_model_1]
-    }
-)
    -
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    -
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    Next steps

    -

    You can look at the output produced by the ValidMind Library right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -

    3. Expand the following sections and take a look around:

      -
        -
      • 2. Data Preparation
        • -
      • 3. Model Development
        • -
      4. -
    -

    What you can see now is a much more easily consumable version of the documentation, including the results of the tests you just performed, along with other parts of your model documentation that still need to be completed. There is a wealth of information that gets uploaded when you run the full test suite, so take a closer look around, especially at test results that might need attention (hint: some of the tests in 2.1 Data description look like they need some attention). From here, you can make qualitative edits to model documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

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    Discover more learning resources

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    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

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    Or, visit our documentation to learn more about ValidMind.

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    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
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    Retrieve the information for the currently installed version of ValidMind:

    -
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    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
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    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
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    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

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    - - - - - - \ No newline at end of file diff --git a/site/notebooks/use_cases/time_series/quickstart_time_series_high_code.html b/site/notebooks/use_cases/time_series/quickstart_time_series_high_code.html deleted file mode 100644 index 8fbc5db88..000000000 --- a/site/notebooks/use_cases/time_series/quickstart_time_series_high_code.html +++ /dev/null @@ -1,2125 +0,0 @@ - - - - - - - - - -Document a time series forecasting model – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    -
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    About ValidMind

    -

    ValidMind is a suite of tools for managing risk, including risk associated with AI and statistical models.

    -

    You use the ValidMind Library to automate documentation and validation tests, and then use the ValidMind Platform to collaborate on documentation. Together, these products simplify risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and validators.

    -

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    Before you begin

    -

    This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

    -

    If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

    -

    -
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    New to ValidMind?

    -

    If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting records such as models and running tests, as well as find code samples and our Python Library API reference.

    -
    -For access to all features available in this notebook, you'll need access to a ValidMind account.

    Register with ValidMind -
    -

    -
    -
    -

    Key concepts

    -

    record: A tool tracked in the ValidMind inventory, such as a model. Records include traditional statistical models, legacy systems, artificial intelligence/machine learning models, large language models (LLMs), agentic AI systems, and other documentable items that benefit from oversight, testing, and lifecycle management.

    -

    model: SR 26-2 (which supersedes SR 11-7) defines a model as a "complex quantitative method, system, or approach that applies statistical, economic, or financial theories to process input data into quantitative estimates." Simple arithmetic, deterministic rule-based processes, or software without statistical, economic, or financial theories underpinning their design or use are generally outside SR 26-2’s definition of a model. Within ValidMind, a model is a type of record tracked in the inventory.

    -

    documentation, model documentation: A structured and detailed document pertaining to a record, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. Within the realm of risk management, this documentation serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the record's application.

    -

    document template: Lays out the structure of documents, segmented into various sections and sub-sections, and functions as a test suite specifying the tests that should be run, and how the results should be displayed. Document templates help automate your development, validation, monitoring, and other risk management processes. Document templates are available for default ValidMind document types as well as custom document types.

    -

    documentation template: A default ValidMind document type that serves as a standardized framework for developing and documenting records, including sections designated for record details, data descriptions, test results, and performance metrics. By outlining required documentation and recommended analyses, document templates ensure consistency and completeness across documentation and help guide developers through a systematic development process while promoting comparability and traceability of development outcomes.

    -

    test: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or record. Test results are logged to the ValidMind Platform, where they are attached to documents. Tests are the building blocks of ValidMind, used to evaluate and document records and datasets, and can be run individually or as part of a suite defined by your templates.

    -

    test suite: A collection of tests designed to run together to automate and generate documentation end-to-end for specific use cases. (Learn more: test_suites)

    -

    metric: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

    -

    custom test: Functions that you define to evaluate your record or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

    -

    inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

    -
      -
    • model: A single record that has been initialized in ValidMind with init_model(). Despite the naming convention, model objects can be any type of record you want to test, document, validate, or monitor with ValidMind.
      • -
    • dataset: A single dataset that has been initialized in ValidMind with init_dataset().
      • -
    • models: A list of ValidMind records - usually this is used when you want to compare multiple records in your custom tests.
      • -
    • datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom tests. (Learn more: Run tests with multiple datasets)
      • -
    -

    parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a test, customize its behavior, or provide additional context.

    -

    outputs: Custom tests can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

    -

    -
    -
    -
    -

    Setting up

    -

    -
    -

    Install the ValidMind Library

    -

    To install the library:

    -
    -
    %pip install -q validmind
    -
    -

    -
    -
    -

    Initialize the ValidMind Library

    -

    -
    -

    Register sample model

    -

    Let's first register a sample record (model) for use with this notebook:

    -
      -

    1. In a browser, log in to ValidMind.

      2. -

    2. In the left sidebar, select Inventory.

      3. -

    3. Under the RECORD TYPE drop-down, select Model and click + Register Model. (Learn more: Register records in the inventory)

      4. -

    4. Enter the model details and click Next > to continue to assignment of inventory record stakeholders.

      5. -

    5. Select your own name under the RECORD OWNER drop-down.

      6. -

    6. Click Register Model to add the model to your inventory.

      7. -
    -

    -
    -
    -

    Apply documentation template

    -

    Once you've registered your model, let's select a documentation template. A template predefines sections for your documentation and provides a general outline to follow, making the documentation process much easier.

    -
      -

    1. In the left sidebar that appears for your model, click Documents and select Development.

      -

      If you cannot locate your Development document, make sure Development type documents are enabled for model records and create a new document. (Learn more: Manage documents)

      2. -

    2. Under TEMPLATE, select Time Series Forecasting with ML.

      3. -

    3. Click Use Template to apply the template.

      4. -
    -

    -
    -
    -

    Get your code snippet

    -

    Initialize the ValidMind Library with the code snippet unique to each record per document, ensuring your test results are uploaded to the correct record and automatically populated in the right document in the ValidMind Platform when you run the Library.

    -
      -

    1. On the left sidebar that appears for your model, select Getting Started and select Development from the DOCUMENT drop-down menu.

      2. -

    2. Click Copy snippet to clipboard.

      3. -

    3. Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

      4. -
    -
    -
    # Load your model identifier credentials from an `.env` file
-
-%load_ext dotenv
-%dotenv .env
-
-# Or replace with your code snippet
-
-import validmind as vm
-
-vm.init(
-    # api_host="...",
-    # api_key="...",
-    # api_secret="...",
-    # model="...",
-    document="documentation",
-)
    -
    -

    -
    -
    -
    -

    Initialize the Python environment

    -

    Next, let's import the necessary libraries and set up your Python environment for data analysis:

    -
    -
    from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
-from sklearn.metrics import mean_squared_error, r2_score
-from sklearn.model_selection import train_test_split
-
-%matplotlib inline
    -
    -

    -
    -
    -

    Preview the documentation template

    -

    Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for your model.

    -

    You will upload documentation and test results unique to your model based on this template later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library and note the empty sections:

    -
    -
    vm.preview_template()
    -
    -

    -
    -
    -
    -

    Load the sample dataset

    -

    The sample dataset used here is provided by the ValidMind library. To be able to use it, you need to import the dataset and load it into a pandas DataFrame, a two-dimensional tabular data structure that makes use of rows and columns:

    -
    -
    from validmind.datasets.regression import fred_timeseries 
-
-target_column = fred_timeseries.target_column
-
-print(
-    f"Loaded demo dataset with: \n\n\t• Target column: '{target_column}'"
-)
-
-raw_df = fred_timeseries.load_data()
-raw_df.head()
    -
    -

    -
    -
    -

    Document the model

    -

    As part of documenting the model with the ValidMind Library, you need to preprocess the raw dataset, initialize some training and test datasets, initialize a model object you can use for testing, and then run the full suite of tests.

    -

    -
    -

    Prepocess the raw dataset

    -

    Preprocessing performs a number of operations to get ready for the subsequent steps: - Split the dataset: Divide the original dataset into training and test sets for the primary model with an 80/20 split, without shuffling. - Difference the data: Calculate the first difference of the train and test datasets to remove trends and seasonality, then drop any resulting NaN values. - Extract features and target variables: Separate the feature columns (predictors) and the target variable from the differenced train and test datasets.

    -
    -
    # Split the raw dataset into training and test sets 
-train_df, test_df = train_test_split(raw_df, test_size=0.2, shuffle=False)
-
-# Take the first difference of the training and test sets
-train_diff_df = train_df.diff().dropna()
-test_diff_df = test_df.diff().dropna()
-
-# Extract the features and target variable from the training set
-X_diff_train = train_diff_df.drop(target_column, axis=1)
-y_diff_train = train_diff_df[target_column]
-
-# Extract the features and target variable from the test set
-X_diff_test = test_diff_df.drop(target_column, axis=1)
-y_diff_test = test_diff_df[target_column]
    -
    -

    -
    -
    -

    Train random forests and gradient boosting regressor models

    -

    This section trains random forest and gradient boosting models on differenced data, transforms predictions back to the original scale, and evaluates model performance using Mean Squared Error (MSE) and R-squared (R²) scores.

    -

    The following helper functions are used to post-process predictions and evaluate model performance:

    -
      -
    • transform_to_levels: Reconstructs the original values from differenced predictions by cumulatively summing them, starting from a given initial value.
      • -
    • evaluate_model: Calculates the Mean Squared Error (MSE) and R-squared (R²) score to evaluate the accuracy of the predictions against the true values.
      • -
    -
    -
    def transform_to_levels(y_diff_pred, first_value=0): 
-    y_pred = [first_value]
-    for pred in y_diff_pred:
-        y_pred.append(y_pred[-1] + pred)
-    return y_pred
-
-def evaluate_model(y_true, y_pred):
-    mse = mean_squared_error(y_true, y_pred)
-    r2 = r2_score(y_true, y_pred)
-    return mse, r2
    -
    -
    -
    # Fit the random forest model
-model_rf = RandomForestRegressor(n_estimators=1500, random_state=0)
-model_rf.fit(X_diff_train, y_diff_train)
-
-# Make predictions on the training and test sets
-y_diff_train_pred = model_rf.predict(X_diff_train)
-y_diff_test_pred = model_rf.predict(X_diff_test)
-
-# Transform the predictions back to the original scale
-y_train_rf_pred = transform_to_levels(y_diff_train_pred, first_value=train_df[target_column].iloc[0])
-y_test_rf_pred = transform_to_levels(y_diff_test_pred, first_value=test_df[target_column].iloc[0])
-
-# Evaluate the model's performance on the training and test sets
-mse_train, r2_train = evaluate_model(train_df[target_column], y_train_rf_pred)
-mse_test, r2_test = evaluate_model(test_df[target_column], y_test_rf_pred)
-
-print(f"Train Mean Squared Error: {mse_train}")
-print(f"Train R-Squared: {r2_train}")
-print(f"Test Mean Squared Error: {mse_test}")
-print(f"Test R-Squared: {r2_test}")
    -
    -
    -
    # Fit the gradient boost model
-model_gb = GradientBoostingRegressor(n_estimators=1500, random_state=0)
-model_gb.fit(X_diff_train, y_diff_train)
-
-# Make predictions on the training and test sets
-y_diff_train_pred = model_gb.predict(X_diff_train)
-y_diff_test_pred = model_gb.predict(X_diff_test)
-
-# Transform the predictions back to the original scale
-y_train_gb_pred = transform_to_levels(y_diff_train_pred, first_value=train_df[target_column].iloc[0])
-y_test_gb_pred = transform_to_levels(y_diff_test_pred, first_value=test_df[target_column].iloc[0])
-
-# Evaluate the model's performance on the training and test sets
-mse_train, r2_train = evaluate_model(train_df[target_column], y_train_gb_pred)
-mse_test, r2_test = evaluate_model(test_df[target_column], y_test_gb_pred)
-
-print(f"Train Mean Squared Error: {mse_train}")
-print(f"Train R-Squared: {r2_train}")
-print(f"Test Mean Squared Error: {mse_test}")
-print(f"Test R-Squared: {r2_test}")
    -
    -

    -
    -
    -

    Initialize the ValidMind datasets

    -

    Before you can run tests, you must first initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module.

    -

    This function takes a number of arguments:

    -
      -
    • dataset — the raw dataset that you want to provide as input to tests
      • -
    • input_id - a unique identifier that allows tracking what inputs are used when running each individual test
      • -
    • target_column — a required argument if tests require access to true values. This is the name of the target column in the dataset
      • -
    -

    With all dataframes ready, you can now initialize the ValidMind datasets objects using vm.init_dataset():

    -
      -
    • vm_raw_ds: contains the raw, unprocessed data with the specified target column.
      • -
    • vm_train_diff_ds: contains the training data with the differenced target column, excluding the first row to remove NaN values caused by differencing.
      • -
    • vm_test_diff_ds: contains the test data with the differenced target column, excluding the first row to remove NaN values caused by differencing.
      • -
    • vm_train_ds: contains the training data, excluding the first row to align with the differenced data.
      • -
    • vm_test_ds: includes the test data split from the raw dataset.
      • -
    -
    -
    vm_raw_ds = vm.init_dataset(
-    input_id="raw_ds",
-    dataset=raw_df,
-    target_column=target_column,
-)
-
-vm_train_diff_ds = vm.init_dataset(
-    input_id="train_diff_ds",
-    dataset=train_diff_df,
-    target_column=target_column,
-)
-
-vm_test_diff_ds = vm.init_dataset(
-    input_id="test_diff_ds",
-    dataset=test_diff_df,
-    target_column=target_column,
-)
-
-vm_train_ds = vm.init_dataset(
-    input_id="train_ds",
-    dataset=train_df,
-    target_column=target_column,
-)
-
-vm_test_ds = vm.init_dataset(
-    input_id="test_ds",
-    dataset=test_df,
-    target_column=target_column,
-)
    -
    -

    -
    -
    -

    Initialize the ValidMind models

    -

    You'll also need to initialize ValidMind model objects (vm_model) that can be passed to other functions for analysis and tests on the data for our models.

    -
      -
    • Despite the naming convention, ValidMind model objects can be any type of record you want to test, document, validate, or monitor with the ValidMind Library.
      • -
    • From classical statistical and machine learning models, to generative and agentic AI systems and more, the ValidMind model object provides a consistent wrapper around your record so it can be passed as a unified input to any ValidMind test or test suite, with results sent directly to the ValidMind Platform.
      • -
    -

    Initialize your model object with vm.init_model():

    -
    -
    vm_model_rf = vm.init_model(
-    model_rf,
-    input_id="random_forests_model",
-)
-
-vm_model_gb = vm.init_model(
-    model_gb,
-    input_id="gradient_boosting_model",
-)
    -
    -

    -
    -
    -

    Assign predictions to the datasets

    -

    We can now use the assign_predictions() method from the Dataset object to link existing predictions to any model. If no prediction values are passed, the method will compute predictions automatically:

    -
    -
    vm_train_ds.assign_predictions(
-    model=vm_model_rf,
-    prediction_values=y_train_rf_pred,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_model_rf,
-    prediction_values=y_test_rf_pred,
-)
-
-vm_train_ds.assign_predictions(
-    model=vm_model_gb,
-    prediction_values=y_train_gb_pred,
-)
-
-vm_test_ds.assign_predictions(
-    model=vm_model_gb,
-    prediction_values=y_test_gb_pred,
-)
    -
    -
    -
    from validmind.utils import preview_test_config
-
-test_config = fred_timeseries.get_demo_test_config()
-preview_test_config(test_config)
    -
    -

    -
    -
    -

    Run data validation tests

    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.TimeSeriesDescription",
-    input_grid={
-        "dataset": ["raw_ds", "train_diff_ds", "test_diff_ds", "train_ds", "test_ds"],
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.TimeSeriesLinePlot",
-    input_grid={
-        "dataset": ["raw_ds"],
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.TimeSeriesMissingValues",
-    input_grid={
-        "dataset": ["raw_ds", "train_diff_ds", "test_diff_ds", "train_ds", "test_ds"],
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.SeasonalDecompose",
-    input_grid={
-        "dataset": ["raw_ds"],
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.TimeSeriesDescriptiveStatistics",
-    input_grid={
-        "dataset": ["train_diff_ds", "test_diff_ds"],
-    },
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.TimeSeriesOutliers",
-    input_grid={
-        "dataset": ["train_diff_ds", "test_diff_ds"],
-    },
-    params={
-        "zscore_threshold": 4
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.TimeSeriesHistogram",
-    input_grid={
-        "dataset": [ "train_diff_ds", "test_diff_ds"],
-    },
-    params={
-        "nbins": 100
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.data_validation.DatasetSplit",
-    inputs={
-        "datasets": ["train_diff_ds", "test_diff_ds"],
-    }
-)
-test.log()
    -
    -

    -
    -
    -

    Run model validation tests

    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.ModelMetadata",
-    input_grid={
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.sklearn.RegressionErrors",
-    input_grid={
-        "dataset": ["train_ds", "test_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.sklearn.RegressionR2Square",
-    input_grid={
-        "dataset": ["train_ds", "test_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.TimeSeriesR2SquareBySegments:train_data",
-    input_grid={
-        "dataset": ["train_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.TimeSeriesR2SquareBySegments:test_data",
-    input_grid={
-        "dataset": ["test_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    },
-    params={
-        "segments":{
-            "start_date": ["2012-11-01","2018-02-01"],
-            "end_date": ["2018-01-01","2023-03-01"]
-        }
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.TimeSeriesPredictionsPlot",
-    input_grid={
-        "dataset": ["train_ds", "test_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.TimeSeriesPredictionWithCI",
-    input_grid={
-        "dataset": ["train_ds", "test_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.ModelPredictionResiduals",
-    input_grid={
-        "dataset": ["train_ds", "test_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.sklearn.FeatureImportance",
-    input_grid={
-        "dataset": ["train_ds", "test_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -
    -
    test = vm.tests.run_test(
-    "validmind.model_validation.sklearn.PermutationFeatureImportance",
-    input_grid={
-        "dataset": ["train_ds", "test_ds"],
-        "model": ["random_forests_model", "gradient_boosting_model"],
-    }
-)
-test.log()
    -
    -

    -
    -
    -
    -

    Next steps

    -

    You can look at the results of this test suite right in the notebook where you ran the code, as you would expect. But there is a better way — use the ValidMind Platform to work with your model documentation.

    -

    -
    -

    Work with your documentation

    -
      -

    1. From the Inventory in the ValidMind Platform, go to the model you registered earlier. (Learn more: Working with the inventory)

      2. -

    2. In the left sidebar that appears for your model, click Development under Documents.

      3. -
    -

    What you see is the full draft of your documentation in a more easily consumable version. From here, you can make qualitative edits to documentation, view guidelines, collaborate with validators, and submit your documentation for approval when it's ready. (Learn more: Working with documentation)

    -

    -
    -
    -

    Discover more learning resources

    -

    We also offer many interactive notebooks to help you use the ValidMind Library to streamline your work:

    - -

    Or, visit our documentation to learn more about ValidMind.

    -

    -
    -
    -
    -

    Upgrade ValidMind

    -
    -After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements. -
    -

    Retrieve the information for the currently installed version of ValidMind:

    -
    -
    %pip show validmind
    -
    -

    If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

    -
    %pip install --upgrade validmind
    -

    You may need to restart your kernel after running the upgrade package for changes to be applied.

    - -

    -
    -

    Copyright © 2023-2026 ValidMind Inc. All rights reserved.
    Refer to LICENSE for details.
    SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial

    - - -
    - -
    - - -
    - - - - - - \ No newline at end of file diff --git a/site/reference/validmind-rest-api-vm-listing.json b/site/reference/validmind-rest-api-vm-listing.json deleted file mode 100644 index 08fa25bc5..000000000 --- a/site/reference/validmind-rest-api-vm-listing.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "listing": "/reference/validmind-rest-api-vm.html", - "items": [ - "/reference/vm-api.html" - ] -} \ No newline at end of file diff --git a/site/reference/validmind-rest-api-vm.html b/site/reference/validmind-rest-api-vm.html deleted file mode 100644 index 9f8d89efd..000000000 --- a/site/reference/validmind-rest-api-vm.html +++ /dev/null @@ -1,1654 +0,0 @@ - - - - - - - - - - -​ValidMind Public REST API – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - -
    - -
    - - -
    - - - -
    - -
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    ​ValidMind Public REST API

    -
    - - - -
    - - -
    -
    Published
    -
    -

    June 1, 2026

    -
    -
    - - -
    - - - -
    - - - -

    The ValidMind Platform provides a RESTful APIs to programmatically interact with:

    - -
    -

    Base URL

    -
    https://api.prod.validmind.ai/vm/api/v1/
    -
    -
    -

    Versioning

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    A screenshot showing the new ValidMind Platform onboarding guide

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    You can hover over each task for more details. The guide can be minimized, leaving a trigger in the bottom right corner, or dismissed entirely. To reactivate the guide, go to Settings > Profile.

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    Improved test descriptions for the ValidMind Library
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    How to upgrade

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    ValidMind Platform

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    To access the latest version of the ValidMind Platform,1 hard refresh your browser tab:

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    • Windows: Ctrl + Shift + R OR Ctrl + F5
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    • MacOS: ⌘ Cmd + Shift + R OR hold down ⌘ Cmd and click the Reload button
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    ValidMind Library

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    To upgrade the ValidMind Library:2

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    1. In your Jupyter Notebook:

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      • Using JupyterHub: Hard refresh your browser tab.
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      • In your own developer environment: Restart your notebook.
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    2. Then within a code cell or your terminal, run:

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    You may need to restart your kernel after running the upgrade package for changes to be applied.

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    To access the latest version of the ValidMind Platform,1 hard refresh your browser tab:

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    ValidMind Library

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    How to upgrade

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    ValidMind Platform

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    To access the latest version of the ValidMind Platform,1 hard refresh your browser tab:

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    • Windows: Ctrl + Shift + R OR Ctrl + F5
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    • MacOS: ⌘ Cmd + Shift + R OR hold down ⌘ Cmd and click the Reload button
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    ValidMind Library

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    To upgrade the ValidMind Library:2

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    1. In your Jupyter Notebook:

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      • Using JupyterHub: Hard refresh your browser tab.
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      • In your own developer environment: Restart your notebook.
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    2. Then within a code cell or your terminal, run:

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    You may need to restart your kernel after running the upgrade package for changes to be applied.

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    We've chosen some new names for our product offerings, expanded our training courses, introduced new large language model (LLM) features — and much, much, more!

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    ValidMind Library (v2.5.25)

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    ​ValidMind Developer Framework > ValidMind Library

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    To reduce ambiguity and highlight the capabilities of our developer tools, as of v2.5.25 what was previously referred to as the ​ValidMind Developer Framework is now known as the ValidMind Library.

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    ValidMind Library

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    ValidMind Library API reference

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    validmind-library

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    In addition to the enhancements in this release, references to the developer framework have been replaced across the ValidMind Library package and open-source repository, as well as the ValidMind Platform and the user documentation.

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    Code samples

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    RAG Model Documentation Demo
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    Check out our RAG Model Documentation Demo Jupyter Notebook!

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    Learn how to build a basic RAG (Retrieval-Augmented Generation) model to help automatically answer RFP (Request for Proposal) questions using GenAI.

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    RAG Model Documentation Demo

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    From in-depth tests assessing generation quality to checks detecting bias and toxicity, use LangChain2 and ​ValidMind to build, evaluate, and document a simple RAG model as it is being developed.

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    ValidMind Platform (v1.27.7)

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    To reduce ambiguity and highlight the capabilities of our cloud-hosted interface, as of v1.27.7 what was previously referred to as the ValidMind Platform UI is now simply known as the ValidMind Platform.

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    ValidMind Platform guides

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    Accessing ​ValidMind

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    In addition to the enhancements in this release, references to the platform UI have been replaced across the ValidMind Library package and open-source repository, as well as the ValidMind Platform and the user documentation.

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    Submit product feedback directly

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    You can now submit feature requests, bug reports, or any other feedback directly within the ValidMind Platform.

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    Click on the Talk to us. tab on the right-hand side of any screen and follow three easy steps:

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    Submit feedback

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    The Talk to us. feedback panel within the ValidMind Platform
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    Support for custom analytics

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    You can now add, edit, or remove custom data to your analytics within the ValidMind Platform in two steps:

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    Documentation

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    User guide updates

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    In concert with our product renaming across the ValidMind Library and the ValidMind Platform, we've edited all user guides and external facing documentation to reflect these updates.

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    Along with keeping our content up to date,4 we've refreshed the look and feel of our documentation to reflect ​ValidMind's new brand colors used on our main website5 and within the ValidMind Platform.

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    Improved documentation homepage
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    We've overhauled the documentation homepage. Check it out:

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    To complement our rebranding,6 our ValidMind Academy training now features its own theme and landing page to distinguish the material from the rest of the documentation along with content updates.7

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    ValidMind Library (v2.5.25)

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    In our Jupyter Notebook samples, we've now included information within the notebooks themselves on how to upgrade to the latest version of the ValidMind Library.

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    documentation

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    ValidMind Platform

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    To access the latest version of the ValidMind Platform,12 hard refresh your browser tab:

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    To upgrade the ValidMind Library:13

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    We've improved the ​ValidMind user experience, from more supportive documentation templates, easier specification of inputs, and better filtering within the library, to the ability to view which user ran actions within the platform.

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    ValidMind Library (v1.26.6)

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    To make use of this new feature, you simply add a unique result_id identifier as a suffix to a content_id identifier in the content block definition of a metric or test content type.

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-- content_type: metric
-  content_id: validmind.data_validation.Skewness:test_data
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    You can configure each of these unique content_id identifiers by passing the appropriate config and inputs in run_documentation_tests() or run_test(). For example, to configure two separate tests for Skewness using different datasets and parameters:

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    test = vm.tests.run_test(
-    test_id="validmind.data_validation.Skewness:training_data",
-    params={
-        "max_threshold": 1
-    },
-    dataset=vm_train_ds,
-)
-test.log()
-
-test = vm.tests.run_test(
-    test_id="validmind.data_validation.Skewness:test_data",
-    params={
-        "max_threshold": 1.5
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-    dataset=vm_test_ds
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    Easier specification of inputs for individual tests

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    The run_documentation_tests() function has been updated to allow passing both test inputs and params via the config parameter.

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    Previously, config could already pass params to each test that you declare. In this example, the test SomeTest receives a custom value for the param min_threshold:

    -
    full_suite = vm.run_documentation_tests(
-    inputs = {
-        ...
-    },
-    config={
-        "validmind.data_validation.SomeTest": {
-            "min_threshold": 1
-        }
-    }
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    run_documentation_tests()

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-    inputs = {
-        "dataset": vm_dataset,
-        "model": xgb_model
-    },
-    config = {
-        "validmind..model_validation.Accuracy:xgb_model": {
-            "params": { threshold: 0.5 },
-            "inputs": { "model": xgb_model }
-        },
-        "validmind..model_validation.Accuracy:lr_model": {
-            "params": { threshold: 0.3 },
-            "inputs": { "model": lr_model }
-        },
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    Screenshot showing the Explore tests notebook with the Understanding Tags and Task Types code cells run successfully

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    Explore tests notebook with the Understanding Tags and Task Types code cells run successfully
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    ValidMind Library documentation inputs tracking

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    Getting Started page for a model with the custom code snippet
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    Search, filter, and sort records

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    Manage inventory fields

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    Managing users

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    ValidMind Platform

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    To access the latest version of the ValidMind Platform,1 hard refresh your browser tab:

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    ValidMind Library

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    To upgrade the ValidMind Library:2

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    1. In your Jupyter Notebook:

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    You may need to restart your kernel after running the upgrade package for changes to be applied.

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    This release includes numerous improvements to the library, including new features for model and dataset initialization, easier testing, support for additional inputs and the Azure OpenAI API, updated notebooks, bug fixes, and much more.

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-     dataset=df,
-     feature_columns=[...],
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-
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-    dataset=train_df,
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-    feature_columns=feature_columns
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-    inputs = {
-        ...
-    },
-    section="section_1",
-    config={
-        "validmind.tests.data_validation.ClassImbalance": ...
-    } 
-)
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-full_suite = vm.run_documentation_tests(
-    inputs = {
-        ...
-    },
-    section="section_2",
-    config={
-        "validmind.tests.data_validation.Duplicates": ...
-    } 
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    New example usage: A single function call runs multiple sections.

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-    inputs = {
-        ...
-    },
-    section=["section_1", "section_2"],
-    config={
-        "validmind.tests.data_validation.ClassImbalance": ...,
-        "validmind.tests.data_validation.Duplicates": ...
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    New recommended syntax for passing inputs:

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-    inputs={
-        "dataset": vm_dataset,
-        "model": vm_model,
-    },
-)
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    2 To learn more about configuring Azure OpenAI Service, see Authentication in the official Microsoft documentation.

    To work with Azure OpenAI API endpoints, you need to set the following environment variables before calling init():

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-      dataset=vm_dataset,
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    New recommended usage for passing inputs:

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    Documentation

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    How to upgrade

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    ValidMind Platform

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    To access the latest version of the ValidMind Platform,4 hard refresh your browser tab:

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    • Windows: Ctrl + Shift + R OR Ctrl + F5
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    • MacOS: ⌘ Cmd + Shift + R OR hold down ⌘ Cmd and click the Reload button
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    ValidMind Library

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    To upgrade the ValidMind Library:5

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    1. In your Jupyter Notebook:

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      • Using JupyterHub: Hard refresh your browser tab.
        • -
      • In your own developer environment: Restart your notebook.
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    You may need to restart your kernel after running the upgrade package for changes to be applied.

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    Within the ValidMind Platform, you can now edit the names of your business units.

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    Manage business units

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    A gif showing you the edit business unit name capability

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    How to upgrade

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    ValidMind Platform

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    To access the latest version of the ValidMind Platform,1 hard refresh your browser tab:

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    ValidMind Library

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    To upgrade the ValidMind Library:2

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    1. In your Jupyter Notebook:

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      • Using JupyterHub: Hard refresh your browser tab.
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      • In your own developer environment: Restart your notebook.
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    2. Then within a code cell or your terminal, run:

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    You may need to restart your kernel after running the upgrade package for changes to be applied.

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    - - - - - - - - \ No newline at end of file diff --git a/site/releases/2024/2024-may-22/release-notes-listing.json b/site/releases/2024/2024-may-22/release-notes-listing.json deleted file mode 100644 index b33ec316c..000000000 --- a/site/releases/2024/2024-may-22/release-notes-listing.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "listing": "/releases/2024/2024-may-22/release-notes.html", - "items": [ - "/training/training.html" - ] -} \ No newline at end of file diff --git a/site/releases/2024/2024-may-22/release-notes.html b/site/releases/2024/2024-may-22/release-notes.html deleted file mode 100644 index 23eb888f9..000000000 --- a/site/releases/2024/2024-may-22/release-notes.html +++ /dev/null @@ -1,2157 +0,0 @@ - - - - - - - - - - -May 22, 2024 – ValidMind - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    May 22, 2024

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    This includes a new introductory notebook for mode developers, support for external models and custom functions, and many improvements to the ValidMind Platform.

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    Release highlights

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    ValidMind Library (v2.2.2)

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    Code samples

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    Introductory notebook for model developers
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    Our new end-to-end notebook gives you a full introduction to the ValidMind Library.

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    You can use this notebook to learn how the end-to-end documentation process works, based on common scenarios you encounter in model development settings.

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    Try notebook on JupyterHub

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    A screenshot showing the new introductory notebook for developers opened in JupyterHub

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    New introductionary notebook for developers opened in JupyterHub
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    High level sections include:

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    1. Initializing the ValidMind Library — The library provides tools for documentation and testing, from dataset descriptions to model validation using various open-source frameworks.
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    2. Starting the model development process — Access the test repository1 to use individual tests as building blocks for model development. Learn to run tests, analyze results, and add evidence to model documentation.
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    3. Implementing custom tests — Include custom tests in model documentation. Learn how to add these tests and use them as additional evidence.
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    4. Finalizing testing and documentation — Ensure model documentation includes custom tests and configure settings for all tests in the template. By the end, you should have a fully documented model ready for review.
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    1 Browse tests and read descriptions in the ​ValidMind test sandbox.

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    Support for external models

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    You can now run documentation tests without passing a Python-native model object.

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    This change enables you to document:

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    To run tests for these models, you typically must load model predictions from a file, dataset, and so on.

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    • The new init_model interface does not enforce a Python model object anymore.
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    Initializing an external model
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    Since there is no native Python object to pass to init_model, you instead pass attributes that describe the model:

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    # Assume you want to load predictions for a PySpark ML model
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-model_attributes = {
-    "architecture": "Spark",
-    "language": "PySpark",
-}
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-# Or maybe you're loading predictions for a SageMaker endpoint (model API)
-model_attributes = {
-    "architecture": "SageMaker Model",
-    "language": "Python",
-}
-
-# Call `init_model` without passing a model. Pass `attributes` instead.
-vm_model = vm.init_model(
-    attributes=model_attributes,
-    input_id="model",
-)
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    Assigning predictions
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    Since there's no model object available, the library won't be able to call model.predict() or model.predict_proba(); you need to load predictions and probabilities manually.

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    For example:

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    vm_train_ds.assign_predictions(
-    model=vm_model,
-    prediction_values=prediction_values,
-    prediction_probabilities=prediction_probabilities,
-)
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    You can proceed to run tests on your data as you would under normal conditions, without needing to modify any other parts of your code.

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    Custom test function decorators

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    We introduced a new metric decorator that turns any function into a ​ValidMind test that you can use in your documentation. To learn what this decorator can do for you, try our code sample on JupyterHub!

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    Try notebook on JupyterHub

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    • Custom tests offer added flexibility. They allow you to extend the library of default tests provided by ​ValidMind which enables you to document any type of model or use case.
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    ValidMind Platform (v1.20.2)

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    New Document Overview

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    Our new Document Overview is designed to help you find the information you need more quickly.

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    Document Overview

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    Now more distinct from the model details landing page, the new overview page provides easier navigation and enhanced data visualization to better understand the progress of the documentation stage:

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    A screenshot showing the new Document Overview page in the ValidMind Platform

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    New Document Overview page in the ValidMind Platform
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    Documentation outline page with conversations

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    We created a new Documentation outline page which replaces the existing model overview page.

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    Working with model documentation

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    This page shows a section-by-section outline of your model's documentation:

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    A screenshot showing the new documentation outline page, with numbered model documentation sections from 1. to 3.4

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    New documentation outline page, with numbered model documentation sections from 1. to 3.4
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    It also includes a count of every unresolved conversation within each section.

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    A screenshot showing a preview of four unresolved conversations

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    Four unresolved conversations preview
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    Business unit and risk areas settings

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    We have introduced several updates to the Organization settings page, enabling you to manage business units and risk areas for your organization.

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    Set up your organization

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    The following features are now available:

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    • Add or remove risk areas from your organization.
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    A gif showing new business unit and risk area settings for your organization

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    New business unit and risk area settings for your organization
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    New documentation template editor

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    • This update allows you to edit documentation templates visually, eliminating the need to manually edit YAML files.
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    Customize document templates

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    Enhanced template setting page
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    Edit mode functionality
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    This new visual editing interface streamlines the template editing process, making it more intuitive and user-friendly:

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    A gif showing edits in the new enhanced documentation template editor

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    Enhanced template setting page
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    A gif a template in edit mode

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    Enhancements

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    ValidMind Library (v2.2.2)

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    Add extra columns on the fly

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    We added support for two new VMDataset methods:

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    You can now register arbitrary extra columns in a dataset when a test needs to compute metrics outside of the existing sets of columns (features, targets, predictions).

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    add_extra_column()

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    Example usage:

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    # Init your dataset as usual
-vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="train_dataset",
-    target_column=customer_churn.target_column,
-)
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-# Generate scores using a user defined function:
-scores = compute_my_scores(x_train)
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-# Assign a new "scores" column to vm_train_ds:
-vm_train_ds.add_extra_column("scores", scores)
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    This function returns an error if no column values are passed:

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    It's also possible to use init_dataset with a dataset that has precomputed scores, for example:

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    > train_df.columns
-Index(['CreditScore', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts',
-       'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited',
-       'Geography_France', 'Geography_Germany', 'Geography_Spain'],
-      dtype='object')
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    > train_df["my_scores"] = scores
-> train_df.columns
-Index(['CreditScore', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts',
-       'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited',
-       'Geography_France', 'Geography_Germany', 'Geography_Spain',
-       'my_scores'],
-      dtype='object')
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    Make sure you set the feature_columns correctly:

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    vm_train_ds = vm.init_dataset(
-    dataset=train_df,
-    input_id="another_ds",
-    feature_columns=[
-        "CreditScore",
-        "Gender",
-        "Age",
-        "Tenure",
-        "Balance",
-        "NumOfProducts",
-        "HasCrCard",
-        "IsActiveMember",
-        "EstimatedSalary",
-        "Exited",
-        "Geography_France",
-        "Geography_Germany",
-        "Geography_Spain",
-    ],
-    target_column=customer_churn.target_column,
-)
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    Then, call add_extra_column() to register the extra column:

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-Column my_scores exists in the dataset, registering as an extra column
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    You can use this inside a test to retrieve the extra column values.

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    Example usage:

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    Create composite tests

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    ​ValidMind now supports the ability to compose multiple Unit Tests into complex outputs.

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    New text data validation tests

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    The following tests for text data validation have been added (browse descriptions in the ​ValidMind test sandbox):

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    Use test decorators with test providers

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    Try notebook on JupyterHub

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    Support for metadata in new metric decorator

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    We added new decorators to support task type and tag metadata in functional metrics.

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    Usage example:

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-from validmind import tags, tasks
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-@tasks("classification")
-@tags("classification", "sklearn", "accuracy")
-def Accuracy(dataset, model):
-    """Calculates the accuracy of a model"""
-    return accuracy_score(dataset.y, dataset.y_pred(model))
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-# the above decorator is syntactic sugar for the following:
-Accuracy.__tags__ = ["classification"]
-Accuracy.__tasks__ = ["classification", "sklearn", "accuracy"]
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    Assign prediction probabilities

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    We added support for assigning prediction_probabilities to assign_predictions. This support enables you to:

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    Access notebook on GitHub

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    ValidMind Platform (v1.20.2)

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    Associate findings with a documentation section

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    You can now associate model findings with sections within your model documentation. Doing so will allow you to track findings by severity, section-by-section, in the Document Overview page.

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    Log artifacts

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    An animated gif demonstrating how to add a finding to a documentation section

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    Adding a finding to a documentation section
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    Enhanced workflow customization

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    Our revamped workflows configuration enables more granular management of model and documentation lifecycles and deep integration with model inventory attributes.

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    Configure workflows

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    The new workflows configuration includes the following features:

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    Documentation section statuses

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    We added a status picker to each section of the model Documentation page.

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    Assign documentation section statuses

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    A screenshot of the Document Overview page displaying a total count of how many sections have been completed

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    Document Overview page displaying a total count of how many sections have been completed
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    A screenshot of the section status drop-down of a model documentation page

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    Section status drop-down of a model documentation page
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    Templates for rich text inventory fields

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    We added a new property to the Long Text inventory field type: enable rich text formatting

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    Manage inventory fields

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    Validation Report overview page

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    We created a new Validation Report overview page which shows a section-by-section outline of a model's validation report, including a total compliance summary for all risk areas associated with the model.

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    • You can hover over any section in the report outline to view the current compliance status in the document section.
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    Preparing validation reports

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    Working with model findings

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    A screenshot showing the section-by-section outline of the validation report, along with a total compliance summary

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    Section-by-section outline of the validation report, along with a total compliance summary
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    Flag vendor models

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    We added the ability to flag models as is vendor model and specify a vendor name during model registration.

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    Register records in the inventory

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    A screenshot showing how you can flag vendor models when registering a model in the inventory

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    Flag vendor models when registering a model in the inventory
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    Improved model inventory filters

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    Also available is an improved look and functionality for filtering the Inventory.

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    Search, filter, and sort records

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    A screenshot showing model inventory filtering options

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    Model inventory filtering options
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    Adjust groups on models

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    You can now modify the group the model belongs to.

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    Manage groups

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    A screenshot showing the Group field on the model

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    Group field on the model
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    Bug fixes

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    ValidMind Platform (v1.20.2)

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    Missing required field card view

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    We added a tooltip for required missing fields on the Inventory card view.

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    Inventory display views

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    A screenshot showing a tooltip on the Inventory card view

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    Tooltip on the Inventory card view
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    Template bug fixes

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    We now validate whether a template has duplicate section IDs and return an error if a duplicate ID is found.

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    Customize document templates

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    A screenshot showing template YAML

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    Template YAML
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    A screenshot showing the error message 'Duplicate section ID test found in template'

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    Error message: 'Duplicate section ID test found in template'
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    Documentation

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    Code samples

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    More contextual information in Jupyter Notebooks

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    Many of our Jupyter Notebooks have received improvements to make them easier to consume and more standalone:

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    Try ​ValidMind on JupyterHub

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    • Introductions now include more contextual information
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    Site enhancements

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    Improved site navigation with new "About" section

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    A screenshot of the ValidMind docs site showing the new About section

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    ​ValidMind docs site showing the new About section
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    About ​ValidMind
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    We've revamped our documentation for a cleaner, more intuitive experience. The update features a brand new About section:

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    About ​ValidMind

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    • Features includes product overviews and our glossary.
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    • Contributing includes community information and our new style guide.
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    Contributing
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    Find a brand new set of guides on contributing to our open source software. Learn how to engage with the ​ValidMind community, read about our brand voice and vision, and more:

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    ​ValidMind community

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    • ​ValidMind community: You're part of the ​ValidMind community. Come learn and play with us.
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    ​ValidMind style guide
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    Check out the first official version of the ​ValidMind style guide!

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    ​ValidMind style guide

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    • Learn about the ​ValidMind voice
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    • Understand our shared vision and goals
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    Quickstart improvements

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    We reworked our quickstart experience to shorten the number of clicks it takes to get you started.

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    You can now access the quickstart directly from the homepage of our docs site, where we direct you to the preferred quickstart on JupyterHub right away.

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    Documentation homepage

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    To upgrade the ValidMind Library:3

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    1. In your Jupyter Notebook:

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      • Using JupyterHub: Hard refresh your browser tab.
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      • In your own developer environment: Restart your notebook.
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30'%3e%3cpath stroke='%232e3133' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%232e3133' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #222425;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #222425;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23222425'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2815.6, 51.2, 90.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(147, 191.5, 241);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(15.6, 51.2, 90.8);--bs-accordion-active-bg: rgb(211.8, 229.6, 249.4)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(34, 36, 37, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(34, 36, 37, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #DE257E;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #DE257E;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #DE257E;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(34, 36, 37, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #222425;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(34, 36, 37, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #222425;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(34, 36, 37, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(34, 36, 37, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Inter;font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #e9ecef;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #222425;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: var(--bs-popover-border-color);z-index:var(--bs-popover-zindex);display:block;max-width:var(--bs-popover-max-width);font-family:Inter;font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-popover-font-size);word-wrap:break-word;background-color:var(--bs-popover-bg);background-clip:padding-box;border:var(--bs-popover-border-width) solid var(--bs-popover-border-color)}.popover 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300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card 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rgb(15.6, 51.2, 90.8);--quarto-scss-export-blue-900: rgb(7.8, 25.6, 45.4);--quarto-scss-export-indigo-100: rgb(224.4, 207.2, 252.4);--quarto-scss-export-indigo-200: rgb(193.8, 159.4, 249.8);--quarto-scss-export-indigo-300: rgb(163.2, 111.6, 247.2);--quarto-scss-export-indigo-400: rgb(132.6, 63.8, 244.6);--quarto-scss-export-indigo-500: #6610f2;--quarto-scss-export-indigo-600: rgb(81.6, 12.8, 193.6);--quarto-scss-export-indigo-700: rgb(61.2, 9.6, 145.2);--quarto-scss-export-indigo-800: rgb(40.8, 6.4, 96.8);--quarto-scss-export-indigo-900: rgb(20.4, 3.2, 48.4);--quarto-scss-export-purple-100: rgb(223.4, 216.2, 228.8);--quarto-scss-export-purple-200: rgb(191.8, 177.4, 202.6);--quarto-scss-export-purple-300: rgb(160.2, 138.6, 176.4);--quarto-scss-export-purple-400: rgb(128.6, 99.8, 150.2);--quarto-scss-export-purple-500: #613d7c;--quarto-scss-export-purple-600: rgb(77.6, 48.8, 99.2);--quarto-scss-export-purple-700: rgb(58.2, 36.6, 74.4);--quarto-scss-export-purple-800: rgb(38.8, 24.4, 49.6);--quarto-scss-export-purple-900: rgb(19.4, 12.2, 24.8);--quarto-scss-export-pink-100: rgb(250.4, 216.4, 232);--quarto-scss-export-pink-200: rgb(245.8, 177.8, 209);--quarto-scss-export-pink-300: rgb(241.2, 139.2, 186);--quarto-scss-export-pink-400: rgb(236.6, 100.6, 163);--quarto-scss-export-pink-500: #e83e8c;--quarto-scss-export-pink-600: rgb(185.6, 49.6, 112);--quarto-scss-export-pink-700: rgb(139.2, 37.2, 84);--quarto-scss-export-pink-800: rgb(92.8, 24.8, 56);--quarto-scss-export-pink-900: rgb(46.4, 12.4, 28);--quarto-scss-export-red-100: rgb(255, 204, 215.4);--quarto-scss-export-red-200: rgb(255, 153, 175.8);--quarto-scss-export-red-300: rgb(255, 102, 136.2);--quarto-scss-export-red-400: rgb(255, 51, 96.6);--quarto-scss-export-red-500: #ff0039;--quarto-scss-export-red-600: rgb(204, 0, 45.6);--quarto-scss-export-red-700: rgb(153, 0, 34.2);--quarto-scss-export-red-800: rgb(102, 0, 22.8);--quarto-scss-export-red-900: rgb(51, 0, 11.4);--quarto-scss-export-orange-100: rgb(252, 238.6, 219.6);--quarto-scss-export-orange-200: rgb(249, 222.2, 184.2);--quarto-scss-export-orange-300: rgb(246, 205.8, 148.8);--quarto-scss-export-orange-400: rgb(243, 189.4, 113.4);--quarto-scss-export-orange-500: #f0ad4e;--quarto-scss-export-orange-600: rgb(192, 138.4, 62.4);--quarto-scss-export-orange-700: rgb(144, 103.8, 46.8);--quarto-scss-export-orange-800: rgb(96, 69.2, 31.2);--quarto-scss-export-orange-900: rgb(48, 34.6, 15.6);--quarto-scss-export-yellow-100: rgb(255, 227.4, 208.8);--quarto-scss-export-yellow-200: rgb(255, 199.8, 162.6);--quarto-scss-export-yellow-300: rgb(255, 172.2, 116.4);--quarto-scss-export-yellow-400: rgb(255, 144.6, 70.2);--quarto-scss-export-yellow-500: #ff7518;--quarto-scss-export-yellow-600: rgb(204, 93.6, 19.2);--quarto-scss-export-yellow-700: rgb(153, 70.2, 14.4);--quarto-scss-export-yellow-800: rgb(102, 46.8, 9.6);--quarto-scss-export-yellow-900: rgb(51, 23.4, 4.8);--quarto-scss-export-green-100: rgb(216.6, 240.4, 208.8);--quarto-scss-export-green-200: rgb(178.2, 225.8, 162.6);--quarto-scss-export-green-300: rgb(139.8, 211.2, 116.4);--quarto-scss-export-green-400: rgb(101.4, 196.6, 70.2);--quarto-scss-export-green-500: #3fb618;--quarto-scss-export-green-600: rgb(50.4, 145.6, 19.2);--quarto-scss-export-green-700: rgb(37.8, 109.2, 14.4);--quarto-scss-export-green-800: rgb(25.2, 72.8, 9.6);--quarto-scss-export-green-900: rgb(12.6, 36.4, 4.8);--quarto-scss-export-teal-100: rgb(210.4, 244.2, 234.2);--quarto-scss-export-teal-200: rgb(165.8, 233.4, 213.4);--quarto-scss-export-teal-300: rgb(121.2, 222.6, 192.6);--quarto-scss-export-teal-400: rgb(76.6, 211.8, 171.8);--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: rgb(25.6, 160.8, 120.8);--quarto-scss-export-teal-700: rgb(19.2, 120.6, 90.6);--quarto-scss-export-teal-800: rgb(12.8, 80.4, 60.4);--quarto-scss-export-teal-900: rgb(6.4, 40.2, 30.2);--quarto-scss-export-cyan-100: rgb(234.6, 220.8, 241.4);--quarto-scss-export-cyan-200: rgb(214.2, 186.6, 227.8);--quarto-scss-export-cyan-300: rgb(193.8, 152.4, 214.2);--quarto-scss-export-cyan-400: rgb(173.4, 118.2, 200.6);--quarto-scss-export-cyan-500: #9954bb;--quarto-scss-export-cyan-600: rgb(122.4, 67.2, 149.6);--quarto-scss-export-cyan-700: rgb(91.8, 50.4, 112.2);--quarto-scss-export-cyan-800: rgb(61.2, 33.6, 74.8);--quarto-scss-export-cyan-900: rgb(30.6, 16.8, 37.4);--quarto-scss-export-default: #343a40;--quarto-scss-export-primary-text-emphasis: rgb(15.6, 51.2, 90.8);--quarto-scss-export-secondary-text-emphasis: rgb(20.8, 23.2, 25.6);--quarto-scss-export-success-text-emphasis: rgb(25.2, 72.8, 9.6);--quarto-scss-export-info-text-emphasis: rgb(61.2, 33.6, 74.8);--quarto-scss-export-warning-text-emphasis: rgb(102, 46.8, 9.6);--quarto-scss-export-danger-text-emphasis: rgb(102, 0, 22.8);--quarto-scss-export-light-text-emphasis: #495057;--quarto-scss-export-dark-text-emphasis: #495057;--quarto-scss-export-primary-bg-subtle: rgb(211.8, 229.6, 249.4);--quarto-scss-export-secondary-bg-subtle: rgb(214.4, 215.6, 216.8);--quarto-scss-export-success-bg-subtle: rgb(216.6, 240.4, 208.8);--quarto-scss-export-info-bg-subtle: rgb(234.6, 220.8, 241.4);--quarto-scss-export-warning-bg-subtle: rgb(255, 227.4, 208.8);--quarto-scss-export-danger-bg-subtle: rgb(255, 204, 215.4);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: rgb(168.6, 204.2, 243.8);--quarto-scss-export-secondary-border-subtle: rgb(173.8, 176.2, 178.6);--quarto-scss-export-success-border-subtle: rgb(178.2, 225.8, 162.6);--quarto-scss-export-info-border-subtle: rgb(214.2, 186.6, 227.8);--quarto-scss-export-warning-border-subtle: rgb(255, 199.8, 162.6);--quarto-scss-export-danger-border-subtle: rgb(255, 153, 175.8);--quarto-scss-export-light-border-subtle: #e9ecef;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-bg: #fff;--quarto-scss-export-body-secondary-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-body-secondary-bg: #e9ecef;--quarto-scss-export-body-tertiary-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #2780e3;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #e9ecef;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(255, 227.4, 208.8);--quarto-scss-export-table-color: #222425;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #222425;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #222425;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #222425;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-btn-color: #222425;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #DE257E;--quarto-scss-export-btn-link-hover-color: #DE257E;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #222425;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-input-focus-color: #222425;--quarto-scss-export-input-placeholder-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-input-plaintext-color: #222425;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: rgb(147, 191.5, 241);--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: rgb(147, 191.5, 241);--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #222425;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #222425;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: rgb(190.2, 216.9, 246.6);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(34, 36, 37, 0.75);--quarto-scss-export-form-file-button-color: #222425;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #DE257E;--quarto-scss-export-nav-link-hover-color: #DE257E;--quarto-scss-export-nav-link-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #222425;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #222425;--quarto-scss-export-dropdown-link-hover-color: #222425;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #DE257E;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #DE257E;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #DE257E;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #222425;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #222425;--quarto-scss-export-accordion-button-bg: 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.nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #2e3133;--bs-navbar-hover-color: rgba(222, 37, 126, 0.8);--bs-navbar-disabled-color: rgba(46, 49, 51, 0.75);--bs-navbar-active-color: #de257e;--bs-navbar-brand-color: #2e3133;--bs-navbar-brand-hover-color: #de257e;--bs-navbar-toggler-border-color: rgba(46, 49, 51, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%232e3133' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%232e3133' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #222425;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #222425;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23222425'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2815.6, 51.2, 90.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(147, 191.5, 241);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(15.6, 51.2, 90.8);--bs-accordion-active-bg: rgb(211.8, 229.6, 249.4)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(34, 36, 37, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(34, 36, 37, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #DE257E;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #DE257E;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #DE257E;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(34, 36, 37, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #222425;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(34, 36, 37, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #222425;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(34, 36, 37, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(34, 36, 37, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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767.98px){.offcanvas-md{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 767.98px)and (prefers-reduced-motion: reduce){.offcanvas-md{transition:none}}@media(max-width: 767.98px){.offcanvas-md.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-md.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-md.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) 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!important}.link-light{color:RGBA(var(--bs-light-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-light-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-light:hover,.link-light:focus{color:RGBA(249, 250, 251, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(249, 250, 251, var(--bs-link-underline-opacity, 1)) !important}.link-dark{color:RGBA(var(--bs-dark-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-dark-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-dark:hover,.link-dark:focus{color:RGBA(42, 46, 51, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(42, 46, 51, var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 1)) 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var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box 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300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid 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37.4);--quarto-scss-export-default: #343a40;--quarto-scss-export-primary-text-emphasis: rgb(15.6, 51.2, 90.8);--quarto-scss-export-secondary-text-emphasis: rgb(20.8, 23.2, 25.6);--quarto-scss-export-success-text-emphasis: rgb(25.2, 72.8, 9.6);--quarto-scss-export-info-text-emphasis: rgb(61.2, 33.6, 74.8);--quarto-scss-export-warning-text-emphasis: rgb(102, 46.8, 9.6);--quarto-scss-export-danger-text-emphasis: rgb(102, 0, 22.8);--quarto-scss-export-light-text-emphasis: #495057;--quarto-scss-export-dark-text-emphasis: #495057;--quarto-scss-export-primary-bg-subtle: rgb(211.8, 229.6, 249.4);--quarto-scss-export-secondary-bg-subtle: rgb(214.4, 215.6, 216.8);--quarto-scss-export-success-bg-subtle: rgb(216.6, 240.4, 208.8);--quarto-scss-export-info-bg-subtle: rgb(234.6, 220.8, 241.4);--quarto-scss-export-warning-bg-subtle: rgb(255, 227.4, 208.8);--quarto-scss-export-danger-bg-subtle: rgb(255, 204, 215.4);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: rgb(168.6, 204.2, 243.8);--quarto-scss-export-secondary-border-subtle: rgb(173.8, 176.2, 178.6);--quarto-scss-export-success-border-subtle: rgb(178.2, 225.8, 162.6);--quarto-scss-export-info-border-subtle: rgb(214.2, 186.6, 227.8);--quarto-scss-export-warning-border-subtle: rgb(255, 199.8, 162.6);--quarto-scss-export-danger-border-subtle: rgb(255, 153, 175.8);--quarto-scss-export-light-border-subtle: #e9ecef;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-bg: #fff;--quarto-scss-export-body-secondary-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-body-secondary-bg: #e9ecef;--quarto-scss-export-body-tertiary-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #2780e3;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #e9ecef;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(255, 227.4, 208.8);--quarto-scss-export-table-color: #222425;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #222425;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #222425;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #222425;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-btn-color: #222425;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #DE257E;--quarto-scss-export-btn-link-hover-color: #DE257E;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #222425;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-input-focus-color: #222425;--quarto-scss-export-input-placeholder-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-input-plaintext-color: #222425;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: rgb(147, 191.5, 241);--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: rgb(147, 191.5, 241);--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #222425;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #222425;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: rgb(190.2, 216.9, 246.6);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(34, 36, 37, 0.75);--quarto-scss-export-form-file-button-color: #222425;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #DE257E;--quarto-scss-export-nav-link-hover-color: #DE257E;--quarto-scss-export-nav-link-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #222425;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #222425;--quarto-scss-export-dropdown-link-hover-color: #222425;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #DE257E;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #DE257E;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #DE257E;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #222425;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #222425;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: rgb(211.8, 229.6, 249.4);--quarto-scss-export-accordion-button-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-accordion-button-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-accordion-icon-color: #222425;--quarto-scss-export-accordion-icon-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #222425;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #222425;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #222425;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #222425;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #222425;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: white;--quarto-scss-export-bslib-page-sidebar-title-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: #222425;--quarto-scss-export-sidebar-hover-color: rgba(166.5, 27.75, 94.5, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 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30'%3e%3cpath stroke='%232e3133' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%232e3133' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #222425;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #222425;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23222425'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2815.6, 51.2, 90.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(147, 191.5, 241);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(15.6, 51.2, 90.8);--bs-accordion-active-bg: rgb(211.8, 229.6, 249.4)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(34, 36, 37, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(34, 36, 37, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #DE257E;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #DE257E;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #DE257E;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(34, 36, 37, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #222425;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(34, 36, 37, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #222425;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(34, 36, 37, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(34, 36, 37, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Inter;font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #e9ecef;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #222425;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: var(--bs-popover-border-color);z-index:var(--bs-popover-zindex);display:block;max-width:var(--bs-popover-max-width);font-family:Inter;font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-popover-font-size);word-wrap:break-word;background-color:var(--bs-popover-bg);background-clip:padding-box;border:var(--bs-popover-border-width) solid var(--bs-popover-border-color)}.popover 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300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card 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rgb(15.6, 51.2, 90.8);--quarto-scss-export-blue-900: rgb(7.8, 25.6, 45.4);--quarto-scss-export-indigo-100: rgb(224.4, 207.2, 252.4);--quarto-scss-export-indigo-200: rgb(193.8, 159.4, 249.8);--quarto-scss-export-indigo-300: rgb(163.2, 111.6, 247.2);--quarto-scss-export-indigo-400: rgb(132.6, 63.8, 244.6);--quarto-scss-export-indigo-500: #6610f2;--quarto-scss-export-indigo-600: rgb(81.6, 12.8, 193.6);--quarto-scss-export-indigo-700: rgb(61.2, 9.6, 145.2);--quarto-scss-export-indigo-800: rgb(40.8, 6.4, 96.8);--quarto-scss-export-indigo-900: rgb(20.4, 3.2, 48.4);--quarto-scss-export-purple-100: rgb(223.4, 216.2, 228.8);--quarto-scss-export-purple-200: rgb(191.8, 177.4, 202.6);--quarto-scss-export-purple-300: rgb(160.2, 138.6, 176.4);--quarto-scss-export-purple-400: rgb(128.6, 99.8, 150.2);--quarto-scss-export-purple-500: #613d7c;--quarto-scss-export-purple-600: rgb(77.6, 48.8, 99.2);--quarto-scss-export-purple-700: rgb(58.2, 36.6, 74.4);--quarto-scss-export-purple-800: rgb(38.8, 24.4, 49.6);--quarto-scss-export-purple-900: rgb(19.4, 12.2, 24.8);--quarto-scss-export-pink-100: rgb(250.4, 216.4, 232);--quarto-scss-export-pink-200: rgb(245.8, 177.8, 209);--quarto-scss-export-pink-300: rgb(241.2, 139.2, 186);--quarto-scss-export-pink-400: rgb(236.6, 100.6, 163);--quarto-scss-export-pink-500: #e83e8c;--quarto-scss-export-pink-600: rgb(185.6, 49.6, 112);--quarto-scss-export-pink-700: rgb(139.2, 37.2, 84);--quarto-scss-export-pink-800: rgb(92.8, 24.8, 56);--quarto-scss-export-pink-900: rgb(46.4, 12.4, 28);--quarto-scss-export-red-100: rgb(255, 204, 215.4);--quarto-scss-export-red-200: rgb(255, 153, 175.8);--quarto-scss-export-red-300: rgb(255, 102, 136.2);--quarto-scss-export-red-400: rgb(255, 51, 96.6);--quarto-scss-export-red-500: #ff0039;--quarto-scss-export-red-600: rgb(204, 0, 45.6);--quarto-scss-export-red-700: rgb(153, 0, 34.2);--quarto-scss-export-red-800: rgb(102, 0, 22.8);--quarto-scss-export-red-900: rgb(51, 0, 11.4);--quarto-scss-export-orange-100: rgb(252, 238.6, 219.6);--quarto-scss-export-orange-200: rgb(249, 222.2, 184.2);--quarto-scss-export-orange-300: rgb(246, 205.8, 148.8);--quarto-scss-export-orange-400: rgb(243, 189.4, 113.4);--quarto-scss-export-orange-500: #f0ad4e;--quarto-scss-export-orange-600: rgb(192, 138.4, 62.4);--quarto-scss-export-orange-700: rgb(144, 103.8, 46.8);--quarto-scss-export-orange-800: rgb(96, 69.2, 31.2);--quarto-scss-export-orange-900: rgb(48, 34.6, 15.6);--quarto-scss-export-yellow-100: rgb(255, 227.4, 208.8);--quarto-scss-export-yellow-200: rgb(255, 199.8, 162.6);--quarto-scss-export-yellow-300: rgb(255, 172.2, 116.4);--quarto-scss-export-yellow-400: rgb(255, 144.6, 70.2);--quarto-scss-export-yellow-500: #ff7518;--quarto-scss-export-yellow-600: rgb(204, 93.6, 19.2);--quarto-scss-export-yellow-700: rgb(153, 70.2, 14.4);--quarto-scss-export-yellow-800: rgb(102, 46.8, 9.6);--quarto-scss-export-yellow-900: rgb(51, 23.4, 4.8);--quarto-scss-export-green-100: rgb(216.6, 240.4, 208.8);--quarto-scss-export-green-200: rgb(178.2, 225.8, 162.6);--quarto-scss-export-green-300: rgb(139.8, 211.2, 116.4);--quarto-scss-export-green-400: rgb(101.4, 196.6, 70.2);--quarto-scss-export-green-500: #3fb618;--quarto-scss-export-green-600: rgb(50.4, 145.6, 19.2);--quarto-scss-export-green-700: rgb(37.8, 109.2, 14.4);--quarto-scss-export-green-800: rgb(25.2, 72.8, 9.6);--quarto-scss-export-green-900: rgb(12.6, 36.4, 4.8);--quarto-scss-export-teal-100: rgb(210.4, 244.2, 234.2);--quarto-scss-export-teal-200: rgb(165.8, 233.4, 213.4);--quarto-scss-export-teal-300: rgb(121.2, 222.6, 192.6);--quarto-scss-export-teal-400: rgb(76.6, 211.8, 171.8);--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: rgb(25.6, 160.8, 120.8);--quarto-scss-export-teal-700: rgb(19.2, 120.6, 90.6);--quarto-scss-export-teal-800: rgb(12.8, 80.4, 60.4);--quarto-scss-export-teal-900: rgb(6.4, 40.2, 30.2);--quarto-scss-export-cyan-100: rgb(234.6, 220.8, 241.4);--quarto-scss-export-cyan-200: rgb(214.2, 186.6, 227.8);--quarto-scss-export-cyan-300: rgb(193.8, 152.4, 214.2);--quarto-scss-export-cyan-400: rgb(173.4, 118.2, 200.6);--quarto-scss-export-cyan-500: #9954bb;--quarto-scss-export-cyan-600: rgb(122.4, 67.2, 149.6);--quarto-scss-export-cyan-700: rgb(91.8, 50.4, 112.2);--quarto-scss-export-cyan-800: rgb(61.2, 33.6, 74.8);--quarto-scss-export-cyan-900: rgb(30.6, 16.8, 37.4);--quarto-scss-export-default: #343a40;--quarto-scss-export-primary-text-emphasis: rgb(15.6, 51.2, 90.8);--quarto-scss-export-secondary-text-emphasis: rgb(20.8, 23.2, 25.6);--quarto-scss-export-success-text-emphasis: rgb(25.2, 72.8, 9.6);--quarto-scss-export-info-text-emphasis: rgb(61.2, 33.6, 74.8);--quarto-scss-export-warning-text-emphasis: rgb(102, 46.8, 9.6);--quarto-scss-export-danger-text-emphasis: rgb(102, 0, 22.8);--quarto-scss-export-light-text-emphasis: #495057;--quarto-scss-export-dark-text-emphasis: #495057;--quarto-scss-export-primary-bg-subtle: rgb(211.8, 229.6, 249.4);--quarto-scss-export-secondary-bg-subtle: rgb(214.4, 215.6, 216.8);--quarto-scss-export-success-bg-subtle: rgb(216.6, 240.4, 208.8);--quarto-scss-export-info-bg-subtle: rgb(234.6, 220.8, 241.4);--quarto-scss-export-warning-bg-subtle: rgb(255, 227.4, 208.8);--quarto-scss-export-danger-bg-subtle: rgb(255, 204, 215.4);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: rgb(168.6, 204.2, 243.8);--quarto-scss-export-secondary-border-subtle: rgb(173.8, 176.2, 178.6);--quarto-scss-export-success-border-subtle: rgb(178.2, 225.8, 162.6);--quarto-scss-export-info-border-subtle: rgb(214.2, 186.6, 227.8);--quarto-scss-export-warning-border-subtle: rgb(255, 199.8, 162.6);--quarto-scss-export-danger-border-subtle: rgb(255, 153, 175.8);--quarto-scss-export-light-border-subtle: #e9ecef;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-bg: #fff;--quarto-scss-export-body-secondary-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-body-secondary-bg: #e9ecef;--quarto-scss-export-body-tertiary-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #2780e3;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #e9ecef;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(255, 227.4, 208.8);--quarto-scss-export-table-color: #222425;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #222425;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #222425;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #222425;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-btn-color: #222425;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #DE257E;--quarto-scss-export-btn-link-hover-color: #DE257E;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #222425;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-input-focus-color: #222425;--quarto-scss-export-input-placeholder-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-input-plaintext-color: #222425;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: rgb(147, 191.5, 241);--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: rgb(147, 191.5, 241);--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #222425;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #222425;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: rgb(190.2, 216.9, 246.6);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(34, 36, 37, 0.75);--quarto-scss-export-form-file-button-color: #222425;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #DE257E;--quarto-scss-export-nav-link-hover-color: #DE257E;--quarto-scss-export-nav-link-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #222425;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #222425;--quarto-scss-export-dropdown-link-hover-color: #222425;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #DE257E;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #DE257E;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #DE257E;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #222425;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #222425;--quarto-scss-export-accordion-button-bg: 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.nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #2e3133;--bs-navbar-hover-color: rgba(222, 37, 126, 0.8);--bs-navbar-disabled-color: rgba(46, 49, 51, 0.75);--bs-navbar-active-color: #de257e;--bs-navbar-brand-color: #2e3133;--bs-navbar-brand-hover-color: #de257e;--bs-navbar-toggler-border-color: rgba(46, 49, 51, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%232e3133' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%232e3133' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #222425;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #222425;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23222425'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2815.6, 51.2, 90.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(147, 191.5, 241);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(15.6, 51.2, 90.8);--bs-accordion-active-bg: rgb(211.8, 229.6, 249.4)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(34, 36, 37, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(34, 36, 37, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #DE257E;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #DE257E;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #DE257E;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(34, 36, 37, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #222425;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(34, 36, 37, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #222425;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(34, 36, 37, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(34, 36, 37, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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767.98px){.offcanvas-md{position:fixed;bottom:0;z-index:var(--bs-offcanvas-zindex);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;max-width:100%;color:var(--bs-offcanvas-color);visibility:hidden;background-color:var(--bs-offcanvas-bg);background-clip:padding-box;outline:0;transition:var(--bs-offcanvas-transition)}}@media(max-width: 767.98px)and (prefers-reduced-motion: reduce){.offcanvas-md{transition:none}}@media(max-width: 767.98px){.offcanvas-md.offcanvas-start{top:0;left:0;width:var(--bs-offcanvas-width);border-right:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(-100%)}.offcanvas-md.offcanvas-end{top:0;right:0;width:var(--bs-offcanvas-width);border-left:var(--bs-offcanvas-border-width) solid var(--bs-offcanvas-border-color);transform:translateX(100%)}.offcanvas-md.offcanvas-top{top:0;right:0;left:0;height:var(--bs-offcanvas-height);max-height:100%;border-bottom:var(--bs-offcanvas-border-width) 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!important}.link-light{color:RGBA(var(--bs-light-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-light-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-light:hover,.link-light:focus{color:RGBA(249, 250, 251, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(249, 250, 251, var(--bs-link-underline-opacity, 1)) !important}.link-dark{color:RGBA(var(--bs-dark-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-dark-rgb), var(--bs-link-underline-opacity, 1)) !important}.link-dark:hover,.link-dark:focus{color:RGBA(42, 46, 51, var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(42, 46, 51, var(--bs-link-underline-opacity, 1)) !important}.link-body-emphasis{color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-opacity, 1)) !important;text-decoration-color:RGBA(var(--bs-emphasis-color-rgb), var(--bs-link-underline-opacity, 1)) 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var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box 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300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid 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rgb(211.8, 229.6, 249.4);--quarto-scss-export-secondary-bg-subtle: rgb(214.4, 215.6, 216.8);--quarto-scss-export-success-bg-subtle: rgb(216.6, 240.4, 208.8);--quarto-scss-export-info-bg-subtle: rgb(234.6, 220.8, 241.4);--quarto-scss-export-warning-bg-subtle: rgb(255, 227.4, 208.8);--quarto-scss-export-danger-bg-subtle: rgb(255, 204, 215.4);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: rgb(168.6, 204.2, 243.8);--quarto-scss-export-secondary-border-subtle: rgb(173.8, 176.2, 178.6);--quarto-scss-export-success-border-subtle: rgb(178.2, 225.8, 162.6);--quarto-scss-export-info-border-subtle: rgb(214.2, 186.6, 227.8);--quarto-scss-export-warning-border-subtle: rgb(255, 199.8, 162.6);--quarto-scss-export-danger-border-subtle: rgb(255, 153, 175.8);--quarto-scss-export-light-border-subtle: #e9ecef;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-bg: #fff;--quarto-scss-export-body-secondary-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-body-secondary-bg: #e9ecef;--quarto-scss-export-body-tertiary-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #2780e3;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #e9ecef;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(255, 227.4, 208.8);--quarto-scss-export-table-color: #222425;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #222425;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #222425;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #222425;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(39, 128, 227, 0.25);--quarto-scss-export-btn-color: #222425;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #DE257E;--quarto-scss-export-btn-link-hover-color: #DE257E;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #222425;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-input-focus-color: #222425;--quarto-scss-export-input-placeholder-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-input-plaintext-color: #222425;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: rgb(147, 191.5, 241);--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: rgb(147, 191.5, 241);--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #222425;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #222425;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: rgb(190.2, 216.9, 246.6);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(34, 36, 37, 0.75);--quarto-scss-export-form-file-button-color: #222425;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #DE257E;--quarto-scss-export-nav-link-hover-color: #DE257E;--quarto-scss-export-nav-link-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #222425;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #222425;--quarto-scss-export-dropdown-link-hover-color: #222425;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #DE257E;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #DE257E;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #DE257E;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #222425;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #222425;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: rgb(211.8, 229.6, 249.4);--quarto-scss-export-accordion-button-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-accordion-button-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-accordion-icon-color: #222425;--quarto-scss-export-accordion-icon-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #222425;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #222425;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #222425;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #222425;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #222425;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: white;--quarto-scss-export-bslib-page-sidebar-title-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: #222425;--quarto-scss-export-sidebar-hover-color: rgba(166.5, 27.75, 94.5, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} \ No 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auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #222425;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #222425;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23222425'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2815.6, 51.2, 90.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(147, 191.5, 241);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(15.6, 51.2, 90.8);--bs-accordion-active-bg: rgb(211.8, 229.6, 249.4)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28125.4, 178.8, 238.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(34, 36, 37, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(34, 36, 37, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #DE257E;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #DE257E;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #DE257E;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(34, 36, 37, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #222425;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(34, 36, 37, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #222425;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(34, 36, 37, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(34, 36, 37, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Inter;font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) 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0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #e9ecef;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #222425;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: var(--bs-popover-border-color);z-index:var(--bs-popover-zindex);display:block;max-width:var(--bs-popover-max-width);font-family:Inter;font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-popover-font-size);word-wrap:break-word;background-color:var(--bs-popover-bg);background-clip:padding-box;border:var(--bs-popover-border-width) solid var(--bs-popover-border-color)}.popover 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300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card 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.accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.navbar+.container-fluid:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-sm:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-md:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-lg:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-xl:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-xxl:has(>.tab-content>.tab-pane.active.html-fill-container){padding-left:0;padding-right:0}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container{padding:var(--bslib-spacer, 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1rem);gap:var(--bslib-spacer, 1rem)}@media(max-width: 575.98px){.bslib-page-fill{height:var(--bslib-page-fill-mobile-height, auto)}}:root{--bslib-page-sidebar-title-bg: white;--bslib-page-sidebar-title-color: #000}.bslib-page-title{background-color:var(--bslib-page-sidebar-title-bg);color:var(--bslib-page-sidebar-title-color);font-size:1.25rem;font-weight:300;padding:var(--bslib-spacer, 1rem);padding-left:1.5rem;margin-bottom:0;border-bottom:1px solid #dee2e6}.bslib-sidebar-layout{--bslib-sidebar-transition-duration: 500ms;--bslib-sidebar-transition-easing-x: cubic-bezier(0.8, 0.78, 0.22, 1.07);--bslib-sidebar-border: var(--bs-card-border-width, 1px) solid var(--bs-card-border-color, rgba(0, 0, 0, 0.175));--bslib-sidebar-border-radius: var(--bs-border-radius);--bslib-sidebar-vert-border: var(--bs-card-border-width, 1px) solid var(--bs-card-border-color, rgba(0, 0, 0, 0.175));--bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--bslib-sidebar-fg: 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#fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #222425;--quarto-scss-export-dropdown-link-hover-color: #222425;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(34, 36, 37, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #DE257E;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #DE257E;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #DE257E;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #222425;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #222425;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: rgb(211.8, 229.6, 249.4);--quarto-scss-export-accordion-button-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-accordion-button-focus-border-color: rgb(147, 191.5, 241);--quarto-scss-export-accordion-icon-color: #222425;--quarto-scss-export-accordion-icon-active-color: rgb(15.6, 51.2, 90.8);--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #222425;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #222425;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #222425;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #222425;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #222425;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: white;--quarto-scss-export-bslib-page-sidebar-title-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: #222425;--quarto-scss-export-sidebar-hover-color: rgba(166.5, 27.75, 94.5, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(34, 36, 37, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} \ No newline at end of file diff --git a/site/site_libs/bootstrap/bootstrap-icons.css b/site/site_libs/bootstrap/bootstrap-icons.css deleted file mode 100644 index 82b40f57e..000000000 --- a/site/site_libs/bootstrap/bootstrap-icons.css +++ /dev/null @@ -1,2106 +0,0 @@ -/*! - * Bootstrap Icons v1.13.1 (https://icons.getbootstrap.com/) - * Copyright 2019-2024 The Bootstrap Authors - * Licensed under MIT 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return 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Diese Technologien nutzen wir außerdem, um Ergebnisse zu messen, um zu verstehen, woher unsere Besucher kommen oder um unsere Website weiter zu entwickeln.","nb_title":"Ihre Privatsphäre ist uns wichtig","pc_fnct_text_1":"Funktions Cookies","pc_fnct_text_2":"Diese Cookies werden verwendet, um Ihnen ein persönlicheres Erlebnis auf unserer Website zu ermöglichen und um sich an Ihre Entscheidungen zu erinnern, die Sie bei der Nutzung unserer Website getroffen haben.","pc_fnct_text_3":"Beispielsweise können wir Funktions-Cookies verwenden, um Ihre Spracheinstellungen oder Ihre Anmeldedaten zu speichern.","pc_minfo_text_1":"Mehr Informationen","pc_minfo_text_2":"Bei Fragen in Bezug auf unseren Umgang mit Cookies und Ihrer Privatsphäre kontaktieren Sie uns bitte.","pc_minfo_text_3":"Details finden Sie in unserer Datenschutzrichtlinie.","pc_save":"Einstellungen speichern","pc_sncssr_text_1":"Technisch notwendige Cookies","pc_sncssr_text_2":"Diese Cookies sind für die Bereitstellung von Diensten, die über unsere Website verfügbar sind, und für die Verwendung bestimmter Funktionen unserer Website von wesentlicher Bedeutung.","pc_sncssr_text_3":"Ohne diese Cookies können wir Ihnen bestimmte Dienste auf unserer Website nicht zur Verfügung stellen.","pc_title":"Cookie Einstellungen","pc_trck_text_1":"Tracking und Performance Cookies","pc_trck_text_2":"Diese Cookies werden zum Sammeln von Informationen verwendet, um den Verkehr auf unserer Website und die Nutzung unserer Website durch Besucher zu analysieren.","pc_trck_text_3":"Diese Cookies können beispielsweise nachverfolgen, wie lange Sie auf der Website verweilen oder welche Seiten Sie besuchen. So können wir verstehen, wie wir unsere Website für Sie verbessern können.","pc_trck_text_4":"Die durch diese Tracking- und Performance-Cookies gesammelten Informationen identifizieren keinen einzelnen Besucher.","pc_trgt_text_1":"Targeting und Werbung Cookies","pc_trgt_text_2":"Diese Cookies werden genutzt, um Werbung anzuzeigen, die Sie aufgrund Ihrer Surfgewohnheiten wahrscheinlich interessieren wird.","pc_trgt_text_3":"Diese Cookies, die von unseren Inhalten und / oder Werbeanbietern bereitgestellt werden, können Informationen, die sie von unserer Website gesammelt haben, mit anderen Informationen kombinieren, welche sie durch Aktivitäten Ihres Webbrowsers in Ihrem Netzwerk von Websites gesammelt haben.","pc_trgt_text_4":"Wenn Sie diese Targeting- oder Werbe-Cookies entfernen oder deaktivieren, werden weiterhin Anzeigen angezeigt. Diese sind für Sie jedoch möglicherweise nicht relevant.","pc_yprivacy_text_1":"Ihre Privatsphäre ist uns wichtig","pc_yprivacy_text_2":"Cookies sind sehr kleine Textdateien, die auf Ihrem Rechner gespeichert werden, wenn Sie eine Website besuchen. Wir verwenden Cookies für eine Reihe von Auswertungen, um damit Ihren Besuch auf unserer Website kontinuierlich verbessern zu können (z. B. damit Ihnen Ihre Login-Daten erhalten bleiben).","pc_yprivacy_text_3":"Sie können Ihre Einstellungen ändern und verschiedenen Arten von Cookies erlauben, auf Ihrem Rechner gespeichert zu werden, während Sie unsere Webseite besuchen. Sie können auf Ihrem Rechner gespeicherte Cookies ebenso weitgehend wieder entfernen. Bitte bedenken Sie aber, dass dadurch Teile unserer Website möglicherweise nicht mehr in der gedachten Art und Weise nutzbar sind.","pc_yprivacy_title":"Ihre Privatsphäre","privacy_policy":"Datenschutzrichtlinie"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Actif","always_active":"Toujours activé","impressum":"Impressum","inactive":"Inactif","nb_agree":"J\'accepte","nb_changep":"Changer mes préférences","nb_ok":"OK","nb_reject":"Je refuse","nb_text":"Nous utilisons des cookies et d\'autres technologies de suivi pour améliorer votre expérience de navigation sur notre site, pour vous montrer un contenu personnalisé et des publicités ciblées, pour analyser le trafic de notre site et pour comprendre la provenance de nos visiteurs.","nb_title":"Nous utilisons des cookies","pc_fnct_text_1":"Cookies de Fonctionnalité","pc_fnct_text_2":"Ces cookies servent à vous offrir une expérience plus personnalisée sur notre site Web et à mémoriser les choix que vous faites lorsque vous utilisez notre site Web.","pc_fnct_text_3":"Par exemple, nous pouvons utiliser des cookies de fonctionnalité pour mémoriser vos préférences de langue ou vos identifiants de connexion.","pc_minfo_text_1":"Plus d\'information","pc_minfo_text_2":"Pour toute question relative à notre politique en matière de cookies et à vos choix, veuillez nous contacter.","pc_minfo_text_3":"Pour en savoir plus, merci de consulter notre Politique de confidentialité.","pc_save":"Sauvegarder mes préférences","pc_sncssr_text_1":"Cookies strictement nécessaires","pc_sncssr_text_2":"Ces cookies sont essentiels pour vous fournir les services disponibles sur notre site Web et vous permettre d’utiliser certaines fonctionnalités de notre site Web.","pc_sncssr_text_3":"Sans ces cookies, nous ne pouvons pas vous fournir certains services sur notre site Web.","pc_title":"Espace de Préférences des Cookies","pc_trck_text_1":"Cookies de suivi et de performance","pc_trck_text_2":"Ces cookies sont utilisés pour collecter des informations permettant d\'analyser le trafic sur notre site et la manière dont les visiteurs utilisent notre site.","pc_trck_text_3":"Par exemple, ces cookies peuvent suivre des choses telles que le temps que vous passez sur le site Web ou les pages que vous visitez, ce qui nous aide à comprendre comment nous pouvons améliorer notre site Web pour vous.","pc_trck_text_4":"Les informations collectées via ces cookies de suivi et de performance n\' identifient aucun visiteur en particulier.","pc_trgt_text_1":"Cookies de ciblage et de publicité","pc_trgt_text_2":"Ces cookies sont utilisés pour afficher des publicités susceptibles de vous intéresser en fonction de vos habitudes de navigation.","pc_trgt_text_3":"Ces cookies, tels que servis par nos fournisseurs de contenu et / ou de publicité, peuvent associer des informations qu\'ils ont collectées sur notre site Web à d\'autres informations qu\'ils ont collectées de manière indépendante et concernant les activités du votre navigateur Web sur son réseau de sites Web.","pc_trgt_text_4":"Si vous choisissez de supprimer ou de désactiver ces cookies de ciblage ou de publicité, vous verrez toujours des annonces, mais elles risquent de ne pas être pertinentes.","pc_yprivacy_text_1":"Votre confidentialité est importante pour nous","pc_yprivacy_text_2":"Les cookies sont de très petits fichiers texte qui sont stockés sur votre ordinateur lorsque vous visitez un site Web. Nous utilisons des cookies à diverses fins et pour améliorer votre expérience en ligne sur notre site Web (par exemple, pour mémoriser les informations de connexion de votre compte).","pc_yprivacy_text_3":"Vous pouvez modifier vos préférences et refuser l\'enregistrement de certains types de cookies sur votre ordinateur lors de la navigation sur notre site. Vous pouvez également supprimer les cookies déjà stockés sur votre ordinateur, mais gardez à l\'esprit que leur suppression peut vous empêcher d\'utiliser des éléments de notre site Web.","pc_yprivacy_title":"Votre confidentialité","privacy_policy":"Politique de confidentialité"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Activo","always_active":"Siempre activo","impressum":"Impressum","inactive":"Inactivo","nb_agree":"Aceptar","nb_changep":"Configurar","nb_ok":"OK","nb_reject":"Rechazar","nb_text":"Usamos cookies y otras técnicas de rastreo para mejorar tu experiencia de navegación en nuestra web, para mostrarte contenidos personalizados y anuncios adecuados, para analizar el tráfico en nuestra web y para comprender de donde llegan nuestros visitantes.","nb_title":"Utilizamos cookies","pc_fnct_text_1":"Cookies de funcionalidad","pc_fnct_text_2":"Estas cookies son utilizadas para proveerte una experiencia más personalizada y recordar tus elecciones en nuestra web.","pc_fnct_text_3":"Por ejemplo, podemos utilizar cookies de funcionalidad para recordar tus preferencias de idioma o tus detalles de acceso.","pc_minfo_text_1":"Más información","pc_minfo_text_2":"Para cualquier pregunta en relación con nuestra política de cookies y tus preferencias, contacta con nosotros, por favor.","pc_minfo_text_3":"Para saber más, visita nuestra página sobre la Política de privacidad.","pc_save":"Guardar mis preferencias","pc_sncssr_text_1":"Cookies estrictamente necesarias","pc_sncssr_text_2":"Estos cookies son esenciales para proveerte los servicios disponibles en nuestra web y para permitirte utilizar algunas características de nuestra web.","pc_sncssr_text_3":"Sin estas cookies, no podemos proveer algunos servicios de nuestro sitio web.","pc_title":"Centro de Preferencias de Cookies","pc_trck_text_1":"Cookies de rastreo y rendimiento","pc_trck_text_2":"Estas cookies son utilizadas para recopilar información, para analizar el tráfico y la forma en que los usuarios utilizan nuestra web.","pc_trck_text_3":"Por ejemplo, estas cookies pueden recopilar datos como cuánto tiempo llevas navegado en nuestro sitio web o qué páginas visitas, cosa que nos ayuda a comprender cómo podemos mejorar nuestra web para ti.","pc_trck_text_4":"La información recopilada con estas cookies de rastreo y rendimiento no identifican a ningún visitante individual.","pc_trgt_text_1":"Cookies de seguimiento y publicidad","pc_trgt_text_2":"Estas cookies son utilizadas para enseñarte anuncios que pueden ser interesantes basados en tus costumbres de navegación.","pc_trgt_text_3":"Estas cookies, servidas por nuestros proveedores de contenido y/o de publicidad, pueden combinar la información que ellos recogieron de nuestro sitio web con otra información recopilada por ellos en relación con las actividades de su navegador a través de su red de sitios web.","pc_trgt_text_4":"Si eliges cancelar o inhabilitar las cookies de seguimiento y publicidad, seguirás viendo anuncios pero estos podrían no ser de tu interés.","pc_yprivacy_text_1":"Tu privacidad es importante para nosotros","pc_yprivacy_text_2":"Las cookies son pequeños archivos de texto que se almacenan en tu navegador cuando visitas nuestra web. Utilizamos cookies para diferentes objetivos y para mejorar tu experiencia en nuestro sitio web (por ejemplo, para recordar tus detalles de acceso).","pc_yprivacy_text_3":"Puedes cambiar tus preferencias y rechazar que algunos tipos de cookies sean almacenados mientras estás navegando en nuestra web. También puedes cancelar cualquier cookie ya almacenada en tu navegador, pero recuerda que cancelar las cookies puede impedirte utilizar algunas partes de nuestra web.","pc_yprivacy_title":"Tu privacidad","privacy_policy":"Política de privacidad"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Actiu","always_active":"Sempre actiu","impressum":"Impressum","inactive":"Inactiu","nb_agree":"Estic d’acord","nb_changep":"Canviar preferències","nb_ok":"OK","nb_reject":"Declino","nb_text":"Fem servir cookies i altres tecnologies de seguiment per millorar la teva experiència de navegació al nostre lloc web, per mostrar-te contingut personalitzat i anuncis interessants per a tu, per analitzar el nostre tràfic i entendre d’on venen els nostres visitants.","nb_title":"Fem servir cookies","pc_fnct_text_1":"Cookies de funcionalitat","pc_fnct_text_2":"Aquestes cookies ens permeten oferir-vos una experiència personalitzada i recordar la vostra configuració quan feu servir el nostre lloc web.","pc_fnct_text_3":"Per exemple, podem fer servir funcionalitat per recordar el vostre idioma o les vostres credencials.","pc_minfo_text_1":"Més informació","pc_minfo_text_2":"Per qualsevol pregunta relacionada amb la nostra política de cookies i les vostres opcions, si us plau contacti’ns.","pc_minfo_text_3":"Per saber més, si us plau visiti la nostra Política de privacitat.","pc_save":"Guarda les meves preferències","pc_sncssr_text_1":"Cookies estrictament necessàries","pc_sncssr_text_2":"Aquestes cookies són essencials per oferir-vos el nostres serveis i funcionalitats al nostre lloc web.","pc_sncssr_text_3":"Sense aquestes cookies, no us podem oferir alguns serveis.","pc_title":"Centre de Preferències de Cookies","pc_trck_text_1":"Cookies de seguiment i rendiment","pc_trck_text_2":"Aquestes cookies es fan servir per recollir informació, analitzar el tràfic i veure com es fa servir el nostre lloc web.","pc_trck_text_3":"Per exemple, aquestes cookies podrien fer el seguiment de quant de temps visiteu el nostre web o quines pàgines visiteu les quals ens poden ajudar a entendre com millorar el lloc web per vosaltres.","pc_trck_text_4":"La informació recollida gràcies a aquestes cookies de seguiment i rendiment no us identifiquen de forma individual.","pc_trgt_text_1":"Cookies de publicitat i focalització","pc_trgt_text_2":"Aquestes cookies es fan servir per mostrar anuncis que poden ser del vostre interès basats en els vostres hàbits d’us.","pc_trgt_text_3":"Aquestes cookies, servides tal i com ho fan els nostres proveïdors de publicitat i contingut, poden combinar informació recollida al nostre lloc web amb altra informació que hagin recollit independentment relacionada amb activitat a la seva xarxa de llocs web.","pc_trgt_text_4":"Si vostè decideix eliminar o deshabilitat aquestes cookies, encara veurà publicitat però aquesta pot no ser rellevant per vostè.","pc_yprivacy_text_1":"La vostra privacitat és important per nosaltres","pc_yprivacy_text_2":"Les cookies són uns arxius de text molt petits que es guarden al vostre ordinador quan visiteu un lloc web. Fem servir cookies per una varietat de finalitats i millorar la vostra experiència al nostre lloc web (per exemple, per recordar les vostres credencials).","pc_yprivacy_text_3":"Pot canviar les vostres preferències i rebutjar l’emmagatzematge al vostre ordinador de certs tipus de cookies mentres navega pel nostre. Pot eliminar qualsevol cookie ja emmagatzemada al vostre ordinador, però tingui en compte que eliminar cookies pot impedir que faci servir parts del nostre lloc web.","pc_yprivacy_title":"La vostra privacitat","privacy_policy":"Política de privacitat"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Attivo","always_active":"Sempre attivo","impressum":"Impressum","inactive":"Inattivo","nb_agree":"Accetto","nb_changep":"Cambia le mie impostazioni","nb_ok":"OK","nb_reject":"Rifiuto","nb_text":"Noi usiamo i cookies e altre tecniche di tracciamento per migliorare la tua esperienza di navigazione nel nostro sito, per mostrarti contenuti personalizzati e annunci mirati, per analizzare il traffico sul nostro sito, e per capire da dove arrivano i nostri visitatori.","nb_title":"Noi usiamo i cookies","pc_fnct_text_1":"Cookies funzionali","pc_fnct_text_2":"Questi cookies sono utilizzati per offrirti un’esperienza più personalizzata nel nostro sito e per ricordare le scelte che hai fatto mentre usavi il nostro sito.","pc_fnct_text_3":"Per esempio, possiamo usare cookies funzionali per memorizzare le tue preferenze sulla lingua o i tuoi dettagli di accesso.","pc_minfo_text_1":"Più informazioni","pc_minfo_text_2":"Per qualsiasi domanda relativa alla nostra politica sui cookies e le tue scelte, per favore contattaci.","pc_minfo_text_3":"Per saperne di più, visita per favore la nostra pagina sulla Politica sulla riservatezza.","pc_save":"Salva le mie impostazioni","pc_sncssr_text_1":"Cookies strettamente necessari","pc_sncssr_text_2":"Questi cookies sono essenziali per fornirti i servizi disponibili nel nostro sito e per renderti disponibili alcune funzionalità del nostro sito web.","pc_sncssr_text_3":"Senza questi cookies, non possiamo fornirti alcuni servizi del nostro sito.","pc_title":"Centro Preferenze sui Cookies","pc_trck_text_1":"Cookies di tracciamento e prestazione","pc_trck_text_2":"Questi cookies sono utilizzati per raccogliere informazioni per analizzare il traffico verso il nostro sito e il modo in cui i visitatori utilizzano il nostro sito.","pc_trck_text_3":"Per esempio, questi cookies possono tracciare cose come quanto a lungo ti fermi nel nostro sito o le pagine che visiti, cosa che ci aiuta a capire come possiamo migliorare il nostro sito per te.","pc_trck_text_4":"Le informazioni raccolte attraverso questi cookies di tracciamento e performance non identificano alcun visitatore individuale.","pc_trgt_text_1":"Cookies di targeting e pubblicità","pc_trgt_text_2":"Questi cookies sono usati per mostrare annunci pubblicitari che possano verosimilmente essere di tuo interesse in base alle tue abitudini di navigazione.","pc_trgt_text_3":"Questi cookies, cosí come forniti dai nostri fornitori di contenuti o annunci pubblicitari, possono combinare le informazioni che raccolgono dal nostro sito web con quelle che hanno indipendentemente raccolto in relazione all’attività del tuo browser attraverso la loro rete di siti web.","pc_trgt_text_4":"Se scegli di rimuovere o disabilitare questo tipo di cookies di targeting e pubblicità, vedrai ancora annunci pubblicitari ma potrebbero essere irrilevanti per te.","pc_yprivacy_text_1":"La tua privacy è importante per noi","pc_yprivacy_text_2":"I cookies sono dei piccolissimi file di testo che vengono memorizzati nel tuo computer quando visiti un sito web. Noi usiamo i cookies per una varietà di scopi e per migliorare la tua esperienza online nel nostro sito web (per esempio, per ricordare i tuoi dettagli di accesso).","pc_yprivacy_text_3":"Tu puoi cambiare le tue impostazioni e rifiutare che alcuni tipi di cookies vengano memorizzati sul tuo computer mentre stai navigando nel nostro sito web. Puoi anche rimuovere qualsiasi cookie già memorizzato nel tuo computer, ma ricorda che cancellare i cookies può impedirti di utilizzare alcune parti del nostro sito.","pc_yprivacy_title":"La tua privacy","privacy_policy":"Politica sulla riservatezza"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktiv","always_active":"Alltid aktiv","impressum":"Impressum","inactive":"Inaktiv","nb_agree":"Jag accepterar","nb_changep":"Ändra mina inställningar","nb_ok":"OK","nb_reject":"Jag avböjer","nb_text":"Vi använder cookies och andra spårningsteknologier för att förbättra din surfupplevelse på vår webbplats, för att visa dig personligt innehåll och riktade annonser, för att analysera vår webbplatstrafik och för att förstå var våra besökare kommer ifrån.","nb_title":"Vi använder oss av cookies","pc_fnct_text_1":"Funktionella cookies","pc_fnct_text_2":"Dessa cookies används för att ge dig en mer personlig upplevelse på vår webbplats och för att komma ihåg val du gör när du använder vår webbplats.","pc_fnct_text_3":"Vi kan till exempel använda funktions cookies för att komma ihåg dina språkinställningar eller dina inloggningsuppgifter.","pc_minfo_text_1":"Mer information","pc_minfo_text_2":"Kontakta oss om du har frågor angående vår policy om cookies och dina val.","pc_minfo_text_3":"För att ta reda på mer, läs vår integritetspolicy.","pc_save":"Spara mina inställningar","pc_sncssr_text_1":"Absolut nödvändiga cookies","pc_sncssr_text_2":"Dessa cookies är viktiga för att förse dig med tjänster som är tillgängliga via vår webbplats och för att du ska kunna använda vissa funktioner på vår webbplats.","pc_sncssr_text_3":"Utan dessa cookies kan vi inte tillhandahålla vissa tjänster på vår webbplats.","pc_title":"Cookies Inställningar","pc_trck_text_1":"Spårnings- och prestanda cookies","pc_trck_text_2":"Dessa cookies används för att samla in information för att analysera trafiken på vår webbplats och hur våra besökare använder den.","pc_trck_text_3":"Dessa cookies kan till exempel spåra hur länge du spenderar på webbplatsen eller vilka sidor du besöker vilket hjälper oss att förstå hur vi kan förbättra vår webbplats för dig.","pc_trck_text_4":"Informationen som samlas in genom dessa spårnings- och prestanda cookies identifierar ingen enskild besökare.","pc_trgt_text_1":"Inriktnings- och reklamcookies","pc_trgt_text_2":"Dessa cookies används för att visa reklam som sannolikt kommer att vara av intresse för dig baserat på dina surfvanor.","pc_trgt_text_3":"Dessa kakor, som betjänas av vårt innehåll och / eller reklamleverantörer, kan kombinera information som de samlat in från vår webbplats med annan information som de har samlat in oberoende om din webbläsares aktiviteter i deras nätverk av webbplatser.","pc_trgt_text_4":"Om du väljer att ta bort eller inaktivera dessa inriktnings- och reklamcookies kommer du fortfarande att se annonser men de kanske inte är relevanta för dig.","pc_yprivacy_text_1":"Din integritet är viktig för oss","pc_yprivacy_text_2":"Cookies är mycket små textfiler som lagras på din dator när du besöker en webbplats. Vi använder cookies till olika ändamål och för att kunna förbättra din onlineupplevelse på vår webbplats (till exempel som att komma ihåg dina inloggningsuppgifter).","pc_yprivacy_text_3":"Du kan ändra dina inställningar och avaktivera vissa typer av cookies som ska lagras på din dator när du surfar på vår webbplats. Du kan också ta bort alla cookies som redan är lagrade på din dator, men kom ihåg att radering av cookies kan hindra dig från att använda delar av vår webbplats.","pc_yprivacy_title":"Din integritet","privacy_policy":"Integritetspolicy"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Actief","always_active":"Altijd actief","impressum":"Impressum","inactive":"Inactief","nb_agree":"Ik ga akkoord","nb_changep":"Wijzig mijn voorkeuren","nb_ok":"OK","nb_reject":"Ik weiger","nb_text":"Wij maken gebruik van cookies en andere tracking-technologieën om uw surfervaring op onze website te verbeteren, om gepersonaliseerde inhoud en advertenties te tonen, om ons websiteverkeer te analyseren en om te begrijpen waar onze bezoekers vandaan komen.","nb_title":"Wij gebruiken cookies","pc_fnct_text_1":"Functionele cookies","pc_fnct_text_2":"Deze cookies worden gebruikt om u een persoonlijkere ervaring op onze website te bieden en om keuzes te onthouden die u maakt wanneer u onze website gebruikt.","pc_fnct_text_3":"Functionele cookies worden bijvoorbeeld gebruikt om uw taalvoorkeuren of inloggegevens te onthouden.","pc_minfo_text_1":"Meer informatie","pc_minfo_text_2":"Voor vragen in verband met ons cookiebeleid en uw keuzes kan u ons contacteren.","pc_minfo_text_3":"Voor meer informatie, bezoek ons Privacybeleid.","pc_save":"Sla mijn voorkeuren op","pc_sncssr_text_1":"Strikt noodzakelijke cookies","pc_sncssr_text_2":"Deze cookies zijn essentieel om u de diensten aan te bieden die beschikbaar zijn via onze website en om u in staat te stellen bepaalde functies van onze website te gebruiken.","pc_sncssr_text_3":"Zonder deze cookies kunnen we u bepaalde diensten op onze website niet aanbieden.","pc_title":"Cookie instellingen","pc_trck_text_1":"Tracking- en prestatie cookies","pc_trck_text_2":"Deze cookies worden gebruikt om informatie te verzamelen om het verkeer naar onze website te analyseren en hoe bezoekers onze website gebruiken.","pc_trck_text_3":"Deze cookies kunnen gegevens zoals hoe lang u op de website doorbrengt of de pagina\'s die u bezoekt, bijhouden. Dit helpt ons te begrijpen hoe we onze website voor u kunnen verbeteren.","pc_trck_text_4":"Individuele bezoekers kunnen niet geïdentificeerd worden aan hand van de informatie in deze cookies.","pc_trgt_text_1":"Targeting- en advertentie cookies","pc_trgt_text_2":"Deze cookies worden gebruikt om advertenties weer te geven die u waarschijnlijk interesseren op basis van uw surfgedrag.","pc_trgt_text_3":"Deze cookies, zoals aangeboden op basis van de inhoud van onze site en/of reclame aanbieders, kunnen informatie die ze van onze website hebben verzameld combineren met andere informatie die ze onafhankelijk hebben verzameld met betrekking tot de activiteiten van uw webbrowser via hun netwerk van websites.","pc_trgt_text_4":"Als u ervoor kiest deze targeting- of advertentiecookies te verwijderen of uit te schakelen, ziet u nog steeds advertenties, maar deze zijn mogelijk niet relevant voor u.","pc_yprivacy_text_1":"Uw privacy is belangrijk voor ons","pc_yprivacy_text_2":"Cookies zijn kleine tekstbestanden die bij het bezoeken van een website op uw computer worden opgeslagen. We gebruiken cookies voor verschillende doeleinden en om uw online ervaring op onze website te verbeteren (bijvoorbeeld om de inloggegevens voor uw account te onthouden).","pc_yprivacy_text_3":"U kunt uw voorkeuren wijzigen en bepaalde soorten cookies weigeren die op uw computer worden opgeslagen tijdens het browsen op onze website. U kunt ook alle cookies verwijderen die al op uw computer zijn opgeslagen, maar houd er rekening mee dat het verwijderen van cookies ertoe kan leiden dat u delen van onze website niet kunt gebruiken.","pc_yprivacy_title":"Jouw privacy","privacy_policy":"Privacybeleid"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Ativo","always_active":"Sempre ativo","impressum":"Impressum","inactive":"Inativo","nb_agree":"Concordo","nb_changep":"Alterar as minhas preferências","nb_ok":"OK","nb_reject":"Eu recuso","nb_text":"Utilizamos cookies e outras tecnologias de medição para melhorar a sua experiência de navegação no nosso site, de forma a mostrar conteúdo personalizado, anúncios direcionados, analisar o tráfego do site e entender de onde vêm os visitantes.","nb_title":"O nosso site usa cookies","pc_fnct_text_1":"Cookies de funcionalidade","pc_fnct_text_2":"Estes cookies são usados ​​para fornecer uma experiência mais personalizada no nosso site e para lembrar as escolhas que faz ao usar o nosso site.","pc_fnct_text_3":"Por exemplo, podemos usar cookies de funcionalidade para se lembrar das suas preferências de idioma e/ ou os seus detalhes de login.","pc_minfo_text_1":"Mais Informações","pc_minfo_text_2":"Para qualquer dúvida sobre a nossa política de cookies e as suas opções, entre em contato connosco.","pc_minfo_text_3":"Para obter mais detalhes, por favor consulte a nossa Política de Privacidade.","pc_save":"Guardar as minhas preferências","pc_sncssr_text_1":"Cookies estritamente necessários","pc_sncssr_text_2":"Estes cookies são essenciais para fornecer serviços disponíveis no nosso site e permitir que possa usar determinados recursos no nosso site.","pc_sncssr_text_3":"Sem estes cookies, não podemos fornecer certos serviços no nosso site.","pc_title":"Centro de preferências de cookies","pc_trck_text_1":"Cookies de medição e desempenho","pc_trck_text_2":"Estes cookies são usados ​​para coletar informações para analisar o tráfego no nosso site e entender como é que os visitantes estão a usar o nosso site.","pc_trck_text_3":"Por exemplo, estes cookies podem medir fatores como o tempo despendido no site ou as páginas visitadas, isto vai permitir entender como podemos melhorar o nosso site para os utilizadores.","pc_trck_text_4":"As informações coletadas por meio destes cookies de medição e desempenho não identificam nenhum visitante individual.","pc_trgt_text_1":"Cookies de segmentação e publicidade","pc_trgt_text_2":"Estes cookies são usados ​​para mostrar publicidade que provavelmente lhe pode interessar com base nos seus hábitos e comportamentos de navegação.","pc_trgt_text_3":"Estes cookies, servidos pelo nosso conteúdo e/ ou fornecedores de publicidade, podem combinar as informações coletadas no nosso site com outras informações coletadas independentemente relacionadas com as atividades na rede de sites do seu navegador.","pc_trgt_text_4":"Se optar por remover ou desativar estes cookies de segmentação ou publicidade, ainda verá anúncios, mas estes poderão não ser relevantes para si.","pc_yprivacy_text_1":"A sua privacidade é importante para nós","pc_yprivacy_text_2":"Cookies são pequenos arquivos de texto que são armazenados no seu computador quando visita um site. Utilizamos cookies para diversos fins e para aprimorar sua experiência no nosso site (por exemplo, para se lembrar dos detalhes de login da sua conta).","pc_yprivacy_text_3":"Pode alterar as suas preferências e recusar o armazenamento de certos tipos de cookies no seu computador enquanto navega no nosso site. Pode também remover todos os cookies já armazenados no seu computador, mas lembre-se de que a exclusão de cookies pode impedir o uso de determinadas áreas no nosso site.","pc_yprivacy_title":"A sua privacidade","privacy_policy":"Política de Privacidade"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Päällä","always_active":"Aina päällä","impressum":"Impressum","inactive":"Pois päältä","nb_agree":"Hyväksyn","nb_changep":"Muuta asetuksiani","nb_ok":"OK","nb_reject":"Kieltäydyn","nb_text":"Käytämme evästeitä ja muita seurantateknologioita parantaaksemme käyttäjäkokemusta verkkosivustollamme, näyttääksemme sinulle personoituja sisältöjä ja mainoksia, analysoidaksemme verkkoliikennettä sekä lisätäksemme ymmärrystämme käyttäjiemme sijainnista.","nb_title":"Käytämme evästeitä","pc_fnct_text_1":"Toiminnallisuusevästeet","pc_fnct_text_2":"Näitä evästeitä käytetään personoidumman käyttäjäkokemuksen luomiseksi sekä valintojesi tallentamiseksi sivustollamme.","pc_fnct_text_3":"Esim. voimme käyttää toiminnallisuusevästeitä muistaaksemme kielivalintasi sekä kirjautumistietosi.","pc_minfo_text_1":"Lisätietoa","pc_minfo_text_2":"Evästeisiin liittyvissä kysymyksissä ole hyvä ja ota meihin yhteyttä.","pc_minfo_text_3":"Lue lisää Tietosuojakäytäntö.","pc_save":"Tallenna asetukseni","pc_sncssr_text_1":"Tärkeät evästeet","pc_sncssr_text_2":"Nämä evästeet mahdollistavat verkkosivustomme palveluiden sekä tiettyjen ominaisuuksien käyttämisen.","pc_sncssr_text_3":"Ilman näitä evästeitä emme voi tarjota sinulle tiettyjä palveluita sivustollamme.","pc_title":"Evästeasetukset","pc_trck_text_1":"Seuranta- ja tehokkuusevästeet","pc_trck_text_2":"Näiden evästeiden avulla kerätään tietoa sivustomme liikenteestä sekä käyttötavoista.","pc_trck_text_3":"Esim. nämä evästeet voivat seurata sitä, paljonko aikaa vietät sivustollamme, mikä auttaa meitä parantamaan sivustomme käyttökokemusta jatkossa.","pc_trck_text_4":"Näiden evästeiden avulla kerätty tietoa ei voida yhdistää yksittäiseen käyttäjään.","pc_trgt_text_1":"Kohdennus- ja mainosevästeet","pc_trgt_text_2":"Näitä evästeitä käytetään näyttämään mainoksia, jotka selauskäytöksesi perusteella todennäköisesti kiinnostavat sinua.","pc_trgt_text_3":"Nämä sisältö- ja/tai mainoskumppanimme tarjoamat evästeet voivat yhdistää sivustoltamme kerättyä tietoa muilta heidän verkostoonsa kuuluvilta sivustoilta kerättyihin tietoihin.","pc_trgt_text_4":"Jos päätät poistaa tai kytkeä pois päältä nämä kohdennus- ja mainosevästeet, näet yhä mainoksia, mutta ne eivät välttämättä ole sinulle oleellisia.","pc_yprivacy_text_1":"Yksityisyytesi on meille tärkeää","pc_yprivacy_text_2":"Evästeet ovat pieniä tekstitiedostoja, jotka tallennetaan laitteeseesi verkkosivulla vieraillessasi. Käytämme evästeitä useaan tarkoitukseen ja parantaaksesi käyttökokemustasi verkkosivustollamme (esim. muistaaksemme kirjautumistietosi).","pc_yprivacy_text_3":"Voit muuttaa asetuksiasi ja kieltää sivustoltamme tiettyjen evästetyyppien tallentamisen laitteellesi. Voit myös poistaa minkä tahansa jo tallennetun evästeen laitteeltasi, mutta huomaathan, että evästeiden poistaminen saattaa estää sinua käyttämästä osaa sivustomme sisällöstä.","pc_yprivacy_title":"Yksityisyytesi","privacy_policy":"Tietosuojakäytäntö"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktív","always_active":"Mindig aktív","impressum":"Impressum","inactive":"Inaktív","nb_agree":"Elfogadom","nb_changep":"Beállítások megváltoztatása","nb_ok":"OK","nb_reject":"Elutasítom","nb_text":"Az oldal sütiket és egyéb nyomkövető technológiákat alkalmaz, hogy javítsa a böngészési élményét, azzal hogy személyre szabott tartalmakat és célzott hirdetéseket jelenít meg, és elemzi a weboldalunk forgalmát, hogy megtudjuk honnan érkeztek a látogatóink.","nb_title":"Az oldal sütiket használ","pc_fnct_text_1":"Funkcionális sütik","pc_fnct_text_2":"Ezeket a sütiket arra használjuk, hogy személyre szabottabb élményt nyújtsunk weboldalunkon, és hogy az oldal rögzítse a webhelyünk használata során tett döntéseket.","pc_fnct_text_3":"Például arra használhatunk funkcionális sütiket, hogy emlékezzünk a nyelvi beállításokra, vagy a bejelentkezési adataira.","pc_minfo_text_1":"Egyéb információk","pc_minfo_text_2":"A sütikre vonatkozó irányelveinkkel és az Ön választásával kapcsolatosan felmerülő bármilyen kérdésével keressen meg bennünket.","pc_minfo_text_3":"Ha többet szeretne megtudni, kérjük, keresse fel a Adatvédelmi irányelvek.","pc_save":"Beállítások mentése","pc_sncssr_text_1":"Feltétlenül szükséges sütik","pc_sncssr_text_2":"Ezek a sütik elengedhetetlenek a weboldalunkon elérhető szolgáltatások nyújtásához, valamint weboldalunk bizonyos funkcióinak használatához.","pc_sncssr_text_3":"A feltétlenül szükséges sütik használata nélkül weboldalunkon nem tudunk bizonyos szolgáltatásokat nyújtani Önnek.","pc_title":"Sütikre beállítási központ","pc_trck_text_1":"Követési és teljesítménnyel kapcsolatos sütik","pc_trck_text_2":"Ezeket a sütiket arra használjuk, hogy információkat gyűjtsünk weboldalunk forgalmáról és látogatóiról, webhelyünk használatának elemzéséhez.","pc_trck_text_3":"Például ezek a sütik nyomon követhetik a webhelyen töltött időt vagy a meglátogatott oldalakat, amely segít megérteni, hogyan javíthatjuk webhelyünket az Ön nagyobb megelégedettségére.","pc_trck_text_4":"Ezekkel a nyomkövető és teljesítménnyel kapcsolatos sütikkel összegyűjtött információk egyetlen személyt sem azonosítanak.","pc_trgt_text_1":"Célirányos és hirdetési sütik","pc_trgt_text_2":"Ezeket a sütiket olyan hirdetések megjelenítésére használjuk, amelyek valószínűleg érdekli Önt a böngészési szokásai alapján.","pc_trgt_text_3":"Ezek a sütik, amelyeket a tartalom és / vagy a reklámszolgáltatók szolgáltatnak, egyesíthetik a weboldalunktól gyűjtött információkat más információkkal, amelyeket önállóan összegyűjtöttek az Ön böngészőjének tevékenységeivel kapcsolatban a webhely-hálózaton keresztül.","pc_trgt_text_4":"Ha Ön úgy dönt, hogy eltávolítja vagy letiltja ezeket a célirányos vagy hirdetési sütiket, akkor is látni fogja a hirdetéseket, de lehet, hogy nem lesznek relevánsak az Ön számára.","pc_yprivacy_text_1":"Az ön adatainak védelem fontos számunkra","pc_yprivacy_text_2":"A sütik egészen kicsi szöveges fájlok, amelyeket a számítógépén tárolnak, amikor meglátogat egy weboldalt. Sütiket használunk különféle célokra, és weboldalunkon az online élmény fokozása érdekében (például a fiókjának bejelentkezési adatainak megjegyzésére).","pc_yprivacy_text_3":"Webhelyünk böngészése közben megváltoztathatja a beállításait, és elutasíthatja a számítógépén tárolni kívánt bizonyos típusú sütik használatát. A számítógépen már tárolt sütiket eltávolíthatja, de ne feledje, hogy a sütik törlése megakadályozhatja weboldalunk egyes részeinek használatát.","pc_yprivacy_title":"Az ön adatai védelme","privacy_policy":"Adatvédelmi irányelvek"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktivno","always_active":"Uvijek aktivno","impressum":"Impressum","inactive":"Neaktivno","nb_agree":"Slažem se","nb_changep":"Promjeni moje postavke","nb_ok":"OK","nb_reject":"Odbijam","nb_text":"Koristimo kolačiće i druge tehnologije praćenja da bismo poboljšali vaše korisničko iskustvo na našoj web stranici, kako bismo vam prikazali personalizirani sadržaj i ciljane oglase, analizirali promet na našoj web stranici i razumjeli odakle dolaze naši posjetitelji.","nb_title":"Mi koristimo kolačiće","pc_fnct_text_1":"Kolačići funkcionalnosti","pc_fnct_text_2":"Ovi se kolačići koriste kako bi vam pružili personalizirano korisničko iskustvo na našoj web stranici i za pamćenje izbora koje napravite kada koristite našu web stranicu.","pc_fnct_text_3":"Na primjer, možemo koristiti kolačiće funkcionalnosti da bismo zapamtili vaše jezične postavke ili upamtili vaše podatke za prijavu.","pc_minfo_text_1":"Više informacija","pc_minfo_text_2":"Za sve upite vezane uz naša pravila o kolačićima i vašim izborima, molimo da nas kontaktirate.","pc_minfo_text_3":"Da bi saznali više, posjetite naša Pravila o privatnosti.","pc_save":"Spremi moje postavke","pc_sncssr_text_1":"Strogo potrebni kolačići","pc_sncssr_text_2":"Ovi su kolačići neophodni za pružanje usluga dostupnih putem naše web stranice i omogućavanje korištenja određenih značajki naše web stranice.","pc_sncssr_text_3":"Bez ovih kolačića ne možemo vam pružiti određene usluge na našoj web stranici.","pc_title":"Centar za postavke kolačića","pc_trck_text_1":"Kolačići za praćenje i performanse","pc_trck_text_2":"Ovi se kolačići koriste za prikupljanje podataka za analizu prometa na našoj web stranici i za informaciju kako posjetitelji koriste našu web stranicu.","pc_trck_text_3":"Na primjer, ti kolačići mogu pratiti stvari poput dugovanja na web stranici ili stranicama koje posjetite što nam pomaže da shvatimo kako možemo poboljšati vaše korisničko iskustvo na našoj web stranici.","pc_trck_text_4":"Informacije prikupljene ovim praćenjem i kolačići izvedbe ne identificiraju nijednog pojedinačnog posjetitelja.","pc_trgt_text_1":"Kolačići za ciljano oglašavanje","pc_trgt_text_2":"Ovi se kolačići koriste za prikazivanje oglasa koji bi vas mogli zanimati na temelju vaših navika pregledavanja web stranica.","pc_trgt_text_3":"Ovi kolačići, posluženi od naših pružatelja sadržaja i / ili oglašavanja, mogu kombinirati podatke koje su prikupili s naše web stranice s drugim podacima koje su neovisno prikupili, a odnose se na aktivnosti vašeg web preglednika kroz njihovu mrežu web stranica.","pc_trgt_text_4":"Ako odlučite ukloniti ili onemogućiti ove kolačiće za ciljano oglašavanje, i dalje ćete vidjeti oglase, ali oni možda nisu relevantni za vas.","pc_yprivacy_text_1":"Vaša privatnost nam je važna","pc_yprivacy_text_2":"Kolačići su vrlo male tekstualne datoteke koje se pohranjuju na vašem računalu kada posjetite web stranicu. Mi koristimo kolačiće za različite svrhe i za poboljšanje vašeg mrežnog iskustva na našoj web stranici (na primjer, za pamćenje podataka za prijavu na vaš korisnički račun).","pc_yprivacy_text_3":"Možete promijeniti svoje postavke i odbiti određene vrste kolačića koji će se pohraniti na vašem računalu tijekom pregledavanja naše web stranice. Također možete ukloniti sve kolačiće koji su već pohranjeni na vašem računalu, ali imajte na umu da vas brisanje kolačića može spriječiti da koristite dijelove naše web stranice.","pc_yprivacy_title":"Vaša privatnost","privacy_policy":"Pravila o privatnosti"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktivní","always_active":"Vždy aktivní","impressum":"Impressum","inactive":"Neaktivní","nb_agree":"Souhlasím","nb_changep":"Upravit mé předvolby","nb_ok":"OK","nb_reject":"Odmítám","nb_text":"Tyto webové stránky používají soubory cookies a další sledovací nástroje s cílem vylepšení uživatelského prostředí, zobrazení přizpůsobeného obsahu a reklam, analýzy návštěvnosti webových stránek a zjištění zdroje návštěvnosti.","nb_title":"Používáme soubory cookies","pc_fnct_text_1":"Cookies pro funkcionality","pc_fnct_text_2":"Tyto soubory cookie se používají k tomu, aby vám na našich webových stránkách poskytovaly personalizovaný uživatelský zážitek a aby si pamatovaly vaše volby, které jste použili při používání našich webových stránek.","pc_fnct_text_3":"Můžeme například používat soubory cookie k zapamatování vašeho jazyka nebo k zapamatování vašich přihlašovacích údajů.","pc_minfo_text_1":"Další informace","pc_minfo_text_2":"V případě jakýchkoliv dotazů ohledně našich zásad týkajících se souborů cookie a vašich možností nás prosím kontaktujte.","pc_minfo_text_3":"Pro více informací navštivte naši stránku Zásady ochrany osobních údajů.","pc_save":"Uložit mé předvolby","pc_sncssr_text_1":"Bezpodmínečně nutné soubory cookies","pc_sncssr_text_2":"Tyto soubory cookies jsou nezbytné k tomu, abychom vám mohli poskytovat služby dostupné prostřednictvím našeho webu a abychom vám umožnili používat určité funkce našeho webu.","pc_sncssr_text_3":"Bez těchto cookies vám nemůžeme na naší webové stránce poskytovat určité služby.","pc_title":"Centrum předvoleb souborů Cookies","pc_trck_text_1":"Sledovací a výkonnostní soubory cookies","pc_trck_text_2":"Tyto soubory cookies se používají ke shromažďování informací pro analýzu provozu na našich webových stránkách a sledování používání našich webových stránek uživateli.","pc_trck_text_3":"Tyto soubory cookies mohou například sledovat věci jako je doba kterou na webu trávíte, nebo stránky, které navštěvujete, což nám pomáhá pochopit, jak pro vás můžeme vylepšit náš web.","pc_trck_text_4":"Informace shromážděné prostřednictvím těchto sledovacích a výkonnostních cookies neidentifikují žádné osoby.","pc_trgt_text_1":"Cookies pro cílení a reklamu","pc_trgt_text_2":"Tyto soubory cookie se používají k zobrazování reklamy, která vás pravděpodobně bude zajímat na základě vašich zvyků při procházení.","pc_trgt_text_3":"Tyto soubory cookie, jsou požadovány námi/nebo poskytovateli reklam, mohou kombinovat informace shromážděné z našich webových stránek s dalšími informacemi, které nezávisle shromáždily z jiných webových stránek, týkající se činností vašeho internetového prohlížeče v rámci jejich reklamní sítě webových stránek.","pc_trgt_text_4":"Pokud se rozhodnete tyto soubory cookies pro cílení nebo reklamu odstranit nebo deaktivovat, budou se vám reklamy stále zobrazovat, ale nemusí pro vás být nadále personalizované a relevantní.","pc_yprivacy_text_1":"Vaše soukromí je pro nás důležité","pc_yprivacy_text_2":"Soubory cookies jsou velmi malé textové soubory, které se ukládají do vašeho zařízení při navštěvování webových stránek. Soubory Cookies používáme pro různé účely a pro vylepšení vašeho online zážitku na webové stránce (například pro zapamatování přihlašovacích údajů k vašemu účtu).","pc_yprivacy_text_3":"Při procházení našich webových stránek můžete změnit své předvolby a odmítnout určité typy cookies, které se mají ukládat do vašeho počítače. Můžete také odstranit všechny soubory cookie, které jsou již uloženy ve vašem počítači, ale mějte na paměti, že odstranění souborů cookie vám může zabránit v používání částí našeho webu.","pc_yprivacy_title":"Vaše soukromí","privacy_policy":"Zásady ochrany osobních údajů"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktiv","always_active":"Altid aktiv","impressum":"Impressum","inactive":"Inaktiv","nb_agree":"Jeg accepterer","nb_changep":"Skift indstillinger","nb_ok":"OK","nb_reject":"Jeg nægter","nb_text":"Vi bruger cookies og andre tracking teknologier for at forbedre din oplevelse på vores website, til at vise personaliseret indhold, målrettede annoncer og til at forstå hvor vores besøgende kommer fra.","nb_title":"Vi bruger cookies","pc_fnct_text_1":"Funktions cookies","pc_fnct_text_2":"Disse cookies anvendes for at kunne give dig en personaliseret oplevelse af vores hjemmeside, og for at kunne huske valg du har truffet.","pc_fnct_text_3":"Eksempelvis kan vi bruge funktions cookies til at huske sprog-indstillinger eller dine login informationer.","pc_minfo_text_1":"Mere information","pc_minfo_text_2":"Har du spørgsmål vedr. vores cookiepolitik og dine valgmuligheder, så kontakt os venligst.","pc_minfo_text_3":"For at finde ud af mere, så læs venligst vores Fortrolighedspolitik.","pc_save":"Gem mine indstillinger","pc_sncssr_text_1":"Nødvendige cookies","pc_sncssr_text_2":"Disse Cookies er essentielle for at du kan bruge vores hjemmeside.","pc_sncssr_text_3":"Uden disse cookies kan vi ikke garantere vores hjemmeside virker ordentligt.","pc_title":"Cookie indstillinger","pc_trck_text_1":"Tracking og performance cookies","pc_trck_text_2":"Disse cookies anvendes til at analysere besøg på vores hjemmeside, og hvordan du bruger vores hjemmeside.","pc_trck_text_3":"Eksempelvis kan vi tracke hvor lang tid du bruger hjemmesiden, eller hvilke sider du kigger på. Det hjælper os til at forstå hvordan vi kan forbedre hjemmesiden.","pc_trck_text_4":"Informationerne kan ikke identificere dig som individ og er derfor anonyme.","pc_trgt_text_1":"Målretning og annoncecookies","pc_trgt_text_2":"Disse cookies anvendes for at kunne vise annoncer, som sandsynligvis er interessante for dig, baseret på dine browser profil.","pc_trgt_text_3":"Disse cookies, som sættes af vores indhold og/eller annoncepartnere, kan kombinere information fra flere hjemmesider i hele det netværk som partnerne styrer.","pc_trgt_text_4":"Hvis du deaktiverer denne indstilling vil du fortsat se reklamer, men de vil ikke længere være målrettet til dig.","pc_yprivacy_text_1":"Dit privatliv er vigtigt for os","pc_yprivacy_text_2":"Cookies er en lille tekstfil, som gemmes på din computer, når du besøger et website. Vi bruger cookies til en række formål, og for at forbedre din oplevelse på vores website (eksempelvis for at huske dine login oplysninger).","pc_yprivacy_text_3":"Du kan ændre dine indstillinger og afvise forskellige typer cookies, som gemmes på din computer, når du besøger vores website. Du kan også fjerne cookies som allerede er gemt på din computer, men bemærk venligst at sletning af cookies kan betyde der er dele af hjemmesiden som ikke virker.","pc_yprivacy_title":"Dit privatliv","privacy_policy":"Fortrolighedspolitik"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Active","always_active":"Întotdeauna active","impressum":"Impressum","inactive":"Inactive","nb_agree":"Sunt de acord","nb_changep":"Vreau să schimb setările","nb_ok":"OK","nb_reject":"Refuz","nb_text":"Folosim cookie-uri și alte tehnologii de urmărire pentru a îmbunătăți experiența ta de navigare pe website-ul nostru, pentru afișa conținut și reclame personalizate, pentru a analiza traficul de pe website-ul nostru și pentru a înțelege de unde vin vizitatorii noștri.","nb_title":"Folosim cookie-uri","pc_fnct_text_1":"Cookie-uri funcționale","pc_fnct_text_2":"Aceste cookie-uri sunt folosite pentru a-ți asigura o experiență personalizată pe website-ul nostru și pentru salvarea alegerilor pe care le faci când folosești website-ul nostru.","pc_fnct_text_3":"De exemplu, putem folosi cookie-uri funcționale pentru a salva preferințele tale legate de limba website-ului nostru sau datele de logare.","pc_minfo_text_1":"Mai multe informații","pc_minfo_text_2":"Pentru mai multe informații cu privire la politica noastră de cookie-uri și preferințele tale, te rugăm să ne contactezi.","pc_minfo_text_3":"Pentru a afla mai multe, te rugăm să citești Politica noastră de confidențialitate.","pc_save":"Salvează","pc_sncssr_text_1":"Cookie-uri strict necesare","pc_sncssr_text_2":"Aceste cookie-uri sunt esențiale pentru a putea beneficia de serviciile disponibile pe website-ul nostru.","pc_sncssr_text_3":"Fără aceste cookie-uri nu poți folosi anumite funcționalități ale website-ului nostru.","pc_title":"Preferințe pentru Cookie-uri","pc_trck_text_1":"Cookie-uri de analiză și performanță","pc_trck_text_2":"Acest tip de cookie-uri sunt folosite pentru a colecta informații în vederea analizării traficului pe website-ul nostru și modul în care vizitatorii noștri folosesc website-ul.","pc_trck_text_3":"De exemplu, aceste cookie-uri pot urmări cât timp petreci pe website sau paginile pe care le vizitezi, ceea ce ne ajută să înțelegem cum putem îmbunătăți website-ul pentru tine.","pc_trck_text_4":"Informațiile astfel colectate nu identifică individual vizitatorii.","pc_trgt_text_1":"Cookie-uri pentru marketing și publicitate","pc_trgt_text_2":"Aceste cookie-uri sunt folosite pentru a-ți afișa reclame cât mai pe interesul tău, în funcție de obiceiurile tale de navigare.","pc_trgt_text_3":"Aceste cookie-uri, așa cum sunt afișate de furnizori noștri de conținut și/sau publicitate, pot combina informații de pe website-ul nostru cu alte informații pe care furnizori noștri le-au colectat în mod independent cu privire la activitatea ta în rețeaua lor de website-uri.","pc_trgt_text_4":"Dacă alegi să ștergi sau să dezactivezi aceste cookie-uri tot vei vedea reclame, dar se poate ca aceste reclame să nu fie relevante pentru tine.","pc_yprivacy_text_1":"Confidențialitatea ta este importantă pentru noi","pc_yprivacy_text_2":"Cookie-urile sunt fișiere text foarte mici ce sunt salvate în browser-ul tău atunci când vizitezi un website. Folosim cookie-uri pentru mai multe scopuri, dar și pentru a îți oferi cea mai bună experiență de utilizare posibilă (de exemplu, să reținem datele tale de logare în cont).","pc_yprivacy_text_3":"Îți poți modifica preferințele și poți refuza ca anumite tipuri de cookie-uri să nu fie salvate în browser în timp ce navigezi pe website-ul nostru. Deasemenea poți șterge cookie-urile salvate deja în browser, dar reține că este posibil să nu poți folosi anumite părți ale website-ul nostru în acest caz.","pc_yprivacy_title":"Confidențialitatea ta","privacy_policy":"Politica noastră de confidențialitate"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktívne","always_active":"Vždy aktívne","impressum":"Impressum","inactive":"Neaktívne","nb_agree":"Súhlasím","nb_changep":"Zmeniť moje nastavenia","nb_ok":"OK","nb_reject":"Odmietam","nb_text":"Súbory cookie a ďalšie technológie sledovania používame na zlepšenie vášho zážitku z prehliadania našich webových stránok, na to, aby sme vám zobrazovali prispôsobený obsah a cielené reklamy, na analýzu návštevnosti našich webových stránok a na pochopenie toho, odkiaľ naši návštevníci prichádzajú.","nb_title":"Používame cookies","pc_fnct_text_1":"Funkčné cookies","pc_fnct_text_2":"Tieto súbory cookie sa používajú na to, aby vám poskytli osobnejšie prostredie na našej webovej stránke, a na zapamätanie si rozhodnutí, ktoré urobíte pri používaní našej webovej stránky.","pc_fnct_text_3":"Napríklad môžeme použiť funkčné cookies na zapamätanie vašich jazykových preferencií alebo na zapamätanie vašich prihlasovacích údajov.","pc_minfo_text_1":"Viac informácií","pc_minfo_text_2":"Ak máte akékoľvek otázky týkajúce sa našich zásad týkajúcich sa súborov cookie a vašich možností, kontaktujte nás.","pc_minfo_text_3":"Ak sa chcete dozvedieť viac, navštívte Zásady ochrany osobných údajov.","pc_save":"Ulož moje predvoľby","pc_sncssr_text_1":"Nevyhnutne potrebné cookies","pc_sncssr_text_2":"Tieto súbory cookie sú nevyhnutné na to, aby sme vám mohli poskytovať služby dostupné prostredníctvom našej webovej stránky a aby ste mohli používať určité funkcie našej webovej stránky.","pc_sncssr_text_3":"Bez týchto súborov cookie vám nemôžeme poskytnúť určité služby na našom webe.","pc_title":"Centrum predvolieb cookies","pc_trck_text_1":"Sledovacie a výkonnostné cookies","pc_trck_text_2":"Tieto súbory cookie sa používajú na zhromažďovanie informácií na analýzu prenosu na našom webe a toho, ako návštevníci používajú náš web.","pc_trck_text_3":"Tieto súbory cookie môžu napríklad sledovať napríklad to, koľko času strávite na webových stránkach alebo navštívených stránkach, čo nám pomáha pochopiť, ako môžeme pre vás vylepšiť naše webové stránky.","pc_trck_text_4":"Informácie zhromaždené prostredníctvom týchto súborov cookie na sledovanie a výkonnosť neidentifikujú žiadneho jednotlivého návštevníka.","pc_trgt_text_1":"Zacielenie a reklamné cookies","pc_trgt_text_2":"Tieto súbory cookie sa používajú na zobrazovanie reklám, ktoré by vás mohli pravdepodobne zaujímať na základe vašich zvykov pri prehliadaní.","pc_trgt_text_3":"Tieto súbory cookie, ktoré slúžia pre náš obsah a/alebo poskytovateľov reklám, môžu kombinovať informácie zhromaždené z našej webovej stránky s ďalšími informáciami, ktoré nezávisle zhromaždili, týkajúce sa aktivít vášho webového prehliadača v rámci ich siete webových stránok.","pc_trgt_text_4":"Ak sa rozhodnete odstrániť alebo zakázať tieto súbory cookie pre zacielenie alebo reklamu, stále sa vám budú zobrazovať reklamy, ktoré však pre vás nemusia byť relevantné.","pc_yprivacy_text_1":"Vaše súkromie je pre nás dôležité","pc_yprivacy_text_2":"Súbory cookie sú veľmi malé textové súbory, ktoré sa ukladajú do vášho počítača pri návšteve webovej stránky. Súbory cookie používame na rôzne účely a na zlepšenie vášho online zážitku z našej webovej stránky (napríklad na zapamätanie prihlasovacích údajov vášho účtu).","pc_yprivacy_text_3":"Môžete zmeniť svoje predvoľby a odmietnuť určité typy súborov cookie, ktoré sa majú ukladať vo vašom počítači pri prehliadaní našich webových stránok. Môžete tiež odstrániť všetky súbory cookie, ktoré sú už uložené vo vašom počítači, ale nezabudnite, že vymazanie súborov cookie vám môže zabrániť v používaní častí našej webovej stránky.","pc_yprivacy_title":"Vaše súkromie","privacy_policy":"Zásady ochrany osobných údajov"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktivni","always_active":"Vedno aktivni","impressum":"Impressum","inactive":"Neaktivni","nb_agree":"Se strinjam","nb_changep":"Spremeni moje nastavitve","nb_ok":"V redu","nb_reject":"Zavračam","nb_text":"Piškotke in druge sledilne tehnologije uporabljamo za izboljšanje vaše uporabniške izkušnje med brskanjem po naši spletni strani, za prikazovanje personaliziranih vsebin oz. targetiranih oglasov, za analizo obiskov naše spletne strani in za vpogled v to, iz kje prihajajo naši gostje.","nb_title":"Uporabljamo piškotke","pc_fnct_text_1":"Funkcionalni piškotki (ang. functionality cookies)","pc_fnct_text_2":"Ti piškotki se uporabljajo za zagotavljanje bolj personalizirane izkušnje na naši spletni strani in za shranjevanje vaših odločitev ob uporabi naše spletne strani.","pc_fnct_text_3":"Funkcionalne piškotke lahko, na primer, uporabljamo za to, da si zapomnimo vaše jezikovne nastavitve oz. podatke za vpis v vaš račun.","pc_minfo_text_1":"Več informacij","pc_minfo_text_2":"Če imate kakršnakoli vprašanja v zvezi z našim pravilnikom o piškotkih in vaših izbirah, nas prosim kontaktirajte.","pc_minfo_text_3":"Za več informacij si prosim oglejte naš Politika zasebnosti.","pc_save":"Shrani moje nastavitve","pc_sncssr_text_1":"Nujno potrebni piškotki (ang. strictly necessary cookies)","pc_sncssr_text_2":"Ti piškotki so ključnega pomena pri zagotavljanju storitev, ki so na voljo na naši spletni strani, in pri omogočanju določenih funkcionalnosti naše spletne strani.","pc_sncssr_text_3":"Brez teh piškotkov vam ne moremo zagotoviti določenih storitev na naši spletni strani.","pc_title":"Nastavitve piškotkov","pc_trck_text_1":"Sledilni in izvedbeni piškotki (ang. tracking and performance cookies)","pc_trck_text_2":"Ti piškotki se uporabljajo za zbiranje podatkov za analizo obiskov naše spletne strani in vpogled v to, kako gostje uporabljajo našo spletno stran.","pc_trck_text_3":"Ti piškotki lahko, na primer, spremljajo stvari kot so to, koliko časa preživite na naši spletni strani oz. katere strani obiščete, kar nam pomaga pri razumevanju, kako lahko za vas izboljšamo spletno stran.","pc_trck_text_4":"Podatki, ki jih zbirajo ti piškotki, ne identificirajo nobenega posameznega uporabnika.","pc_trgt_text_1":"Ciljni in oglaševalski piškotki (ang. targeting and advertising cookies)","pc_trgt_text_2":"Ti piškotki se uporabljajo za prikazovanje spletnih oglasov, ki vas bodo na podlagi vaših navad pri brskanju verjetno zanimali.","pc_trgt_text_3":"Ti piškotki, ki jih uporabljajo naši oglaševalski ponudniki oz. ponudniki vsebine, lahko združujejo podatke, ki so jih zbrali na naši spletni strani, z drugimi podatki, ki so jih zbrali neodvisno v povezavi z dejavnostmi vašega spletnega brskalnika na njihovi mreži spletnih mest.","pc_trgt_text_4":"Če se odločite izbrisati oz. onemogočiti te ciljne in oglaševalske piškotke, boste še vedno videvali oglase, vendar ti morda ne bodo relevantni za vas.","pc_yprivacy_text_1":"Cenimo vašo zasebnost","pc_yprivacy_text_2":"Piškotki so majhne besedilne datoteke, ki se shranijo na vašo napravo ob obisku spletne strani. Piškotke uporabljamo v več namenov, predvsem pa za izboljšanje vaše spletne izkušnje na naši strani (na primer za shranjevanje podatkov ob vpisu v vaš račun).","pc_yprivacy_text_3":"Vaše nastavitve lahko spremenite in onemogočite določenim vrstam piškotkov, da bi se shranili na vašo napravo med brskanjem po naši spletni strani. Poleg tega lahko odstranite katerekoli piškotke, ki so že shranjeni v vaši napravi, a upoštevajte, da vam bo po izbrisu piškotkov morda onemogočeno uporabljati dele naše spletne strani.","pc_yprivacy_title":"Vaša zasebnost","privacy_policy":"Politika zasebnosti"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktywne","always_active":"Zawsze aktywne","impressum":"Impressum","inactive":"Nieaktywne","nb_agree":"Zgoda","nb_changep":"Zmiana ustawień","nb_ok":"OK","nb_reject":"Odmawiam","nb_text":"Używamy plików cookie i innych technologii śledzenia, aby poprawić jakość przeglądania naszej witryny, wyświetlać spersonalizowane treści i reklamy, analizować ruch w naszej witrynie i wiedzieć, skąd pochodzą nasi użytkownicy.","nb_title":"Używamy plików cookie","pc_fnct_text_1":"Funkcjonalne","pc_fnct_text_2":"Te pliki cookie służą do bardziej spersonalizowanego korzystania z naszej strony internetowej i do zapamiętywania wyborów dokonywanych podczas korzystania z naszej strony internetowej.","pc_fnct_text_3":"Na przykład możemy używać funkcjonalnych plików cookie do zapamiętywania preferencji językowych lub zapamiętywania danych logowania.","pc_minfo_text_1":"Więcej informacji","pc_minfo_text_2":"W przypadku jakichkolwiek pytań dotyczących naszej polityki dotyczącej plików cookie i Twoich wyborów, skontaktuj się z nami.","pc_minfo_text_3":"Aby dowiedzieć się więcej, odwiedź naszą Polityka prywatności.","pc_save":"Zapisz ustawienia","pc_sncssr_text_1":"Niezbędne","pc_sncssr_text_2":"Te pliki cookie są niezbędne do świadczenia usług dostępnych za pośrednictwem naszej strony internetowej i umożliwienia korzystania z niektórych funkcji naszej strony internetowej.","pc_sncssr_text_3":"Bez tych plików cookie nie możemy zapewnić usług na naszej stronie internetowej.","pc_title":"Centrum ustawień cookie","pc_trck_text_1":"Śledzenie i wydajność","pc_trck_text_2":"Te pliki cookie służą do zbierania informacji w celu analizy ruchu na naszej stronie internetowej i sposobu, w jaki użytkownicy korzystają z naszej strony internetowej.","pc_trck_text_3":"Na przykład te pliki cookie mogą śledzić takie rzeczy, jak czas spędzony na stronie lub odwiedzane strony, co pomaga nam zrozumieć, w jaki sposób możemy ulepszyć naszą witrynę internetową.","pc_trck_text_4":"Informacje zebrane przez te pliki nie identyfikują żadnego konkretnego użytkownika.","pc_trgt_text_1":"Targeting i reklama","pc_trgt_text_2":"Te pliki cookie służą do wyświetlania reklam, które mogą Cię zainteresować na podstawie Twoich zwyczajów przeglądania.","pc_trgt_text_3":"Pliki te tworzone przez naszych dostawców treści i/lub reklam, mogą łączyć informacje zebrane z naszej strony z innymi informacjami, które gromadzili niezależnie w związku z działaniami przeglądarki internetowej w ich sieci witryn.","pc_trgt_text_4":"Jeśli zdecydujesz się usunąć lub wyłączyć te pliki cookie, reklamy nadal będą wyświetlane, ale mogą one nie być odpowiednie dla Ciebie.","pc_yprivacy_text_1":"Twoja prywatność jest dla nas ważna","pc_yprivacy_text_2":"Pliki cookie to bardzo małe pliki tekstowe, które są tworzone i przechowywane na komputerze użytkownika podczas odwiedzania strony internetowej. Używamy plików cookie do różnych celów, w tym do ulepszania obsługi online na naszej stronie internetowej (na przykład, aby zapamiętać dane logowania do konta).","pc_yprivacy_text_3":"Możesz zmienić swoje ustawienia i odrzucić niektóre rodzaje plików cookie, które mają być przechowywane na twoim komputerze podczas przeglądania naszej strony. Możesz również usunąć wszystkie pliki cookie już zapisane na komputerze, ale pamiętaj, że usunięcie plików cookie może uniemożliwić korzystanie z części naszej strony internetowej.","pc_yprivacy_title":"Twoja prywatność","privacy_policy":"Polityka prywatności"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktivno","always_active":"Uvek aktivno","impressum":"Impressum","inactive":"Neaktivno","nb_agree":"Slažem se","nb_changep":"Promeni moja podešavanja","nb_ok":"OK","nb_reject":"Odbijam","nb_text":"Mi koristimo kolačiće i ostale tehnologije za praćenje kako bismo unapredili vašu pretragu na našem veb sajtu, prikazali personalizovani sadržaj i ciljane reklame, analizirali posete na našem sajtu i razumeli odakle dolaze naši posetioci sajta.","nb_title":"Mi koristimo kolačiće","pc_fnct_text_1":"Funkcionalni kolačići","pc_fnct_text_2":"Ovi kolačići koriste se za pružanje personalizovanijeg iskustva na našem veb sajtu i za pamćenje izbora koje pravite kada koristite naš veb sajt.","pc_fnct_text_3":"Na primer, možemo da koristimo funkcionalne kolačiće da bismo zapamtili vaše jezičke postavke ili vaše podatke za prijavu.","pc_minfo_text_1":"Više informacija","pc_minfo_text_2":"Za bilo koja pitanja vezana za našu politiku o kolačićma i vašim izborima, molimo vas kontaktirajte nas.","pc_minfo_text_3":"Da saznate više, pogledajte našu Pravila o privatnosti.","pc_save":"Sačuvaj moja podešavanja","pc_sncssr_text_1":"Obavezni kolačići","pc_sncssr_text_2":"Ovi kolačići su neophodni za pružanje usluga dostupnih putem našeg veb sajta i za omogućavanje korišćenja određenih funkcija našeg veb sajta.","pc_sncssr_text_3":"Bez ovih kolačića ne možemo vam pružiti određene usluge na našem veb sajtu.","pc_title":"Centar za podešavanje kolačića","pc_trck_text_1":"Kolačići za praćenje i performanse","pc_trck_text_2":"Ovi kolačići koriste se za prikupljanje informacija za analizu saobraćaja na našem veb sajtu i kako posetioci koriste naš veb sajt.","pc_trck_text_3":"Na primer, ovi kolačići mogu pratiti stvari poput vremena koliko provodite na veb stranici ili stranicama koje posećujete što nam pomaže da shvatimo kako možemo da poboljšamo naš veb sajt.","pc_trck_text_4":"Informacije prikupljene ovim kolačićima za praćenje i performanse ne identifikuju nijednog pojedinačnog posetioca.","pc_trgt_text_1":"Kolačići za ciljanje i oglašavanje","pc_trgt_text_2":"Ovi kolačići koriste se za prikazivanje reklama koje će vas verovatno zanimati na osnovu vaših navika pregledanja.","pc_trgt_text_3":"Ovi kolačići, opsluženi od strane naših dobavljača sadržaja i / ili oglašavanja, mogu kombinovati informacije koje su sakupili sa našeg veb sajta sa drugim informacijama koje su nezavisno prikupili u vezi sa aktivnostima vašeg veb pretraživača kroz mrežu njihovih veb sajtova.","pc_trgt_text_4":"Ako odlučite da uklonite ili onemogućite ove ciljane ili reklamne kolačiće i dalje ćete videti reklame, ali one možda neće biti relevantne za vas.","pc_yprivacy_text_1":"Vaša privatnost je važna za nas","pc_yprivacy_text_2":"Kolačići su veoma mali tekstualni fajlovi koji su sačuvani na vašem računaru kada posetite veb sajt. Mi koristimo kolačiće za različite svrhe i kako bi unapredili vaše onlajn iskustvo na našem veb sajtu (na primer, kako bi zapamtili vaše pristupne podatke).","pc_yprivacy_text_3":"Vi možete promeniti vaša podešavanja i odbiti određenu vrstu kolačića koji će biti sačuvani na vašem računaru dok pregledate naš veb sajt. Takođe možete izbrisati bilo koje kolačiće koji su već sačuvani u vašem računaru, ali imajte na umu da brisanjem kolačića možete onemogućiti pristup nekim delovima našeg veb sajta.","pc_yprivacy_title":"Vaša privatnost","privacy_policy":"Pravila o privatnosti"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Įjungta","always_active":"Visada įjungta","impressum":"Impressum","inactive":"Išjungta","nb_agree":"Sutinku","nb_changep":"Keisti mano pasirinkimus","nb_ok":"Gerai","nb_reject":"Aš atsisakau","nb_text":"Mes naudojame slapukus ir kitas stebėjimo technologijas, siekdami pagerinti jūsų naršymo mūsų svetainėje patirtį, parodyti jums pritaikytą turinį ir tikslinius skelbimus, išanalizuoti mūsų svetainės srautą ir suprasti, iš kur ateina mūsų lankytojai.","nb_title":"Mes naudojame slapukus","pc_fnct_text_1":"Funkcionalumo slapukai","pc_fnct_text_2":"Šie slapukai naudojami siekiant suteikti jums asmeniškesnę patirtį mūsų svetainėje ir prisiminti pasirinkimus, kuriuos atlikote, kai naudojatės mūsų svetaine.","pc_fnct_text_3":"Pvz., Mes galime naudoti funkcinius slapukus, kad prisimintume jūsų kalbos nustatymus arba prisimintume jūsų prisijungimo duomenis.","pc_minfo_text_1":"Daugiau informacijos","pc_minfo_text_2":"Dėl bet kokių klausimų, susijusių su mūsų slapukų politika ir jūsų pasirinkimais, susisiekite su mumis.","pc_minfo_text_3":"Norėdami sužinoti daugiau, susipažinkite su mūsų Privatumo politika.","pc_save":"Išsaugoti mano pasirinkimus","pc_sncssr_text_1":"Privalomi slapukai","pc_sncssr_text_2":"Šie slapukai yra būtini norint suteikti jums paslaugas, pasiekiamas mūsų svetainėje, ir leisti naudotis tam tikromis mūsų svetainės funkcijomis.","pc_sncssr_text_3":"Be šių slapukų mes negalime jums suteikti tam tikrų paslaugų mūsų svetainėje.","pc_title":"Slapukų Pasirinkimo Centras","pc_trck_text_1":"Stebėjimo ir našumo slapukai","pc_trck_text_2":"Šie slapukai naudojami rinkti informaciją, siekiant analizuoti srautą į mūsų svetainę ir tai, kaip lankytojai naudojasi mūsų svetaine.","pc_trck_text_3":"Pavyzdžiui, šie slapukai gali sekti kiek laiko praleidžiate svetainėje ar lankomuose puslapiuose, o tai padeda mums suprasti, kaip galime patobulinti savo svetainę.","pc_trck_text_4":"Informacija, surinkta naudojant šiuos stebėjimo ir našumo slapukus, neatpažįsta konkretaus lankytojo.","pc_trgt_text_1":"Tiksliniai ir reklaminiai slapukai","pc_trgt_text_2":"Šie slapukai naudojami rodyti reklamą, kuri greičiausiai jus domina, atsižvelgiant į jūsų naršymo įpročius.","pc_trgt_text_3":"Šie slapukai, kuriuos teikia mūsų turinio ir (arba) reklamos teikėjai, gali apjungti informaciją, kurią jie surinko iš mūsų svetainės, su kita informacija, kurią jie rinko nepriklausomai, apie jūsų interneto naršyklės veiklą jų svetainių tinkle.","pc_trgt_text_4":"Jei nuspręsite pašalinti arba išjungti šiuos tikslinius ar reklamavimo slapukus, vis tiek pamatysite skelbimus, tačiau jie gali būti jums neaktualūs.","pc_yprivacy_text_1":"Mums rūpi jūsų privatumas","pc_yprivacy_text_2":"Slapukai yra labai maži tekstiniai failai, kurie saugomi jūsų kompiuteryje, kai apsilankote svetainėje. Mes naudojame slapukus įvairiais tikslais ir siekdami pagerinti jūsų internetinę patirtį mūsų svetainėje (pavyzdžiui, jei norite, kad būtu įsimenami jūsų prisijungimo duomenys).","pc_yprivacy_text_3":"Naršydami mūsų svetainėje galite pakeisti savo nustatymus ir atsisakyti tam tikrų tipų slapukų, kurie bus saugomi jūsų kompiuteryje. Taip pat galite pašalinti visus slapukus, jau saugomus jūsų kompiuteryje, tačiau nepamirškite, kad ištrynę slapukus galite nepilnai naudotis mūsų svetaine.","pc_yprivacy_title":"Jūsų privatumas","privacy_policy":"Privatumo politika"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktīvs","always_active":"Vienmēr aktīvs","impressum":"Impressum","inactive":"Neaktīvs","nb_agree":"Es piekrītu","nb_changep":"Mainīt manas preferences","nb_ok":"OK","nb_reject":"Es noraidu","nb_text":"Mēs izmantojam sīkdatnes un citas izsekošanas tehnoloģijas, lai uzlabotu Jūsu pārlūkošanas pieredzi mūsu vietnē, parādītu Jums personalizētu saturu un mērķētas reklāmas, analizētu mūsu vietnes datplūsmu un saprastu, no kurienes nāk mūsu apmeklētāji.","nb_title":"Mēs izmantojam sīkdatnes","pc_fnct_text_1":"Funkcionalitātes sīkdatnes","pc_fnct_text_2":"Šīs sīkdatnes tiek izmantotas, lai Jūs nodrošinātu ar personalizētu pieredzi mūsu mājaslapā un lai atcerētos izvēles, kuras veicat izmantojot mūsu mājaslapu.","pc_fnct_text_3":"Piemēram, mēs varam izmantot funkcionalitātes sīkdatnes, lai atcerētos Jūsu valodas preferences vai konta pieteikšanās datus.","pc_minfo_text_1":"Vairāk informācijas","pc_minfo_text_2":"Par jautājumiem saistītiem ar mūsu sīkdatņu politiku un Jūsu izvēlēm, lūdzu, sazinieties ar mums.","pc_minfo_text_3":"Lai uzzinātu vairāk, lūdzu apmeklējiet mūsu Privacy Policy.","pc_save":"Saglabāt manas preferences","pc_sncssr_text_1":"Strikti nepieciešamās sīkdatnes","pc_sncssr_text_2":"Šīs sīkdatnes ir nepieciešamas, lai nodrošinātu Jums pakalpojumus, kas pieejami caur mūsu mājaslapu un ļautu Jums izmantot noteiktas mūsu vietnes funkcijas.","pc_sncssr_text_3":"Bez šīm sīkdatnēm, mēs nevaram Jums nodrošināt noteiktus pakalpojumus mūsu mājaslapā.","pc_title":"Sīkdatņu Preferenču Centrs","pc_trck_text_1":"Izsekošanas sīkdatnes","pc_trck_text_2":"Šīs sīkdatnes tiek izmantotas informācijas apkopošanai, lai analizētu mūsu mājaslapas datplūsmu, un kā apmeklētāji izmanto mūsu mājaslapu.","pc_trck_text_3":"Piemēram, šīs sīkdatnes var izsekot cik daudz laika Jūs pavadāt mājaslapā vai Jūsu apmeklētās lapas, kas mums palīdz saprast, kā mēs Jums varam uzlabot mūsu mājaslapu.","pc_trck_text_4":"Informācija, kas savākta, izmantojot šīs izsekošanas un veiktspējas sīkdatnes, neidentificē nevienu atsevišķu apmeklētāju.","pc_trgt_text_1":"Mērķauditorijas atlases un reklāmas sīkdatnes","pc_trgt_text_2":"Šīs sīkdatnes tiek izmantotas, lai rādītu reklāmas, kas iespējams, Jūs interesēs, pamatojoties uz Jūsu pārlūkošanas paradumiem.","pc_trgt_text_3":"Šīs sīkdatnes, ko apkalpo mūsu satura un/vai reklāmas nodrošinātāji, var apvienot informāciju , kas savākta no mūsu mājaslapas ar citu viņu rīcībā esošo informāciju, ko viņi ir neatkarīgi apkopojuši, kas saistīta ar Jūsu tīmekļa pārlūkprogrammas darbību viņu vietņu tīklā.","pc_trgt_text_4":"Ja Jūs izvēlaties noņemt vai atspējot šīs mērķauditorijas atlases vai reklāmas sīkdatnes, Jūs joprojām redzēsiet reklāmas, bet tās var nebūt Jums aktuālas.","pc_yprivacy_text_1":"Mums ir svarīgs Jūsu privātums","pc_yprivacy_text_2":"Sīkdatnes ir ļoti mazi teksta faili, kas tiek saglabāti Jūsu datorā, kad apmeklējat mājaslapu. Mēs izmantojam sīkdatnes dažādiem mērķiem, un lai uzlabotu Jūsu tiešsaistes pieredzi mūsu mājaslapā (piemēram, lai atcerētos Jūsu konta pieteikšanās datus).","pc_yprivacy_text_3":"Jūs varat mainīt savas preferences un noraidīt noteiktus sīkfailu veidus, kas saglabātos Jūsu datorā, pārlūkojot mūsu mājaslapu. Jūs varat arī noņemt sīkfailus, kas jau ir saglabāti Jūsu datorā, taču paturiet prātā, ka sīkdatņu dzēšana var liegt Jums izmantot atsevišķas daļas no mūsu mājaslapas.","pc_yprivacy_title":"Jūsu privātums","privacy_policy":"Privacy Policy"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Активно","always_active":"Всегда активно","impressum":"Impressum","inactive":"Неактивно","nb_agree":"Я согласен","nb_changep":"Изменить мои предпочтения","nb_ok":"Ок","nb_reject":"Я отказываюсь","nb_text":"Мы используем файлы куки и другие технологии отслеживания для улучшения вашего просмотра на нашем веб-сайте, чтобы показывать вам персонализированный контент и таргетированную рекламу, анализировать трафик нашего веб-сайта и понимать, откуда приходят наши посетители.","nb_title":"Мы используем куки","pc_fnct_text_1":"Функциональные куки","pc_fnct_text_2":"Файлы куки используются, чтобы предоставить вам более персонализированный опыт на нашем веб-сайте и запомнить выбор, который вы делаете при использовании нашего веб-сайта.","pc_fnct_text_3":"Например, мы можем использовать функциональные файлы куки, чтобы запомнить ваши языковые предпочтения или данные для входа.","pc_minfo_text_1":"Больше информации.","pc_minfo_text_2":"По любым вопросам, касающимся нашей политики в отношении файлов куки и вашего выбора, свяжитесь с нами.","pc_minfo_text_3":"Чтобы узнать больше, посетите наш сайт Privacy Policy.","pc_save":"Сохранить мои предпочтения","pc_sncssr_text_1":"Необходимые куки","pc_sncssr_text_2":"Файлы куки необходимы для предоставления вам услуг, доступных через наш веб-сайт, и для того, чтобы вы могли использовать определенные функции нашего веб-сайта.","pc_sncssr_text_3":"Без этих файлов куки мы не можем предоставлять вам определенные функции на нашем веб-сайте.","pc_title":"Центр настроек файлов куки","pc_trck_text_1":"Отслеживание куки","pc_trck_text_2":"Файлы куки используются для сбора информации для анализа трафика на наш веб-сайт и того, как посетители используют наш веб-сайт.","pc_trck_text_3":"Например, эти файлы куки могут отслеживать такие вещи, как время, которое вы проводите на веб-сайте или посещаемые вами страницы, что помогает нам понять, как мы можем улучшить наш веб-сайт для вас.","pc_trck_text_4":"Информация, собранная с помощью файлов куки для отслеживания и производительности, не идентифицирует отдельного посетителя.","pc_trgt_text_1":"Целевые и рекламные файлы куки","pc_trgt_text_2":"Эти файлы куки используются для показа рекламы, которая может быть вам интересна в зависимости от ваших привычек просмотра.","pc_trgt_text_3":"Эти файлы куки, обслуживаемые нашими поставщиками контента и / или рекламы, могут объединять информацию, собранную ими с нашего веб-сайта, с другой информацией, которую они независимо собирали относительно действий вашего браузера в их сети веб-сайтов.","pc_trgt_text_4":"Если вы решите удалить или отключить эти целевые или рекламные файлы куки, вы все равно будете видеть рекламу, но она может не иметь отношения к вам.","pc_yprivacy_text_1":"Ваша конфиденциальность важна для нас","pc_yprivacy_text_2":"Куки - это небольшие текстовые файлы, которые сохраняются на вашем компьютере, когда Вы посещаете веб-сайт. Мы используем куки для различных целей, в том числе для того, чтобы улучшить ваше пребывание на нашем веб-сайте (например, чтобы запомнить данные для входа в вашу учетную запись).","pc_yprivacy_text_3":"Вы можете изменить свои предпочтения и отказаться от сохранения определенных типов файлов cookie на вашем компьютере во время просмотра нашего веб-сайта. Вы также можете удалить любые файлы куки, уже хранящиеся на вашем компьютере, но имейте в виду, что удаление файлов cookie может помешать вам использовать некоторые части нашего веб-сайта.","pc_yprivacy_title":"Ваша конфиденциальность","privacy_policy":"Privacy Policy"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Aktiv","always_active":"Alltid aktiv","impressum":"Impressum","inactive":"Inaktiv","nb_agree":"Godta alle","nb_changep":"Endre innstillinger","nb_ok":"OK","nb_reject":"Avvis alle","nb_text":"Vi bruker informasjonskapsler og andre sporingsteknologier for å forbedre din nettleseropplevelse på nettstedet vårt, for å vise deg personlig tilpasset innhold og målrettede annonser, for å analysere nettstedstrafikken vår og for å forstå hvor våre besøkende kommer fra.","nb_title":"Vi bruker informasjonskapsler","pc_fnct_text_1":"Funksjonalitetscookies","pc_fnct_text_2":"Disse informasjonskapslene brukes til å gi deg en mer personlig opplevelse på nettstedet vårt og til å huske valg du tar når du bruker nettstedet vårt.","pc_fnct_text_3":"For eksempel kan vi bruke funksjonalitetscookies for å huske språkinnstillingene dine eller huske påloggingsinformasjonen din.","pc_minfo_text_1":"Mer informasjon","pc_minfo_text_2":"For spørsmål angående våre retningslinjer for informasjonskapsler og dine valg, vennligst kontakt oss.","pc_minfo_text_3":"For å finne ut mer, besøk vår personvernpolicy.","pc_save":"Lagre mine preferanser","pc_sncssr_text_1":"Strengt nødvendige informasjonskapsler","pc_sncssr_text_2":"Disse informasjonskapslene er viktige for å gi deg tjenester tilgjengelig via nettstedet vårt og for å gjøre det mulig for deg å bruke visse funksjoner på nettstedet vårt.","pc_sncssr_text_3":"Uten disse informasjonskapslene kan vi ikke tilby deg visse tjenester på nettstedet vårt.","pc_title":"Informasjonssenter for informasjonskapsler","pc_trck_text_1":"Sporings- og ytelses-informasjonskapsler","pc_trck_text_2":"Disse informasjonskapslene brukes til å samle inn informasjon for å analysere trafikken til nettstedet vårt og hvordan besøkende bruker nettstedet vårt","pc_trck_text_3":"Disse informasjonskapslene kan for eksempel spore ting som hvor lang tid du bruker på nettstedet eller sidene du besøker, noe som hjelper oss å forstå hvordan vi kan forbedre nettstedet vårt for deg.","pc_trck_text_4":"Informasjonen som samles inn gjennom disse sporings- og ytelseskapslene, identifiserer ikke noen individuell besøkende.","pc_trgt_text_1":"Målretting og annonsering av informasjonskapsler","pc_trgt_text_2":"Disse informasjonskapslene brukes til å vise reklame som sannsynligvis vil være av interesse for deg basert på nettleservaner.","pc_trgt_text_3":"Disse informasjonskapslene, som serveres av innholds- og / eller reklameleverandører, kan kombinere informasjon de har samlet inn fra nettstedet vårt med annen informasjon de har samlet uavhengig av nettleserens aktiviteter på tvers av nettverket av nettsteder.","pc_trgt_text_4":"Hvis du velger å fjerne eller deaktivere disse målrettings- eller annonseringskapslene, vil du fremdeles se annonser, men de er kanskje ikke relevante for deg.","pc_yprivacy_text_1":"Ditt personvern er viktig for oss","pc_yprivacy_text_2":"Informasjonskapsler er veldig små tekstfiler som lagres på datamaskinen din når du besøker et nettsted. Vi bruker informasjonskapsler for en rekke formål og for å forbedre din online opplevelse på nettstedet vårt (for eksempel for å huske påloggingsinformasjonen din).","pc_yprivacy_text_3":"Du kan endre innstillingene dine og avvise visse typer informasjonskapsler som skal lagres på datamaskinen din mens du surfer på nettstedet vårt. Du kan også fjerne alle informasjonskapsler som allerede er lagret på datamaskinen din, men husk at sletting av informasjonskapsler kan forhindre deg i å bruke deler av nettstedet vårt.","pc_yprivacy_title":"Ditt personvern","privacy_policy":"Personvernpolicy"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"В действие са бисквитките","always_active":"Винаги в действие са бисквитките","impressum":"Impressum","inactive":"Неактивни бисквитки","nb_agree":"Съгласен съм","nb_changep":"Промяна на предпочитанията ми","nb_ok":"Добре","nb_reject":"Аз отказвам","nb_text":"Ние използваме бисквитки и други, проследяващи, технологии, за да подобрим сърфирането ви в нашия сайт, като ви покажем персонализирано съдържание и реклами, да анализираме трафика на нашия сайт и да разберем откъде идват нашите посетители.","nb_title":"Ние използваме бисквитки","pc_fnct_text_1":"Функционални бисквитки","pc_fnct_text_2":"Тези бисквитки се използват, за да ви осигурят още по-персонализирано изживяване на нашия уебсайт и да бъдат запомнени изборите, които сте направили, когато използвахте нашия уебсайт.","pc_fnct_text_3":"Например: може да използваме функционални бисквитки, за да запомним предпочитания ви език или да запомним детайли по влизането ви в уебсайта.","pc_minfo_text_1":"Още информация","pc_minfo_text_2":"За всякакви въпроси във връзка с нашата политика за бисквитките и вашите избори, моля, свържете се с нас.","pc_minfo_text_3":"За да научите повече, моля, посетете нашата Страница за поверителност.","pc_save":"Запази предпочитанията ми","pc_sncssr_text_1":"Строго задължителни бисквитки","pc_sncssr_text_2":"Тези бисквитки са съществен елемент, който осигурява услуги, достъпни чрез нашия уебсайт и дават възможност за използване на определени функции на нашия уебсайт.","pc_sncssr_text_3":"Без тези бисквитки не можем да ви доставим определени услуги на нашия уебсайт.","pc_title":"Център за настройка на бисквитки","pc_trck_text_1":"Бисквитки за проследяване и за производителност","pc_trck_text_2":"Тези бисквитки се използват за събиране на информация за анализ на трафика към нашия уебсайт и как посетителите използват нашия уебсайт.","pc_trck_text_3":"Например, тези бисквитки могат да проследяват неща като колко време прекарвате на уебсайта или на посещаваните от вас страници, което ни помага да разберем как можем да подобрим нашия сайт за вас.","pc_trck_text_4":"Информацията, събрана чрез тези бисквитки за проследяване и производителност, не идентифицира всеки отделен посетител.","pc_trgt_text_1":"Насочване и рекламни бисквитки","pc_trgt_text_2":"Тези бисквитки се използват за показване на реклама, която вероятно ще ви заинтересова въз основа на навиците ви за сърфиране.","pc_trgt_text_3":"Тези бисквитки, обслужвани от нашите доставчици на съдържание и / или реклама, могат да комбинират информацията, която са събрали от нашия уебсайт, с друга информация, която са събрали независимо, свързана с дейностите на вашия уеб браузър в тяхната мрежа от уебсайтове.","pc_trgt_text_4":"Ако решите да премахнете или деактивирате тези бисквитки за определени потребителски групи или реклама, пак ще видите реклами, но те може да не са от подходящи за вас.","pc_yprivacy_text_1":"Вашата поверителност е важна за нас","pc_yprivacy_text_2":"Бисквитките са много малки текстови файлове, които се съхраняват на вашия компютър, когато посетите уебсайт. Ние използваме бисквитки за множество от цели и да подобрим сърфирането ви из нашия сайт (например: за да запомним детайлите на вашия акаунт за влизане).","pc_yprivacy_text_3":"Можете да промените предпочитанията си и да откажете определени видове бисквитки, които да се съхраняват на вашия компютър, докато сърфирате в нашия уебсайт. Можете също да премахнете някои бисквитки, които вече са запазени на вашия компютър, но имайте предвид, че изтриването на бисквитки може да ви попречи да използвате части от нашия уебсайт.","pc_yprivacy_title":"Вашата поверителност","privacy_policy":"Страница за поверителност"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Ενεργό","always_active":"Πάντα ενεργό","impressum":"Impressum","inactive":"Ανενεργό","nb_agree":"Συμφωνώ","nb_changep":"Αλλαγή των προτιμήσεών μου","nb_ok":"OK","nb_reject":"Αρνούμαι","nb_text":"Χρησιμοποιούμε cookies και άλλες τεχνολογίες εντοπισμού για την βελτίωση της εμπειρίας περιήγησης στην ιστοσελίδα μας, για την εξατομίκευση περιεχομένου και διαφημίσεων, την παροχή λειτουργιών κοινωνικών μέσων και την ανάλυση της επισκεψιμότητάς μας.","nb_title":"Αυτή η ιστοσελίδα χρησιμοποιεί cookies","pc_fnct_text_1":"Cookies Λειτουργικότητας","pc_fnct_text_2":"Αυτά τα cookies χρησιμοποιούνται για να σας παρέχουν μία πιο προσωποποιημένη εμπειρία στην ιστοσελίδα μας και για να θυμούνται επιλογές που κάνετε όταν χρησιμοποιείτε την ιστοσελίδα μας.","pc_fnct_text_3":"Για παράδειγμα, μπορεί να χρησιμοποιήσουμε cookies λειτουργικότητας για να θυμόμαστε την επιλογή γλώσσας ή τα στοιχεία εισόδου σας.","pc_minfo_text_1":"Περισσότερες πληροφορίες","pc_minfo_text_2":"Για οποιαδήποτε απορία σε σχέση με την πολιτική μας σχετικά με τα cookies και τις επιλογές σας, παρακαλούμε να έρθετε σε επαφή μαζί μας.","pc_minfo_text_3":"Για να μάθετε περισσότερα, παρακαλούμε επισκεφθείτε την σελίδα περί Πολιτική απορρήτου.","pc_save":"Αποθήκευση των προτιμήσεών μου","pc_sncssr_text_1":"Άκρως απαραίτητα cookies","pc_sncssr_text_2":"Τα απαραίτητα cookies βοηθούν στο να γίνει χρηστική μία ιστοσελίδα, επιτρέποντας βασικές λειτουργίες όπως την πλοήγηση και την πρόσβαση σε ασφαλείς περιοχές της ιστοσελίδας.","pc_sncssr_text_3":"Η ιστοσελίδα δεν μπορεί να λειτουργήσει σωστά χωρίς αυτά τα cookies.","pc_title":"Κέντρο Προτιμήσεων Cookies","pc_trck_text_1":"Cookies εντοπισμού και αποδοτικότητας","pc_trck_text_2":"Αυτά τα cookies χρησιμοποιούνται για να συλλέγουν πληροφορίες σχετικές με την ανάλυση της επισκεψιμότητας της ιστοσελίδας μας και με το πώς οι χρήστες την χρησιμοποιούν.","pc_trck_text_3":"Για παράδειγμα, αυτά τα cookies μπορεί να εντοπίσουν πόσο χρόνο αφιερώνετε στην ιστοσελίδα μας ή ποιες σελίδες της επισκέπτεστε, πράγμα που μας βοηθάει να καταλάβουμε πώς να βελτιώσουμε την ιστοσελίδα μας.","pc_trck_text_4":"Οι πληροφορίες που συλλέγονται μέσω αυτών των cookies δεν αναγνωρίζουν μεμονωμένους χρήστες.","pc_trgt_text_1":"Cookies εξατομικευμένου περιεχομένου και διαφημίσεων","pc_trgt_text_2":"Αυτά τα cookies χρησιμοποιούνται για να δείχνουν διαφημίσεις που μπορεί να σας ενδιαφέρουν με βάση τις συνήθειες περιήγησής σας στο Διαδίκτυο.","pc_trgt_text_3":"Αυτά τα cookies, παρέχονται από τους παρόχους περιεχομένου ή/και διαφημίσεων, μπορεί να συνδυάζουν πληροφορίες που συλλέγουν από την ιστοσελίδα μας με άλλες που έχουν ανεξάρτητα συλλέξει από άλλα δίκτυα ή ιστοσελίδες σχετικά με τις δραστηριότητές σας στον φυλλομετρητή σας.","pc_trgt_text_4":"Εάν επιλέξετε να αφαιρέσετε ή να απενεργοποιήσετε αυτά τα cookies, θα συνεχίσετε να βλέπετε διαφημίσεις, αλλά αυτές μπορεί να μην είναι πλέον σχετικές με τα ενδιαφέροντά σας.","pc_yprivacy_text_1":"Η ιδιωτικότητά σας είναι σημαντική για εμάς","pc_yprivacy_text_2":"Τα cookies είναι πολύ μικρά αρχεία κειμένου που αποθηκεύονται στον υπολογιστή σας όταν επισκέπτεστε μια ιστοσελίδα. Χρησιμοποιούμε cookies για διάφορους λόγους και για να βελτιώσουμε την διαδικτυακή σας εμπειρία στην ιστοσελίδα μας (π.χ., για υπενθύμιση των στοιχείων πρόσβασής σας στην ιστοσελίδα).","pc_yprivacy_text_3":"Μπορείτε να αλλάξετε τις προτιμήσεις σας και να μην επιτρέψετε σε κάποιους τύπους cookies να αποθηκευτούν στον υπολογιστή σας όσο περιηγείστε στην ιστοσελίδα μας. Μπορείτε επίσης να διαγράψετε οποιαδήποτε cookies είναι ήδη αποθηκευμένα στον υπολογιστή σας, αλλά να έχετε υπόψιν ότι διαγράφοντας cookies μπορεί να σας αποτρέψει από το να χρησιμοποιήσετε μέρη της ιστοσελίδας μας.","pc_yprivacy_title":"Η ιδιωτικότητά σας","privacy_policy":"Πολιτική απορρήτου"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"פעיל","always_active":"תמיד פעיל","impressum":"רושם","inactive":"לא פעיל","nb_agree":"אני מסכים/ה","nb_changep":"שנה את ההגדרות שלי","nb_ok":"אוקיי","nb_reject":"אני מסרב/ת","nb_text":"אנו משתמשים בעוגיות ובטכנולוגיות מעקב אחרות כדי לשפר את חווית הגלישה שלך באתר האינטרנט שלנו, כדי להציג לך תוכן מותאם אישית ומודעות ממוקדות, לנתח את התנועה באתר שלנו ולהבין מהיכן מגיעים המבקרים שלנו.","nb_title":"אנו משתמשים בעוגיות","pc_fnct_text_1":"עוגיות פונקציונליות","pc_fnct_text_2":"עוגיות אלה משמשות כדי לספק לך חוויה מותאמת אישית יותר באתר האינטרנט שלנו וכדי לזכור בחירות שאתה עושה כשאתה משתמש באתר שלנו.","pc_fnct_text_3":"לדוגמה, אנו עשויים להשתמש בעוגיות פונקציונליות כדי לזכור את העדפות השפה שלך או לזכור את פרטי ההתחברות שלך.","pc_minfo_text_1":"מידע נוסף","pc_minfo_text_2":"לכל שאלה בנוגע למדיניות שלנו בנושא קובצי עוגיות והבחירות שלך, אנא צור איתנו קשר.","pc_minfo_text_3":"למידע נוסף, בקר במדיניות הפרטיות שלנו.","pc_save":"שמור את ההעדפות שלי","pc_sncssr_text_1":"עוגיות נחוצות בלבד","pc_sncssr_text_2":"עוגיות אלו חיוניות כדי לספק לך שירותים הזמינים דרך האתר שלנו וכדי לאפשר לך להשתמש בתכונות מסוימות של האתר שלנו.","pc_sncssr_text_3":"ללא עוגיות אלה, איננו יכולים לספק לך שירותים מסוימים באתר שלנו.","pc_title":"מרכז העדפות עוגיות","pc_trck_text_1":"עוגיות מעקב","pc_trck_text_2":"עוגיות אלו משמשות לאיסוף מידע כדי לנתח את התנועה לאתר שלנו וכיצד המבקרים משתמשים באתר שלנו.","pc_trck_text_3":"לדוגמה, קובצי עוגיות אלה עשויים לעקוב אחר דברים כגון משך הזמן שאתה מבלה באתר או הדפים שבהם אתה מבקר, מה שעוזר לנו להבין כיצד אנו יכולים לשפר עבורך את אתר האינטרנט שלנו.","pc_trck_text_4":"המידע שנאסף באמצעות עוגיות מעקב וביצועים אלה אינו מזהה אף מבקר בודד.","pc_trgt_text_1":"עוגיות מיקוד ופרסום","pc_trgt_text_2":"עוגיות אלו משמשות להצגת פרסומות שסביר להניח שיעניינו אותך בהתבסס על הרגלי הגלישה שלך.","pc_trgt_text_3":"קובצי עוגיות אלה, כפי שמוצגים על ידי ספקי התוכן ו/או הפרסום שלנו, עשויים לשלב מידע שהם אספו מהאתר שלנו עם מידע אחר שהם אספו באופן עצמאי הקשור לפעילויות של דפדפן האינטרנט שלך ברחבי רשת האתרים שלהם.","pc_trgt_text_4":"אם תבחר להסיר או להשבית את קובצי המיקוד או קובצי הפרסום הללו, עדיין תראה פרסומות אך ייתכן שהן לא יהיו רלוונטיות עבורך.","pc_yprivacy_text_1":"הפרטיות שלך חשובה לנו","pc_yprivacy_text_2":"קובצי עוגיות הם קבצי טקסט קטנים מאוד המאוחסנים במחשב שלך כאשר אתה מבקר באתר. אנו משתמשים בקובצי עוגיות למגוון מטרות וכדי לשפר את החוויה המקוונת שלך באתר האינטרנט שלנו (לדוגמה, כדי לזכור את פרטי הכניסה לחשבון שלך).","pc_yprivacy_text_3":"אתה יכול לשנות את ההעדפות שלך ולדחות סוגים מסוימים של עוגיות שישמרו במחשב שלך בזמן הגלישה באתר שלנו. אתה יכול גם להסיר קובצי עוגיות שכבר מאוחסנים במחשב שלך, אך זכור שמחיקת קובצי עוגיות עלולה למנוע ממך להשתמש בחלקים מהאתר שלנו.","pc_yprivacy_title":"הפרטיות שלך","privacy_policy":"מדיניות פרטיות"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Активно","always_active":"Секогаш активно","impressum":"Impressum","inactive":"Неактивно","nb_agree":"Се согласувам","nb_changep":"Промени ги моите преференции","nb_ok":"Се согласувам","nb_reject":"Одбивам","nb_text":"Ние користиме колачиња и други технологии за следење за да го подобриме вашето искуство со прелистувањето на нашата веб страна, за да ви прикажеме персонализирана содржина и таргетирани реклами, да го анализираме сообраќајот на нашата веб страна и да разбереме од каде доаѓаат нашите посетители.","nb_title":"Ние користиме колачиња","pc_fnct_text_1":"Колачиња за функционалност","pc_fnct_text_2":"Овие колачиња се користат за да ви овозможат поперсонализирано искуство на нашата веб страна и да ги запомнат изборите што ги правите кога ја користите нашата веб страна.","pc_fnct_text_3":"На пример, може да користиме функционални колачиња за да ги запомниме вашите јазични преференции или да ги запомниме вашите детали за најавување.","pc_minfo_text_1":"Повеќе информации","pc_minfo_text_2":"За било какви прашања во врска со нашата политика за колачиња и вашиот избор, ве молиме контактирајте не.","pc_minfo_text_3":"За да дознаете повеќе, ве молиме посетете ја нашата Политика за Приватност.","pc_save":"Зачувај ги моите преференции","pc_sncssr_text_1":"Строго неопходни колачиња","pc_sncssr_text_2":"Овие колачиња се од суштинско значење за да ви овозможат услуги достапни преку нашата веб страна, и да ви овозможат да користите одредени функции на нашата веб страна.","pc_sncssr_text_3":"Без овие колачиња, ние не можеме да ви обезбедиме одредени услуги на нашата веб страна.","pc_title":"Центар за преференци за колачиња","pc_trck_text_1":"Колачиња за следење","pc_trck_text_2":"Овие колачиња се користат за собирање информации за анализа на сообраќајот кон нашата веб страна, и за тоа како посетителите ја користат нашата веб страна.","pc_trck_text_3":"Овие колачиња може да следат работи како на пример, колку време поминувате на веб страната, или страниците што ги посетувате што ни помага да разбереме како можеме да ја подобриме нашата веб страна за вас.","pc_trck_text_4":"Информациите собрани преку овие колачиња за следење и перформанси не идентификуваат поединечни посетители.","pc_trgt_text_1":"Колачиња за таргетирање и рекламирање","pc_trgt_text_2":"Овие колачиња се користат за прикажување реклами што најверојатно ќе ве интересираат врз основа на вашите навики на прелистување.","pc_trgt_text_3":"Овие колачиња, сервирани од нашата содржина и/или провајдери за рекламирање, може да ги комбинираат информациите што ги собрале од нашата веб страна со други информации што независно ги собрале во врска со активностите на вашиот веб-прелистувач низ нивната мрежа на веб страни.","pc_trgt_text_4":"Ако изберете да ги отстраните или оневозможите овие колачиња за таргетирање или рекламирање, сè уште ќе гледате реклами, но тие можеби нема да бидат релевантни за вас.","pc_yprivacy_text_1":"Вашата приватност е важна за нас","pc_yprivacy_text_2":"Колачињата се многу мали текстуални датотеки што се складираат на вашиот компјутер кога посетувате веб страна. Ние користиме колачиња за различни цели и за да го подобриме вашето онлајн искуство на нашата веб страна (на пример, за да ги запомниме деталите за најавување на вашата сметка).","pc_yprivacy_text_3":"Можете да ги промените вашите параметри и да одбиете одредени видови колачиња да се складираат на вашиот компјутер додека ја прелистувате нашата веб страна. Можете исто така да ги отстраните сите колачиња што се веќе зачувани на вашиот компјутер, но имајте во предвид дека бришењето колачиња може да ве спречи да користите делови од нашата веб страна.","pc_yprivacy_title":"Вашата приватност","privacy_policy":"Политика за Приватност"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Gweithredol","always_active":"Yn weithredol bob tro","impressum":"Impressum","inactive":"Anweithredol","nb_agree":"Rwy\'n cytuno","nb_changep":"Newid fy newisiadau","nb_ok":"Iawn","nb_reject":"Rwy\'n gwrthod","nb_text":"Rydym yn defnyddio cwcis a thechnolegau tracio eraill i wella eich profiad o bori ar ein gwefan, i ddangos cynnwys wedi ei bersonoli a hysbysebion wedi\'u targedu, i ddadansoddi traffig ar ein gwefan ac i ddeall o ble daw ein hymwelwyr.","nb_title":"Rydym yn defnyddio cwcis","pc_fnct_text_1":"Cwcis swyddogaeth","pc_fnct_text_2":"Mae\'r cwcis yma yn cael eu defnyddio i ddarparu profiad mwy personol ichi ar ein gwefan, ac i gofio dewisiadau a wnewch wrth ddefnyddio ein gwefan.","pc_fnct_text_3":"Er enghraifft, gallem ddefnyddio cwcis swyddogaeth i gofio\'ch dewis iaith neu gofio\'ch manylion mewngofnodi.","pc_minfo_text_1":"Rhagor o wybodaeth","pc_minfo_text_2":"Os oes gennych chi unrhyw ymholiadau yn ymwneud â\'n polisi cwcis a\'ch dewisiadau, a wnewch chi gysylltu â ni.","pc_minfo_text_3":"I ganfod mwy, ewch at ein Polasaí Príobháideachta.","pc_save":"Cadw fy newisiadau","pc_sncssr_text_1":"Cwcis hollol hanfodol","pc_sncssr_text_2":"Mae\'r cwcis yma yn hanfodol er mwyn ichi dderbyn gwasanaethau drwy ein gwefan a\'ch galluogi i ddefnyddio nodweddion penodol ar ein gwefan.","pc_sncssr_text_3":"Heb y cwcis yma, ni fedrwn ddarparu rhai gwasanaethau penodol ichi ar ein gwefan.","pc_title":"Canolfan Dewisiadau Cwcis","pc_trck_text_1":"Cwcis tracio a pherfformiad","pc_trck_text_2":"Mae\'r cwcis yma yn cael eu defnyddio i gasglu gwybodaeth a dadansoddi traffig i\'n gwefan a sut mae ymwelwyr yn defnyddio\'n gwefan.","pc_trck_text_3":"Er enghraifft, gall y cwcis yma dracio faint o amser rydych yn ei dreulio ar y wefan neu\'r tudalennau rydych yn ymweld â hwy a\'n cynorthwyo i ddeall sut y gallwn wella ein gwefan ar eich cyfer.","pc_trck_text_4":"Nid yw\'r wybodaeth a gesglir drwy\'r cwcis tracio a pherfformiad yn adnabod unrhyw ymwelydd unigol.","pc_trgt_text_1":"Cwcis targedu a hysbysebu","pc_trgt_text_2":"Mae\'r cwcis yma yn cael eu defnyddio i ddangos hysbysebion sydd yn debygol o fod o ddiddordeb i chi yn seiliedig ar eich arferion pori.","pc_trgt_text_3":"Gall y cwcis yma, fel y\'u gweinyddir gan ein darparwyr cynnwys a/neu hysbysebion, gyfuno gwybodaeth a gasglwyd ganddynt o\'n gwefan gyda gwybodaeth arall maent wedi ei chasglu\'n annibynnol yn seiliedig ar eich gweithgareddau pori ar y rhyngrwyd ar draws eu rhwydweithiau o wefannau.","pc_trgt_text_4":"Os byddwch yn dewis tynnu neu atal y cwcis targedu neu hysbysebu yma, byddwch yn parhau i weld hysbysebion ond mae\'n bosib na fyddant yn berthnasol i chi.","pc_yprivacy_text_1":"Mae eich preifatrwydd yn bwysig i ni","pc_yprivacy_text_2":"Ffeiliau testun bach eu maint yw cwcis sydd yn cael eu storio ar eich cyfrifiadur wrth ichi ymweld â gwefan. Rydym yn defnyddio cwcis i sawl diben ac i wella eich profiad ar-lein ar ein gwefan (er enghraifft, cofio eich manylion mewngofnodi i\'ch cyfrif).","pc_yprivacy_text_3":"Gallwch newid eich dewisiadau ac atal rhai mathau o gwcis rhag cael eu storio ar eich cyfrifiadur. Gallwch hefyd dynnu unrhyw gwcis sydd eisoes wedi eu storio ar eich cyfrifiadur, ond cofiwch y gall.","pc_yprivacy_title":"Eich preifatrwydd","privacy_policy":"Polasaí Príobháideachta"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"アクティブ","always_active":"常にアクティブ","impressum":"Impressum","inactive":"停止中","nb_agree":"同意","nb_changep":"設定変更","nb_ok":"承諾","nb_reject":"拒否","nb_text":"訪問者の当ウェブサイトの閲覧体験を向上させるため、パーソナライズされたコンテンツやターゲット広告を表示するため、当ウェブサイトのトラフィックを分析するため、および当ウェブサイトへの訪問者がどこから来ているかを理解するために、Cookieおよびその他の追跡技術を使用しています。","nb_title":"クッキーの使用","pc_fnct_text_1":"機能性クッキー","pc_fnct_text_2":"これらのクッキーは、当ウェブサイトでよりカスタマイズされた体験を提供するため、および当ウェブサイトを利用する際に行った選択を記憶するために使用されます。","pc_fnct_text_3":"例えば、訪問者の言語設定を記憶したり、ログイン情報を記憶するために、機能性クッキーを使用することがあります。","pc_minfo_text_1":"詳細情報","pc_minfo_text_2":"クッキーに関する方針や訪問者の選択に関連したご質問については、当方までお問い合わせください。","pc_minfo_text_3":"詳しくは、プライバシーポリシー をご覧ください。","pc_save":"設定を保存","pc_sncssr_text_1":"ウェブサイトの動作に必要不可欠なクッキー","pc_sncssr_text_2":"これらのクッキーは、訪問者が当ウェブサイトを通じて利用可能なサービスを提供したり、当ウェブサイトの特定の機能を利用したりするために不可欠なものです。","pc_sncssr_text_3":"これらのクッキーをブロックした場合、当ウェブサイトでの特定のサービスを提供できません。","pc_title":"クッキー設定センター","pc_trck_text_1":"トラッキングクッキー","pc_trck_text_2":"これらのクッキーは、当ウェブサイトへのトラフィックや訪問者がどのように当ウェブサイトを利用しているかを分析するための情報を収集するために使用されます。","pc_trck_text_3":"例えば、これらのクッキーは、訪問者が当ウェブサイトに滞在した時間や訪問したページなどを追跡することがあり、これは、訪問者のために当ウェブサイトの利便性向上に役立てます。","pc_trck_text_4":"これらのトラッキングおよびパフォーマンスクッキーによって収集された情報は、特定の個人を特定することはありません。","pc_trgt_text_1":"ターゲティングおよび広告用クッキー","pc_trgt_text_2":"これらのクッキーは、訪問者の閲覧習慣に基づいて、訪問者が興味を持ちそうな広告を表示するために使用されます。","pc_trgt_text_3":"これらのクッキーは、コンテンツプロバイダーおよび/または広告プロバイダーによって提供され、当ウェブサイトから収集した情報と、そのネットワーク上での訪問者のウェブブラウザの活動に関連して独自に収集した他の情報とを組み合わせることがあります。","pc_trgt_text_4":"訪問者がこれらのターゲティングクッキーや広告用クッキーを削除または無効を選択した場合でも、広告は表示されますが、訪問者に関連したものではない可能性があります。","pc_yprivacy_text_1":"お客様のプライバシーを尊重します","pc_yprivacy_text_2":"クッキーとは、訪問者がウェブサイトにアクセスした際に訪問者のコンピュータに保存される非常に小さなテキストファイルです。当ウェブサイトは、さまざまな目的でクッキーを使用し、当ウェブサイトでの訪問者のオンライン利便性を向上させています。（例えば、訪問者のアカウントのログイン情報を記憶するためなど。）","pc_yprivacy_text_3":"訪問者は、設定を変更して、当ウェブサイトを閲覧中のコンピュータに保存される特定の種類のクッキーを拒否することができます。また、すでに訪問者のコンピュータに保存されているクッキーを削除することもできますが、クッキーを削除すると、当ウェブサイト機能の一部が利用できなくなる可能性がありますので、ご注意ください。","pc_yprivacy_title":"プライバシー","privacy_policy":"プライバシーポリシー"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"تغيير تفضيلاتي","always_active":"مفعل دائمًا","impressum":"Impressum","inactive":"غير مفعل","nb_agree":"موافق","nb_changep":"تغيير تفضيلاتي","nb_ok":"فهمت","nb_reject":"أرفض","nb_text":"نحن نستخدم ملفات تعريف الارتباط وتقنيات التتبع الأخرى لتحسين تجربة التصفح الخاصة بك على موقعنا الإلكتروني ، ولإظهار المحتوى المخصص والإعلانات المستهدفة لك ، وتحليل حركة المرور على موقعنا ، وفهم من أين يأتي زوارنا.","nb_title":"نحنُ نستخدم ملفات تعريف الارتباط","pc_fnct_text_1":"ملفات تعريف الارتباط الوظيفية","pc_fnct_text_2":"تُستخدم ملفات تعريف الارتباط هذه لتزويدك بتجربة أكثر تخصيصًا على موقعنا الإلكتروني ولتذكر الخيارات التي تتخذها عند استخدامك لموقعنا.","pc_fnct_text_3":"على سبيل المثال ، قد نستخدم ملفات تعريف الارتباط الوظيفية لتذكر تفضيلات اللغة الخاصة بك أو تذكر تفاصيل تسجيل الدخول الخاصة بك.","pc_minfo_text_1":"معلومات أكثر.","pc_minfo_text_2":"لأي استفسارات تتعلق بسياستنا الخاصة بملفات تعريف الارتباط ، وخياراتك، يرجى التواصل معنا.","pc_minfo_text_3":"\\nالخاصة بنا لمعرفة المزيد ، يرجى زيارةسياسة الخصوصية .\\n","pc_save":"حفظ تفضيلاتي","pc_sncssr_text_1":"ملفات تعريف الارتباط الضرورية للغاية","pc_sncssr_text_2":"تعد ملفات تعريف الارتباط هذه ضرورية لتزويدك بالخدمات المتاحة عبر موقعنا على الويب ولتمكينك من استخدام ميزات معينة في موقعنا .","pc_sncssr_text_3":"بدون ملفات تعريف الارتباط هذه ، لا يمكننا تقديم خدمات معينة لك على موقعنا.","pc_title":"مركز تفضيلات ملفات تعريف الارتباط","pc_trck_text_1":"ملفات تعريف الارتباط للتتبع والأداء","pc_trck_text_2":"\\nتُستخدم ملفات تعريف الارتباط هذه لجمع المعلومات لتحليل حركة المرور إلى موقعنا الإلكتروني وكيفية استخدام الزوار لموقعنا.","pc_trck_text_3":"\\nعلى سبيل المثال ، قد تتعقب ملفات تعريف الارتباط هذه أشياء مثل المدة التي تقضيها على موقع الويب أو الصفحات التي تزورها مما يساعدنا على فهم كيف يمكننا تحسين موقعنا على الويب من أجلك.","pc_trck_text_4":"\\nالمعلومات التي يتم جمعها من خلال ملفات تعريف الارتباط الخاصة بالتتبع والأداء هذه لا تحدد أي زائر فردي.\\n","pc_trgt_text_1":"ملفات تعريف الارتباط الخاصة بالاستهداف والإعلان","pc_trgt_text_2":"تُستخدم ملفات تعريف الارتباط هذه لإظهار الإعلانات التي من المحتمل أن تهمك بناءً على عاداتك في التصفح.","pc_trgt_text_3":"\\nقد تدمج ملفات تعريف الارتباط هذه ، كما يقدمها المحتوى و / أو موفرو الإعلانات لدينا ، المعلومات التي جمعوها من موقعنا الإلكتروني مع المعلومات الأخرى التي جمعوها بشكل مستقل فيما يتعلق بأنشطة متصفح الويب الخاص بك عبر شبكة مواقعهم الإلكترونية.\\n","pc_trgt_text_4":"إذا اخترت إزالة أو تعطيل ملفات تعريف الارتباط الخاصة بالاستهداف أو الإعلانات ، فستظل تشاهد إعلانات ولكنها قد لا تكون ذات صلة بك.","pc_yprivacy_text_1":"خصوصيتك مهمة بالنسبة لنا","pc_yprivacy_text_2":"من الأغراض ولتعزيز تجربتك عبر الإنترنت على موقعنا (على سبيل المثال ، لتذكر تفاصيل تسجيل الدخول إلى حسابك).","pc_yprivacy_text_3":"يمكنك تغيير تفضيلاتك ورفض أنواع معينة من ملفات تعريف الارتباط ليتم تخزينها على جهاز الكمبيوتر الخاص بك أثناء تصفح موقعنا على الويب. يمكنك أيضًا إزالة أي ملفات تعريف ارتباط مخزنة بالفعل على جهاز الكمبيوتر الخاص بك ، ولكن ضع في اعتبارك أن حذف ملفات تعريف الارتباط قد يمنعك من استخدام أجزاء من موقعنا.","pc_yprivacy_title":"خصوصيتك","privacy_policy":"\\nةسياسة الخصوصية\\n"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Etkin","always_active":"Her zaman etkin","impressum":"Impressum","inactive":"Etkin değil","nb_agree":"Kabul et","nb_changep":"Tercihleri değiştir","nb_ok":"Tamam","nb_reject":"Reddet","nb_text":"Web sitemizde gezinme deneyiminizi geliştirmek, size kişiselleştirilmiş içerik ve hedefli reklamlar göstermek, web sitesi trafiğimizi analiz etmek ve ziyaretçilerimizin nereden geldiğini anlamak için çerezleri ve diğer izleme teknolojilerini kullanıyoruz.","nb_title":"Çerezleri kullanıyoruz","pc_fnct_text_1":"İşlevsellik çerezleri","pc_fnct_text_2":"Bu çerezler, web sitemizde size daha kişiselleştirilmiş bir deneyim sağlamak ve web sitemizi kullanırken yaptığınız seçimleri hatırlamak için kullanılır.","pc_fnct_text_3":"Örneğin, dil tercihlerinizi veya oturum açma bilgilerinizi hatırlamak için işlevsellik tanımlama bilgilerini kullanabiliriz.","pc_minfo_text_1":"Daha fazla bilgi","pc_minfo_text_2":"Çerezlere ilişkin politikamız ve seçimlerinizle ilgili herhangi bir sorunuz için lütfen bizimle iletişime geçin","pc_minfo_text_3":"Daha fazlasını öğrenmek için lütfen Gizlilik Politikası ziyaret edin.","pc_save":"Tercihleri Kaydet","pc_sncssr_text_1":"Kesinlikle gerekli çerezler","pc_sncssr_text_2":"Bu çerezler, size web sitemiz aracılığıyla sunulan hizmetleri sağlamak ve web sitemizin belirli özelliklerini kullanmanızı sağlamak için gereklidir.","pc_sncssr_text_3":"Bu çerezler olmadan, web sitemizde size belirli hizmetleri sağlayamayız.","pc_title":"Çerez Tercihleri Merkezi","pc_trck_text_1":"İzleme ve performans çerezleri","pc_trck_text_2":"Bu çerezler, web sitemize gelen trafiği ve ziyaretçilerin web sitemizi nasıl kullandığını analiz etmek için bilgi toplamak amacıyla kullanılır.","pc_trck_text_3":"Örneğin, çerezler, web sitesinde ne kadar zaman geçirdiğiniz veya ziyaret ettiğiniz sayfalar gibi şeyleri izleyebilir ve bu da web sitemizi sizin için nasıl iyileştirebileceğimizi anlamamıza yardımcı olur.","pc_trck_text_4":"Bu izleme ve performans çerezleri aracılığıyla toplanan bilgiler anonim olup herhangi bir bireysel ziyaretçiyi tanımlamaz.","pc_trgt_text_1":"Hedefleme ve reklam çerezleri","pc_trgt_text_2":"Bu çerezler, arama/gezinme alışkanlıklarınıza göre ilginizi çekebilecek reklamları göstermek için kullanılır.","pc_trgt_text_3":"Bu çerezler, içerik ve/veya reklam sağlayıcılarımız tarafından, web sitemizden topladıkları bilgileri, web tarayıcınızın kendi web siteleri ağlarındaki faaliyetleriyle ilgili olarak bağımsız olarak topladıkları diğer bilgilerle birleştirilebilir.","pc_trgt_text_4":"Bu hedefleme veya reklam çerezlerini kaldırmayı veya devre dışı bırakmayı seçerseniz, reklamları görmeye devam edersiniz, ancak bunlar sizinle alakalı olmayabilir.","pc_yprivacy_text_1":"Gizliliğiniz bizim için önemlidir","pc_yprivacy_text_2":"Çerezler, bir web sitesini ziyaret ettiğinizde bilgisayarınızda depolanan çok küçük metin dosyalarıdır. Çerezleri çeşitli amaçlarla ve web sitemizdeki çevrimiçi deneyiminizi geliştirmek için (örneğin, hesap giriş bilgilerinizi hatırlamak için) kullanıyoruz.","pc_yprivacy_text_3":"Web sitemizde gezinirken tercihlerinizi değiştirebilir ve bilgisayarınızda saklanacak belirli çerez türlerini reddedebilirsiniz. Ayrıca, bilgisayarınızda depolanmış olan çerezleri de kaldırabilirsiniz, ancak çerezleri silmenin web sitemizin bölümlerini kullanmanızı engelleyebileceğini unutmayın.","pc_yprivacy_title":"Gizliliğiniz","privacy_policy":"Gizlilik Politikası"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"啟用","always_active":"永遠啟用","impressum":"Impressum","inactive":"停用","nb_agree":"我同意","nb_changep":"更改我的偏好","nb_ok":"確定","nb_reject":"我拒絕","nb_text":"我們使用cookies和其他追蹤技術來改善您在我們網站上的瀏覽體驗，對您顯示個性化的內容和有針對性的廣告，分析我們的網站流量，並了解我們的訪客來自哪裡。","nb_title":"我們使用cookies","pc_fnct_text_1":"功能性cookies","pc_fnct_text_2":"這些cookies用於在我們的網站上為您提供更加個人化的體驗，並記住您在使用我們網站時做出的選擇。","pc_fnct_text_3":"例如，我們可能使用功能性cookies來記住您的語言偏好或記住您的登入資訊。","pc_minfo_text_1":"更多資訊","pc_minfo_text_2":"如果對我們的cookies政策或您的選擇有任何疑問，請聯繫我們。","pc_minfo_text_3":"想了解更多資訊，請前往我們的隱私權政策.","pc_save":"儲存我的偏好","pc_sncssr_text_1":"必要的cookies","pc_sncssr_text_2":"這些cookies對於向您提供透過我們網站的服務以及使您能夠使用我們網站的某些功能是不可或缺的。","pc_sncssr_text_3":"沒有這些cookies，我們就不能在我們的網站上為您提供某些服務。","pc_title":"Cookies偏好中心","pc_trck_text_1":"追蹤cookies","pc_trck_text_2":"這些cookies用於收集資訊，以分析我們網站的流量以及訪客如何使用我們的網站。","pc_trck_text_3":"例如，這些cookies可能會跟追蹤如您在網站上花費的時間或您造訪的頁面，這有助於我們了解如何為您改進我們的網站。","pc_trck_text_4":"透過這些追蹤和性能cookies收集的資訊不會識別任何個人訪客。","pc_trgt_text_1":"定位和廣告cookies","pc_trgt_text_2":"這些cookies被用來根據您的瀏覽習慣顯示您可能感興趣的廣告。","pc_trgt_text_3":"由我們的內容或廣告供應商提供的這些cookies，可能會將他們從我們的網站上收集的資訊和他們獨立收集的與您的瀏覽器在其網站中的活動有關的其他資訊結合起來。","pc_trgt_text_4":"如果您選擇刪除或禁用這些定位或廣告cookies，您仍然會看到廣告，但它們可能與您無關。","pc_yprivacy_text_1":"您的隱私對我們很重要","pc_yprivacy_text_2":"Cookies是非常小的文本文件，當您造訪網站時存儲在您的裝置上。我們將cookies用於各種目的，並提高您在我們網站的使用體驗（例如，記住您帳號的登入資訊）。","pc_yprivacy_text_3":"在瀏覽我們的網站時，您可以改變您的偏好，拒絕某些類型的cookies儲存在您的裝置上。您也可以刪除已經儲存在您裝置上的任何cookies，但請記住，刪除cookies可能會導致您無法使用我們網站的部分內容。","pc_yprivacy_title":"您的隱私","privacy_policy":"隱私權政策"}}')},function(e){e.exports=JSON.parse('{"i18n":{"active":"Activats","always_active":"Totjorn activats","inactive":"Desactivats","nb_agree":"Accèpti","nb_changep":"Cambiar mas preferéncias","nb_ok":"D\'acòrdi","nb_reject":"Regèti","nb_text":"Utilizam de cookies e d’autras tecnologias de seguiment per melhorar vòstra experiéncia de navegacion sus nòstre site web, per vos afichar de contenguts personalizats, de publicitats cibladas, per analisar nòstra audiéncia e per comprendre d’ont venon nòstres visitaires.","nb_title":"Utilizam de cookies","pc_fnct_text_1":"Cookies foncionals","pc_fnct_text_2":"Aquestes cookies servisson per vos fornir una experiéncia mai personalizada sus nòstre site web e per memorizar vòstras causidas quand navegatz sus nòstre site web.","pc_fnct_text_3":"Per exemple, podèm utilizar de cookies foncionals per memorizar vòstras preferéncias lingüisticas o nos remembrar de vòstre identificant de connexion.","pc_minfo_text_1":"Mai d\'informacions","pc_minfo_text_2":"Per quina question que siá tocant nòstra politica de cookies e vòstras causidas, contactat-nos.","pc_minfo_text_3":"Per ne saber mai, consultatz nòstra Politica de confidencialitat.","pc_save":"Enregistrar mas preferéncias","pc_sncssr_text_1":"Cookies formalament necessaris","pc_sncssr_text_2":"Aquestes cookies son essencials per vos fornir los servicis disponibles via nòstre site web e per vos permetre d’utilizar d’unas foncionalitats de nòstre site web.","pc_sncssr_text_3":"Sens aquestes cookies podèm pas vos provesir certans servicis sus nòstre site web.","pc_title":"Centre de preferéncias dels cookies","pc_trck_text_1":"Cookies de seguiment","pc_trck_text_2":"Aquestes cookies s’emplegan per collectar d’informacions per analisar lo trafic de nòstre site web e coma los visitaires l’utilizan.","pc_trck_text_3":"Per exemple, aquestes cookies poirián pistar las causas coma quant de temps passatz sus un site web o las paginas que consultatz, çò que nos permet de comprendre coma podèm melhorar nòstre site web per vos.","pc_trck_text_4":"Las informacions collectadas via aqueles cookies de seguiment e de performància identifican pas individualament cap de visitaire.","pc_trgt_text_1":"Cookies de ciblatge e publicitat","pc_trgt_text_2":"Aquestes cookies servisson per afichar de publicitats que vos interessaràn probablament basadas sus vòstras costumas de navegacion.","pc_trgt_text_3":"Aquestes cookies, servits per nòstres provesidors de contenguts e/o publicitats, pòdon combinar d’informacions que collèctan de nòstre site web amb d’autras informacions qu’an collectadas independentament en relacion amb las activitats de vòstre navegador a travèrs lor malhum de sites web.","pc_trgt_text_4":"Se causissètz de suprimir o desactivar aquestes cookies publicitaris o de ciblatge, veiretz totjorn de reclamas mas seràn pas pertinentas per vos.","pc_yprivacy_text_1":"Vòstra vida privada nos impòrta","pc_yprivacy_text_2":"Los cookies son de plan pichons fichièrs tèxt que son gardas dins vòstre ordenador quand visitatz un site. 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before focus: "+r,"info"),null===(i=r)||void 0===i||i.focus(),e.preventDefault()):document.activeElement===r&&(t.cookieConsent.log("preferencesCenterOverlayModal_firstFocusableElement before focus: "+n,"info"),null===(o=n)||void 0===o||o.focus(),e.preventDefault()))})),t.cookieConsent.log("preferencesCenterOverlayModal_firstFocusableElement before focus: "+n,"info"),null===(e=n)||void 0===e||e.focus(),this.preferencesCenterOverlay.classList.add("termsfeed-com---lang-"+t.cookieConsent.i18n.userLang)},e.prototype.hidePreferencesCenter=function(){this.preferencesCenterOverlay.classList.remove("termsfeed-com---is-visible"),this.cookieConsent.log("Preferences Center hidden","info")},e.prototype.refreshPreferencesCenter=function(){if(null!==this.preferencesCenterOverlay)return this.preferencesCenterOverlay.parentNode.removeChild(this.preferencesCenterOverlay),this.preferencesCenterOverlay=null,this.showPreferencesCenter()},e.prototype.createPreferencesCenterOverlayAndDialog=function(){var e=this,t=document.createElement("div");t.classList.add("termsfeed-com---pc-overlay"),t.classList.add(e.cookieConsent.colorPalette.getClass()),t.classList.add("termsfeed-com---reset"),t.id="termsfeed-com---preferences-center",t.setAttribute("id","termsfeed-com---preferences-center");var i=document.createElement("div");i.classList.add("termsfeed-com---pc-dialog");var n=document.createElement("div");n.classList.add("cc-pc-container");var o=document.createElement("div");o.classList.add("cc-pc-head");var r=document.createElement("div");if(r.classList.add("cc-pc-head-title"),e.cookieConsent.ownerWebsiteName.length>2){var s=document.createElement("p");s.classList.add("cc-pc-head-title-text"),s.innerText=e.cookieConsent.ownerWebsiteName,a.appendChild(r,s)}var c=document.createElement("p");c.classList.add("cc-pc-head-title-headline"),c.setAttribute("id","cc-pc-head-title-headline"),c.innerHTML=e.cookieConsent.i18n.$t("i18n","pc_title"),a.appendChild(r,c);var l=document.createElement("div");l.classList.add("cc-pc-head-lang");var p=this.obtainLanguageSelector();a.appendChild(l,p);var d=document.createElement("button");d.classList.add("cc-pc-head-close"),d.innerHTML="✕",d.addEventListener("click",(function(){document.dispatchEvent(e.cookieConsent.events.cc_preferencesCenterClosePressed)})),a.appendChild(o,r),a.appendChild(o,l),!1===e.cookieConsent.ownerPreferencesCenterCloseButtonHide&&a.appendChild(l,d,"afterbegin");var u=document.createElement("div");u.classList.add("cc-cp-body");var m=this.getMenuContainer(),_=this.getContentContainer();a.appendChild(u,m),a.appendChild(u,_);var k=this.getFooterContainer();return a.appendChild(n,o),a.appendChild(n,u),a.appendChild(n,k),a.appendChild(i,n),a.appendChild(t,i),t},e.prototype.obtainLanguageSelector=function(){var e=this,t=document.createElement("select");return t.classList.add("cc-pc-head-lang-select"),[].forEach.call(e.cookieConsent.i18n.availableLanguages,(function(i){var n=document.createElement("option");n.text=i.title,n.value=i.value,e.cookieConsent.i18n.userLang===n.value&&n.setAttribute("selected","selected"),t.add(n)})),t.addEventListener("change",(function(){e.cookieConsent.i18n.userLang=t.value,e.cookieConsent.cookieLevels.languageChanged(),e.refreshPreferencesCenter(),document.dispatchEvent(e.cookieConsent.events.cc_userLanguageChanged)})),t},e.prototype.getContentContainer=function(){var e=this,t=document.createElement("div");t.classList.add("cc-cp-body-content");var i=0;return e.cookieConsent.cookieLevels.preferenceItems.forEach((function(n){var o=document.createElement("div");if(o.classList.add("cc-cp-body-content-entry"),o.setAttribute("id",n.content_container),o.setAttribute("role","tabpanel"),o.setAttribute("aria-labelledby",n.title_container),o.setAttribute("hidden",""),o.setAttribute("tabindex","0"),o.setAttribute("content_layout",n.content_container),o.setAttribute("active","false"),o.innerHTML=n.content,0===i&&(o.setAttribute("active","true"),o.removeAttribute("hidden")),i++,n.id){var r=e._getLevelCheckbox(n);a.appendChild(o,r)}a.appendChild(t,o)})),t},e.prototype.getMenuContainer=function(){var e=this,t=document.createElement("ul");t.classList.add("cc-cp-body-tabs"),t.setAttribute("role","tablist"),t.setAttribute("aria-label","Menu");var i=0;return e.cookieConsent.cookieLevels.preferenceItems.forEach((function(n){var o=document.createElement("li");o.classList.add("cc-cp-body-tabs-item");var r=document.createElement("button");r.classList.add("cc-cp-body-tabs-item-link"),r.setAttribute("id",n.title_container),r.setAttribute("role","tab"),r.setAttribute("aria-selected","false"),r.setAttribute("aria-controls",n.content_container),r.setAttribute("tabindex","-1"),r.setAttribute("t",n.content_container),r.innerHTML=n.title,0===i&&(o.setAttribute("active","true"),r.setAttribute("aria-selected","true"),r.setAttribute("tabindex","0")),i++,r.addEventListener("click",(function(t){t.preventDefault(),e.cookieConsent.log("Preferences Center tab item clicked: "+n.title,"info");var i=document.querySelectorAll('li[active="true"]');[].forEach.call(i,(function(e){e.setAttribute("active","false"),e.firstElementChild.setAttribute("aria-selected","false"),e.firstElementChild.setAttribute("tabindex","-1")})),o.setAttribute("active","true"),o.firstElementChild.setAttribute("aria-selected","true"),o.firstElementChild.setAttribute("tabindex","0");try{var a=document.querySelectorAll("div[content_layout]");[].forEach.call(a,(function(e){e.setAttribute("active","false"),e.setAttribute("hidden","")}));var r=document.querySelector('div[content_layout="'+n.content_container+'"]');r.setAttribute("active","true"),r.removeAttribute("hidden")}catch(t){}}));var s=0,c=document.getElementsByClassName("cc-cp-body-tabs-item-link");t.addEventListener("keydown",(function(e){"ArrowDown"!==e.key&&"ArrowUp"!==e.key&&"ArrowLeft"!==e.key&&"ArrowRight"!==e.key||(c[s].setAttribute("tabindex","-1"),"ArrowDown"===e.key||"ArrowRight"===e.key?++s>=c.length&&(s=0):"ArrowUp"!==e.key&&"ArrowLeft"!==e.key||--s<0&&(s=c.length-1),c[s].setAttribute("tabindex","0"),c[s].focus())})),a.appendChild(o,r),a.appendChild(t,o)})),t},e.prototype.getFooterContainer=function(){var e=this,t=document.createElement("div");t.classList.add("cc-cp-foot");var 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s=document.createElement("span");s.classList.add("cc-nb-text-urls"),s.innerHTML=" ";var c=document.createElement("span");c.classList.add("cc-nb-text-urls-privacy"),c.setAttribute("role","link");var l=document.createElement("span");l.classList.add("cc-nb-text-urls-impressum"),l.setAttribute("role","link");var p=document.createElement("span");p.classList.add("cc-nb-text-urls-separator"),p.innerHTML=" | 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var(--fa-style-family, "Font Awesome 6 Free"); - font-weight: var(--fa-style, 900); } - -.fa, -.fas, -.fa-solid, -.far, -.fa-regular, -.fal, -.fa-light, -.fat, -.fa-thin, -.fad, -.fa-duotone, -.fab, -.fa-brands { - -moz-osx-font-smoothing: grayscale; - -webkit-font-smoothing: antialiased; - display: var(--fa-display, inline-block); - font-style: normal; - font-variant: normal; - line-height: 1; - text-rendering: auto; } - -.fa-1x { - font-size: 1em; } - -.fa-2x { - font-size: 2em; } - -.fa-3x { - font-size: 3em; } - -.fa-4x { - font-size: 4em; } - -.fa-5x { - font-size: 5em; } - -.fa-6x { - font-size: 6em; } - -.fa-7x { - font-size: 7em; } - -.fa-8x { - font-size: 8em; } - -.fa-9x { - font-size: 9em; } - -.fa-10x { - font-size: 10em; } - -.fa-2xs { - font-size: 0.625em; - line-height: 0.1em; - vertical-align: 0.225em; } - -.fa-xs { - font-size: 0.75em; - line-height: 0.08333em; - vertical-align: 0.125em; } - -.fa-sm { - font-size: 0.875em; - line-height: 0.07143em; - vertical-align: 0.05357em; } - -.fa-lg { - font-size: 1.25em; - line-height: 0.05em; - vertical-align: -0.075em; } - -.fa-xl { - font-size: 1.5em; - line-height: 0.04167em; - vertical-align: -0.125em; } - -.fa-2xl { - font-size: 2em; - line-height: 0.03125em; - vertical-align: -0.1875em; } - -.fa-fw { - text-align: center; - width: 1.25em; } - -.fa-ul { - list-style-type: none; - margin-left: var(--fa-li-margin, 2.5em); - padding-left: 0; } - .fa-ul > li { - position: relative; } - -.fa-li { - left: calc(var(--fa-li-width, 2em) * -1); - position: absolute; - text-align: center; - width: var(--fa-li-width, 2em); - line-height: inherit; } - -.fa-border { - border-color: var(--fa-border-color, #eee); - border-radius: var(--fa-border-radius, 0.1em); - border-style: var(--fa-border-style, solid); - border-width: var(--fa-border-width, 0.08em); - padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } - -.fa-pull-left { - float: left; - margin-right: var(--fa-pull-margin, 0.3em); } - -.fa-pull-right { - float: right; - margin-left: var(--fa-pull-margin, 0.3em); } - -.fa-beat { - -webkit-animation-name: fa-beat; - animation-name: fa-beat; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); - animation-timing-function: var(--fa-animation-timing, ease-in-out); } - -.fa-bounce { - -webkit-animation-name: fa-bounce; - animation-name: fa-bounce; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } - -.fa-fade { - -webkit-animation-name: fa-fade; - animation-name: fa-fade; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } - -.fa-beat-fade { - -webkit-animation-name: fa-beat-fade; - animation-name: fa-beat-fade; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); - animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } - -.fa-flip { - -webkit-animation-name: fa-flip; - animation-name: fa-flip; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); - animation-timing-function: var(--fa-animation-timing, ease-in-out); } - -.fa-shake { - -webkit-animation-name: fa-shake; - animation-name: fa-shake; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, linear); - animation-timing-function: var(--fa-animation-timing, linear); } - -.fa-spin { - -webkit-animation-name: fa-spin; - animation-name: fa-spin; - -webkit-animation-delay: var(--fa-animation-delay, 0); - animation-delay: var(--fa-animation-delay, 0); - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 2s); - animation-duration: var(--fa-animation-duration, 2s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, linear); - animation-timing-function: var(--fa-animation-timing, linear); } - -.fa-spin-reverse { - --fa-animation-direction: reverse; } - -.fa-pulse, -.fa-spin-pulse { - -webkit-animation-name: fa-spin; - animation-name: fa-spin; - -webkit-animation-direction: var(--fa-animation-direction, normal); - animation-direction: var(--fa-animation-direction, normal); - -webkit-animation-duration: var(--fa-animation-duration, 1s); - animation-duration: var(--fa-animation-duration, 1s); - -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); - animation-iteration-count: var(--fa-animation-iteration-count, infinite); - -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); - animation-timing-function: var(--fa-animation-timing, steps(8)); } - -@media (prefers-reduced-motion: reduce) { - .fa-beat, - .fa-bounce, - .fa-fade, - .fa-beat-fade, - .fa-flip, - .fa-pulse, - .fa-shake, - .fa-spin, - .fa-spin-pulse { - -webkit-animation-delay: -1ms; - animation-delay: -1ms; - -webkit-animation-duration: 1ms; - animation-duration: 1ms; - -webkit-animation-iteration-count: 1; - animation-iteration-count: 1; - transition-delay: 0s; - transition-duration: 0s; } } - -@-webkit-keyframes fa-beat { - 0%, 90% { - -webkit-transform: scale(1); - transform: scale(1); } - 45% { - -webkit-transform: scale(var(--fa-beat-scale, 1.25)); - transform: scale(var(--fa-beat-scale, 1.25)); } } - -@keyframes fa-beat { - 0%, 90% { - -webkit-transform: scale(1); - transform: scale(1); } - 45% { - -webkit-transform: scale(var(--fa-beat-scale, 1.25)); - transform: scale(var(--fa-beat-scale, 1.25)); } } - -@-webkit-keyframes fa-bounce { - 0% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 10% { - -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); - transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } - 30% { - -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); - transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } - 50% { - -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); - transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } - 57% { - -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); - transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } - 64% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 100% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } } - -@keyframes fa-bounce { - 0% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 10% { - -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); - transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } - 30% { - -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); - transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } - 50% { - -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); - transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } - 57% { - -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); - transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } - 64% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } - 100% { - -webkit-transform: scale(1, 1) translateY(0); - transform: scale(1, 1) translateY(0); } } - -@-webkit-keyframes fa-fade { - 50% { - opacity: var(--fa-fade-opacity, 0.4); } } - -@keyframes fa-fade { - 50% { - opacity: var(--fa-fade-opacity, 0.4); } } - -@-webkit-keyframes fa-beat-fade { - 0%, 100% { - opacity: var(--fa-beat-fade-opacity, 0.4); - -webkit-transform: scale(1); - transform: scale(1); } - 50% { - opacity: 1; - -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); - transform: scale(var(--fa-beat-fade-scale, 1.125)); } } - -@keyframes fa-beat-fade { - 0%, 100% { - opacity: var(--fa-beat-fade-opacity, 0.4); - -webkit-transform: scale(1); - transform: scale(1); } - 50% { - opacity: 1; - -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); - transform: scale(var(--fa-beat-fade-scale, 1.125)); } } - -@-webkit-keyframes fa-flip { - 50% { - -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); - transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } - -@keyframes fa-flip { - 50% { - -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); - transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } - -@-webkit-keyframes fa-shake { - 0% { - -webkit-transform: rotate(-15deg); - transform: rotate(-15deg); } - 4% { - -webkit-transform: rotate(15deg); - transform: rotate(15deg); } - 8%, 24% { - -webkit-transform: rotate(-18deg); - transform: rotate(-18deg); } - 12%, 28% { - -webkit-transform: rotate(18deg); - transform: rotate(18deg); } - 16% { - -webkit-transform: rotate(-22deg); - transform: rotate(-22deg); } - 20% { - -webkit-transform: rotate(22deg); - transform: rotate(22deg); } - 32% { - -webkit-transform: rotate(-12deg); - transform: rotate(-12deg); } - 36% { - -webkit-transform: rotate(12deg); - transform: rotate(12deg); } - 40%, 100% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } } - -@keyframes fa-shake { - 0% { - -webkit-transform: rotate(-15deg); - transform: rotate(-15deg); } - 4% { - -webkit-transform: rotate(15deg); - transform: rotate(15deg); } - 8%, 24% { - -webkit-transform: rotate(-18deg); - transform: rotate(-18deg); } - 12%, 28% { - -webkit-transform: rotate(18deg); - transform: rotate(18deg); } - 16% { - -webkit-transform: rotate(-22deg); - transform: rotate(-22deg); } - 20% { - -webkit-transform: rotate(22deg); - transform: rotate(22deg); } - 32% { - -webkit-transform: rotate(-12deg); - transform: rotate(-12deg); } - 36% { - -webkit-transform: rotate(12deg); - transform: rotate(12deg); } - 40%, 100% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } } - -@-webkit-keyframes fa-spin { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); } } - -@keyframes fa-spin { - 0% { - -webkit-transform: rotate(0deg); - transform: rotate(0deg); } - 100% { - -webkit-transform: rotate(360deg); - transform: rotate(360deg); } } - -.fa-rotate-90 { - -webkit-transform: rotate(90deg); - transform: rotate(90deg); } - -.fa-rotate-180 { - -webkit-transform: rotate(180deg); - transform: rotate(180deg); } - -.fa-rotate-270 { - -webkit-transform: rotate(270deg); - transform: rotate(270deg); } - -.fa-flip-horizontal { - -webkit-transform: scale(-1, 1); - transform: scale(-1, 1); } - -.fa-flip-vertical { - -webkit-transform: scale(1, -1); - transform: scale(1, -1); } - -.fa-flip-both, -.fa-flip-horizontal.fa-flip-vertical { - -webkit-transform: scale(-1, -1); - transform: scale(-1, -1); } - -.fa-rotate-by { - -webkit-transform: rotate(var(--fa-rotate-angle, none)); - transform: rotate(var(--fa-rotate-angle, none)); } - -.fa-stack { - display: inline-block; - height: 2em; - line-height: 2em; - position: relative; - vertical-align: middle; - width: 2.5em; } - -.fa-stack-1x, -.fa-stack-2x { - left: 0; - position: absolute; - text-align: center; - width: 100%; - z-index: var(--fa-stack-z-index, auto); } - -.fa-stack-1x { - line-height: inherit; } - -.fa-stack-2x { - font-size: 2em; } - -.fa-inverse { - color: var(--fa-inverse, #fff); } - -/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen -readers do not read off random characters that represent icons */ -.fa-0::before { - content: "\30"; } - -.fa-1::before { - content: "\31"; } - -.fa-2::before { - content: "\32"; } - -.fa-3::before { - content: "\33"; } - -.fa-4::before { - content: "\34"; } - -.fa-5::before { - content: "\35"; } - -.fa-6::before { - content: "\36"; } - -.fa-7::before { - content: "\37"; } - -.fa-8::before { - content: "\38"; } - -.fa-9::before { - content: "\39"; } - -.fa-a::before { - content: "\41"; } - -.fa-address-book::before { - content: "\f2b9"; } - -.fa-contact-book::before { - content: "\f2b9"; } - -.fa-address-card::before { - content: "\f2bb"; } - -.fa-contact-card::before { - content: "\f2bb"; } - -.fa-vcard::before { - content: "\f2bb"; } - -.fa-align-center::before { - content: "\f037"; } - -.fa-align-justify::before { - content: "\f039"; } - -.fa-align-left::before { - content: "\f036"; } - -.fa-align-right::before { - content: "\f038"; } - -.fa-anchor::before { - content: "\f13d"; } - -.fa-anchor-circle-check::before { - content: "\e4aa"; } - -.fa-anchor-circle-exclamation::before { - content: "\e4ab"; } - -.fa-anchor-circle-xmark::before { - content: "\e4ac"; } - -.fa-anchor-lock::before { - content: "\e4ad"; } - -.fa-angle-down::before { - content: "\f107"; } - -.fa-angle-left::before { - content: "\f104"; } - -.fa-angle-right::before { - content: "\f105"; } - -.fa-angle-up::before { - content: "\f106"; } - -.fa-angles-down::before { - content: "\f103"; } - -.fa-angle-double-down::before { - content: "\f103"; } - -.fa-angles-left::before { - content: "\f100"; } - -.fa-angle-double-left::before { - content: "\f100"; } - -.fa-angles-right::before { - content: "\f101"; } - -.fa-angle-double-right::before { - content: "\f101"; } - -.fa-angles-up::before { - content: "\f102"; } - -.fa-angle-double-up::before { - content: "\f102"; } - -.fa-ankh::before { - content: "\f644"; } - -.fa-apple-whole::before { - content: "\f5d1"; } - -.fa-apple-alt::before { - content: "\f5d1"; } - -.fa-archway::before { - content: "\f557"; } - -.fa-arrow-down::before { - content: "\f063"; } - -.fa-arrow-down-1-9::before { - content: "\f162"; } - -.fa-sort-numeric-asc::before { - content: "\f162"; } - -.fa-sort-numeric-down::before { - content: "\f162"; } - -.fa-arrow-down-9-1::before { - content: "\f886"; } - -.fa-sort-numeric-desc::before { - content: "\f886"; } - -.fa-sort-numeric-down-alt::before { - content: "\f886"; } - -.fa-arrow-down-a-z::before { - content: "\f15d"; } - -.fa-sort-alpha-asc::before { - content: "\f15d"; } - -.fa-sort-alpha-down::before { - content: "\f15d"; } - -.fa-arrow-down-long::before { - content: "\f175"; } - -.fa-long-arrow-down::before { - content: "\f175"; } - -.fa-arrow-down-short-wide::before { - content: "\f884"; } - -.fa-sort-amount-desc::before { - content: "\f884"; } - -.fa-sort-amount-down-alt::before { - content: "\f884"; } - -.fa-arrow-down-up-across-line::before { - content: "\e4af"; } - -.fa-arrow-down-up-lock::before { - content: "\e4b0"; } - -.fa-arrow-down-wide-short::before { - content: "\f160"; } - -.fa-sort-amount-asc::before { - content: "\f160"; } - -.fa-sort-amount-down::before { - content: "\f160"; } - -.fa-arrow-down-z-a::before { - content: "\f881"; } - -.fa-sort-alpha-desc::before { - content: "\f881"; } - -.fa-sort-alpha-down-alt::before { - content: "\f881"; } - -.fa-arrow-left::before { - content: "\f060"; } - -.fa-arrow-left-long::before { - content: "\f177"; } - -.fa-long-arrow-left::before { - content: "\f177"; } - -.fa-arrow-pointer::before { - content: "\f245"; } - -.fa-mouse-pointer::before { - content: "\f245"; } - -.fa-arrow-right::before { - content: "\f061"; } - -.fa-arrow-right-arrow-left::before { - content: "\f0ec"; } - -.fa-exchange::before { - content: "\f0ec"; } - -.fa-arrow-right-from-bracket::before { - content: "\f08b"; } - -.fa-sign-out::before { - content: "\f08b"; } - -.fa-arrow-right-long::before { - content: "\f178"; } - -.fa-long-arrow-right::before { - content: "\f178"; } - -.fa-arrow-right-to-bracket::before { - content: "\f090"; } - -.fa-sign-in::before { - content: "\f090"; } - -.fa-arrow-right-to-city::before { - content: "\e4b3"; } - -.fa-arrow-rotate-left::before { - content: "\f0e2"; } - -.fa-arrow-left-rotate::before { - content: "\f0e2"; } - -.fa-arrow-rotate-back::before { - content: "\f0e2"; } - -.fa-arrow-rotate-backward::before { - content: "\f0e2"; } - -.fa-undo::before { - content: "\f0e2"; } - -.fa-arrow-rotate-right::before { - content: "\f01e"; } - -.fa-arrow-right-rotate::before { - content: "\f01e"; } - -.fa-arrow-rotate-forward::before { - content: "\f01e"; } - -.fa-redo::before { - content: "\f01e"; } - -.fa-arrow-trend-down::before { - content: "\e097"; } - -.fa-arrow-trend-up::before { - content: "\e098"; } - -.fa-arrow-turn-down::before { - content: "\f149"; } - -.fa-level-down::before { - content: "\f149"; } - -.fa-arrow-turn-up::before { - content: "\f148"; } - -.fa-level-up::before { - content: "\f148"; } - -.fa-arrow-up::before { - content: "\f062"; } - -.fa-arrow-up-1-9::before { - content: "\f163"; } - -.fa-sort-numeric-up::before { - content: "\f163"; } - -.fa-arrow-up-9-1::before { - content: "\f887"; } - -.fa-sort-numeric-up-alt::before { - content: "\f887"; } - -.fa-arrow-up-a-z::before { - content: "\f15e"; } - -.fa-sort-alpha-up::before { - content: "\f15e"; } - -.fa-arrow-up-from-bracket::before { - content: "\e09a"; } - -.fa-arrow-up-from-ground-water::before { - content: "\e4b5"; } - -.fa-arrow-up-from-water-pump::before { - content: "\e4b6"; } - -.fa-arrow-up-long::before { - content: "\f176"; } - -.fa-long-arrow-up::before { - content: "\f176"; } - -.fa-arrow-up-right-dots::before { - content: "\e4b7"; } - -.fa-arrow-up-right-from-square::before { - content: "\f08e"; } - -.fa-external-link::before { - content: "\f08e"; } - -.fa-arrow-up-short-wide::before { - content: "\f885"; } - -.fa-sort-amount-up-alt::before { - content: "\f885"; } - -.fa-arrow-up-wide-short::before { - content: "\f161"; } - -.fa-sort-amount-up::before { - content: "\f161"; } - -.fa-arrow-up-z-a::before { - content: "\f882"; } - -.fa-sort-alpha-up-alt::before { - content: "\f882"; } - -.fa-arrows-down-to-line::before { - content: "\e4b8"; } - -.fa-arrows-down-to-people::before { - content: "\e4b9"; } - -.fa-arrows-left-right::before { - content: "\f07e"; } - -.fa-arrows-h::before { - content: "\f07e"; } - -.fa-arrows-left-right-to-line::before { - content: "\e4ba"; } - -.fa-arrows-rotate::before { - content: "\f021"; } - -.fa-refresh::before { - content: "\f021"; } - -.fa-sync::before { - content: "\f021"; } - -.fa-arrows-spin::before { - content: "\e4bb"; } - -.fa-arrows-split-up-and-left::before { - content: "\e4bc"; } - -.fa-arrows-to-circle::before { - content: "\e4bd"; } - -.fa-arrows-to-dot::before { - content: "\e4be"; } - -.fa-arrows-to-eye::before { - content: "\e4bf"; } - -.fa-arrows-turn-right::before { - content: "\e4c0"; } - -.fa-arrows-turn-to-dots::before { - content: "\e4c1"; } - -.fa-arrows-up-down::before { - content: "\f07d"; } - -.fa-arrows-v::before { - content: "\f07d"; } - -.fa-arrows-up-down-left-right::before { - content: "\f047"; } - -.fa-arrows::before { - content: "\f047"; } - -.fa-arrows-up-to-line::before { - content: "\e4c2"; } - -.fa-asterisk::before { - content: "\2a"; } - -.fa-at::before { - content: "\40"; } - -.fa-atom::before { - content: "\f5d2"; } - -.fa-audio-description::before { - content: "\f29e"; } - -.fa-austral-sign::before { - content: "\e0a9"; } - -.fa-award::before { - content: "\f559"; } - -.fa-b::before { - content: "\42"; } - -.fa-baby::before { - content: "\f77c"; } - -.fa-baby-carriage::before { - content: "\f77d"; } - -.fa-carriage-baby::before { - content: "\f77d"; } - -.fa-backward::before { - content: "\f04a"; } - -.fa-backward-fast::before { - content: "\f049"; } - -.fa-fast-backward::before { - content: "\f049"; } - -.fa-backward-step::before { - content: "\f048"; } - -.fa-step-backward::before { - content: "\f048"; } - -.fa-bacon::before { - content: "\f7e5"; } - -.fa-bacteria::before { - content: "\e059"; } - -.fa-bacterium::before { - content: "\e05a"; } - -.fa-bag-shopping::before { - content: "\f290"; } - -.fa-shopping-bag::before { - content: "\f290"; } - -.fa-bahai::before { - content: "\f666"; } - -.fa-baht-sign::before { - content: "\e0ac"; } - -.fa-ban::before { - content: "\f05e"; } - -.fa-cancel::before { - content: "\f05e"; } - -.fa-ban-smoking::before { - content: "\f54d"; } - -.fa-smoking-ban::before { - content: "\f54d"; } - -.fa-bandage::before { - content: "\f462"; } - -.fa-band-aid::before { - content: "\f462"; } - -.fa-barcode::before { - content: "\f02a"; } - -.fa-bars::before { - content: "\f0c9"; } - -.fa-navicon::before { - content: "\f0c9"; } - -.fa-bars-progress::before { - content: "\f828"; } - -.fa-tasks-alt::before { - content: "\f828"; } - -.fa-bars-staggered::before { - content: "\f550"; } - -.fa-reorder::before { - content: "\f550"; } - -.fa-stream::before { - content: "\f550"; } - -.fa-baseball::before { - content: "\f433"; } - -.fa-baseball-ball::before { - content: "\f433"; } - -.fa-baseball-bat-ball::before { - content: "\f432"; } - -.fa-basket-shopping::before { - content: "\f291"; } - -.fa-shopping-basket::before { - content: "\f291"; } - -.fa-basketball::before { - content: "\f434"; } - -.fa-basketball-ball::before { - content: "\f434"; } - -.fa-bath::before { - content: "\f2cd"; } - -.fa-bathtub::before { - content: "\f2cd"; } - -.fa-battery-empty::before { - content: "\f244"; } - -.fa-battery-0::before { - content: "\f244"; } - -.fa-battery-full::before { - content: "\f240"; } - -.fa-battery::before { - content: "\f240"; } - -.fa-battery-5::before { - content: "\f240"; } - -.fa-battery-half::before { - content: "\f242"; } - -.fa-battery-3::before { - content: "\f242"; } - -.fa-battery-quarter::before { - content: "\f243"; } - -.fa-battery-2::before { - content: "\f243"; } - -.fa-battery-three-quarters::before { - content: "\f241"; } - -.fa-battery-4::before { - content: "\f241"; } - -.fa-bed::before { - content: "\f236"; } - -.fa-bed-pulse::before { - content: "\f487"; } - -.fa-procedures::before { - content: "\f487"; } - -.fa-beer-mug-empty::before { - content: "\f0fc"; } - -.fa-beer::before { - content: "\f0fc"; } - -.fa-bell::before { - content: "\f0f3"; } - -.fa-bell-concierge::before { - content: "\f562"; } - -.fa-concierge-bell::before { - content: "\f562"; } - -.fa-bell-slash::before { - content: "\f1f6"; } - -.fa-bezier-curve::before { - content: "\f55b"; } - -.fa-bicycle::before { - content: "\f206"; } - -.fa-binoculars::before { - content: "\f1e5"; } - -.fa-biohazard::before { - content: "\f780"; } - -.fa-bitcoin-sign::before { - content: "\e0b4"; } - -.fa-blender::before { - content: "\f517"; } - -.fa-blender-phone::before { - content: "\f6b6"; } - -.fa-blog::before { - content: "\f781"; } - -.fa-bold::before { - content: "\f032"; } - -.fa-bolt::before { - content: "\f0e7"; } - -.fa-zap::before { - content: "\f0e7"; } - -.fa-bolt-lightning::before { - content: "\e0b7"; } - -.fa-bomb::before { - content: "\f1e2"; } - -.fa-bone::before { - content: "\f5d7"; } - -.fa-bong::before { - content: "\f55c"; } - -.fa-book::before { - content: "\f02d"; } - -.fa-book-atlas::before { - content: "\f558"; } - -.fa-atlas::before { - content: "\f558"; } - -.fa-book-bible::before { - content: "\f647"; } - -.fa-bible::before { - content: "\f647"; } - -.fa-book-bookmark::before { - content: "\e0bb"; } - -.fa-book-journal-whills::before { - content: "\f66a"; } - -.fa-journal-whills::before { - content: "\f66a"; } - -.fa-book-medical::before { - content: "\f7e6"; } - -.fa-book-open::before { - content: "\f518"; } - -.fa-book-open-reader::before { - content: "\f5da"; } - -.fa-book-reader::before { - content: "\f5da"; } - -.fa-book-quran::before { - content: "\f687"; } - -.fa-quran::before { - content: "\f687"; } - -.fa-book-skull::before { - content: "\f6b7"; } - -.fa-book-dead::before { - content: "\f6b7"; } - -.fa-bookmark::before { - content: "\f02e"; } - -.fa-border-all::before { - content: "\f84c"; } - -.fa-border-none::before { - content: "\f850"; } - -.fa-border-top-left::before { - content: "\f853"; } - -.fa-border-style::before { - content: "\f853"; } - -.fa-bore-hole::before { - content: "\e4c3"; } - -.fa-bottle-droplet::before { - content: "\e4c4"; } - -.fa-bottle-water::before { - content: "\e4c5"; } - -.fa-bowl-food::before { - content: "\e4c6"; } - -.fa-bowl-rice::before { - content: "\e2eb"; } - -.fa-bowling-ball::before { - content: "\f436"; } - -.fa-box::before { - content: "\f466"; } - -.fa-box-archive::before { - content: "\f187"; } - -.fa-archive::before { - content: "\f187"; } - -.fa-box-open::before { - content: "\f49e"; } - -.fa-box-tissue::before { - content: "\e05b"; } - -.fa-boxes-packing::before { - content: "\e4c7"; } - -.fa-boxes-stacked::before { - content: "\f468"; } - -.fa-boxes::before { - content: "\f468"; } - -.fa-boxes-alt::before { - content: "\f468"; } - -.fa-braille::before { - content: "\f2a1"; } - -.fa-brain::before { - content: "\f5dc"; } - -.fa-brazilian-real-sign::before { - content: "\e46c"; } - -.fa-bread-slice::before { - content: "\f7ec"; } - -.fa-bridge::before { - content: "\e4c8"; } - -.fa-bridge-circle-check::before { - content: "\e4c9"; } - -.fa-bridge-circle-exclamation::before { - content: "\e4ca"; } - -.fa-bridge-circle-xmark::before { - content: "\e4cb"; } - -.fa-bridge-lock::before { - content: "\e4cc"; } - -.fa-bridge-water::before { - content: "\e4ce"; } - -.fa-briefcase::before { - content: "\f0b1"; } - -.fa-briefcase-medical::before { - content: "\f469"; } - -.fa-broom::before { - content: "\f51a"; } - -.fa-broom-ball::before { - content: "\f458"; } - -.fa-quidditch::before { - content: "\f458"; } - -.fa-quidditch-broom-ball::before { - content: "\f458"; } - -.fa-brush::before { - content: "\f55d"; } - -.fa-bucket::before { - content: "\e4cf"; } - -.fa-bug::before { - content: "\f188"; } - -.fa-bug-slash::before { - content: "\e490"; } - -.fa-bugs::before { - content: "\e4d0"; } - -.fa-building::before { - content: "\f1ad"; } - -.fa-building-circle-arrow-right::before { - content: "\e4d1"; } - -.fa-building-circle-check::before { - content: "\e4d2"; } - -.fa-building-circle-exclamation::before { - content: "\e4d3"; } - -.fa-building-circle-xmark::before { - content: "\e4d4"; } - -.fa-building-columns::before { - content: "\f19c"; } - -.fa-bank::before { - content: "\f19c"; } - -.fa-institution::before { - content: "\f19c"; } - -.fa-museum::before { - content: "\f19c"; } - -.fa-university::before { - content: "\f19c"; } - -.fa-building-flag::before { - content: "\e4d5"; } - -.fa-building-lock::before { - content: "\e4d6"; } - -.fa-building-ngo::before { - content: "\e4d7"; } - -.fa-building-shield::before { - content: "\e4d8"; } - -.fa-building-un::before { - content: "\e4d9"; } - -.fa-building-user::before { - content: "\e4da"; } - -.fa-building-wheat::before { - content: "\e4db"; } - -.fa-bullhorn::before { - content: "\f0a1"; } - -.fa-bullseye::before { - content: "\f140"; } - -.fa-burger::before { - content: "\f805"; } - -.fa-hamburger::before { - content: "\f805"; } - -.fa-burst::before { - content: "\e4dc"; } - -.fa-bus::before { - content: "\f207"; } - -.fa-bus-simple::before { - content: "\f55e"; } - -.fa-bus-alt::before { - content: "\f55e"; } - -.fa-business-time::before { - content: "\f64a"; } - -.fa-briefcase-clock::before { - content: "\f64a"; } - -.fa-c::before { - content: "\43"; } - -.fa-cake-candles::before { - content: "\f1fd"; } - -.fa-birthday-cake::before { - content: "\f1fd"; } - -.fa-cake::before { - content: "\f1fd"; } - -.fa-calculator::before { - content: "\f1ec"; } - -.fa-calendar::before { - content: "\f133"; } - -.fa-calendar-check::before { - content: "\f274"; } - -.fa-calendar-day::before { - content: "\f783"; } - -.fa-calendar-days::before { - content: "\f073"; } - -.fa-calendar-alt::before { - content: "\f073"; } - -.fa-calendar-minus::before { - content: "\f272"; } - -.fa-calendar-plus::before { - content: "\f271"; } - -.fa-calendar-week::before { - content: "\f784"; } - -.fa-calendar-xmark::before { - content: "\f273"; } - -.fa-calendar-times::before { - content: "\f273"; } - -.fa-camera::before { - content: "\f030"; } - -.fa-camera-alt::before { - content: "\f030"; } - -.fa-camera-retro::before { - content: "\f083"; } - -.fa-camera-rotate::before { - content: "\e0d8"; } - -.fa-campground::before { - content: "\f6bb"; } - -.fa-candy-cane::before { - content: "\f786"; } - -.fa-cannabis::before { - content: "\f55f"; } - -.fa-capsules::before { - content: "\f46b"; } - -.fa-car::before { - content: "\f1b9"; } - -.fa-automobile::before { - content: "\f1b9"; } - -.fa-car-battery::before { - content: "\f5df"; } - -.fa-battery-car::before { - content: "\f5df"; } - -.fa-car-burst::before { - content: "\f5e1"; } - -.fa-car-crash::before { - content: "\f5e1"; } - -.fa-car-on::before { - content: "\e4dd"; } - -.fa-car-rear::before { - content: "\f5de"; } - -.fa-car-alt::before { - content: "\f5de"; } - -.fa-car-side::before { - content: "\f5e4"; } - -.fa-car-tunnel::before { - content: "\e4de"; } - -.fa-caravan::before { - content: "\f8ff"; } - -.fa-caret-down::before { - content: "\f0d7"; } - -.fa-caret-left::before { - content: "\f0d9"; } - -.fa-caret-right::before { - content: "\f0da"; } - -.fa-caret-up::before { - content: "\f0d8"; } - -.fa-carrot::before { - content: "\f787"; } - -.fa-cart-arrow-down::before { - content: "\f218"; } - -.fa-cart-flatbed::before { - content: "\f474"; } - -.fa-dolly-flatbed::before { - content: "\f474"; } - -.fa-cart-flatbed-suitcase::before { - content: "\f59d"; } - -.fa-luggage-cart::before { - content: "\f59d"; } - -.fa-cart-plus::before { - content: "\f217"; } - -.fa-cart-shopping::before { - content: "\f07a"; } - -.fa-shopping-cart::before { - content: "\f07a"; } - -.fa-cash-register::before { - content: "\f788"; } - -.fa-cat::before { - content: "\f6be"; } - -.fa-cedi-sign::before { - content: "\e0df"; } - -.fa-cent-sign::before { - content: "\e3f5"; } - -.fa-certificate::before { - content: "\f0a3"; } - -.fa-chair::before { - content: "\f6c0"; } - -.fa-chalkboard::before { - content: "\f51b"; } - -.fa-blackboard::before { - content: "\f51b"; } - -.fa-chalkboard-user::before { - content: "\f51c"; } - -.fa-chalkboard-teacher::before { - content: "\f51c"; } - -.fa-champagne-glasses::before { - content: "\f79f"; } - -.fa-glass-cheers::before { - content: "\f79f"; } - -.fa-charging-station::before { - content: "\f5e7"; } - -.fa-chart-area::before { - content: "\f1fe"; } - -.fa-area-chart::before { - content: "\f1fe"; } - -.fa-chart-bar::before { - content: "\f080"; } - -.fa-bar-chart::before { - content: "\f080"; } - -.fa-chart-column::before { - content: "\e0e3"; } - -.fa-chart-gantt::before { - content: "\e0e4"; } - -.fa-chart-line::before { - content: "\f201"; } - -.fa-line-chart::before { - content: "\f201"; } - -.fa-chart-pie::before { - content: "\f200"; } - -.fa-pie-chart::before { - content: "\f200"; } - -.fa-chart-simple::before { - content: "\e473"; } - -.fa-check::before { - content: "\f00c"; } - -.fa-check-double::before { - content: "\f560"; } - -.fa-check-to-slot::before { - content: "\f772"; } - -.fa-vote-yea::before { - content: "\f772"; } - -.fa-cheese::before { - content: "\f7ef"; } - -.fa-chess::before { - content: "\f439"; } - -.fa-chess-bishop::before { - content: "\f43a"; } - -.fa-chess-board::before { - content: "\f43c"; } - -.fa-chess-king::before { - content: "\f43f"; } - -.fa-chess-knight::before { - content: "\f441"; } - -.fa-chess-pawn::before { - content: "\f443"; } - -.fa-chess-queen::before { - content: "\f445"; } - -.fa-chess-rook::before { - content: "\f447"; } - -.fa-chevron-down::before { - content: "\f078"; } - -.fa-chevron-left::before { - content: "\f053"; } - -.fa-chevron-right::before { - content: "\f054"; } - -.fa-chevron-up::before { - content: "\f077"; } - -.fa-child::before { - content: "\f1ae"; } - -.fa-child-dress::before { - content: "\e59c"; } - -.fa-child-reaching::before { - content: "\e59d"; } - -.fa-child-rifle::before { - content: "\e4e0"; } - -.fa-children::before { - content: "\e4e1"; } - -.fa-church::before { - content: "\f51d"; } - -.fa-circle::before { - content: "\f111"; } - -.fa-circle-arrow-down::before { - content: "\f0ab"; } - -.fa-arrow-circle-down::before { - content: "\f0ab"; } - -.fa-circle-arrow-left::before { - content: "\f0a8"; } - -.fa-arrow-circle-left::before { - content: "\f0a8"; } - -.fa-circle-arrow-right::before { - content: "\f0a9"; } - -.fa-arrow-circle-right::before { - content: "\f0a9"; } - -.fa-circle-arrow-up::before { - content: "\f0aa"; } - -.fa-arrow-circle-up::before { - content: "\f0aa"; } - -.fa-circle-check::before { - content: "\f058"; } - -.fa-check-circle::before { - content: "\f058"; } - -.fa-circle-chevron-down::before { - content: "\f13a"; } - -.fa-chevron-circle-down::before { - content: "\f13a"; } - -.fa-circle-chevron-left::before { - content: "\f137"; } - -.fa-chevron-circle-left::before { - content: "\f137"; } - -.fa-circle-chevron-right::before { - content: "\f138"; } - -.fa-chevron-circle-right::before { - content: "\f138"; } - -.fa-circle-chevron-up::before { - content: "\f139"; } - -.fa-chevron-circle-up::before { - content: "\f139"; } - -.fa-circle-dollar-to-slot::before { - content: "\f4b9"; } - -.fa-donate::before { - content: "\f4b9"; } - -.fa-circle-dot::before { - content: "\f192"; } - -.fa-dot-circle::before { - content: "\f192"; } - -.fa-circle-down::before { - content: "\f358"; } - -.fa-arrow-alt-circle-down::before { - content: "\f358"; } - -.fa-circle-exclamation::before { - content: "\f06a"; } - -.fa-exclamation-circle::before { - content: "\f06a"; } - -.fa-circle-h::before { - content: "\f47e"; } - -.fa-hospital-symbol::before { - content: "\f47e"; } - -.fa-circle-half-stroke::before { - content: "\f042"; } - -.fa-adjust::before { - content: "\f042"; } - -.fa-circle-info::before { - content: "\f05a"; } - -.fa-info-circle::before { - content: "\f05a"; } - -.fa-circle-left::before { - content: "\f359"; } - -.fa-arrow-alt-circle-left::before { - content: "\f359"; } - -.fa-circle-minus::before { - content: "\f056"; } - -.fa-minus-circle::before { - content: "\f056"; } - -.fa-circle-nodes::before { - content: "\e4e2"; } - -.fa-circle-notch::before { - content: "\f1ce"; } - -.fa-circle-pause::before { - content: "\f28b"; } - -.fa-pause-circle::before { - content: "\f28b"; } - -.fa-circle-play::before { - content: "\f144"; } - -.fa-play-circle::before { - content: "\f144"; } - -.fa-circle-plus::before { - content: "\f055"; } - -.fa-plus-circle::before { - content: "\f055"; } - -.fa-circle-question::before { - content: "\f059"; } - -.fa-question-circle::before { - content: "\f059"; } - -.fa-circle-radiation::before { - content: "\f7ba"; } - -.fa-radiation-alt::before { - content: "\f7ba"; } - -.fa-circle-right::before { - content: "\f35a"; } - -.fa-arrow-alt-circle-right::before { - content: "\f35a"; } - -.fa-circle-stop::before { - content: "\f28d"; } - -.fa-stop-circle::before { - content: "\f28d"; } - -.fa-circle-up::before { - content: "\f35b"; } - -.fa-arrow-alt-circle-up::before { - content: "\f35b"; } - -.fa-circle-user::before { - content: "\f2bd"; } - -.fa-user-circle::before { - content: "\f2bd"; } - -.fa-circle-xmark::before { - content: "\f057"; } - -.fa-times-circle::before { - content: "\f057"; } - -.fa-xmark-circle::before { - content: "\f057"; } - -.fa-city::before { - content: "\f64f"; } - -.fa-clapperboard::before { - content: "\e131"; } - -.fa-clipboard::before { - content: "\f328"; } - -.fa-clipboard-check::before { - content: "\f46c"; } - -.fa-clipboard-list::before { - content: "\f46d"; } - -.fa-clipboard-question::before { - content: "\e4e3"; } - -.fa-clipboard-user::before { - content: "\f7f3"; } - -.fa-clock::before { - content: "\f017"; } - -.fa-clock-four::before { - content: "\f017"; } - -.fa-clock-rotate-left::before { - content: "\f1da"; } - -.fa-history::before { - content: "\f1da"; } - -.fa-clone::before { - content: "\f24d"; } - -.fa-closed-captioning::before { - content: "\f20a"; } - -.fa-cloud::before { - content: "\f0c2"; } - -.fa-cloud-arrow-down::before { - content: "\f0ed"; } - -.fa-cloud-download::before { - content: "\f0ed"; } - -.fa-cloud-download-alt::before { - content: "\f0ed"; } - -.fa-cloud-arrow-up::before { - content: "\f0ee"; } - -.fa-cloud-upload::before { - content: "\f0ee"; } - -.fa-cloud-upload-alt::before { - content: "\f0ee"; } - -.fa-cloud-bolt::before { - content: "\f76c"; } - -.fa-thunderstorm::before { - content: "\f76c"; } - -.fa-cloud-meatball::before { - content: "\f73b"; } - -.fa-cloud-moon::before { - content: "\f6c3"; } - -.fa-cloud-moon-rain::before { - content: "\f73c"; } - -.fa-cloud-rain::before { - content: "\f73d"; } - -.fa-cloud-showers-heavy::before { - content: "\f740"; } - -.fa-cloud-showers-water::before { - content: "\e4e4"; } - -.fa-cloud-sun::before { - content: "\f6c4"; } - -.fa-cloud-sun-rain::before { - content: "\f743"; } - -.fa-clover::before { - content: "\e139"; } - -.fa-code::before { - content: "\f121"; } - -.fa-code-branch::before { - content: "\f126"; } - -.fa-code-commit::before { - content: "\f386"; } - -.fa-code-compare::before { - content: "\e13a"; } - -.fa-code-fork::before { - content: "\e13b"; } - -.fa-code-merge::before { - content: "\f387"; } - -.fa-code-pull-request::before { - content: "\e13c"; } - -.fa-coins::before { - content: "\f51e"; } - -.fa-colon-sign::before { - content: "\e140"; } - -.fa-comment::before { - content: "\f075"; } - -.fa-comment-dollar::before { - content: "\f651"; } - -.fa-comment-dots::before { - content: "\f4ad"; } - -.fa-commenting::before { - content: "\f4ad"; } - -.fa-comment-medical::before { - content: "\f7f5"; } - -.fa-comment-slash::before { - content: "\f4b3"; } - -.fa-comment-sms::before { - content: "\f7cd"; } - -.fa-sms::before { - content: "\f7cd"; } - -.fa-comments::before { - content: "\f086"; } - -.fa-comments-dollar::before { - content: "\f653"; } - -.fa-compact-disc::before { - content: "\f51f"; } - -.fa-compass::before { - content: "\f14e"; } - -.fa-compass-drafting::before { - content: "\f568"; } - -.fa-drafting-compass::before { - content: "\f568"; } - -.fa-compress::before { - content: "\f066"; } - -.fa-computer::before { - content: "\e4e5"; } - -.fa-computer-mouse::before { - content: "\f8cc"; } - -.fa-mouse::before { - content: "\f8cc"; } - -.fa-cookie::before { - content: "\f563"; } - -.fa-cookie-bite::before { - content: "\f564"; } - -.fa-copy::before { - content: "\f0c5"; } - -.fa-copyright::before { - content: "\f1f9"; } - -.fa-couch::before { - content: "\f4b8"; } - -.fa-cow::before { - content: "\f6c8"; } - -.fa-credit-card::before { - content: "\f09d"; } - -.fa-credit-card-alt::before { - content: "\f09d"; } - -.fa-crop::before { - content: "\f125"; } - -.fa-crop-simple::before { - content: "\f565"; } - -.fa-crop-alt::before { - content: "\f565"; } - -.fa-cross::before { - content: "\f654"; } - -.fa-crosshairs::before { - content: "\f05b"; } - -.fa-crow::before { - content: "\f520"; } - -.fa-crown::before { - content: "\f521"; } - -.fa-crutch::before { - content: "\f7f7"; } - -.fa-cruzeiro-sign::before { - content: "\e152"; } - -.fa-cube::before { - content: "\f1b2"; } - -.fa-cubes::before { - content: "\f1b3"; } - -.fa-cubes-stacked::before { - content: "\e4e6"; } - -.fa-d::before { - content: "\44"; } - -.fa-database::before { - content: "\f1c0"; } - -.fa-delete-left::before { - content: "\f55a"; } - -.fa-backspace::before { - content: "\f55a"; } - -.fa-democrat::before { - content: "\f747"; } - -.fa-desktop::before { - content: "\f390"; } - -.fa-desktop-alt::before { - content: "\f390"; } - -.fa-dharmachakra::before { - content: "\f655"; } - -.fa-diagram-next::before { - content: "\e476"; } - -.fa-diagram-predecessor::before { - content: "\e477"; } - -.fa-diagram-project::before { - content: "\f542"; } - -.fa-project-diagram::before { - content: "\f542"; } - -.fa-diagram-successor::before { - content: "\e47a"; } - -.fa-diamond::before { - content: "\f219"; } - -.fa-diamond-turn-right::before { - content: "\f5eb"; } - -.fa-directions::before { - content: "\f5eb"; } - -.fa-dice::before { - content: "\f522"; } - -.fa-dice-d20::before { - content: "\f6cf"; } - -.fa-dice-d6::before { - content: "\f6d1"; } - -.fa-dice-five::before { - content: "\f523"; } - -.fa-dice-four::before { - content: "\f524"; } - -.fa-dice-one::before { - content: "\f525"; } - -.fa-dice-six::before { - content: "\f526"; } - -.fa-dice-three::before { - content: "\f527"; } - -.fa-dice-two::before { - content: "\f528"; } - -.fa-disease::before { - content: "\f7fa"; } - -.fa-display::before { - content: "\e163"; } - -.fa-divide::before { - content: "\f529"; } - -.fa-dna::before { - content: "\f471"; } - -.fa-dog::before { - content: "\f6d3"; } - -.fa-dollar-sign::before { - content: "\24"; } - -.fa-dollar::before { - content: "\24"; } - -.fa-usd::before { - content: "\24"; } - -.fa-dolly::before { - content: "\f472"; } - -.fa-dolly-box::before { - content: "\f472"; } - -.fa-dong-sign::before { - content: "\e169"; } - -.fa-door-closed::before { - content: "\f52a"; } - -.fa-door-open::before { - content: "\f52b"; } - -.fa-dove::before { - content: "\f4ba"; } - -.fa-down-left-and-up-right-to-center::before { - content: "\f422"; } - -.fa-compress-alt::before { - content: "\f422"; } - -.fa-down-long::before { - content: "\f309"; } - -.fa-long-arrow-alt-down::before { - content: "\f309"; } - -.fa-download::before { - content: "\f019"; } - -.fa-dragon::before { - content: "\f6d5"; } - -.fa-draw-polygon::before { - content: "\f5ee"; } - -.fa-droplet::before { - content: "\f043"; } - -.fa-tint::before { - content: "\f043"; } - -.fa-droplet-slash::before { - content: "\f5c7"; } - -.fa-tint-slash::before { - content: "\f5c7"; } - -.fa-drum::before { - content: "\f569"; } - -.fa-drum-steelpan::before { - content: "\f56a"; } - -.fa-drumstick-bite::before { - content: "\f6d7"; } - -.fa-dumbbell::before { - content: "\f44b"; } - -.fa-dumpster::before { - content: "\f793"; } - -.fa-dumpster-fire::before { - content: "\f794"; } - -.fa-dungeon::before { - content: "\f6d9"; } - -.fa-e::before { - content: "\45"; } - -.fa-ear-deaf::before { - content: "\f2a4"; } - -.fa-deaf::before { - content: "\f2a4"; } - -.fa-deafness::before { - content: "\f2a4"; } - -.fa-hard-of-hearing::before { - content: "\f2a4"; } - -.fa-ear-listen::before { - content: "\f2a2"; } - -.fa-assistive-listening-systems::before { - content: "\f2a2"; } - -.fa-earth-africa::before { - content: "\f57c"; } - -.fa-globe-africa::before { - content: "\f57c"; } - -.fa-earth-americas::before { - content: "\f57d"; } - -.fa-earth::before { - content: "\f57d"; } - -.fa-earth-america::before { - content: "\f57d"; } - -.fa-globe-americas::before { - content: "\f57d"; } - -.fa-earth-asia::before { - content: "\f57e"; } - -.fa-globe-asia::before { - content: "\f57e"; } - -.fa-earth-europe::before { - content: "\f7a2"; } - -.fa-globe-europe::before { - content: "\f7a2"; } - -.fa-earth-oceania::before { - content: "\e47b"; } - -.fa-globe-oceania::before { - content: "\e47b"; } - -.fa-egg::before { - content: "\f7fb"; } - -.fa-eject::before { - content: "\f052"; } - -.fa-elevator::before { - content: "\e16d"; } - -.fa-ellipsis::before { - content: "\f141"; } - -.fa-ellipsis-h::before { - content: "\f141"; } - -.fa-ellipsis-vertical::before { - content: "\f142"; } - -.fa-ellipsis-v::before { - content: "\f142"; } - -.fa-envelope::before { - content: "\f0e0"; } - -.fa-envelope-circle-check::before { - content: "\e4e8"; } - -.fa-envelope-open::before { - content: "\f2b6"; } - -.fa-envelope-open-text::before { - content: "\f658"; } - -.fa-envelopes-bulk::before { - content: "\f674"; } - -.fa-mail-bulk::before { - content: "\f674"; } - -.fa-equals::before { - content: "\3d"; } - -.fa-eraser::before { - content: "\f12d"; } - -.fa-ethernet::before { - content: "\f796"; } - -.fa-euro-sign::before { - content: "\f153"; } - -.fa-eur::before { - content: "\f153"; } - -.fa-euro::before { - content: "\f153"; } - -.fa-exclamation::before { - content: "\21"; } - -.fa-expand::before { - content: "\f065"; } - -.fa-explosion::before { - content: "\e4e9"; } - -.fa-eye::before { - content: "\f06e"; } - -.fa-eye-dropper::before { - content: "\f1fb"; } - -.fa-eye-dropper-empty::before { - content: "\f1fb"; } - -.fa-eyedropper::before { - content: "\f1fb"; } - -.fa-eye-low-vision::before { - content: "\f2a8"; } - -.fa-low-vision::before { - content: "\f2a8"; } - -.fa-eye-slash::before { - content: "\f070"; } - -.fa-f::before { - content: "\46"; } - -.fa-face-angry::before { - content: "\f556"; } - -.fa-angry::before { - content: "\f556"; } - -.fa-face-dizzy::before { - content: "\f567"; } - -.fa-dizzy::before { - content: "\f567"; } - -.fa-face-flushed::before { - content: "\f579"; } - -.fa-flushed::before { - content: "\f579"; } - -.fa-face-frown::before { - content: "\f119"; } - -.fa-frown::before { - content: "\f119"; } - -.fa-face-frown-open::before { - content: "\f57a"; } - -.fa-frown-open::before { - content: "\f57a"; } - -.fa-face-grimace::before { - content: "\f57f"; } - -.fa-grimace::before { - content: "\f57f"; } - -.fa-face-grin::before { - content: "\f580"; } - -.fa-grin::before { - content: "\f580"; } - -.fa-face-grin-beam::before { - content: "\f582"; } - -.fa-grin-beam::before { - content: "\f582"; } - -.fa-face-grin-beam-sweat::before { - content: "\f583"; } - -.fa-grin-beam-sweat::before { - content: "\f583"; } - -.fa-face-grin-hearts::before { - content: "\f584"; } - -.fa-grin-hearts::before { - content: "\f584"; } - -.fa-face-grin-squint::before { - content: "\f585"; } - -.fa-grin-squint::before { - content: "\f585"; } - -.fa-face-grin-squint-tears::before { - content: "\f586"; } - -.fa-grin-squint-tears::before { - content: "\f586"; } - -.fa-face-grin-stars::before { - content: "\f587"; } - -.fa-grin-stars::before { - content: "\f587"; } - -.fa-face-grin-tears::before { - content: "\f588"; } - -.fa-grin-tears::before { - content: "\f588"; } - -.fa-face-grin-tongue::before { - content: "\f589"; } - -.fa-grin-tongue::before { - content: "\f589"; } - -.fa-face-grin-tongue-squint::before { - content: "\f58a"; } - -.fa-grin-tongue-squint::before { - content: "\f58a"; } - -.fa-face-grin-tongue-wink::before { - content: "\f58b"; } - -.fa-grin-tongue-wink::before { - content: "\f58b"; } - -.fa-face-grin-wide::before { - content: "\f581"; } - -.fa-grin-alt::before { - content: "\f581"; } - -.fa-face-grin-wink::before { - content: "\f58c"; } - -.fa-grin-wink::before { - content: "\f58c"; } - -.fa-face-kiss::before { - content: "\f596"; } - -.fa-kiss::before { - content: "\f596"; } - -.fa-face-kiss-beam::before { - content: "\f597"; } - -.fa-kiss-beam::before { - content: "\f597"; } - -.fa-face-kiss-wink-heart::before { - content: "\f598"; } - -.fa-kiss-wink-heart::before { - content: "\f598"; } - -.fa-face-laugh::before { - content: "\f599"; } - -.fa-laugh::before { - content: "\f599"; } - -.fa-face-laugh-beam::before { - content: "\f59a"; } - -.fa-laugh-beam::before { - content: "\f59a"; } - -.fa-face-laugh-squint::before { - content: "\f59b"; } - -.fa-laugh-squint::before { - content: "\f59b"; } - -.fa-face-laugh-wink::before { - content: "\f59c"; } - -.fa-laugh-wink::before { - content: "\f59c"; } - -.fa-face-meh::before { - content: "\f11a"; } - -.fa-meh::before { - content: "\f11a"; } - -.fa-face-meh-blank::before { - content: "\f5a4"; } - -.fa-meh-blank::before { - content: "\f5a4"; } - -.fa-face-rolling-eyes::before { - content: "\f5a5"; } - -.fa-meh-rolling-eyes::before { - content: "\f5a5"; } - -.fa-face-sad-cry::before { - content: "\f5b3"; } - -.fa-sad-cry::before { - content: "\f5b3"; } - -.fa-face-sad-tear::before { - content: "\f5b4"; } - -.fa-sad-tear::before { - content: "\f5b4"; } - -.fa-face-smile::before { - content: "\f118"; } - -.fa-smile::before { - content: "\f118"; } - -.fa-face-smile-beam::before { - content: "\f5b8"; } - -.fa-smile-beam::before { - content: "\f5b8"; } - -.fa-face-smile-wink::before { - content: "\f4da"; } - -.fa-smile-wink::before { - content: "\f4da"; } - -.fa-face-surprise::before { - content: "\f5c2"; } - -.fa-surprise::before { - content: "\f5c2"; } - -.fa-face-tired::before { - content: "\f5c8"; } - -.fa-tired::before { - content: "\f5c8"; } - -.fa-fan::before { - content: "\f863"; } - -.fa-faucet::before { - content: "\e005"; } - -.fa-faucet-drip::before { - content: "\e006"; } - -.fa-fax::before { - content: "\f1ac"; } - -.fa-feather::before { - content: "\f52d"; } - -.fa-feather-pointed::before { - content: "\f56b"; } - -.fa-feather-alt::before { - content: "\f56b"; } - -.fa-ferry::before { - content: "\e4ea"; } - -.fa-file::before { - content: "\f15b"; } - -.fa-file-arrow-down::before { - content: "\f56d"; } - -.fa-file-download::before { - content: "\f56d"; } - -.fa-file-arrow-up::before { - content: "\f574"; } - -.fa-file-upload::before { - content: "\f574"; } - -.fa-file-audio::before { - content: "\f1c7"; } - -.fa-file-circle-check::before { - content: "\e493"; } - -.fa-file-circle-exclamation::before { - content: "\e4eb"; } - -.fa-file-circle-minus::before { - content: "\e4ed"; } - -.fa-file-circle-plus::before { - content: "\e4ee"; } - -.fa-file-circle-question::before { - content: "\e4ef"; } - -.fa-file-circle-xmark::before { - content: "\e494"; } - -.fa-file-code::before { - content: "\f1c9"; } - -.fa-file-contract::before { - content: "\f56c"; } - -.fa-file-csv::before { - content: "\f6dd"; } - -.fa-file-excel::before { - content: "\f1c3"; } - -.fa-file-export::before { - content: "\f56e"; } - -.fa-arrow-right-from-file::before { - content: "\f56e"; } - -.fa-file-image::before { - content: "\f1c5"; } - -.fa-file-import::before { - content: "\f56f"; } - -.fa-arrow-right-to-file::before { - content: "\f56f"; } - -.fa-file-invoice::before { - content: "\f570"; } - -.fa-file-invoice-dollar::before { - content: "\f571"; } - -.fa-file-lines::before { - content: "\f15c"; } - -.fa-file-alt::before { - content: "\f15c"; } - -.fa-file-text::before { - content: "\f15c"; } - -.fa-file-medical::before { - content: "\f477"; } - -.fa-file-pdf::before { - content: "\f1c1"; } - -.fa-file-pen::before { - content: "\f31c"; } - -.fa-file-edit::before { - content: "\f31c"; } - -.fa-file-powerpoint::before { - content: "\f1c4"; } - -.fa-file-prescription::before { - content: "\f572"; } - -.fa-file-shield::before { - content: "\e4f0"; } - -.fa-file-signature::before { - content: "\f573"; } - -.fa-file-video::before { - content: "\f1c8"; } - -.fa-file-waveform::before { - content: "\f478"; } - -.fa-file-medical-alt::before { - content: "\f478"; } - -.fa-file-word::before { - content: "\f1c2"; } - -.fa-file-zipper::before { - content: "\f1c6"; } - -.fa-file-archive::before { - content: "\f1c6"; } - -.fa-fill::before { - content: "\f575"; } - -.fa-fill-drip::before { - content: "\f576"; } - -.fa-film::before { - content: "\f008"; } - -.fa-filter::before { - content: "\f0b0"; } - -.fa-filter-circle-dollar::before { - content: "\f662"; } - -.fa-funnel-dollar::before { - content: "\f662"; } - -.fa-filter-circle-xmark::before { - content: "\e17b"; } - -.fa-fingerprint::before { - content: "\f577"; } - -.fa-fire::before { - content: "\f06d"; } - -.fa-fire-burner::before { - content: "\e4f1"; } - -.fa-fire-extinguisher::before { - content: "\f134"; } - -.fa-fire-flame-curved::before { - content: "\f7e4"; } - -.fa-fire-alt::before { - content: "\f7e4"; } - -.fa-fire-flame-simple::before { - content: "\f46a"; } - -.fa-burn::before { - content: "\f46a"; } - -.fa-fish::before { - content: "\f578"; } - -.fa-fish-fins::before { - content: "\e4f2"; } - -.fa-flag::before { - content: "\f024"; } - -.fa-flag-checkered::before { - content: "\f11e"; } - -.fa-flag-usa::before { - content: "\f74d"; } - -.fa-flask::before { - content: "\f0c3"; } - -.fa-flask-vial::before { - content: "\e4f3"; } - -.fa-floppy-disk::before { - content: "\f0c7"; } - -.fa-save::before { - content: "\f0c7"; } - -.fa-florin-sign::before { - content: "\e184"; } - -.fa-folder::before { - content: "\f07b"; } - -.fa-folder-blank::before { - content: "\f07b"; } - -.fa-folder-closed::before { - content: "\e185"; } - -.fa-folder-minus::before { - content: "\f65d"; } - -.fa-folder-open::before { - content: "\f07c"; } - -.fa-folder-plus::before { - content: "\f65e"; } - -.fa-folder-tree::before { - content: "\f802"; } - -.fa-font::before { - content: "\f031"; } - -.fa-football::before { - content: "\f44e"; } - -.fa-football-ball::before { - content: "\f44e"; } - -.fa-forward::before { - content: "\f04e"; } - -.fa-forward-fast::before { - content: "\f050"; } - -.fa-fast-forward::before { - content: "\f050"; } - -.fa-forward-step::before { - content: "\f051"; } - -.fa-step-forward::before { - content: "\f051"; } - -.fa-franc-sign::before { - content: "\e18f"; } - -.fa-frog::before { - content: "\f52e"; } - -.fa-futbol::before { - content: "\f1e3"; } - -.fa-futbol-ball::before { - content: "\f1e3"; } - -.fa-soccer-ball::before { - content: "\f1e3"; } - -.fa-g::before { - content: "\47"; } - -.fa-gamepad::before { - content: "\f11b"; } - -.fa-gas-pump::before { - content: "\f52f"; } - -.fa-gauge::before { - content: "\f624"; } - -.fa-dashboard::before { - content: "\f624"; } - -.fa-gauge-med::before { - content: "\f624"; } - -.fa-tachometer-alt-average::before { - content: "\f624"; } - -.fa-gauge-high::before { - content: "\f625"; } - -.fa-tachometer-alt::before { - content: "\f625"; } - -.fa-tachometer-alt-fast::before { - content: "\f625"; } - -.fa-gauge-simple::before { - content: "\f629"; } - -.fa-gauge-simple-med::before { - content: "\f629"; } - -.fa-tachometer-average::before { - content: "\f629"; } - -.fa-gauge-simple-high::before { - content: "\f62a"; } - -.fa-tachometer::before { - content: "\f62a"; } - -.fa-tachometer-fast::before { - content: "\f62a"; } - -.fa-gavel::before { - content: "\f0e3"; } - -.fa-legal::before { - content: "\f0e3"; } - -.fa-gear::before { - content: "\f013"; } - -.fa-cog::before { - content: "\f013"; } - -.fa-gears::before { - content: "\f085"; } - -.fa-cogs::before { - content: "\f085"; } - -.fa-gem::before { - content: "\f3a5"; } - -.fa-genderless::before { - content: "\f22d"; } - -.fa-ghost::before { - content: "\f6e2"; } - -.fa-gift::before { - content: "\f06b"; } - -.fa-gifts::before { - content: "\f79c"; } - -.fa-glass-water::before { - content: "\e4f4"; } - -.fa-glass-water-droplet::before { - content: "\e4f5"; } - -.fa-glasses::before { - content: "\f530"; } - -.fa-globe::before { - content: "\f0ac"; } - -.fa-golf-ball-tee::before { - content: "\f450"; } - -.fa-golf-ball::before { - content: "\f450"; } - -.fa-gopuram::before { - content: "\f664"; } - -.fa-graduation-cap::before { - content: "\f19d"; } - -.fa-mortar-board::before { - content: "\f19d"; } - -.fa-greater-than::before { - content: "\3e"; } - -.fa-greater-than-equal::before { - content: "\f532"; } - -.fa-grip::before { - content: "\f58d"; } - -.fa-grip-horizontal::before { - content: "\f58d"; } - -.fa-grip-lines::before { - content: "\f7a4"; } - -.fa-grip-lines-vertical::before { - content: "\f7a5"; } - -.fa-grip-vertical::before { - content: "\f58e"; } - -.fa-group-arrows-rotate::before { - content: "\e4f6"; } - -.fa-guarani-sign::before { - content: "\e19a"; } - -.fa-guitar::before { - content: "\f7a6"; } - -.fa-gun::before { - content: "\e19b"; } - -.fa-h::before { - content: "\48"; } - -.fa-hammer::before { - content: "\f6e3"; } - -.fa-hamsa::before { - content: "\f665"; } - -.fa-hand::before { - content: "\f256"; } - -.fa-hand-paper::before { - content: "\f256"; } - -.fa-hand-back-fist::before { - content: "\f255"; } - -.fa-hand-rock::before { - content: "\f255"; } - -.fa-hand-dots::before { - content: "\f461"; } - -.fa-allergies::before { - content: "\f461"; } - -.fa-hand-fist::before { - content: "\f6de"; } - -.fa-fist-raised::before { - content: "\f6de"; } - -.fa-hand-holding::before { - content: "\f4bd"; } - -.fa-hand-holding-dollar::before { - content: "\f4c0"; } - -.fa-hand-holding-usd::before { - content: "\f4c0"; } - -.fa-hand-holding-droplet::before { - content: "\f4c1"; } - -.fa-hand-holding-water::before { - content: "\f4c1"; } - -.fa-hand-holding-hand::before { - content: "\e4f7"; } - -.fa-hand-holding-heart::before { - content: "\f4be"; } - -.fa-hand-holding-medical::before { - content: "\e05c"; } - -.fa-hand-lizard::before { - content: "\f258"; } - -.fa-hand-middle-finger::before { - content: "\f806"; } - -.fa-hand-peace::before { - content: "\f25b"; } - -.fa-hand-point-down::before { - content: "\f0a7"; } - -.fa-hand-point-left::before { - content: "\f0a5"; } - -.fa-hand-point-right::before { - content: "\f0a4"; } - -.fa-hand-point-up::before { - content: "\f0a6"; } - -.fa-hand-pointer::before { - content: "\f25a"; } - -.fa-hand-scissors::before { - content: "\f257"; } - -.fa-hand-sparkles::before { - content: "\e05d"; } - -.fa-hand-spock::before { - content: "\f259"; } - -.fa-handcuffs::before { - content: "\e4f8"; } - -.fa-hands::before { - content: "\f2a7"; } - -.fa-sign-language::before { - content: "\f2a7"; } - -.fa-signing::before { - content: "\f2a7"; } - -.fa-hands-asl-interpreting::before { - content: "\f2a3"; } - -.fa-american-sign-language-interpreting::before { - content: "\f2a3"; } - -.fa-asl-interpreting::before { - content: "\f2a3"; } - -.fa-hands-american-sign-language-interpreting::before { - content: "\f2a3"; } - -.fa-hands-bound::before { - content: "\e4f9"; } - -.fa-hands-bubbles::before { - content: "\e05e"; } - -.fa-hands-wash::before { - content: "\e05e"; } - -.fa-hands-clapping::before { - content: "\e1a8"; } - -.fa-hands-holding::before { - content: "\f4c2"; } - -.fa-hands-holding-child::before { - content: "\e4fa"; } - -.fa-hands-holding-circle::before { - content: "\e4fb"; } - -.fa-hands-praying::before { - content: "\f684"; } - -.fa-praying-hands::before { - content: "\f684"; } - -.fa-handshake::before { - content: "\f2b5"; } - -.fa-handshake-angle::before { - content: "\f4c4"; } - -.fa-hands-helping::before { - content: "\f4c4"; } - -.fa-handshake-simple::before { - content: "\f4c6"; } - -.fa-handshake-alt::before { - content: "\f4c6"; } - -.fa-handshake-simple-slash::before { - content: "\e05f"; } - -.fa-handshake-alt-slash::before { - content: "\e05f"; } - -.fa-handshake-slash::before { - content: "\e060"; } - -.fa-hanukiah::before { - content: "\f6e6"; } - -.fa-hard-drive::before { - content: "\f0a0"; } - -.fa-hdd::before { - content: "\f0a0"; } - -.fa-hashtag::before { - content: "\23"; } - -.fa-hat-cowboy::before { - content: "\f8c0"; } - -.fa-hat-cowboy-side::before { - content: "\f8c1"; } - -.fa-hat-wizard::before { - content: "\f6e8"; } - -.fa-head-side-cough::before { - content: "\e061"; } - -.fa-head-side-cough-slash::before { - content: "\e062"; } - -.fa-head-side-mask::before { - content: "\e063"; } - -.fa-head-side-virus::before { - content: "\e064"; } - -.fa-heading::before { - content: "\f1dc"; } - -.fa-header::before { - content: "\f1dc"; } - -.fa-headphones::before { - content: "\f025"; } - -.fa-headphones-simple::before { - content: "\f58f"; } - -.fa-headphones-alt::before { - content: "\f58f"; } - -.fa-headset::before { - content: "\f590"; } - -.fa-heart::before { - content: "\f004"; } - -.fa-heart-circle-bolt::before { - content: "\e4fc"; } - -.fa-heart-circle-check::before { - content: "\e4fd"; } - -.fa-heart-circle-exclamation::before { - content: "\e4fe"; } - -.fa-heart-circle-minus::before { - content: "\e4ff"; } - -.fa-heart-circle-plus::before { - content: "\e500"; } - -.fa-heart-circle-xmark::before { - content: "\e501"; } - -.fa-heart-crack::before { - content: "\f7a9"; } - -.fa-heart-broken::before { - content: "\f7a9"; } - -.fa-heart-pulse::before { - content: "\f21e"; } - -.fa-heartbeat::before { - content: "\f21e"; } - -.fa-helicopter::before { - content: "\f533"; } - -.fa-helicopter-symbol::before { - content: "\e502"; } - -.fa-helmet-safety::before { - content: "\f807"; } - -.fa-hard-hat::before { - content: "\f807"; } - -.fa-hat-hard::before { - content: "\f807"; } - -.fa-helmet-un::before { - content: "\e503"; } - -.fa-highlighter::before { - content: "\f591"; } - -.fa-hill-avalanche::before { - content: "\e507"; } - -.fa-hill-rockslide::before { - content: "\e508"; } - -.fa-hippo::before { - content: "\f6ed"; } - -.fa-hockey-puck::before { - content: "\f453"; } - -.fa-holly-berry::before { - content: "\f7aa"; } - -.fa-horse::before { - content: "\f6f0"; } - -.fa-horse-head::before { - content: "\f7ab"; } - -.fa-hospital::before { - content: "\f0f8"; } - -.fa-hospital-alt::before { - content: "\f0f8"; } - -.fa-hospital-wide::before { - content: "\f0f8"; } - -.fa-hospital-user::before { - content: "\f80d"; } - -.fa-hot-tub-person::before { - content: "\f593"; } - -.fa-hot-tub::before { - content: "\f593"; } - -.fa-hotdog::before { - content: "\f80f"; } - -.fa-hotel::before { - content: "\f594"; } - -.fa-hourglass::before { - content: "\f254"; } - -.fa-hourglass-2::before { - content: "\f254"; } - -.fa-hourglass-half::before { - content: "\f254"; } - -.fa-hourglass-empty::before { - content: "\f252"; } - -.fa-hourglass-end::before { - content: "\f253"; } - -.fa-hourglass-3::before { - content: "\f253"; } - -.fa-hourglass-start::before { - content: "\f251"; } - -.fa-hourglass-1::before { - content: "\f251"; } - -.fa-house::before { - content: "\f015"; } - -.fa-home::before { - content: "\f015"; } - -.fa-home-alt::before { - content: "\f015"; } - -.fa-home-lg-alt::before { - content: "\f015"; } - -.fa-house-chimney::before { - content: "\e3af"; } - -.fa-home-lg::before { - content: "\e3af"; } - -.fa-house-chimney-crack::before { - content: "\f6f1"; } - -.fa-house-damage::before { - content: "\f6f1"; } - -.fa-house-chimney-medical::before { - content: "\f7f2"; } - -.fa-clinic-medical::before { - content: "\f7f2"; } - -.fa-house-chimney-user::before { - content: "\e065"; } - -.fa-house-chimney-window::before { - content: "\e00d"; } - -.fa-house-circle-check::before { - content: "\e509"; } - -.fa-house-circle-exclamation::before { - content: "\e50a"; } - -.fa-house-circle-xmark::before { - content: "\e50b"; } - -.fa-house-crack::before { - content: "\e3b1"; } - -.fa-house-fire::before { - content: "\e50c"; } - -.fa-house-flag::before { - content: "\e50d"; } - -.fa-house-flood-water::before { - content: "\e50e"; } - -.fa-house-flood-water-circle-arrow-right::before { - content: "\e50f"; } - -.fa-house-laptop::before { - content: "\e066"; } - -.fa-laptop-house::before { - content: "\e066"; } - -.fa-house-lock::before { - content: "\e510"; } - -.fa-house-medical::before { - content: "\e3b2"; } - -.fa-house-medical-circle-check::before { - content: "\e511"; } - -.fa-house-medical-circle-exclamation::before { - content: "\e512"; } - -.fa-house-medical-circle-xmark::before { - content: "\e513"; } - -.fa-house-medical-flag::before { - content: "\e514"; } - -.fa-house-signal::before { - content: "\e012"; } - -.fa-house-tsunami::before { - content: "\e515"; } - -.fa-house-user::before { - content: "\e1b0"; } - -.fa-home-user::before { - content: "\e1b0"; } - -.fa-hryvnia-sign::before { - content: "\f6f2"; } - -.fa-hryvnia::before { - content: "\f6f2"; } - -.fa-hurricane::before { - content: "\f751"; } - -.fa-i::before { - content: "\49"; } - -.fa-i-cursor::before { - content: "\f246"; } - -.fa-ice-cream::before { - content: "\f810"; } - -.fa-icicles::before { - content: "\f7ad"; } - -.fa-icons::before { - content: "\f86d"; } - -.fa-heart-music-camera-bolt::before { - content: "\f86d"; } - -.fa-id-badge::before { - content: "\f2c1"; } - -.fa-id-card::before { - content: "\f2c2"; } - -.fa-drivers-license::before { - content: "\f2c2"; } - -.fa-id-card-clip::before { - content: "\f47f"; } - -.fa-id-card-alt::before { - content: "\f47f"; } - -.fa-igloo::before { - content: "\f7ae"; } - -.fa-image::before { - content: "\f03e"; } - -.fa-image-portrait::before { - content: "\f3e0"; } - -.fa-portrait::before { - content: "\f3e0"; } - -.fa-images::before { - content: "\f302"; } - -.fa-inbox::before { - content: "\f01c"; } - -.fa-indent::before { - content: "\f03c"; } - -.fa-indian-rupee-sign::before { - content: "\e1bc"; } - -.fa-indian-rupee::before { - content: "\e1bc"; } - -.fa-inr::before { - content: "\e1bc"; } - -.fa-industry::before { - content: "\f275"; } - -.fa-infinity::before { - content: "\f534"; } - -.fa-info::before { - content: "\f129"; } - -.fa-italic::before { - content: "\f033"; } - -.fa-j::before { - content: "\4a"; } - -.fa-jar::before { - content: "\e516"; } - -.fa-jar-wheat::before { - content: "\e517"; } - -.fa-jedi::before { - content: "\f669"; } - -.fa-jet-fighter::before { - content: "\f0fb"; } - -.fa-fighter-jet::before { - content: "\f0fb"; } - -.fa-jet-fighter-up::before { - content: "\e518"; } - -.fa-joint::before { - content: "\f595"; } - -.fa-jug-detergent::before { - content: "\e519"; } - -.fa-k::before { - content: "\4b"; } - -.fa-kaaba::before { - content: "\f66b"; } - -.fa-key::before { - content: "\f084"; } - -.fa-keyboard::before { - content: "\f11c"; } - -.fa-khanda::before { - content: "\f66d"; } - -.fa-kip-sign::before { - content: "\e1c4"; } - -.fa-kit-medical::before { - content: "\f479"; } - -.fa-first-aid::before { - content: "\f479"; } - -.fa-kitchen-set::before { - content: "\e51a"; } - -.fa-kiwi-bird::before { - content: "\f535"; } - -.fa-l::before { - content: "\4c"; } - -.fa-land-mine-on::before { - content: "\e51b"; } - -.fa-landmark::before { - content: "\f66f"; } - -.fa-landmark-dome::before { - content: "\f752"; } - -.fa-landmark-alt::before { - content: "\f752"; } - -.fa-landmark-flag::before { - content: "\e51c"; } - -.fa-language::before { - content: "\f1ab"; } - -.fa-laptop::before { - content: "\f109"; } - -.fa-laptop-code::before { - content: "\f5fc"; } - -.fa-laptop-file::before { - content: "\e51d"; } - -.fa-laptop-medical::before { - content: "\f812"; } - -.fa-lari-sign::before { - content: "\e1c8"; } - -.fa-layer-group::before { - content: "\f5fd"; } - -.fa-leaf::before { - content: "\f06c"; } - -.fa-left-long::before { - content: "\f30a"; } - -.fa-long-arrow-alt-left::before { - content: "\f30a"; } - -.fa-left-right::before { - content: "\f337"; } - -.fa-arrows-alt-h::before { - content: "\f337"; } - -.fa-lemon::before { - content: "\f094"; } - -.fa-less-than::before { - content: "\3c"; } - -.fa-less-than-equal::before { - content: "\f537"; } - -.fa-life-ring::before { - content: "\f1cd"; } - -.fa-lightbulb::before { - content: "\f0eb"; } - -.fa-lines-leaning::before { - content: "\e51e"; } - -.fa-link::before { - content: "\f0c1"; } - -.fa-chain::before { - content: "\f0c1"; } - -.fa-link-slash::before { - content: "\f127"; } - -.fa-chain-broken::before { - content: "\f127"; } - -.fa-chain-slash::before { - content: "\f127"; } - -.fa-unlink::before { - content: "\f127"; } - -.fa-lira-sign::before { - content: "\f195"; } - -.fa-list::before { - content: "\f03a"; } - -.fa-list-squares::before { - content: "\f03a"; } - -.fa-list-check::before { - content: "\f0ae"; } - -.fa-tasks::before { - content: "\f0ae"; } - -.fa-list-ol::before { - content: "\f0cb"; } - -.fa-list-1-2::before { - content: "\f0cb"; } - -.fa-list-numeric::before { - content: "\f0cb"; } - -.fa-list-ul::before { - content: "\f0ca"; } - -.fa-list-dots::before { - content: "\f0ca"; } - -.fa-litecoin-sign::before { - content: "\e1d3"; } - -.fa-location-arrow::before { - content: "\f124"; } - -.fa-location-crosshairs::before { - content: "\f601"; } - -.fa-location::before { - content: "\f601"; } - -.fa-location-dot::before { - content: "\f3c5"; } - -.fa-map-marker-alt::before { - content: "\f3c5"; } - -.fa-location-pin::before { - content: "\f041"; } - -.fa-map-marker::before { - content: "\f041"; } - -.fa-location-pin-lock::before { - content: "\e51f"; } - -.fa-lock::before { - content: "\f023"; } - -.fa-lock-open::before { - content: "\f3c1"; } - -.fa-locust::before { - content: "\e520"; } - -.fa-lungs::before { - content: "\f604"; } - -.fa-lungs-virus::before { - content: "\e067"; } - -.fa-m::before { - content: "\4d"; } - -.fa-magnet::before { - content: "\f076"; } - -.fa-magnifying-glass::before { - content: "\f002"; } - -.fa-search::before { - content: "\f002"; } - -.fa-magnifying-glass-arrow-right::before { - content: "\e521"; } - -.fa-magnifying-glass-chart::before { - content: "\e522"; } - -.fa-magnifying-glass-dollar::before { - content: "\f688"; } - -.fa-search-dollar::before { - content: "\f688"; } - -.fa-magnifying-glass-location::before { - content: "\f689"; } - -.fa-search-location::before { - content: "\f689"; } - -.fa-magnifying-glass-minus::before { - content: "\f010"; } - -.fa-search-minus::before { - content: "\f010"; } - -.fa-magnifying-glass-plus::before { - content: "\f00e"; } - -.fa-search-plus::before { - content: "\f00e"; } - -.fa-manat-sign::before { - content: "\e1d5"; } - -.fa-map::before { - content: "\f279"; } - -.fa-map-location::before { - content: "\f59f"; } - -.fa-map-marked::before { - content: "\f59f"; } - -.fa-map-location-dot::before { - content: "\f5a0"; } - -.fa-map-marked-alt::before { - content: "\f5a0"; } - -.fa-map-pin::before { - content: "\f276"; } - -.fa-marker::before { - content: "\f5a1"; } - -.fa-mars::before { - content: "\f222"; } - -.fa-mars-and-venus::before { - content: "\f224"; } - -.fa-mars-and-venus-burst::before { - content: "\e523"; } - -.fa-mars-double::before { - content: "\f227"; } - -.fa-mars-stroke::before { - content: "\f229"; } - -.fa-mars-stroke-right::before { - content: "\f22b"; } - -.fa-mars-stroke-h::before { - content: "\f22b"; } - -.fa-mars-stroke-up::before { - content: "\f22a"; } - -.fa-mars-stroke-v::before { - content: "\f22a"; } - -.fa-martini-glass::before { - content: "\f57b"; } - -.fa-glass-martini-alt::before { - content: "\f57b"; } - -.fa-martini-glass-citrus::before { - content: "\f561"; } - -.fa-cocktail::before { - content: "\f561"; } - -.fa-martini-glass-empty::before { - content: "\f000"; } - -.fa-glass-martini::before { - content: "\f000"; } - -.fa-mask::before { - content: "\f6fa"; } - -.fa-mask-face::before { - content: "\e1d7"; } - -.fa-mask-ventilator::before { - content: "\e524"; } - -.fa-masks-theater::before { - content: "\f630"; } - -.fa-theater-masks::before { - content: "\f630"; } - -.fa-mattress-pillow::before { - content: "\e525"; } - -.fa-maximize::before { - content: "\f31e"; } - -.fa-expand-arrows-alt::before { - content: "\f31e"; } - -.fa-medal::before { - content: "\f5a2"; } - -.fa-memory::before { - content: "\f538"; } - -.fa-menorah::before { - content: "\f676"; } - -.fa-mercury::before { - content: "\f223"; } - -.fa-message::before { - content: "\f27a"; } - -.fa-comment-alt::before { - content: "\f27a"; } - -.fa-meteor::before { - content: "\f753"; } - -.fa-microchip::before { - content: "\f2db"; } - -.fa-microphone::before { - content: "\f130"; } - -.fa-microphone-lines::before { - content: "\f3c9"; } - -.fa-microphone-alt::before { - content: "\f3c9"; } - -.fa-microphone-lines-slash::before { - content: "\f539"; } - -.fa-microphone-alt-slash::before { - content: "\f539"; } - -.fa-microphone-slash::before { - content: "\f131"; } - -.fa-microscope::before { - content: "\f610"; } - -.fa-mill-sign::before { - content: "\e1ed"; } - -.fa-minimize::before { - content: "\f78c"; } - -.fa-compress-arrows-alt::before { - content: "\f78c"; } - -.fa-minus::before { - content: "\f068"; } - -.fa-subtract::before { - content: "\f068"; } - -.fa-mitten::before { - content: "\f7b5"; } - -.fa-mobile::before { - content: "\f3ce"; } - -.fa-mobile-android::before { - content: "\f3ce"; } - -.fa-mobile-phone::before { - content: "\f3ce"; } - -.fa-mobile-button::before { - content: "\f10b"; } - -.fa-mobile-retro::before { - content: "\e527"; } - -.fa-mobile-screen::before { - content: "\f3cf"; } - -.fa-mobile-android-alt::before { - content: "\f3cf"; } - -.fa-mobile-screen-button::before { - content: "\f3cd"; } - -.fa-mobile-alt::before { - content: "\f3cd"; } - -.fa-money-bill::before { - content: "\f0d6"; } - -.fa-money-bill-1::before { - content: "\f3d1"; } - -.fa-money-bill-alt::before { - content: "\f3d1"; } - -.fa-money-bill-1-wave::before { - content: "\f53b"; } - -.fa-money-bill-wave-alt::before { - content: "\f53b"; } - -.fa-money-bill-transfer::before { - content: "\e528"; } - -.fa-money-bill-trend-up::before { - content: "\e529"; } - -.fa-money-bill-wave::before { - content: "\f53a"; } - -.fa-money-bill-wheat::before { - content: "\e52a"; } - -.fa-money-bills::before { - content: "\e1f3"; } - -.fa-money-check::before { - content: "\f53c"; } - -.fa-money-check-dollar::before { - content: "\f53d"; } - -.fa-money-check-alt::before { - content: "\f53d"; } - -.fa-monument::before { - content: "\f5a6"; } - -.fa-moon::before { - content: "\f186"; } - -.fa-mortar-pestle::before { - content: "\f5a7"; } - -.fa-mosque::before { - content: "\f678"; } - -.fa-mosquito::before { - content: "\e52b"; } - -.fa-mosquito-net::before { - content: "\e52c"; } - -.fa-motorcycle::before { - content: "\f21c"; } - -.fa-mound::before { - content: "\e52d"; } - -.fa-mountain::before { - content: "\f6fc"; } - -.fa-mountain-city::before { - content: "\e52e"; } - -.fa-mountain-sun::before { - content: "\e52f"; } - -.fa-mug-hot::before { - content: "\f7b6"; } - -.fa-mug-saucer::before { - content: "\f0f4"; } - -.fa-coffee::before { - content: "\f0f4"; } - -.fa-music::before { - content: "\f001"; } - -.fa-n::before { - content: "\4e"; } - -.fa-naira-sign::before { - content: "\e1f6"; } - -.fa-network-wired::before { - content: "\f6ff"; } - -.fa-neuter::before { - content: "\f22c"; } - -.fa-newspaper::before { - content: "\f1ea"; } - -.fa-not-equal::before { - content: "\f53e"; } - -.fa-note-sticky::before { - content: "\f249"; } - -.fa-sticky-note::before { - content: "\f249"; } - -.fa-notes-medical::before { - content: "\f481"; } - -.fa-o::before { - content: "\4f"; } - -.fa-object-group::before { - content: "\f247"; } - -.fa-object-ungroup::before { - content: "\f248"; } - -.fa-oil-can::before { - content: "\f613"; } - -.fa-oil-well::before { - content: "\e532"; } - -.fa-om::before { - content: "\f679"; } - -.fa-otter::before { - content: "\f700"; } - -.fa-outdent::before { - content: "\f03b"; } - -.fa-dedent::before { - content: "\f03b"; } - -.fa-p::before { - content: "\50"; } - -.fa-pager::before { - content: "\f815"; } - -.fa-paint-roller::before { - content: "\f5aa"; } - -.fa-paintbrush::before { - content: "\f1fc"; } - -.fa-paint-brush::before { - content: "\f1fc"; } - -.fa-palette::before { - content: "\f53f"; } - -.fa-pallet::before { - content: "\f482"; } - -.fa-panorama::before { - content: "\e209"; } - -.fa-paper-plane::before { - content: "\f1d8"; } - -.fa-paperclip::before { - content: "\f0c6"; } - -.fa-parachute-box::before { - content: "\f4cd"; } - -.fa-paragraph::before { - content: "\f1dd"; } - -.fa-passport::before { - content: "\f5ab"; } - -.fa-paste::before { - content: "\f0ea"; } - -.fa-file-clipboard::before { - content: "\f0ea"; } - -.fa-pause::before { - content: "\f04c"; } - -.fa-paw::before { - content: "\f1b0"; } - -.fa-peace::before { - content: "\f67c"; } - -.fa-pen::before { - content: "\f304"; } - -.fa-pen-clip::before { - content: "\f305"; } - -.fa-pen-alt::before { - content: "\f305"; } - -.fa-pen-fancy::before { - content: "\f5ac"; } - -.fa-pen-nib::before { - content: "\f5ad"; } - -.fa-pen-ruler::before { - content: "\f5ae"; } - -.fa-pencil-ruler::before { - content: "\f5ae"; } - -.fa-pen-to-square::before { - content: "\f044"; } - -.fa-edit::before { - content: "\f044"; } - -.fa-pencil::before { - content: "\f303"; } - -.fa-pencil-alt::before { - content: "\f303"; } - -.fa-people-arrows-left-right::before { - content: "\e068"; } - -.fa-people-arrows::before { - content: "\e068"; } - -.fa-people-carry-box::before { - content: "\f4ce"; } - -.fa-people-carry::before { - content: "\f4ce"; } - -.fa-people-group::before { - content: "\e533"; } - -.fa-people-line::before { - content: "\e534"; } - -.fa-people-pulling::before { - content: "\e535"; } - -.fa-people-robbery::before { - content: "\e536"; } - -.fa-people-roof::before { - content: "\e537"; } - -.fa-pepper-hot::before { - content: "\f816"; } - -.fa-percent::before { - content: "\25"; } - -.fa-percentage::before { - content: "\25"; } - -.fa-person::before { - content: "\f183"; } - -.fa-male::before { - content: "\f183"; } - -.fa-person-arrow-down-to-line::before { - content: "\e538"; } - -.fa-person-arrow-up-from-line::before { - content: "\e539"; } - -.fa-person-biking::before { - content: "\f84a"; } - -.fa-biking::before { - content: "\f84a"; } - -.fa-person-booth::before { - content: "\f756"; } - -.fa-person-breastfeeding::before { - content: "\e53a"; } - -.fa-person-burst::before { - content: "\e53b"; } - -.fa-person-cane::before { - content: "\e53c"; } - -.fa-person-chalkboard::before { - content: "\e53d"; } - -.fa-person-circle-check::before { - content: "\e53e"; } - -.fa-person-circle-exclamation::before { - content: "\e53f"; } - -.fa-person-circle-minus::before { - content: "\e540"; } - -.fa-person-circle-plus::before { - content: "\e541"; } - -.fa-person-circle-question::before { - content: "\e542"; } - -.fa-person-circle-xmark::before { - content: "\e543"; } - -.fa-person-digging::before { - content: "\f85e"; } - -.fa-digging::before { - content: "\f85e"; } - -.fa-person-dots-from-line::before { - content: "\f470"; } - -.fa-diagnoses::before { - content: "\f470"; } - -.fa-person-dress::before { - content: "\f182"; } - -.fa-female::before { - content: "\f182"; } - -.fa-person-dress-burst::before { - content: "\e544"; } - -.fa-person-drowning::before { - content: "\e545"; } - -.fa-person-falling::before { - content: "\e546"; } - -.fa-person-falling-burst::before { - content: "\e547"; } - -.fa-person-half-dress::before { - content: "\e548"; } - -.fa-person-harassing::before { - content: "\e549"; } - -.fa-person-hiking::before { - content: "\f6ec"; } - -.fa-hiking::before { - content: "\f6ec"; } - -.fa-person-military-pointing::before { - content: "\e54a"; } - -.fa-person-military-rifle::before { - content: "\e54b"; } - -.fa-person-military-to-person::before { - content: "\e54c"; } - -.fa-person-praying::before { - content: "\f683"; } - -.fa-pray::before { - content: "\f683"; } - -.fa-person-pregnant::before { - content: "\e31e"; } - -.fa-person-rays::before { - content: "\e54d"; } - -.fa-person-rifle::before { - content: "\e54e"; } - -.fa-person-running::before { - content: "\f70c"; } - -.fa-running::before { - content: "\f70c"; } - -.fa-person-shelter::before { - content: "\e54f"; } - -.fa-person-skating::before { - content: "\f7c5"; } - -.fa-skating::before { - content: "\f7c5"; } - -.fa-person-skiing::before { - content: "\f7c9"; } - -.fa-skiing::before { - content: "\f7c9"; } - -.fa-person-skiing-nordic::before { - content: "\f7ca"; } - -.fa-skiing-nordic::before { - content: "\f7ca"; } - -.fa-person-snowboarding::before { - content: "\f7ce"; } - -.fa-snowboarding::before { - content: "\f7ce"; } - -.fa-person-swimming::before { - content: "\f5c4"; } - -.fa-swimmer::before { - content: "\f5c4"; } - -.fa-person-through-window::before { - content: "\e433"; } - -.fa-person-walking::before { - content: "\f554"; } - -.fa-walking::before { - content: "\f554"; } - -.fa-person-walking-arrow-loop-left::before { - content: "\e551"; } - -.fa-person-walking-arrow-right::before { - content: "\e552"; } - -.fa-person-walking-dashed-line-arrow-right::before { - content: "\e553"; } - -.fa-person-walking-luggage::before { - content: "\e554"; } - -.fa-person-walking-with-cane::before { - content: "\f29d"; } - -.fa-blind::before { - content: "\f29d"; } - -.fa-peseta-sign::before { - content: "\e221"; } - -.fa-peso-sign::before { - content: "\e222"; } - -.fa-phone::before { - content: "\f095"; } - -.fa-phone-flip::before { - content: "\f879"; } - -.fa-phone-alt::before { - content: "\f879"; } - -.fa-phone-slash::before { - content: "\f3dd"; } - -.fa-phone-volume::before { - content: "\f2a0"; } - -.fa-volume-control-phone::before { - content: "\f2a0"; } - -.fa-photo-film::before { - content: "\f87c"; } - -.fa-photo-video::before { - content: "\f87c"; } - -.fa-piggy-bank::before { - content: "\f4d3"; } - -.fa-pills::before { - content: "\f484"; } - -.fa-pizza-slice::before { - content: "\f818"; } - -.fa-place-of-worship::before { - content: "\f67f"; } - -.fa-plane::before { - content: "\f072"; } - -.fa-plane-arrival::before { - content: "\f5af"; } - -.fa-plane-circle-check::before { - content: "\e555"; } - -.fa-plane-circle-exclamation::before { - content: "\e556"; } - -.fa-plane-circle-xmark::before { - content: "\e557"; } - -.fa-plane-departure::before { - content: "\f5b0"; } - -.fa-plane-lock::before { - content: "\e558"; } - -.fa-plane-slash::before { - content: "\e069"; } - -.fa-plane-up::before { - content: "\e22d"; } - -.fa-plant-wilt::before { - content: "\e43b"; } - -.fa-plate-wheat::before { - content: "\e55a"; } - -.fa-play::before { - content: "\f04b"; } - -.fa-plug::before { - content: "\f1e6"; } - -.fa-plug-circle-bolt::before { - content: "\e55b"; } - -.fa-plug-circle-check::before { - content: "\e55c"; } - -.fa-plug-circle-exclamation::before { - content: "\e55d"; } - -.fa-plug-circle-minus::before { - content: "\e55e"; } - -.fa-plug-circle-plus::before { - content: "\e55f"; } - -.fa-plug-circle-xmark::before { - content: "\e560"; } - -.fa-plus::before { - content: "\2b"; } - -.fa-add::before { - content: "\2b"; } - -.fa-plus-minus::before { - content: "\e43c"; } - -.fa-podcast::before { - content: "\f2ce"; } - -.fa-poo::before { - content: "\f2fe"; } - -.fa-poo-storm::before { - content: "\f75a"; } - -.fa-poo-bolt::before { - content: "\f75a"; } - -.fa-poop::before { - content: "\f619"; } - -.fa-power-off::before { - content: "\f011"; } - -.fa-prescription::before { - content: "\f5b1"; } - -.fa-prescription-bottle::before { - content: "\f485"; } - -.fa-prescription-bottle-medical::before { - content: "\f486"; } - -.fa-prescription-bottle-alt::before { - content: "\f486"; } - -.fa-print::before { - content: "\f02f"; } - -.fa-pump-medical::before { - content: "\e06a"; } - -.fa-pump-soap::before { - content: "\e06b"; } - -.fa-puzzle-piece::before { - content: "\f12e"; } - -.fa-q::before { - content: "\51"; } - -.fa-qrcode::before { - content: "\f029"; } - -.fa-question::before { - content: "\3f"; } - -.fa-quote-left::before { - content: "\f10d"; } - -.fa-quote-left-alt::before { - content: "\f10d"; } - -.fa-quote-right::before { - content: "\f10e"; } - -.fa-quote-right-alt::before { - content: "\f10e"; } - -.fa-r::before { - content: "\52"; } - -.fa-radiation::before { - content: "\f7b9"; } - -.fa-radio::before { - content: "\f8d7"; } - -.fa-rainbow::before { - content: "\f75b"; } - -.fa-ranking-star::before { - content: "\e561"; } - -.fa-receipt::before { - content: "\f543"; } - -.fa-record-vinyl::before { - content: "\f8d9"; } - -.fa-rectangle-ad::before { - content: "\f641"; } - -.fa-ad::before { - content: "\f641"; } - -.fa-rectangle-list::before { - content: "\f022"; } - -.fa-list-alt::before { - content: "\f022"; } - -.fa-rectangle-xmark::before { - content: "\f410"; } - -.fa-rectangle-times::before { - content: "\f410"; } - -.fa-times-rectangle::before { - content: "\f410"; } - -.fa-window-close::before { - content: "\f410"; } - -.fa-recycle::before { - content: "\f1b8"; } - -.fa-registered::before { - content: "\f25d"; } - -.fa-repeat::before { - content: "\f363"; } - -.fa-reply::before { - content: "\f3e5"; } - -.fa-mail-reply::before { - content: "\f3e5"; } - -.fa-reply-all::before { - content: "\f122"; } - -.fa-mail-reply-all::before { - content: "\f122"; } - -.fa-republican::before { - content: "\f75e"; } - -.fa-restroom::before { - content: "\f7bd"; } - -.fa-retweet::before { - content: "\f079"; } - -.fa-ribbon::before { - content: "\f4d6"; } - -.fa-right-from-bracket::before { - content: "\f2f5"; } - -.fa-sign-out-alt::before { - content: "\f2f5"; } - -.fa-right-left::before { - content: "\f362"; } - -.fa-exchange-alt::before { - content: "\f362"; } - -.fa-right-long::before { - content: "\f30b"; } - -.fa-long-arrow-alt-right::before { - content: "\f30b"; } - -.fa-right-to-bracket::before { - content: "\f2f6"; } - -.fa-sign-in-alt::before { - content: "\f2f6"; } - -.fa-ring::before { - content: "\f70b"; } - -.fa-road::before { - content: "\f018"; } - -.fa-road-barrier::before { - content: "\e562"; } - -.fa-road-bridge::before { - content: "\e563"; } - -.fa-road-circle-check::before { - content: "\e564"; } - -.fa-road-circle-exclamation::before { - content: "\e565"; } - -.fa-road-circle-xmark::before { - content: "\e566"; } - -.fa-road-lock::before { - content: "\e567"; } - -.fa-road-spikes::before { - content: "\e568"; } - -.fa-robot::before { - content: "\f544"; } - -.fa-rocket::before { - content: "\f135"; } - -.fa-rotate::before { - content: "\f2f1"; } - -.fa-sync-alt::before { - content: "\f2f1"; } - -.fa-rotate-left::before { - content: "\f2ea"; } - -.fa-rotate-back::before { - content: "\f2ea"; } - -.fa-rotate-backward::before { - content: "\f2ea"; } - -.fa-undo-alt::before { - content: "\f2ea"; } - -.fa-rotate-right::before { - content: "\f2f9"; } - -.fa-redo-alt::before { - content: "\f2f9"; } - -.fa-rotate-forward::before { - content: "\f2f9"; } - -.fa-route::before { - content: "\f4d7"; } - -.fa-rss::before { - content: "\f09e"; } - -.fa-feed::before { - content: "\f09e"; } - -.fa-ruble-sign::before { - content: "\f158"; } - -.fa-rouble::before { - content: "\f158"; } - -.fa-rub::before { - content: "\f158"; } - -.fa-ruble::before { - content: "\f158"; } - -.fa-rug::before { - content: "\e569"; } - -.fa-ruler::before { - content: "\f545"; } - -.fa-ruler-combined::before { - content: "\f546"; } - -.fa-ruler-horizontal::before { - content: "\f547"; } - -.fa-ruler-vertical::before { - content: "\f548"; } - -.fa-rupee-sign::before { - content: "\f156"; } - -.fa-rupee::before { - content: "\f156"; } - -.fa-rupiah-sign::before { - content: "\e23d"; } - -.fa-s::before { - content: "\53"; } - -.fa-sack-dollar::before { - content: "\f81d"; } - -.fa-sack-xmark::before { - content: "\e56a"; } - -.fa-sailboat::before { - content: "\e445"; } - -.fa-satellite::before { - content: "\f7bf"; } - -.fa-satellite-dish::before { - content: "\f7c0"; } - -.fa-scale-balanced::before { - content: "\f24e"; } - -.fa-balance-scale::before { - content: "\f24e"; } - -.fa-scale-unbalanced::before { - content: "\f515"; } - -.fa-balance-scale-left::before { - content: "\f515"; } - -.fa-scale-unbalanced-flip::before { - content: "\f516"; } - -.fa-balance-scale-right::before { - content: "\f516"; } - -.fa-school::before { - content: "\f549"; } - -.fa-school-circle-check::before { - content: "\e56b"; } - -.fa-school-circle-exclamation::before { - content: "\e56c"; } - -.fa-school-circle-xmark::before { - content: "\e56d"; } - -.fa-school-flag::before { - content: "\e56e"; } - -.fa-school-lock::before { - content: "\e56f"; } - -.fa-scissors::before { - content: "\f0c4"; } - -.fa-cut::before { - content: "\f0c4"; } - -.fa-screwdriver::before { - content: "\f54a"; } - -.fa-screwdriver-wrench::before { - content: "\f7d9"; } - -.fa-tools::before { - content: "\f7d9"; } - -.fa-scroll::before { - content: "\f70e"; } - -.fa-scroll-torah::before { - content: "\f6a0"; } - -.fa-torah::before { - content: "\f6a0"; } - -.fa-sd-card::before { - content: "\f7c2"; } - -.fa-section::before { - content: "\e447"; } - -.fa-seedling::before { - content: "\f4d8"; } - -.fa-sprout::before { - content: "\f4d8"; } - -.fa-server::before { - content: "\f233"; } - -.fa-shapes::before { - content: "\f61f"; } - -.fa-triangle-circle-square::before { - content: "\f61f"; } - -.fa-share::before { - content: "\f064"; } - -.fa-arrow-turn-right::before { - content: "\f064"; } - -.fa-mail-forward::before { - content: "\f064"; } - -.fa-share-from-square::before { - content: "\f14d"; } - -.fa-share-square::before { - content: "\f14d"; } - -.fa-share-nodes::before { - content: "\f1e0"; } - -.fa-share-alt::before { - content: "\f1e0"; } - -.fa-sheet-plastic::before { - content: "\e571"; } - -.fa-shekel-sign::before { - content: "\f20b"; } - -.fa-ils::before { - content: "\f20b"; } - -.fa-shekel::before { - content: "\f20b"; } - -.fa-sheqel::before { - content: "\f20b"; } - -.fa-sheqel-sign::before { - content: "\f20b"; } - -.fa-shield::before { - content: "\f132"; } - -.fa-shield-blank::before { - content: "\f132"; } - -.fa-shield-cat::before { - content: "\e572"; } - -.fa-shield-dog::before { - content: "\e573"; } - -.fa-shield-halved::before { - content: "\f3ed"; } - -.fa-shield-alt::before { - content: "\f3ed"; } - -.fa-shield-heart::before { - content: "\e574"; } - -.fa-shield-virus::before { - content: "\e06c"; } - -.fa-ship::before { - content: "\f21a"; } - -.fa-shirt::before { - content: "\f553"; } - -.fa-t-shirt::before { - content: "\f553"; } - -.fa-tshirt::before { - content: "\f553"; } - -.fa-shoe-prints::before { - content: "\f54b"; } - -.fa-shop::before { - content: "\f54f"; } - -.fa-store-alt::before { - content: "\f54f"; } - -.fa-shop-lock::before { - content: "\e4a5"; } - -.fa-shop-slash::before { - content: "\e070"; } - -.fa-store-alt-slash::before { - content: "\e070"; } - -.fa-shower::before { - content: "\f2cc"; } - -.fa-shrimp::before { - content: "\e448"; } - -.fa-shuffle::before { - content: "\f074"; } - -.fa-random::before { - content: "\f074"; } - -.fa-shuttle-space::before { - content: "\f197"; } - -.fa-space-shuttle::before { - content: "\f197"; } - -.fa-sign-hanging::before { - content: "\f4d9"; } - -.fa-sign::before { - content: "\f4d9"; } - -.fa-signal::before { - content: "\f012"; } - -.fa-signal-5::before { - content: "\f012"; } - -.fa-signal-perfect::before { - content: "\f012"; } - -.fa-signature::before { - content: "\f5b7"; } - -.fa-signs-post::before { - content: "\f277"; } - -.fa-map-signs::before { - content: "\f277"; } - -.fa-sim-card::before { - content: "\f7c4"; } - -.fa-sink::before { - content: "\e06d"; } - -.fa-sitemap::before { - content: "\f0e8"; } - -.fa-skull::before { - content: "\f54c"; } - -.fa-skull-crossbones::before { - content: "\f714"; } - -.fa-slash::before { - content: "\f715"; } - -.fa-sleigh::before { - content: "\f7cc"; } - -.fa-sliders::before { - content: "\f1de"; } - -.fa-sliders-h::before { - content: "\f1de"; } - -.fa-smog::before { - content: "\f75f"; } - -.fa-smoking::before { - content: "\f48d"; } - -.fa-snowflake::before { - content: "\f2dc"; } - -.fa-snowman::before { - content: "\f7d0"; } - -.fa-snowplow::before { - content: "\f7d2"; } - -.fa-soap::before { - content: "\e06e"; } - -.fa-socks::before { - content: "\f696"; } - -.fa-solar-panel::before { - content: "\f5ba"; } - -.fa-sort::before { - content: "\f0dc"; } - -.fa-unsorted::before { - content: "\f0dc"; } - -.fa-sort-down::before { - content: "\f0dd"; } - -.fa-sort-desc::before { - content: "\f0dd"; } - -.fa-sort-up::before { - content: "\f0de"; } - -.fa-sort-asc::before { - content: "\f0de"; } - -.fa-spa::before { - content: "\f5bb"; } - -.fa-spaghetti-monster-flying::before { - content: "\f67b"; } - -.fa-pastafarianism::before { - content: "\f67b"; } - -.fa-spell-check::before { - content: "\f891"; } - -.fa-spider::before { - content: "\f717"; } - -.fa-spinner::before { - content: "\f110"; } - -.fa-splotch::before { - content: "\f5bc"; } - -.fa-spoon::before { - content: "\f2e5"; } - -.fa-utensil-spoon::before { - content: "\f2e5"; } - -.fa-spray-can::before { - content: "\f5bd"; } - -.fa-spray-can-sparkles::before { - content: "\f5d0"; } - -.fa-air-freshener::before { - content: "\f5d0"; } - -.fa-square::before { - content: "\f0c8"; } - -.fa-square-arrow-up-right::before { - content: "\f14c"; } - -.fa-external-link-square::before { - content: "\f14c"; } - -.fa-square-caret-down::before { - content: "\f150"; } - -.fa-caret-square-down::before { - content: "\f150"; } - -.fa-square-caret-left::before { - content: "\f191"; } - -.fa-caret-square-left::before { - content: "\f191"; } - -.fa-square-caret-right::before { - content: "\f152"; } - -.fa-caret-square-right::before { - content: "\f152"; } - -.fa-square-caret-up::before { - content: "\f151"; } - -.fa-caret-square-up::before { - content: "\f151"; } - -.fa-square-check::before { - content: "\f14a"; } - -.fa-check-square::before { - content: "\f14a"; } - -.fa-square-envelope::before { - content: "\f199"; } - -.fa-envelope-square::before { - content: "\f199"; } - -.fa-square-full::before { - content: "\f45c"; } - -.fa-square-h::before { - content: "\f0fd"; } - -.fa-h-square::before { - content: "\f0fd"; } - -.fa-square-minus::before { - content: "\f146"; } - -.fa-minus-square::before { - content: "\f146"; } - -.fa-square-nfi::before { - content: "\e576"; } - -.fa-square-parking::before { - content: "\f540"; } - -.fa-parking::before { - content: "\f540"; } - -.fa-square-pen::before { - content: "\f14b"; } - -.fa-pen-square::before { - content: "\f14b"; } - -.fa-pencil-square::before { - content: "\f14b"; } - -.fa-square-person-confined::before { - content: "\e577"; } - -.fa-square-phone::before { - content: "\f098"; } - -.fa-phone-square::before { - content: "\f098"; } - -.fa-square-phone-flip::before { - content: "\f87b"; } - -.fa-phone-square-alt::before { - content: "\f87b"; } - -.fa-square-plus::before { - content: "\f0fe"; } - -.fa-plus-square::before { - content: "\f0fe"; } - -.fa-square-poll-horizontal::before { - content: "\f682"; } - -.fa-poll-h::before { - content: "\f682"; } - -.fa-square-poll-vertical::before { - content: "\f681"; } - -.fa-poll::before { - content: "\f681"; } - -.fa-square-root-variable::before { - content: "\f698"; } - -.fa-square-root-alt::before { - content: "\f698"; } - -.fa-square-rss::before { - content: "\f143"; } - -.fa-rss-square::before { - content: "\f143"; } - -.fa-square-share-nodes::before { - content: "\f1e1"; } - -.fa-share-alt-square::before { - content: "\f1e1"; } - -.fa-square-up-right::before { - content: "\f360"; } - -.fa-external-link-square-alt::before { - content: "\f360"; } - -.fa-square-virus::before { - content: "\e578"; } - -.fa-square-xmark::before { - content: "\f2d3"; } - -.fa-times-square::before { - content: "\f2d3"; } - -.fa-xmark-square::before { - content: "\f2d3"; } - -.fa-staff-aesculapius::before { - content: "\e579"; } - -.fa-rod-asclepius::before { - content: "\e579"; } - -.fa-rod-snake::before { - content: "\e579"; } - -.fa-staff-snake::before { - content: "\e579"; } - -.fa-stairs::before { - content: "\e289"; } - -.fa-stamp::before { - content: "\f5bf"; } - -.fa-star::before { - content: "\f005"; } - -.fa-star-and-crescent::before { - content: "\f699"; } - -.fa-star-half::before { - content: "\f089"; } - -.fa-star-half-stroke::before { - content: "\f5c0"; } - -.fa-star-half-alt::before { - content: "\f5c0"; } - -.fa-star-of-david::before { - content: "\f69a"; } - -.fa-star-of-life::before { - content: "\f621"; } - -.fa-sterling-sign::before { - content: "\f154"; } - -.fa-gbp::before { - content: "\f154"; } - -.fa-pound-sign::before { - content: "\f154"; } - -.fa-stethoscope::before { - content: "\f0f1"; } - -.fa-stop::before { - content: "\f04d"; } - -.fa-stopwatch::before { - content: "\f2f2"; } - -.fa-stopwatch-20::before { - content: "\e06f"; } - -.fa-store::before { - content: "\f54e"; } - -.fa-store-slash::before { - content: "\e071"; } - -.fa-street-view::before { - content: "\f21d"; } - -.fa-strikethrough::before { - content: "\f0cc"; } - -.fa-stroopwafel::before { - content: "\f551"; } - -.fa-subscript::before { - content: "\f12c"; } - -.fa-suitcase::before { - content: "\f0f2"; } - -.fa-suitcase-medical::before { - content: "\f0fa"; } - -.fa-medkit::before { - content: "\f0fa"; } - -.fa-suitcase-rolling::before { - content: "\f5c1"; } - -.fa-sun::before { - content: "\f185"; } - -.fa-sun-plant-wilt::before { - content: "\e57a"; } - -.fa-superscript::before { - content: "\f12b"; } - -.fa-swatchbook::before { - content: "\f5c3"; } - -.fa-synagogue::before { - content: "\f69b"; } - -.fa-syringe::before { - content: "\f48e"; } - -.fa-t::before { - content: "\54"; } - -.fa-table::before { - content: "\f0ce"; } - -.fa-table-cells::before { - content: "\f00a"; } - -.fa-th::before { - content: "\f00a"; } - -.fa-table-cells-large::before { - content: "\f009"; } - -.fa-th-large::before { - content: "\f009"; } - -.fa-table-columns::before { - content: "\f0db"; } - -.fa-columns::before { - content: "\f0db"; } - -.fa-table-list::before { - content: "\f00b"; } - -.fa-th-list::before { - content: "\f00b"; } - -.fa-table-tennis-paddle-ball::before { - content: "\f45d"; } - -.fa-ping-pong-paddle-ball::before { - content: "\f45d"; } - -.fa-table-tennis::before { - content: "\f45d"; } - -.fa-tablet::before { - content: "\f3fb"; } - -.fa-tablet-android::before { - content: "\f3fb"; } - -.fa-tablet-button::before { - content: "\f10a"; } - -.fa-tablet-screen-button::before { - content: "\f3fa"; } - -.fa-tablet-alt::before { - content: "\f3fa"; } - -.fa-tablets::before { - content: "\f490"; } - -.fa-tachograph-digital::before { - content: "\f566"; } - -.fa-digital-tachograph::before { - content: "\f566"; } - -.fa-tag::before { - content: "\f02b"; } - -.fa-tags::before { - content: "\f02c"; } - -.fa-tape::before { - content: "\f4db"; } - -.fa-tarp::before { - content: "\e57b"; } - -.fa-tarp-droplet::before { - content: "\e57c"; } - -.fa-taxi::before { - content: "\f1ba"; } - -.fa-cab::before { - content: "\f1ba"; } - -.fa-teeth::before { - content: "\f62e"; } - -.fa-teeth-open::before { - content: "\f62f"; } - -.fa-temperature-arrow-down::before { - content: "\e03f"; } - -.fa-temperature-down::before { - content: "\e03f"; } - -.fa-temperature-arrow-up::before { - content: "\e040"; } - -.fa-temperature-up::before { - content: "\e040"; } - -.fa-temperature-empty::before { - content: "\f2cb"; } - -.fa-temperature-0::before { - content: "\f2cb"; } - -.fa-thermometer-0::before { - content: "\f2cb"; } - -.fa-thermometer-empty::before { - content: "\f2cb"; } - -.fa-temperature-full::before { - content: "\f2c7"; } - -.fa-temperature-4::before { - content: "\f2c7"; } - -.fa-thermometer-4::before { - content: "\f2c7"; } - -.fa-thermometer-full::before { - content: "\f2c7"; } - -.fa-temperature-half::before { - content: "\f2c9"; } - -.fa-temperature-2::before { - content: "\f2c9"; } - -.fa-thermometer-2::before { - content: "\f2c9"; } - -.fa-thermometer-half::before { - content: "\f2c9"; } - -.fa-temperature-high::before { - content: "\f769"; } - -.fa-temperature-low::before { - content: "\f76b"; } - -.fa-temperature-quarter::before { - content: "\f2ca"; } - -.fa-temperature-1::before { - content: "\f2ca"; } - -.fa-thermometer-1::before { - content: "\f2ca"; } - -.fa-thermometer-quarter::before { - content: "\f2ca"; } - -.fa-temperature-three-quarters::before { - content: "\f2c8"; } - -.fa-temperature-3::before { - content: "\f2c8"; } - -.fa-thermometer-3::before { - content: "\f2c8"; } - -.fa-thermometer-three-quarters::before { - content: "\f2c8"; } - -.fa-tenge-sign::before { - content: "\f7d7"; } - -.fa-tenge::before { - content: "\f7d7"; } - -.fa-tent::before { - content: "\e57d"; } - -.fa-tent-arrow-down-to-line::before { - content: "\e57e"; } - -.fa-tent-arrow-left-right::before { - content: "\e57f"; } - -.fa-tent-arrow-turn-left::before { - content: "\e580"; } - -.fa-tent-arrows-down::before { - content: "\e581"; } - -.fa-tents::before { - content: "\e582"; } - -.fa-terminal::before { - content: "\f120"; } - -.fa-text-height::before { - content: "\f034"; } - -.fa-text-slash::before { - content: "\f87d"; } - -.fa-remove-format::before { - content: "\f87d"; } - -.fa-text-width::before { - content: "\f035"; } - -.fa-thermometer::before { - content: "\f491"; } - -.fa-thumbs-down::before { - content: "\f165"; } - -.fa-thumbs-up::before { - content: "\f164"; } - -.fa-thumbtack::before { - content: "\f08d"; } - -.fa-thumb-tack::before { - content: "\f08d"; } - -.fa-ticket::before { - content: "\f145"; } - -.fa-ticket-simple::before { - content: "\f3ff"; } - -.fa-ticket-alt::before { - content: "\f3ff"; } - -.fa-timeline::before { - content: "\e29c"; } - -.fa-toggle-off::before { - content: "\f204"; } - -.fa-toggle-on::before { - content: "\f205"; } - -.fa-toilet::before { - content: "\f7d8"; } - -.fa-toilet-paper::before { - content: "\f71e"; } - -.fa-toilet-paper-slash::before { - content: "\e072"; } - -.fa-toilet-portable::before { - content: "\e583"; } - -.fa-toilets-portable::before { - content: "\e584"; } - -.fa-toolbox::before { - content: "\f552"; } - -.fa-tooth::before { - content: "\f5c9"; } - -.fa-torii-gate::before { - content: "\f6a1"; } - -.fa-tornado::before { - content: "\f76f"; } - -.fa-wifi::before { - content: "\f519"; } - -.fa-broadcast-tower::before { - content: "\f519"; } - -.fa-tower-cell::before { - content: "\e585"; } - -.fa-tower-observation::before { - content: "\e586"; } - -.fa-tractor::before { - content: "\f722"; } - -.fa-trademark::before { - content: "\f25c"; } - -.fa-traffic-light::before { - content: "\f637"; } - -.fa-trailer::before { - content: "\e041"; } - -.fa-train::before { - content: "\f238"; } - -.fa-train-subway::before { - content: "\f239"; } - -.fa-subway::before { - content: "\f239"; } - -.fa-train-tram::before { - content: "\f7da"; } - -.fa-tram::before { - content: "\f7da"; } - -.fa-transgender::before { - content: "\f225"; } - -.fa-transgender-alt::before { - content: "\f225"; } - -.fa-trash::before { - content: "\f1f8"; } - -.fa-trash-arrow-up::before { - content: "\f829"; } - -.fa-trash-restore::before { - content: "\f829"; } - -.fa-trash-can::before { - content: "\f2ed"; } - -.fa-trash-alt::before { - content: "\f2ed"; } - -.fa-trash-can-arrow-up::before { - content: "\f82a"; } - -.fa-trash-restore-alt::before { - content: "\f82a"; } - -.fa-tree::before { - content: "\f1bb"; } - -.fa-tree-city::before { - content: "\e587"; } - -.fa-triangle-exclamation::before { - content: "\f071"; } - -.fa-exclamation-triangle::before { - content: "\f071"; } - -.fa-warning::before { - content: "\f071"; } - -.fa-trophy::before { - content: "\f091"; } - -.fa-trowel::before { - content: "\e589"; } - -.fa-trowel-bricks::before { - content: "\e58a"; } - -.fa-truck::before { - content: "\f0d1"; } - -.fa-truck-arrow-right::before { - content: "\e58b"; } - -.fa-truck-droplet::before { - content: "\e58c"; } - -.fa-truck-fast::before { - content: "\f48b"; } - -.fa-shipping-fast::before { - content: "\f48b"; } - -.fa-truck-field::before { - content: "\e58d"; } - -.fa-truck-field-un::before { - content: "\e58e"; } - -.fa-truck-front::before { - content: "\e2b7"; } - -.fa-truck-medical::before { - content: "\f0f9"; } - -.fa-ambulance::before { - content: "\f0f9"; } - -.fa-truck-monster::before { - content: "\f63b"; } - -.fa-truck-moving::before { - content: "\f4df"; } - -.fa-truck-pickup::before { - content: "\f63c"; } - -.fa-truck-plane::before { - content: "\e58f"; } - -.fa-truck-ramp-box::before { - content: "\f4de"; } - -.fa-truck-loading::before { - content: "\f4de"; } - -.fa-tty::before { - content: "\f1e4"; } - -.fa-teletype::before { - content: "\f1e4"; } - -.fa-turkish-lira-sign::before { - content: "\e2bb"; } - -.fa-try::before { - content: "\e2bb"; } - -.fa-turkish-lira::before { - content: "\e2bb"; } - -.fa-turn-down::before { - content: "\f3be"; } - -.fa-level-down-alt::before { - content: "\f3be"; } - -.fa-turn-up::before { - content: "\f3bf"; } - -.fa-level-up-alt::before { - content: "\f3bf"; } - -.fa-tv::before { - content: "\f26c"; } - -.fa-television::before { - content: "\f26c"; } - -.fa-tv-alt::before { - content: "\f26c"; } - -.fa-u::before { - content: "\55"; } - -.fa-umbrella::before { - content: "\f0e9"; } - -.fa-umbrella-beach::before { - content: "\f5ca"; } - -.fa-underline::before { - content: "\f0cd"; } - -.fa-universal-access::before { - content: "\f29a"; } - -.fa-unlock::before { - content: "\f09c"; } - -.fa-unlock-keyhole::before { - content: "\f13e"; } - -.fa-unlock-alt::before { - content: "\f13e"; } - -.fa-up-down::before { - content: "\f338"; } - -.fa-arrows-alt-v::before { - content: "\f338"; } - -.fa-up-down-left-right::before { - content: "\f0b2"; } - -.fa-arrows-alt::before { - content: "\f0b2"; } - -.fa-up-long::before { - content: "\f30c"; } - -.fa-long-arrow-alt-up::before { - content: "\f30c"; } - -.fa-up-right-and-down-left-from-center::before { - content: "\f424"; } - -.fa-expand-alt::before { - content: "\f424"; } - -.fa-up-right-from-square::before { - content: "\f35d"; } - -.fa-external-link-alt::before { - content: "\f35d"; } - -.fa-upload::before { - content: "\f093"; } - -.fa-user::before { - content: "\f007"; } - -.fa-user-astronaut::before { - content: "\f4fb"; } - -.fa-user-check::before { - content: "\f4fc"; } - -.fa-user-clock::before { - content: "\f4fd"; } - -.fa-user-doctor::before { - content: "\f0f0"; } - -.fa-user-md::before { - content: "\f0f0"; } - -.fa-user-gear::before { - content: "\f4fe"; } - -.fa-user-cog::before { - content: "\f4fe"; } - -.fa-user-graduate::before { - content: "\f501"; } - -.fa-user-group::before { - content: "\f500"; } - -.fa-user-friends::before { - content: "\f500"; } - -.fa-user-injured::before { - content: "\f728"; } - -.fa-user-large::before { - content: "\f406"; } - -.fa-user-alt::before { - content: "\f406"; } - -.fa-user-large-slash::before { - content: "\f4fa"; } - -.fa-user-alt-slash::before { - content: "\f4fa"; } - -.fa-user-lock::before { - content: "\f502"; } - -.fa-user-minus::before { - content: "\f503"; } - -.fa-user-ninja::before { - content: "\f504"; } - -.fa-user-nurse::before { - content: "\f82f"; } - -.fa-user-pen::before { - content: "\f4ff"; } - -.fa-user-edit::before { - content: "\f4ff"; } - -.fa-user-plus::before { - content: "\f234"; } - -.fa-user-secret::before { - content: "\f21b"; } - -.fa-user-shield::before { - content: "\f505"; } - -.fa-user-slash::before { - content: "\f506"; } - -.fa-user-tag::before { - content: "\f507"; } - -.fa-user-tie::before { - content: "\f508"; } - -.fa-user-xmark::before { - content: "\f235"; } - -.fa-user-times::before { - content: "\f235"; } - -.fa-users::before { - content: "\f0c0"; } - -.fa-users-between-lines::before { - content: "\e591"; } - -.fa-users-gear::before { - content: "\f509"; } - -.fa-users-cog::before { - content: "\f509"; } - -.fa-users-line::before { - content: "\e592"; } - -.fa-users-rays::before { - content: "\e593"; } - -.fa-users-rectangle::before { - content: "\e594"; } - -.fa-users-slash::before { - content: "\e073"; } - -.fa-users-viewfinder::before { - content: "\e595"; } - -.fa-utensils::before { - content: "\f2e7"; } - -.fa-cutlery::before { - content: "\f2e7"; } - -.fa-v::before { - content: "\56"; } - -.fa-van-shuttle::before { - content: "\f5b6"; } - -.fa-shuttle-van::before { - content: "\f5b6"; } - -.fa-vault::before { - content: "\e2c5"; } - -.fa-vector-square::before { - content: "\f5cb"; } - -.fa-venus::before { - content: "\f221"; } - -.fa-venus-double::before { - content: "\f226"; } - -.fa-venus-mars::before { - content: "\f228"; } - -.fa-vest::before { - content: "\e085"; } - -.fa-vest-patches::before { - content: "\e086"; } - -.fa-vial::before { - content: "\f492"; } - -.fa-vial-circle-check::before { - content: "\e596"; } - -.fa-vial-virus::before { - content: "\e597"; } - -.fa-vials::before { - content: "\f493"; } - -.fa-video::before { - content: "\f03d"; } - -.fa-video-camera::before { - content: "\f03d"; } - -.fa-video-slash::before { - content: "\f4e2"; } - -.fa-vihara::before { - content: "\f6a7"; } - -.fa-virus::before { - content: "\e074"; } - -.fa-virus-covid::before { - content: "\e4a8"; } - -.fa-virus-covid-slash::before { - content: "\e4a9"; } - -.fa-virus-slash::before { - content: "\e075"; } - -.fa-viruses::before { - content: "\e076"; } - -.fa-voicemail::before { - content: "\f897"; } - -.fa-volcano::before { - content: "\f770"; } - -.fa-volleyball::before { - content: "\f45f"; } - -.fa-volleyball-ball::before { - content: "\f45f"; } - -.fa-volume-high::before { - content: "\f028"; } - -.fa-volume-up::before { - content: "\f028"; } - -.fa-volume-low::before { - content: "\f027"; } - -.fa-volume-down::before { - content: "\f027"; } - -.fa-volume-off::before { - content: "\f026"; } - -.fa-volume-xmark::before { - content: "\f6a9"; } - -.fa-volume-mute::before { - content: "\f6a9"; } - -.fa-volume-times::before { - content: "\f6a9"; } - -.fa-vr-cardboard::before { - content: "\f729"; } - -.fa-w::before { - content: "\57"; } - -.fa-walkie-talkie::before { - content: "\f8ef"; } - -.fa-wallet::before { - content: "\f555"; } - -.fa-wand-magic::before { - content: "\f0d0"; } - -.fa-magic::before { - content: "\f0d0"; } - -.fa-wand-magic-sparkles::before { - content: "\e2ca"; } - -.fa-magic-wand-sparkles::before { - content: "\e2ca"; } - -.fa-wand-sparkles::before { - content: "\f72b"; } - -.fa-warehouse::before { - content: "\f494"; } - -.fa-water::before { - content: "\f773"; } - -.fa-water-ladder::before { - content: "\f5c5"; } - -.fa-ladder-water::before { - content: "\f5c5"; } - -.fa-swimming-pool::before { - content: "\f5c5"; } - -.fa-wave-square::before { - content: "\f83e"; } - -.fa-weight-hanging::before { - content: "\f5cd"; } - -.fa-weight-scale::before { - content: "\f496"; } - -.fa-weight::before { - content: "\f496"; } - -.fa-wheat-awn::before { - content: "\e2cd"; } - -.fa-wheat-alt::before { - content: "\e2cd"; } - -.fa-wheat-awn-circle-exclamation::before { - content: "\e598"; } - -.fa-wheelchair::before { - content: "\f193"; } - -.fa-wheelchair-move::before { - content: "\e2ce"; } - -.fa-wheelchair-alt::before { - content: "\e2ce"; } - -.fa-whiskey-glass::before { - content: "\f7a0"; } - -.fa-glass-whiskey::before { - content: "\f7a0"; } - -.fa-wifi::before { - content: "\f1eb"; } - -.fa-wifi-3::before { - content: "\f1eb"; } - -.fa-wifi-strong::before { - content: "\f1eb"; } - -.fa-wind::before { - content: "\f72e"; } - -.fa-window-maximize::before { - content: "\f2d0"; } - -.fa-window-minimize::before { - content: "\f2d1"; } - -.fa-window-restore::before { - content: "\f2d2"; } - -.fa-wine-bottle::before { - content: "\f72f"; } - -.fa-wine-glass::before { - content: "\f4e3"; } - -.fa-wine-glass-empty::before { - content: "\f5ce"; } - -.fa-wine-glass-alt::before { - content: "\f5ce"; } - -.fa-won-sign::before { - content: "\f159"; } - -.fa-krw::before { - content: "\f159"; } - -.fa-won::before { - content: "\f159"; } - -.fa-worm::before { - content: "\e599"; } - -.fa-wrench::before { - content: "\f0ad"; } - -.fa-x::before { - content: "\58"; } - -.fa-x-ray::before { - content: "\f497"; } - -.fa-xmark::before { - content: "\f00d"; } - -.fa-close::before { - content: "\f00d"; } - -.fa-multiply::before { - content: "\f00d"; } - -.fa-remove::before { - content: "\f00d"; } - -.fa-times::before { - content: "\f00d"; } - -.fa-xmarks-lines::before { - content: "\e59a"; } - -.fa-y::before { - content: "\59"; } - -.fa-yen-sign::before { - content: "\f157"; } - -.fa-cny::before { - content: "\f157"; } - -.fa-jpy::before { - content: "\f157"; } - -.fa-rmb::before { - content: "\f157"; } - -.fa-yen::before { - content: "\f157"; } - -.fa-yin-yang::before { - content: "\f6ad"; } - -.fa-z::before { - content: "\5a"; } - -.sr-only, -.fa-sr-only { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - white-space: nowrap; - border-width: 0; } - -.sr-only-focusable:not(:focus), -.fa-sr-only-focusable:not(:focus) { - position: absolute; - width: 1px; - height: 1px; - padding: 0; - margin: -1px; - overflow: hidden; - clip: rect(0, 0, 0, 0); - white-space: nowrap; - border-width: 0; } -:root, :host { - --fa-font-brands: normal 400 1em/1 "Font Awesome 6 Brands"; } - -@font-face { - font-family: 'Font Awesome 6 Brands'; - font-style: normal; - font-weight: 400; - font-display: block; - src: url("1e21o67/fa-brands-400.woff2") format("woff2"), url("1e21o67/fa-brands-400.ttf") format("truetype"); } - -.fab, -.fa-brands { - font-family: 'Font Awesome 6 Brands'; - font-weight: 400; } - -.fa-42-group:before { - content: "\e080"; } - -.fa-innosoft:before { - content: "\e080"; } - -.fa-500px:before { - content: "\f26e"; } - -.fa-accessible-icon:before { - content: "\f368"; } - -.fa-accusoft:before { - content: "\f369"; } - -.fa-adn:before { - content: "\f170"; } - -.fa-adversal:before { - content: "\f36a"; } - -.fa-affiliatetheme:before { - content: "\f36b"; } - -.fa-airbnb:before { - content: "\f834"; } - -.fa-algolia:before { - content: "\f36c"; } - -.fa-alipay:before { - content: "\f642"; } - -.fa-amazon:before { - content: "\f270"; } - -.fa-amazon-pay:before { - content: "\f42c"; } - -.fa-amilia:before { - content: "\f36d"; } - -.fa-android:before { - content: "\f17b"; } - -.fa-angellist:before { - content: "\f209"; } - -.fa-angrycreative:before { - content: "\f36e"; } - -.fa-angular:before { - content: "\f420"; } - -.fa-app-store:before { - content: "\f36f"; } - -.fa-app-store-ios:before { - content: "\f370"; } - -.fa-apper:before { - content: "\f371"; } - -.fa-apple:before { - content: "\f179"; } - -.fa-apple-pay:before { - content: "\f415"; } - -.fa-artstation:before { - content: "\f77a"; } - -.fa-asymmetrik:before { - content: "\f372"; } - -.fa-atlassian:before { - content: "\f77b"; } - -.fa-audible:before { - content: "\f373"; } - -.fa-autoprefixer:before { - content: "\f41c"; } - -.fa-avianex:before { - content: "\f374"; } - -.fa-aviato:before { - content: "\f421"; } - -.fa-aws:before { - content: "\f375"; } - -.fa-bandcamp:before { - content: "\f2d5"; } - -.fa-battle-net:before { - content: "\f835"; } - -.fa-behance:before { - content: "\f1b4"; } - -.fa-behance-square:before { - content: "\f1b5"; } - -.fa-bilibili:before { - content: "\e3d9"; } - -.fa-bimobject:before { - content: "\f378"; } - -.fa-bitbucket:before { - content: "\f171"; } - -.fa-bitcoin:before { - content: "\f379"; } - -.fa-bity:before { - content: "\f37a"; } - -.fa-black-tie:before { - content: "\f27e"; } - -.fa-blackberry:before { - content: "\f37b"; } - -.fa-blogger:before { - content: "\f37c"; } - -.fa-blogger-b:before { - content: "\f37d"; } - -.fa-bluetooth:before { - content: "\f293"; } - -.fa-bluetooth-b:before { - content: "\f294"; } - -.fa-bootstrap:before { - content: "\f836"; } - -.fa-bots:before { - content: "\e340"; } - -.fa-btc:before { - content: "\f15a"; } - -.fa-buffer:before { - content: "\f837"; } - -.fa-buromobelexperte:before { - content: "\f37f"; } - -.fa-buy-n-large:before { - content: "\f8a6"; } - -.fa-buysellads:before { - content: "\f20d"; } - -.fa-canadian-maple-leaf:before { - content: "\f785"; } - -.fa-cc-amazon-pay:before { - content: "\f42d"; } - -.fa-cc-amex:before { - content: "\f1f3"; } - -.fa-cc-apple-pay:before { - content: "\f416"; } - -.fa-cc-diners-club:before { - content: "\f24c"; } - -.fa-cc-discover:before { - content: "\f1f2"; } - -.fa-cc-jcb:before { - content: "\f24b"; } - -.fa-cc-mastercard:before { - content: "\f1f1"; } - -.fa-cc-paypal:before { - content: "\f1f4"; } - -.fa-cc-stripe:before { - content: "\f1f5"; } - -.fa-cc-visa:before { - content: "\f1f0"; } - -.fa-centercode:before { - content: "\f380"; } - -.fa-centos:before { - content: "\f789"; } - -.fa-chrome:before { - content: "\f268"; } - -.fa-chromecast:before { - content: "\f838"; } - -.fa-cloudflare:before { - content: "\e07d"; } - -.fa-cloudscale:before { - content: "\f383"; } - -.fa-cloudsmith:before { - content: "\f384"; } - -.fa-cloudversify:before { - content: "\f385"; } - -.fa-cmplid:before { - content: "\e360"; } - -.fa-codepen:before { - content: "\f1cb"; } - -.fa-codiepie:before { - content: "\f284"; } - -.fa-confluence:before { - content: "\f78d"; } - -.fa-connectdevelop:before { - content: "\f20e"; } - -.fa-contao:before { - content: "\f26d"; } - -.fa-cotton-bureau:before { - content: "\f89e"; } - -.fa-cpanel:before { - content: "\f388"; } - -.fa-creative-commons:before { - content: "\f25e"; } - -.fa-creative-commons-by:before { - content: "\f4e7"; } - -.fa-creative-commons-nc:before { - content: "\f4e8"; } - -.fa-creative-commons-nc-eu:before { - content: "\f4e9"; } - -.fa-creative-commons-nc-jp:before { - content: "\f4ea"; } - -.fa-creative-commons-nd:before { - content: "\f4eb"; } - -.fa-creative-commons-pd:before { - content: "\f4ec"; } - -.fa-creative-commons-pd-alt:before { - content: "\f4ed"; } - -.fa-creative-commons-remix:before { - content: "\f4ee"; } - -.fa-creative-commons-sa:before { - content: "\f4ef"; } - -.fa-creative-commons-sampling:before { - content: "\f4f0"; } - -.fa-creative-commons-sampling-plus:before { - content: "\f4f1"; } - -.fa-creative-commons-share:before { - content: "\f4f2"; } - -.fa-creative-commons-zero:before { - content: "\f4f3"; } - -.fa-critical-role:before { - content: "\f6c9"; } - -.fa-css3:before { - content: "\f13c"; } - -.fa-css3-alt:before { - content: "\f38b"; } - -.fa-cuttlefish:before { - content: "\f38c"; } - -.fa-d-and-d:before { - content: "\f38d"; } - -.fa-d-and-d-beyond:before { - content: "\f6ca"; } - -.fa-dailymotion:before { - content: "\e052"; } - -.fa-dashcube:before { - content: "\f210"; } - -.fa-deezer:before { - content: "\e077"; } - -.fa-delicious:before { - content: "\f1a5"; } - -.fa-deploydog:before { - content: "\f38e"; } - -.fa-deskpro:before { - content: "\f38f"; } - -.fa-dev:before { - content: "\f6cc"; } - -.fa-deviantart:before { - content: "\f1bd"; } - -.fa-dhl:before { - content: "\f790"; } - -.fa-diaspora:before { - content: "\f791"; } - -.fa-digg:before { - content: "\f1a6"; } - -.fa-digital-ocean:before { - content: "\f391"; } - -.fa-discord:before { - content: "\f392"; } - -.fa-discourse:before { - content: "\f393"; } - -.fa-dochub:before { - content: "\f394"; } - -.fa-docker:before { - content: "\f395"; } - -.fa-draft2digital:before { - content: "\f396"; } - -.fa-dribbble:before { - content: "\f17d"; } - -.fa-dribbble-square:before { - content: "\f397"; } - -.fa-dropbox:before { - content: "\f16b"; } - -.fa-drupal:before { - content: "\f1a9"; } - -.fa-dyalog:before { - content: "\f399"; } - -.fa-earlybirds:before { - content: "\f39a"; } - -.fa-ebay:before { - content: "\f4f4"; } - -.fa-edge:before { - content: "\f282"; } - -.fa-edge-legacy:before { - content: "\e078"; } - -.fa-elementor:before { - content: "\f430"; } - -.fa-ello:before { - content: "\f5f1"; } - -.fa-ember:before { - content: "\f423"; } - -.fa-empire:before { - content: "\f1d1"; } - -.fa-envira:before { - content: "\f299"; } - -.fa-erlang:before { - content: "\f39d"; } - -.fa-ethereum:before { - content: "\f42e"; } - -.fa-etsy:before { - content: "\f2d7"; } - -.fa-evernote:before { - content: "\f839"; } - -.fa-expeditedssl:before { - content: "\f23e"; } - -.fa-facebook:before { - content: "\f09a"; } - -.fa-facebook-f:before { - content: "\f39e"; } - -.fa-facebook-messenger:before { - content: "\f39f"; } - -.fa-facebook-square:before { - content: "\f082"; } - -.fa-fantasy-flight-games:before { - content: "\f6dc"; } - -.fa-fedex:before { - content: "\f797"; } - -.fa-fedora:before { - content: "\f798"; } - -.fa-figma:before { - content: "\f799"; } - -.fa-firefox:before { - content: "\f269"; } - -.fa-firefox-browser:before { - content: "\e007"; } - -.fa-first-order:before { - content: "\f2b0"; } - -.fa-first-order-alt:before { - content: "\f50a"; } - -.fa-firstdraft:before { - content: "\f3a1"; } - -.fa-flickr:before { - content: "\f16e"; } - -.fa-flipboard:before { - content: "\f44d"; } - -.fa-fly:before { - content: "\f417"; } - -.fa-font-awesome:before { - content: "\f2b4"; } - -.fa-font-awesome-flag:before { - content: "\f2b4"; } - -.fa-font-awesome-logo-full:before { - content: "\f2b4"; } - -.fa-fonticons:before { - content: "\f280"; } - -.fa-fonticons-fi:before { - content: "\f3a2"; } - -.fa-fort-awesome:before { - content: "\f286"; } - -.fa-fort-awesome-alt:before { - content: "\f3a3"; } - -.fa-forumbee:before { - content: "\f211"; } - -.fa-foursquare:before { - content: "\f180"; } - -.fa-free-code-camp:before { - content: "\f2c5"; } - -.fa-freebsd:before { - content: "\f3a4"; } - -.fa-fulcrum:before { - content: "\f50b"; } - -.fa-galactic-republic:before { - content: "\f50c"; } - -.fa-galactic-senate:before { - content: "\f50d"; } - -.fa-get-pocket:before { - content: "\f265"; } - -.fa-gg:before { - content: "\f260"; } - -.fa-gg-circle:before { - content: "\f261"; } - -.fa-git:before { - content: "\f1d3"; } - -.fa-git-alt:before { - content: "\f841"; } - -.fa-git-square:before { - content: "\f1d2"; } - -.fa-github:before { - content: "\f09b"; } - -.fa-github-alt:before { - content: "\f113"; } - -.fa-github-square:before { - content: "\f092"; } - -.fa-gitkraken:before { - content: "\f3a6"; } - -.fa-gitlab:before { - content: "\f296"; } - -.fa-gitter:before { - content: "\f426"; } - -.fa-glide:before { - content: "\f2a5"; } - -.fa-glide-g:before { - content: "\f2a6"; } - -.fa-gofore:before { - content: "\f3a7"; } - -.fa-golang:before { - content: "\e40f"; } - -.fa-goodreads:before { - content: "\f3a8"; } - -.fa-goodreads-g:before { - content: "\f3a9"; } - -.fa-google:before { - content: "\f1a0"; } - -.fa-google-drive:before { - content: "\f3aa"; } - -.fa-google-pay:before { - content: "\e079"; } - -.fa-google-play:before { - content: "\f3ab"; } - -.fa-google-plus:before { - content: "\f2b3"; } - -.fa-google-plus-g:before { - content: "\f0d5"; } - -.fa-google-plus-square:before { - content: "\f0d4"; } - -.fa-google-wallet:before { - content: "\f1ee"; } - -.fa-gratipay:before { - content: "\f184"; } - -.fa-grav:before { - content: "\f2d6"; } - -.fa-gripfire:before { - content: "\f3ac"; } - -.fa-grunt:before { - content: "\f3ad"; } - -.fa-guilded:before { - content: "\e07e"; } - -.fa-gulp:before { - content: "\f3ae"; } - -.fa-hacker-news:before { - content: "\f1d4"; } - -.fa-hacker-news-square:before { - content: "\f3af"; } - -.fa-hackerrank:before { - content: "\f5f7"; } - -.fa-hashnode:before { - content: "\e499"; } - -.fa-hips:before { - content: "\f452"; } - -.fa-hire-a-helper:before { - content: "\f3b0"; } - -.fa-hive:before { - content: "\e07f"; } - -.fa-hooli:before { - content: "\f427"; } - -.fa-hornbill:before { - content: "\f592"; } - -.fa-hotjar:before { - content: "\f3b1"; } - -.fa-houzz:before { - content: "\f27c"; } - -.fa-html5:before { - content: "\f13b"; } - -.fa-hubspot:before { - content: "\f3b2"; } - -.fa-ideal:before { - content: "\e013"; } - -.fa-imdb:before { - content: "\f2d8"; } - -.fa-instagram:before { - content: "\f16d"; } - -.fa-instagram-square:before { - content: "\e055"; } - -.fa-instalod:before { - content: "\e081"; } - -.fa-intercom:before { - content: "\f7af"; } - -.fa-internet-explorer:before { - content: "\f26b"; } - -.fa-invision:before { - content: "\f7b0"; } - -.fa-ioxhost:before { - content: "\f208"; } - -.fa-itch-io:before { - content: "\f83a"; } - -.fa-itunes:before { - content: "\f3b4"; } - -.fa-itunes-note:before { - content: "\f3b5"; } - -.fa-java:before { - content: "\f4e4"; } - -.fa-jedi-order:before { - content: "\f50e"; } - -.fa-jenkins:before { - content: "\f3b6"; } - -.fa-jira:before { - content: "\f7b1"; } - -.fa-joget:before { - content: "\f3b7"; } - -.fa-joomla:before { - content: "\f1aa"; } - -.fa-js:before { - content: "\f3b8"; } - -.fa-js-square:before { - content: "\f3b9"; } - -.fa-jsfiddle:before { - content: "\f1cc"; } - -.fa-kaggle:before { 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t=this.preV,i=e.touches.length,n=e.touches[0].pageX,s=e.touches[0].pageY;if(this.isDoubleTap=!1,i>1){var l=e.touches[1].pageX,o=e.touches[1].pageY,r={x:e.touches[1].pageX-n,y:e.touches[1].pageY-s};null!==t.x&&(this.pinchStartLen>0&&(e.zoom=Y(r)/this.pinchStartLen,this.pinch.dispatch(e,this.element)),e.angle=q(r,t),this.rotate.dispatch(e,this.element)),t.x=r.x,t.y=r.y,null!==this.x2&&null!==this.sx2?(e.deltaX=(n-this.x2+l-this.sx2)/2,e.deltaY=(s-this.y2+o-this.sy2)/2):(e.deltaX=0,e.deltaY=0),this.twoFingerPressMove.dispatch(e,this.element),this.sx2=l,this.sy2=o}else{if(null!==this.x2){e.deltaX=n-this.x2,e.deltaY=s-this.y2;var a=Math.abs(this.x1-this.x2),h=Math.abs(this.y1-this.y2);(a>10||h>10)&&(this._preventTap=!0)}else e.deltaX=0,e.deltaY=0;this.pressMove.dispatch(e,this.element)}this.touchMove.dispatch(e,this.element),this._cancelLongTap(),this.x2=n,this.y2=s,i>1&&e.preventDefault()}}},{key:"end",value:function(e){if(e.changedTouches){this._cancelLongTap();var t=this;e.touches.length<2&&(this.multipointEnd.dispatch(e,this.element),this.sx2=this.sy2=null),this.x2&&Math.abs(this.x1-this.x2)>30||this.y2&&Math.abs(this.y1-this.y2)>30?(e.direction=this._swipeDirection(this.x1,this.x2,this.y1,this.y2),this.swipeTimeout=setTimeout((function(){t.swipe.dispatch(e,t.element)}),0)):(this.tapTimeout=setTimeout((function(){t._preventTap||t.tap.dispatch(e,t.element),t.isDoubleTap&&(t.doubleTap.dispatch(e,t.element),t.isDoubleTap=!1)}),0),t.isDoubleTap||(t.singleTapTimeout=setTimeout((function(){t.singleTap.dispatch(e,t.element)}),250))),this.touchEnd.dispatch(e,this.element),this.preV.x=0,this.preV.y=0,this.zoom=1,this.pinchStartLen=null,this.x1=this.x2=this.y1=this.y2=null}}},{key:"cancelAll",value:function(){this._preventTap=!0,clearTimeout(this.singleTapTimeout),clearTimeout(this.tapTimeout),clearTimeout(this.longTapTimeout),clearTimeout(this.swipeTimeout)}},{key:"cancel",value:function(e){this.cancelAll(),this.touchCancel.dispatch(e,this.element)}},{key:"_cancelLongTap",value:function(){clearTimeout(this.longTapTimeout)}},{key:"_cancelSingleTap",value:function(){clearTimeout(this.singleTapTimeout)}},{key:"_swipeDirection",value:function(e,t,i,n){return Math.abs(e-t)>=Math.abs(i-n)?e-t>0?"Left":"Right":i-n>0?"Up":"Down"}},{key:"on",value:function(e,t){this[e]&&this[e].add(t)}},{key:"off",value:function(e,t){this[e]&&this[e].del(t)}},{key:"destroy",value:function(){return this.singleTapTimeout&&clearTimeout(this.singleTapTimeout),this.tapTimeout&&clearTimeout(this.tapTimeout),this.longTapTimeout&&clearTimeout(this.longTapTimeout),this.swipeTimeout&&clearTimeout(this.swipeTimeout),this.element.removeEventListener("touchstart",this.start),this.element.removeEventListener("touchmove",this.move),this.element.removeEventListener("touchend",this.end),this.element.removeEventListener("touchcancel",this.cancel),this.rotate.del(),this.touchStart.del(),this.multipointStart.del(),this.multipointEnd.del(),this.pinch.del(),this.swipe.del(),this.tap.del(),this.doubleTap.del(),this.longTap.del(),this.singleTap.del(),this.pressMove.del(),this.twoFingerPressMove.del(),this.touchMove.del(),this.touchEnd.del(),this.touchCancel.del(),this.preV=this.pinchStartLen=this.zoom=this.isDoubleTap=this.delta=this.last=this.now=this.tapTimeout=this.singleTapTimeout=this.longTapTimeout=this.swipeTimeout=this.x1=this.x2=this.y1=this.y2=this.preTapPosition=this.rotate=this.touchStart=this.multipointStart=this.multipointEnd=this.pinch=this.swipe=this.tap=this.doubleTap=this.longTap=this.singleTap=this.pressMove=this.touchMove=this.touchEnd=this.touchCancel=this.twoFingerPressMove=null,window.removeEventListener("scroll",this._cancelAllHandler),null}}]),e}();function W(e){var t=function(){var e,t=document.createElement("fakeelement"),i={transition:"transitionend",OTransition:"oTransitionEnd",MozTransition:"transitionend",WebkitTransition:"webkitTransitionEnd"};for(e in i)if(void 0!==t.style[e])return i[e]}(),i=window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth,n=c(e,"gslide-media")?e:e.querySelector(".gslide-media"),s=u(n,".ginner-container"),l=e.querySelector(".gslide-description");i>769&&(n=s),h(n,"greset"),v(n,"translate3d(0, 0, 0)"),a(t,{onElement:n,once:!0,withCallback:function(e,t){d(n,"greset")}}),n.style.opacity="",l&&(l.style.opacity="")}function B(e){if(e.events.hasOwnProperty("touch"))return!1;var t,i,n,s=y(),l=s.width,o=s.height,r=!1,a=null,g=null,f=null,p=!1,m=1,x=1,b=!1,S=!1,w=null,T=null,C=null,k=null,E=0,A=0,L=!1,I=!1,O={},P={},M=0,z=0,X=document.getElementById("glightbox-slider"),Y=document.querySelector(".goverlay"),q=new _(X,{touchStart:function(t){if(r=!0,(c(t.targetTouches[0].target,"ginner-container")||u(t.targetTouches[0].target,".gslide-desc")||"a"==t.targetTouches[0].target.nodeName.toLowerCase())&&(r=!1),u(t.targetTouches[0].target,".gslide-inline")&&!c(t.targetTouches[0].target.parentNode,"gslide-inline")&&(r=!1),r){if(P=t.targetTouches[0],O.pageX=t.targetTouches[0].pageX,O.pageY=t.targetTouches[0].pageY,M=t.targetTouches[0].clientX,z=t.targetTouches[0].clientY,a=e.activeSlide,g=a.querySelector(".gslide-media"),n=a.querySelector(".gslide-inline"),f=null,c(g,"gslide-image")&&(f=g.querySelector("img")),(window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth)>769&&(g=a.querySelector(".ginner-container")),d(Y,"greset"),t.pageX>20&&t.pageXo){var a=O.pageX-P.pageX;if(Math.abs(a)<=13)return!1}p=!0;var h,d=s.targetTouches[0].clientX,c=s.targetTouches[0].clientY,u=M-d,m=z-c;if(Math.abs(u)>Math.abs(m)?(L=!1,I=!0):(I=!1,L=!0),t=P.pageX-O.pageX,E=100*t/l,i=P.pageY-O.pageY,A=100*i/o,L&&f&&(h=1-Math.abs(i)/o,Y.style.opacity=h,e.settings.touchFollowAxis&&(E=0)),I&&(h=1-Math.abs(t)/l,g.style.opacity=h,e.settings.touchFollowAxis&&(A=0)),!f)return v(g,"translate3d(".concat(E,"%, 0, 0)"));v(g,"translate3d(".concat(E,"%, ").concat(A,"%, 0)"))}},touchEnd:function(){if(r){if(p=!1,S||b)return C=w,void(k=T);var t=Math.abs(parseInt(A)),i=Math.abs(parseInt(E));if(!(t>29&&f))return t<29&&i<25?(h(Y,"greset"),Y.style.opacity=1,W(g)):void 0;e.close()}},multipointEnd:function(){setTimeout((function(){b=!1}),50)},multipointStart:function(){b=!0,m=x||1},pinch:function(e){if(!f||p)return!1;b=!0,f.scaleX=f.scaleY=m*e.zoom;var t=m*e.zoom;if(S=!0,t<=1)return S=!1,t=1,k=null,C=null,w=null,T=null,void f.setAttribute("style","");t>4.5&&(t=4.5),f.style.transform="scale3d(".concat(t,", ").concat(t,", 1)"),x=t},pressMove:function(e){if(S&&!b){var t=P.pageX-O.pageX,i=P.pageY-O.pageY;C&&(t+=C),k&&(i+=k),w=t,T=i;var n="translate3d(".concat(t,"px, ").concat(i,"px, 0)");x&&(n+=" scale3d(".concat(x,", ").concat(x,", 1)")),v(f,n)}},swipe:function(t){if(!S)if(b)b=!1;else{if("Left"==t.direction){if(e.index==e.elements.length-1)return W(g);e.nextSlide()}if("Right"==t.direction){if(0==e.index)return W(g);e.prevSlide()}}}});e.events.touch=q}var H=function(){function e(i,n){var s=this,l=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null;if(t(this,e),this.img=i,this.slide=n,this.onclose=l,this.img.setZoomEvents)return!1;this.active=!1,this.zoomedIn=!1,this.dragging=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.img.addEventListener("mousedown",(function(e){return s.dragStart(e)}),!1),this.img.addEventListener("mouseup",(function(e){return s.dragEnd(e)}),!1),this.img.addEventListener("mousemove",(function(e){return s.drag(e)}),!1),this.img.addEventListener("click",(function(e){return s.slide.classList.contains("dragging-nav")?(s.zoomOut(),!1):s.zoomedIn?void(s.zoomedIn&&!s.dragging&&s.zoomOut()):s.zoomIn()}),!1),this.img.setZoomEvents=!0}return n(e,[{key:"zoomIn",value:function(){var e=this.widowWidth();if(!(this.zoomedIn||e<=768)){var t=this.img;if(t.setAttribute("data-style",t.getAttribute("style")),t.style.maxWidth=t.naturalWidth+"px",t.style.maxHeight=t.naturalHeight+"px",t.naturalWidth>e){var i=e/2-t.naturalWidth/2;this.setTranslate(this.img.parentNode,i,0)}this.slide.classList.add("zoomed"),this.zoomedIn=!0}}},{key:"zoomOut",value:function(){this.img.parentNode.setAttribute("style",""),this.img.setAttribute("style",this.img.getAttribute("data-style")),this.slide.classList.remove("zoomed"),this.zoomedIn=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.onclose&&"function"==typeof this.onclose&&this.onclose()}},{key:"dragStart",value:function(e){e.preventDefault(),this.zoomedIn?("touchstart"===e.type?(this.initialX=e.touches[0].clientX-this.xOffset,this.initialY=e.touches[0].clientY-this.yOffset):(this.initialX=e.clientX-this.xOffset,this.initialY=e.clientY-this.yOffset),e.target===this.img&&(this.active=!0,this.img.classList.add("dragging"))):this.active=!1}},{key:"dragEnd",value:function(e){var t=this;e.preventDefault(),this.initialX=this.currentX,this.initialY=this.currentY,this.active=!1,setTimeout((function(){t.dragging=!1,t.img.isDragging=!1,t.img.classList.remove("dragging")}),100)}},{key:"drag",value:function(e){this.active&&(e.preventDefault(),"touchmove"===e.type?(this.currentX=e.touches[0].clientX-this.initialX,this.currentY=e.touches[0].clientY-this.initialY):(this.currentX=e.clientX-this.initialX,this.currentY=e.clientY-this.initialY),this.xOffset=this.currentX,this.yOffset=this.currentY,this.img.isDragging=!0,this.dragging=!0,this.setTranslate(this.img,this.currentX,this.currentY))}},{key:"onMove",value:function(e){if(this.zoomedIn){var t=e.clientX-this.img.naturalWidth/2,i=e.clientY-this.img.naturalHeight/2;this.setTranslate(this.img,t,i)}}},{key:"setTranslate",value:function(e,t,i){e.style.transform="translate3d("+t+"px, "+i+"px, 0)"}},{key:"widowWidth",value:function(){return window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth}}]),e}(),V=function(){function e(){var i=this,n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};t(this,e);var s=n.dragEl,l=n.toleranceX,o=void 0===l?40:l,r=n.toleranceY,a=void 0===r?65:r,h=n.slide,d=void 0===h?null:h,c=n.instance,u=void 0===c?null:c;this.el=s,this.active=!1,this.dragging=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.direction=null,this.lastDirection=null,this.toleranceX=o,this.toleranceY=a,this.toleranceReached=!1,this.dragContainer=this.el,this.slide=d,this.instance=u,this.el.addEventListener("mousedown",(function(e){return i.dragStart(e)}),!1),this.el.addEventListener("mouseup",(function(e){return i.dragEnd(e)}),!1),this.el.addEventListener("mousemove",(function(e){return i.drag(e)}),!1)}return n(e,[{key:"dragStart",value:function(e){if(this.slide.classList.contains("zoomed"))this.active=!1;else{"touchstart"===e.type?(this.initialX=e.touches[0].clientX-this.xOffset,this.initialY=e.touches[0].clientY-this.yOffset):(this.initialX=e.clientX-this.xOffset,this.initialY=e.clientY-this.yOffset);var t=e.target.nodeName.toLowerCase();e.target.classList.contains("nodrag")||u(e.target,".nodrag")||-1!==["input","select","textarea","button","a"].indexOf(t)?this.active=!1:(e.preventDefault(),(e.target===this.el||"img"!==t&&u(e.target,".gslide-inline"))&&(this.active=!0,this.el.classList.add("dragging"),this.dragContainer=u(e.target,".ginner-container")))}}},{key:"dragEnd",value:function(e){var t=this;e&&e.preventDefault(),this.initialX=0,this.initialY=0,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.active=!1,this.doSlideChange&&(this.instance.preventOutsideClick=!0,"right"==this.doSlideChange&&this.instance.prevSlide(),"left"==this.doSlideChange&&this.instance.nextSlide()),this.doSlideClose&&this.instance.close(),this.toleranceReached||this.setTranslate(this.dragContainer,0,0,!0),setTimeout((function(){t.instance.preventOutsideClick=!1,t.toleranceReached=!1,t.lastDirection=null,t.dragging=!1,t.el.isDragging=!1,t.el.classList.remove("dragging"),t.slide.classList.remove("dragging-nav"),t.dragContainer.style.transform="",t.dragContainer.style.transition=""}),100)}},{key:"drag",value:function(e){if(this.active){e.preventDefault(),this.slide.classList.add("dragging-nav"),"touchmove"===e.type?(this.currentX=e.touches[0].clientX-this.initialX,this.currentY=e.touches[0].clientY-this.initialY):(this.currentX=e.clientX-this.initialX,this.currentY=e.clientY-this.initialY),this.xOffset=this.currentX,this.yOffset=this.currentY,this.el.isDragging=!0,this.dragging=!0,this.doSlideChange=!1,this.doSlideClose=!1;var t=Math.abs(this.currentX),i=Math.abs(this.currentY);if(t>0&&t>=Math.abs(this.currentY)&&(!this.lastDirection||"x"==this.lastDirection)){this.yOffset=0,this.lastDirection="x",this.setTranslate(this.dragContainer,this.currentX,0);var n=this.shouldChange();if(!this.instance.settings.dragAutoSnap&&n&&(this.doSlideChange=n),this.instance.settings.dragAutoSnap&&n)return this.instance.preventOutsideClick=!0,this.toleranceReached=!0,this.active=!1,this.instance.preventOutsideClick=!0,this.dragEnd(null),"right"==n&&this.instance.prevSlide(),void("left"==n&&this.instance.nextSlide())}if(this.toleranceY>0&&i>0&&i>=t&&(!this.lastDirection||"y"==this.lastDirection)){this.xOffset=0,this.lastDirection="y",this.setTranslate(this.dragContainer,0,this.currentY);var s=this.shouldClose();return!this.instance.settings.dragAutoSnap&&s&&(this.doSlideClose=!0),void(this.instance.settings.dragAutoSnap&&s&&this.instance.close())}}}},{key:"shouldChange",value:function(){var e=!1;if(Math.abs(this.currentX)>=this.toleranceX){var t=this.currentX>0?"right":"left";("left"==t&&this.slide!==this.slide.parentNode.lastChild||"right"==t&&this.slide!==this.slide.parentNode.firstChild)&&(e=t)}return e}},{key:"shouldClose",value:function(){var e=!1;return Math.abs(this.currentY)>=this.toleranceY&&(e=!0),e}},{key:"setTranslate",value:function(e,t,i){var n=arguments.length>3&&void 0!==arguments[3]&&arguments[3];e.style.transition=n?"all .2s ease":"",e.style.transform="translate3d(".concat(t,"px, ").concat(i,"px, 0)")}}]),e}();function j(e,t,i,n){var s=e.querySelector(".gslide-media"),l=new Image,o="gSlideTitle_"+i,r="gSlideDesc_"+i;l.addEventListener("load",(function(){T(n)&&n()}),!1),l.src=t.href,""!=t.sizes&&""!=t.srcset&&(l.sizes=t.sizes,l.srcset=t.srcset),l.alt="",I(t.alt)||""===t.alt||(l.alt=t.alt),""!==t.title&&l.setAttribute("aria-labelledby",o),""!==t.description&&l.setAttribute("aria-describedby",r),t.hasOwnProperty("_hasCustomWidth")&&t._hasCustomWidth&&(l.style.width=t.width),t.hasOwnProperty("_hasCustomHeight")&&t._hasCustomHeight&&(l.style.height=t.height),s.insertBefore(l,s.firstChild)}function F(e,t,i,n){var s=this,l=e.querySelector(".ginner-container"),o="gvideo"+i,r=e.querySelector(".gslide-media"),a=this.getAllPlayers();h(l,"gvideo-container"),r.insertBefore(m('

    '),r.firstChild);var d=e.querySelector(".gvideo-wrapper");S(this.settings.plyr.css,"Plyr");var c=t.href,u=null==t?void 0:t.videoProvider,g=!1;r.style.maxWidth=t.width,S(this.settings.plyr.js,"Plyr",(function(){if(!u&&c.match(/vimeo\.com\/([0-9]*)/)&&(u="vimeo"),!u&&(c.match(/(youtube\.com|youtube-nocookie\.com)\/watch\?v=([a-zA-Z0-9\-_]+)/)||c.match(/youtu\.be\/([a-zA-Z0-9\-_]+)/)||c.match(/(youtube\.com|youtube-nocookie\.com)\/embed\/([a-zA-Z0-9\-_]+)/))&&(u="youtube"),"local"===u||!u){u="local";var l='")}var r=g||m('
    '));h(d,"".concat(u,"-video gvideo")),d.appendChild(r),d.setAttribute("data-id",o),d.setAttribute("data-index",i);var v=O(s.settings.plyr,"config")?s.settings.plyr.config:{},f=new Plyr("#"+o,v);f.on("ready",(function(e){a[o]=e.detail.plyr,T(n)&&n()})),b((function(){return e.querySelector("iframe")&&"true"==e.querySelector("iframe").dataset.ready}),(function(){s.resize(e)})),f.on("enterfullscreen",R),f.on("exitfullscreen",R)}))}function R(e){var t=u(e.target,".gslide-media");"enterfullscreen"===e.type&&h(t,"fullscreen"),"exitfullscreen"===e.type&&d(t,"fullscreen")}function G(e,t,i,n){var s,l=this,o=e.querySelector(".gslide-media"),r=!(!O(t,"href")||!t.href)&&t.href.split("#").pop().trim(),d=!(!O(t,"content")||!t.content)&&t.content;if(d&&(C(d)&&(s=m('
    '.concat(d,"
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t=e;if(null!==(e=e.toLowerCase()).match(/\.(jpeg|jpg|jpe|gif|png|apn|webp|avif|svg)/))return"image";if(e.match(/(youtube\.com|youtube-nocookie\.com)\/watch\?v=([a-zA-Z0-9\-_]+)/)||e.match(/youtu\.be\/([a-zA-Z0-9\-_]+)/)||e.match(/(youtube\.com|youtube-nocookie\.com)\/embed\/([a-zA-Z0-9\-_]+)/))return"video";if(e.match(/vimeo\.com\/([0-9]*)/))return"video";if(null!==e.match(/\.(mp4|ogg|webm|mov)/))return"video";if(null!==e.match(/\.(mp3|wav|wma|aac|ogg)/))return"audio";if(e.indexOf("#")>-1&&""!==t.split("#").pop().trim())return"inline";return e.indexOf("goajax=true")>-1?"ajax":"external"}},{key:"parseConfig",value:function(e,t){var i=this,n=l({descPosition:t.descPosition},this.defaults);if(L(e)&&!k(e)){O(e,"type")||(O(e,"content")&&e.content?e.type="inline":O(e,"href")&&(e.type=this.sourceType(e.href)));var s=l(n,e);return this.setSize(s,t),s}var r="",a=e.getAttribute("data-glightbox"),h=e.nodeName.toLowerCase();if("a"===h&&(r=e.href),"img"===h&&(r=e.src,n.alt=e.alt),n.href=r,o(n,(function(s,l){O(t,l)&&"width"!==l&&(n[l]=t[l]);var o=e.dataset[l];I(o)||(n[l]=i.sanitizeValue(o))})),n.content&&(n.type="inline"),!n.type&&r&&(n.type=this.sourceType(r)),I(a)){if(!n.title&&"a"==h){var d=e.title;I(d)||""===d||(n.title=d)}if(!n.title&&"img"==h){var c=e.alt;I(c)||""===c||(n.title=c)}}else{var u=[];o(n,(function(e,t){u.push(";\\s?"+t)})),u=u.join("\\s?:|"),""!==a.trim()&&o(n,(function(e,t){var s=a,l=new RegExp("s?"+t+"s?:s?(.*?)("+u+"s?:|$)"),o=s.match(l);if(o&&o.length&&o[1]){var r=o[1].trim().replace(/;\s*$/,"");n[t]=i.sanitizeValue(r)}}))}if(n.description&&"."===n.description.substring(0,1)){var g;try{g=document.querySelector(n.description).innerHTML}catch(e){if(!(e instanceof DOMException))throw e}g&&(n.description=g)}if(!n.description){var v=e.querySelector(".glightbox-desc");v&&(n.description=v.innerHTML)}return this.setSize(n,t,e),this.slideConfig=n,n}},{key:"setSize",value:function(e,t){var i=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,n="video"==e.type?this.checkSize(t.videosWidth):this.checkSize(t.width),s=this.checkSize(t.height);return e.width=O(e,"width")&&""!==e.width?this.checkSize(e.width):n,e.height=O(e,"height")&&""!==e.height?this.checkSize(e.height):s,i&&"image"==e.type&&(e._hasCustomWidth=!!i.dataset.width,e._hasCustomHeight=!!i.dataset.height),e}},{key:"checkSize",value:function(e){return M(e)?"".concat(e,"px"):e}},{key:"sanitizeValue",value:function(e){return"true"!==e&&"false"!==e?e:"true"===e}}]),e}(),$=function(){function e(i,n,s){t(this,e),this.element=i,this.instance=n,this.index=s}return n(e,[{key:"setContent",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(c(t,"loaded"))return!1;var n=this.instance.settings,s=this.slideConfig,l=w();T(n.beforeSlideLoad)&&n.beforeSlideLoad({index:this.index,slide:t,player:!1});var o=s.type,r=s.descPosition,a=t.querySelector(".gslide-media"),d=t.querySelector(".gslide-title"),u=t.querySelector(".gslide-desc"),g=t.querySelector(".gdesc-inner"),v=i,f="gSlideTitle_"+this.index,p="gSlideDesc_"+this.index;if(T(n.afterSlideLoad)&&(v=function(){T(i)&&i(),n.afterSlideLoad({index:e.index,slide:t,player:e.instance.getSlidePlayerInstance(e.index)})}),""==s.title&&""==s.description?g&&g.parentNode.parentNode.removeChild(g.parentNode):(d&&""!==s.title?(d.id=f,d.innerHTML=s.title):d.parentNode.removeChild(d),u&&""!==s.description?(u.id=p,l&&n.moreLength>0?(s.smallDescription=this.slideShortDesc(s.description,n.moreLength,n.moreText),u.innerHTML=s.smallDescription,this.descriptionEvents(u,s)):u.innerHTML=s.description):u.parentNode.removeChild(u),h(a.parentNode,"desc-".concat(r)),h(g.parentNode,"description-".concat(r))),h(a,"gslide-".concat(o)),h(t,"loaded"),"video"!==o){if("external"!==o)return"inline"===o?(G.apply(this.instance,[t,s,this.index,v]),void(s.draggable&&new V({dragEl:t.querySelector(".gslide-inline"),toleranceX:n.dragToleranceX,toleranceY:n.dragToleranceY,slide:t,instance:this.instance}))):void("image"!==o?T(v)&&v():j(t,s,this.index,(function(){var i=t.querySelector("img");s.draggable&&new V({dragEl:i,toleranceX:n.dragToleranceX,toleranceY:n.dragToleranceY,slide:t,instance:e.instance}),s.zoomable&&i.naturalWidth>i.offsetWidth&&(h(i,"zoomable"),new H(i,t,(function(){e.instance.resize()}))),T(v)&&v()})));Z.apply(this,[t,s,this.index,v])}else F.apply(this.instance,[t,s,this.index,v])}},{key:"slideShortDesc",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:50,i=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=document.createElement("div");n.innerHTML=e;var s=n.innerText,l=i;if((e=s.trim()).length<=t)return e;var o=e.substr(0,t-1);return l?(n=null,o+'...     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n=e.getAttribute("data-gallery");n&&(this.fullElementsList=this.elements,this.elements=this.getGalleryElements(this.elements,n)),I(i)&&(i=this.getElementIndex(e))<0&&(i=0)}M(i)||(i=0),this.build(),g(this.overlay,"none"===this.settings.openEffect?"none":this.settings.cssEfects.fade.in);var s=document.body,l=window.innerWidth-document.documentElement.clientWidth;if(l>0){var o=document.createElement("style");o.type="text/css",o.className="gcss-styles",o.innerText=".gscrollbar-fixer {margin-right: ".concat(l,"px}"),document.head.appendChild(o),h(s,"gscrollbar-fixer")}h(s,"glightbox-open"),h(Q,"glightbox-open"),J&&(h(document.body,"glightbox-mobile"),this.settings.slideEffect="slide"),this.showSlide(i,!0),1===this.elements.length?(h(this.prevButton,"glightbox-button-hidden"),h(this.nextButton,"glightbox-button-hidden")):(d(this.prevButton,"glightbox-button-hidden"),d(this.nextButton,"glightbox-button-hidden")),this.lightboxOpen=!0,this.trigger("open"),T(this.settings.onOpen)&&this.settings.onOpen(),K&&this.settings.touchNavigation&&B(this),this.settings.keyboardNavigation&&X(this)}},{key:"openAt",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;this.open(null,e)}},{key:"showSlide",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,i=arguments.length>1&&void 0!==arguments[1]&&arguments[1];f(this.loader),this.index=parseInt(t);var n=this.slidesContainer.querySelector(".current");n&&d(n,"current"),this.slideAnimateOut();var s=this.slidesContainer.querySelectorAll(".gslide")[t];if(c(s,"loaded"))this.slideAnimateIn(s,i),p(this.loader);else{f(this.loader);var l=this.elements[t],o={index:this.index,slide:s,slideNode:s,slideConfig:l.slideConfig,slideIndex:this.index,trigger:l.node,player:null};this.trigger("slide_before_load",o),l.instance.setContent(s,(function(){p(e.loader),e.resize(),e.slideAnimateIn(s,i),e.trigger("slide_after_load",o)}))}this.slideDescription=s.querySelector(".gslide-description"),this.slideDescriptionContained=this.slideDescription&&c(this.slideDescription.parentNode,"gslide-media"),this.settings.preload&&(this.preloadSlide(t+1),this.preloadSlide(t-1)),this.updateNavigationClasses(),this.activeSlide=s}},{key:"preloadSlide",value:function(e){var t=this;if(e<0||e>this.elements.length-1)return!1;if(I(this.elements[e]))return!1;var i=this.slidesContainer.querySelectorAll(".gslide")[e];if(c(i,"loaded"))return!1;var n=this.elements[e],s=n.type,l={index:e,slide:i,slideNode:i,slideConfig:n.slideConfig,slideIndex:e,trigger:n.node,player:null};this.trigger("slide_before_load",l),"video"===s||"external"===s?setTimeout((function(){n.instance.setContent(i,(function(){t.trigger("slide_after_load",l)}))}),200):n.instance.setContent(i,(function(){t.trigger("slide_after_load",l)}))}},{key:"prevSlide",value:function(){this.goToSlide(this.index-1)}},{key:"nextSlide",value:function(){this.goToSlide(this.index+1)}},{key:"goToSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];if(this.prevActiveSlide=this.activeSlide,this.prevActiveSlideIndex=this.index,!this.loop()&&(e<0||e>this.elements.length-1))return!1;e<0?e=this.elements.length-1:e>=this.elements.length&&(e=0),this.showSlide(e)}},{key:"insertSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1;t<0&&(t=this.elements.length);var i=new $(e,this,t),n=i.getConfig(),s=l({},n),o=i.create(),r=this.elements.length-1;s.index=t,s.node=!1,s.instance=i,s.slideConfig=n,this.elements.splice(t,0,s);var a=null,h=null;if(this.slidesContainer){if(t>r)this.slidesContainer.appendChild(o);else{var d=this.slidesContainer.querySelectorAll(".gslide")[t];this.slidesContainer.insertBefore(o,d)}(this.settings.preload&&0==this.index&&0==t||this.index-1==t||this.index+1==t)&&this.preloadSlide(t),0===this.index&&0===t&&(this.index=1),this.updateNavigationClasses(),a=this.slidesContainer.querySelectorAll(".gslide")[t],h=this.getSlidePlayerInstance(t),s.slideNode=a}this.trigger("slide_inserted",{index:t,slide:a,slideNode:a,slideConfig:n,slideIndex:t,trigger:null,player:h}),T(this.settings.slideInserted)&&this.settings.slideInserted({index:t,slide:a,player:h})}},{key:"removeSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:-1;if(e<0||e>this.elements.length-1)return!1;var t=this.slidesContainer&&this.slidesContainer.querySelectorAll(".gslide")[e];t&&(this.getActiveSlideIndex()==e&&(e==this.elements.length-1?this.prevSlide():this.nextSlide()),t.parentNode.removeChild(t)),this.elements.splice(e,1),this.trigger("slide_removed",e),T(this.settings.slideRemoved)&&this.settings.slideRemoved(e)}},{key:"slideAnimateIn",value:function(e,t){var i=this,n=e.querySelector(".gslide-media"),s=e.querySelector(".gslide-description"),l={index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,slideNode:this.prevActiveSlide,slideIndex:this.prevActiveSlide,slideConfig:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].slideConfig,trigger:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].node,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},o={index:this.index,slide:this.activeSlide,slideNode:this.activeSlide,slideConfig:this.elements[this.index].slideConfig,slideIndex:this.index,trigger:this.elements[this.index].node,player:this.getSlidePlayerInstance(this.index)};if(n.offsetWidth>0&&s&&(p(s),s.style.display=""),d(e,this.effectsClasses),t)g(e,this.settings.cssEfects[this.settings.openEffect].in,(function(){i.settings.autoplayVideos&&i.slidePlayerPlay(e),i.trigger("slide_changed",{prev:l,current:o}),T(i.settings.afterSlideChange)&&i.settings.afterSlideChange.apply(i,[l,o])}));else{var r=this.settings.slideEffect,a="none"!==r?this.settings.cssEfects[r].in:r;this.prevActiveSlideIndex>this.index&&"slide"==this.settings.slideEffect&&(a=this.settings.cssEfects.slideBack.in),g(e,a,(function(){i.settings.autoplayVideos&&i.slidePlayerPlay(e),i.trigger("slide_changed",{prev:l,current:o}),T(i.settings.afterSlideChange)&&i.settings.afterSlideChange.apply(i,[l,o])}))}setTimeout((function(){i.resize(e)}),100),h(e,"current")}},{key:"slideAnimateOut",value:function(){if(!this.prevActiveSlide)return!1;var e=this.prevActiveSlide;d(e,this.effectsClasses),h(e,"prev");var t=this.settings.slideEffect,i="none"!==t?this.settings.cssEfects[t].out:t;this.slidePlayerPause(e),this.trigger("slide_before_change",{prev:{index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,slideNode:this.prevActiveSlide,slideIndex:this.prevActiveSlideIndex,slideConfig:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].slideConfig,trigger:I(this.prevActiveSlideIndex)?null:this.elements[this.prevActiveSlideIndex].node,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},current:{index:this.index,slide:this.activeSlide,slideNode:this.activeSlide,slideIndex:this.index,slideConfig:this.elements[this.index].slideConfig,trigger:this.elements[this.index].node,player:this.getSlidePlayerInstance(this.index)}}),T(this.settings.beforeSlideChange)&&this.settings.beforeSlideChange.apply(this,[{index:this.prevActiveSlideIndex,slide:this.prevActiveSlide,player:this.getSlidePlayerInstance(this.prevActiveSlideIndex)},{index:this.index,slide:this.activeSlide,player:this.getSlidePlayerInstance(this.index)}]),this.prevActiveSlideIndex>this.index&&"slide"==this.settings.slideEffect&&(i=this.settings.cssEfects.slideBack.out),g(e,i,(function(){var t=e.querySelector(".ginner-container"),i=e.querySelector(".gslide-media"),n=e.querySelector(".gslide-description");t.style.transform="",i.style.transform="",d(i,"greset"),i.style.opacity="",n&&(n.style.opacity=""),d(e,"prev")}))}},{key:"getAllPlayers",value:function(){return this.videoPlayers}},{key:"getSlidePlayerInstance",value:function(e){var t="gvideo"+e,i=this.getAllPlayers();return!(!O(i,t)||!i[t])&&i[t]}},{key:"stopSlideVideo",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}console.log("stopSlideVideo is deprecated, use slidePlayerPause");var i=this.getSlidePlayerInstance(e);i&&i.playing&&i.pause()}},{key:"slidePlayerPause",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}var i=this.getSlidePlayerInstance(e);i&&i.playing&&i.pause()}},{key:"playSlideVideo",value:function(e){if(k(e)){var t=e.querySelector(".gvideo-wrapper");t&&(e=t.getAttribute("data-index"))}console.log("playSlideVideo is deprecated, use slidePlayerPlay");var i=this.getSlidePlayerInstance(e);i&&!i.playing&&i.play()}},{key:"slidePlayerPlay",value:function(e){var t;if(!J||null!==(t=this.settings.plyr.config)&&void 0!==t&&t.muted){if(k(e)){var i=e.querySelector(".gvideo-wrapper");i&&(e=i.getAttribute("data-index"))}var n=this.getSlidePlayerInstance(e);n&&!n.playing&&(n.play(),this.settings.autofocusVideos&&n.elements.container.focus())}}},{key:"setElements",value:function(e){var t=this;this.settings.elements=!1;var i=[];e&&e.length&&o(e,(function(e,n){var s=new $(e,t,n),o=s.getConfig(),r=l({},o);r.slideConfig=o,r.instance=s,r.index=n,i.push(r)})),this.elements=i,this.lightboxOpen&&(this.slidesContainer.innerHTML="",this.elements.length&&(o(this.elements,(function(){var e=m(t.settings.slideHTML);t.slidesContainer.appendChild(e)})),this.showSlide(0,!0)))}},{key:"getElementIndex",value:function(e){var t=!1;return o(this.elements,(function(i,n){if(O(i,"node")&&i.node==e)return t=n,!0})),t}},{key:"getElements",value:function(){var e=this,t=[];this.elements=this.elements?this.elements:[],!I(this.settings.elements)&&E(this.settings.elements)&&this.settings.elements.length&&o(this.settings.elements,(function(i,n){var s=new $(i,e,n),o=s.getConfig(),r=l({},o);r.node=!1,r.index=n,r.instance=s,r.slideConfig=o,t.push(r)}));var i=!1;return this.getSelector()&&(i=document.querySelectorAll(this.getSelector())),i?(o(i,(function(i,n){var s=new $(i,e,n),o=s.getConfig(),r=l({},o);r.node=i,r.index=n,r.instance=s,r.slideConfig=o,r.gallery=i.getAttribute("data-gallery"),t.push(r)})),t):t}},{key:"getGalleryElements",value:function(e,t){return e.filter((function(e){return e.gallery==t}))}},{key:"getSelector",value:function(){return!this.settings.elements&&(this.settings.selector&&"data-"==this.settings.selector.substring(0,5)?"*[".concat(this.settings.selector,"]"):this.settings.selector)}},{key:"getActiveSlide",value:function(){return this.slidesContainer.querySelectorAll(".gslide")[this.index]}},{key:"getActiveSlideIndex",value:function(){return this.index}},{key:"getAnimationClasses",value:function(){var e=[];for(var t in this.settings.cssEfects)if(this.settings.cssEfects.hasOwnProperty(t)){var i=this.settings.cssEfects[t];e.push("g".concat(i.in)),e.push("g".concat(i.out))}return e.join(" ")}},{key:"build",value:function(){var e=this;if(this.built)return!1;var t=document.body.childNodes,i=[];o(t,(function(e){e.parentNode==document.body&&"#"!==e.nodeName.charAt(0)&&e.hasAttribute&&!e.hasAttribute("aria-hidden")&&(i.push(e),e.setAttribute("aria-hidden","true"))}));var n=O(this.settings.svg,"next")?this.settings.svg.next:"",s=O(this.settings.svg,"prev")?this.settings.svg.prev:"",l=O(this.settings.svg,"close")?this.settings.svg.close:"",r=this.settings.lightboxHTML;r=m(r=(r=(r=r.replace(/{nextSVG}/g,n)).replace(/{prevSVG}/g,s)).replace(/{closeSVG}/g,l)),document.body.appendChild(r);var d=document.getElementById("glightbox-body");this.modal=d;var g=d.querySelector(".gclose");this.prevButton=d.querySelector(".gprev"),this.nextButton=d.querySelector(".gnext"),this.overlay=d.querySelector(".goverlay"),this.loader=d.querySelector(".gloader"),this.slidesContainer=document.getElementById("glightbox-slider"),this.bodyHiddenChildElms=i,this.events={},h(this.modal,"glightbox-"+this.settings.skin),this.settings.closeButton&&g&&(this.events.close=a("click",{onElement:g,withCallback:function(t,i){t.preventDefault(),e.close()}})),g&&!this.settings.closeButton&&g.parentNode.removeChild(g),this.nextButton&&(this.events.next=a("click",{onElement:this.nextButton,withCallback:function(t,i){t.preventDefault(),e.nextSlide()}})),this.prevButton&&(this.events.prev=a("click",{onElement:this.prevButton,withCallback:function(t,i){t.preventDefault(),e.prevSlide()}})),this.settings.closeOnOutsideClick&&(this.events.outClose=a("click",{onElement:d,withCallback:function(t,i){e.preventOutsideClick||c(document.body,"glightbox-mobile")||u(t.target,".ginner-container")||u(t.target,".gbtn")||c(t.target,"gnext")||c(t.target,"gprev")||e.close()}})),o(this.elements,(function(t,i){e.slidesContainer.appendChild(t.instance.create()),t.slideNode=e.slidesContainer.querySelectorAll(".gslide")[i]})),K&&h(document.body,"glightbox-touch"),this.events.resize=a("resize",{onElement:window,withCallback:function(){e.resize()}}),this.built=!0}},{key:"resize",value:function(){var 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m=g.split(":"),x=this.settings.videosWidth,b=this.settings.videosWidth,S=(b=M(x)||-1!==x.indexOf("px")?parseInt(x):-1!==x.indexOf("vw")?l*parseInt(x)/100:-1!==x.indexOf("vh")?o*parseInt(x)/100:-1!==x.indexOf("%")?l*parseInt(x)/100:parseInt(i.clientWidth))/(parseInt(m[0])/parseInt(m[1]));if(S=Math.floor(S),r&&(o-=s.offsetHeight),b>l||S>o||ob){var w=i.offsetWidth,T=i.offsetHeight,C=o/T,k={width:w*C,height:T*C};i.parentNode.setAttribute("style","max-width: ".concat(k.width,"px")),r&&s.setAttribute("style","max-width: ".concat(k.width,"px;"))}else i.parentNode.style.maxWidth="".concat(x),r&&s.setAttribute("style","max-width: ".concat(x,";"))}}}}},{key:"reload",value:function(){this.init()}},{key:"updateNavigationClasses",value:function(){var e=this.loop();d(this.nextButton,"disabled"),d(this.prevButton,"disabled"),0==this.index&&this.elements.length-1==0?(h(this.prevButton,"disabled"),h(this.nextButton,"disabled")):0!==this.index||e?this.index!==this.elements.length-1||e||h(this.nextButton,"disabled"):h(this.prevButton,"disabled")}},{key:"loop",value:function(){var e=O(this.settings,"loopAtEnd")?this.settings.loopAtEnd:null;return e=O(this.settings,"loop")?this.settings.loop:e,e}},{key:"close",value:function(){var e=this;if(!this.lightboxOpen){if(this.events){for(var t in this.events)this.events.hasOwnProperty(t)&&this.events[t].destroy();this.events=null}return!1}if(this.closing)return!1;this.closing=!0,this.slidePlayerPause(this.activeSlide),this.fullElementsList&&(this.elements=this.fullElementsList),this.bodyHiddenChildElms.length&&o(this.bodyHiddenChildElms,(function(e){e.removeAttribute("aria-hidden")})),h(this.modal,"glightbox-closing"),g(this.overlay,"none"==this.settings.openEffect?"none":this.settings.cssEfects.fade.out),g(this.activeSlide,this.settings.cssEfects[this.settings.closeEffect].out,(function(){if(e.activeSlide=null,e.prevActiveSlideIndex=null,e.prevActiveSlide=null,e.built=!1,e.events){for(var t in e.events)e.events.hasOwnProperty(t)&&e.events[t].destroy();e.events=null}var i=document.body;d(Q,"glightbox-open"),d(i,"glightbox-open touching gdesc-open glightbox-touch glightbox-mobile gscrollbar-fixer"),e.modal.parentNode.removeChild(e.modal),e.trigger("close"),T(e.settings.onClose)&&e.settings.onClose();var n=document.querySelector(".gcss-styles");n&&n.parentNode.removeChild(n),e.lightboxOpen=!1,e.closing=null}))}},{key:"destroy",value:function(){this.close(),this.clearAllEvents(),this.baseEvents&&this.baseEvents.destroy()}},{key:"on",value:function(e,t){var i=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if(!e||!T(t))throw new TypeError("Event name and callback must be defined");this.apiEvents.push({evt:e,once:i,callback:t})}},{key:"once",value:function(e,t){this.on(e,t,!0)}},{key:"trigger",value:function(e){var t=this,i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:null,n=[];o(this.apiEvents,(function(t,s){var l=t.evt,o=t.once,r=t.callback;l==e&&(r(i),o&&n.push(s))})),n.length&&o(n,(function(e){return t.apiEvents.splice(e,1)}))}},{key:"clearAllEvents",value:function(){this.apiEvents.splice(0,this.apiEvents.length)}},{key:"version",value:function(){return"3.1.0"}}]),e}();return function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=new te(e);return t.init(),t}})); \ No newline at end of file diff --git a/site/site_libs/quarto-contrib/glightbox/lightbox.css b/site/site_libs/quarto-contrib/glightbox/lightbox.css deleted file mode 100644 index 46432d990..000000000 --- a/site/site_libs/quarto-contrib/glightbox/lightbox.css +++ /dev/null @@ -1,26 +0,0 @@ -body:not(.glightbox-mobile) div.gslide div.gslide-description, -body:not(.glightbox-mobile) div.gslide-description .gslide-title, -body:not(.glightbox-mobile) div.gslide-description .gslide-desc { - color: var(--quarto-body-color); - background-color: var(--quarto-body-bg); -} - -body:not(.glightbox-mobile) div.gslide-media { - background-color: var(--quarto-body-bg); -} - -.goverlay { - background: rgba(0, 0, 0, 0.7); -} - -div.gslide-description .gslide-title { - margin-top: 0.25em; - margin-bottom: 0.25em; - font-weight: 500; - font-family: inherit; -} - -div.gslide-description .gslide-desc { - padding-bottom: 0.5em; - font-family: inherit; -} diff --git a/site/site_libs/quarto-contrib/quarto-project/nrichers/preview/preview.css b/site/site_libs/quarto-contrib/quarto-project/nrichers/preview/preview.css deleted file mode 100644 index ac250f4e1..000000000 --- a/site/site_libs/quarto-contrib/quarto-project/nrichers/preview/preview.css +++ /dev/null @@ -1,24 +0,0 @@ -.preview { - position: relative; - display: inline-block; - box-shadow: 2px 2px 4px rgba(0, 0, 0, 0.2); - border-radius: 5px; - background: #DE257E; - border: 1px solid #DE257E; -} - -.preview iframe { - width: 100%; - height: 100%; - border: none; - border-radius: 5px; -} - -.preview a { - position: absolute; - top: 0; - left: 0; - width: 100%; - height: 100%; - z-index: 10; -} diff --git a/site/site_libs/quarto-contrib/tachyons-4.12.0/quarto-resets.css b/site/site_libs/quarto-contrib/tachyons-4.12.0/quarto-resets.css deleted file mode 100644 index e5b0441de..000000000 --- a/site/site_libs/quarto-contrib/tachyons-4.12.0/quarto-resets.css +++ /dev/null @@ -1,25 +0,0 @@ -/* Fixes for tachyons and pandoc code styles interactions */ -code span { /* BaseN */ } -code span.bn { - border-style: inherit; - border-width: inherit; -} -code span.cf:before, code span.cf:after { - content: unset; - display: inline; -} -code span.cf:after { - clear: unset; -} -code span.cf { - zoom: inherit; -} -code span.cn { - clear: unset; -} -code span.dt { - display: inline; -} -code span.fl { - float: unset; -} \ No newline at end of file diff --git a/site/site_libs/quarto-contrib/tachyons-4.12.0/tachyons.css b/site/site_libs/quarto-contrib/tachyons-4.12.0/tachyons.css deleted file mode 100644 index 70f20f21e..000000000 --- a/site/site_libs/quarto-contrib/tachyons-4.12.0/tachyons.css +++ /dev/null @@ -1,3325 +0,0 @@ -/*! TACHYONS v4.12.0 | http://tachyons.io */ -/* - * - * ________ ______ - * ___ __/_____ _________ /______ ______________________ - * __ / _ __ `/ ___/_ __ \_ / / / __ \_ __ \_ ___/ - * _ / / /_/ // /__ _ / / / /_/ // /_/ / / / /(__ ) - * /_/ \__,_/ \___/ /_/ /_/_\__, / \____//_/ /_//____/ - * /____/ - * - * TABLE OF CONTENTS - * - * 1. External Library Includes - * - Normalize.css | http://normalize.css.github.io - * 2. Tachyons Modules - * 3. Variables - * - Media Queries - * - Colors - * 4. Debugging - * - Debug all - * - Debug children - * - */ -/* External Library Includes */ -/*! normalize.css v8.0.0 | MIT License | github.com/necolas/normalize.css */ -/* Document - ========================================================================== */ -/** - * 1. Correct the line height in all browsers. - * 2. Prevent adjustments of font size after orientation changes in iOS. - */ -html { line-height: 1.15; /* 1 */ -webkit-text-size-adjust: 100%; /* 2 */ } -/* Sections - ========================================================================== */ -/** - * Remove the margin in all browsers. - */ -body { margin: 0; } -/** - * Correct the font size and margin on `h1` elements within `section` and - * `article` contexts in Chrome, Firefox, and Safari. - */ -h1 { font-size: 2em; margin: .67em 0; } -/* Grouping content - ========================================================================== */ -/** - * 1. Add the correct box sizing in Firefox. - * 2. Show the overflow in Edge and IE. - */ -hr { box-sizing: content-box; /* 1 */ height: 0; /* 1 */ overflow: visible; /* 2 */ } -/** - * 1. Correct the inheritance and scaling of font size in all browsers. - * 2. Correct the odd `em` font sizing in all browsers. - */ -pre { font-family: monospace, monospace; /* 1 */ font-size: 1em; /* 2 */ } -/* Text-level semantics - ========================================================================== */ -/** - * Remove the gray background on active links in IE 10. - */ -a { background-color: transparent; } -/** - * 1. Remove the bottom border in Chrome 57- - * 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari. - */ -abbr[title] { border-bottom: none; /* 1 */ text-decoration: underline; /* 2 */ -webkit-text-decoration: underline dotted; text-decoration: underline dotted; /* 2 */ } -/** - * Add the correct font weight in Chrome, Edge, and Safari. - */ -b, strong { font-weight: bolder; } -/** - * 1. Correct the inheritance and scaling of font size in all browsers. - * 2. Correct the odd `em` font sizing in all browsers. - */ -code, kbd, samp { font-family: monospace, monospace; /* 1 */ font-size: 1em; /* 2 */ } -/** - * Add the correct font size in all browsers. - */ -small { font-size: 80%; } -/** - * Prevent `sub` and `sup` elements from affecting the line height in - * all browsers. - */ -sub, sup { font-size: 75%; line-height: 0; position: relative; vertical-align: baseline; } -sub { bottom: -0.25em; } -sup { top: -0.5em; } -/* Embedded content - ========================================================================== */ -/** - * Remove the border on images inside links in IE 10. - */ -img { border-style: none; } -/* Forms - ========================================================================== */ -/** - * 1. Change the font styles in all browsers. - * 2. Remove the margin in Firefox and Safari. - */ -button, input, optgroup, select, textarea { font-family: inherit; /* 1 */ font-size: 100%; /* 1 */ line-height: 1.15; /* 1 */ margin: 0; /* 2 */ } -/** - * Show the overflow in IE. - * 1. Show the overflow in Edge. - */ -button, input {/* 1 */ overflow: visible; } -/** - * Remove the inheritance of text transform in Edge, Firefox, and IE. - * 1. Remove the inheritance of text transform in Firefox. - */ -button, select {/* 1 */ text-transform: none; } -/** - * Correct the inability to style clickable types in iOS and Safari. - */ -button, [type="button"], [type="reset"], [type="submit"] { -webkit-appearance: button; } -/** - * Remove the inner border and padding in Firefox. - */ -button::-moz-focus-inner, [type="button"]::-moz-focus-inner, -[type="reset"]::-moz-focus-inner, [type="submit"]::-moz-focus-inner { border-style: none; padding: 0; } -/** - * Restore the focus styles unset by the previous rule. - */ -button:-moz-focusring, [type="button"]:-moz-focusring, -[type="reset"]:-moz-focusring, [type="submit"]:-moz-focusring { outline: 1px dotted ButtonText; } -/** - * Correct the padding in Firefox. - */ -fieldset { padding: .35em .75em .625em; } -/** - * 1. Correct the text wrapping in Edge and IE. - * 2. Correct the color inheritance from `fieldset` elements in IE. - * 3. Remove the padding so developers are not caught out when they zero out - * `fieldset` elements in all browsers. - */ -legend { box-sizing: border-box; /* 1 */ color: inherit; /* 2 */ display: table; /* 1 */ max-width: 100%; /* 1 */ padding: 0; /* 3 */ white-space: normal; /* 1 */ } -/** - * Add the correct vertical alignment in Chrome, Firefox, and Opera. - */ -progress { vertical-align: baseline; } -/** - * Remove the default vertical scrollbar in IE 10+. - */ -textarea { overflow: auto; } -/** - * 1. Add the correct box sizing in IE 10. - * 2. Remove the padding in IE 10. - */ -[type="checkbox"], [type="radio"] { box-sizing: border-box; /* 1 */ padding: 0; /* 2 */ } -/** - * Correct the cursor style of increment and decrement buttons in Chrome. - */ -[type="number"]::-webkit-inner-spin-button, -[type="number"]::-webkit-outer-spin-button { height: auto; } -/** - * 1. Correct the odd appearance in Chrome and Safari. - * 2. Correct the outline style in Safari. - */ -[type="search"] { -webkit-appearance: textfield; /* 1 */ outline-offset: -2px; /* 2 */ } -/** - * Remove the inner padding in Chrome and Safari on macOS. - */ -[type="search"]::-webkit-search-decoration { -webkit-appearance: none; } -/** - * 1. Correct the inability to style clickable types in iOS and Safari. - * 2. Change font properties to `inherit` in Safari. - */ -::-webkit-file-upload-button { -webkit-appearance: button; /* 1 */ font: inherit; /* 2 */ } -/* Interactive - ========================================================================== */ -/* - * Add the correct display in Edge, IE 10+, and Firefox. - */ -details { display: block; } -/* - * Add the correct display in all browsers. - */ -summary { display: list-item; } -/* Misc - ========================================================================== */ -/** - * Add the correct display in IE 10+. - */ -template { display: none; } -/** - * Add the correct display in IE 10. - */ -[hidden] { display: none; } -/* Modules */ -/* - - BOX SIZING - -*/ -html, body, div, article, aside, section, main, nav, footer, header, form, -fieldset, legend, pre, code, a, h1, h2, h3, h4, h5, h6, p, ul, ol, li, dl, dt, -dd, blockquote, figcaption, figure, textarea, table, td, th, tr, -input[type="email"], input[type="number"], input[type="password"], -input[type="tel"], input[type="text"], input[type="url"], .border-box { box-sizing: border-box; } -/* - - ASPECT RATIOS - -*/ -/* This is for fluid media that is embedded from third party sites like youtube, vimeo etc. - * Wrap the outer element in aspect-ratio and then extend it with the desired ratio i.e - * Make sure there are no height and width attributes on the embedded media. - * Adapted from: https://github.com/suitcss/components-flex-embed - * - * Example: - * - *
    - * - *
    - * - * */ -.aspect-ratio { height: 0; position: relative; } -.aspect-ratio--16x9 { padding-bottom: 56.25%; } -.aspect-ratio--9x16 { padding-bottom: 177.77%; } -.aspect-ratio--4x3 { padding-bottom: 75%; } -.aspect-ratio--3x4 { padding-bottom: 133.33%; } -.aspect-ratio--6x4 { padding-bottom: 66.6%; } -.aspect-ratio--4x6 { padding-bottom: 150%; } -.aspect-ratio--8x5 { padding-bottom: 62.5%; } -.aspect-ratio--5x8 { padding-bottom: 160%; } -.aspect-ratio--7x5 { padding-bottom: 71.42%; } -.aspect-ratio--5x7 { padding-bottom: 140%; } -.aspect-ratio--1x1 { padding-bottom: 100%; } -.aspect-ratio--object { position: absolute; top: 0; right: 0; bottom: 0; left: 0; width: 100%; height: 100%; z-index: 100; } -/* - - IMAGES - Docs: http://tachyons.io/docs/elements/images/ - -*/ -/* Responsive images! */ -img { max-width: 100%; } -/* - - BACKGROUND SIZE - Docs: http://tachyons.io/docs/themes/background-size/ - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -/* - Often used in combination with background image set as an inline style - on an html element. -*/ -.cover { background-size: cover !important; } -.contain { background-size: contain !important; } -/* - - BACKGROUND POSITION - - Base: - bg = background - - Modifiers: - -center = center center - -top = top center - -right = center right - -bottom = bottom center - -left = center left - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - - */ -.bg-center { background-repeat: no-repeat; background-position: center center; } -.bg-top { background-repeat: no-repeat; background-position: top center; } -.bg-right { background-repeat: no-repeat; background-position: center right; } -.bg-bottom { background-repeat: no-repeat; background-position: bottom center; } -.bg-left { background-repeat: no-repeat; background-position: center left; } -/* - - OUTLINES - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.outline { outline: 1px solid; } -.outline-transparent { outline: 1px solid transparent; } -.outline-0 { outline: 0; } -/* - - BORDERS - Docs: http://tachyons.io/docs/themes/borders/ - - Base: - b = border - - Modifiers: - a = all - t = top - r = right - b = bottom - l = left - n = none - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.ba { border-style: solid; border-width: 1px; } -.bt { border-top-style: solid; border-top-width: 1px; } -.br { border-right-style: solid; border-right-width: 1px; } -.bb { border-bottom-style: solid; border-bottom-width: 1px; } -.bl { border-left-style: solid; border-left-width: 1px; } -.bn { border-style: none; border-width: 0; } -/* - - BORDER COLORS - Docs: http://tachyons.io/docs/themes/borders/ - - Border colors can be used to extend the base - border classes ba,bt,bb,br,bl found in the _borders.css file. - - The base border class by default will set the color of the border - to that of the current text color. These classes are for the cases - where you desire for the text and border colors to be different. - - Base: - b = border - - Modifiers: - --color-name = each color variable name is also a border color name - -*/ -.b--black { border-color: #000; } -.b--near-black { border-color: #111; } -.b--dark-gray { border-color: #333; } -.b--mid-gray { border-color: #555; } -.b--gray { border-color: #777; } -.b--silver { border-color: #999; } -.b--light-silver { border-color: #aaa; } -.b--moon-gray { border-color: #ccc; } -.b--light-gray { border-color: #eee; } -.b--near-white { border-color: #f4f4f4; } -.b--white { border-color: #fff; } -.b--white-90 { border-color: rgba( 255, 255, 255, .9 ); } -.b--white-80 { border-color: rgba( 255, 255, 255, .8 ); } -.b--white-70 { border-color: rgba( 255, 255, 255, .7 ); } -.b--white-60 { border-color: rgba( 255, 255, 255, .6 ); } -.b--white-50 { border-color: rgba( 255, 255, 255, .5 ); } -.b--white-40 { border-color: rgba( 255, 255, 255, .4 ); } -.b--white-30 { border-color: rgba( 255, 255, 255, .3 ); } -.b--white-20 { border-color: rgba( 255, 255, 255, .2 ); } -.b--white-10 { border-color: rgba( 255, 255, 255, .1 ); } -.b--white-05 { border-color: rgba( 255, 255, 255, .05 ); } -.b--white-025 { border-color: rgba( 255, 255, 255, .025 ); } -.b--white-0125 { border-color: rgba( 255, 255, 255, .0125 ); } -.b--black-90 { border-color: rgba( 0, 0, 0, .9 ); } -.b--black-80 { border-color: rgba( 0, 0, 0, .8 ); } -.b--black-70 { border-color: rgba( 0, 0, 0, .7 ); } -.b--black-60 { border-color: rgba( 0, 0, 0, .6 ); } -.b--black-50 { border-color: rgba( 0, 0, 0, .5 ); } -.b--black-40 { border-color: rgba( 0, 0, 0, .4 ); } -.b--black-30 { border-color: rgba( 0, 0, 0, .3 ); } -.b--black-20 { border-color: rgba( 0, 0, 0, .2 ); } -.b--black-10 { border-color: rgba( 0, 0, 0, .1 ); } -.b--black-05 { border-color: rgba( 0, 0, 0, .05 ); } -.b--black-025 { border-color: rgba( 0, 0, 0, .025 ); } -.b--black-0125 { border-color: rgba( 0, 0, 0, .0125 ); } -.b--dark-red { border-color: #e7040f; } -.b--red { border-color: #ff4136; } -.b--light-red { border-color: #ff725c; } -.b--orange { border-color: #ff6300; } -.b--gold { border-color: #ffb700; } -.b--yellow { border-color: #ffd700; } -.b--light-yellow { border-color: #fbf1a9; } -.b--purple { border-color: #5e2ca5; } -.b--light-purple { border-color: #a463f2; } -.b--dark-pink { border-color: #d5008f; } -.b--hot-pink { border-color: #ff41b4; } -.b--pink { border-color: #ff80cc; } -.b--light-pink { border-color: #ffa3d7; } -.b--dark-green { border-color: #137752; } -.b--green { border-color: #19a974; } -.b--light-green { border-color: #9eebcf; } -.b--navy { border-color: #001b44; } -.b--dark-blue { border-color: #00449e; } -.b--blue { border-color: #357edd; } -.b--light-blue { border-color: #96ccff; } -.b--lightest-blue { border-color: #cdecff; } -.b--washed-blue { border-color: #f6fffe; } -.b--washed-green { border-color: #e8fdf5; } -.b--washed-yellow { border-color: #fffceb; } -.b--washed-red { border-color: #ffdfdf; } -.b--transparent { border-color: transparent; } -.b--inherit { border-color: inherit; } -.b--initial { border-color: initial; } -.b--unset { border-color: unset; } -/* - - BORDER RADIUS - Docs: http://tachyons.io/docs/themes/border-radius/ - - Base: - br = border-radius - - Modifiers: - 0 = 0/none - 1 = 1st step in scale - 2 = 2nd step in scale - 3 = 3rd step in scale - 4 = 4th step in scale - - Literal values: - -100 = 100% - -pill = 9999px - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.br0 { border-radius: 0; } -.br1 { border-radius: .125rem; } -.br2 { border-radius: .25rem; } -.br3 { border-radius: .5rem; } -.br4 { border-radius: 1rem; } -.br-100 { border-radius: 100%; } -.br-pill { border-radius: 9999px; } -.br--bottom { border-top-left-radius: 0; border-top-right-radius: 0; } -.br--top { border-bottom-left-radius: 0; border-bottom-right-radius: 0; } -.br--right { border-top-left-radius: 0; border-bottom-left-radius: 0; } -.br--left { border-top-right-radius: 0; border-bottom-right-radius: 0; } -.br-inherit { border-radius: inherit; } -.br-initial { border-radius: initial; } -.br-unset { border-radius: unset; } -/* - - BORDER STYLES - Docs: http://tachyons.io/docs/themes/borders/ - - Depends on base border module in _borders.css - - Base: - b = border-style - - Modifiers: - --none = none - --dotted = dotted - --dashed = dashed - --solid = solid - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - - */ -.b--dotted { border-style: dotted; } -.b--dashed { border-style: dashed; } -.b--solid { border-style: solid; } -.b--none { border-style: none; } -/* - - BORDER WIDTHS - Docs: http://tachyons.io/docs/themes/borders/ - - Base: - bw = border-width - - Modifiers: - 0 = 0 width border - 1 = 1st step in border-width scale - 2 = 2nd step in border-width scale - 3 = 3rd step in border-width scale - 4 = 4th step in border-width scale - 5 = 5th step in border-width scale - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.bw0 { border-width: 0; } -.bw1 { border-width: .125rem; } -.bw2 { border-width: .25rem; } -.bw3 { border-width: .5rem; } -.bw4 { border-width: 1rem; } -.bw5 { border-width: 2rem; } -/* Resets */ -.bt-0 { border-top-width: 0; } -.br-0 { border-right-width: 0; } -.bb-0 { border-bottom-width: 0; } -.bl-0 { border-left-width: 0; } -/* - - BOX-SHADOW - Docs: http://tachyons.io/docs/themes/box-shadow/ - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - - */ -.shadow-1 { box-shadow: 0 0 4px 2px rgba( 0, 0, 0, .2 ); } -.shadow-2 { box-shadow: 0 0 8px 2px rgba( 0, 0, 0, .2 ); } -.shadow-3 { box-shadow: 2px 2px 4px 2px rgba( 0, 0, 0, .2 ); } -.shadow-4 { box-shadow: 2px 2px 8px 0 rgba( 0, 0, 0, .2 ); } -.shadow-5 { box-shadow: 4px 4px 8px 0 rgba( 0, 0, 0, .2 ); } -/* - - CODE - -*/ -.pre { overflow-x: auto; overflow-y: hidden; overflow: scroll; } -/* - - COORDINATES - Docs: http://tachyons.io/docs/layout/position/ - - Use in combination with the position module. - - Base: - top - bottom - right - left - - Modifiers: - -0 = literal value 0 - -1 = literal value 1 - -2 = literal value 2 - --1 = literal value -1 - --2 = literal value -2 - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.top-0 { top: 0; } -.right-0 { right: 0; } -.bottom-0 { bottom: 0; } -.left-0 { left: 0; } -.top-1 { top: 1rem; } -.right-1 { right: 1rem; } -.bottom-1 { bottom: 1rem; } -.left-1 { left: 1rem; } -.top-2 { top: 2rem; } -.right-2 { right: 2rem; } -.bottom-2 { bottom: 2rem; } -.left-2 { left: 2rem; } -.top--1 { top: -1rem; } -.right--1 { right: -1rem; } -.bottom--1 { bottom: -1rem; } -.left--1 { left: -1rem; } -.top--2 { top: -2rem; } -.right--2 { right: -2rem; } -.bottom--2 { bottom: -2rem; } -.left--2 { left: -2rem; } -.absolute--fill { top: 0; right: 0; bottom: 0; left: 0; } -/* - - CLEARFIX - http://tachyons.io/docs/layout/clearfix/ - -*/ -/* Nicolas Gallaghers Clearfix solution - Ref: http://nicolasgallagher.com/micro-clearfix-hack/ */ -.cf:before, .cf:after { content: " "; display: table; } -.cf:after { clear: both; } -.cf { *zoom: 1; } -.cl { clear: left; } -.cr { clear: right; } -.cb { clear: both; } -.cn { clear: none; } -/* - - DISPLAY - Docs: http://tachyons.io/docs/layout/display - - Base: - d = display - - Modifiers: - n = none - b = block - ib = inline-block - it = inline-table - t = table - tc = table-cell - t-row = table-row - t-columm = table-column - t-column-group = table-column-group - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.dn { display: none; } -.di { display: inline; } -.db { display: block; } -.dib { display: inline-block; } -.dit { display: inline-table; } -.dt { display: table; } -.dtc { display: table-cell; } -.dt-row { display: table-row; } -.dt-row-group { display: table-row-group; } -.dt-column { display: table-column; } -.dt-column-group { display: table-column-group; } -/* - This will set table to full width and then - all cells will be equal width -*/ -.dt--fixed { table-layout: fixed; width: 100%; } -/* - - FLEXBOX - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.flex { display: flex; } -.inline-flex { display: inline-flex; } -/* 1. Fix for Chrome 44 bug. - * https://code.google.com/p/chromium/issues/detail?id=506893 */ -.flex-auto { flex: 1 1 auto; min-width: 0; /* 1 */ min-height: 0; /* 1 */ } -.flex-none { flex: none; } -.flex-column { flex-direction: column; } -.flex-row { flex-direction: row; } -.flex-wrap { flex-wrap: wrap; } -.flex-nowrap { flex-wrap: nowrap; } -.flex-wrap-reverse { flex-wrap: wrap-reverse; } -.flex-column-reverse { flex-direction: column-reverse; } -.flex-row-reverse { flex-direction: row-reverse; } -.items-start { align-items: flex-start; } -.items-end { align-items: flex-end; } -.items-center { align-items: center; } -.items-baseline { align-items: baseline; } -.items-stretch { align-items: stretch; } -.self-start { align-self: flex-start; } -.self-end { align-self: flex-end; } -.self-center { align-self: center; } -.self-baseline { align-self: baseline; } -.self-stretch { align-self: stretch; } -.justify-start { justify-content: flex-start; } -.justify-end { justify-content: flex-end; } -.justify-center { justify-content: center; } -.justify-between { justify-content: space-between; } -.justify-around { justify-content: space-around; } -.content-start { align-content: flex-start; } -.content-end { align-content: flex-end; } -.content-center { align-content: center; } -.content-between { align-content: space-between; } -.content-around { align-content: space-around; } -.content-stretch { align-content: stretch; } -.order-0 { order: 0; } -.order-1 { order: 1; } -.order-2 { order: 2; } -.order-3 { order: 3; } -.order-4 { order: 4; } -.order-5 { order: 5; } -.order-6 { order: 6; } -.order-7 { order: 7; } -.order-8 { order: 8; } -.order-last { order: 99999; } -.flex-grow-0 { flex-grow: 0; } -.flex-grow-1 { flex-grow: 1; } -.flex-shrink-0 { flex-shrink: 0; } -.flex-shrink-1 { flex-shrink: 1; } -/* - - FLOATS - http://tachyons.io/docs/layout/floats/ - - 1. Floated elements are automatically rendered as block level elements. - Setting floats to display inline will fix the double margin bug in - ie6. You know... just in case. - - 2. Don't forget to clearfix your floats with .cf - - Base: - f = float - - Modifiers: - l = left - r = right - n = none - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.fl { float: left; _display: inline; } -.fr { float: right; _display: inline; } -.fn { float: none; } -/* - - FONT FAMILY GROUPS - Docs: http://tachyons.io/docs/typography/font-family/ - -*/ -.sans-serif { font-family: -apple-system, BlinkMacSystemFont, 'avenir next', avenir, 'helvetica neue', helvetica, ubuntu, roboto, noto, 'segoe ui', arial, sans-serif; } -.serif { font-family: georgia, times, serif; } -.system-sans-serif { font-family: sans-serif; } -.system-serif { font-family: serif; } -/* Monospaced Typefaces (for code) */ -/* From http://cssfontstack.com */ -code, .code { font-family: Consolas, monaco, monospace; } -.courier { font-family: 'Courier Next', courier, monospace; } -/* Sans-Serif Typefaces */ -.helvetica { font-family: 'helvetica neue', helvetica, sans-serif; } -.avenir { font-family: 'avenir next', avenir, sans-serif; } -/* Serif Typefaces */ -.athelas { font-family: athelas, georgia, serif; } -.georgia { font-family: georgia, serif; } -.times { font-family: times, serif; } -.bodoni { font-family: "Bodoni MT", serif; } -.calisto { font-family: "Calisto MT", serif; } -.garamond { font-family: garamond, serif; } -.baskerville { font-family: baskerville, serif; } -/* - - FONT STYLE - Docs: http://tachyons.io/docs/typography/font-style/ - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.i { font-style: italic; } -.fs-normal { font-style: normal; } -/* - - FONT WEIGHT - Docs: http://tachyons.io/docs/typography/font-weight/ - - Base - fw = font-weight - - Modifiers: - 1 = literal value 100 - 2 = literal value 200 - 3 = literal value 300 - 4 = literal value 400 - 5 = literal value 500 - 6 = literal value 600 - 7 = literal value 700 - 8 = literal value 800 - 9 = literal value 900 - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.normal { font-weight: normal; } -.b { font-weight: bold; } -.fw1 { font-weight: 100; } -.fw2 { font-weight: 200; } -.fw3 { font-weight: 300; } -.fw4 { font-weight: 400; } -.fw5 { font-weight: 500; } -.fw6 { font-weight: 600; } -.fw7 { font-weight: 700; } -.fw8 { font-weight: 800; } -.fw9 { font-weight: 900; } -/* - - FORMS - -*/ -.input-reset { -webkit-appearance: none; -moz-appearance: none; } -.button-reset::-moz-focus-inner, .input-reset::-moz-focus-inner { border: 0; padding: 0; } -/* - - HEIGHTS - Docs: http://tachyons.io/docs/layout/heights/ - - Base: - h = height - min-h = min-height - min-vh = min-height vertical screen height - vh = vertical screen height - - Modifiers - 1 = 1st step in height scale - 2 = 2nd step in height scale - 3 = 3rd step in height scale - 4 = 4th step in height scale - 5 = 5th step in height scale - - -25 = literal value 25% - -50 = literal value 50% - -75 = literal value 75% - -100 = literal value 100% - - -auto = string value of auto - -inherit = string value of inherit - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -/* Height Scale */ -.h1 { height: 1rem; } -.h2 { height: 2rem; } -.h3 { height: 4rem; } -.h4 { height: 8rem; } -.h5 { height: 16rem; } -/* Height Percentages - Based off of height of parent */ -.h-25 { height: 25%; } -.h-50 { height: 50%; } -.h-75 { height: 75%; } -.h-100 { height: 100%; } -.min-h-100 { min-height: 100%; } -/* Screen Height Percentage */ -.vh-25 { height: 25vh; } -.vh-50 { height: 50vh; } -.vh-75 { height: 75vh; } -.vh-100 { height: 100vh; } -.min-vh-100 { min-height: 100vh; } -/* String Properties */ -.h-auto { height: auto; } -.h-inherit { height: inherit; } -/* - - LETTER SPACING - Docs: http://tachyons.io/docs/typography/tracking/ - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.tracked { letter-spacing: .1em; } -.tracked-tight { letter-spacing: -.05em; } -.tracked-mega { letter-spacing: .25em; } -/* - - LINE HEIGHT / LEADING - Docs: http://tachyons.io/docs/typography/line-height - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.lh-solid { line-height: 1; } -.lh-title { line-height: 1.25; } -.lh-copy { line-height: 1.5; } -/* - - LINKS - Docs: http://tachyons.io/docs/elements/links/ - -*/ -.link { text-decoration: none; transition: color .15s ease-in; } -.link:link, .link:visited { transition: color .15s ease-in; } -.link:hover { transition: color .15s ease-in; } -.link:active { transition: color .15s ease-in; } -.link:focus { transition: color .15s ease-in; outline: 1px dotted currentColor; } -/* - - LISTS - http://tachyons.io/docs/elements/lists/ - -*/ -.list { list-style-type: none; } -/* - - MAX WIDTHS - Docs: http://tachyons.io/docs/layout/max-widths/ - - Base: - mw = max-width - - Modifiers - 1 = 1st step in width scale - 2 = 2nd step in width scale - 3 = 3rd step in width scale - 4 = 4th step in width scale - 5 = 5th step in width scale - 6 = 6st step in width scale - 7 = 7nd step in width scale - 8 = 8rd step in width scale - 9 = 9th step in width scale - - -100 = literal value 100% - - -none = string value none - - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -/* Max Width Percentages */ -.mw-100 { max-width: 100%; } -/* Max Width Scale */ -.mw1 { max-width: 1rem; } -.mw2 { max-width: 2rem; } -.mw3 { max-width: 4rem; } -.mw4 { max-width: 8rem; } -.mw5 { max-width: 16rem; } -.mw6 { max-width: 32rem; } -.mw7 { max-width: 48rem; } -.mw8 { max-width: 64rem; } -.mw9 { max-width: 96rem; } -/* Max Width String Properties */ -.mw-none { max-width: none; } -/* - - WIDTHS - Docs: http://tachyons.io/docs/layout/widths/ - - Base: - w = width - - Modifiers - 1 = 1st step in width scale - 2 = 2nd step in width scale - 3 = 3rd step in width scale - 4 = 4th step in width scale - 5 = 5th step in width scale - - -10 = literal value 10% - -20 = literal value 20% - -25 = literal value 25% - -30 = literal value 30% - -33 = literal value 33% - -34 = literal value 34% - -40 = literal value 40% - -50 = literal value 50% - -60 = literal value 60% - -70 = literal value 70% - -75 = literal value 75% - -80 = literal value 80% - -90 = literal value 90% - -100 = literal value 100% - - -third = 100% / 3 (Not supported in opera mini or IE8) - -two-thirds = 100% / 1.5 (Not supported in opera mini or IE8) - -auto = string value auto - - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -/* Width Scale */ -.w1 { width: 1rem; } -.w2 { width: 2rem; } -.w3 { width: 4rem; } -.w4 { width: 8rem; } -.w5 { width: 16rem; } -.w-10 { width: 10%; } -.w-20 { width: 20%; } -.w-25 { width: 25%; } -.w-30 { width: 30%; } -.w-33 { width: 33%; } -.w-34 { width: 34%; } -.w-40 { width: 40%; } -.w-50 { width: 50%; } -.w-60 { width: 60%; } -.w-70 { width: 70%; } -.w-75 { width: 75%; } -.w-80 { width: 80%; } -.w-90 { width: 90%; } -.w-100 { width: 100%; } -.w-third { width: 33.33333%; } -.w-two-thirds { width: 66.66667%; } -.w-auto { width: auto; } -/* - - OVERFLOW - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - - */ -.overflow-visible { overflow: visible; } -.overflow-hidden { overflow: hidden; } -.overflow-scroll { overflow: scroll; } -.overflow-auto { overflow: auto; } -.overflow-x-visible { overflow-x: visible; } -.overflow-x-hidden { overflow-x: hidden; } -.overflow-x-scroll { overflow-x: scroll; } -.overflow-x-auto { overflow-x: auto; } -.overflow-y-visible { overflow-y: visible; } -.overflow-y-hidden { overflow-y: hidden; } -.overflow-y-scroll { overflow-y: scroll; } -.overflow-y-auto { overflow-y: auto; } -/* - - POSITIONING - Docs: http://tachyons.io/docs/layout/position/ - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.static { position: static; } -.relative { position: relative; } -.absolute { position: absolute; } -.fixed { position: fixed; } -/* - - OPACITY - Docs: http://tachyons.io/docs/themes/opacity/ - -*/ -.o-100 { opacity: 1; } -.o-90 { opacity: .9; } -.o-80 { opacity: .8; } -.o-70 { opacity: .7; } -.o-60 { opacity: .6; } -.o-50 { opacity: .5; } -.o-40 { opacity: .4; } -.o-30 { opacity: .3; } -.o-20 { opacity: .2; } -.o-10 { opacity: .1; } -.o-05 { opacity: .05; } -.o-025 { opacity: .025; } -.o-0 { opacity: 0; } -/* - - ROTATIONS - -*/ -.rotate-45 { -webkit-transform: rotate( 45deg ); transform: rotate( 45deg ); } -.rotate-90 { -webkit-transform: rotate( 90deg ); transform: rotate( 90deg ); } -.rotate-135 { -webkit-transform: rotate( 135deg ); transform: rotate( 135deg ); } -.rotate-180 { -webkit-transform: rotate( 180deg ); transform: rotate( 180deg ); } -.rotate-225 { -webkit-transform: rotate( 225deg ); transform: rotate( 225deg ); } -.rotate-270 { -webkit-transform: rotate( 270deg ); transform: rotate( 270deg ); } -.rotate-315 { -webkit-transform: rotate( 315deg ); transform: rotate( 315deg ); } -/* - - SKINS - Docs: http://tachyons.io/docs/themes/skins/ - - Classes for setting foreground and background colors on elements. - If you haven't declared a border color, but set border on an element, it will - be set to the current text color. - -*/ -/* Text colors */ -.black-90 { color: rgba( 0, 0, 0, .9 ); } -.black-80 { color: rgba( 0, 0, 0, .8 ); } -.black-70 { color: rgba( 0, 0, 0, .7 ); } -.black-60 { color: rgba( 0, 0, 0, .6 ); } -.black-50 { color: rgba( 0, 0, 0, .5 ); } -.black-40 { color: rgba( 0, 0, 0, .4 ); } -.black-30 { color: rgba( 0, 0, 0, .3 ); } -.black-20 { color: rgba( 0, 0, 0, .2 ); } -.black-10 { color: rgba( 0, 0, 0, .1 ); } -.black-05 { color: rgba( 0, 0, 0, .05 ); } -.white-90 { color: rgba( 255, 255, 255, .9 ); } -.white-80 { color: rgba( 255, 255, 255, .8 ); } -.white-70 { color: rgba( 255, 255, 255, .7 ); } -.white-60 { color: rgba( 255, 255, 255, .6 ); } -.white-50 { color: rgba( 255, 255, 255, .5 ); } -.white-40 { color: rgba( 255, 255, 255, .4 ); } -.white-30 { color: rgba( 255, 255, 255, .3 ); } -.white-20 { color: rgba( 255, 255, 255, .2 ); } -.white-10 { color: rgba( 255, 255, 255, .1 ); } -.black { color: #000; } -.near-black { color: #111; } -.dark-gray { color: #333; } -.mid-gray { color: #555; } -.gray { color: #777; } -.silver { color: #999; } -.light-silver { color: #aaa; } -.moon-gray { color: #ccc; } -.light-gray { color: #eee; } -.near-white { color: #f4f4f4; } -.white { color: #fff; } -.dark-red { color: #e7040f; } -.red { color: #ff4136; } -.light-red { color: #ff725c; } -.orange { color: #ff6300; } -.gold { color: #ffb700; } -.yellow { color: #ffd700; } -.light-yellow { color: #fbf1a9; } -.purple { color: #5e2ca5; } -.light-purple { color: #a463f2; } -.dark-pink { color: #d5008f; } -.hot-pink { color: #ff41b4; } -.pink { color: #ff80cc; } -.light-pink { color: #ffa3d7; } -.dark-green { color: #137752; } -.green { color: #19a974; } -.light-green { color: #9eebcf; } -.navy { color: #001b44; } -.dark-blue { color: #00449e; } -.blue { color: #357edd; } -.light-blue { color: #96ccff; } -.lightest-blue { color: #cdecff; } -.washed-blue { color: #f6fffe; } -.washed-green { color: #e8fdf5; } -.washed-yellow { color: #fffceb; } -.washed-red { color: #ffdfdf; } -.color-inherit { color: inherit; } -/* Background colors */ -.bg-black-90 { background-color: rgba( 0, 0, 0, .9 ); } -.bg-black-80 { background-color: rgba( 0, 0, 0, .8 ); } -.bg-black-70 { background-color: rgba( 0, 0, 0, .7 ); } -.bg-black-60 { background-color: rgba( 0, 0, 0, .6 ); } -.bg-black-50 { background-color: rgba( 0, 0, 0, .5 ); } -.bg-black-40 { background-color: rgba( 0, 0, 0, .4 ); } -.bg-black-30 { background-color: rgba( 0, 0, 0, .3 ); } -.bg-black-20 { background-color: rgba( 0, 0, 0, .2 ); } -.bg-black-10 { background-color: rgba( 0, 0, 0, .1 ); } -.bg-black-05 { background-color: rgba( 0, 0, 0, .05 ); } -.bg-white-90 { background-color: rgba( 255, 255, 255, .9 ); } -.bg-white-80 { background-color: rgba( 255, 255, 255, .8 ); } -.bg-white-70 { background-color: rgba( 255, 255, 255, .7 ); } -.bg-white-60 { background-color: rgba( 255, 255, 255, .6 ); } -.bg-white-50 { background-color: rgba( 255, 255, 255, .5 ); } -.bg-white-40 { background-color: rgba( 255, 255, 255, .4 ); } -.bg-white-30 { background-color: rgba( 255, 255, 255, .3 ); } -.bg-white-20 { background-color: rgba( 255, 255, 255, .2 ); } -.bg-white-10 { background-color: rgba( 255, 255, 255, .1 ); } -.bg-black { background-color: #000; } -.bg-near-black { background-color: #111; } -.bg-dark-gray { background-color: #333; } -.bg-mid-gray { background-color: #555; } -.bg-gray { background-color: #777; } -.bg-silver { background-color: #999; } -.bg-light-silver { background-color: #aaa; } -.bg-moon-gray { background-color: #ccc; } -.bg-light-gray { background-color: #eee; } -.bg-near-white { background-color: #f4f4f4; } -.bg-white { background-color: #fff; } -.bg-transparent { background-color: transparent; } -.bg-dark-red { background-color: #e7040f; } -.bg-red { background-color: #ff4136; } -.bg-light-red { background-color: #ff725c; } -.bg-orange { background-color: #ff6300; } -.bg-gold { background-color: #ffb700; } -.bg-yellow { background-color: #ffd700; } -.bg-light-yellow { background-color: #fbf1a9; } -.bg-purple { background-color: #5e2ca5; } -.bg-light-purple { background-color: #a463f2; } -.bg-dark-pink { background-color: #d5008f; } -.bg-hot-pink { background-color: #ff41b4; } -.bg-pink { background-color: #ff80cc; } -.bg-light-pink { background-color: #ffa3d7; } -.bg-dark-green { background-color: #137752; } -.bg-green { background-color: #19a974; } -.bg-light-green { background-color: #9eebcf; } -.bg-navy { background-color: #001b44; } -.bg-dark-blue { background-color: #00449e; } -.bg-blue { background-color: #357edd; } -.bg-light-blue { background-color: #96ccff; } -.bg-lightest-blue { background-color: #cdecff; } -.bg-washed-blue { background-color: #f6fffe; } -.bg-washed-green { background-color: #e8fdf5; } -.bg-washed-yellow { background-color: #fffceb; } -.bg-washed-red { background-color: #ffdfdf; } -.bg-inherit { background-color: inherit; } -/* - - SKINS:PSEUDO - - Customize the color of an element when - it is focused or hovered over. - - */ -.hover-black:hover { color: #000; } -.hover-black:focus { color: #000; } -.hover-near-black:hover { color: #111; } -.hover-near-black:focus { color: #111; } -.hover-dark-gray:hover { color: #333; } -.hover-dark-gray:focus { color: #333; } -.hover-mid-gray:hover { color: #555; } -.hover-mid-gray:focus { color: #555; } -.hover-gray:hover { color: #777; } -.hover-gray:focus { color: #777; } -.hover-silver:hover { color: #999; } -.hover-silver:focus { color: #999; } -.hover-light-silver:hover { color: #aaa; } -.hover-light-silver:focus { color: #aaa; } -.hover-moon-gray:hover { color: #ccc; } -.hover-moon-gray:focus { color: #ccc; } -.hover-light-gray:hover { color: #eee; } -.hover-light-gray:focus { color: #eee; } -.hover-near-white:hover { color: #f4f4f4; } -.hover-near-white:focus { color: #f4f4f4; } -.hover-white:hover { color: #fff; } -.hover-white:focus { color: #fff; } -.hover-black-90:hover { color: rgba( 0, 0, 0, .9 ); } -.hover-black-90:focus { color: rgba( 0, 0, 0, .9 ); } -.hover-black-80:hover { color: rgba( 0, 0, 0, .8 ); } -.hover-black-80:focus { color: rgba( 0, 0, 0, .8 ); } -.hover-black-70:hover { color: rgba( 0, 0, 0, .7 ); } -.hover-black-70:focus { color: rgba( 0, 0, 0, .7 ); } -.hover-black-60:hover { color: rgba( 0, 0, 0, .6 ); } -.hover-black-60:focus { color: rgba( 0, 0, 0, .6 ); } -.hover-black-50:hover { color: rgba( 0, 0, 0, .5 ); } -.hover-black-50:focus { color: rgba( 0, 0, 0, .5 ); } -.hover-black-40:hover { color: rgba( 0, 0, 0, .4 ); } -.hover-black-40:focus { color: rgba( 0, 0, 0, .4 ); } -.hover-black-30:hover { color: rgba( 0, 0, 0, .3 ); } -.hover-black-30:focus { color: rgba( 0, 0, 0, .3 ); } -.hover-black-20:hover { color: rgba( 0, 0, 0, .2 ); } -.hover-black-20:focus { color: rgba( 0, 0, 0, .2 ); } -.hover-black-10:hover { color: rgba( 0, 0, 0, .1 ); } -.hover-black-10:focus { color: rgba( 0, 0, 0, .1 ); } -.hover-white-90:hover { color: rgba( 255, 255, 255, .9 ); } -.hover-white-90:focus { color: rgba( 255, 255, 255, .9 ); } -.hover-white-80:hover { color: rgba( 255, 255, 255, .8 ); } -.hover-white-80:focus { color: rgba( 255, 255, 255, .8 ); } -.hover-white-70:hover { color: rgba( 255, 255, 255, .7 ); } -.hover-white-70:focus { color: rgba( 255, 255, 255, .7 ); } -.hover-white-60:hover { color: rgba( 255, 255, 255, .6 ); } -.hover-white-60:focus { color: rgba( 255, 255, 255, .6 ); } -.hover-white-50:hover { color: rgba( 255, 255, 255, .5 ); } -.hover-white-50:focus { color: rgba( 255, 255, 255, .5 ); } -.hover-white-40:hover { color: rgba( 255, 255, 255, .4 ); } -.hover-white-40:focus { color: rgba( 255, 255, 255, .4 ); } -.hover-white-30:hover { color: rgba( 255, 255, 255, .3 ); } -.hover-white-30:focus { color: rgba( 255, 255, 255, .3 ); } -.hover-white-20:hover { color: rgba( 255, 255, 255, .2 ); } -.hover-white-20:focus { color: rgba( 255, 255, 255, .2 ); } -.hover-white-10:hover { color: rgba( 255, 255, 255, .1 ); } -.hover-white-10:focus { color: rgba( 255, 255, 255, .1 ); } -.hover-inherit:hover, .hover-inherit:focus { color: inherit; } -.hover-bg-black:hover { background-color: #000; } -.hover-bg-black:focus { background-color: #000; } -.hover-bg-near-black:hover { background-color: #111; } -.hover-bg-near-black:focus { background-color: #111; } -.hover-bg-dark-gray:hover { background-color: #333; } -.hover-bg-dark-gray:focus { background-color: #333; } -.hover-bg-mid-gray:hover { background-color: #555; } -.hover-bg-mid-gray:focus { background-color: #555; } -.hover-bg-gray:hover { background-color: #777; } -.hover-bg-gray:focus { background-color: #777; } -.hover-bg-silver:hover { background-color: #999; } -.hover-bg-silver:focus { background-color: #999; } -.hover-bg-light-silver:hover { background-color: #aaa; } -.hover-bg-light-silver:focus { background-color: #aaa; } -.hover-bg-moon-gray:hover { background-color: #ccc; } -.hover-bg-moon-gray:focus { background-color: #ccc; } -.hover-bg-light-gray:hover { background-color: #eee; } -.hover-bg-light-gray:focus { background-color: #eee; } -.hover-bg-near-white:hover { background-color: #f4f4f4; } -.hover-bg-near-white:focus { background-color: #f4f4f4; } -.hover-bg-white:hover { background-color: #fff; } -.hover-bg-white:focus { background-color: #fff; } -.hover-bg-transparent:hover { background-color: transparent; } -.hover-bg-transparent:focus { background-color: transparent; } -.hover-bg-black-90:hover { background-color: rgba( 0, 0, 0, .9 ); } -.hover-bg-black-90:focus { background-color: rgba( 0, 0, 0, .9 ); } -.hover-bg-black-80:hover { background-color: rgba( 0, 0, 0, .8 ); } -.hover-bg-black-80:focus { background-color: rgba( 0, 0, 0, .8 ); } -.hover-bg-black-70:hover { background-color: rgba( 0, 0, 0, .7 ); } -.hover-bg-black-70:focus { background-color: rgba( 0, 0, 0, .7 ); } -.hover-bg-black-60:hover { background-color: rgba( 0, 0, 0, .6 ); } -.hover-bg-black-60:focus { background-color: rgba( 0, 0, 0, .6 ); } -.hover-bg-black-50:hover { background-color: rgba( 0, 0, 0, .5 ); } -.hover-bg-black-50:focus { background-color: rgba( 0, 0, 0, .5 ); } -.hover-bg-black-40:hover { background-color: rgba( 0, 0, 0, .4 ); } -.hover-bg-black-40:focus { background-color: rgba( 0, 0, 0, .4 ); } -.hover-bg-black-30:hover { background-color: rgba( 0, 0, 0, .3 ); } -.hover-bg-black-30:focus { background-color: rgba( 0, 0, 0, .3 ); } -.hover-bg-black-20:hover { background-color: rgba( 0, 0, 0, .2 ); } -.hover-bg-black-20:focus { background-color: rgba( 0, 0, 0, .2 ); } -.hover-bg-black-10:hover { background-color: rgba( 0, 0, 0, .1 ); } -.hover-bg-black-10:focus { background-color: rgba( 0, 0, 0, .1 ); } -.hover-bg-white-90:hover { background-color: rgba( 255, 255, 255, .9 ); } -.hover-bg-white-90:focus { background-color: rgba( 255, 255, 255, .9 ); } -.hover-bg-white-80:hover { background-color: rgba( 255, 255, 255, .8 ); } -.hover-bg-white-80:focus { background-color: rgba( 255, 255, 255, .8 ); } -.hover-bg-white-70:hover { background-color: rgba( 255, 255, 255, .7 ); } -.hover-bg-white-70:focus { background-color: rgba( 255, 255, 255, .7 ); } -.hover-bg-white-60:hover { background-color: rgba( 255, 255, 255, .6 ); } -.hover-bg-white-60:focus { background-color: rgba( 255, 255, 255, .6 ); } -.hover-bg-white-50:hover { background-color: rgba( 255, 255, 255, .5 ); } -.hover-bg-white-50:focus { background-color: rgba( 255, 255, 255, .5 ); } -.hover-bg-white-40:hover { background-color: rgba( 255, 255, 255, .4 ); } -.hover-bg-white-40:focus { background-color: rgba( 255, 255, 255, .4 ); } -.hover-bg-white-30:hover { background-color: rgba( 255, 255, 255, .3 ); } -.hover-bg-white-30:focus { background-color: rgba( 255, 255, 255, .3 ); } -.hover-bg-white-20:hover { background-color: rgba( 255, 255, 255, .2 ); } -.hover-bg-white-20:focus { background-color: rgba( 255, 255, 255, .2 ); } -.hover-bg-white-10:hover { background-color: rgba( 255, 255, 255, .1 ); } -.hover-bg-white-10:focus { background-color: rgba( 255, 255, 255, .1 ); } -.hover-dark-red:hover { color: #e7040f; } -.hover-dark-red:focus { color: #e7040f; } -.hover-red:hover { color: #ff4136; } -.hover-red:focus { color: #ff4136; } -.hover-light-red:hover { color: #ff725c; } -.hover-light-red:focus { color: #ff725c; } -.hover-orange:hover { color: #ff6300; } -.hover-orange:focus { color: #ff6300; } -.hover-gold:hover { color: #ffb700; } -.hover-gold:focus { color: #ffb700; } -.hover-yellow:hover { color: #ffd700; } -.hover-yellow:focus { color: #ffd700; } -.hover-light-yellow:hover { color: #fbf1a9; } -.hover-light-yellow:focus { color: #fbf1a9; } -.hover-purple:hover { color: #5e2ca5; } -.hover-purple:focus { color: #5e2ca5; } -.hover-light-purple:hover { color: #a463f2; } -.hover-light-purple:focus { color: #a463f2; } -.hover-dark-pink:hover { color: #d5008f; } -.hover-dark-pink:focus { color: #d5008f; } -.hover-hot-pink:hover { color: #ff41b4; } -.hover-hot-pink:focus { color: #ff41b4; } -.hover-pink:hover { color: #ff80cc; } -.hover-pink:focus { color: #ff80cc; } -.hover-light-pink:hover { color: #ffa3d7; } -.hover-light-pink:focus { color: #ffa3d7; } -.hover-dark-green:hover { color: #137752; } -.hover-dark-green:focus { color: #137752; } -.hover-green:hover { color: #19a974; } -.hover-green:focus { color: #19a974; } -.hover-light-green:hover { color: #9eebcf; } -.hover-light-green:focus { color: #9eebcf; } -.hover-navy:hover { color: #001b44; } -.hover-navy:focus { color: #001b44; } -.hover-dark-blue:hover { color: #00449e; } -.hover-dark-blue:focus { color: #00449e; } -.hover-blue:hover { color: #357edd; } -.hover-blue:focus { color: #357edd; } -.hover-light-blue:hover { color: #96ccff; } -.hover-light-blue:focus { color: #96ccff; } -.hover-lightest-blue:hover { color: #cdecff; } -.hover-lightest-blue:focus { color: #cdecff; } -.hover-washed-blue:hover { color: #f6fffe; } -.hover-washed-blue:focus { color: #f6fffe; } -.hover-washed-green:hover { color: #e8fdf5; } -.hover-washed-green:focus { color: #e8fdf5; } -.hover-washed-yellow:hover { color: #fffceb; } -.hover-washed-yellow:focus { color: #fffceb; } -.hover-washed-red:hover { color: #ffdfdf; } -.hover-washed-red:focus { color: #ffdfdf; } -.hover-bg-dark-red:hover { background-color: #e7040f; } -.hover-bg-dark-red:focus { background-color: #e7040f; } -.hover-bg-red:hover { background-color: #ff4136; } -.hover-bg-red:focus { background-color: #ff4136; } -.hover-bg-light-red:hover { background-color: #ff725c; } -.hover-bg-light-red:focus { background-color: #ff725c; } -.hover-bg-orange:hover { background-color: #ff6300; } -.hover-bg-orange:focus { background-color: #ff6300; } -.hover-bg-gold:hover { background-color: #ffb700; } -.hover-bg-gold:focus { background-color: #ffb700; } -.hover-bg-yellow:hover { background-color: #ffd700; } -.hover-bg-yellow:focus { background-color: #ffd700; } -.hover-bg-light-yellow:hover { background-color: #fbf1a9; } -.hover-bg-light-yellow:focus { background-color: #fbf1a9; } -.hover-bg-purple:hover { background-color: #5e2ca5; } -.hover-bg-purple:focus { background-color: #5e2ca5; } -.hover-bg-light-purple:hover { background-color: #a463f2; } -.hover-bg-light-purple:focus { background-color: #a463f2; } -.hover-bg-dark-pink:hover { background-color: #d5008f; } -.hover-bg-dark-pink:focus { background-color: #d5008f; } -.hover-bg-hot-pink:hover { background-color: #ff41b4; } -.hover-bg-hot-pink:focus { background-color: #ff41b4; } -.hover-bg-pink:hover { background-color: #ff80cc; } -.hover-bg-pink:focus { background-color: #ff80cc; } -.hover-bg-light-pink:hover { background-color: #ffa3d7; } -.hover-bg-light-pink:focus { background-color: #ffa3d7; } -.hover-bg-dark-green:hover { background-color: #137752; } -.hover-bg-dark-green:focus { background-color: #137752; } -.hover-bg-green:hover { background-color: #19a974; } -.hover-bg-green:focus { background-color: #19a974; } -.hover-bg-light-green:hover { background-color: #9eebcf; } -.hover-bg-light-green:focus { background-color: #9eebcf; } -.hover-bg-navy:hover { background-color: #001b44; } -.hover-bg-navy:focus { background-color: #001b44; } -.hover-bg-dark-blue:hover { background-color: #00449e; } -.hover-bg-dark-blue:focus { background-color: #00449e; } -.hover-bg-blue:hover { background-color: #357edd; } -.hover-bg-blue:focus { background-color: #357edd; } -.hover-bg-light-blue:hover { background-color: #96ccff; } -.hover-bg-light-blue:focus { background-color: #96ccff; } -.hover-bg-lightest-blue:hover { background-color: #cdecff; } -.hover-bg-lightest-blue:focus { background-color: #cdecff; } -.hover-bg-washed-blue:hover { background-color: #f6fffe; } -.hover-bg-washed-blue:focus { background-color: #f6fffe; } -.hover-bg-washed-green:hover { background-color: #e8fdf5; } -.hover-bg-washed-green:focus { background-color: #e8fdf5; } -.hover-bg-washed-yellow:hover { background-color: #fffceb; } -.hover-bg-washed-yellow:focus { background-color: #fffceb; } -.hover-bg-washed-red:hover { background-color: #ffdfdf; } -.hover-bg-washed-red:focus { background-color: #ffdfdf; } -.hover-bg-inherit:hover, .hover-bg-inherit:focus { background-color: inherit; } -/* Variables */ -/* - SPACING - Docs: http://tachyons.io/docs/layout/spacing/ - - An eight step powers of two scale ranging from 0 to 16rem. - - Base: - p = padding - m = margin - - Modifiers: - a = all - h = horizontal - v = vertical - t = top - r = right - b = bottom - l = left - - 0 = none - 1 = 1st step in spacing scale - 2 = 2nd step in spacing scale - 3 = 3rd step in spacing scale - 4 = 4th step in spacing scale - 5 = 5th step in spacing scale - 6 = 6th step in spacing scale - 7 = 7th step in spacing scale - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.pa0 { padding: 0; } -.pa1 { padding: .25rem; } -.pa2 { padding: .5rem; } -.pa3 { padding: 1rem; } -.pa4 { padding: 2rem; } -.pa5 { padding: 4rem; } -.pa6 { padding: 8rem; } -.pa7 { padding: 16rem; } -.pl0 { padding-left: 0; } -.pl1 { padding-left: .25rem; } -.pl2 { padding-left: .5rem; } -.pl3 { padding-left: 1rem; } -.pl4 { padding-left: 2rem; } -.pl5 { padding-left: 4rem; } -.pl6 { padding-left: 8rem; } -.pl7 { padding-left: 16rem; } -.pr0 { padding-right: 0; } -.pr1 { padding-right: .25rem; } -.pr2 { padding-right: .5rem; } -.pr3 { padding-right: 1rem; } -.pr4 { padding-right: 2rem; } -.pr5 { padding-right: 4rem; } -.pr6 { padding-right: 8rem; } -.pr7 { padding-right: 16rem; } -.pb0 { padding-bottom: 0; } -.pb1 { padding-bottom: .25rem; } -.pb2 { padding-bottom: .5rem; } -.pb3 { padding-bottom: 1rem; } -.pb4 { padding-bottom: 2rem; } -.pb5 { padding-bottom: 4rem; } -.pb6 { padding-bottom: 8rem; } -.pb7 { padding-bottom: 16rem; } -.pt0 { padding-top: 0; } -.pt1 { padding-top: .25rem; } -.pt2 { padding-top: .5rem; } -.pt3 { padding-top: 1rem; } -.pt4 { padding-top: 2rem; } -.pt5 { padding-top: 4rem; } -.pt6 { padding-top: 8rem; } -.pt7 { padding-top: 16rem; } -.pv0 { padding-top: 0; padding-bottom: 0; } -.pv1 { padding-top: .25rem; padding-bottom: .25rem; } -.pv2 { padding-top: .5rem; padding-bottom: .5rem; } -.pv3 { padding-top: 1rem; padding-bottom: 1rem; } -.pv4 { padding-top: 2rem; padding-bottom: 2rem; } -.pv5 { padding-top: 4rem; padding-bottom: 4rem; } -.pv6 { padding-top: 8rem; padding-bottom: 8rem; } -.pv7 { padding-top: 16rem; padding-bottom: 16rem; } -.ph0 { padding-left: 0; padding-right: 0; } -.ph1 { padding-left: .25rem; padding-right: .25rem; } -.ph2 { padding-left: .5rem; padding-right: .5rem; } -.ph3 { padding-left: 1rem; padding-right: 1rem; } -.ph4 { padding-left: 2rem; padding-right: 2rem; } -.ph5 { padding-left: 4rem; padding-right: 4rem; } -.ph6 { padding-left: 8rem; padding-right: 8rem; } -.ph7 { padding-left: 16rem; padding-right: 16rem; } -.ma0 { margin: 0; } -.ma1 { margin: .25rem; } -.ma2 { margin: .5rem; } -.ma3 { margin: 1rem; } -.ma4 { margin: 2rem; } -.ma5 { margin: 4rem; } -.ma6 { margin: 8rem; } -.ma7 { margin: 16rem; } -.ml0 { margin-left: 0; } -.ml1 { margin-left: .25rem; } -.ml2 { margin-left: .5rem; } -.ml3 { margin-left: 1rem; } -.ml4 { margin-left: 2rem; } -.ml5 { margin-left: 4rem; } -.ml6 { margin-left: 8rem; } -.ml7 { margin-left: 16rem; } -.mr0 { margin-right: 0; } -.mr1 { margin-right: .25rem; } -.mr2 { margin-right: .5rem; } -.mr3 { margin-right: 1rem; } -.mr4 { margin-right: 2rem; } -.mr5 { margin-right: 4rem; } -.mr6 { margin-right: 8rem; } -.mr7 { margin-right: 16rem; } -.mb0 { margin-bottom: 0; } -.mb1 { margin-bottom: .25rem; } -.mb2 { margin-bottom: .5rem; } -.mb3 { margin-bottom: 1rem; } -.mb4 { margin-bottom: 2rem; } -.mb5 { margin-bottom: 4rem; } -.mb6 { margin-bottom: 8rem; } -.mb7 { margin-bottom: 16rem; } -.mt0 { margin-top: 0; } -.mt1 { margin-top: .25rem; } -.mt2 { margin-top: .5rem; } -.mt3 { margin-top: 1rem; } -.mt4 { margin-top: 2rem; } -.mt5 { margin-top: 4rem; } -.mt6 { margin-top: 8rem; } -.mt7 { margin-top: 16rem; } -.mv0 { margin-top: 0; margin-bottom: 0; } -.mv1 { margin-top: .25rem; margin-bottom: .25rem; } -.mv2 { margin-top: .5rem; margin-bottom: .5rem; } -.mv3 { margin-top: 1rem; margin-bottom: 1rem; } -.mv4 { margin-top: 2rem; margin-bottom: 2rem; } -.mv5 { margin-top: 4rem; margin-bottom: 4rem; } -.mv6 { margin-top: 8rem; margin-bottom: 8rem; } -.mv7 { margin-top: 16rem; margin-bottom: 16rem; } -.mh0 { margin-left: 0; margin-right: 0; } -.mh1 { margin-left: .25rem; margin-right: .25rem; } -.mh2 { margin-left: .5rem; margin-right: .5rem; } -.mh3 { margin-left: 1rem; margin-right: 1rem; } -.mh4 { margin-left: 2rem; margin-right: 2rem; } -.mh5 { margin-left: 4rem; margin-right: 4rem; } -.mh6 { margin-left: 8rem; margin-right: 8rem; } -.mh7 { margin-left: 16rem; margin-right: 16rem; } -/* - NEGATIVE MARGINS - - Base: - n = negative - - Modifiers: - a = all - t = top - r = right - b = bottom - l = left - - 1 = 1st step in spacing scale - 2 = 2nd step in spacing scale - 3 = 3rd step in spacing scale - 4 = 4th step in spacing scale - 5 = 5th step in spacing scale - 6 = 6th step in spacing scale - 7 = 7th step in spacing scale - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.na1 { margin: -0.25rem; } -.na2 { margin: -0.5rem; } -.na3 { margin: -1rem; } -.na4 { margin: -2rem; } -.na5 { margin: -4rem; } -.na6 { margin: -8rem; } -.na7 { margin: -16rem; } -.nl1 { margin-left: -0.25rem; } -.nl2 { margin-left: -0.5rem; } -.nl3 { margin-left: -1rem; } -.nl4 { margin-left: -2rem; } -.nl5 { margin-left: -4rem; } -.nl6 { margin-left: -8rem; } -.nl7 { margin-left: -16rem; } -.nr1 { margin-right: -0.25rem; } -.nr2 { margin-right: -0.5rem; } -.nr3 { margin-right: -1rem; } -.nr4 { margin-right: -2rem; } -.nr5 { margin-right: -4rem; } -.nr6 { margin-right: -8rem; } -.nr7 { margin-right: -16rem; } -.nb1 { margin-bottom: -0.25rem; } -.nb2 { margin-bottom: -0.5rem; } -.nb3 { margin-bottom: -1rem; } -.nb4 { margin-bottom: -2rem; } -.nb5 { margin-bottom: -4rem; } -.nb6 { margin-bottom: -8rem; } -.nb7 { margin-bottom: -16rem; } -.nt1 { margin-top: -0.25rem; } -.nt2 { margin-top: -0.5rem; } -.nt3 { margin-top: -1rem; } -.nt4 { margin-top: -2rem; } -.nt5 { margin-top: -4rem; } -.nt6 { margin-top: -8rem; } -.nt7 { margin-top: -16rem; } -/* - - TABLES - Docs: http://tachyons.io/docs/elements/tables/ - -*/ -.collapse { border-collapse: collapse; border-spacing: 0; } -.striped--light-silver:nth-child(odd) { background-color: #aaa; } -.striped--moon-gray:nth-child(odd) { background-color: #ccc; } -.striped--light-gray:nth-child(odd) { background-color: #eee; } -.striped--near-white:nth-child(odd) { background-color: #f4f4f4; } -.stripe-light:nth-child(odd) { background-color: rgba( 255, 255, 255, .1 ); } -.stripe-dark:nth-child(odd) { background-color: rgba( 0, 0, 0, .1 ); } -/* - - TEXT DECORATION - Docs: http://tachyons.io/docs/typography/text-decoration/ - - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.strike { text-decoration: line-through; } -.underline { text-decoration: underline; } -.no-underline { text-decoration: none; } -/* - - TEXT ALIGN - Docs: http://tachyons.io/docs/typography/text-align/ - - Base - t = text-align - - Modifiers - l = left - r = right - c = center - j = justify - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.tl { text-align: left; } -.tr { text-align: right; } -.tc { text-align: center; } -.tj { text-align: justify; } -/* - - TEXT TRANSFORM - Docs: http://tachyons.io/docs/typography/text-transform/ - - Base: - tt = text-transform - - Modifiers - c = capitalize - l = lowercase - u = uppercase - n = none - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.ttc { text-transform: capitalize; } -.ttl { text-transform: lowercase; } -.ttu { text-transform: uppercase; } -.ttn { text-transform: none; } -/* - - TYPE SCALE - Docs: http://tachyons.io/docs/typography/scale/ - - Base: - f = font-size - - Modifiers - 1 = 1st step in size scale - 2 = 2nd step in size scale - 3 = 3rd step in size scale - 4 = 4th step in size scale - 5 = 5th step in size scale - 6 = 6th step in size scale - 7 = 7th step in size scale - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large -*/ -/* - * For Hero/Marketing Titles - * - * These generally are too large for mobile - * so be careful using them on smaller screens. - * */ -.f-6, .f-headline { font-size: 6rem; } -.f-5, .f-subheadline { font-size: 5rem; } -/* Type Scale */ -.f1 { font-size: 3rem; } -.f2 { font-size: 2.25rem; } -.f3 { font-size: 1.5rem; } -.f4 { font-size: 1.25rem; } -.f5 { font-size: 1rem; } -.f6 { font-size: .875rem; } -.f7 { font-size: .75rem; } -/* Small and hard to read for many people so use with extreme caution */ -/* - - TYPOGRAPHY - http://tachyons.io/docs/typography/measure/ - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -/* Measure is limited to ~66 characters */ -.measure { max-width: 30em; } -/* Measure is limited to ~80 characters */ -.measure-wide { max-width: 34em; } -/* Measure is limited to ~45 characters */ -.measure-narrow { max-width: 20em; } -/* Book paragraph style - paragraphs are indented with no vertical spacing. */ -.indent { text-indent: 1em; margin-top: 0; margin-bottom: 0; } -.small-caps { font-variant: small-caps; } -/* Combine this class with a width to truncate text (or just leave as is to truncate at width of containing element. */ -.truncate { white-space: nowrap; overflow: hidden; text-overflow: ellipsis; } -/* - - UTILITIES - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -/* Equivalent to .overflow-y-scroll */ -.overflow-container { overflow-y: scroll; } -.center { margin-right: auto; margin-left: auto; } -.mr-auto { margin-right: auto; } -.ml-auto { margin-left: auto; } -/* - - VISIBILITY - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -/* - Text that is hidden but accessible - Ref: http://snook.ca/archives/html_and_css/hiding-content-for-accessibility -*/ -.clip { position: fixed !important; _position: absolute !important; clip: rect( 1px 1px 1px 1px ); /* IE6, IE7 */ clip: rect( 1px, 1px, 1px, 1px ); } -/* - - WHITE SPACE - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.ws-normal { white-space: normal; } -.nowrap { white-space: nowrap; } -.pre { white-space: pre; } -/* - - VERTICAL ALIGN - - Media Query Extensions: - -ns = not-small - -m = medium - -l = large - -*/ -.v-base { vertical-align: baseline; } -.v-mid { vertical-align: middle; } -.v-top { vertical-align: top; } -.v-btm { vertical-align: bottom; } -/* - - HOVER EFFECTS - Docs: http://tachyons.io/docs/themes/hovers/ - - - Dim - - Glow - - Hide Child - - Underline text - - Grow - - Pointer - - Shadow - -*/ -/* - - Dim element on hover by adding the dim class. - -*/ -.dim { opacity: 1; transition: opacity .15s ease-in; } -.dim:hover, .dim:focus { opacity: .5; transition: opacity .15s ease-in; } -.dim:active { opacity: .8; transition: opacity .15s ease-out; } -/* - - Animate opacity to 100% on hover by adding the glow class. - -*/ -.glow { transition: opacity .15s ease-in; } -.glow:hover, .glow:focus { opacity: 1; transition: opacity .15s ease-in; } -/* - - Hide child & reveal on hover: - - Put the hide-child class on a parent element and any nested element with the - child class will be hidden and displayed on hover or focus. - -
    -
    Hidden until hover or focus
    -
    Hidden until hover or focus
    -
    Hidden until hover or focus
    -
    Hidden until hover or focus
    -
    -*/ -.hide-child .child { opacity: 0; transition: opacity .15s ease-in; } -.hide-child:hover .child, .hide-child:focus .child, .hide-child:active .child { opacity: 1; transition: opacity .15s ease-in; } -.underline-hover:hover, .underline-hover:focus { text-decoration: underline; } -/* Can combine this with overflow-hidden to make background images grow on hover - * even if you are using background-size: cover */ -.grow { -moz-osx-font-smoothing: grayscale; -webkit-backface-visibility: hidden; backface-visibility: hidden; -webkit-transform: translateZ( 0 ); transform: translateZ( 0 ); transition: -webkit-transform .25s ease-out; transition: transform .25s ease-out; transition: transform .25s ease-out, -webkit-transform .25s ease-out; } -.grow:hover, .grow:focus { -webkit-transform: scale( 1.05 ); transform: scale( 1.05 ); } -.grow:active { -webkit-transform: scale( .90 ); transform: scale( .90 ); } -.grow-large { -moz-osx-font-smoothing: grayscale; -webkit-backface-visibility: hidden; backface-visibility: hidden; -webkit-transform: translateZ( 0 ); transform: translateZ( 0 ); transition: -webkit-transform .25s ease-in-out; transition: transform .25s ease-in-out; transition: transform .25s ease-in-out, -webkit-transform .25s ease-in-out; } -.grow-large:hover, .grow-large:focus { -webkit-transform: scale( 1.2 ); transform: scale( 1.2 ); } -.grow-large:active { -webkit-transform: scale( .95 ); transform: scale( .95 ); } -/* Add pointer on hover */ -.pointer:hover { cursor: pointer; } -/* - Add shadow on hover. - - Performant box-shadow animation pattern from - http://tobiasahlin.com/blog/how-to-animate-box-shadow/ -*/ -.shadow-hover { cursor: pointer; position: relative; transition: all .5s cubic-bezier( .165, .84, .44, 1 ); } -.shadow-hover::after { content: ''; box-shadow: 0 0 16px 2px rgba( 0, 0, 0, .2 ); border-radius: inherit; opacity: 0; position: absolute; top: 0; left: 0; width: 100%; height: 100%; z-index: -1; transition: opacity .5s cubic-bezier( .165, .84, .44, 1 ); } -.shadow-hover:hover::after, .shadow-hover:focus::after { opacity: 1; } -/* Combine with classes in skins and skins-pseudo for - * many different transition possibilities. */ -.bg-animate, .bg-animate:hover, .bg-animate:focus { transition: background-color .15s ease-in-out; } -/* - - Z-INDEX - - Base - z = z-index - - Modifiers - -0 = literal value 0 - -1 = literal value 1 - -2 = literal value 2 - -3 = literal value 3 - -4 = literal value 4 - -5 = literal value 5 - -999 = literal value 999 - -9999 = literal value 9999 - - -max = largest accepted z-index value as integer - - -inherit = string value inherit - -initial = string value initial - -unset = string value unset - - MDN: https://developer.mozilla.org/en/docs/Web/CSS/z-index - Spec: http://www.w3.org/TR/CSS2/zindex.html - Articles: - https://philipwalton.com/articles/what-no-one-told-you-about-z-index/ - - Tips on extending: - There might be a time worth using negative z-index values. - Or if you are using tachyons with another project, you might need to - adjust these values to suit your needs. - -*/ -.z-0 { z-index: 0; } -.z-1 { z-index: 1; } -.z-2 { z-index: 2; } -.z-3 { z-index: 3; } -.z-4 { z-index: 4; } -.z-5 { z-index: 5; } -.z-999 { z-index: 999; } -.z-9999 { z-index: 9999; } -.z-max { z-index: 2147483647; } -.z-inherit { z-index: inherit; } -.z-initial { z-index: initial; } -.z-unset { z-index: unset; } -/* - - NESTED - Tachyons module for styling nested elements - that are generated by a cms. - -*/ -.nested-copy-line-height p, .nested-copy-line-height ul, -.nested-copy-line-height ol { line-height: 1.5; } -.nested-headline-line-height h1, .nested-headline-line-height h2, -.nested-headline-line-height h3, .nested-headline-line-height h4, -.nested-headline-line-height h5, .nested-headline-line-height h6 { line-height: 1.25; } -.nested-list-reset ul, .nested-list-reset ol { padding-left: 0; margin-left: 0; list-style-type: none; } -.nested-copy-indent p+p { text-indent: 1em; margin-top: 0; margin-bottom: 0; } -.nested-copy-separator p+p { margin-top: 1.5em; } -.nested-img img { width: 100%; max-width: 100%; display: block; } -.nested-links a { color: #357edd; transition: color .15s ease-in; } -.nested-links a:hover { color: #96ccff; transition: color .15s ease-in; } -.nested-links a:focus { color: #96ccff; transition: color .15s ease-in; } -/* - - STYLES - - Add custom styles here. - -*/ -/* Variables */ -/* Importing here will allow you to override any variables in the modules */ -/* - - Tachyons - COLOR VARIABLES - - Grayscale - - Solids - - Transparencies - Colors - -*/ -/* - - CUSTOM MEDIA QUERIES - - Media query values can be changed to fit your own content. - There are no magic bullets when it comes to media query width values. - They should be declared in em units - and they should be set to meet - the needs of your content. You can also add additional media queries, - or remove some of the existing ones. - - These media queries can be referenced like so: - - @media (--breakpoint-not-small) { - .medium-and-larger-specific-style { - background-color: red; - } - } - - @media (--breakpoint-medium) { - .medium-screen-specific-style { - background-color: red; - } - } - - @media (--breakpoint-large) { - .large-and-larger-screen-specific-style { - background-color: red; - } - } - -*/ -/* Media Queries */ -/* Debugging */ -/* - - DEBUG CHILDREN - Docs: http://tachyons.io/docs/debug/ - - Just add the debug class to any element to see outlines on its - children. - -*/ -.debug * { outline: 1px solid gold; } -.debug-white * { outline: 1px solid white; } -.debug-black * { outline: 1px solid black; } -/* - - DEBUG GRID - http://tachyons.io/docs/debug-grid/ - - Can be useful for debugging layout issues - or helping to make sure things line up perfectly. - Just tack one of these classes onto a parent element. - -*/ -.debug-grid { background: transparent url( data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAICAYAAADED76LAAAAFElEQVR4AWPAC97/9x0eCsAEPgwAVLshdpENIxcAAAAASUVORK5CYII= ) repeat top left; } -.debug-grid-16 { background: transparent url( data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAMklEQVR4AWOgCLz/b0epAa6UGuBOqQHOQHLUgFEDnAbcBZ4UGwDOkiCnkIhdgNgNxAYAiYlD+8sEuo8AAAAASUVORK5CYII= ) repeat top left; } -.debug-grid-8-solid { background: white url( data:image/gif;base64,R0lGODdhCAAIAPEAAADw/wDx/////wAAACwAAAAACAAIAAACDZQvgaeb/lxbAIKA8y0AOw== ) repeat top left; } -.debug-grid-16-solid { background: white url( data:image/gif;base64,R0lGODdhEAAQAPEAAADw/wDx/xXy/////ywAAAAAEAAQAAACIZyPKckYDQFsb6ZqD85jZ2+BkwiRFKehhqQCQgDHcgwEBQA7 ) repeat top left; } -/* Uncomment out the line below to help debug layout issues */ -/* @import './_debug'; */ -@media screen and (min-width: 30em) { - .aspect-ratio-ns { height: 0; position: relative; } - .aspect-ratio--16x9-ns { padding-bottom: 56.25%; } - .aspect-ratio--9x16-ns { padding-bottom: 177.77%; } - .aspect-ratio--4x3-ns { padding-bottom: 75%; } - .aspect-ratio--3x4-ns { padding-bottom: 133.33%; } - .aspect-ratio--6x4-ns { padding-bottom: 66.6%; } - .aspect-ratio--4x6-ns { padding-bottom: 150%; } - .aspect-ratio--8x5-ns { padding-bottom: 62.5%; } - .aspect-ratio--5x8-ns { padding-bottom: 160%; } - .aspect-ratio--7x5-ns { padding-bottom: 71.42%; } - .aspect-ratio--5x7-ns { padding-bottom: 140%; } - .aspect-ratio--1x1-ns { padding-bottom: 100%; } - .aspect-ratio--object-ns { position: absolute; top: 0; right: 0; bottom: 0; left: 0; width: 100%; height: 100%; z-index: 100; } - .cover-ns { background-size: cover !important; } - .contain-ns { background-size: contain !important; } - .bg-center-ns { background-repeat: no-repeat; background-position: center center; } - .bg-top-ns { background-repeat: no-repeat; background-position: top center; } - .bg-right-ns { background-repeat: no-repeat; background-position: center right; } - .bg-bottom-ns { background-repeat: no-repeat; background-position: bottom center; } - .bg-left-ns { background-repeat: no-repeat; background-position: center left; } - .outline-ns { outline: 1px solid; } - .outline-transparent-ns { outline: 1px solid transparent; } - .outline-0-ns { outline: 0; } - .ba-ns { border-style: solid; border-width: 1px; } - .bt-ns { border-top-style: solid; border-top-width: 1px; } - .br-ns { border-right-style: solid; border-right-width: 1px; } - .bb-ns { border-bottom-style: solid; border-bottom-width: 1px; } - .bl-ns { border-left-style: solid; border-left-width: 1px; } - .bn-ns { border-style: none; border-width: 0; } - .br0-ns { border-radius: 0; } - .br1-ns { border-radius: .125rem; } - .br2-ns { border-radius: .25rem; } - .br3-ns { border-radius: .5rem; } - .br4-ns { border-radius: 1rem; } - .br-100-ns { border-radius: 100%; } - .br-pill-ns { border-radius: 9999px; } - .br--bottom-ns { border-top-left-radius: 0; border-top-right-radius: 0; } - .br--top-ns { border-bottom-left-radius: 0; border-bottom-right-radius: 0; } - .br--right-ns { border-top-left-radius: 0; border-bottom-left-radius: 0; } - .br--left-ns { border-top-right-radius: 0; border-bottom-right-radius: 0; } - .br-inherit-ns { border-radius: inherit; } - .br-initial-ns { border-radius: initial; } - .br-unset-ns { border-radius: unset; } - .b--dotted-ns { border-style: dotted; } - .b--dashed-ns { border-style: dashed; } - .b--solid-ns { border-style: solid; } - .b--none-ns { border-style: none; } - .bw0-ns { border-width: 0; } - .bw1-ns { border-width: .125rem; } - .bw2-ns { border-width: .25rem; } - .bw3-ns { border-width: .5rem; } - .bw4-ns { border-width: 1rem; } - .bw5-ns { border-width: 2rem; } - .bt-0-ns { border-top-width: 0; } - .br-0-ns { border-right-width: 0; } - .bb-0-ns { border-bottom-width: 0; } - .bl-0-ns { border-left-width: 0; } - .shadow-1-ns { box-shadow: 0 0 4px 2px rgba( 0, 0, 0, .2 ); } - .shadow-2-ns { box-shadow: 0 0 8px 2px rgba( 0, 0, 0, .2 ); } - .shadow-3-ns { box-shadow: 2px 2px 4px 2px rgba( 0, 0, 0, .2 ); } - .shadow-4-ns { box-shadow: 2px 2px 8px 0 rgba( 0, 0, 0, .2 ); } - .shadow-5-ns { box-shadow: 4px 4px 8px 0 rgba( 0, 0, 0, .2 ); } - .top-0-ns { top: 0; } - .left-0-ns { left: 0; } - .right-0-ns { right: 0; } - .bottom-0-ns { bottom: 0; } - .top-1-ns { top: 1rem; } - .left-1-ns { left: 1rem; } - .right-1-ns { right: 1rem; } - .bottom-1-ns { bottom: 1rem; } - .top-2-ns { top: 2rem; } - .left-2-ns { left: 2rem; } - .right-2-ns { right: 2rem; } - .bottom-2-ns { bottom: 2rem; } - .top--1-ns { top: -1rem; } - .right--1-ns { right: -1rem; } - .bottom--1-ns { bottom: -1rem; } - .left--1-ns { left: -1rem; } - .top--2-ns { top: -2rem; } - .right--2-ns { right: -2rem; } - .bottom--2-ns { bottom: -2rem; } - .left--2-ns { left: -2rem; } - .absolute--fill-ns { top: 0; right: 0; bottom: 0; left: 0; } - .cl-ns { clear: left; } - .cr-ns { clear: right; } - .cb-ns { clear: both; } - .cn-ns { clear: none; } - .dn-ns { display: none; } - .di-ns { display: inline; } - .db-ns { display: block; } - .dib-ns { display: inline-block; } - .dit-ns { display: inline-table; } - .dt-ns { display: table; } - .dtc-ns { display: table-cell; } - .dt-row-ns { display: table-row; } - .dt-row-group-ns { display: table-row-group; } - .dt-column-ns { display: table-column; } - .dt-column-group-ns { display: table-column-group; } - .dt--fixed-ns { table-layout: fixed; width: 100%; } - .flex-ns { display: flex; } - .inline-flex-ns { display: inline-flex; } - .flex-auto-ns { flex: 1 1 auto; min-width: 0; /* 1 */ min-height: 0; /* 1 */ } - .flex-none-ns { flex: none; } - .flex-column-ns { flex-direction: column; } - .flex-row-ns { flex-direction: row; } - .flex-wrap-ns { flex-wrap: wrap; } - .flex-nowrap-ns { flex-wrap: nowrap; } - .flex-wrap-reverse-ns { flex-wrap: wrap-reverse; } - .flex-column-reverse-ns { flex-direction: column-reverse; } - .flex-row-reverse-ns { flex-direction: row-reverse; } - .items-start-ns { align-items: flex-start; } - .items-end-ns { align-items: flex-end; } - .items-center-ns { align-items: center; } - .items-baseline-ns { align-items: baseline; } - .items-stretch-ns { align-items: stretch; } - .self-start-ns { align-self: flex-start; } - .self-end-ns { align-self: flex-end; } - .self-center-ns { align-self: center; } - .self-baseline-ns { align-self: baseline; } - .self-stretch-ns { align-self: stretch; } - .justify-start-ns { justify-content: flex-start; } - .justify-end-ns { justify-content: flex-end; } - .justify-center-ns { justify-content: center; } - .justify-between-ns { justify-content: space-between; } - .justify-around-ns { justify-content: space-around; } - .content-start-ns { align-content: flex-start; } - .content-end-ns { align-content: flex-end; } - .content-center-ns { align-content: center; } - .content-between-ns { align-content: space-between; } - .content-around-ns { align-content: space-around; } - .content-stretch-ns { align-content: stretch; } - .order-0-ns { order: 0; } - .order-1-ns { order: 1; } - .order-2-ns { order: 2; } - .order-3-ns { order: 3; } - .order-4-ns { order: 4; } - .order-5-ns { order: 5; } - .order-6-ns { order: 6; } - .order-7-ns { order: 7; } - .order-8-ns { order: 8; } - .order-last-ns { order: 99999; } - .flex-grow-0-ns { flex-grow: 0; } - .flex-grow-1-ns { flex-grow: 1; } - .flex-shrink-0-ns { flex-shrink: 0; } - .flex-shrink-1-ns { flex-shrink: 1; } - .fl-ns { float: left; _display: inline; } - .fr-ns { float: right; _display: inline; } - .fn-ns { float: none; } - .i-ns { font-style: italic; } - .fs-normal-ns { font-style: normal; } - .normal-ns { font-weight: normal; } - .b-ns { font-weight: bold; } - .fw1-ns { font-weight: 100; } - .fw2-ns { font-weight: 200; } - .fw3-ns { font-weight: 300; } - .fw4-ns { font-weight: 400; } - .fw5-ns { font-weight: 500; } - .fw6-ns { font-weight: 600; } - .fw7-ns { font-weight: 700; } - .fw8-ns { font-weight: 800; } - .fw9-ns { font-weight: 900; } - .h1-ns { height: 1rem; } - .h2-ns { height: 2rem; } - .h3-ns { height: 4rem; } - .h4-ns { height: 8rem; } - .h5-ns { height: 16rem; } - .h-25-ns { height: 25%; } - .h-50-ns { height: 50%; } - .h-75-ns { height: 75%; } - .h-100-ns { height: 100%; } - .min-h-100-ns { min-height: 100%; } - .vh-25-ns { height: 25vh; } - .vh-50-ns { height: 50vh; } - .vh-75-ns { height: 75vh; } - .vh-100-ns { height: 100vh; } - .min-vh-100-ns { min-height: 100vh; } - .h-auto-ns { height: auto; } - .h-inherit-ns { height: inherit; } - .tracked-ns { letter-spacing: .1em; } - .tracked-tight-ns { letter-spacing: -.05em; } - .tracked-mega-ns { letter-spacing: .25em; } - .lh-solid-ns { line-height: 1; } - .lh-title-ns { line-height: 1.25; } - .lh-copy-ns { line-height: 1.5; } - .mw-100-ns { max-width: 100%; } - .mw1-ns { max-width: 1rem; } - .mw2-ns { max-width: 2rem; } - .mw3-ns { max-width: 4rem; } - .mw4-ns { max-width: 8rem; } - .mw5-ns { max-width: 16rem; } - .mw6-ns { max-width: 32rem; } - .mw7-ns { max-width: 48rem; } - .mw8-ns { max-width: 64rem; } - .mw9-ns { max-width: 96rem; } - .mw-none-ns { max-width: none; } - .w1-ns { width: 1rem; } - .w2-ns { width: 2rem; } - .w3-ns { width: 4rem; } - .w4-ns { width: 8rem; } - .w5-ns { width: 16rem; } - .w-10-ns { width: 10%; } - .w-20-ns { width: 20%; } - .w-25-ns { width: 25%; } - .w-30-ns { width: 30%; } - .w-33-ns { width: 33%; } - .w-34-ns { width: 34%; } - .w-40-ns { width: 40%; } - .w-50-ns { width: 50%; } - .w-60-ns { width: 60%; } - .w-70-ns { width: 70%; } - .w-75-ns { width: 75%; } - .w-80-ns { width: 80%; } - .w-90-ns { width: 90%; } - .w-100-ns { width: 100%; } - .w-third-ns { width: 33.33333%; } - .w-two-thirds-ns { width: 66.66667%; } - .w-auto-ns { width: auto; } - .overflow-visible-ns { overflow: visible; } - .overflow-hidden-ns { overflow: hidden; } - .overflow-scroll-ns { overflow: scroll; } - .overflow-auto-ns { overflow: auto; } - .overflow-x-visible-ns { overflow-x: visible; } - .overflow-x-hidden-ns { overflow-x: hidden; } - .overflow-x-scroll-ns { overflow-x: scroll; } - .overflow-x-auto-ns { overflow-x: auto; } - .overflow-y-visible-ns { overflow-y: visible; } - .overflow-y-hidden-ns { overflow-y: hidden; } - .overflow-y-scroll-ns { overflow-y: scroll; } - .overflow-y-auto-ns { overflow-y: auto; } - .static-ns { position: static; } - .relative-ns { position: relative; } - .absolute-ns { position: absolute; } - .fixed-ns { position: fixed; } - .rotate-45-ns { -webkit-transform: rotate( 45deg ); transform: rotate( 45deg ); } - .rotate-90-ns { -webkit-transform: rotate( 90deg ); transform: rotate( 90deg ); } - .rotate-135-ns { -webkit-transform: rotate( 135deg ); transform: rotate( 135deg ); } - .rotate-180-ns { -webkit-transform: rotate( 180deg ); transform: rotate( 180deg ); } - .rotate-225-ns { -webkit-transform: rotate( 225deg ); transform: rotate( 225deg ); } - .rotate-270-ns { -webkit-transform: rotate( 270deg ); transform: rotate( 270deg ); } - .rotate-315-ns { -webkit-transform: rotate( 315deg ); transform: rotate( 315deg ); } - .pa0-ns { padding: 0; } - .pa1-ns { padding: .25rem; } - .pa2-ns { padding: .5rem; } - .pa3-ns { padding: 1rem; } - .pa4-ns { padding: 2rem; } - .pa5-ns { padding: 4rem; } - .pa6-ns { padding: 8rem; } - .pa7-ns { padding: 16rem; } - .pl0-ns { padding-left: 0; } - .pl1-ns { padding-left: .25rem; } - .pl2-ns { padding-left: .5rem; } - .pl3-ns { padding-left: 1rem; } - .pl4-ns { padding-left: 2rem; } - .pl5-ns { padding-left: 4rem; } - .pl6-ns { padding-left: 8rem; } - .pl7-ns { padding-left: 16rem; } - .pr0-ns { padding-right: 0; } - .pr1-ns { padding-right: .25rem; } - .pr2-ns { padding-right: .5rem; } - .pr3-ns { padding-right: 1rem; } - .pr4-ns { padding-right: 2rem; } - .pr5-ns { padding-right: 4rem; } - .pr6-ns { padding-right: 8rem; } - .pr7-ns { padding-right: 16rem; } - .pb0-ns { padding-bottom: 0; } - .pb1-ns { padding-bottom: .25rem; } - .pb2-ns { padding-bottom: .5rem; } - .pb3-ns { padding-bottom: 1rem; } - .pb4-ns { padding-bottom: 2rem; } - .pb5-ns { padding-bottom: 4rem; } - .pb6-ns { padding-bottom: 8rem; } - .pb7-ns { padding-bottom: 16rem; } - .pt0-ns { padding-top: 0; } - .pt1-ns { padding-top: .25rem; } - .pt2-ns { padding-top: .5rem; } - .pt3-ns { padding-top: 1rem; } - .pt4-ns { padding-top: 2rem; } - .pt5-ns { padding-top: 4rem; } - .pt6-ns { padding-top: 8rem; } - .pt7-ns { padding-top: 16rem; } - .pv0-ns { padding-top: 0; padding-bottom: 0; } - .pv1-ns { padding-top: .25rem; padding-bottom: .25rem; } - .pv2-ns { padding-top: .5rem; padding-bottom: .5rem; } - .pv3-ns { padding-top: 1rem; padding-bottom: 1rem; } - .pv4-ns { padding-top: 2rem; padding-bottom: 2rem; } - .pv5-ns { padding-top: 4rem; padding-bottom: 4rem; } - .pv6-ns { padding-top: 8rem; padding-bottom: 8rem; } - .pv7-ns { padding-top: 16rem; padding-bottom: 16rem; } - .ph0-ns { padding-left: 0; padding-right: 0; } - .ph1-ns { padding-left: .25rem; padding-right: .25rem; } - .ph2-ns { padding-left: .5rem; padding-right: .5rem; } - .ph3-ns { padding-left: 1rem; padding-right: 1rem; } - .ph4-ns { padding-left: 2rem; padding-right: 2rem; } - .ph5-ns { padding-left: 4rem; padding-right: 4rem; } - .ph6-ns { padding-left: 8rem; padding-right: 8rem; } - .ph7-ns { padding-left: 16rem; padding-right: 16rem; } - .ma0-ns { margin: 0; } - .ma1-ns { margin: .25rem; } - .ma2-ns { margin: .5rem; } - .ma3-ns { margin: 1rem; } - .ma4-ns { margin: 2rem; } - .ma5-ns { margin: 4rem; } - .ma6-ns { margin: 8rem; } - .ma7-ns { margin: 16rem; } - .ml0-ns { margin-left: 0; } - .ml1-ns { margin-left: .25rem; } - .ml2-ns { margin-left: .5rem; } - .ml3-ns { margin-left: 1rem; } - .ml4-ns { margin-left: 2rem; } - .ml5-ns { margin-left: 4rem; } - .ml6-ns { margin-left: 8rem; } - .ml7-ns { margin-left: 16rem; } - .mr0-ns { margin-right: 0; } - .mr1-ns { margin-right: .25rem; } - .mr2-ns { margin-right: .5rem; } - .mr3-ns { margin-right: 1rem; } - .mr4-ns { margin-right: 2rem; } - .mr5-ns { margin-right: 4rem; } - .mr6-ns { margin-right: 8rem; } - .mr7-ns { margin-right: 16rem; } - .mb0-ns { margin-bottom: 0; } - .mb1-ns { margin-bottom: .25rem; } - .mb2-ns { margin-bottom: .5rem; } - .mb3-ns { margin-bottom: 1rem; } - .mb4-ns { margin-bottom: 2rem; } - .mb5-ns { margin-bottom: 4rem; } - .mb6-ns { margin-bottom: 8rem; } - .mb7-ns { margin-bottom: 16rem; } - .mt0-ns { margin-top: 0; } - .mt1-ns { margin-top: .25rem; } - .mt2-ns { margin-top: .5rem; } - .mt3-ns { margin-top: 1rem; } - .mt4-ns { margin-top: 2rem; } - .mt5-ns { margin-top: 4rem; } - .mt6-ns { margin-top: 8rem; } - .mt7-ns { margin-top: 16rem; } - .mv0-ns { margin-top: 0; margin-bottom: 0; } - .mv1-ns { margin-top: .25rem; margin-bottom: .25rem; } - .mv2-ns { margin-top: .5rem; margin-bottom: .5rem; } - .mv3-ns { margin-top: 1rem; margin-bottom: 1rem; } - .mv4-ns { margin-top: 2rem; margin-bottom: 2rem; } - .mv5-ns { margin-top: 4rem; margin-bottom: 4rem; } - .mv6-ns { margin-top: 8rem; margin-bottom: 8rem; } - .mv7-ns { margin-top: 16rem; margin-bottom: 16rem; } - .mh0-ns { margin-left: 0; margin-right: 0; } - .mh1-ns { margin-left: .25rem; margin-right: .25rem; } - .mh2-ns { margin-left: .5rem; margin-right: .5rem; } - .mh3-ns { margin-left: 1rem; margin-right: 1rem; } - .mh4-ns { margin-left: 2rem; margin-right: 2rem; } - .mh5-ns { margin-left: 4rem; margin-right: 4rem; } - .mh6-ns { margin-left: 8rem; margin-right: 8rem; } - .mh7-ns { margin-left: 16rem; margin-right: 16rem; } - .na1-ns { margin: -0.25rem; } - .na2-ns { margin: -0.5rem; } - .na3-ns { margin: -1rem; } - .na4-ns { margin: -2rem; } - .na5-ns { margin: -4rem; } - .na6-ns { margin: -8rem; } - .na7-ns { margin: -16rem; } - .nl1-ns { margin-left: -0.25rem; } - .nl2-ns { margin-left: -0.5rem; } - .nl3-ns { margin-left: -1rem; } - .nl4-ns { margin-left: -2rem; } - .nl5-ns { margin-left: -4rem; } - .nl6-ns { margin-left: -8rem; } - .nl7-ns { margin-left: -16rem; } - .nr1-ns { margin-right: -0.25rem; } - .nr2-ns { margin-right: -0.5rem; } - .nr3-ns { margin-right: -1rem; } - .nr4-ns { margin-right: -2rem; } - .nr5-ns { margin-right: -4rem; } - .nr6-ns { margin-right: -8rem; } - .nr7-ns { margin-right: -16rem; } - .nb1-ns { margin-bottom: -0.25rem; } - .nb2-ns { margin-bottom: -0.5rem; } - .nb3-ns { margin-bottom: -1rem; } - .nb4-ns { margin-bottom: -2rem; } - .nb5-ns { margin-bottom: -4rem; } - .nb6-ns { margin-bottom: -8rem; } - .nb7-ns { margin-bottom: -16rem; } - .nt1-ns { margin-top: -0.25rem; } - .nt2-ns { margin-top: -0.5rem; } - .nt3-ns { margin-top: -1rem; } - .nt4-ns { margin-top: -2rem; } - .nt5-ns { margin-top: -4rem; } - .nt6-ns { margin-top: -8rem; } - .nt7-ns { margin-top: -16rem; } - .strike-ns { text-decoration: line-through; } - .underline-ns { text-decoration: underline; } - .no-underline-ns { text-decoration: none; } - .tl-ns { text-align: left; } - .tr-ns { text-align: right; } - .tc-ns { text-align: center; } - .tj-ns { text-align: justify; } - .ttc-ns { text-transform: capitalize; } - .ttl-ns { text-transform: lowercase; } - .ttu-ns { text-transform: uppercase; } - .ttn-ns { text-transform: none; } - .f-6-ns, .f-headline-ns { font-size: 6rem; } - .f-5-ns, .f-subheadline-ns { font-size: 5rem; } - .f1-ns { font-size: 3rem; } - .f2-ns { font-size: 2.25rem; } - .f3-ns { font-size: 1.5rem; } - .f4-ns { font-size: 1.25rem; } - .f5-ns { font-size: 1rem; } - .f6-ns { font-size: .875rem; } - .f7-ns { font-size: .75rem; } - .measure-ns { max-width: 30em; } - .measure-wide-ns { max-width: 34em; } - .measure-narrow-ns { max-width: 20em; } - .indent-ns { text-indent: 1em; margin-top: 0; margin-bottom: 0; } - .small-caps-ns { font-variant: small-caps; } - .truncate-ns { white-space: nowrap; overflow: hidden; text-overflow: ellipsis; } - .center-ns { margin-right: auto; margin-left: auto; } - .mr-auto-ns { margin-right: auto; } - .ml-auto-ns { margin-left: auto; } - .clip-ns { position: fixed !important; _position: absolute !important; clip: rect( 1px 1px 1px 1px ); /* IE6, IE7 */ clip: rect( 1px, 1px, 1px, 1px ); } - .ws-normal-ns { white-space: normal; } - .nowrap-ns { white-space: nowrap; } - .pre-ns { white-space: pre; } - .v-base-ns { vertical-align: baseline; } - .v-mid-ns { vertical-align: middle; } - .v-top-ns { vertical-align: top; } - .v-btm-ns { vertical-align: bottom; } -} -@media screen and (min-width: 30em) and (max-width: 60em) { - .aspect-ratio-m { height: 0; position: relative; } - .aspect-ratio--16x9-m { padding-bottom: 56.25%; } - .aspect-ratio--9x16-m { padding-bottom: 177.77%; } - .aspect-ratio--4x3-m { padding-bottom: 75%; } - .aspect-ratio--3x4-m { padding-bottom: 133.33%; } - .aspect-ratio--6x4-m { padding-bottom: 66.6%; } - .aspect-ratio--4x6-m { padding-bottom: 150%; } - .aspect-ratio--8x5-m { padding-bottom: 62.5%; } - .aspect-ratio--5x8-m { padding-bottom: 160%; } - .aspect-ratio--7x5-m { padding-bottom: 71.42%; } - .aspect-ratio--5x7-m { padding-bottom: 140%; } - .aspect-ratio--1x1-m { padding-bottom: 100%; } - .aspect-ratio--object-m { position: absolute; top: 0; right: 0; bottom: 0; left: 0; width: 100%; height: 100%; z-index: 100; } - .cover-m { background-size: cover !important; } - .contain-m { background-size: contain !important; } - .bg-center-m { background-repeat: no-repeat; background-position: center center; } - .bg-top-m { background-repeat: no-repeat; background-position: top center; } - .bg-right-m { background-repeat: no-repeat; background-position: center right; } - .bg-bottom-m { background-repeat: no-repeat; background-position: bottom center; } - .bg-left-m { background-repeat: no-repeat; background-position: center left; } - .outline-m { outline: 1px solid; } - .outline-transparent-m { outline: 1px solid transparent; } - .outline-0-m { outline: 0; } - .ba-m { border-style: solid; border-width: 1px; } - .bt-m { border-top-style: solid; border-top-width: 1px; } - .br-m { border-right-style: solid; border-right-width: 1px; } - .bb-m { border-bottom-style: solid; border-bottom-width: 1px; } - .bl-m { border-left-style: solid; border-left-width: 1px; } - .bn-m { border-style: none; border-width: 0; } - .br0-m { border-radius: 0; } - .br1-m { border-radius: .125rem; } - .br2-m { border-radius: .25rem; } - .br3-m { border-radius: .5rem; } - .br4-m { border-radius: 1rem; } - .br-100-m { border-radius: 100%; } - .br-pill-m { border-radius: 9999px; } - .br--bottom-m { border-top-left-radius: 0; border-top-right-radius: 0; } - .br--top-m { border-bottom-left-radius: 0; border-bottom-right-radius: 0; } - .br--right-m { border-top-left-radius: 0; border-bottom-left-radius: 0; } - .br--left-m { border-top-right-radius: 0; border-bottom-right-radius: 0; } - .br-inherit-m { border-radius: inherit; } - .br-initial-m { border-radius: initial; } - .br-unset-m { border-radius: unset; } - .b--dotted-m { border-style: dotted; } - .b--dashed-m { border-style: dashed; } - .b--solid-m { border-style: solid; } - .b--none-m { border-style: none; } - .bw0-m { border-width: 0; } - .bw1-m { border-width: .125rem; } - .bw2-m { border-width: .25rem; } - .bw3-m { border-width: .5rem; } - .bw4-m { border-width: 1rem; } - .bw5-m { border-width: 2rem; } - .bt-0-m { border-top-width: 0; } - .br-0-m { border-right-width: 0; } - .bb-0-m { border-bottom-width: 0; } - .bl-0-m { border-left-width: 0; } - .shadow-1-m { box-shadow: 0 0 4px 2px rgba( 0, 0, 0, .2 ); } - .shadow-2-m { box-shadow: 0 0 8px 2px rgba( 0, 0, 0, .2 ); } - .shadow-3-m { box-shadow: 2px 2px 4px 2px rgba( 0, 0, 0, .2 ); } - .shadow-4-m { box-shadow: 2px 2px 8px 0 rgba( 0, 0, 0, .2 ); } - .shadow-5-m { box-shadow: 4px 4px 8px 0 rgba( 0, 0, 0, .2 ); } - .top-0-m { top: 0; } - .left-0-m { left: 0; } - .right-0-m { right: 0; } - .bottom-0-m { bottom: 0; } - .top-1-m { top: 1rem; } - .left-1-m { left: 1rem; } - .right-1-m { right: 1rem; } - .bottom-1-m { bottom: 1rem; } - .top-2-m { top: 2rem; } - .left-2-m { left: 2rem; } - .right-2-m { right: 2rem; } - .bottom-2-m { bottom: 2rem; } - .top--1-m { top: -1rem; } - .right--1-m { right: -1rem; } - .bottom--1-m { bottom: -1rem; } - .left--1-m { left: -1rem; } - .top--2-m { top: -2rem; } - .right--2-m { right: -2rem; } - .bottom--2-m { bottom: -2rem; } - .left--2-m { left: -2rem; } - .absolute--fill-m { top: 0; right: 0; bottom: 0; left: 0; } - .cl-m { clear: left; } - .cr-m { clear: right; } - .cb-m { clear: both; } - .cn-m { clear: none; } - .dn-m { display: none; } - .di-m { display: inline; } - .db-m { display: block; } - .dib-m { display: inline-block; } - .dit-m { display: inline-table; } - .dt-m { display: table; } - .dtc-m { display: table-cell; } - .dt-row-m { display: table-row; } - .dt-row-group-m { display: table-row-group; } - .dt-column-m { display: table-column; } - .dt-column-group-m { display: table-column-group; } - .dt--fixed-m { table-layout: fixed; width: 100%; } - .flex-m { display: flex; } - .inline-flex-m { display: inline-flex; } - .flex-auto-m { flex: 1 1 auto; min-width: 0; /* 1 */ min-height: 0; /* 1 */ } - .flex-none-m { flex: none; } - .flex-column-m { flex-direction: column; } - .flex-row-m { flex-direction: row; } - .flex-wrap-m { flex-wrap: wrap; } - .flex-nowrap-m { flex-wrap: nowrap; } - .flex-wrap-reverse-m { flex-wrap: wrap-reverse; } - .flex-column-reverse-m { flex-direction: column-reverse; } - .flex-row-reverse-m { flex-direction: row-reverse; } - .items-start-m { align-items: flex-start; } - .items-end-m { align-items: flex-end; } - .items-center-m { align-items: center; } - .items-baseline-m { align-items: baseline; } - .items-stretch-m { align-items: stretch; } - .self-start-m { align-self: flex-start; } - .self-end-m { align-self: flex-end; } - .self-center-m { align-self: center; } - .self-baseline-m { align-self: baseline; } - .self-stretch-m { align-self: stretch; } - .justify-start-m { justify-content: flex-start; } - .justify-end-m { justify-content: flex-end; } - .justify-center-m { justify-content: center; } - .justify-between-m { justify-content: space-between; } - .justify-around-m { justify-content: space-around; } - .content-start-m { align-content: flex-start; } - .content-end-m { align-content: flex-end; } - .content-center-m { align-content: center; } - .content-between-m { align-content: space-between; } - .content-around-m { align-content: space-around; } - .content-stretch-m { align-content: stretch; } - .order-0-m { order: 0; } - .order-1-m { order: 1; } - .order-2-m { order: 2; } - .order-3-m { order: 3; } - .order-4-m { order: 4; } - .order-5-m { order: 5; } - .order-6-m { order: 6; } - .order-7-m { order: 7; } - .order-8-m { order: 8; } - .order-last-m { order: 99999; } - .flex-grow-0-m { flex-grow: 0; } - .flex-grow-1-m { flex-grow: 1; } - .flex-shrink-0-m { flex-shrink: 0; } - .flex-shrink-1-m { flex-shrink: 1; } - .fl-m { float: left; _display: inline; } - .fr-m { float: right; _display: inline; } - .fn-m { float: none; } - .i-m { font-style: italic; } - .fs-normal-m { font-style: normal; } - .normal-m { font-weight: normal; } - .b-m { font-weight: bold; } - .fw1-m { font-weight: 100; } - .fw2-m { font-weight: 200; } - .fw3-m { font-weight: 300; } - .fw4-m { font-weight: 400; } - .fw5-m { font-weight: 500; } - .fw6-m { font-weight: 600; } - .fw7-m { font-weight: 700; } - .fw8-m { font-weight: 800; } - .fw9-m { font-weight: 900; } - .h1-m { height: 1rem; } - .h2-m { height: 2rem; } - .h3-m { height: 4rem; } - .h4-m { height: 8rem; } - .h5-m { height: 16rem; } - .h-25-m { height: 25%; } - .h-50-m { height: 50%; } - .h-75-m { height: 75%; } - .h-100-m { height: 100%; } - .min-h-100-m { min-height: 100%; } - .vh-25-m { height: 25vh; } - .vh-50-m { height: 50vh; } - .vh-75-m { height: 75vh; } - .vh-100-m { height: 100vh; } - .min-vh-100-m { min-height: 100vh; } - .h-auto-m { height: auto; } - .h-inherit-m { height: inherit; } - .tracked-m { letter-spacing: .1em; } - .tracked-tight-m { letter-spacing: -.05em; } - .tracked-mega-m { letter-spacing: .25em; } - .lh-solid-m { line-height: 1; } - .lh-title-m { line-height: 1.25; } - .lh-copy-m { line-height: 1.5; } - .mw-100-m { max-width: 100%; } - .mw1-m { max-width: 1rem; } - .mw2-m { max-width: 2rem; } - .mw3-m { max-width: 4rem; } - .mw4-m { max-width: 8rem; } - .mw5-m { max-width: 16rem; } - .mw6-m { max-width: 32rem; } - .mw7-m { max-width: 48rem; } - .mw8-m { max-width: 64rem; } - .mw9-m { max-width: 96rem; } - .mw-none-m { max-width: none; } - .w1-m { width: 1rem; } - .w2-m { width: 2rem; } - .w3-m { width: 4rem; } - .w4-m { width: 8rem; } - .w5-m { width: 16rem; } - .w-10-m { width: 10%; } - .w-20-m { width: 20%; } - .w-25-m { width: 25%; } - .w-30-m { width: 30%; } - .w-33-m { width: 33%; } - .w-34-m { width: 34%; } - .w-40-m { width: 40%; } - .w-50-m { width: 50%; } - .w-60-m { width: 60%; } - .w-70-m { width: 70%; } - .w-75-m { width: 75%; } - .w-80-m { width: 80%; } - .w-90-m { width: 90%; } - .w-100-m { width: 100%; } - .w-third-m { width: 33.33333%; } - .w-two-thirds-m { width: 66.66667%; } - .w-auto-m { width: auto; } - .overflow-visible-m { overflow: visible; } - .overflow-hidden-m { overflow: hidden; } - .overflow-scroll-m { overflow: scroll; } - .overflow-auto-m { overflow: auto; } - .overflow-x-visible-m { overflow-x: visible; } - .overflow-x-hidden-m { overflow-x: hidden; } - .overflow-x-scroll-m { overflow-x: scroll; } - .overflow-x-auto-m { overflow-x: auto; } - .overflow-y-visible-m { overflow-y: visible; } - .overflow-y-hidden-m { overflow-y: hidden; } - .overflow-y-scroll-m { overflow-y: scroll; } - .overflow-y-auto-m { overflow-y: auto; } - .static-m { position: static; } - .relative-m { position: relative; } - .absolute-m { position: absolute; } - .fixed-m { position: fixed; } - .rotate-45-m { -webkit-transform: rotate( 45deg ); transform: rotate( 45deg ); } - .rotate-90-m { -webkit-transform: rotate( 90deg ); transform: rotate( 90deg ); } - .rotate-135-m { -webkit-transform: rotate( 135deg ); transform: rotate( 135deg ); } - .rotate-180-m { -webkit-transform: rotate( 180deg ); transform: rotate( 180deg ); } - .rotate-225-m { -webkit-transform: rotate( 225deg ); transform: rotate( 225deg ); } - .rotate-270-m { -webkit-transform: rotate( 270deg ); transform: rotate( 270deg ); } - .rotate-315-m { -webkit-transform: rotate( 315deg ); transform: rotate( 315deg ); } - .pa0-m { padding: 0; } - .pa1-m { padding: .25rem; } - .pa2-m { padding: .5rem; } - .pa3-m { padding: 1rem; } - .pa4-m { padding: 2rem; } - .pa5-m { padding: 4rem; } - .pa6-m { padding: 8rem; } - .pa7-m { padding: 16rem; } - .pl0-m { padding-left: 0; } - .pl1-m { padding-left: .25rem; } - .pl2-m { padding-left: .5rem; } - .pl3-m { padding-left: 1rem; } - .pl4-m { padding-left: 2rem; } - .pl5-m { padding-left: 4rem; } - .pl6-m { padding-left: 8rem; } - .pl7-m { padding-left: 16rem; } - .pr0-m { padding-right: 0; } - .pr1-m { padding-right: .25rem; } - .pr2-m { padding-right: .5rem; } - .pr3-m { padding-right: 1rem; } - .pr4-m { padding-right: 2rem; } - .pr5-m { padding-right: 4rem; } - .pr6-m { padding-right: 8rem; } - .pr7-m { padding-right: 16rem; } - .pb0-m { padding-bottom: 0; } - .pb1-m { padding-bottom: .25rem; } - .pb2-m { padding-bottom: .5rem; } - .pb3-m { padding-bottom: 1rem; } - .pb4-m { padding-bottom: 2rem; } - .pb5-m { padding-bottom: 4rem; } - .pb6-m { padding-bottom: 8rem; } - .pb7-m { padding-bottom: 16rem; } - .pt0-m { padding-top: 0; } - .pt1-m { padding-top: .25rem; } - .pt2-m { padding-top: .5rem; } - .pt3-m { padding-top: 1rem; } - .pt4-m { padding-top: 2rem; } - .pt5-m { padding-top: 4rem; } - .pt6-m { padding-top: 8rem; } - .pt7-m { padding-top: 16rem; } - .pv0-m { padding-top: 0; padding-bottom: 0; } - .pv1-m { padding-top: .25rem; padding-bottom: .25rem; } - .pv2-m { padding-top: .5rem; padding-bottom: .5rem; } - .pv3-m { padding-top: 1rem; padding-bottom: 1rem; } - .pv4-m { padding-top: 2rem; padding-bottom: 2rem; } - .pv5-m { padding-top: 4rem; padding-bottom: 4rem; } - .pv6-m { padding-top: 8rem; padding-bottom: 8rem; } - .pv7-m { padding-top: 16rem; padding-bottom: 16rem; } - .ph0-m { padding-left: 0; padding-right: 0; } - .ph1-m { padding-left: .25rem; padding-right: .25rem; } - .ph2-m { padding-left: .5rem; padding-right: .5rem; } - .ph3-m { padding-left: 1rem; padding-right: 1rem; } - .ph4-m { padding-left: 2rem; padding-right: 2rem; } - .ph5-m { padding-left: 4rem; padding-right: 4rem; } - .ph6-m { padding-left: 8rem; padding-right: 8rem; } - .ph7-m { padding-left: 16rem; padding-right: 16rem; } - .ma0-m { margin: 0; } - .ma1-m { margin: .25rem; } - .ma2-m { margin: .5rem; } - .ma3-m { margin: 1rem; } - .ma4-m { margin: 2rem; } - .ma5-m { margin: 4rem; } - .ma6-m { margin: 8rem; } - .ma7-m { margin: 16rem; } - .ml0-m { margin-left: 0; } - .ml1-m { margin-left: .25rem; } - .ml2-m { margin-left: .5rem; } - .ml3-m { margin-left: 1rem; } - .ml4-m { margin-left: 2rem; } - .ml5-m { margin-left: 4rem; } - .ml6-m { margin-left: 8rem; } - .ml7-m { margin-left: 16rem; } - .mr0-m { margin-right: 0; } - .mr1-m { margin-right: .25rem; } - .mr2-m { margin-right: .5rem; } - .mr3-m { margin-right: 1rem; } - .mr4-m { margin-right: 2rem; } - .mr5-m { margin-right: 4rem; } - .mr6-m { margin-right: 8rem; } - .mr7-m { margin-right: 16rem; } - .mb0-m { margin-bottom: 0; } - .mb1-m { margin-bottom: .25rem; } - .mb2-m { margin-bottom: .5rem; } - .mb3-m { margin-bottom: 1rem; } - .mb4-m { margin-bottom: 2rem; } - .mb5-m { margin-bottom: 4rem; } - .mb6-m { margin-bottom: 8rem; } - .mb7-m { margin-bottom: 16rem; } - .mt0-m { margin-top: 0; } - .mt1-m { margin-top: .25rem; } - .mt2-m { margin-top: .5rem; } - .mt3-m { margin-top: 1rem; } - .mt4-m { margin-top: 2rem; } - .mt5-m { margin-top: 4rem; } - .mt6-m { margin-top: 8rem; } - .mt7-m { margin-top: 16rem; } - .mv0-m { margin-top: 0; margin-bottom: 0; } - .mv1-m { margin-top: .25rem; margin-bottom: .25rem; } - .mv2-m { margin-top: .5rem; margin-bottom: .5rem; } - .mv3-m { margin-top: 1rem; margin-bottom: 1rem; } - .mv4-m { margin-top: 2rem; margin-bottom: 2rem; } - .mv5-m { margin-top: 4rem; margin-bottom: 4rem; } - .mv6-m { margin-top: 8rem; margin-bottom: 8rem; } - .mv7-m { margin-top: 16rem; margin-bottom: 16rem; } - .mh0-m { margin-left: 0; margin-right: 0; } - .mh1-m { margin-left: .25rem; margin-right: .25rem; } - .mh2-m { margin-left: .5rem; margin-right: .5rem; } - .mh3-m { margin-left: 1rem; margin-right: 1rem; } - .mh4-m { margin-left: 2rem; margin-right: 2rem; } - .mh5-m { margin-left: 4rem; margin-right: 4rem; } - .mh6-m { margin-left: 8rem; margin-right: 8rem; } - .mh7-m { margin-left: 16rem; margin-right: 16rem; } - .na1-m { margin: -0.25rem; } - .na2-m { margin: -0.5rem; } - .na3-m { margin: -1rem; } - .na4-m { margin: -2rem; } - .na5-m { margin: -4rem; } - .na6-m { margin: -8rem; } - .na7-m { margin: -16rem; } - .nl1-m { margin-left: -0.25rem; } - .nl2-m { margin-left: -0.5rem; } - .nl3-m { margin-left: -1rem; } - .nl4-m { margin-left: -2rem; } - .nl5-m { margin-left: -4rem; } - .nl6-m { margin-left: -8rem; } - .nl7-m { margin-left: -16rem; } - .nr1-m { margin-right: -0.25rem; } - .nr2-m { margin-right: -0.5rem; } - .nr3-m { margin-right: -1rem; } - .nr4-m { margin-right: -2rem; } - .nr5-m { margin-right: -4rem; } - .nr6-m { margin-right: -8rem; } - .nr7-m { margin-right: -16rem; } - .nb1-m { margin-bottom: -0.25rem; } - .nb2-m { margin-bottom: -0.5rem; } - .nb3-m { margin-bottom: -1rem; } - .nb4-m { margin-bottom: -2rem; } - .nb5-m { margin-bottom: -4rem; } - .nb6-m { margin-bottom: -8rem; } - .nb7-m { margin-bottom: -16rem; } - .nt1-m { margin-top: -0.25rem; } - .nt2-m { margin-top: -0.5rem; } - .nt3-m { margin-top: -1rem; } - .nt4-m { margin-top: -2rem; } - .nt5-m { margin-top: -4rem; } - .nt6-m { margin-top: -8rem; } - .nt7-m { margin-top: -16rem; } - .strike-m { text-decoration: line-through; } - .underline-m { text-decoration: underline; } - .no-underline-m { text-decoration: none; } - .tl-m { text-align: left; } - .tr-m { text-align: right; } - .tc-m { text-align: center; } - .tj-m { text-align: justify; } - .ttc-m { text-transform: capitalize; } - .ttl-m { text-transform: lowercase; } - .ttu-m { text-transform: uppercase; } - .ttn-m { text-transform: none; } - .f-6-m, .f-headline-m { font-size: 6rem; } - .f-5-m, .f-subheadline-m { font-size: 5rem; } - .f1-m { font-size: 3rem; } - .f2-m { font-size: 2.25rem; } - .f3-m { font-size: 1.5rem; } - .f4-m { font-size: 1.25rem; } - .f5-m { font-size: 1rem; } - .f6-m { font-size: .875rem; } - .f7-m { font-size: .75rem; } - .measure-m { max-width: 30em; } - .measure-wide-m { max-width: 34em; } - .measure-narrow-m { max-width: 20em; } - .indent-m { text-indent: 1em; margin-top: 0; margin-bottom: 0; } - .small-caps-m { font-variant: small-caps; } - .truncate-m { white-space: nowrap; overflow: hidden; text-overflow: ellipsis; } - .center-m { margin-right: auto; margin-left: auto; } - .mr-auto-m { margin-right: auto; } - .ml-auto-m { margin-left: auto; } - .clip-m { position: fixed !important; _position: absolute !important; clip: rect( 1px 1px 1px 1px ); /* IE6, IE7 */ clip: rect( 1px, 1px, 1px, 1px ); } - .ws-normal-m { white-space: normal; } - .nowrap-m { white-space: nowrap; } - .pre-m { white-space: pre; } - .v-base-m { vertical-align: baseline; } - .v-mid-m { vertical-align: middle; } - .v-top-m { vertical-align: top; } - .v-btm-m { vertical-align: bottom; } -} -@media screen and (min-width: 60em) { - .aspect-ratio-l { height: 0; position: relative; } - .aspect-ratio--16x9-l { padding-bottom: 56.25%; } - .aspect-ratio--9x16-l { padding-bottom: 177.77%; } - .aspect-ratio--4x3-l { padding-bottom: 75%; } - .aspect-ratio--3x4-l { padding-bottom: 133.33%; } - .aspect-ratio--6x4-l { padding-bottom: 66.6%; } - .aspect-ratio--4x6-l { padding-bottom: 150%; } - .aspect-ratio--8x5-l { padding-bottom: 62.5%; } - .aspect-ratio--5x8-l { padding-bottom: 160%; } - .aspect-ratio--7x5-l { padding-bottom: 71.42%; } - .aspect-ratio--5x7-l { padding-bottom: 140%; } - .aspect-ratio--1x1-l { padding-bottom: 100%; } - .aspect-ratio--object-l { position: absolute; top: 0; right: 0; bottom: 0; left: 0; width: 100%; height: 100%; z-index: 100; } - .cover-l { background-size: cover !important; } - .contain-l { background-size: contain !important; } - .bg-center-l { background-repeat: no-repeat; background-position: center center; } - .bg-top-l { background-repeat: no-repeat; background-position: top center; } - .bg-right-l { background-repeat: no-repeat; background-position: center right; } - .bg-bottom-l { background-repeat: no-repeat; background-position: bottom center; } - .bg-left-l { background-repeat: no-repeat; background-position: center left; } - .outline-l { outline: 1px solid; } - .outline-transparent-l { outline: 1px solid transparent; } - .outline-0-l { outline: 0; } - .ba-l { border-style: solid; border-width: 1px; } - .bt-l { border-top-style: solid; border-top-width: 1px; } - .br-l { border-right-style: solid; border-right-width: 1px; } - .bb-l { border-bottom-style: solid; border-bottom-width: 1px; } - .bl-l { border-left-style: solid; border-left-width: 1px; } - .bn-l { border-style: none; border-width: 0; } - .br0-l { border-radius: 0; } - .br1-l { border-radius: .125rem; } - .br2-l { border-radius: .25rem; } - .br3-l { border-radius: .5rem; } - .br4-l { border-radius: 1rem; } - .br-100-l { border-radius: 100%; } - .br-pill-l { border-radius: 9999px; } - .br--bottom-l { border-top-left-radius: 0; border-top-right-radius: 0; } - .br--top-l { border-bottom-left-radius: 0; border-bottom-right-radius: 0; } - .br--right-l { border-top-left-radius: 0; border-bottom-left-radius: 0; } - .br--left-l { border-top-right-radius: 0; border-bottom-right-radius: 0; } - .br-inherit-l { border-radius: inherit; } - .br-initial-l { border-radius: initial; } - .br-unset-l { border-radius: unset; } - .b--dotted-l { border-style: dotted; } - .b--dashed-l { border-style: dashed; } - .b--solid-l { border-style: solid; } - .b--none-l { border-style: none; } - .bw0-l { border-width: 0; } - .bw1-l { border-width: .125rem; } - .bw2-l { border-width: .25rem; } - .bw3-l { border-width: .5rem; } - .bw4-l { border-width: 1rem; } - .bw5-l { border-width: 2rem; } - .bt-0-l { border-top-width: 0; } - .br-0-l { border-right-width: 0; } - .bb-0-l { border-bottom-width: 0; } - .bl-0-l { border-left-width: 0; } - .shadow-1-l { box-shadow: 0 0 4px 2px rgba( 0, 0, 0, .2 ); } - .shadow-2-l { box-shadow: 0 0 8px 2px rgba( 0, 0, 0, .2 ); } - .shadow-3-l { box-shadow: 2px 2px 4px 2px rgba( 0, 0, 0, .2 ); } - .shadow-4-l { box-shadow: 2px 2px 8px 0 rgba( 0, 0, 0, .2 ); } - .shadow-5-l { box-shadow: 4px 4px 8px 0 rgba( 0, 0, 0, .2 ); } - .top-0-l { top: 0; } - .left-0-l { left: 0; } - .right-0-l { right: 0; } - .bottom-0-l { bottom: 0; } - .top-1-l { top: 1rem; } - .left-1-l { left: 1rem; } - .right-1-l { right: 1rem; } - .bottom-1-l { bottom: 1rem; } - .top-2-l { top: 2rem; } - .left-2-l { left: 2rem; } - .right-2-l { right: 2rem; } - .bottom-2-l { bottom: 2rem; } - .top--1-l { top: -1rem; } - .right--1-l { right: -1rem; } - .bottom--1-l { bottom: -1rem; } - .left--1-l { left: -1rem; } - .top--2-l { top: -2rem; } - .right--2-l { right: -2rem; } - .bottom--2-l { bottom: -2rem; } - .left--2-l { left: -2rem; } - .absolute--fill-l { top: 0; right: 0; bottom: 0; left: 0; } - .cl-l { clear: left; } - .cr-l { clear: right; } - .cb-l { clear: both; } - .cn-l { clear: none; } - .dn-l { display: none; } - .di-l { display: inline; } - .db-l { display: block; } - .dib-l { display: inline-block; } - .dit-l { display: inline-table; } - .dt-l { display: table; } - .dtc-l { display: table-cell; } - .dt-row-l { display: table-row; } - .dt-row-group-l { display: table-row-group; } - .dt-column-l { display: table-column; } - .dt-column-group-l { display: table-column-group; } - .dt--fixed-l { table-layout: fixed; width: 100%; } - .flex-l { display: flex; } - .inline-flex-l { display: inline-flex; } - .flex-auto-l { flex: 1 1 auto; min-width: 0; /* 1 */ min-height: 0; /* 1 */ } - .flex-none-l { flex: none; } - .flex-column-l { flex-direction: column; } - .flex-row-l { flex-direction: row; } - .flex-wrap-l { flex-wrap: wrap; } - .flex-nowrap-l { flex-wrap: nowrap; } - .flex-wrap-reverse-l { flex-wrap: wrap-reverse; } - .flex-column-reverse-l { flex-direction: column-reverse; } - .flex-row-reverse-l { flex-direction: row-reverse; } - .items-start-l { align-items: flex-start; } - .items-end-l { align-items: flex-end; } - .items-center-l { align-items: center; } - .items-baseline-l { align-items: baseline; } - .items-stretch-l { align-items: stretch; } - .self-start-l { align-self: flex-start; } - .self-end-l { align-self: flex-end; } - .self-center-l { align-self: center; } - .self-baseline-l { align-self: baseline; } - .self-stretch-l { align-self: stretch; } - .justify-start-l { justify-content: flex-start; } - .justify-end-l { justify-content: flex-end; } - .justify-center-l { justify-content: center; } - .justify-between-l { justify-content: space-between; } - .justify-around-l { justify-content: space-around; } - .content-start-l { align-content: flex-start; } - .content-end-l { align-content: flex-end; } - .content-center-l { align-content: center; } - .content-between-l { align-content: space-between; } - .content-around-l { align-content: space-around; } - .content-stretch-l { align-content: stretch; } - .order-0-l { order: 0; } - .order-1-l { order: 1; } - .order-2-l { order: 2; } - .order-3-l { order: 3; } - .order-4-l { order: 4; } - .order-5-l { order: 5; } - .order-6-l { order: 6; } - .order-7-l { order: 7; } - .order-8-l { order: 8; } - .order-last-l { order: 99999; } - .flex-grow-0-l { flex-grow: 0; } - .flex-grow-1-l { flex-grow: 1; } - .flex-shrink-0-l { flex-shrink: 0; } - .flex-shrink-1-l { flex-shrink: 1; } - .fl-l { float: left; _display: inline; } - .fr-l { float: right; _display: inline; } - .fn-l { float: none; } - .i-l { font-style: italic; } - .fs-normal-l { font-style: normal; } - .normal-l { font-weight: normal; } - .b-l { font-weight: bold; } - .fw1-l { font-weight: 100; } - .fw2-l { font-weight: 200; } - .fw3-l { font-weight: 300; } - .fw4-l { font-weight: 400; } - .fw5-l { font-weight: 500; } - .fw6-l { font-weight: 600; } - .fw7-l { font-weight: 700; } - .fw8-l { font-weight: 800; } - .fw9-l { font-weight: 900; } - .h1-l { height: 1rem; } - .h2-l { height: 2rem; } - .h3-l { height: 4rem; } - .h4-l { height: 8rem; } - .h5-l { height: 16rem; } - .h-25-l { height: 25%; } - .h-50-l { height: 50%; } - .h-75-l { height: 75%; } - .h-100-l { height: 100%; } - .min-h-100-l { min-height: 100%; } - .vh-25-l { height: 25vh; } - .vh-50-l { height: 50vh; } - .vh-75-l { height: 75vh; } - .vh-100-l { height: 100vh; } - .min-vh-100-l { min-height: 100vh; } - .h-auto-l { height: auto; } - .h-inherit-l { height: inherit; } - .tracked-l { letter-spacing: .1em; } - .tracked-tight-l { letter-spacing: -.05em; } - .tracked-mega-l { letter-spacing: .25em; } - .lh-solid-l { line-height: 1; } - .lh-title-l { line-height: 1.25; } - .lh-copy-l { line-height: 1.5; } - .mw-100-l { max-width: 100%; } - .mw1-l { max-width: 1rem; } - .mw2-l { max-width: 2rem; } - .mw3-l { max-width: 4rem; } - .mw4-l { max-width: 8rem; } - .mw5-l { max-width: 16rem; } - .mw6-l { max-width: 32rem; } - .mw7-l { max-width: 48rem; } - .mw8-l { max-width: 64rem; } - .mw9-l { max-width: 96rem; } - .mw-none-l { max-width: none; } - .w1-l { width: 1rem; } - .w2-l { width: 2rem; } - .w3-l { width: 4rem; } - .w4-l { width: 8rem; } - .w5-l { width: 16rem; } - .w-10-l { width: 10%; } - .w-20-l { width: 20%; } - .w-25-l { width: 25%; } - .w-30-l { width: 30%; } - .w-33-l { width: 33%; } - .w-34-l { width: 34%; } - .w-40-l { width: 40%; } - .w-50-l { width: 50%; } - .w-60-l { width: 60%; } - .w-70-l { width: 70%; } - .w-75-l { width: 75%; } - .w-80-l { width: 80%; } - .w-90-l { width: 90%; } - .w-100-l { width: 100%; } - .w-third-l { width: 33.33333%; } - .w-two-thirds-l { width: 66.66667%; } - .w-auto-l { width: auto; } - .overflow-visible-l { overflow: visible; } - .overflow-hidden-l { overflow: hidden; } - .overflow-scroll-l { overflow: scroll; } - .overflow-auto-l { overflow: auto; } - .overflow-x-visible-l { overflow-x: visible; } - .overflow-x-hidden-l { overflow-x: hidden; } - .overflow-x-scroll-l { overflow-x: scroll; } - .overflow-x-auto-l { overflow-x: auto; } - .overflow-y-visible-l { overflow-y: visible; } - .overflow-y-hidden-l { overflow-y: hidden; } - .overflow-y-scroll-l { overflow-y: scroll; } - .overflow-y-auto-l { overflow-y: auto; } - .static-l { position: static; } - .relative-l { position: relative; } - .absolute-l { position: absolute; } - .fixed-l { position: fixed; } - .rotate-45-l { -webkit-transform: rotate( 45deg ); transform: rotate( 45deg ); } - .rotate-90-l { -webkit-transform: rotate( 90deg ); transform: rotate( 90deg ); } - .rotate-135-l { -webkit-transform: rotate( 135deg ); transform: rotate( 135deg ); } - .rotate-180-l { -webkit-transform: rotate( 180deg ); transform: rotate( 180deg ); } - .rotate-225-l { -webkit-transform: rotate( 225deg ); transform: rotate( 225deg ); } - .rotate-270-l { -webkit-transform: rotate( 270deg ); transform: rotate( 270deg ); } - .rotate-315-l { -webkit-transform: rotate( 315deg ); transform: rotate( 315deg ); } - .pa0-l { padding: 0; } - .pa1-l { padding: .25rem; } - .pa2-l { padding: .5rem; } - .pa3-l { padding: 1rem; } - .pa4-l { padding: 2rem; } - .pa5-l { padding: 4rem; } - .pa6-l { padding: 8rem; } - .pa7-l { padding: 16rem; } - .pl0-l { padding-left: 0; } - .pl1-l { padding-left: .25rem; } - .pl2-l { padding-left: .5rem; } - .pl3-l { padding-left: 1rem; } - .pl4-l { padding-left: 2rem; } - .pl5-l { padding-left: 4rem; } - .pl6-l { padding-left: 8rem; } - .pl7-l { padding-left: 16rem; } - .pr0-l { padding-right: 0; } - .pr1-l { padding-right: .25rem; } - .pr2-l { padding-right: .5rem; } - .pr3-l { padding-right: 1rem; } - .pr4-l { padding-right: 2rem; } - .pr5-l { padding-right: 4rem; } - .pr6-l { padding-right: 8rem; } - .pr7-l { padding-right: 16rem; } - .pb0-l { padding-bottom: 0; } - .pb1-l { padding-bottom: .25rem; } - .pb2-l { padding-bottom: .5rem; } - .pb3-l { padding-bottom: 1rem; } - .pb4-l { padding-bottom: 2rem; } - .pb5-l { padding-bottom: 4rem; } - .pb6-l { padding-bottom: 8rem; } - .pb7-l { padding-bottom: 16rem; } - .pt0-l { padding-top: 0; } - .pt1-l { padding-top: .25rem; } - .pt2-l { padding-top: .5rem; } - .pt3-l { padding-top: 1rem; } - .pt4-l { padding-top: 2rem; } - .pt5-l { padding-top: 4rem; } - .pt6-l { padding-top: 8rem; } - .pt7-l { padding-top: 16rem; } - .pv0-l { padding-top: 0; padding-bottom: 0; } - .pv1-l { padding-top: .25rem; padding-bottom: .25rem; } - .pv2-l { padding-top: .5rem; padding-bottom: .5rem; } - .pv3-l { padding-top: 1rem; padding-bottom: 1rem; } - .pv4-l { padding-top: 2rem; padding-bottom: 2rem; } - .pv5-l { padding-top: 4rem; padding-bottom: 4rem; } - .pv6-l { padding-top: 8rem; padding-bottom: 8rem; } - .pv7-l { padding-top: 16rem; padding-bottom: 16rem; } - .ph0-l { padding-left: 0; padding-right: 0; } - .ph1-l { padding-left: .25rem; padding-right: .25rem; } - .ph2-l { padding-left: .5rem; padding-right: .5rem; } - .ph3-l { padding-left: 1rem; padding-right: 1rem; } - .ph4-l { padding-left: 2rem; padding-right: 2rem; } - .ph5-l { padding-left: 4rem; padding-right: 4rem; } - .ph6-l { padding-left: 8rem; padding-right: 8rem; } - .ph7-l { padding-left: 16rem; padding-right: 16rem; } - .ma0-l { margin: 0; } - .ma1-l { margin: .25rem; } - .ma2-l { margin: .5rem; } - .ma3-l { margin: 1rem; } - .ma4-l { margin: 2rem; } - .ma5-l { margin: 4rem; } - .ma6-l { margin: 8rem; } - .ma7-l { margin: 16rem; } - .ml0-l { margin-left: 0; } - .ml1-l { margin-left: .25rem; } - .ml2-l { margin-left: .5rem; } - .ml3-l { margin-left: 1rem; } - .ml4-l { margin-left: 2rem; } - .ml5-l { margin-left: 4rem; } - .ml6-l { margin-left: 8rem; } - .ml7-l { margin-left: 16rem; } - .mr0-l { margin-right: 0; } - .mr1-l { margin-right: .25rem; } - .mr2-l { margin-right: .5rem; } - .mr3-l { margin-right: 1rem; } - .mr4-l { margin-right: 2rem; } - .mr5-l { margin-right: 4rem; } - .mr6-l { margin-right: 8rem; } - .mr7-l { margin-right: 16rem; } - .mb0-l { margin-bottom: 0; } - .mb1-l { margin-bottom: .25rem; } - .mb2-l { margin-bottom: .5rem; } - .mb3-l { margin-bottom: 1rem; } - .mb4-l { margin-bottom: 2rem; } - .mb5-l { margin-bottom: 4rem; } - .mb6-l { margin-bottom: 8rem; } - .mb7-l { margin-bottom: 16rem; } - .mt0-l { margin-top: 0; } - .mt1-l { margin-top: .25rem; } - .mt2-l { margin-top: .5rem; } - .mt3-l { margin-top: 1rem; } - .mt4-l { margin-top: 2rem; } - .mt5-l { margin-top: 4rem; } - .mt6-l { margin-top: 8rem; } - .mt7-l { margin-top: 16rem; } - .mv0-l { margin-top: 0; margin-bottom: 0; } - .mv1-l { margin-top: .25rem; margin-bottom: .25rem; } - .mv2-l { margin-top: .5rem; margin-bottom: .5rem; } - .mv3-l { margin-top: 1rem; margin-bottom: 1rem; } - .mv4-l { margin-top: 2rem; margin-bottom: 2rem; } - .mv5-l { margin-top: 4rem; margin-bottom: 4rem; } - .mv6-l { margin-top: 8rem; margin-bottom: 8rem; } - .mv7-l { margin-top: 16rem; margin-bottom: 16rem; } - .mh0-l { margin-left: 0; margin-right: 0; } - .mh1-l { margin-left: .25rem; margin-right: .25rem; } - .mh2-l { margin-left: .5rem; margin-right: .5rem; } - .mh3-l { margin-left: 1rem; margin-right: 1rem; } - .mh4-l { margin-left: 2rem; margin-right: 2rem; } - .mh5-l { margin-left: 4rem; margin-right: 4rem; } - .mh6-l { margin-left: 8rem; margin-right: 8rem; } - .mh7-l { margin-left: 16rem; margin-right: 16rem; } - .na1-l { margin: -0.25rem; } - .na2-l { margin: -0.5rem; } - .na3-l { margin: -1rem; } - .na4-l { margin: -2rem; } - .na5-l { margin: -4rem; } - .na6-l { margin: -8rem; } - .na7-l { margin: -16rem; } - .nl1-l { margin-left: -0.25rem; } - .nl2-l { margin-left: -0.5rem; } - .nl3-l { margin-left: -1rem; } - .nl4-l { margin-left: -2rem; } - .nl5-l { margin-left: -4rem; } - .nl6-l { margin-left: -8rem; } - .nl7-l { margin-left: -16rem; } - .nr1-l { margin-right: -0.25rem; } - .nr2-l { margin-right: -0.5rem; } - .nr3-l { margin-right: -1rem; } - .nr4-l { margin-right: -2rem; } - .nr5-l { margin-right: -4rem; } - .nr6-l { margin-right: -8rem; } - .nr7-l { margin-right: -16rem; } - .nb1-l { margin-bottom: -0.25rem; } - .nb2-l { margin-bottom: -0.5rem; } - .nb3-l { margin-bottom: -1rem; } - .nb4-l { margin-bottom: -2rem; } - .nb5-l { margin-bottom: -4rem; } - .nb6-l { margin-bottom: -8rem; } - .nb7-l { margin-bottom: -16rem; } - .nt1-l { margin-top: -0.25rem; } - .nt2-l { margin-top: -0.5rem; } - .nt3-l { margin-top: -1rem; } - .nt4-l { margin-top: -2rem; } - .nt5-l { margin-top: -4rem; } - .nt6-l { margin-top: -8rem; } - .nt7-l { margin-top: -16rem; } - .strike-l { text-decoration: line-through; } - .underline-l { text-decoration: underline; } - .no-underline-l { text-decoration: none; } - .tl-l { text-align: left; } - .tr-l { text-align: right; } - .tc-l { text-align: center; } - .tj-l { text-align: justify; } - .ttc-l { text-transform: capitalize; } - .ttl-l { text-transform: lowercase; } - .ttu-l { text-transform: uppercase; } - .ttn-l { text-transform: none; } - .f-6-l, .f-headline-l { font-size: 6rem; } - .f-5-l, .f-subheadline-l { font-size: 5rem; } - .f1-l { font-size: 3rem; } - .f2-l { font-size: 2.25rem; } - .f3-l { font-size: 1.5rem; } - .f4-l { font-size: 1.25rem; } - .f5-l { font-size: 1rem; } - .f6-l { font-size: .875rem; } - .f7-l { font-size: .75rem; } - .measure-l { max-width: 30em; } - .measure-wide-l { max-width: 34em; } - .measure-narrow-l { max-width: 20em; } - .indent-l { text-indent: 1em; margin-top: 0; margin-bottom: 0; } - .small-caps-l { font-variant: small-caps; } - .truncate-l { white-space: nowrap; overflow: hidden; text-overflow: ellipsis; } - .center-l { margin-right: auto; margin-left: auto; } - .mr-auto-l { margin-right: auto; } - .ml-auto-l { margin-left: auto; } - .clip-l { position: fixed !important; _position: absolute !important; clip: rect( 1px 1px 1px 1px ); /* IE6, IE7 */ clip: rect( 1px, 1px, 1px, 1px ); } - .ws-normal-l { white-space: normal; } - .nowrap-l { white-space: nowrap; } - .pre-l { white-space: pre; } - .v-base-l { vertical-align: baseline; } - .v-mid-l { vertical-align: middle; } - .v-top-l { vertical-align: top; } - .v-btm-l { vertical-align: bottom; } -} - -/* Fixes for tachyons and pandoc code styles interactions */ -code span { - &.bn { border-style: inherit; border-width: inherit } /* BaseN */ - &.cf:before, &.cf:after { content: unset; display: inline; } - &.cf:after { clear: unset; } - &.cf { zoom: inherit; } - &.cn { clear: unset; } - &.dt { display: inline; } - &.fl { float: unset; } -} \ No newline at end of file diff --git a/site/site_libs/quarto-contrib/validbeck-sidebar-slim-1.0.4/sidebar-slim.css b/site/site_libs/quarto-contrib/validbeck-sidebar-slim-1.0.4/sidebar-slim.css deleted file mode 100644 index 7e1897296..000000000 --- a/site/site_libs/quarto-contrib/validbeck-sidebar-slim-1.0.4/sidebar-slim.css +++ /dev/null @@ -1,89 +0,0 @@ -/* - Collapsible site sidebar: narrows to a slim vertical strip (desktop). - Activated when sidebar-slim metadata is true (see sidebar-slim.lua). -*/ - -@media (min-width: 992px) { - .quarto-sidebar-slim-toolbar { - display: flex; - align-items: center; - justify-content: flex-end; - gap: 0.25rem; - padding: 0.35rem 0.4rem 0.45rem; - flex-shrink: 0; - } - - body.quarto-sidebar-slim-enabled #quarto-sidebar.sidebar { - display: flex; - flex-direction: column; - transition: - width 0.22s ease, - min-width 0.22s ease, - max-width 0.22s ease, - flex 0.22s ease; - } - - body.quarto-sidebar-slim-enabled #quarto-sidebar .quarto-sidebar-slim-toolbar { - order: -1; - } - - /* - Collapsed: narrow the sidebar element inside its grid cell only — do not change - grid-column on the sidebar or main content, so the article column does not widen. - */ - body.quarto-sidebar-slim-enabled.quarto-sidebar-slim-collapsed - #quarto-sidebar.sidebar { - width: 100% !important; - max-width: 2.75rem !important; - min-width: 0 !important; - justify-self: start; - overflow: hidden; - } - - /* - #quarto-sidebar is

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