From 17f26e56f4afebda9850a872d0a876d5d1ecc875 Mon Sep 17 00:00:00 2001 From: Luis Herrmann Date: Thu, 30 Jul 2026 01:39:14 +0200 Subject: [PATCH 1/9] Bump version to 1.0.0 for first public release Co-Authored-By: Claude Sonnet 5 --- .gitignore | 1 + pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index ff1b4f4..d38d21d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ *__pycache__ *.pyc +dist/ data/* !*.gitkeep diff --git a/pyproject.toml b/pyproject.toml index 70b1270..9b61868 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "udm" -version = "0.2.0" +version = "1.0.0" description = "DataModule used for unified access to UKBB data across BIH projects." authors = [ "Luis Herrmann ", From 7719518d4e699a478da461f6bd117f0387325ba8 Mon Sep 17 00:00:00 2001 From: Luis Herrmann Date: Thu, 30 Jul 2026 02:53:17 +0200 Subject: [PATCH 2/9] Minor improvements to README.md. --- README.md | 63 ++++++++++++++++++++++++++++--------------------------- 1 file changed, 32 insertions(+), 31 deletions(-) diff --git a/README.md b/README.md index eff702b..aed8611 100644 --- a/README.md +++ b/README.md @@ -21,11 +21,11 @@ The documentation is available at [luisherrmann.github.io/udm](https://luisherrm ### Installation from GitHub --- -You can install the package with `pip` directly from github: +You can install the package with `pip` directly from GitHub: ``` pip install git+https://:@github.com/luisherrmann/udm.git#egg=udm --extra-index-url https://download.pytorch.org/whl/cu116 ``` -Note that the `--extra-index-url` parameter is required to install the correct pytorch dependencies. +Note that the `--extra-index-url` parameter is required to install the correct PyTorch dependencies. If you are using `poetry` for dependency management, you can also install the package by adding these lines @@ -40,18 +40,18 @@ secondary = true udm = {git = "https://:@github.com/luisherrmann/udm.git"} ``` -to the `pyproject.toml` of your project, and running either `poetry install` or `poetry update` in your project. where `` should be replaced with your GitHub username and `` with your private access token. +to the `pyproject.toml` of your project, and run either `poetry install` or `poetry update` in your project, where `` should be replaced with your GitHub username and `` with your private access token. ### Installation from local source --- -If you want to install the UDM a local source, activate the Python environment where you wish to install the UDM (e.g. if you are managing your environments with `conda`, use `conda activate ` to activate the environment) and install the UDM using +If you want to install the UDM from a local source, activate the Python environment where you wish to install the UDM (e.g. if you are managing your environments with `conda`, use `conda activate ` to activate the environment) and install the UDM using ``` bash pip install -e ``` -It is highly recommended that you install in development mode (i.e. providing the `-e` editable-flag), since it may be necessary to introduce additions or modifications for your own project, requiring the package to be editable. +It is highly recommended that you install in development mode (i.e. providing the `-e` editable flag), since it may be necessary to introduce additions or modifications for your own project, requiring the package to be editable. If you are using `poetry` for dependency and environment management, include the following line @@ -67,13 +67,13 @@ in your `pyproject.toml` and install or update the dependencies. ### Post installation --- -If your project is already using hydra configs, it is recommended that you use the hydra configs in the `config` folder in a starting point for building your own hydra configs by cloning the config folder into your respective `config` subfolder. For instance, in the OGM/Umbrella project, the config templates of the UDM were included through +If your project is already using Hydra configs, it is recommended that you use the Hydra configs in the `config` folder as a starting point for building your own Hydra configs by cloning the config folder into your respective `config` subfolder. For instance, in the OGM/Umbrella project, the config templates of the UDM were included through ``` bash cp -r config umbrella/config/run/datamodule ``` -so the config of the UDM becomes a subconfig `datamodule` of the `run` config. +so the config of the UDM becomes a `datamodule` subconfig of the `run` config. ## Overview: How it works --- @@ -86,13 +86,13 @@ The UDM relies on the following three classes 2. `GeneralDataset` 3. `GeneralDatamodule` -Additional helper classes are provide to enable +Additional helper classes are provided to enable 4. Transforms (`PluginTransform`, `DatasetTransform`) 5. Filtering (`PluginRowFilter`, `PluginColFilter` and more) ### 1. DataPlugin -The `DataPlugin` class is an abstract class that defines the general interface for interacting with different data modalities. The idea is that for each data modality or each way of interacting with the data, there should be a class extending from `DataPlugin`. For instance there could be a `GeneticPlugin`, `CovariatePlugin`, ... class implemeting the interface of `DataPlugin` and providing additional methods specfic to the respective data modality. There is also a pre-existing generic class `TabularPlugin` which can be used for reading generic tabular data from a `.feather` file. +The `DataPlugin` class is an abstract class that defines the general interface for interacting with different data modalities. The idea is that for each data modality or each way of interacting with the data, there should be a class extending from `DataPlugin`. For instance, there could be a `GeneticPlugin`, `CovariatePlugin`, ... class implementing the interface of `DataPlugin` and providing additional methods specific to the respective data modality. There is also a pre-existing generic class `TabularPlugin` which can be used for reading generic tabular data from a `.feather` file. The `__getitem__()` method is called with an eid of the `DataPlugin` instance and an optional dictionary of feature selections and returns the respective data sample corresponding to that eid for the features provided. @@ -101,18 +101,18 @@ An important aspect to keep in mind is that the eids between different applicati 1. ***native eids*** are ids that are native to the application used by the `DataPlugin`. 2. ***master eids*** are the ids that are used to retrieve elements from the `DataPlugin`. -The user needs to ensure that all `DataPlugin`s use the same ***master eids***. If they use different ***native eids***, one set of eids are taken as the master eids and the `DataPlugin`s using different native eids need to be provided with a `.csv` mapping file to map native eids to master eids. This can be done using the `eid_map_path` option to specify the mapping file, as well as `eid_map_from` and `eid_map_to` to specify the columns containing the native and master eids, respectively. +The user needs to ensure that all `DataPlugin`s use the same ***master eids***. If they use different ***native eids***, one set of eids is taken as the master eids and the `DataPlugin`s using different native eids need to be provided with a `.csv` mapping file to map native eids to master eids. This can be done using the `eid_map_path` option to specify the mapping file, as well as `eid_map_from` and `eid_map_to` to specify the columns containing the native and master eids, respectively. -If the user needs to retrieve metadata from the dataplugin, this can be done using the `get_metadata()` method. This method should provide at least the following info: +If the user needs to retrieve metadata from the `DataPlugin`, this can be done using the `get_metadata()` method. This method should provide at least the following info: 1. `'features'`: A list of the feature names of the data controlled by the `DataPlugin`. -2. `feature_types`: A list of the data type used by the respective feature names. +2. `'feature_types'`: A list of the data types used by the respective feature names. 3. `'eids'`: A list of all the master eids controlled by this `DataPlugin`. -4. `'tags'`: A list of strings that can be used to tag `DataPlugin` for later identification and metadata aggregation across multiple `DataPlugin` instances. These are passed to the `DataPlugin` during initialization. +4. `'tags'`: A list of strings that can be used to tag the `DataPlugin` for later identification and metadata aggregation across multiple `DataPlugin` instances. These are passed to the `DataPlugin` during initialization. ### 2. GeneralDataset -The `GeneralDataset` class extends from the torch `Dataset` (`torch.utils.data`) and allows for the creation of a dataset from which to build a `DataLoader`. It expects a list of (optionally named) `DataPlugin` instances and a list of eids to use from those plugins. For instance, given +The `GeneralDataset` class extends from the PyTorch `Dataset` (`torch.utils.data`) and allows for the creation of a dataset from which to build a `DataLoader`. It expects a list of (optionally named) `DataPlugin` instances and a list of eids to use from those plugins. For instance, given ``` python >>> plugins = { @@ -121,7 +121,7 @@ The `GeneralDataset` class extends from the torch `Dataset` (`torch.utils.data`) ... } ``` -containing data for hypothetical eids `[1, 2, ..., 100]`, one could define datasets representing train-, validation- and test-split through `GeneralDataset` instances: +containing data for hypothetical eids `[1, 2, ..., 100]`, one could define datasets representing train, validation, and test splits through `GeneralDataset` instances: ``` python >>> train_ds = GeneralDataset(plugins, eids=[0, ..., 80], ...) @@ -165,7 +165,7 @@ The `get_metadata()` method returns metadata of the respective `DataPlugins` as ### 3. GeneralDatamodule -The `GeneralDatamodule` extends from the `LightningDatamodule` of Pytorch Lightning. It is initialized with a config of plugins, as well as mapping of eids denoting he respective splits. For instance, a `GeneralDatamodule` using the `CovariatePlugin` and the `GeneticPlugin` might be initialized by something like +The `GeneralDatamodule` extends from the `LightningDatamodule` of PyTorch Lightning. It is initialized with a config of plugins, as well as a mapping of eids denoting the respective splits. For instance, a `GeneralDatamodule` using the `CovariatePlugin` and the `GeneticPlugin` might be initialized by something like ``` python datamodule = GeneralDatamodule( @@ -190,19 +190,19 @@ datamodule = GeneralDatamodule( ``` -Ideally, the eids of all the plugins should align. However, when they do not, eids available from the DataPlugin will be obtained through `intersect` or through `union` operation of the respective eid sets (the behaviour is controlled by the `combine_eids_as` parameter). +Ideally, the eids of all the plugins should align. However, when they do not, eids available from the `DataPlugin` will be obtained through an `intersect` or `union` operation of the respective eid sets (the behaviour is controlled by the `combine_eids_as` parameter). Another thing to take into account is that the `GeneralDatamodule` supports passing of multiple splits. The respective datasets will all be set during the setup of the module. The splits to be used for training, validation and testing can be reassigned at any point during the lifecycle of the UDM. However, you will have to rerun `prepare_data()` and `setup()` for the new splits to be used rather than the old ones. ### 4. Transforms -All `DataPlugin` and `GeneralDataset` subclasses can be instantiated with additional transformations that are applied on top of the data of the `DataPlugin` or `GeneralDataset` every time an single element (or batch thereof) is sampled from the respective instance. The `DataPlugin` needs to be provided with an instance of `PluginTransform`, while a `GeneralDataset` must be provided with a `DatasetTransform` at initialization. +All `DataPlugin` and `GeneralDataset` subclasses can be instantiated with additional transformations that are applied on top of the data of the `DataPlugin` or `GeneralDataset` every time a single element (or batch thereof) is sampled from the respective instance. The `DataPlugin` needs to be provided with an instance of `PluginTransform`, while a `GeneralDataset` must be provided with a `DatasetTransform` at initialization. These transforms can be understood as follows: - `TensorTransform` -A function that takes a tensor as input and returns a tensor as output `torch.Tensor -> torch.Tensor`. You can use the `get_transform()` function from the `transforms.tensor_transforms.factory` package to instantiate transformations from hydra configurations. By default, all transforms from `torchvision.transforms` and `torch.nn.functional` are also supported. +A function that takes a tensor as input and returns a tensor as output `torch.Tensor -> torch.Tensor`. You can use the `get_transform()` function from the `transforms.tensor_transforms.factory` package to instantiate transformations from Hydra configurations. By default, all transforms from `torchvision.transforms` and `torch.nn.functional` are also supported. For example: ```python from omegaconf import DictConfig @@ -219,7 +219,7 @@ data_ = transform(data) ``` - `PluginTransform` -A mapping of component names to `TensorTransform` instances to be applied to the respective component. For instance, the `EHRPlugin` could be equipped with a single transformation `{records: cnormalize}`, where `cnormalize = lambda x: normalize(x, mean=(0.0, 0.5), std=(0.278, 1.023)`. Every time an element gets sampled from the dataplugin, the tensor of component `records` is modified by the `cnormalize` function. +A mapping of component names to `TensorTransform` instances to be applied to the respective component. For instance, the `EHRPlugin` could be equipped with a single transformation `{records: cnormalize}`, where `cnormalize = lambda x: normalize(x, mean=(0.0, 0.5), std=(0.278, 1.023))`. Every time an element gets sampled from the `DataPlugin`, the tensor of component `records` is modified by the `cnormalize` function. For example: ```python data = { @@ -237,14 +237,14 @@ data_ = transform(data) ``` - `DatasetTransform` -A mapping of plugin names to `PluginTransform` instances to be applied to the respective `DataPlugin`s when sampling. Every time an element gets sampled from the dataset, the tensor of every plugin is modified by the respective `PluginTransform`. Note that if a `DataPlugin` `plugin1`, controlled by the dataset `ds` was initialized with a `PluginTransform` `plugin1_trafo`, then the transformation will be applied in any dataset controlling `plugin1`. If a dataset `ds` has an own plugin `ds_plugin1_trafo`, then sampling an original element `x` from `ds` will see lead transformations `x[plugin1] -> plugin1_trafo -> ds_plugin1_trafo` of the original output. +A mapping of plugin names to `PluginTransform` instances to be applied to the respective `DataPlugin`s when sampling. Every time an element gets sampled from the dataset, the tensor of every plugin is modified by the respective `PluginTransform`. Note that if a `DataPlugin` `plugin1`, controlled by the dataset `ds`, was initialized with a `PluginTransform` `plugin1_trafo`, then the transformation will be applied in any dataset controlling `plugin1`. If a dataset `ds` has its own plugin transform `ds_plugin1_trafo`, then sampling an original element `x` from `ds` will lead to transformations `x[plugin1] -> plugin1_trafo -> ds_plugin1_trafo` of the original output. ### 5. Filtering -All `DataPlugin` instances can be provided with an instance of `PluginRowFilter` or `PluginColFilter` to filter rows or columns of the data, respectively. The filter will be applied once to the entire dataset during the `setup()` of the respective plugin, and the dataset will only keep rows and filters which satisfy the filtering condition (assuming a non-empty `row_filter` or `col_filter` argument is provided). Subsequent sampling of filtered eids will cause a `KeyError`, as will the selection of filtered columns. The hierarchy of filters works as follows: +All `DataPlugin` instances can be provided with an instance of `PluginRowFilter` or `PluginColFilter` to filter rows or columns of the data, respectively. The filter will be applied once to the entire dataset during the `setup()` of the respective plugin, and the dataset will only keep rows and columns which satisfy the filtering condition (assuming a non-empty `row_filter` or `col_filter` argument is provided). Subsequent sampling of filtered eids will cause a `KeyError`, as will the selection of filtered columns. The hierarchy of filters works as follows: - `Filter` -A filter is essentially a function instantiated with certain parameters which can be **called with a 2D tensor** $X$ **of shape** $M \times N$, and which **returns a boolean tensor** mask $\mu$ of length $M$, where $\mu_i$ indicates whether row $X_{i,:}$ should be kept or not. Column filtering for 2D tensor $X$ can be handled analogously by calling a filter with $X^T$. +A filter is essentially a function instantiated with certain parameters which can be **called with a 2D tensor** $X$ **of shape** $M \times N$, and which **returns a boolean tensor** mask $\mu$ of length $M$, where $\mu_i$ indicates whether row $X_{i,:}$ should be kept or not. Column filtering for a 2D tensor $X$ can be handled analogously by calling a filter with $X^T$. For example: ```python v_filter = AnyNan() @@ -312,7 +312,7 @@ mask = row_filter(eids, data) ``` - `PluginColFilter` -Instantiated with an optional mapping of component name to `KeyFilter`, and an optional mapping of component name to `Filter`. The key filters and value filters are applied to each component of the provided data separately and the filter returns a dictionary mapping components to masks to be applied to each component separately. Only columns satisfying their respective `KeyFilter` and `Filter` are marked to preservation. +Instantiated with an optional mapping of component name to `KeyFilter`, and an optional mapping of component name to `Filter`. The key filters and value filters are applied to each component of the provided data separately and the filter returns a dictionary mapping components to masks to be applied to each component separately. Only columns satisfying their respective `KeyFilter` and `Filter` are marked for preservation. Consider the following example: ```python features = { @@ -352,7 +352,7 @@ masks = col_filters(data) # } ``` -**NOTE**: Some of the plugins, such as the `H5adPlugin` had their own systems for filtering columns in place (e.g `usecols` and `dropcols` arguments). These individual filtering systems are still in place for backward compatibility reasons, but will eventually be removed. +**NOTE**: Some of the plugins, such as the `H5adPlugin`, had their own systems for filtering columns in place (e.g. `usecols` and `dropcols` arguments). These individual filtering systems are still in place for backward compatibility reasons, but will eventually be removed. ## Extending the UDM --- @@ -375,7 +375,8 @@ class ProteomicsPlugin(DataPlugin): ... ``` -which extends from `DataPlugin` that enables the use of proteomics data, and that the code for this class is in a file called `proteomics_plugin.py`. Following Python convention, the source code for each DataPlugin subclass should be in a file containing no other classes than the subclass itself, and the classname should be written in camel case (e.g. `ProteomicsPlugin`), while the file name should be written in snake case (e.g. `proteomics_plugin`). NOTE: Please make sure the name of the plugin does not match the name of any other pre-existing DataPlugin in the project! +which extends from `DataPlugin`, enables the use of proteomics data, and has its code in a file called `proteomics_plugin.py`. Following Python convention, the source code for each `DataPlugin` subclass should be in a file containing no other classes than the subclass itself, and the class name should be written in camel case (e.g. `ProteomicsPlugin`), while the file name should be written in snake case (e.g. `proteomics_plugin`). +**NOTE**: Please make sure the name of the plugin does not match the name of any other pre-existing `DataPlugin` in the project! To include this DataPlugin in the repository, you could put the file `proteomics_plugin.py` in a subdirectory of `plugins` like so: @@ -405,7 +406,7 @@ Or better yet: └── ... ``` -Arbitrary levels of nesting are possible, as the GeneralDatamoulde will automatically discover all subclasses of `DataPlugin` within the `plugins` package. However, we encourage you to apply a low amount of nesting to keep a clean directory structure. +Arbitrary levels of nesting are possible, as the `GeneralDatamodule` will automatically discover all subclasses of `DataPlugin` within the `plugins` package. However, we encourage you to use a low amount of nesting to keep a clean directory structure. ### 2. Adding config files --- @@ -420,9 +421,9 @@ src_path: memmap: false ``` -i.e. the fields `name` giving the name of the class, the field `__init__` giving the method by which to initialize an instance of the class and default values for the arguments to be passed to the init function of the class. The first two values are mandatory, because they are required by the `DataModule` to know which DataPlugins to prepare, and what method to prepare for the initialization. By default, the initialization method will be the regular `__init__` method, but in some cases it may be useful to define different `__init__` methods for different ways of initializing the DataPlugin for interfacing with the respective data. +i.e. the field `name` gives the name of the class, the field `__init__` gives the method by which to initialize an instance of the class, and the remaining fields give default values for the arguments to be passed to the init function of the class. The first two values are mandatory, because they are required by the `DataModule` to know which `DataPlugin`s to prepare, and what method to use for the initialization. By default, the initialization method will be the regular `__init__` method, but in some cases it may be useful to define different `__init__` methods for different ways of initializing the `DataPlugin` for interfacing with the respective data. -For example, let's say the proteomics data to be accessed through the `ProteomicsPlugin` can also be retrieved from a database. Then, a good pattern would be to enable the `ProteomicsPlugin` to be initialized through another method `from_db()`, where database connection arguments are provided. It would be recommendable to have a separate default configuration for this case, e.g.: +For example, let's say the proteomics data to be accessed through the `ProteomicsPlugin` can also be retrieved from a database. Then, a good pattern would be to enable the `ProteomicsPlugin` to be initialized through another method `from_db()`, where database connection arguments are provided. It would be recommended to have a separate default configuration for this case, e.g.: #### *`from_db.yaml`* ``` yaml @@ -433,7 +434,7 @@ db_pass: locked db_table: ukbb_processed ``` -Putting everything together, these two config files should be placed in a subdirectory of configs, preferrably mirroring the directory structure of `udm/plugins`, like so: +Putting everything together, these two config files should be placed in a subdirectory of configs, preferably mirroring the directory structure of `udm/plugins`, like so: ``` . @@ -454,7 +455,7 @@ Putting everything together, these two config files should be placed in a subdir └── ... ``` -And that's it, you can now create DataModule configurations +And that's it, you can now create your own DataModule configurations. ### 3. Add tests From 2733b0706b923982ee520ea15ed2fc12bceadcaf Mon Sep 17 00:00:00 2001 From: Luis Herrmann Date: Thu, 30 Jul 2026 02:53:17 +0200 Subject: [PATCH 3/9] Minor improvements to README.md. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index aed8611..f4b3630 100644 --- a/README.md +++ b/README.md @@ -457,7 +457,7 @@ Putting everything together, these two config files should be placed in a subdir And that's it, you can now create your own DataModule configurations. -### 3. Add tests +### 3. Adding tests In order to ensure that your `DataPlugin` subclass works properly, it is highly encouraged that you write unit tests to check that your plugin works as intended on small test datasets. From 59d3ceb90dc1d31440ac4d9f4470ccc554cdf048 Mon Sep 17 00:00:00 2001 From: Luis Herrmann Date: Thu, 30 Jul 2026 09:38:29 +0200 Subject: [PATCH 4/9] Update README.md header, outdated reference to private token, and installation instructions. --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index f4b3630..7a31c2a 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,9 @@ [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) -![UDM logo](res/img/logo.svg) +

+ UDM logo +

# UDM - Unified Data Module @@ -23,7 +25,7 @@ The documentation is available at [luisherrmann.github.io/udm](https://luisherrm You can install the package with `pip` directly from GitHub: ``` -pip install git+https://:@github.com/luisherrmann/udm.git#egg=udm --extra-index-url https://download.pytorch.org/whl/cu116 +pip install "udm @ git+https://github.com/luisherrmann/udm.git" --extra-index-url https://download.pytorch.org/whl/cu116 ``` Note that the `--extra-index-url` parameter is required to install the correct PyTorch dependencies. @@ -37,10 +39,10 @@ url = "https://download.pytorch.org/whl/cu116" secondary = true [tool.poetry.dependencies] -udm = {git = "https://:@github.com/luisherrmann/udm.git"} +udm = {git = "https://github.com/luisherrmann/udm.git"} ``` -to the `pyproject.toml` of your project, and run either `poetry install` or `poetry update` in your project, where `` should be replaced with your GitHub username and `` with your private access token. +to the `pyproject.toml` of your project, and run either `poetry install` or `poetry update` in your project. ### Installation from local source --- From 336a1203c1e0b2d6dccc943fa6011c59fdb7c914 Mon Sep 17 00:00:00 2001 From: Luis Herrmann Date: Sat, 1 Aug 2026 01:08:39 +0200 Subject: [PATCH 5/9] Fix broken factory get_transform(). --- udm/transforms/tensor_transforms/factory.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/udm/transforms/tensor_transforms/factory.py b/udm/transforms/tensor_transforms/factory.py index 8ddf9e4..e8a3449 100644 --- a/udm/transforms/tensor_transforms/factory.py +++ b/udm/transforms/tensor_transforms/factory.py @@ -1,5 +1,6 @@ from typing import Callable from abc import ABC +from functools import partial from omegaconf import OmegaConf, DictConfig from inspect import isabstract, isclass, isroutine import torch @@ -157,7 +158,7 @@ def get_transform(trafo_config: DictConfig, custom={}): trafo_config_subset = { k: v for k, v in trafo_config.items() if k in KEY_SUBSET } - transform = lambda tensor: module(tensor, **trafo_config_subset) + transform = partial(module, **trafo_config_subset) return transform else: raise KeyError(f"{trafo_config['name']} is not a supported transform!") From 1890042b91fede64121641c1beacaebf7a8df9a0 Mon Sep 17 00:00:00 2001 From: Luis Herrmann Date: Sun, 2 Aug 2026 00:37:16 +0200 Subject: [PATCH 6/9] Fix docs workflow. --- .github/workflows/docs.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 7c1a641..b4b6208 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -3,6 +3,8 @@ on: workflow_dispatch: {} push: branches: [main] # branch to trigger deployment +permissions: + contents: write jobs: build: runs-on: ubuntu-latest From 1ffbb135d20e84306ebe4b6b8bcc7dab328897ab Mon Sep 17 00:00:00 2001 From: Luis Herrmann Date: Sat, 1 Aug 2026 03:11:37 +0200 Subject: [PATCH 7/9] Add clean fake datasets and tests. --- .github/workflows/test.yml | 2 +- config/plugins/genetic/debug.yaml | 2 +- .../additional_pheno_extended_tr.h5ad.X.npy | Bin 0 -> 4224 bytes .../additional_pheno_extended_tr.h5ad.obs.csv | 2049 +++++++++++++++++ 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.../fake/pheno/cad_extended_tr.h5ad.X.npy | Bin 0 -> 4224 bytes .../fake/pheno/cad_extended_tr.h5ad.obs.csv | 2049 +++++++++++++++++ .../fake/pheno/cad_extended_tr.h5ad.var.csv | 2 + res/datasets/fake/pheno/cad_test.h5ad.X.npy | Bin 0 -> 2176 bytes res/datasets/fake/pheno/cad_test.h5ad.obs.csv | 1025 +++++++++ res/datasets/fake/pheno/cad_test.h5ad.var.csv | 2 + res/datasets/fake/pheno/cad_valid.h5ad.X.npy | Bin 0 -> 2176 bytes .../fake/pheno/cad_valid.h5ad.obs.csv | 1025 +++++++++ .../fake/pheno/cad_valid.h5ad.var.csv | 2 + res/datasets/fake/tf_target_motif.csv | 12 + test/__init__.py | 0 test/filters/__init__.py | 0 test/filters/composed_test.py | 344 +++ test/filters/factory_test.py | 72 + test/filters/plugin_filters_test.py | 532 +++++ test/filters/simple_test.py | 278 +++ test/general_datamodule_test.py | 1342 +++++++++++ test/general_dataset_test.py | 679 ++++++ test/integration/__init__.py | 0 test/integration/test_dummy.py | 3 + test/plugins/__init__.py | 0 test/plugins/covariates/__init__.py | 0 .../covariates/covariates_plugin_test.py | 511 ++++ test/plugins/dataplugin_test.py | 669 ++++++ test/plugins/ehr/__init__.py | 0 test/plugins/ehr/ehr_plugin_test.py | 1040 +++++++++ test/plugins/genetic/__init__.py | 0 test/plugins/genetic/genetic_plugin_test.py | 738 ++++++ .../plugins/genetic/genetic_ts_plugin_test.py | 734 ++++++ test/plugins/h5ad/__init__.py | 0 test/plugins/h5ad/h5ad_plugin_test.py | 755 ++++++ test/plugins/tabular/__init__.py | 0 test/plugins/tabular/tabular_plugin_test.py | 900 ++++++++ test/pytest.ini | 4 + test/transforms/__init__.py | 0 test/transforms/dict_transforms_test.py | 339 +++ test/transforms/tensor_transforms/__init__.py | 0 .../tensor_transforms/factory_test.py | 98 + test/utils.py | 71 + 125 files changed, 51619 insertions(+), 2 deletions(-) create mode 100644 res/datasets/fake/additional_pheno/additional_pheno_extended_tr.h5ad.X.npy create mode 100644 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res/datasets/fake/pheno/cad_valid.h5ad.obs.csv create mode 100644 res/datasets/fake/pheno/cad_valid.h5ad.var.csv create mode 100644 res/datasets/fake/tf_target_motif.csv create mode 100644 test/__init__.py create mode 100644 test/filters/__init__.py create mode 100644 test/filters/composed_test.py create mode 100644 test/filters/factory_test.py create mode 100644 test/filters/plugin_filters_test.py create mode 100644 test/filters/simple_test.py create mode 100644 test/general_datamodule_test.py create mode 100644 test/general_dataset_test.py create mode 100644 test/integration/__init__.py create mode 100644 test/integration/test_dummy.py create mode 100644 test/plugins/__init__.py create mode 100644 test/plugins/covariates/__init__.py create mode 100644 test/plugins/covariates/covariates_plugin_test.py create mode 100644 test/plugins/dataplugin_test.py create mode 100644 test/plugins/ehr/__init__.py create mode 100644 test/plugins/ehr/ehr_plugin_test.py create mode 100644 test/plugins/genetic/__init__.py create mode 100644 test/plugins/genetic/genetic_plugin_test.py create mode 100644 test/plugins/genetic/genetic_ts_plugin_test.py create mode 100644 test/plugins/h5ad/__init__.py create mode 100644 test/plugins/h5ad/h5ad_plugin_test.py create mode 100644 test/plugins/tabular/__init__.py create mode 100644 test/plugins/tabular/tabular_plugin_test.py create mode 100644 test/pytest.ini create mode 100644 test/transforms/__init__.py create mode 100644 test/transforms/dict_transforms_test.py create mode 100644 test/transforms/tensor_transforms/__init__.py create mode 100644 test/transforms/tensor_transforms/factory_test.py create mode 100644 test/utils.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 79e5ca6..2431278 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -26,7 +26,7 @@ jobs: version: 1.8.5 - name: Install dependencies and run tests run: | - export CI_CD=1 + export BIH_HPC=0 poetry install poetry run task coverage-test - name: Generate coverage badge diff --git a/config/plugins/genetic/debug.yaml b/config/plugins/genetic/debug.yaml index 8947334..4086187 100644 --- a/config/plugins/genetic/debug.yaml +++ b/config/plugins/genetic/debug.yaml @@ -1,6 +1,6 @@ name: GeneticPlugin __init__: __init__ -src: ['all_chrs_extended_tr.fincad100', 'all_chrs_valid.fincad100', 'all_chrs_test.fincad100'] +src: ['genetic_extended_tr', 'genetic_valid', 'genetic_test'] src_names: ['train', 'valid', 'test'] genetic_json: 'res/ukb_genetic.json' ontology: 'molecular_function' diff --git a/res/datasets/fake/additional_pheno/additional_pheno_extended_tr.h5ad.X.npy b/res/datasets/fake/additional_pheno/additional_pheno_extended_tr.h5ad.X.npy new file mode 100644 index 0000000000000000000000000000000000000000..130c2af5c020810d4efe4e5ccf9a8910523c3503 GIT binary patch literal 4224 zcmbW4=U~e2|CjGFV#3r>Rr#r7M@$)26-Vf11cneT(hxE? z_Z(J1iAG;vXCqz7{#9U0!4#6;6;}%4X!OC#`rtJBIz?;Pci>S@> 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b/res/datasets/fake/covariate/covariates_new_base_test.h5ad.var.csv new file mode 100644 index 0000000..0032efc --- /dev/null +++ b/res/datasets/fake/covariate/covariates_new_base_test.h5ad.var.csv @@ -0,0 +1,10 @@ +Unnamed: 0,id,Field,ValueType,Transformation +0,1,genetic_principal_components_f22009_0_1,float,"{""zscore"": {""std"": [2948.33267068], ""mean"": [-1.12466556]}}" +1,2,genetic_principal_components_f22009_0_2,float,"{""zscore"": {""std"": [776.16257928], ""mean"": [0.35880318]}}" +2,3,genetic_principal_components_f22009_0_3,float,"{""zscore"": {""std"": [221.79799481], ""mean"": [-0.08920376]}}" +3,4,genetic_principal_components_f22009_0_4,float,"{""zscore"": {""std"": [108.2274961], ""mean"": [0.12935057]}}" +4,5,genetic_principal_components_f22009_0_5,float,"{""zscore"": {""std"": [57.29823059], ""mean"": [0.06411575]}}" +5,6,age,float,"{""zscore"": {""std"": [65.83748872], ""mean"": [68.67234809]}}" +6,7,batch,uint8, +7,8,sex_f31_0_0_Female,uint8, 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b/res/datasets/fake/covariate/covariates_new_extended_categorical_extended_tr.h5ad.var.csv new file mode 100644 index 0000000..62c15c7 --- /dev/null +++ b/res/datasets/fake/covariate/covariates_new_extended_categorical_extended_tr.h5ad.var.csv @@ -0,0 +1,38 @@ +Unnamed: 0,id,Field,ValueType,Transformation +0,1,medications_drugs_for_acid_related_disorders,uint8, +1,2,medications_vitamins,uint8, +2,3,medications_diuretics,uint8, +3,4,medications_beta_blocking_agents,uint8, +4,5,medications_agents_acting_on_the_renin-angiotensin_system,uint8, +5,6,medications_lipid_modifying_agents,uint8, +6,7,medications_thyroid_therapy,uint8, +7,8,medications_antiinflammatory_and_antirheumatic_products,uint8, +8,9,medications_analgesics,uint8, +9,10,medications_psychoanaleptics,uint8, +10,11,medications_nasal_preparations,uint8, +11,12,medications_drugs_for_obstructive_airway_diseases,uint8, +12,13,medications_statins,uint8, +13,14,family_fh_alzheimer's_disease/dementia,uint8, +14,15,family_fh_bowel_cancer,uint8, +15,16,family_fh_breast_cancer,uint8, +16,17,family_fh_chronic_bronchitis/emphysema,uint8, +17,18,family_fh_diabetes,uint8, +18,19,family_fh_heart_disease,uint8, +19,20,family_fh_high_blood_pressure,uint8, +20,21,family_fh_lung_cancer,uint8, +21,22,family_fh_parkinson's_disease,uint8, +22,23,family_fh_severe_depression,uint8, +23,24,family_fh_stroke,uint8, +24,25,questionaire_overall_health_rating_f2178_0_0_Poor,uint8, +25,26,questionaire_overall_health_rating_f2178_0_0_Fair,uint8, +26,27,questionaire_overall_health_rating_f2178_0_0_Good,uint8, +27,28,questionaire_overall_health_rating_f2178_0_0_Excellent,uint8, +28,29,questionaire_smoking_status_f20116_0_0_Current,uint8, +29,30,questionaire_smoking_status_f20116_0_0_Previous,uint8, +30,31,questionaire_smoking_status_f20116_0_0_Never,uint8, +31,32,questionaire_alcohol_intake_frequency_f1558_0_0_Daily or almost daily,uint8, +32,33,questionaire_alcohol_intake_frequency_f1558_0_0_Three or four times a 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+6,6,TFAP2A,ENSG00000137203,ENSG00000003249,0,0,0,1,1 +7,7,TFAP2A,ENSG00000137203,ENSG00000003402,0,0,0,1,1 +8,8,TFAP2A,ENSG00000137203,ENSG00000003756,0,0,0,1,1 +9,9,TFAP2A,ENSG00000137203,ENSG00000004399,0,0,0,1,1 +10,10,TFAFAKE1,ENSG00000230368,ENSG00000162571,0,0,0,1,1 \ No newline at end of file diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/filters/__init__.py b/test/filters/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/filters/composed_test.py b/test/filters/composed_test.py new file mode 100644 index 0000000..a28279c --- /dev/null +++ b/test/filters/composed_test.py @@ -0,0 +1,344 @@ +import pytest +import numpy as np +import torch + +from udm.filters.simple import IsIn, AnyIn, HasNan +from udm.filters.composed import And, Or, Not + + +class TestAnd(object): + @pytest.mark.parametrize( + "filters,error", + [ + ([], Exception), + ([IsIn([0, 1, 2])], None), + ([IsIn([0, 1, 2]), IsIn([3, 4, 5])], None), + ([IsIn([0, 1, 2]), IsIn([3, 4, 5]), IsIn([6, 7, 8])], None), + ([IsIn([0, 1, 2]), HasNan()], None), + ], + ) + def test__init__(self, filters, error): + if error: + with pytest.raises(error): + and_filter = And(filters) + else: + and_filter = And(filters) + + @pytest.mark.parametrize( + "filters,val,exp_result", + [ + ([IsIn([0, 1, 2])], [0, 3], torch.tensor([True, False], dtype=bool)), + ( + [IsIn([0, 1]), IsIn([0, 2])], + [0, 1, 2, 3], + torch.tensor([True, False, False, False], dtype=bool), + ), + ( + [AnyIn(torch.tensor([0, 1])), Not(HasNan())], + torch.tensor([0.0, np.nan, 2.0]).reshape(3, 1), + torch.tensor([True, False, False], dtype=bool), + ), + ], + ) + def test__call__(self, filters, val, exp_result): + and_filter = And(filters) + result = and_filter(val) + assert torch.all(result == exp_result) + + @pytest.mark.parametrize( + "filters,vals,exp_result", + [ + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + ], + torch.tensor([False, True, False, False]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + torch.tensor([0.0, 0.0, 0.0, 0.0]).reshape(-1, 1), + ], + torch.tensor([False, True, True, False]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + torch.tensor([1.0, 1.0, 1.0, 1.0]).reshape(-1, 1), + ], + torch.tensor([True, True, True, True]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + torch.tensor([2.0, 2.0, 2.0, 2.0]).reshape(-1, 1), + ], + torch.tensor([True, True, False, False]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + torch.tensor([3.0, 4.0, 5.0, 6.0]).reshape(-1, 1), + ], + torch.tensor([False, True, False, False]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), Not(HasNan())], + [ + torch.tensor( + [0.0, 0.0, 0.0, 2.0, 2.0, 2.0, np.nan, np.nan, np.nan] + ).reshape(-1, 1), + torch.tensor( + [1.0, 3.0, np.nan, 1.0, 3.0, np.nan, 1.0, 3.0, np.nan] + ).reshape(-1, 1), + ], + torch.tensor( + [True, True, False, True, False, False, False, False, False], + dtype=bool, + ), + ), + ], + ) + def test_compute(self, filters, vals, exp_result): + and_filter = And(filters) + for v in vals: + and_filter.update(v) + result = and_filter.compute() + assert torch.all(result == exp_result) + + +class TestOr(object): + @pytest.mark.parametrize( + "filters,error", + [ + ([], Exception), + ([IsIn([0, 1, 2])], None), + ([IsIn([0, 1, 2]), IsIn([3, 4, 5])], None), + ([IsIn([0, 1, 2]), IsIn([3, 4, 5]), IsIn([6, 7, 8])], None), + ([IsIn([0, 1, 2]), HasNan()], None), + ], + ) + def test__init__(self, filters, error): + if error: + with pytest.raises(error): + and_filter = Or(filters) + else: + and_filter = Or(filters) + + @pytest.mark.parametrize( + "filters,val,exp_result", + [ + ([IsIn([0, 1, 2])], [0, 3], torch.tensor([True, False], dtype=bool)), + ( + [IsIn([0, 1]), IsIn([0, 2])], + [0, 1, 2, 3], + torch.tensor([True, True, True, False], dtype=bool), + ), + ( + [AnyIn(torch.tensor([0, 1])), HasNan()], + torch.tensor([0.0, 2.0, np.nan]).reshape(3, 1), + torch.tensor([True, False, True], dtype=bool), + ), + ], + ) + def test__call__(self, filters, val, exp_result): + or_filter = Or(filters) + result = or_filter(val) + assert torch.all(result == exp_result) + + @pytest.mark.parametrize( + "filters,vals,exp_result", + [ + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + ], + torch.tensor([True, True, True, False]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + torch.tensor([0.0, 0.0, 0.0, 0.0]).reshape(-1, 1), + ], + torch.tensor([True, True, True, True]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + torch.tensor([1.0, 1.0, 1.0, 1.0]).reshape(-1, 1), + ], + torch.tensor([True, True, True, True]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + torch.tensor([2.0, 2.0, 2.0, 2.0]).reshape(-1, 1), + ], + torch.tensor([True, True, True, True]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), AnyIn(torch.tensor([1.0, 2.0]))], + [ + torch.tensor([0.0, 1.0, 2.0, 3.0]).reshape(-1, 1), + torch.tensor([3.0, 4.0, 5.0, 6.0]).reshape(-1, 1), + ], + torch.tensor([True, True, True, False]), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), Not(HasNan())], + [ + torch.tensor([0.0, 0.0, 0.0]).reshape(-1, 1), + torch.tensor([1.0, 3.0, np.nan]).reshape(-1, 1), + ], + torch.tensor([True, True, True], dtype=bool), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), Not(HasNan())], + [ + torch.tensor([2.0, 2.0, 2.0]).reshape(-1, 1), + torch.tensor([1.0, 3.0, np.nan]).reshape(-1, 1), + ], + torch.tensor([True, True, False], dtype=bool), + ), + ( + [AnyIn(torch.tensor([0.0, 1.0])), Not(HasNan())], + [ + torch.tensor([np.nan, np.nan, np.nan]).reshape(-1, 1), + torch.tensor([1.0, 3.0, np.nan]).reshape(-1, 1), + ], + torch.tensor([True, False, False], dtype=bool), + ), + ], + ) + def test_compute(self, filters, vals, exp_result): + or_filter = Or(filters) + for v in vals: + or_filter.update(v) + result = or_filter.compute() + assert torch.all(result == exp_result) + + +class TestNot(object): + @pytest.mark.parametrize( + "filters,error", + [ + (None, Exception), + ([], Exception), + ([IsIn([0, 1, 2]), IsIn([0, 1, 2])], Exception), + (IsIn([0, 1, 2]), None), + (And([IsIn([0, 1, 2]), IsIn([3, 4, 5])]), None), + ], + ) + def test__init__(self, filters, error): + if error: + with pytest.raises(error): + not_filter = Not(filters) + else: + not_filter = Not(filters) + + @pytest.mark.parametrize( + "filters,val,exp_result", + [ + (IsIn([0, 1, 2]), [0, 3], torch.tensor([False, True], dtype=bool)), + ( + Not(IsIn([0, 1, 2])), + [0, 3], + torch.tensor([True, False], dtype=bool), + ), + ( + HasNan(), + torch.tensor([[0.0, 1.0], [np.nan, 2.0]]), + ~torch.tensor([False, True], dtype=bool), + ), + ( + And([IsIn([0, 1]), IsIn([0, 2])]), + [0, 1, 2, 3], + ~torch.tensor([True, False, False, False], dtype=bool), + ), + ], + ) + def test__call__(self, filters, val, exp_result): + not_filter = Not(filters) + result = not_filter(val) + assert torch.all(result == exp_result) + + @pytest.mark.parametrize( + "filters, vals, exp_result", + [ + ( + HasNan(), + [torch.tensor([[0.0, 1.0], [np.nan, 2.0]])], + ~torch.tensor([False, True], dtype=bool), + ), + ( + Not(HasNan()), + [torch.tensor([[0.0, 1.0], [np.nan, 2.0]])], + ~torch.tensor([True, False], dtype=bool), + ), + ( + HasNan(), + [ + torch.tensor( + [[0.0, 1.0], [0.0, 1.0], [np.nan, 2.0], [np.nan, 2.0]] + ), + torch.tensor( + [[1.0, 2.0], [3.0, np.nan], [1.0, 2.0], [3.0, np.nan]] + ), + ], + ~torch.tensor([False, True, True, True], dtype=bool), + ), + ( + Not(HasNan()), + [ + torch.tensor( + [[0.0, 1.0], [0.0, 1.0], [np.nan, 2.0], [np.nan, 2.0]] + ), + torch.tensor( + [[1.0, 2.0], [3.0, np.nan], [1.0, 2.0], [3.0, np.nan]] + ), + ], + ~torch.tensor([True, False, False, False], dtype=bool), + ), + ( + AnyIn(torch.tensor([1.0])), + [torch.tensor([[0.0], [1.0]])], + ~torch.tensor([False, True], dtype=bool), + ), + ( + Not(AnyIn(torch.tensor([1.0]))), + [torch.tensor([[0.0], [1.0]])], + ~torch.tensor([True, False], dtype=bool), + ), + ( + AnyIn(torch.tensor([1.0])), + [ + torch.tensor([0.0, 0.0, 1.0, 1.0]).reshape(-1, 1), + torch.tensor([2.0, 1.0, 2.0, 1.0]).reshape(-1, 1), + ], + ~torch.tensor([False, True, True, True]), + ), + ( + Not(AnyIn(torch.tensor([1.0]))), + [ + torch.tensor([0.0, 0.0, 1.0, 1.0]).reshape(-1, 1), + torch.tensor([2.0, 1.0, 2.0, 1.0]).reshape(-1, 1), + ], + ~torch.tensor([True, False, False, False]), + ), + ], + ) + def test_compute(self, filters, vals, exp_result): + not_filter = Not(filters) + for v in vals: + not_filter.update(v) + result = not_filter.compute() + assert torch.all(result == exp_result) diff --git a/test/filters/factory_test.py b/test/filters/factory_test.py new file mode 100644 index 0000000..e35e79e --- /dev/null +++ b/test/filters/factory_test.py @@ -0,0 +1,72 @@ +import pytest +import numpy as np +import torch +from omegaconf import DictConfig + +from udm.filters import get_filter + + +@pytest.fixture +def IsInCfg(): + return DictConfig({"name": "IsIn", "__init__": "__init__", "whitelist": [0, 1]}) + + +@pytest.fixture +def AnyInCfg(): + return DictConfig( + {"name": "AnyIn", "__init__": "from_sequence", "whitelist": [0, 1]} + ) + + +@pytest.fixture +def HasNanCfg(): + return DictConfig({"name": "HasNan", "__init__": "__init__"}) + + +@pytest.fixture +def AndCfg(): + return DictConfig( + { + "name": "And", + "__init__": "__init__", + "filters": [ + {"name": "IsIn", "__init__": "__init__", "whitelist": [0, 1]}, + {"name": "IsIn", "__init__": "__init__", "whitelist": [0, 2]}, + ], + } + ) + + +@pytest.mark.parametrize( + "config,custom_filters,val,exp_result", + [ + ( + pytest.lazy_fixture("IsInCfg"), + {}, + [0, 3], + torch.tensor([True, False], dtype=bool), + ), + ( + pytest.lazy_fixture("AnyInCfg"), + {}, + torch.tensor([[0.0, 1.0], [2.0, 3.0]]), + torch.tensor([True, False], dtype=bool), + ), + ( + pytest.lazy_fixture("HasNanCfg"), + {}, + torch.tensor([[0.0, 1.0], [np.nan, 2.0]]), + torch.tensor([False, True], dtype=bool), + ), + ( + pytest.lazy_fixture("AndCfg"), + {}, + [0, 1, 2, 3], + torch.tensor([True, False, False, False], dtype=bool), + ), + ], +) +def test_get_filter(config, custom_filters, val, exp_result): + filter = get_filter(config, custom=custom_filters) + result = filter(val) + assert torch.all(result == exp_result) diff --git a/test/filters/plugin_filters_test.py b/test/filters/plugin_filters_test.py new file mode 100644 index 0000000..94f0d53 --- /dev/null +++ b/test/filters/plugin_filters_test.py @@ -0,0 +1,532 @@ +import pytest +import numpy as np +import torch +from omegaconf import DictConfig, ListConfig + +from udm.filters.simple import IsIn, HasNan +from udm.filters.composed import Not +from udm.filters.plugin_filters import PluginRowFilter, PluginColFilter + + +@pytest.fixture +def PluginRowFilterCfg_key_val(): + return DictConfig( + { + "key_filter": {"name": "IsIn", "__init__": "__init__", "whitelist": [0, 1]}, + "val_filters": { + "data": { + "name": "Not", + "__init__": "__init__", + "filters": [{"name": "HasNan", "__init__": "__init__"}], + } + }, + } + ) + + +@pytest.fixture +def PluginRowFilterCfg_val(): + return DictConfig( + { + "key_filter": None, + "val_filters": { + "x": {"name": "HasNan", "__init__": "__init__"}, + "y": {"name": "HasNan", "__init__": "__init__"}, + }, + } + ) + + +@pytest.fixture +def PluginColFilterCfg_key(): + return DictConfig( + { + "key_filters": { + "x": { + "name": "Not", + "filters": [ + {"name": "IsIn", "__init__": "__init__", "whitelist": [1]} + ], + }, + "y": { + "name": "Not", + "filters": [ + {"name": "IsIn", "__init__": "__init__", "whitelist": [2]}, + ], + }, + }, + "val_filters": {}, + } + ) + + +@pytest.fixture +def PluginColFilterCfg_2d_key(PluginColFilterCfg_key): + PluginColFilterCfg_key.key_filters.x = ListConfig( + [ + { + "name": "Not", + "filters": [{"name": "IsIn", "__init__": "__init__", "whitelist": [1]}], + }, + None, + ] + ) + return PluginColFilterCfg_key + + +@pytest.fixture +def PluginColFilterCfg_val(): + return DictConfig( + { + "key_filters": {}, + "val_filters": { + "x": {"name": "HasNan", "__init__": "__init__"}, + "y": {"name": "HasNan", "__init__": "__init__"}, + }, + } + ) + + +@pytest.fixture +def PluginColFilterCfg_2d_val(PluginColFilterCfg_val): + PluginColFilterCfg_val.val_filters.x = ListConfig( + [{"name": "HasNan", "__init__": "__init__"}, None] + ) + return PluginColFilterCfg_val + + +@pytest.fixture +def PluginColFilterCfg_key_val(): + return DictConfig( + { + "key_filters": { + "data": {"name": "IsIn", "__init__": "__init__", "whitelist": [0, 1]} + }, + "val_filters": { + "data": { + "name": "Not", + "__init__": "__init__", + "filters": [{"name": "HasNan", "__init__": "__init__"}], + } + }, + } + ) + + +@pytest.fixture +def PluginColFilterCfg_key_multidim(): + return DictConfig( + { + "key_filters": { + "x": (None, {"name": "IsIn", "__init__": "__init__", "whitelist": [1]}), + "y": ( + { + "name": "Not", + "filters": [ + {"name": "IsIn", "__init__": "__init__", "whitelist": [0]} + ], + }, + { + "name": "Not", + "filters": [ + {"name": "IsIn", "__init__": "__init__", "whitelist": [2]} + ], + }, + ), + }, + "val_filters": {}, + } + ) + + +@pytest.fixture +def PluginColFilterCfg_val_multidim(): + return DictConfig( + { + "key_filters": {}, + "val_filters": { + "x": ( + {"name": "HasNan", "__init__": "__init__"}, + { + "name": "Not", + "filters": [ + { + "name": "AnyIn", + "__init__": "from_sequence", + "whitelist": [1.0], + } + ], + }, + ), + "y": (None, {"name": "HasNan", "__init__": "__init__"}), + }, + } + ) + + +@pytest.fixture +def PluginColFilterCfg_key_val_multidim( + PluginColFilterCfg_key_multidim, PluginColFilterCfg_val_multidim +): + return DictConfig( + { + "key_filters": PluginColFilterCfg_key_multidim.key_filters, + "val_filters": PluginColFilterCfg_val_multidim.val_filters, + } + ) + + +class TestPluginRowFilter(object): + @pytest.mark.parametrize( + "key_filter,val_filters,error", + [ + (None, {}, Exception), + (IsIn([0, 1, 2]), {}, None), + (None, {"y": Not(HasNan())}, None), + (None, {"x": HasNan(), "y": Not(HasNan())}, None), + (IsIn([0, 1, 2]), {"x": HasNan(), "y": Not(HasNan())}, None), + ], + ) + def test__init__(self, key_filter, val_filters, error): + if error: + with pytest.raises(error): + plugin_filter = PluginRowFilter( + key_filter=key_filter, val_filters=val_filters + ) + else: + plugin_filter = PluginRowFilter( + key_filter=key_filter, val_filters=val_filters + ) + + @pytest.mark.parametrize( + "config,error", + [ + (pytest.lazy_fixture("PluginRowFilterCfg_key_val"), None), + (pytest.lazy_fixture("PluginRowFilterCfg_val"), None), + ], + ) + def test_from_config(self, config, error): + if error: + with pytest.raises(error): + plugin_filter = PluginRowFilter.from_config(config) + else: + plugin_filter = PluginRowFilter.from_config(config) + + @pytest.mark.parametrize( + "cfg_or_filters,eid,val,exp_result", + [ + ( + (IsIn([0, 1, 2]), {}), + [0, 3], + {"data": torch.tensor([0.0, 1.0])}, + torch.tensor([True, False], dtype=bool), + ), + ( + (None, {"data": Not(HasNan())}), + [0, 1], + {"data": torch.tensor([0.0, np.nan])}, + torch.tensor([True, False], dtype=bool), + ), + ( + (IsIn([0, 1]), {"data": Not(HasNan())}), + [0, 1, 2, 3], + {"data": torch.tensor([0.0, np.nan, 0.0, np.nan])}, + torch.tensor([True, False, False, False], dtype=bool), + ), + ( + (None, {"x": HasNan(), "y": HasNan()}), + [0, 1, 2, 3], + { + "x": torch.tensor([np.nan, np.nan, 0.0, 0.0]), + "y": torch.tensor([np.nan, 0.0, np.nan, 0.0]), + }, + torch.tensor([True, False, False, False], dtype=bool), + ), + ( + pytest.lazy_fixture("PluginRowFilterCfg_key_val"), + [0, 1, 2, 3], + {"data": torch.tensor([0.0, np.nan, 0.0, np.nan])}, + torch.tensor([True, False, False, False], dtype=bool), + ), + ( + pytest.lazy_fixture("PluginRowFilterCfg_val"), + [0, 1, 2, 3], + { + "x": torch.tensor([np.nan, np.nan, 0.0, 0.0]), + "y": torch.tensor([np.nan, 0.0, np.nan, 0.0]), + }, + torch.tensor([True, False, False, False], dtype=bool), + ), + ], + ) + def test__call__(self, cfg_or_filters, eid, val, exp_result): + if isinstance(cfg_or_filters, DictConfig): + plugin_filter = PluginRowFilter.from_config(cfg_or_filters) + else: + key_filter, val_filters = cfg_or_filters + plugin_filter = PluginRowFilter( + key_filter=key_filter, val_filters=val_filters + ) + result = plugin_filter(eid, val) + assert torch.all(result == exp_result) + + +class TestPluginColFilter(object): + # @pytest.mark.parametrize( + # "key_filter,val_filters,error", + # [ + # (None, {}, Exception), + # (IsIn([0, 1, 2]), {}, None), + # (None, {"y": Not(HasNan())}, None), + # (None, {"x": HasNan(), "y": Not(HasNan())}, None), + # (IsIn([0, 1, 2]), {"x": HasNan(), "y": Not(HasNan())}, None), + # ], + # ) + # def test__init__(self, key_filter, val_filters, error): + # if error: + # with pytest.raises(error): + # plugin_filter = PluginRowFilter( + # key_filter=key_filter, val_filters=val_filters + # ) + # else: + # plugin_filter = PluginRowFilter(key_filter=key_filter, val_filters=val_filters) + + # @pytest.mark.parametrize( + # "config,error", + # [ + # (pytest.lazy_fixture("PluginRowFilterCfg_key_val"), None), + # (pytest.lazy_fixture("PluginRowFilterCfg_val"), None), + # ], + # ) + # def test_from_config(self, config, error): + # if error: + # with pytest.raises(error): + # plugin_filter = PluginRowFilter.from_config(config) + # else: + # plugin_filter = PluginRowFilter.from_config(config) + + @pytest.mark.parametrize( + "cfg_or_filters,eid,vals,exp_result", + [ + ( + ({"data": IsIn([0, 1, 2])}, {}), + {"data": [0, 3]}, + [ + {"data": torch.tensor([[0.0, 1.0]])}, + {"data": torch.tensor([[3.0, 4.0]])}, + ], + {"data": (torch.tensor([True, False], dtype=bool),)}, + ), + ( + ({}, {"data": Not(HasNan())}), + {"data": [0, 1, 2, 3]}, + [ + {"data": torch.tensor([[0.0, 0.0, np.nan, np.nan]])}, + {"data": torch.tensor([[1.0, np.nan, 1.0, np.nan]])}, + ], + {"data": (torch.tensor([True, False, False, False], dtype=bool),)}, + ), + ( + ({"data": IsIn([0, 1])}, {"data": Not(HasNan())}), + {"data": [0, 1, 2, 3]}, + [ + {"data": torch.tensor([[0.0, 1.0, 2.0, 3.0]])}, + {"data": torch.tensor([[1.0, np.nan, 1.0, np.nan]])}, + ], + {"data": (torch.tensor([True, False, False, False], dtype=bool),)}, + ), + ( + ({"data": IsIn([0, 1])}, {"data": Not(HasNan())}), + {"data": [0, 1, 2, 3]}, + [ + {"data": torch.tensor([[np.nan, 1.0, np.nan, 3.0]])}, + {"data": torch.tensor([[1.0, np.nan, 1.0, np.nan]])}, + ], + {"data": (torch.tensor([False, False, False, False], dtype=bool),)}, + ), + ( + ({}, {"x": HasNan(), "y": HasNan()}), + { + "x": ["col0", "col1", "col2", "col3"], + "y": ["col4", "col5", "col6", "col7"], + }, + [ + { + "x": torch.tensor([[0.0, 1.0, 2.0, 3.0]]), + "y": torch.tensor([[1.0, 2.0, 3.0, 4.0]]), + }, + { + "x": torch.tensor([[np.nan, np.nan, 0.0, 0.0]]), + "y": torch.tensor([[np.nan, 0.0, np.nan, 0.0]]), + }, + ], + { + "x": (torch.tensor([True, True, False, False], dtype=bool),), + "y": (torch.tensor([True, False, True, False], dtype=bool),), + }, + ), + ( + ({}, {"x": HasNan(), "y": HasNan()}), + { + "x": ["col0", "col1", "col2", "col3"], + "y": ["col4", "col5", "col6", "col7"], + }, + [ + { + "x": torch.tensor([[0.0, 1.0, np.nan, np.nan]]), + "y": torch.tensor([[1.0, np.nan, 3.0, np.nan]]), + }, + { + "x": torch.tensor([[np.nan, np.nan, 0.0, 0.0]]), + "y": torch.tensor([[np.nan, 0.0, np.nan, 0.0]]), + }, + ], + { + "x": (torch.tensor([True, True, True, True], dtype=bool),), + "y": (torch.tensor([True, True, True, True], dtype=bool),), + }, + ), + ( + pytest.lazy_fixture("PluginColFilterCfg_key_multidim"), + {"x": [[0, 1, 2], [0, 1]], "y": [[0, 1, 2], [2, 3]]}, + [ + { + "x": torch.tensor( + [ + [[0.0, np.nan], [2.0, 3.0], [4.0, 5.0]], + ] + ), + "y": torch.tensor( + [ + [[0.0, 1.0], [np.nan, 3.0], [4.0, 5.0]], + ] + ), + }, + { + "x": torch.tensor( + [ + [[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]], + ] + ), + "y": torch.tensor( + [ + [[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]], + ] + ), + }, + ], + { + "x": ( + torch.tensor([True, True, True], dtype=bool), + torch.tensor([False, True], dtype=bool), + ), + "y": ( + torch.tensor([False, True, True], dtype=bool), + torch.tensor([False, True], dtype=bool), + ), + }, + ), + ( + pytest.lazy_fixture("PluginColFilterCfg_val_multidim"), + {"x": [[0, 1, 2], [0, 1]], "y": [[0, 1, 2], [2, 3]]}, + [ + { + "x": torch.tensor( + [ + [[0.0, np.nan], [2.0, 3.0], [4.0, 5.0]], + ] + ), + "y": torch.tensor( + [ + [[0.0, 1.0], [np.nan, 3.0], [4.0, 5.0]], + ] + ), + }, + { + "x": torch.tensor( + [ + [[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]], + ] + ), + "y": torch.tensor( + [ + [[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]], + ] + ), + }, + ], + { + "x": ( + torch.tensor([True, False, False], dtype=bool), + torch.tensor([True, False], dtype=bool), + ), + "y": ( + torch.tensor([True, True, True], dtype=bool), + torch.tensor([True, False], dtype=bool), + ), + }, + ), + ( + pytest.lazy_fixture("PluginColFilterCfg_key_val_multidim"), + {"x": [[0, 1, 2], [0, 1]], "y": [[0, 1, 2], [2, 3]]}, + [ + { + "x": torch.tensor( + [ + [[0.0, np.nan], [2.0, 3.0], [4.0, 5.0]], + ] + ), + "y": torch.tensor( + [ + [[0.0, 1.0], [np.nan, 3.0], [4.0, 5.0]], + ] + ), + }, + { + "x": torch.tensor( + [ + [[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]], + ] + ), + "y": torch.tensor( + [ + [[0.0, 1.0], [2.0, 3.0], [4.0, 5.0]], + ] + ), + }, + ], + { + "x": ( + # torch.tensor([True, True, True], dtype=bool) & torch.tensor([True, False, False], dtype=bool) + torch.tensor([True, False, False], dtype=bool), + # torch.tensor([False, True], dtype=bool) & torch.tensor([True, False], dtype=bool) + torch.tensor([False, False], dtype=bool), + ), + "y": ( + # torch.tensor([False, True, True], dtype=bool) & torch.tensor([True, True, True], dtype=bool) + torch.tensor([False, True, True], dtype=bool), + # torch.tensor([False, True], dtype=bool) & torch.tensor([True, False], dtype=bool) + torch.tensor([False, False], dtype=bool), + ), + }, + ), + ], + ) + def test_compute(self, cfg_or_filters, eid, vals, exp_result): + if isinstance(cfg_or_filters, DictConfig): + plugin_filter = PluginColFilter.from_config(cfg_or_filters) + else: + key_filters, val_filters = cfg_or_filters + plugin_filter = PluginColFilter( + key_filters=key_filters, val_filters=val_filters + ) + plugin_filter.init_state(eid) + for v in vals: + plugin_filter.update(v) + result = plugin_filter.compute() + assert set(result.keys()) == set(exp_result.keys()) + for k in result.keys(): + assert isinstance(result[k], tuple) + assert len(result[k]) == len(exp_result[k]) + for dim_result, dim_exp_result in zip(result[k], exp_result[k]): + assert torch.all(dim_result == dim_exp_result) diff --git a/test/filters/simple_test.py b/test/filters/simple_test.py new file mode 100644 index 0000000..dba23e2 --- /dev/null +++ b/test/filters/simple_test.py @@ -0,0 +1,278 @@ +import pytest +import numpy as np +import torch +from typing import ( + Any, + Dict, + Iterable, +) + +from udm.filters.simple import Filter, StatefulFilter, IsIn, AnyIn, HasNan + + +class AnyOne(Filter, StatefulFilter): + def __init__(self): + super().__init__() + + def __call__(self, value): + return torch.any(value == 1, dim=1) + + def _update(self, input: Iterable[Any], state: Dict[str, Any]) -> Dict[str, Any]: + """Updates the filter state""" + mask = self.__call__(input) + if state["mask"] is None: + state["mask"] = mask + else: + state["mask"] = torch.logical_or(state["mask"], mask) + return state + + def _compute(self, state: Dict[str, Any]) -> torch.BoolTensor: + """Computes the mask using the state.""" + return state["mask"] + + def _init_state(self) -> Dict[str, Any]: + """Initializes the filter state""" + state = {"mask": None} + return state + + +class AnyTwo(Filter, StatefulFilter): + def __init__(self): + super().__init__() + + def __call__(self, value): + return torch.any(value == 2, dim=1) + + def _update(self, input: Iterable[Any], state: Dict[str, Any]) -> Dict[str, Any]: + """Updates the filter state""" + mask = self.__call__(input) + if state["mask"] is None: + state["mask"] = mask + else: + state["mask"] = torch.logical_or(state["mask"], mask) + return state + + def _compute(self, state: Dict[str, Any]) -> torch.BoolTensor: + """Computes the mask using the state.""" + return state["mask"] + + def _init_state(self) -> Dict[str, Any]: + """Initializes the filter state""" + state = {"mask": None} + return state + + +class TestIsIn(object): + @pytest.mark.parametrize("whitelist", [[], [0, 1, 2], ["a", "b", "c"]]) + def test__init__(self, whitelist): + isin_filter = IsIn(whitelist) + + @pytest.mark.parametrize( + "whitelist,eid,exp_result", + [ + ([], [0], torch.tensor([False], dtype=bool)), + ([0, 1, 2], [0], torch.tensor([True], dtype=bool)), + ([0, 1, 2], [3], torch.tensor([False], dtype=bool)), + ([0, 1, 2], [0, 3], torch.tensor([True, False], dtype=bool)), + ( + [0, 1, 2], + [0, 3, 1, 4, 2, 5], + torch.tensor([True, False, True, False, True, False], dtype=bool), + ), + ([], ["a"], torch.tensor([False], dtype=bool)), + (["a", "b", "c"], ["a"], torch.tensor([True], dtype=bool)), + (["a", "b", "c"], ["d"], torch.tensor([False], dtype=bool)), + (["a", "b", "c"], ["a", "d"], torch.tensor([True, False], dtype=bool)), + ( + ["a", "b", "c"], + ["a", "d", "b", "e", "c", "f"], + torch.tensor([True, False, True, False, True, False], dtype=bool), + ), + ], + ) + def test__call__(self, whitelist, eid, exp_result): + isin_filter = IsIn(whitelist) + result = isin_filter(eid) + assert torch.all(result == exp_result) + + +class TestAnyIn(object): + @pytest.mark.parametrize( + "whitelist,error", + [ + (torch.tensor([]), AssertionError), + (torch.tensor([0.0]), None), + (torch.tensor([0.0, 1.0, 2.0, 3.0]), None), + (torch.tensor([[0.0, 1.0], [2.0, 3.0]]), None), + ], + ) + def test__init__(self, whitelist, error): + if error: + with pytest.raises(error): + anyin_filter = AnyIn(whitelist) + else: + anyin_filter = AnyIn(whitelist) + + @pytest.mark.parametrize( + "whitelist,val,exp_result", + [ + ( + torch.tensor([0.0]), + torch.tensor([0.0]), + torch.tensor([True], dtype=bool), + ), + ( + torch.tensor([0.0]), + torch.tensor([1.0]), + torch.tensor([False], dtype=bool), + ), + ( + torch.tensor([0.0, 1.0]), + torch.tensor([0.0, 1.0, 2.0, 3.0]), + torch.tensor([True, True, False, False], dtype=bool), + ), + ( + torch.tensor([0.0, 1.0]), + torch.tensor([[0.0, 1.0], [2.0, 3.0]]), + torch.tensor([True, False], dtype=bool), + ), + ], + ) + def test__call__(self, whitelist, val, exp_result): + anyin_filter = AnyIn(whitelist) + result = anyin_filter(val) + assert torch.all(result == exp_result) + + @pytest.mark.parametrize( + "whitelist,vals,exp_result", + [ + ( + torch.tensor([0.0]), + [torch.tensor([0.0])], + torch.tensor([True], dtype=bool), + ), + ( + torch.tensor([0.0]), + [torch.tensor([1.0])], + torch.tensor([False], dtype=bool), + ), + ( + torch.tensor([0.0]), + [ + torch.tensor([0.0]), + torch.tensor([1.0]), + ], + torch.tensor([True], dtype=bool), + ), + ( + torch.tensor([0.0]), + [ + torch.tensor([1.0]), + torch.tensor([0.0]), + ], + torch.tensor([True], dtype=bool), + ), + ( + torch.tensor([0.0, 1.0]), + [torch.tensor([0.0, 1.0, 2.0, 3.0])], + torch.tensor([True, True, False, False], dtype=bool), + ), + ( + torch.tensor([0.0, 1.0]), + [torch.tensor([[0.0, 1.0], [2.0, 3.0]])], + torch.tensor([True, False], dtype=bool), + ), + ( + torch.tensor([0.0, 1.0]), + [ + torch.tensor( + [ + [1.0, 2.0], # True + [1.0, 2.0], # True + [2.0, 3.0], # False + [2.0, 3.0], # False + ] + ), + torch.tensor( + [ + [1.0, 2.0], # True + [3.0, 4.0], # False + [1.0, 2.0], # True + [3.0, 4.0], # False + ] + ), + ], + torch.tensor([True, True, True, False], dtype=bool), + ), + ], + ) + def test_compute(self, whitelist, vals, exp_result): + anyin_filter = AnyIn(whitelist) + for v in vals: + anyin_filter.update(v) + result = anyin_filter.compute() + assert torch.all(result == exp_result) + + +class TestHasNan(object): + def test__init__(self): + hasnan_filter = HasNan() + + @pytest.mark.parametrize( + "val,exp_result", + [ + # 1D tensor + (torch.tensor([0.0]), torch.tensor([False], dtype=bool)), + (torch.tensor([np.nan]), torch.tensor([True], dtype=bool)), + ( + torch.tensor([0.0, np.nan]), + torch.tensor([False, True], dtype=bool), + ), + # 2D tensor + (torch.tensor([[0.0]]), torch.tensor([False], dtype=bool)), + (torch.tensor([[np.nan]]), torch.tensor([True], dtype=bool)), + (torch.tensor([[0.0, np.nan]]), torch.tensor([True], dtype=bool)), + ( + torch.tensor([[0.0, 1.0], [np.nan, 2.0]]), + torch.tensor([False, True], dtype=bool), + ), + ], + ) + def test__call__(self, val, exp_result): + hasnan_filter = HasNan() + if isinstance(exp_result, Exception): + with pytest.raises(exp_result): + hasnan_filter(val) + else: + result = hasnan_filter(val) + assert torch.all(result == exp_result) + + @pytest.mark.parametrize( + "vals,exp_result", + [ + ([torch.tensor([[0.0]])], torch.tensor([False], dtype=bool)), + ([torch.tensor([[np.nan]])], torch.tensor([True], dtype=bool)), + ([torch.tensor([[0.0, np.nan]])], torch.tensor([True], dtype=bool)), + ( + [torch.tensor([[0.0, 1.0], [np.nan, 2.0]])], + torch.tensor([False, True], dtype=bool), + ), + ( + [ + torch.tensor( + [[0.0, 1.0], [0.0, 1.0], [np.nan, 2.0], [np.nan, 2.0]] + ), + torch.tensor( + [[2.0, 3.0], [3.0, np.nan], [2.0, 3.0], [3.0, np.nan]] + ), + ], + torch.tensor([False, True, True, True], dtype=bool), + ), + ], + ) + def test_compute(self, vals, exp_result): + hasnan_filter = HasNan() + for v in vals: + hasnan_filter.update(v) + result = hasnan_filter.compute() + assert torch.all(result == exp_result) diff --git a/test/general_datamodule_test.py b/test/general_datamodule_test.py new file mode 100644 index 0000000..0041382 --- /dev/null +++ b/test/general_datamodule_test.py @@ -0,0 +1,1342 @@ +import os +import pathlib +from copy import deepcopy +from inspect import isclass +from math import ceil +from os.path import abspath, dirname +from typing import Any +import yaml +from tempfile import TemporaryDirectory +import hydra +import numpy as np +import pandas as pd +import pytest +from pytest_lazyfixture import lazy_fixture +import torch +from omegaconf import DictConfig, OmegaConf + +from udm.general_datamodule import GeneralDatamodule +from udm.plugins.serialized.serialized_plugin import SerializedPlugin +from test.transforms.tensor_transforms.factory_test import ScaleTrafo +from test.general_dataset_test import assertBatchEqual +from test.utils import SKIPIF_NOT_BIH_HPC, try_clear_hydra +from test.plugins.dataplugin_test import MinimalPlugin +from test.plugins.genetic.genetic_plugin_test import ( + geno_debug_config, + mini_train_data, + prepare_mini_train as geno_prepare_mini_train, + prepare_mini_valid as geno_prepare_mini_valid, +) # noqa +from test.plugins.ehr.ehr_plugin_test import ehr_debug_config # noqa +from test.plugins.tabular.tabular_plugin_test import tabular_debug_config # noqa +from test.plugins.covariates.covariates_plugin_test import ( + covariates_debug_config, +) # noqa +from test.plugins.h5ad.h5ad_plugin_test import pheno_config # noqa + + +UDM_HOME = ".." +UDM_HOME_ABS = abspath(os.path.join(dirname(__file__), UDM_HOME)) + + +@pytest.fixture +def temp_dir(): + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +@pytest.fixture +def debug_eids(): + DEBUG_EIDS_PATH = os.path.join( + UDM_HOME, + "data/cvd/data/2_datasets_pre/211110_anewbeginning/artifacts/debug/not_anonymized_debug/eids.yaml", + ) + eids = yaml.load(DEBUG_EIDS_PATH, Loader=yaml.CSafeLoader) + return list(eids) + + +@pytest.fixture +def debug_eids_head(debug_eids): + return debug_eids[:100] + + +def _eid_split(eids, ratio): + n = int(ratio * len(eids)) + return eids[:n], eids[n:] + + +class CustomCollatePlugin(MinimalPlugin): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def collate_fn(self, batch) -> Any: + # Double every sample in the batch + x = batch[0]["data"] + doubled_batch = torch.concat([x, x], dim=1) + return {"data": doubled_batch} + + +@pytest.fixture +def scale_trafo_cfg(): + return DictConfig( + { + "name": "PluginTransform", + "transforms": { + "data": {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2} + }, + } + ) + + +@pytest.fixture +def composed_trafo_cfg(): + return DictConfig( + { + "name": "PluginTransform", + "transforms": { + "data": { + "name": "ListTransform", + "transforms": [ + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + ], + } + }, + } + ) + + +@pytest.fixture +def from_clsmethod_config(): + config = OmegaConf.create( + { + "plugins": { + "minimal": { + "name": "MinimalPlugin", + "__init__": "from_list", + "data": [[0], [1], [2]], + } + }, + "splits": {"train": [0], "valid": [1], "test": [2]}, + "batch_size": 1, + "shuffle": False, + } + ) + yield config + + +@pytest.fixture +def custom_collate_config(): + config = OmegaConf.create( + { + "plugins": { + "minimal": {"name": "MinimalPlugin"}, + "custom_collate": {"name": "CustomCollatePlugin"}, + }, + "splits": {"train": [0, 1], "valid": [2, 3], "test": [4, 5]}, + "batch_size": 2, + "shuffle": False, + } + ) + yield config + + +@pytest.fixture +def minimal_config(): + config = OmegaConf.create( + { + "plugins": { + "minimal": {"name": "MinimalPlugin", "transforms": None}, + }, + "splits": {"train": [0, 1], "valid": [2, 3], "test": [4, 5]}, + "transforms": {}, + "batch_size": 2, + "shuffle": False, + } + ) + yield config + + +@pytest.fixture +def minimal_str_config(): + config = OmegaConf.create( + { + "plugins": { + "minimal": { + "name": "MinimalPlugin", + "eids_type": "str", + "transforms": None, + }, + }, + "splits": { + "train": ["a1", "b2"], + "valid": ["c3", "d4"], + "test": ["e5", "f6"], + }, + "transforms": {}, + "batch_size": 2, + "shuffle": False, + } + ) + yield config + + +@pytest.fixture +def minimal_plg_trafo_config(minimal_config): + config = minimal_config + overrides_cfg = DictConfig( + { + "plugins": { + "minimal": { + # ... + "transforms": { + "name": "PluginTransform", + "transforms": { + "data": { + "name": "ScaleTrafo", + "__init__": "__init__", + "alpha": 2, + } + }, + } + } + } + } + ) + config = OmegaConf.merge(config, overrides_cfg) + yield config + + +def _minimal_ds_trafo_config(dataset_trafo="train"): + return DictConfig( + { + # ... + "transforms": { + # ... other splits/datasets + dataset_trafo: { + "name": "DatasetTransform", + "transforms": { + "minimal": { + "name": "PluginTransform", + "transforms": { + "data": { + "name": "ScaleTrafo", + "__init__": "__init__", + "alpha": 2, + } + }, + } + }, + } + } + } + ) + + +@pytest.fixture +def minimal_train_ds_trafo_config(minimal_config): + overrides_config = _minimal_ds_trafo_config(dataset_trafo="train") + config = OmegaConf.merge(minimal_config, overrides_config) + yield config + + +@pytest.fixture +def minimal_valid_ds_trafo_config(minimal_config): + overrides_config = _minimal_ds_trafo_config(dataset_trafo="valid") + config = OmegaConf.merge(minimal_config, overrides_config) + yield config + + +@pytest.fixture +def minimal_test_ds_trafo_config(minimal_config): + overrides_config = _minimal_ds_trafo_config(dataset_trafo="test") + config = OmegaConf.merge(minimal_config, overrides_config) + yield config + + +@pytest.fixture +def cov_config(covariates_debug_config): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose("default.yaml", overrides=[]) + config.plugins = {"cov": covariates_debug_config} + yield config + + +@pytest.fixture +def geno_config(geno_debug_config): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose("default.yaml", overrides=[]) + config.plugins = {"geno": geno_debug_config} + yield config + + +@pytest.fixture +def ehr_config(ehr_debug_config): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose("default.yaml", overrides=[]) + config.plugins = {"ehr": ehr_debug_config} + yield config + + +@pytest.fixture +def tabular_config(tabular_debug_config): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose("default.yaml", overrides=[]) + config.plugins = {"tabular": tabular_debug_config} + yield config + + +@pytest.fixture +def h5ad_config(pheno_config): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose("default.yaml", overrides=[]) + config.plugins = {"pheno": pheno_config} + yield config + + +@pytest.fixture +def geno_geno_mini_config(geno_prepare_mini_train, geno_prepare_mini_valid): + try_clear_hydra() + TMP_PATH1, TMP_PATH2 = geno_prepare_mini_train, geno_prepare_mini_valid + config_path = os.path.join(UDM_HOME, "config") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + "default.yaml", + overrides=[ + "+plugins/genetic@plugins.geno1=default", + f"plugins.geno1.src=[{TMP_PATH1}]", + "plugins.geno1.src_names=[geno]", + "plugins.geno1.eid_map_path=null", + "+plugins/genetic@plugins.geno2=default", + f"plugins.geno2.src=[{TMP_PATH2}]", + "plugins.geno2.src_names=[geno]", + "plugins.geno2.eid_map_path=null", + "+splits={train: [0], valid: [1], test: [2]}", + "combine_eids_as=intersect", + "batch_size=1", + "keyerr=True", + ], + ) + yield config + + +@pytest.fixture +def geno_geno_mini_map_config(temp_dir, geno_geno_mini_config): + config = geno_geno_mini_config + with TemporaryDirectory(dir=temp_dir) as temp_dir: + config.plugins.geno2.eid_map_path = f"{temp_dir}/eid_map.csv" + config.plugins.geno2.eid_map_from = "eid_map_from" + config.plugins.geno2.eid_map_to = "eid_map_to" + eid_map = pd.DataFrame( + {"eid_map_from": [3, 4, 5, 6], "eid_map_to": [0, 1, 2, 6]} + ) + eid_map_path = f"{temp_dir}/eid_map.csv" + eid_map.to_csv(eid_map_path, sep="\t") + yield config + + +@pytest.fixture +def cov_cov_config(debug_eids_head): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose("default.yaml", overrides=["plugins=cov_cov"]) + data_paths = os.path.join( + "data", + "cvd", + "data", + "2_datasets_pre", + "211110_anewbeginning", + "artifacts", + "debug", + "not_anonymized_debug", + "baseline_covariates_minimal_debug_20220711.feather", + ) + config.plugins.cov1.data_paths = data_paths + config.plugins.cov2.data_paths = data_paths + eids_train_valid, eids_test = _eid_split(debug_eids_head, 0.9) + eids_train, eids_valid = _eid_split(eids_train_valid, 0.9) + config.splits = {"train": eids_train, "valid": eids_valid, "test": eids_test} + yield config + + +@pytest.fixture +def cov_geno_config(debug_eids_head): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + "default.yaml", + overrides=[ + "+plugins/covariates@plugins.cov=default", + "+plugins/genetic@plugins.geno=debug", + ], + ) + # Fix covariate paths + config.plugins.cov.data_paths = os.path.join( + "data", + "cvd", + "data", + "2_datasets_pre", + "211110_anewbeginning", + "artifacts", + "debug", + "not_anonymized_debug", + "baseline_covariates_minimal_debug_20220711.feather", + ) + # Fix geno paths + FAKE_GENO_PATH = os.path.join(UDM_HOME_ABS, "res/datasets/fake/geno/") + config.plugins.geno.dataset_path = FAKE_GENO_PATH + eids_train_valid, eids_test = _eid_split(debug_eids_head, 0.9) + eids_train, eids_valid = _eid_split(eids_train_valid, 0.9) + config.splits = {"train": eids_train, "valid": eids_valid, "test": eids_test} + yield config + + +class TestGeneralDataModule(object): + @pytest.mark.parametrize( + "split1,split2,warning", + [ + ([], [], None), + ([0], [], None), + ([], [0], None), + ([0], [1], None), + ([0], [0], UserWarning), + ([0, 1], [1, 2], UserWarning), + ], + ) + def test_check_two_splits(self, split1, split2, warning): + split1 = np.array(split1, dtype=int) + split2 = np.array(split2, dtype=int) + if warning is None: + GeneralDatamodule._check_two_splits( + "split1", "split2", "train", "valid", split1, split2 + ) + else: + with pytest.warns(warning): + GeneralDatamodule._check_two_splits( + "split1", "split2", "train", "valid", split1, split2 + ) + + @pytest.mark.parametrize( + "config, overrides, custom_plugins, custom_transforms", + [ + ( + lazy_fixture("from_clsmethod_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {}, + ), + (lazy_fixture("minimal_config"), {}, {"MinimalPlugin": MinimalPlugin}, {}), + ( + lazy_fixture("minimal_str_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {}, + ), + ( + lazy_fixture("minimal_plg_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + ), + ( + lazy_fixture("minimal_train_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + ), + ( + lazy_fixture("minimal_valid_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + ), + ( + lazy_fixture("minimal_test_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + ), + pytest.param( + lazy_fixture("cov_config"), {}, {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param( + lazy_fixture("geno_config"), {}, {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param( + lazy_fixture("ehr_config"), {}, {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param( + lazy_fixture("tabular_config"), {}, {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param( + lazy_fixture("h5ad_config"), {}, {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param(lazy_fixture("geno_geno_mini_config"), {}, {}, {}), + pytest.param(lazy_fixture("geno_geno_mini_map_config"), {}, {}, {}), + pytest.param( + lazy_fixture("geno_geno_mini_map_config"), + {"combine_eids_as": "union"}, + {}, + {}, + ), + pytest.param( + lazy_fixture("cov_cov_config"), {}, {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param( + lazy_fixture("cov_geno_config"), {}, {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + ], + ) + def test__init__(self, config, overrides, custom_plugins, custom_transforms): + config.update(overrides) + gdm = GeneralDatamodule( + **config, + custom_plugins=custom_plugins, + custom_transforms=custom_transforms, + ) + + @SKIPIF_NOT_BIH_HPC + @pytest.mark.parametrize( + "config, overrides", + [ + (lazy_fixture("cov_config"), {}), + (lazy_fixture("geno_config"), {}), + (lazy_fixture("ehr_config"), {}), + (lazy_fixture("tabular_config"), {}), + (lazy_fixture("h5ad_config"), {}), + (lazy_fixture("geno_geno_mini_config"), {}), + (lazy_fixture("geno_geno_mini_map_config"), {}), + (lazy_fixture("geno_geno_mini_map_config"), {"combine_eids_as": "union"}), + (lazy_fixture("cov_cov_config"), {}), + (lazy_fixture("cov_geno_config"), {}), + ], + ) + def test_prepare_data(self, config, overrides): + config.update(overrides) + gdm = GeneralDatamodule(**config) + gdm.prepare_data() + + @staticmethod + def _run_setup( + config, custom_plugins={}, custom_transforms={}, update_splits=False + ): + gdm = GeneralDatamodule( + **config, custom_plugins=custom_plugins, custom_transforms=custom_transforms + ) + gdm.prepare_data() + gdm.setup() + if update_splits: + eids = gdm.get_eids() + train_valid_eids, test_eids = _eid_split(eids, 0.9) + train_eids, valid_eids = _eid_split(train_valid_eids, 0.9) + splits = {"train": train_eids, "valid": valid_eids, "test": test_eids} + gdm.update_splits(splits) + # NOTE: This requires pytorch_lightning >= 1.6 + gdm.prepare_data() + gdm.setup() + return gdm + + @SKIPIF_NOT_BIH_HPC + @pytest.mark.parametrize( + "config, overrides, update_splits, warning", + [ + (lazy_fixture("cov_config"), {}, True, None), + (lazy_fixture("geno_config"), {}, True, None), + (lazy_fixture("ehr_config"), {}, True, None), + (lazy_fixture("tabular_config"), {}, True, None), + (lazy_fixture("h5ad_config"), {}, True, None), + (lazy_fixture("geno_geno_mini_config"), {}, False, UserWarning), + (lazy_fixture("geno_geno_mini_map_config"), {}, False, UserWarning), + ( + lazy_fixture("geno_geno_mini_map_config"), + {"combine_eids_as": "union"}, + False, + None, + ), + (lazy_fixture("cov_cov_config"), {}, False, None), + (lazy_fixture("cov_geno_config"), {}, False, None), + ], + ) + def test_setup(self, config, overrides, update_splits, warning): + config.update(overrides) + gdm = TestGeneralDataModule._run_setup(config, update_splits=update_splits) + gdm.prepare_data() + if warning: + with pytest.warns(warning): + gdm.setup() + else: + gdm.setup() + del gdm + + @SKIPIF_NOT_BIH_HPC + @pytest.mark.parametrize( + "config, overrides, exp_eids", + [ + (lazy_fixture("minimal_config"), {}, [0, 1, 2, 3, 4, 5]), + ( + lazy_fixture("minimal_str_config"), + {}, + ["a1", "b2", "c3", "d4", "e5", "f6"], + ), + (lazy_fixture("geno_geno_mini_config"), {}, []), + (lazy_fixture("geno_geno_mini_map_config"), {}, [0, 1]), + ( + lazy_fixture("geno_geno_mini_map_config"), + {"combine_eids_as": "union"}, + [0, 1, 2, 6], + ), + ( + lazy_fixture("geno_geno_mini_config"), + {"combine_eids_as": "union"}, + [0, 1, 2, 3, 4, 6], + ), + ], + ) + def test_get_eids(self, config, overrides, exp_eids): + config.update(overrides) + gdm = GeneralDatamodule(**config) + gdm.prepare_data() + gdm.setup() + eids = gdm.get_eids() + assert eids == exp_eids + del gdm + + @pytest.mark.parametrize( + "config, overrides, custom_plugins, custom_transforms, shapes_only, update_splits, exp_len, exp_batch", + [ + ( + lazy_fixture("custom_collate_config"), + {}, + { + "MinimalPlugin": MinimalPlugin, + "CustomCollatePlugin": CustomCollatePlugin, + }, + {}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 0, 0], [0, 0, 1]], dtype=torch.uint8) + }, + "custom_collate": { + "data": torch.tensor( + [[0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 1]], dtype=torch.uint8 + ) + }, + }, + ), + ( + lazy_fixture("minimal_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 0, 0], [0, 0, 1]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_str_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 0, 0], [0, 0, 1]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_plg_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 0, 0], [0, 0, 2]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_train_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 0, 0], [0, 0, 2]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_valid_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 0, 0], [0, 0, 1]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_test_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 0, 0], [0, 0, 1]], dtype=torch.uint8) + } + }, + ), + pytest.param( + lazy_fixture("cov_config"), + {}, + {}, + {}, + True, + True, + ceil(81 / 16), + {"cov": {"covariates": [16, 31]}}, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_config"), + {}, + {}, + {}, + True, + True, + ceil(4096 * 0.9**2 / 16), + {"geno": {"genetic": [16, 10]}}, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("ehr_config"), + {}, + {}, + {}, + True, + True, + ceil(81 / 16), + { + "ehr": { + "records": [16, 6370], + "censorings": [16, 1], + "exclusions": [16, 1192], + "label_events": [16, 1192], + "label_times": [16, 1192], + "label_auxiliary_times": [16, 5178], + "label_auxiliary_events": [16, 5178], + } + }, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("tabular_config"), + {}, + {}, + {}, + True, + True, + ceil(81 / 16), + {"tabular": {"tabular_data": [16, 9]}}, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("h5ad_config"), + {}, + {}, + {}, + True, + True, + ceil(4096 * 0.9**2 / 16), + {"pheno": {"tabular_data": [16, 1]}}, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_geno_mini_map_config"), + {}, + {}, + {}, + False, + False, + 1, + { + "geno1": { + "genetic": torch.tensor([0, 1, 0], dtype=torch.float16) / 127 + }, + "geno2": { + "genetic": torch.tensor([1, 0, 0], dtype=torch.float16) / 127 + }, + }, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_geno_mini_config"), + {"combine_eids_as": "union"}, + {}, + {}, + False, + False, + 1, + KeyError, + marks=[pytest.mark.xfail], + ), + pytest.param( + lazy_fixture("geno_geno_mini_config"), + {"combine_eids_as": "union", "keyerr": False}, + {}, + {}, + False, + False, + 1, + { + "geno1": { + "genetic": torch.tensor([0, 1, 0], dtype=torch.float16) / 127 + }, + "geno2": {}, + }, + ), + pytest.param( + lazy_fixture("cov_cov_config"), + {}, + {}, + {}, + True, + False, + ceil(81 / 16), + { + "cov1": {"covariates": [16, 31]}, + "cov2": {"covariates": [16, 31]}, + }, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("cov_geno_config"), + {}, + {}, + {}, + True, + False, + ceil(81 / 16), + { + "cov": {"covariates": [16, 31]}, + "geno": {"genetic": [16, 10]}, + }, + marks=SKIPIF_NOT_BIH_HPC, + ), + ], + ) + def test_train_dataloader( + self, + config, + overrides, + custom_plugins, + custom_transforms, + shapes_only, + update_splits, + exp_len, + exp_batch, + ): + config.update(overrides) + gdm = TestGeneralDataModule._run_setup( + config, + custom_plugins=custom_plugins, + custom_transforms=custom_transforms, + update_splits=update_splits, + ) + dataloader = gdm.train_dataloader(drop_last=False) + if isclass(exp_batch) and issubclass(exp_batch, Exception): + with pytest.raises(exp_batch): + iterator = iter(dataloader) + next(iterator) + else: + batch = deepcopy(next(iter(dataloader))) + assert len(dataloader) == exp_len + assertBatchEqual(batch, exp_batch, shapes_only=shapes_only) + del gdm, dataloader + + @SKIPIF_NOT_BIH_HPC + @pytest.mark.parametrize( + "config, overrides, custom_plugins, custom_transforms, shapes_only, update_splits, exp_len, exp_batch", + [ + ( + lazy_fixture("minimal_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 1, 0], [1, 0, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_str_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 1, 0], [1, 0, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_plg_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 2, 0], [2, 0, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_train_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 1, 0], [1, 0, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_valid_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 2, 0], [2, 0, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_test_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[0, 1, 0], [1, 0, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("cov_config"), + {}, + {}, + {}, + True, + True, + 1, + {"cov": {"covariates": [9, 31]}}, + ), + ( + lazy_fixture("geno_config"), + {}, + {}, + {}, + True, + True, + ceil(4096 * 0.9 * 0.1 / 16), + {"geno": {"genetic": [16, 10]}}, + ), + ( + lazy_fixture("ehr_config"), + {}, + {}, + {}, + True, + True, + 1, + { + "ehr": { + "records": [9, 6370], + "censorings": [9, 1], + "exclusions": [9, 1192], + "label_events": [9, 1192], + "label_times": [9, 1192], + "label_auxiliary_times": [9, 5178], + "label_auxiliary_events": [9, 5178], + } + }, + ), + ( + lazy_fixture("h5ad_config"), + {}, + {}, + {}, + True, + True, + ceil(4096 * 0.9 * 0.1 / 16), + {"pheno": {"tabular_data": [16, 1]}}, + ), + ( + lazy_fixture("tabular_config"), + {}, + {}, + {}, + True, + True, + 1, + {"tabular": {"tabular_data": [9, 9]}}, + ), + ( + lazy_fixture("cov_cov_config"), + {}, + {}, + {}, + True, + False, + 1, + { + "cov1": {"covariates": [9, 31]}, + "cov2": {"covariates": [9, 31]}, + }, + ), + ( + lazy_fixture("cov_geno_config"), + {}, + {}, + {}, + True, + False, + 1, + { + "cov": {"covariates": [9, 31]}, + "geno": {"genetic": [9, 10]}, + }, + ), + ], + ) + def test_val_dataloader( + self, + config, + overrides, + custom_plugins, + custom_transforms, + shapes_only, + update_splits, + exp_len, + exp_batch, + ): + config.update(overrides) + gdm = TestGeneralDataModule._run_setup( + config, + custom_plugins=custom_plugins, + custom_transforms=custom_transforms, + update_splits=update_splits, + ) + dataloader = gdm.val_dataloader(drop_last=False) + batch = deepcopy(next(iter(dataloader))) + assert len(dataloader) == exp_len + assertBatchEqual(batch, exp_batch, shapes_only=shapes_only) + del gdm, dataloader + + @SKIPIF_NOT_BIH_HPC + @pytest.mark.parametrize( + "config, overrides, custom_plugins, custom_transforms, shapes_only, update_splits, exp_len, exp_batch", + [ + ( + lazy_fixture("minimal_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[1, 0, 1], [1, 1, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_str_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[1, 0, 1], [1, 1, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_plg_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[2, 0, 2], [2, 2, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_train_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[1, 0, 1], [1, 1, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_valid_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[1, 0, 1], [1, 1, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("minimal_test_ds_trafo_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + {"ScaleTrafo": ScaleTrafo}, + False, + False, + 1, + { + "minimal": { + "data": torch.tensor([[2, 0, 2], [2, 2, 0]], dtype=torch.uint8) + } + }, + ), + ( + lazy_fixture("cov_config"), + {}, + {}, + {}, + True, + True, + 1, + {"cov": {"covariates": [10, 31]}}, + ), + ( + lazy_fixture("geno_config"), + {}, + {}, + {}, + True, + True, + ceil(4096 * 0.1 / 16), + {"geno": {"genetic": [16, 10]}}, + ), + ( + lazy_fixture("ehr_config"), + {}, + {}, + {}, + True, + True, + 1, + { + "ehr": { + "records": [10, 6370], + "censorings": [10, 1], + "exclusions": [10, 1192], + "label_events": [10, 1192], + "label_times": [10, 1192], + "label_auxiliary_times": [10, 5178], + "label_auxiliary_events": [10, 5178], + } + }, + ), + ( + lazy_fixture("tabular_config"), + {}, + {}, + {}, + True, + True, + 1, + {"tabular": {"tabular_data": [10, 9]}}, + ), + ( + lazy_fixture("h5ad_config"), + {}, + {}, + {}, + True, + True, + ceil(4096 * 0.1 / 16), + {"pheno": {"tabular_data": [16, 1]}}, + ), + ( + lazy_fixture("cov_cov_config"), + {}, + {}, + {}, + True, + False, + 1, + { + "cov1": {"covariates": [10, 31]}, + "cov2": {"covariates": [10, 31]}, + }, + ), + ( + lazy_fixture("cov_geno_config"), + {}, + {}, + {}, + True, + False, + 1, + { + "cov": {"covariates": [10, 31]}, + "geno": {"genetic": [10, 10]}, + }, + ), + ], + ) + def test_test_dataloader( + self, + config, + overrides, + custom_plugins, + custom_transforms, + shapes_only, + update_splits, + exp_len, + exp_batch, + ): + config.update(overrides) + gdm = TestGeneralDataModule._run_setup( + config, + custom_plugins=custom_plugins, + custom_transforms=custom_transforms, + update_splits=update_splits, + ) + dataloader = gdm.test_dataloader(drop_last=False) + batch = deepcopy(next(iter(dataloader))) + assert len(dataloader) == exp_len + assertBatchEqual(batch, exp_batch, shapes_only=shapes_only) + del gdm, dataloader + + @pytest.mark.parametrize( + "config, overrides, custom_plugins", + [ + ( + lazy_fixture("from_clsmethod_config"), + {}, + {"MinimalPlugin": MinimalPlugin}, + ), + pytest.param(lazy_fixture("cov_config"), {}, {}, marks=SKIPIF_NOT_BIH_HPC), + pytest.param(lazy_fixture("geno_config"), {}, {}, marks=SKIPIF_NOT_BIH_HPC), + pytest.param(lazy_fixture("ehr_config"), {}, {}, marks=SKIPIF_NOT_BIH_HPC), + pytest.param( + lazy_fixture("tabular_config"), {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param(lazy_fixture("h5ad_config"), {}, {}, marks=SKIPIF_NOT_BIH_HPC), + pytest.param( + lazy_fixture("geno_geno_mini_config"), {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param( + lazy_fixture("geno_geno_mini_map_config"), + {}, + {}, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_geno_mini_map_config"), + {"combine_eids_as": "union"}, + {}, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("cov_cov_config"), {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param( + lazy_fixture("cov_geno_config"), {}, {}, marks=SKIPIF_NOT_BIH_HPC + ), + ], + ) + def test_serialize(self, config, overrides, custom_plugins): + config.update(overrides) + DIR = abspath(os.path.join(dirname(__file__), UDM_HOME)) + with TemporaryDirectory(dir=DIR) as module_dir: + plugin_name = list(config["plugins"].keys())[0] + module_path = ( + pathlib.Path(module_dir) / f"{plugin_name}_plugin_serialized.pkl" + ) + gdm = GeneralDatamodule(**config, custom_plugins=custom_plugins) + gdm.plugins[plugin_name].serialize(module_path) + + plugin = SerializedPlugin.from_compressed_pickle(module_path) diff --git a/test/general_dataset_test.py b/test/general_dataset_test.py new file mode 100644 index 0000000..309d4f0 --- /dev/null +++ b/test/general_dataset_test.py @@ -0,0 +1,679 @@ +import os +from inspect import isclass +from os.path import abspath, dirname +from typing import Callable +import itertools as it +import hydra +import numpy as np +import pandas as pd +import pytest +from pytest_lazyfixture import lazy_fixture +import torch +from omegaconf import OmegaConf +from tempfile import TemporaryDirectory + +from udm.general_dataset import GeneralDataset +from udm.plugins.covariates.covariates_plugin import CovariatesPlugin +from udm.plugins.genetic.genetic_plugin import GeneticPlugin +from udm.transforms.dict_transforms import PluginTransform, DatasetTransform +from test.utils import assertBatchEqual, try_clear_hydra +from test.transforms.tensor_transforms.factory_test import ScaleTrafo +from test.plugins.dataplugin_test import MinimalPlugin +from test.plugins.genetic.genetic_plugin_test import geno_debug_config # noqa +from test.plugins.covariates.covariates_plugin_test import ( + covariates_debug_config, + mini1_df, + prepare_mini1, + covariates_mini1_config, +) # noqa + + +UDM_HOME = ".." +UDM_HOME_ABSPATH = abspath(os.path.join(dirname(__file__), UDM_HOME)) + + +@pytest.fixture +def temp_dir(): + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +def prepare_plugin(plugin_cls, config): + plugin = plugin_cls.from_config(config) + plugin.prepare_data() + plugin.setup() + return plugin + + +@pytest.fixture +def minimal_plugin(): + return prepare_plugin(MinimalPlugin, OmegaConf.create({})) + + +@pytest.fixture +def minimal_str_plugin(): + return prepare_plugin(MinimalPlugin, OmegaConf.create({"eids_type": "str"})) + + +@pytest.fixture +def covariates_mini1_plugin(covariates_mini1_config): + return prepare_plugin(CovariatesPlugin, covariates_mini1_config) + + +@pytest.fixture +def prepare_genetic_mini1(temp_dir): + try_clear_hydra() + with TemporaryDirectory(dir=temp_dir) as temp_dir: + DS_NAME = "genetic1" + genetic_data = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]], dtype=np.uint8) + data_mmap = np.memmap( + f"{temp_dir}/{DS_NAME}.X.npy", + dtype=np.uint8, + mode="w+", + shape=(3, 3), + ) + data_mmap[:, :] = genetic_data[:, :] + data_mmap.flush() + obs = pd.DataFrame({"index": [0, 1, 2]}) + var = pd.DataFrame( + { + "Name": ["snp1", "snp2", "snp3"], + "ID": ["rs1", "rs2", "rs3"], + "GeneticAlt": ["G", "A", "G"], + "Field": ["ENSG001", "ENSG002", "ENSG003"], + } + ) + obs.to_csv(f"{temp_dir}/{DS_NAME}.obs.csv", index=False) + var.to_csv(f"{temp_dir}/{DS_NAME}.var.csv", index=False) + yield temp_dir, DS_NAME + + +@pytest.fixture +def genetic_mini1_plugin(prepare_genetic_mini1): + temp_dir, ds_name = prepare_genetic_mini1 + hydra.initialize( + version_base=None, + config_path=os.path.join(UDM_HOME, "config", "plugins", "genetic"), + ) + base_config = hydra.compose(config_name="default.yaml", overrides=[]) + overrides_config = OmegaConf.create( + { + "src": ds_name, + "src_names": "data", + "dataset_path": temp_dir, + "eid_map_path": None, + } + ) + config = OmegaConf.merge(base_config, overrides_config) + plugin = prepare_plugin(GeneticPlugin, config) + yield plugin + + +@pytest.fixture +def prepare_genetic_mini2(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + DS_NAME = "genetic2" + genetic_data = np.array( + [[1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]], dtype=np.uint8 + ) + data_mmap = np.memmap( + f"{temp_dir}/{DS_NAME}.X.npy", + dtype=np.uint8, + mode="w+", + shape=(4, 3), + ) + data_mmap[:, :] = genetic_data[:, :] + data_mmap.flush() + obs = pd.DataFrame({"index": [0, 1, 2, 3]}) + var = pd.DataFrame( + { + "Name": ["snp4", "snp5", "snp6"], + "ID": ["rs1", "rs2", "rs3"], + "GeneticAlt": ["C", "T", "G"], + "Field": ["ENSG004", "ENSG005", "ENSG006"], + } + ) + obs.to_csv(f"{temp_dir}/{DS_NAME}.obs.csv", index=False) + var.to_csv(f"{temp_dir}/{DS_NAME}.var.csv", index=False) + yield temp_dir, DS_NAME + + +@pytest.fixture +def genetic_mini2_plugin(prepare_genetic_mini2): + try_clear_hydra() + ds_path, ds_name = prepare_genetic_mini2 + hydra.initialize( + version_base=None, + config_path=os.path.join(UDM_HOME, "config", "plugins", "genetic"), + ) + base_config = hydra.compose(config_name="default.yaml", overrides=[]) + overrides_config = OmegaConf.create( + { + "src": ds_name, + "src_names": "data", + "dataset_path": ds_path, + "eid_map_path": None, + } + ) + config = OmegaConf.merge(base_config, overrides_config) + plugin = prepare_plugin(GeneticPlugin, config) + yield plugin + + +@pytest.fixture +def genetic_mini1_mini2_plugin(genetic_mini1_plugin, genetic_mini2_plugin): + yield [genetic_mini1_plugin, genetic_mini2_plugin] + + +@pytest.fixture +def genetic_base_config(): + try_clear_hydra() + hydra.initialize( + version_base=None, + config_path=os.path.join(UDM_HOME, "config", "plugins", "genetic"), + ) + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/geno/") + config = hydra.compose( + config_name="default.yaml", + overrides=[ + f"dataset_path={FAKE_DATA_PATH}", + "eid_map_path=null", + ], + ) + return config + + +@pytest.fixture +def genetic_plugin(genetic_base_config): + config = genetic_base_config + split_config = OmegaConf.create( + { + "src": [ + "genetic_extended_tr", + "genetic_valid", + "genetic_test", + ], + "src_names": ["train", "valid", "test"], + } + ) + config = OmegaConf.merge(config, split_config) + plugin = prepare_plugin(GeneticPlugin, config) + yield plugin + + +@pytest.fixture +def genetic_train_plugin(genetic_base_config): + config = genetic_base_config + split_config = OmegaConf.create( + { + "src": "genetic_extended_tr", + "src_names": "train", + } + ) + config = OmegaConf.merge(config, split_config) + plugin = prepare_plugin(GeneticPlugin, config) + yield plugin + + +@pytest.fixture +def genetic_train_eids(): + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/geno/") + src = "genetic_extended_tr" + obs_path = os.path.join(FAKE_DATA_PATH, f"{src}.obs.csv") + with open(obs_path, "r") as f: + eids = pd.read_csv(f)["index"].tolist() + return eids + + +@pytest.fixture +def genetic_valid_plugin(genetic_base_config): + config = genetic_base_config + split_config = OmegaConf.create( + { + "src": "genetic_valid", + "src_names": "valid", + } + ) + config = OmegaConf.merge(config, split_config) + plugin = prepare_plugin(GeneticPlugin, config) + yield plugin + + +@pytest.fixture +def genetic_valid_eids(): + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/geno/") + src = "genetic_valid" + obs_path = os.path.join(FAKE_DATA_PATH, f"{src}.obs.csv") + with open(obs_path, "r") as f: + eids = pd.read_csv(f)["index"].tolist() + return eids + + +@pytest.fixture +def genetic_test_plugin(genetic_base_config): + config = genetic_base_config + split_config = OmegaConf.create( + { + "src": "genetic_test", + "src_names": "test", + } + ) + config = OmegaConf.merge(config, split_config) + plugin = prepare_plugin(GeneticPlugin, config) + yield plugin + + +@pytest.fixture +def genetic_test_eids(): + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/geno/") + src = "genetic_test" + obs_path = os.path.join(FAKE_DATA_PATH, f"{src}.obs.csv") + with open(obs_path, "r") as f: + eids = pd.read_csv(f)["index"].tolist() + return eids + + +@pytest.fixture +def genetic_eids(genetic_train_eids, genetic_valid_eids, genetic_test_eids): + return [*genetic_train_eids, *genetic_valid_eids, *genetic_test_eids] + + +@pytest.fixture +def genetic_eids_head(genetic_eids): + return genetic_eids[:10] + + +@pytest.fixture +def geno_cov_debug_plugin(geno_debug_config, covariates_debug_config): + geno_debug_plugin = prepare_plugin(GeneticPlugin, geno_debug_config) + covariates_debug_plugin = prepare_plugin(CovariatesPlugin, covariates_debug_config) + yield [geno_debug_plugin, covariates_debug_plugin] + + +class TestGeneralDataset(object): + @pytest.mark.parametrize( + "plugins, names, custom, transforms", + [ + (lazy_fixture("minimal_plugin"), "minimal", {}, None), + (lazy_fixture("minimal_str_plugin"), "minimal", {}, None), + pytest.param( + lazy_fixture("genetic_plugin"), + "genetic", + {}, + None, + ), + pytest.param( + lazy_fixture("genetic_mini1_plugin"), + "genetic", + {}, + None, + ), + pytest.param( + lazy_fixture("genetic_mini1_plugin"), + "genetic", + {}, + DatasetTransform( + {"geno": PluginTransform({"genetic": ScaleTrafo(2.0)})} + ), + ), + ], + ) + def test__init__(self, plugins, names, custom, transforms): + if isinstance(plugins, list): + plugin_eids = [p.get_eids() for p in plugins] + else: + plugin_eids = [plugins.get_eids()] + eids_all = list(set(it.chain(*plugin_eids))) + eids = eids_all[:10] + general_ds = GeneralDataset( + plugins, names, eids, transforms=transforms, keyerr=False + ) + + @pytest.mark.parametrize( + "plugins, eids, exp_len", + [ + (lazy_fixture("minimal_plugin"), None, 6), + (lazy_fixture("minimal_plugin"), [0, 2, 3], 3), + (lazy_fixture("minimal_str_plugin"), ["a1", "b2", "f6"], 3), + pytest.param( + lazy_fixture("genetic_mini1_plugin"), + None, + 3, + ), + pytest.param( + lazy_fixture("genetic_mini2_plugin"), + None, + 4, + ), + pytest.param( + lazy_fixture("genetic_mini1_mini2_plugin"), + [0, 1, 2, 3], + 4, + ), + pytest.param( + lazy_fixture("genetic_plugin"), + lazy_fixture("genetic_eids_head"), + 10, + ), + pytest.param( + lazy_fixture("genetic_plugin"), + lazy_fixture("genetic_train_eids"), + 2048, + ), + pytest.param( + lazy_fixture("genetic_plugin"), + lazy_fixture("genetic_valid_eids"), + 1024, + ), + pytest.param( + lazy_fixture("genetic_plugin"), + lazy_fixture("genetic_test_eids"), + 1024, + ), + pytest.param( + lazy_fixture("genetic_train_plugin"), + lazy_fixture("genetic_train_eids"), + 2048, + ), + pytest.param( + lazy_fixture("genetic_valid_plugin"), + lazy_fixture("genetic_valid_eids"), + 1024, + ), + pytest.param( + lazy_fixture("genetic_test_plugin"), + lazy_fixture("genetic_test_eids"), + 1024, + ), + ], + ) + def test__len__(self, plugins, eids, exp_len): + if isinstance(eids, Callable): + eids = eids(plugins) + general_ds = GeneralDataset(plugins, eids=eids, keyerr=False) + assert len(general_ds) == exp_len + + FEMALE = [1.0, 0.0] + MALE = [0.0, 1.0] + ELF = [1.0, 0.0, 0.0] + HOBBIT = [0.0, 1.0, 0.0] + HUMAN = [0.0, 0.0, 1.0] + AGES = np.array([200.0, 60.0, 57.0]) + AGES = (AGES - np.mean(AGES)) / np.std(AGES) + + @pytest.mark.parametrize( + "plugins, names, custom, transforms, eids, ids, shapes_only, exp_item", + [ + *[ + ( + lazy_fixture("minimal_plugin"), + ["minimal"], + {}, + None, + None, + idx, + False, + { + "minimal": { + "data": torch.tensor( + [ + [0, 0, 0], + [0, 0, 1], + [0, 1, 0], + [1, 0, 0], + [1, 0, 1], + [1, 1, 0], + ], + dtype=torch.uint8, + )[idx, :] + } + }, + ) + for idx in range(6) + ], + ( + lazy_fixture("minimal_plugin"), + ["minimal"], + {}, + None, + None, + [0, 2, 3], + False, + { + "minimal": { + "data": torch.tensor( + [[0, 0, 0], [0, 1, 0], [1, 0, 0]], dtype=torch.uint8 + ) + } + }, + ), + *[ + ( + lazy_fixture("minimal_str_plugin"), + ["minimal"], + {}, + None, + None, + idx, + False, + { + "minimal": { + "data": torch.tensor( + [ + [0, 0, 0], + [0, 0, 1], + [0, 1, 0], + [1, 0, 0], + [1, 0, 1], + [1, 1, 0], + ], + dtype=torch.uint8, + )[idx, :] + } + }, + ) + for idx in range(6) + ], + pytest.param( + lazy_fixture("genetic_mini1_plugin"), + ["genetic_mini1"], + {}, + None, + None, + 0, + False, + { + "genetic_mini1": { + "genetic": torch.tensor([0, 1, 0], dtype=torch.float16) / 127 + }, + }, + ), + pytest.param( + lazy_fixture("genetic_mini1_plugin"), + ["genetic_mini1"], + {}, + DatasetTransform( + {"genetic_mini1": PluginTransform({"genetic": ScaleTrafo(2.0)})} + ), + None, + 0, + False, + { + "genetic_mini1": { + "genetic": torch.tensor([0, 2, 0], dtype=torch.float16) / 127 + }, + }, + ), + pytest.param( + lazy_fixture("genetic_plugin"), + ["genetic"], + {}, + None, + lazy_fixture("genetic_eids_head"), + 5797, + False, + IndexError, + ), + pytest.param( + lazy_fixture("genetic_plugin"), + ["genetic"], + {}, + None, + lazy_fixture("genetic_eids_head"), + 11, + False, + IndexError, + ), + pytest.param( + lazy_fixture("genetic_mini1_mini2_plugin"), + ["genetic_mini1", "genetic_mini2"], + {}, + None, + None, + 0, + False, + { + "genetic_mini1": { + "genetic": torch.tensor([0, 1, 0], dtype=torch.float16) / 127 + }, + "genetic_mini2": { + "genetic": torch.tensor([1, 0, 0], dtype=torch.float16) / 127 + }, + }, + ), + ( + lazy_fixture("covariates_mini1_plugin"), + ["cov_mini1"], + {}, + None, + None, + 0, + False, + { + "cov_mini1": { + "covariates": torch.tensor( + [AGES[0], *FEMALE, *ELF, 1.0], dtype=torch.float64 + ) + }, + }, + ), + ( + lazy_fixture("covariates_mini1_plugin"), + ["cov_mini1"], + {}, + None, + None, + 0, + False, + { + "cov_mini1": { + "covariates": torch.tensor( + [AGES[0], *FEMALE, *ELF, 1.0], dtype=torch.float64 + ) + }, + }, + ), + ], + ) + def test__getitem__( + self, + plugins, + names, + custom, + transforms, + eids, + ids, + shapes_only, + exp_item, + ): + if isinstance(eids, Callable): + eids = eids(plugins) + general_ds = GeneralDataset( + plugins, names=names, eids=eids, transforms=transforms, keyerr=False + ) + if isclass(exp_item) and issubclass(exp_item, Exception): + with pytest.raises(exp_item): + item = general_ds[ids] + else: + item = general_ds[ids] + assertBatchEqual(item, exp_item, shapes_only=shapes_only) + + @pytest.mark.parametrize( + "plugins, names, eids, exp_metadata", + [ + pytest.param( + lazy_fixture("genetic_mini1_plugin"), + ["geno"], + None, + { + "geno": { + "tags": [], + "eids": [0, 1, 2], + "features": {"genetic": (["rs1", "rs2", "rs3"],)}, + "feature_types": {"genetic": np.uint8}, + "variables": { + "0": { + "Name": "snp1", + "ID": "rs1", + "GeneticAlt": "G", + "Field": "ENSG001", + }, + "1": { + "Name": "snp2", + "ID": "rs2", + "GeneticAlt": "A", + "Field": "ENSG002", + }, + "2": { + "Name": "snp3", + "ID": "rs3", + "GeneticAlt": "G", + "Field": "ENSG003", + }, + }, + } + }, + ), + pytest.param( + lazy_fixture("genetic_mini2_plugin"), + ["geno"], + None, + { + "geno": { + "tags": [], + "eids": [0, 1, 2, 3], + "features": {"genetic": (["rs1", "rs2", "rs3"],)}, + "feature_types": {"genetic": np.uint8}, + "variables": { + "0": { + "Name": "snp4", + "ID": "rs1", + "GeneticAlt": "C", + "Field": "ENSG004", + }, + "1": { + "Name": "snp5", + "ID": "rs2", + "GeneticAlt": "T", + "Field": "ENSG005", + }, + "2": { + "Name": "snp6", + "ID": "rs3", + "GeneticAlt": "G", + "Field": "ENSG006", + }, + }, + } + }, + ), + ], + ) + def test_get_metadata(self, plugins, names, eids, exp_metadata): + if isinstance(eids, Callable): + eids = eids(plugins) + general_ds = GeneralDataset(plugins, names=names, eids=eids, keyerr=False) + metadata = general_ds.get_metadata() + KEYS = set(["eids", "features", "feature_types"]) + assert isinstance(metadata, dict) + assert metadata == exp_metadata diff --git a/test/integration/__init__.py b/test/integration/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/integration/test_dummy.py b/test/integration/test_dummy.py new file mode 100644 index 0000000..0e518e0 --- /dev/null +++ b/test/integration/test_dummy.py @@ -0,0 +1,3 @@ +class TestDummy: + def test_dummy(self): + assert True diff --git a/test/plugins/__init__.py b/test/plugins/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/plugins/covariates/__init__.py b/test/plugins/covariates/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/plugins/covariates/covariates_plugin_test.py b/test/plugins/covariates/covariates_plugin_test.py new file mode 100644 index 0000000..be5e081 --- /dev/null +++ b/test/plugins/covariates/covariates_plugin_test.py @@ -0,0 +1,511 @@ +import os +import numpy as np +import hydra +import yaml +import pyarrow as pa +import pyarrow.feather as feather +import pytest +import torch +from tempfile import TemporaryDirectory +from pytest_lazyfixture import lazy_fixture + +from udm.plugins.covariates.covariates_plugin import CovariatesPlugin +from test.utils import Head, SKIPIF_NOT_BIH_HPC, try_clear_hydra + +UDM_HOME = "../../.." + + +@pytest.fixture +def temp_dir(): + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +@pytest.fixture +def mini1_df(): + return { + "eid": [0, 1, 2], + "age": [200.0, 60.0, 57.0], + "sex": ["Female", "Male", "Female"], + "ancestry": ["Elf", "Human", "Hobbit"], + "recruitment_center": ["MiddleEarth", "MiddleEarth", "MiddleEarth"], + } + + +@pytest.fixture +def mini1_str_df(mini1_df): + mini1_df.update({"eid": ["a1", "b2", "c3"]}) + return mini1_df + + +def prepare_temp_file(temp_dir, name, df): + TMP_PATH = os.path.join(temp_dir, name) + covariates_pa = pa.Table.from_pydict(df) + feather.write_feather(covariates_pa, TMP_PATH) + return TMP_PATH + + +@pytest.fixture +def prepare_mini1(temp_dir, mini1_df): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_temp_file(temp_dir, "covariates1.feather", mini1_df) + + +@pytest.fixture +def prepare_mini1_str(temp_dir, mini1_str_df): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_temp_file(temp_dir, "covariates1.feather", mini1_str_df) + + +@pytest.fixture +def mini2_df(): + return { + "eid": [3, 4, 5], + "age": [60.0, 70.0, 1000.0], + "sex": ["Male", "Male", "Male"], + "ancestry": ["Human", "Human", "Dragon"], + "recruitment_center": ["MiddleEarth", "MiddleEarth", "MiddleEarth"], + } + + +@pytest.fixture +def mini2_str_df(mini2_df): + mini2_df.update({"eid": ["d4", "e5", "f6"]}) + return mini2_df + + +@pytest.fixture +def prepare_mini2(temp_dir, mini2_df): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_temp_file(temp_dir, "covariates2.feather", mini2_df) + + +@pytest.fixture +def prepare_mini2_str(temp_dir, mini2_str_df): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_temp_file(temp_dir, "covariates2.feather", mini2_str_df) + + +def covariates_config(config_name, overrides=[]): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "covariates") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose(config_name=config_name, overrides=overrides) + return config + + +@pytest.fixture +def covariates_debug_config(): + yield covariates_config(config_name="debug", overrides=[]) + + +@pytest.fixture +def covariates_mini_config(prepare_mini1, prepare_mini2): + TMP_PATH1, TMP_PATH2 = prepare_mini1, prepare_mini2 + yield covariates_config( + "default.yaml", + overrides=[ + f"data_paths=[{TMP_PATH1}, {TMP_PATH2}]", + "names=[]", + ], + ) + + +@pytest.fixture +def covariates_mini1_config(prepare_mini1): + TMP_PATH = prepare_mini1 + yield covariates_config("default.yaml", overrides=[f"data_paths={TMP_PATH}"]) + + +@pytest.fixture +def covariates_mini1_str_config(prepare_mini1_str): + TMP_PATH = prepare_mini1_str + yield covariates_config("default.yaml", overrides=[f"data_paths={TMP_PATH}"]) + + +@pytest.fixture +def covariates_mini2_config(prepare_mini2): + TMP_PATH = prepare_mini2 + yield covariates_config("default.yaml", overrides=[f"data_paths={TMP_PATH}"]) + + +@pytest.fixture +def covariates_mini2_str_config(prepare_mini2_str): + TMP_PATH = prepare_mini2_str + yield covariates_config("default.yaml", overrides=[f"data_paths={TMP_PATH}"]) + + +@pytest.fixture +def covariates_full_wandb_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "covariates") + hydra.initialize(version_base=None, config_path=config_path) + path_wandb = "data/cvd/data/2_datasets_pre/211110_anewbeginning/artifacts/" + config = hydra.compose( + config_name="from_wandb_default.yaml", + overrides=[f"data_paths=[{path_wandb}]"], + ) + yield config + + +@pytest.fixture +def covariates_debug_eids(): + COVARIATES_DEBUG_EIDS_PATH = os.path.join( + UDM_HOME, + "data/cvd/data/2_datasets_pre/211110_anewbeginning/artifacts/debug/not_anonymized_debug/eids.yaml", + ) + eids = yaml.load(COVARIATES_DEBUG_EIDS_PATH, Loader=yaml.CSafeLoader) + return list(eids) + + +@pytest.fixture +def covariates_debug_eids_head(covariates_debug_eids): + return Head(covariates_debug_eids) + + +@pytest.fixture +def covariates_debug_eid(covariates_debug_eids): + return covariates_debug_eids[101] + + +class TestCovariatesPlugin(object): + @pytest.mark.parametrize( + "config", + [ + pytest.param( + lazy_fixture("covariates_debug_config"), marks=SKIPIF_NOT_BIH_HPC + ), + lazy_fixture("covariates_mini_config"), + lazy_fixture("covariates_mini1_config"), + lazy_fixture("covariates_mini1_str_config"), + lazy_fixture("covariates_mini2_config"), + lazy_fixture("covariates_mini2_str_config"), + pytest.param( + lazy_fixture("covariates_full_wandb_config"), marks=SKIPIF_NOT_BIH_HPC + ), + ], + ) + def test__init__(self, config): + if config["__init__"] == "from_wandb": + covariates_plugin = CovariatesPlugin.from_wandb(**config) + else: + covariates_plugin = CovariatesPlugin.from_config(config) + + FEMALE = [1.0, 0.0] + MALE = [0.0, 1.0] + ELF = [1.0, 0.0, 0.0] + HOBBIT = [0.0, 1.0, 0.0] + HUMAN = [0.0, 0.0, 1.0] + AGES = np.array([200.0, 60.0, 57.0]) + AGES = (AGES - np.mean(AGES)) / np.std(AGES) + + @pytest.mark.parametrize( + "config, eid, features, exp_item", + [ + pytest.param( + lazy_fixture("covariates_debug_config"), + lazy_fixture("covariates_debug_eid"), + None, + None, + marks=SKIPIF_NOT_BIH_HPC, + ), + ( + lazy_fixture("covariates_mini1_config"), + 0, + None, + [AGES[0], *FEMALE, *ELF, 1.0], + ), + ( + lazy_fixture("covariates_mini1_config"), + 1, + None, + [AGES[1], *MALE, *HUMAN, 1.0], + ), + ( + lazy_fixture("covariates_mini1_config"), + 2, + None, + [AGES[2], *FEMALE, *HOBBIT, 1.0], + ), + ( + lazy_fixture("covariates_mini1_str_config"), + "a1", + None, + [AGES[0], *FEMALE, *ELF, 1.0], + ), + ( + lazy_fixture("covariates_mini1_str_config"), + "b2", + None, + [AGES[1], *MALE, *HUMAN, 1.0], + ), + ( + lazy_fixture("covariates_mini1_str_config"), + "c3", + None, + [AGES[2], *FEMALE, *HOBBIT, 1.0], + ), + ( + lazy_fixture("covariates_mini1_config"), + 0, + { + "covariates": ( + torch.tensor( + [True, True, True, False, False, False, False], + dtype=torch.bool, + ), + ) + }, + [AGES[0], *FEMALE], + ), + ( + lazy_fixture("covariates_mini1_config"), + 0, + {"covariates": (["num__age", "cat__sex_Female", "cat__sex_Male"],)}, + [AGES[0], *FEMALE], + ), + ( + lazy_fixture("covariates_mini1_config"), + 1, + { + "covariates": ( + torch.tensor( + [False, False, False, True, True, True, True], + dtype=torch.bool, + ), + ) + }, + [*HUMAN, 1.0], + ), + ( + lazy_fixture("covariates_mini1_config"), + 2, + { + "covariates": ( + torch.tensor( + [True, False, False, False, False, False, True], + dtype=torch.bool, + ), + ) + }, + [AGES[2], 1.0], + ), + ( + lazy_fixture("covariates_mini1_config"), + [0, 2], + None, + [[AGES[0], *FEMALE, *ELF, 1.0], [AGES[2], *FEMALE, *HOBBIT, 1.0]], + ), + ( + lazy_fixture("covariates_mini1_config"), + [0, 2], + {"covariates": ([True, True, True, False, False, False, False],)}, + [[AGES[0], *FEMALE], [AGES[2], *FEMALE]], + ), + ( + lazy_fixture("covariates_mini1_config"), + slice(0, 2), + None, + [[AGES[0], *FEMALE, *ELF, 1.0], [AGES[1], *MALE, *HUMAN, 1.0]], + ), + ], + ) + def test__getitem__(self, config, eid, features, exp_item): + covariates_plugin = CovariatesPlugin.from_config(config) + covariates_plugin.prepare_data() + covariates_plugin.setup() + item = covariates_plugin[eid, features] + assert isinstance(item, dict) + assert list(item.keys()) == ["covariates"] + # One-hot encoding of [Female, Male] + One-hot encoding of [Elf, Hobbit, Human] + if exp_item is not None: + expected = torch.tensor(exp_item, dtype=torch.float64) + assert torch.allclose(item["covariates"], expected) + + @pytest.mark.parametrize( + "config", + [ + pytest.param( + lazy_fixture("covariates_debug_config"), marks=SKIPIF_NOT_BIH_HPC + ), + lazy_fixture("covariates_mini_config"), + lazy_fixture("covariates_mini1_config"), + lazy_fixture("covariates_mini1_str_config"), + lazy_fixture("covariates_mini2_config"), + lazy_fixture("covariates_mini2_str_config"), + pytest.param( + lazy_fixture("covariates_full_wandb_config"), marks=SKIPIF_NOT_BIH_HPC + ), + ], + ) + def test_prepare_data(self, config): + if config["__init__"] == "from_wandb": + covariates_plugin = CovariatesPlugin.from_wandb(**config) + else: + covariates_plugin = CovariatesPlugin.from_config(config) + covariates_plugin.prepare_data() + + @pytest.mark.parametrize( + "config", + [ + pytest.param( + lazy_fixture("covariates_debug_config"), marks=SKIPIF_NOT_BIH_HPC + ), + lazy_fixture("covariates_mini_config"), + lazy_fixture("covariates_mini1_config"), + lazy_fixture("covariates_mini1_str_config"), + lazy_fixture("covariates_mini2_config"), + lazy_fixture("covariates_mini2_str_config"), + pytest.param( + lazy_fixture("covariates_full_wandb_config"), marks=SKIPIF_NOT_BIH_HPC + ), + ], + ) + def test_setup(self, config): + if config["__init__"] == "from_wandb": + covariates_plugin = CovariatesPlugin.from_wandb(**config) + else: + covariates_plugin = CovariatesPlugin.from_config(config) + covariates_plugin.prepare_data() + covariates_plugin.setup() + + @pytest.mark.parametrize( + "config, overrides, exp_columns", + [ + pytest.param( + lazy_fixture("covariates_debug_config"), + {}, + None, + marks=SKIPIF_NOT_BIH_HPC, + ), + (lazy_fixture("covariates_mini1_config"), {}, None), + (lazy_fixture("covariates_mini2_config"), {}, None), + pytest.param( + lazy_fixture("covariates_full_wandb_config"), + {}, + None, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("covariates_debug_config"), + { + "usecols": [], + "dropcols": [], + }, + set( + [ + "age_at_recruitment_f21022_0_0", + "sex_f31_0_0", + "ethnic_background_f21000_0_0", + "uk_biobank_assessment_centre_f54_0_0", + ] + ), + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("covariates_debug_config"), + { + "usecols": ["age_at_recruitment_f21022_0_0", "sex_f31_0_0"], + "dropcols": [], + }, + set(["age_at_recruitment_f21022_0_0", "sex_f31_0_0"]), + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("covariates_debug_config"), + { + "usecols": [], + "dropcols": ["age_at_recruitment_f21022_0_0", "sex_f31_0_0"], + }, + set( + [ + "ethnic_background_f21000_0_0", + "uk_biobank_assessment_centre_f54_0_0", + ] + ), + marks=SKIPIF_NOT_BIH_HPC, + ), + ], + ) + def test_get_metadata(self, config, overrides, exp_columns): + if overrides: + config.update(overrides) + if config["__init__"] == "from_wandb": + covariates_plugin = CovariatesPlugin.from_wandb(**config) + else: + covariates_plugin = CovariatesPlugin.from_config(config) + covariates_plugin.prepare_data() + covariates_plugin.setup() + metadata = covariates_plugin.get_metadata() + assert isinstance(metadata, dict) + KEYS_METADATA = set(["eids", "features", "feature_types"]) + assert set(metadata.keys()).intersection(KEYS_METADATA) == KEYS_METADATA + if exp_columns: + assert set(metadata["features"]) == exp_columns + + @pytest.mark.parametrize( + "config, prepare_data, setup, exp_eids", + [ + pytest.param( + lazy_fixture("covariates_debug_config"), + False, + False, + [], + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("covariates_debug_config"), + True, + False, + lazy_fixture("covariates_debug_eids_head"), + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("covariates_debug_config"), + True, + True, + lazy_fixture("covariates_debug_eids_head"), + marks=SKIPIF_NOT_BIH_HPC, + ), + (lazy_fixture("covariates_mini1_config"), False, False, []), + (lazy_fixture("covariates_mini1_config"), True, False, [0, 1, 2]), + (lazy_fixture("covariates_mini1_config"), True, True, [0, 1, 2]), + ( + lazy_fixture("covariates_mini1_str_config"), + True, + True, + ["a1", "b2", "c3"], + ), + (lazy_fixture("covariates_mini2_config"), True, True, [3, 4, 5]), + ( + lazy_fixture("covariates_mini2_str_config"), + True, + True, + ["d4", "e5", "f6"], + ), + pytest.param( + lazy_fixture("covariates_full_wandb_config"), + True, + True, + None, + marks=SKIPIF_NOT_BIH_HPC, + ), + ], + ) + def test_get_eids(self, config, prepare_data, setup, exp_eids, request): + if config["__init__"] == "from_wandb": + covariates_plugin = CovariatesPlugin.from_wandb(**config) + else: + covariates_plugin = CovariatesPlugin.from_config(config) + if prepare_data: + covariates_plugin.prepare_data() + if setup: + covariates_plugin.setup() + eids = covariates_plugin.get_eids() + assert isinstance(eids, list) + if exp_eids is not None: + # No need to compare ALL eids! + if isinstance(exp_eids, Head): + assert Head(eids) == exp_eids + else: + assert eids == exp_eids diff --git a/test/plugins/dataplugin_test.py b/test/plugins/dataplugin_test.py new file mode 100644 index 0000000..509667c --- /dev/null +++ b/test/plugins/dataplugin_test.py @@ -0,0 +1,669 @@ +import pytest +from pytest_lazyfixture import lazy_fixture +import pandas as pd +import tempfile +import numpy as np +import torch +from inspect import isclass +from collections import OrderedDict +from typing import Any, Dict, Hashable, Iterable, List, Tuple +from omegaconf import OmegaConf, DictConfig + +from udm.plugins.dataplugin import ( + DataPlugin, + PluginFeatureSelection, + mapped, +) +from udm.utils import SliceableDict, numpy_to_torch +from udm.filters import PluginRowFilter, PluginColFilter, IsIn, AnyIn +from udm.transforms.dict_transforms import PluginTransform +from udm.transforms.tensor_transforms import ListTransform +from test.transforms.tensor_transforms.factory_test import ScaleTrafo +from test.filters.simple_test import AnyOne, AnyTwo + + +class MinimalPlugin(DataPlugin): + def __init__(self, eids_type="int", **kwargs): + super().__init__(**kwargs) + self.data = torch.tensor( + [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 0, 1], [1, 1, 0]], + dtype=torch.uint8, + ) + self.eids = ( + ["a1", "b2", "c3", "d4", "e5", "f6"] + if eids_type == "str" + else [0, 1, 2, 3, 4, 5] + ) + self.eid_to_idx = SliceableDict({eid: idx for idx, eid in enumerate(self.eids)}) + + def __len__(self): + return len(self.data) + + @mapped + def __getitem__( + self, eid: int, features: PluginFeatureSelection = None + ) -> Dict[str, torch.Tensor]: + return super().__getitem__(eid, features=features) + + def _getitem(self, eid: int, features: PluginFeatureSelection = None): + idx = self.eid_to_idx[eid] + features = features["data"][0] if features else slice(None) + if isinstance(idx, Iterable) and isinstance(features, Iterable): + return {"data": self.data[np.ix_(idx, features)]} + else: + return {"data": self.data[idx, features]} + + def get_feature_types(self) -> Dict[str, torch.dtype]: + return {"data": torch.uint8} + + def _get_features(self) -> Dict[str, Tuple[List[Hashable]]]: + return {"data": ([0, 1, 2],)} + + @classmethod + def from_list(cls, data: list, **kwargs): + instance = cls() + data = np.array(data) + instance.data = torch.from_numpy(data) + return instance + + def get_eids(self) -> List[int]: + return self.get_native_eids() + + def get_native_eids(self) -> List[int]: + return self.eids + + +class GenericPlugin(DataPlugin): + def __init__(self, path: str, **kwargs): + super().__init__(**kwargs) + self.path = path + + def __len__(self): + return len(self.df) + + @mapped + def __getitem__( + self, eid: int, features: PluginFeatureSelection = None + ) -> Dict[str, torch.Tensor]: + return super().__getitem__(eid, features=features) + + def _getitem( + self, eid: int, features: PluginFeatureSelection = None + ) -> Dict[str, torch.Tensor]: + features_ = features["data"][0] if features else slice(None, None, None) + if isinstance(features_, torch.Tensor): + features_ = features_.tolist() + tensor = torch.tensor(self.df.loc[eid, features_].values) + return {"data": tensor} + + def get_feature_types(self) -> Dict[str, Any]: + dtype_list = df.dtypes.tolist() + return {"data": numpy_to_torch(dtype_list[0])} + + def _get_features(self) -> Dict[str, Tuple[List[Hashable]]]: + return {"data": (list(self.df.columns),)} + + def _setup(self) -> None: + return None + + def prepare_data(self) -> None: + self.df = pd.read_csv(self.path) + + def _get_native_eids(self) -> List[int]: + """Returns the native eids of all samples in this DataPlugin as list.""" + return list(self.df.index.astype(int)) + + def get_native_eids_as_struct(self) -> "OrderedDict[str, List[int]]": + """Returns the native eids of all samples in this DataPlugin as struct.""" + return {0: self.get_native_eids()} + + +@pytest.fixture +def temp_df_path(): + with tempfile.NamedTemporaryFile(mode="w+", suffix=".csv") as temp_file: + df = pd.DataFrame( + {"C1": [1.0, 2.0, 3.0], "C2": [2.0, 3.0, 4.0], "C3": [3.0, 4.0, 5.0]} + ) + temp_path = temp_file.name + df.to_csv(temp_path, index=False) + yield temp_path + + +@pytest.fixture +@pytest.mark.usefixtures("temp_df_path") +def base_cfg(temp_df_path): + return DictConfig( + { + "path": temp_df_path, + "eid_map_path": None, + "eid_map_from": "col1", + "eid_map_to": "col2", + "transforms": None, + "row_filter": None, + "col_filter": None, + "tags": [], + } + ) + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def filter_eids_cfg(base_cfg): + filters_cfg = DictConfig( + { + "row_filter": { + "key_filter": { + "name": "IsIn", + "__init__": "__init__", + "whitelist": [0, 1], + }, + "val_filters": {}, + } + } + ) + config = OmegaConf.merge(base_cfg, filters_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def filter_nanrows_cfg(base_cfg): + filters_cfg = DictConfig( + { + "row_filter": { + "key_filter": None, + "val_filters": { + "data": { + "name": "Not", + "__init__": "__init__", + "filters": [{"name": "HasNan", "__init__": "__init__"}], + } + }, + } + } + ) + config = OmegaConf.merge(base_cfg, filters_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def filter_ones_cfg(base_cfg): + filters_cfg = DictConfig( + { + "row_filter": { + "key_filter": None, + "val_filters": {"data": {"name": "AnyOne", "__init__": "__init__"}}, + } + } + ) + config = OmegaConf.merge(base_cfg, filters_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def filter_anyin_cfg(base_cfg): + filters_cfg = DictConfig( + { + "row_filter": { + "key_filter": None, + "val_filters": { + "data": { + "name": "AnyIn", + "__init__": "from_sequence", + "whitelist": [1, 5], + } + }, + } + } + ) + config = OmegaConf.merge(base_cfg, filters_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def filter_features_cfg(base_cfg): + filters_cfg = DictConfig( + { + "col_filter": { + "key_filters": { + "data": { + "name": "IsIn", + "__init__": "__init__", + "whitelist": ["C1", "C3"], + } + }, + "val_filters": {}, + } + } + ) + config = OmegaConf.merge(base_cfg, filters_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def filter_features_val_cfg(base_cfg): + filters_cfg = DictConfig( + { + "col_filter": { + "key_filters": {}, + "val_filters": { + "data": { + "name": "AnyIn", + "__init__": "from_sequence", + "whitelist": [4, 6], + } + }, + } + } + ) + config = OmegaConf.merge(base_cfg, filters_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def filter_features_keyval_cfg(base_cfg): + filters_cfg = DictConfig( + { + "col_filter": { + "key_filters": { + "data": { + "name": "IsIn", + "__init__": "__init__", + "whitelist": ["C1", "C3"], + } + }, + "val_filters": { + "data": { + "name": "AnyIn", + "__init__": "from_sequence", + "whitelist": [4, 6], + } + }, + } + } + ) + config = OmegaConf.merge(base_cfg, filters_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def filter_features_notones_cfg(base_cfg): + filters_cfg = DictConfig( + { + "col_filter": { + "key_filter": None, + "val_filters": { + "data": { + "name": "Not", + "__init__": "__init__", + "filters": [{"name": "AnyOne", "__init__": "__init__"}], + } + }, + } + } + ) + config = OmegaConf.merge(base_cfg, filters_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def scale_trafo_cfg(base_cfg): + transforms_cfg = DictConfig( + { + "transforms": { + "name": "PluginTransform", + "transforms": { + "data": {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2.0} + }, + } + } + ) + config = OmegaConf.merge(base_cfg, transforms_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("base_cfg") +def list_trafo_cfg(base_cfg): + transforms_cfg = DictConfig( + { + "transforms": { + "name": "PluginTransform", + "transforms": { + "data": { + "name": "ListTransform", + "transforms": [ + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + ], + } + }, + } + } + ) + config = OmegaConf.merge(base_cfg, transforms_cfg) + return config + + +@pytest.fixture +@pytest.mark.usefixtures("scale_trafo_cfg") +def filter_custom_trafo_filter_cfg(scale_trafo_cfg): + filter_cfg = DictConfig( + { + "col_filter": { + "key_filter": None, + "val_filters": { + "data": { + "name": "Not", + "__init__": "__init__", + "filters": [{"name": "AnyTwo", "__init__": "__init__"}], + } + }, + } + } + ) + config = OmegaConf.merge(scale_trafo_cfg, filter_cfg) + return config + + +class TestDataplugin(object): + @pytest.mark.parametrize( + "eid_map_path,eid_map_from,eid_map_to,transforms,row_filter,col_filter,tags", + [ + (None, "col1", "col2", None, None, None, []), + ( + None, + "col1", + "col2", + PluginTransform({"data": ScaleTrafo(2.0)}), + None, + None, + [], + ), + ( + None, + "col1", + "col2", + PluginTransform( + {"data": ListTransform([ScaleTrafo(2.0), ScaleTrafo(2.0)])} + ), + None, + None, + [], + ), + ( + None, + "col1", + "col2", + None, + PluginRowFilter( + key_filter=IsIn([1, 2]), + val_filters={"data": AnyIn(torch.tensor([2, 3]))}, + ), + None, + [], + ), + ( + None, + "col1", + "col2", + None, + PluginColFilter( + key_filters={"data": IsIn(["C1", "C3"])}, + val_filters={"data": AnyIn(torch.tensor([2, 3]))}, + ), + None, + [], + ), + ], + ) + def test__init__( + self, + eid_map_path, + eid_map_from, + eid_map_to, + transforms, + row_filter, + col_filter, + tags, + ): + plugin = GenericPlugin( + path=temp_df_path, + eid_map_path=eid_map_path, + eid_map_from=eid_map_from, + eid_map_to=eid_map_to, + transforms=transforms, + row_filter=row_filter, + col_filter=col_filter, + tags=tags, + ) + + @pytest.mark.parametrize( + "config,custom_transforms", + [ + (lazy_fixture("base_cfg"), {}), + (lazy_fixture("scale_trafo_cfg"), {"ScaleTrafo": ScaleTrafo}), + (lazy_fixture("list_trafo_cfg"), {"ScaleTrafo": ScaleTrafo}), + (lazy_fixture("filter_nanrows_cfg"), {}), + (lazy_fixture("filter_eids_cfg"), {}), + (lazy_fixture("filter_ones_cfg"), {"AnyOne": AnyOne}), + (lazy_fixture("filter_anyin_cfg"), {}), + ], + ) + def test_from_config(self, config, custom_transforms): + plugin = GenericPlugin.from_config(config, custom=custom_transforms) + + @pytest.mark.parametrize( + "args_or_config,custom_transforms,eid,features,exp_item", + [ + ((None, "col1", "col2", None, []), {}, 0, None, {"data": [1.0, 2.0, 3.0]}), + (lazy_fixture("base_cfg"), {}, 0, None, {"data": [1.0, 2.0, 3.0]}), + ((None, "col1", "col2", None, []), 1, {}, None, {"data": [2.0, 3.0, 4.0]}), + (lazy_fixture("base_cfg"), {}, 1, None, {"data": [2.0, 3.0, 4.0]}), + ( + (None, "col1", "col2", PluginTransform({"data": ScaleTrafo(2.0)}), []), + {}, + 0, + None, + {"data": [2.0, 4.0, 6.0]}, + ), + ( + lazy_fixture("scale_trafo_cfg"), + {"ScaleTrafo": ScaleTrafo}, + 0, + None, + {"data": [2.0, 4.0, 6.0]}, + ), + ( + ( + None, + "col1", + "col2", + PluginTransform( + {"data": ListTransform([ScaleTrafo(2.0), ScaleTrafo(2.0)])} + ), + [], + ), + {}, + 0, + None, + {"data": [4.0, 8.0, 12.0]}, + ), + ( + lazy_fixture("list_trafo_cfg"), + {"ScaleTrafo": ScaleTrafo}, + 0, + None, + {"data": [4.0, 8.0, 12.0]}, + ), + (lazy_fixture("base_cfg"), {}, 1, None, {"data": [2.0, 3.0, 4.0]}), + ( + lazy_fixture("base_cfg"), + {}, + 0, + {}, + {}, + ), + ( + lazy_fixture("filter_ones_cfg"), + {"AnyOne": AnyOne}, + 0, + None, + KeyError, + ), + ( + lazy_fixture("filter_ones_cfg"), + {"AnyOne": AnyOne}, + 1, + None, + {"data": [2.0, 3.0, 4.0]}, + ), + ( + lazy_fixture("filter_ones_cfg"), + {"AnyOne": AnyOne}, + 2, + None, + {"data": [3.0, 4.0, 5.0]}, + ), + (lazy_fixture("filter_anyin_cfg"), {}, 0, None, KeyError), + ( + lazy_fixture("filter_anyin_cfg"), + {}, + 1, + None, + {"data": [2.0, 3.0, 4.0]}, + ), + (lazy_fixture("filter_anyin_cfg"), {}, 2, None, KeyError), + ( + lazy_fixture("filter_features_notones_cfg"), + {"AnyOne": AnyOne}, + 0, + None, + {"data": [2.0, 3.0]}, + ), + ( + lazy_fixture("filter_custom_trafo_filter_cfg"), + {"AnyTwo": AnyTwo, "ScaleTrafo": ScaleTrafo}, + 0, + None, + {"data": [4.0, 6.0]}, + ), + ( + lazy_fixture("filter_features_cfg"), + {}, + 0, + {"data": ["C2"]}, + KeyError, + ), + ( + lazy_fixture("filter_features_cfg"), + {}, + 0, + None, + {"data": [1.0, 3.0]}, + ), + ( + lazy_fixture("filter_features_cfg"), + {}, + 1, + None, + {"data": [2.0, 4.0]}, + ), + ( + lazy_fixture("filter_features_cfg"), + {}, + 2, + None, + {"data": [3.0, 5.0]}, + ), + ( + lazy_fixture("filter_features_val_cfg"), + {}, + 0, + None, + {"data": [2.0, 3.0]}, + ), + ( + lazy_fixture("filter_features_val_cfg"), + {}, + 1, + None, + {"data": [3.0, 4.0]}, + ), + ( + lazy_fixture("filter_features_val_cfg"), + {}, + 2, + None, + {"data": [4.0, 5.0]}, + ), + ( + lazy_fixture("filter_features_keyval_cfg"), + {}, + 0, + None, + {"data": [3.0]}, + ), + ( + lazy_fixture("filter_features_keyval_cfg"), + {}, + 1, + None, + {"data": [4.0]}, + ), + ( + lazy_fixture("filter_features_keyval_cfg"), + {}, + 2, + None, + {"data": [5.0]}, + ), + ], + ) + @pytest.mark.usefixtures("temp_df_path") + def test__getitem__( + self, args_or_config, custom_transforms, eid, features, exp_item, temp_df_path + ): + if isclass(exp_item) and issubclass(exp_item, Exception): + with pytest.raises(exp_item): + self._test__getitem__( + args_or_config, + custom_transforms, + eid, + features, + exp_item, + temp_df_path, + ) + else: + self._test__getitem__( + args_or_config, custom_transforms, eid, features, exp_item, temp_df_path + ) + + def _test__getitem__( + self, args_or_config, custom_transforms, eid, features, exp_item, temp_df_path + ): + if isinstance(args_or_config, DictConfig): + plugin = GenericPlugin.from_config(args_or_config, custom=custom_transforms) + else: + eid_map_path, eid_map_from, eid_map_to, transforms, tags = args_or_config + plugin = GenericPlugin( + path=temp_df_path, + eid_map_path=eid_map_path, + eid_map_from=eid_map_from, + eid_map_to=eid_map_to, + transforms=transforms, + ) + plugin.prepare_data() + plugin.setup() + item = plugin[eid, features] + assert isinstance(item, dict) + assert item.keys() == exp_item.keys() + for key in item.keys(): + assert torch.all(item[key] == torch.tensor(exp_item[key])) diff --git a/test/plugins/ehr/__init__.py b/test/plugins/ehr/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/plugins/ehr/ehr_plugin_test.py b/test/plugins/ehr/ehr_plugin_test.py new file mode 100644 index 0000000..86829a7 --- /dev/null +++ b/test/plugins/ehr/ehr_plugin_test.py @@ -0,0 +1,1040 @@ +import os +from tempfile import TemporaryDirectory +import hydra +import numpy as np +import pandas as pd +import pytest +from pytest_lazyfixture import lazy_fixture +import torch +import yaml + +from udm.plugins.ehr.ehr_plugin import EHRPlugin +from test.utils import SKIPIF_NOT_BIH_HPC, Head, try_clear_hydra + + +UDM_HOME = "../../.." + + +@pytest.fixture +def temp_dir(): + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +@pytest.fixture +def data_mini1(): + records_dict1 = { + "concept_id": [ + "OMOP_1548196", + "OMOP_4324588", + "OMOP_4060831", + "phecode_557", + "phecode_247.72", + "OMOP_4306655", + ], + "domain_id": [ + "Drug", + "Observation", + "Condition", + "GI", + "Metab", + "Observation", + ], + "vocabulary": ["OMOP", "OMOP", "OMOP", "phecode", "phecode", "OMOP"], + "origin": [ + "gp_scripts_tpp", + "gp_ctv3", + "gp_ctv3", + "hes_icd10", + "hes_icd10", + "gp_ctv3", + ], + "eid": [327, 578, 578, 111, 111, 111], + "birth_date": [ + "1938-05-20", + "1949-08-07", + "1949-08-07", + "1956-08-16", + "1956-08-16", + "1956-08-16", + ], + "recruitment_date": [ + "2002-03-14", + "2005-08-23", + "2005-08-23", + "2010-01-07", + "2010-01-07", + "2010-01-07", + ], + "death_date": ["nat", "nat", "nat", "nat", "nat", "nat"], + "exit_date": [ + "2019-08-19", + "2019-08-19", + "2019-08-19", + "2019-08-19", + "2019-08-19", + "2019-08-19", + ], + "date": [ + "1947-02-03 13:26:04", + "1972-03-10 02:35:27", + "1955-11-25 18:07:14", + "2009-12-04 01:36:37", + "1993-03-18 01:14:02", + "2019-08-19 17:54:08", + ], + } + + dtype_dict = { + "concept_id": "category", + "domain_id": "category", + "vocabulary": "category", + "origin": "category", + "birth_date": np.datetime64, + "recruitment_date": np.datetime64, + "death_date": np.datetime64, + "exit_date": np.datetime64, + "date": np.datetime64, + } + return records_dict1, dtype_dict + + +@pytest.fixture +def data_mini1_str(data_mini1): + records_dict1, dtype_dict = data_mini1 + records_dict1.update({"eid": ["a327", "b578", "b578", "c111", "c111", "c111"]}) + yield records_dict1, dtype_dict + + +def prepare_feather(temp_dir, filename, records_dict, dtype_dict): + TMP_PATH = os.path.join(temp_dir, f"{filename}.feather") + records_df = pd.DataFrame(records_dict) + records_df = records_df.astype(dtype_dict) + records_df.to_feather(TMP_PATH) + return TMP_PATH + + +@pytest.fixture +def prepare_mini1(temp_dir, data_mini1): + records_dict1, dtype_dict = data_mini1 + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_feather(temp_dir, "records1", records_dict1, dtype_dict) + + +@pytest.fixture +def prepare_mini1_str(temp_dir, data_mini1_str): + records_dict1, dtype_dict = data_mini1_str + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_feather(temp_dir, "records1", records_dict1, dtype_dict) + + +@pytest.fixture +def data_mini2(temp_dir): + records_dict2 = { + "concept_id": [ + "phecode_401", + "OMOP_4306655", + "OMOP_4214579", + "OMOP_19020124", + "phecode_247.72", + ], + "domain_id": ["Cardio", "Observation", "Condition", "Drug", "Metab"], + "vocabulary": ["phecode", "OMOP", "OMOP", "OMOP", "phecode"], + "origin": [ + "hes_icd10", + "gp_ctv3", + "gp_ctv3", + "gp_scripts_emis", + "hes_icd10", + ], + "eid": [181, 181, 996, 733, 733], + "birth_date": [ + "1942-01-07", + "1942-01-07", + "1960-02-23", + "1962-10-25", + "1962-10-25", + ], + # "birth_date":[np.datetime64("1938-05-20"),np.datetime64("1949-08-07")] + "recruitment_date": [ + "1999-02-03", + "1999-02-03", + "2019-12-21", + "2003-08-18", + "2003-08-18", + ], + "death_date": ["2013-03-08", "2013-03-08", "2017-09-01", "nat", "nat"], + "exit_date": [ + "2013-03-08", + "2013-03-08", + "2017-09-01", + "2019-08-19", + "2019-08-19", + ], + "date": [ + "1968-12-30 13:09:34", + "2013-03-08 03:36:19", + "2001-09-05 16:54:37", + "1992-06-21 06:23:06", + "1988-05-20 22:04:36", + ], + } + dtype_dict = { + "concept_id": "category", + "domain_id": "category", + "vocabulary": "category", + "origin": "category", + "birth_date": np.datetime64, + "recruitment_date": np.datetime64, + "death_date": np.datetime64, + "exit_date": np.datetime64, + "date": np.datetime64, + } + return records_dict2, dtype_dict + + +@pytest.fixture +def data_mini2_str(temp_dir, data_mini2): + records_dict2, dtype_dict = data_mini2 + records_dict2.update( + { + "eid": ["d181", "d181", "e996", "f733", "f733"], + } + ) + yield records_dict2, dtype_dict + + +@pytest.fixture +def prepare_mini2(temp_dir, data_mini2): + records_dict2, dtype_dict = data_mini2 + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_feather(temp_dir, "records2", records_dict2, dtype_dict) + + +@pytest.fixture +def prepare_mini2_str(temp_dir, data_mini2_str): + records_dict2, dtype_dict = data_mini2_str + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_feather(temp_dir, "records2", records_dict2, dtype_dict) + + +@pytest.fixture +def prepare_phecode_def_mini(temp_dir): + phecode_definitions_dict = { + "index": [2367, 3789, 6065], + "phecode": ["247.72", "401", "557"], + "phecode_string": [ + "Streptococcus", + "Lichen planus, nitidus, or striatus", + "Family history of genetic condition", + ], + "phecode_category": ["ID", "ID", "Derm"], + "sex": ["Both", "Both", "Both"], + "ICD10_only": [0, 0, 0], + "phecode_top": [1, 2, 3], + "leaf": [0, 1, 1], + } + + with TemporaryDirectory(dir=temp_dir) as temp_dir: + TMP_PATH = os.path.join(temp_dir, "phecode_def.feather") + phecode_definitions_df = pd.DataFrame(phecode_definitions_dict) + phecode_definitions_df.to_feather(TMP_PATH) + yield TMP_PATH + + +def prepare_eids_yaml(temp_dir, eids): + TMP_PATH = os.path.join(temp_dir, "eids.yaml") + with open(TMP_PATH, "w") as eids_outfile: + yaml.dump(eids, eids_outfile, default_flow_style=False) + return TMP_PATH + + +@pytest.fixture +def prepare_eids_mini1(temp_dir): + eids_array = np.array([327, 578, 111]) + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_eids_yaml(temp_dir, eids_array) + + +@pytest.fixture +def prepare_eids_mini1_str(temp_dir): + eids_array = np.array(["a327", "b578", "c111"]) + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_eids_yaml(temp_dir, eids_array) + + +@pytest.fixture +def prepare_eids_mini2(temp_dir): + eids_array = np.array([181, 996, 733]) + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_eids_yaml(temp_dir, eids_array) + + +@pytest.fixture +def prepare_eids_mini2_str(temp_dir): + eids_array = np.array(["d181", "e996", "f733"]) + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_eids_yaml(temp_dir, eids_array) + + +@pytest.fixture +def prepare_record_freq_mini(temp_dir): + record_freq_dict = { + "record": [ + "OMOP_1548196", + "OMOP_4324588", + "OMOP_4060831", + "phecode_557", + "phecode_401", + "OMOP_4214579", + "OMOP_19020124", + "phecode_247.72", + ], + "n": [1, 1, 1, 1, 1, 1, 1, 1], + } + with TemporaryDirectory(dir=temp_dir) as temp_dir: + TMP_PATH = os.path.join(temp_dir, "record_freq.feather") + record_freq_df = pd.DataFrame(record_freq_dict) + record_freq_df.to_feather(TMP_PATH) + yield TMP_PATH + + +@pytest.fixture +def ehr_debug_eids(): + EHR_DEBUG_EIDS_PATH = os.path.join( + UDM_HOME, + "data/cvd/data/2_datasets_pre/211110_anewbeginning/artifacts/debug/eids.yaml", + ) + eids = yaml.load(EHR_DEBUG_EIDS_PATH, Loader=yaml.CSafeLoader) + return list(eids) + + +@pytest.fixture +def ehr_debug_eids_head(ehr_debug_eids): + return Head(ehr_debug_eids) + + +@pytest.fixture +def ehr_debug_three_eids(ehr_debug_eids): + return [ehr_debug_eids[x] for x in [111, 123, 124]] + + +@pytest.fixture +def ehr_debug_eid(ehr_debug_eids): + return ehr_debug_eids[101] + + +@pytest.fixture +def metadata_individuals_mini1(): + return { + "eid": [327, 578, 111], + "birth_date": [ + "1938-05-20", + "1949-08-07", + "1956-08-16", + ], + "recruitment_date": [ + "2002-03-14", + "2005-08-23", + "2010-01-07", + ], + "death_date": ["nat", "nat", "nat"], + "cens_data": [ + "2019-08-19", + "2019-08-19", + "2019-08-19", + ], + "exit_date": [ + "2019-08-19", + "2019-08-19", + "2019-08-19", + ], + } + + +@pytest.fixture +def metadata_individuals_mini1_str(metadata_individuals_mini1): + metadata = metadata_individuals_mini1 + metadata.update({"eid": ["a327", "b578", "c111"]}) + return metadata + + +def prepare_metadata_individuals(temp_dir, metadata_individuals): + TMP_PATH = os.path.join(temp_dir, "metadata_individuals.feather") + metdata_individuals_df = pd.DataFrame(metadata_individuals) + metdata_individuals_df.to_feather(TMP_PATH) + return TMP_PATH + + +@pytest.fixture +def prepare_metadata_individuals_mini1(temp_dir, metadata_individuals_mini1): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_metadata_individuals(temp_dir, metadata_individuals_mini1) + + +@pytest.fixture +def prepare_metadata_individuals_mini1_str(temp_dir, metadata_individuals_mini1_str): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_metadata_individuals(temp_dir, metadata_individuals_mini1_str) + + +@pytest.fixture +def metadata_individuals_mini2(): + return { + "eid": [181, 996, 733], + "birth_date": [ + "1942-01-07", + "1960-02-23", + "1962-10-25", + ], + "recruitment_date": [ + "1999-02-03", + "2019-12-21", + "2003-08-18", + ], + "death_date": ["2013-03-08", "2017-09-01", "nat"], + "cens_data": [ + "2013-03-08", + "2017-09-01", + "2019-08-19", + ], + "exit_date": [ + "2013-03-08", + "2017-09-01", + "2019-08-19", + ], + } + + +@pytest.fixture +def metadata_individuals_mini2_str(metadata_individuals_mini2): + metadata = metadata_individuals_mini2 + metadata.update({"eid": ["d181", "e996", "f733"]}) + return metadata + + +@pytest.fixture +def prepare_metadata_individuals_mini2(temp_dir, metadata_individuals_mini2): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_metadata_individuals(temp_dir, metadata_individuals_mini2) + + +@pytest.fixture +def prepare_metadata_individuals_mini2_str(temp_dir, metadata_individuals_mini2_str): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_metadata_individuals(temp_dir, metadata_individuals_mini2_str) + + +@pytest.fixture +def ehr_debug_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "ehr") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="debug.yaml", + overrides=[ + "min_record_counts=0", + "use_top_n_phecodes=1683", + "t0_mode=recruitment", + ], + ) + config.phecode_definitions_path = os.path.join( + config.data_path, config.phecode_definitions_path + ) + config.records_path = os.path.join(config.data_path, config.records_path) + config.records_frequencies_path = os.path.join( + config.data_path, config.records_frequencies_path + ) + config.metadata_individuals_path = os.path.join( + config.data_path, config.metadata_individuals_path + ) + yield config + + +@pytest.fixture +def ehr_debug_birth_date_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "ehr") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="debug.yaml", + overrides=[ + "min_record_counts=0", + "use_top_n_phecodes=10000", + "label_definition.all_cause_death=False", + "t0_mode=birth_date", + ], + ) + config.phecode_definitions_path = os.path.join( + config.data_path, config.phecode_definitions_path + ) + config.records_path = os.path.join(config.data_path, config.records_path) + config.records_frequencies_path = os.path.join( + config.data_path, config.records_frequencies_path + ) + config.metadata_individuals_path = os.path.join( + config.data_path, config.metadata_individuals_path + ) + yield config + + +@pytest.fixture +def ehr_mini_config( + prepare_mini1, + prepare_mini2, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini1, +): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "ehr") + hydra.initialize(version_base=None, config_path=config_path) + TMP_PATH1, TMP_PATH2, TMP_PATH_PHE_DEF, TMP_PATH_REC_FRE, TMP_PATH_EIDS = ( + prepare_mini1, + prepare_mini2, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini1, + ) + config = hydra.compose( + "debug.yaml", + overrides=[ + f"use_top_n_phecodes=2", + f"data_path=[{TMP_PATH1}, {TMP_PATH2}]", + f"eids_path={TMP_PATH_EIDS}", + f"phecode_definitions_path={TMP_PATH_PHE_DEF}", + f"records_frequencies_path={TMP_PATH_REC_FRE}", + ], + ) + yield config + + +@pytest.fixture +def ehr_mini1_config( + prepare_mini1, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini1, + prepare_metadata_individuals_mini1, +): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "ehr") + hydra.initialize(version_base=None, config_path=config_path) + TMP_PATH, TMP_PATH_PHE_DEF, TMP_PATH_REC_FRE, TMP_PATH_EIDS, TMP_PATH_MET_IND = ( + prepare_mini1, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini1, + prepare_metadata_individuals_mini1, + ) + config = hydra.compose( + "debug.yaml", + overrides=[ + f"records_path={TMP_PATH}", + f"eids_path={TMP_PATH_EIDS}", + f"phecode_definitions_path={TMP_PATH_PHE_DEF}", + f"records_frequencies_path={TMP_PATH_REC_FRE}", + f"metadata_individuals_path={TMP_PATH_MET_IND}", + f"use_top_n_phecodes=2", + f"drop_phecode_strings=[]", + ], + ) + yield config + + +@pytest.fixture +def ehr_mini1_str_config( + prepare_mini1_str, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini1_str, + prepare_metadata_individuals_mini1_str, +): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "ehr") + hydra.initialize(version_base=None, config_path=config_path) + TMP_PATH, TMP_PATH_PHE_DEF, TMP_PATH_REC_FRE, TMP_PATH_EIDS, TMP_PATH_MET_IND = ( + prepare_mini1_str, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini1_str, + prepare_metadata_individuals_mini1_str, + ) + config = hydra.compose( + "debug.yaml", + overrides=[ + f"records_path={TMP_PATH}", + f"eids_path={TMP_PATH_EIDS}", + f"phecode_definitions_path={TMP_PATH_PHE_DEF}", + f"records_frequencies_path={TMP_PATH_REC_FRE}", + f"metadata_individuals_path={TMP_PATH_MET_IND}", + f"use_top_n_phecodes=2", + f"drop_phecode_strings=[]", + ], + ) + yield config + + +@pytest.fixture +def ehr_mini2_config( + prepare_mini2, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini2, + prepare_metadata_individuals_mini2, +): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "ehr") + hydra.initialize(version_base=None, config_path=config_path) + TMP_PATH, TMP_PATH_PHE_DEF, TMP_PATH_REC_FRE, TMP_PATH_EIDS, TMP_PATH_MET_IND = ( + prepare_mini2, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini2, + prepare_metadata_individuals_mini2, + ) + config = hydra.compose( + "debug.yaml", + overrides=[ + f"use_top_n_phecodes=2", + f"records_path={TMP_PATH}", + f"eids_path={TMP_PATH_EIDS}", + f"phecode_definitions_path={TMP_PATH_PHE_DEF}", + f"records_frequencies_path={TMP_PATH_REC_FRE}", + f"metadata_individuals_path={TMP_PATH_MET_IND}", + f"drop_phecode_strings=[]", + ], + ) + yield config + + +@pytest.fixture +def ehr_mini2_str_config( + prepare_mini2_str, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini2_str, + prepare_metadata_individuals_mini2_str, +): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "ehr") + hydra.initialize(version_base=None, config_path=config_path) + TMP_PATH, TMP_PATH_PHE_DEF, TMP_PATH_REC_FRE, TMP_PATH_EIDS, TMP_PATH_MET_IND = ( + prepare_mini2_str, + prepare_phecode_def_mini, + prepare_record_freq_mini, + prepare_eids_mini2_str, + prepare_metadata_individuals_mini2_str, + ) + config = hydra.compose( + "debug.yaml", + overrides=[ + f"use_top_n_phecodes=2", + f"records_path={TMP_PATH}", + f"eids_path={TMP_PATH_EIDS}", + f"phecode_definitions_path={TMP_PATH_PHE_DEF}", + f"records_frequencies_path={TMP_PATH_REC_FRE}", + f"metadata_individuals_path={TMP_PATH_MET_IND}", + f"drop_phecode_strings=[]", + ], + ) + yield config + + +@pytest.fixture +def ehr_full_wandb_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "ehr") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="from_wandb_default", + overrides=[ + "min_record_counts=0", + "use_top_n_phecodes=1683", + "t0_mode=recruitment", + "records_data_name=final_records_omop_subsampled:latest", + ], + ) + yield config + + +class TestEHRPlugin(object): + @pytest.mark.parametrize( + "config", + [ + pytest.param(lazy_fixture("ehr_debug_config"), marks=SKIPIF_NOT_BIH_HPC), + lazy_fixture("ehr_mini_config"), + lazy_fixture("ehr_mini1_config"), + lazy_fixture("ehr_mini1_str_config"), + lazy_fixture("ehr_mini2_config"), + lazy_fixture("ehr_mini2_str_config"), + ], + ) + def test__init__(self, config): + ehr_plugin = EHRPlugin.from_config(config) + + @SKIPIF_NOT_BIH_HPC + @pytest.mark.parametrize( + "config", + [ + pytest.param(lazy_fixture("ehr_full_wandb_config"), marks=pytest.mark.slow), + ], + ) + def test_from_wandb(self, config): + ehr_plugin = EHRPlugin.from_wandb(**config) + + @pytest.mark.parametrize( + "config", + [ + pytest.param(lazy_fixture("ehr_debug_config"), marks=SKIPIF_NOT_BIH_HPC), + pytest.param(lazy_fixture("ehr_mini_config"), marks=pytest.mark.xfail), + lazy_fixture("ehr_mini1_config"), + lazy_fixture("ehr_mini1_str_config"), + lazy_fixture("ehr_mini2_config"), + lazy_fixture("ehr_mini2_str_config"), + pytest.param( + lazy_fixture("ehr_full_wandb_config"), + marks=[SKIPIF_NOT_BIH_HPC, pytest.mark.slow], + ), + ], + ) + def test_prepare_data(self, config): + if config["__init__"] == "from_wandb": + ehr_plugin = EHRPlugin.from_wandb(**config) + else: + ehr_plugin = EHRPlugin.from_config(config) + ehr_plugin.prepare_data() + + @pytest.mark.parametrize( + "config", + [ + pytest.param(lazy_fixture("ehr_debug_config"), marks=SKIPIF_NOT_BIH_HPC), + pytest.param( + lazy_fixture("ehr_debug_birth_date_config"), marks=SKIPIF_NOT_BIH_HPC + ), + pytest.param(lazy_fixture("ehr_mini_config"), marks=pytest.mark.xfail), + lazy_fixture("ehr_mini1_config"), + lazy_fixture("ehr_mini1_str_config"), + lazy_fixture("ehr_mini2_config"), + lazy_fixture("ehr_mini2_str_config"), + pytest.param( + lazy_fixture("ehr_full_wandb_config"), + marks=[SKIPIF_NOT_BIH_HPC, pytest.mark.slow], + ), + ], + ) + def test_setup(self, config): + if config["__init__"] == "from_wandb": + ehr_plugin = EHRPlugin.from_wandb(**config) + else: + ehr_plugin = EHRPlugin.from_config(config) + ehr_plugin.prepare_data() + ehr_plugin.setup() + + @pytest.mark.parametrize( + "config, exp_eids", + [ + pytest.param( + lazy_fixture("ehr_debug_config"), + lazy_fixture("ehr_debug_eids_head"), + marks=SKIPIF_NOT_BIH_HPC, + ), + (lazy_fixture("ehr_mini1_config"), [327, 578, 111]), + (lazy_fixture("ehr_mini1_str_config"), ["a327", "b578", "c111"]), + (lazy_fixture("ehr_mini2_config"), [181, 733]), + (lazy_fixture("ehr_mini2_str_config"), ["d181", "f733"]), + pytest.param( + lazy_fixture("ehr_full_wandb_config"), + None, + marks=[SKIPIF_NOT_BIH_HPC, pytest.mark.slow], + ), + ], + ) + def test_get_eids(self, config, exp_eids): + if config["__init__"] == "from_wandb": + ehr_plugin = EHRPlugin.from_wandb(**config) + else: + ehr_plugin = EHRPlugin.from_config(config) + ehr_plugin.prepare_data() + ehr_plugin.setup() + eids = ehr_plugin.get_eids() + assert isinstance(eids, list) + if exp_eids is not None: + if isinstance(exp_eids, Head): + assert all([eid in eids for eid in exp_eids]) + else: + assert set(eids) == set(exp_eids) + + @pytest.mark.parametrize( + "config", + [ + pytest.param(lazy_fixture("ehr_debug_config"), marks=SKIPIF_NOT_BIH_HPC), + lazy_fixture("ehr_mini1_config"), + lazy_fixture("ehr_mini2_config"), + pytest.param( + lazy_fixture("ehr_full_wandb_config"), + marks=[SKIPIF_NOT_BIH_HPC, pytest.mark.slow], + ), + ], + ) + def test_get_metadata(self, config): + if config["__init__"] == "from_wandb": + ehr_plugin = EHRPlugin.from_wandb(**config) + else: + ehr_plugin = EHRPlugin.from_config(config) + ehr_plugin.prepare_data() + ehr_plugin.setup() + metadata = ehr_plugin.get_metadata() + KEYS_METADATA = set(["eids", "features", "feature_types"]) + assert isinstance(metadata, dict) + assert set(metadata.keys()).intersection(KEYS_METADATA) == KEYS_METADATA + + DEFAULT_KEYS = [ + "records", + "censorings", + "exclusions", + "label_events", + "label_times", + ] + + @pytest.mark.parametrize( + "config, eids, features, subset, exp_item", + [ + pytest.param( + lazy_fixture("ehr_debug_config"), + lazy_fixture("ehr_debug_eid"), + None, + None, + [*DEFAULT_KEYS, "label_auxiliary_events", "label_auxiliary_times"], + marks=[pytest.mark.slow, SKIPIF_NOT_BIH_HPC], + ), + pytest.param( + lazy_fixture("ehr_debug_config"), + lazy_fixture("ehr_debug_three_eids"), + None, + None, + DEFAULT_KEYS, + marks=[pytest.mark.slow, SKIPIF_NOT_BIH_HPC], + ), + pytest.param( + lazy_fixture("ehr_full_wandb_config"), + lazy_fixture("ehr_debug_eid"), + None, + None, + DEFAULT_KEYS, + marks=[pytest.mark.slow, SKIPIF_NOT_BIH_HPC], + ), + ( + lazy_fixture("ehr_mini1_config"), + 327, + None, + None, + {"censorings": torch.tensor([[17.432254791259766]])}, + ), + ( + lazy_fixture("ehr_mini1_str_config"), + "a327", + None, + None, + {"censorings": torch.tensor([[17.432254791259766]])}, + ), + ( + lazy_fixture("ehr_mini1_config"), + [111, 578, 327], + None, + None, + { + "records": torch.tensor( + [ + [0.0, 0.0, 0.0, 0.0, 1.0, 1.0], + [0.0, 1.0, 0.0, 1.0, 0.0, 0.0], + [1.0, 0.0, 0.0, 0.0, 0.0, 0.0], + ] + ), + "censorings": torch.tensor( + [ + [9.612791061401367], + [13.987966537475586], + [17.432254791259766], + ] + ), + "exclusions": torch.tensor( + [[0.0, 1.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]] + ), + "label_events": torch.tensor( + [[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]] + ), + "label_times": torch.tensor( + [ + [9.614636421203613, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + ] + ), + }, + ), + ( + lazy_fixture("ehr_mini1_config"), + [111, 578, 327], + { + "records": (["phecode_247.72", "phecode_557"],), + "censorings": (["censored"],), + "exclusions": ([True, True, False],), + "label_events": ( + torch.tensor([True, True, False], dtype=torch.bool), + ), + "label_times": ([True, True, False],), + }, + None, + { + "records": torch.tensor([[1.0, 1.0], [0.0, 0.0], [0.0, 0.0]]), + "censorings": torch.tensor( + [ + [9.612791061401367], + [13.987966537475586], + [17.432254791259766], + ] + ), + "exclusions": torch.tensor([[0.0, 1.0], [0.0, 0.0], [0.0, 0.0]]), + "label_events": torch.tensor([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0]]), + "label_times": torch.tensor( + [[9.614636421203613, 0.0], [0.0, 0.0], [0.0, 0.0]] + ), + }, + ), + ( + lazy_fixture("ehr_mini1_config"), + [111, 578, 327], + { + "records": (["phecode_247.72", "phecode_557"],), + "exclusions": ([True, True, False],), + "label_times": ([True, True, False],), + }, + None, + { + "records": torch.tensor([[1.0, 1.0], [0.0, 0.0], [0.0, 0.0]]), + "exclusions": torch.tensor([[0.0, 1.0], [0.0, 0.0], [0.0, 0.0]]), + "label_times": torch.tensor( + [[9.614636421203613, 0.0], [0.0, 0.0], [0.0, 0.0]] + ), + }, + ), + ( + lazy_fixture("ehr_mini1_str_config"), + ["c111", "b578", "a327"], + { + "records": (["phecode_247.72", "phecode_557"],), + "exclusions": ([True, True, False],), + "label_times": ([True, True, False],), + }, + None, + { + "records": torch.tensor([[1.0, 1.0], [0.0, 0.0], [0.0, 0.0]]), + "exclusions": torch.tensor([[0.0, 1.0], [0.0, 0.0], [0.0, 0.0]]), + "label_times": torch.tensor( + [[9.614636421203613, 0.0], [0.0, 0.0], [0.0, 0.0]] + ), + }, + ), + (lazy_fixture("ehr_mini2_config"), 733, None, None, None), + ( + # 996 should be filtered + lazy_fixture("ehr_mini2_config"), + [181, 996, 733], + None, + None, + { + "censorings": torch.tensor( + [[14.092007637023926], [16.003067016601562]] + ) + }, + ), + ( + # e996 should be filtered + lazy_fixture("ehr_mini2_str_config"), + ["d181", "e996", "f733"], + None, + None, + { + "censorings": torch.tensor( + [[14.092007637023926], [16.003067016601562]] + ) + }, + ), + ( + lazy_fixture("ehr_mini2_config"), + [181, 733], + None, + None, + { + "records": torch.tensor( + [[0.0, 0.0, 0.0, 0.0, 1.0], [1.0, 0.0, 0.0, 1.0, 0.0]] + ), + "censorings": torch.tensor( + [[14.092007637023926], [16.003067016601562]] + ), + "exclusions": torch.tensor([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]), + "label_events": torch.tensor([[1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]), + "label_times": torch.tensor( + [[14.092129707336426, 0.0, 0.0], [0.0, 0.0, 0.0]] + ), + }, + ), + ( + lazy_fixture("ehr_mini2_config"), + [181, 733], + { + "records": (["OMOP_19020124", "OMOP_4214579", "OMOP_4306655"],), + "censorings": (["censored"],), + "exclusions": ( + torch.tensor([True, False, True], dtype=torch.bool), + ), + "label_events": ( + torch.tensor([True, False, False], dtype=torch.bool), + ), + "label_times": ([True, False, False],), + }, + None, + { + "records": torch.tensor([[0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]), + "censorings": torch.tensor( + [[14.092007637023926], [16.003067016601562]] + ), + "exclusions": torch.tensor([[0.0, 0.0], [0.0, 1.0]]), + "label_events": torch.tensor([[1.0], [0.0]]), + "label_times": torch.tensor([[14.092129707336426], [0.0]]), + }, + ), + ( + lazy_fixture("ehr_mini1_config"), + 327, + None, + ["label_events"], + ["label_events"], + ), + ( + lazy_fixture("ehr_mini1_config"), + [327, 578, 111], + None, + ["label_events"], + ["label_events"], + ), + ], + ) + def test__getitem__(self, config, eids, features, subset, exp_item): + if config["__init__"] == "from_wandb": + ehr_plugin = EHRPlugin.from_wandb(**config) + else: + ehr_plugin = EHRPlugin.from_config(config) + ehr_plugin.prepare_data() + ehr_plugin.setup() + if isinstance(eids, int): + item = ehr_plugin[eids, features] + else: + item = ehr_plugin[eids, features] + assert isinstance(item, dict) + if isinstance(exp_item, set): + assert set(item.keys()) == exp_item + elif isinstance(exp_item, dict): + for key in exp_item.keys(): + assert torch.allclose(item[key], exp_item[key]) diff --git a/test/plugins/genetic/__init__.py b/test/plugins/genetic/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/plugins/genetic/genetic_plugin_test.py b/test/plugins/genetic/genetic_plugin_test.py new file mode 100644 index 0000000..525d663 --- /dev/null +++ b/test/plugins/genetic/genetic_plugin_test.py @@ -0,0 +1,738 @@ +import copy +import inspect +import os +from os.path import abspath, dirname +import torch +import hydra +import numpy as np +import pandas as pd +import pytest +from pytest_lazyfixture import lazy_fixture +import pickle +from tempfile import TemporaryDirectory +from omegaconf import OmegaConf, DictConfig + +from udm.plugins.genetic.genetic_plugin import GeneticPlugin +from test.utils import SKIPIF_NOT_BIH_HPC, try_clear_hydra + +UDM_HOME = "../../.." + + +@pytest.fixture +def temp_dir(): + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +@pytest.fixture +def mini_train_data(): + # Train data + X = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]], dtype=np.uint8) + obs = pd.DataFrame({"index": [0, 1, 2]}) + var = pd.DataFrame( + { + "Name": ["snp1", "snp2", "snp3"], + "ID": ["rs1", "rs2", "rs3"], + "GeneticAlt": ["G", "A", "G"], + "Field": ["ENSG001", "ENSG002", "ENSG003"], + } + ) + return X, obs, var + + +@pytest.fixture +def mini_train_str_data(mini_train_data): + X, _, var = mini_train_data + obs = pd.DataFrame({"index": ["a1", "b2", "c3"]}) + return X, obs, var + + +def prepare_data(temp_dir, name, X, obs, var): + basename = os.path.join(temp_dir, name) + with open(f"{basename}.X.npy", "wb+") as f: + data_mmap = np.memmap(f, dtype=np.uint8, mode="w+", shape=(3, 3)) + data_mmap[:, :] = X[:, :] + data_mmap.flush() + obs.to_csv(f"{basename}.obs.csv", index=False) + var.to_csv(f"{basename}.var.csv") + return basename + + +@pytest.fixture +def prepare_mini_train(temp_dir, mini_train_data): + X, obs, var = mini_train_data + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_data(temp_dir, "train", X, obs, var) + + +@pytest.fixture +def prepare_mini_train_str(temp_dir, mini_train_str_data): + X, obs, var = mini_train_str_data + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_data(temp_dir, "train", X, obs, var) + + +@pytest.fixture +def prepare_mini_train_uns(prepare_mini_train): + basename = prepare_mini_train + uns = {"dtype": "uint8"} + with open(f"{basename}.uns.pkl", "wb+") as f: + pickle.dump(uns, f) + yield basename + + +@pytest.fixture +def prepare_mini_valid(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + # Valid data + valid_data = np.array([[1, 0, 0], [1, 0, 1], [1, 1, 0]]) + obs = pd.DataFrame({"index": [3, 4, 6]}) + var = pd.DataFrame( + { + "Name": ["snp1", "snp2", "snp3"], + "ID": ["rs1", "rs2", "rs3"], + "GeneticAlt": ["G", "A", "G"], + "Field": ["ENSG001", "ENSG002", "ENSG003"], + } + ) + yield prepare_data(temp_dir, "valid", valid_data, obs, var) + + +@pytest.fixture +def prepare_mini_valid_uns(prepare_mini_valid): + basename = prepare_mini_valid + uns = {"dtype": "uint8"} + with open(f"{basename}.uns.pkl", "wb+") as f: + pickle.dump(uns, f) + yield basename + + +@pytest.fixture +def geno_debug_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "genetic") + hydra.initialize(version_base=None, config_path=config_path) + UDM_HOME_ABSPATH = abspath(os.path.join(dirname(__file__), UDM_HOME)) + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/geno/") + overrides_config = OmegaConf.create( + { + "src": [ + "genetic_extended_tr", + "genetic_valid", + "genetic_test", + ], + "src_names": ["train", "valid", "test"], + "dataset_path": FAKE_DATA_PATH, + } + ) + config = hydra.compose(config_name="default.yaml", overrides=[]) + config = OmegaConf.merge(config, overrides_config) + yield config + + +@pytest.fixture +def geno_debug_nomap_config(geno_debug_config): + config = geno_debug_config + config.update({"eid_map_path": None}) + yield config + + +@pytest.fixture +def mini_train_config(prepare_mini_train): + try_clear_hydra() + basename_train = prepare_mini_train + config_path = os.path.join(UDM_HOME, "config", "plugins", "genetic") + hydra.initialize(version_base=None, config_path=config_path) + overrides_config = OmegaConf.create( + { + "src": [basename_train], + "src_names": ["train"], + "eid_map_path": None, + "transforms": None, + } + ) + config = hydra.compose("default.yaml", overrides=[]) + config = OmegaConf.merge(config, overrides_config) + yield config + + +@pytest.fixture +def mini_train_str_config(prepare_mini_train_str): + try_clear_hydra() + basename_train = prepare_mini_train_str + config_path = os.path.join(UDM_HOME, "config", "plugins", "genetic") + hydra.initialize(version_base=None, config_path=config_path) + overrides_config = OmegaConf.create( + { + "src": [basename_train], + "src_names": ["train"], + "eid_map_path": None, + "transforms": None, + } + ) + config = hydra.compose("default.yaml", overrides=[]) + config = OmegaConf.merge(config, overrides_config) + yield config + + +@pytest.fixture +def mini_train_uns_config(prepare_mini_train_uns): + try_clear_hydra() + basename_train = prepare_mini_train_uns + config_path = os.path.join(UDM_HOME, "config", "plugins", "genetic") + hydra.initialize(version_base=None, config_path=config_path) + overrides_config = OmegaConf.create( + { + "src": [basename_train], + "src_names": ["train"], + "eid_map_path": None, + "transforms": None, + } + ) + config = hydra.compose("default.yaml", overrides=[]) + config = OmegaConf.merge(config, overrides_config) + yield config + + +@pytest.fixture +def plugin_trafo_config(): + trafo_cfg = DictConfig( + { + "name": "PluginTransform", + "transforms": { + "data": { + "name": "ListTransform", + "transforms": [ + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + ], + } + }, + } + ) + yield trafo_cfg + + +@pytest.fixture +def mini_train_trafo_config(mini_train_config, plugin_trafo_config): + config = mini_train_config + config.transforms = plugin_trafo_config + yield config + + +@pytest.fixture +def mini_valid_config(prepare_mini_valid): + try_clear_hydra() + basename_valid = prepare_mini_valid + config_path = os.path.join(UDM_HOME, "config", "plugins", "genetic") + hydra.initialize(version_base=None, config_path=config_path) + overrides_config = OmegaConf.create( + { + "src": [basename_valid], + "src_names": ["valid"], + "eid_map_path": None, + "transforms": None, + } + ) + config = hydra.compose("default.yaml", overrides=[]) + config = OmegaConf.merge(config, overrides_config) + yield config + + +@pytest.fixture +def mini_valid_uns_config(prepare_mini_valid_uns): + try_clear_hydra() + basename_valid = prepare_mini_valid_uns + config_path = os.path.join(UDM_HOME, "config", "plugins", "genetic") + hydra.initialize(version_base=None, config_path=config_path) + overrides_config = OmegaConf.create( + { + "src": [basename_valid], + "src_names": ["valid"], + "eid_map_path": None, + "transforms": None, + } + ) + config = hydra.compose("default.yaml", overrides=[]) + config = OmegaConf.merge(config, overrides_config) + yield config + + +@pytest.fixture +def mini_valid_trafo_config(mini_valid_config, plugin_trafo_config): + config = mini_valid_config + config.transforms = plugin_trafo_config + yield config + + +@pytest.fixture +def mini_train_valid_config(temp_dir, prepare_mini_train, prepare_mini_valid): + try_clear_hydra() + basename_train = prepare_mini_train + basename_valid = prepare_mini_valid + config_path = os.path.join(UDM_HOME, "config", "plugins", "genetic") + hydra.initialize(version_base=None, config_path=config_path) + with TemporaryDirectory(dir=temp_dir) as temp_dir: + overrides_config = OmegaConf.create( + { + "src": [basename_train, basename_valid], + "src_names": ["train", "valid"], + "eid_map_path": None, + "transforms": None, + } + ) + config = hydra.compose("default.yaml", overrides=[]) + config = OmegaConf.merge(config, overrides_config) + yield config + + +@pytest.fixture +def mini_train_valid_map_config(temp_dir, mini_train_valid_config): + config = mini_train_valid_config + eid_map = pd.DataFrame( + {"eid_map_from": [0, 1, 2, 3, 4, 6], "eid_map_to": [5, 6, 7, 8, 9, 10]} + ) + eid_map_path = f"{temp_dir}/eid_map.csv" + eid_map.to_csv(eid_map_path, sep="\t") + config.update( + { + "eid_map_path": f"{temp_dir}/eid_map.csv", + "eid_map_from": "eid_map_from", + "eid_map_to": "eid_map_to", + } + ) + yield config + + +class TestGeneticPlugin(object): + + cls = GeneticPlugin + + @pytest.mark.parametrize( + "config", + [ + pytest.param(lazy_fixture("geno_debug_config"), marks=SKIPIF_NOT_BIH_HPC), + lazy_fixture("mini_train_config"), + lazy_fixture("mini_train_str_config"), + lazy_fixture("mini_train_uns_config"), + lazy_fixture("mini_train_trafo_config"), + lazy_fixture("mini_valid_config"), + lazy_fixture("mini_valid_uns_config"), + lazy_fixture("mini_valid_trafo_config"), + ], + ) + def test__init__(self, config): + genetic_plugin = self.cls(**config) + + @pytest.mark.parametrize( + "config", + [ + pytest.param(lazy_fixture("geno_debug_config"), marks=SKIPIF_NOT_BIH_HPC), + lazy_fixture("mini_train_config"), + lazy_fixture("mini_train_str_config"), + lazy_fixture("mini_train_uns_config"), + lazy_fixture("mini_train_trafo_config"), + lazy_fixture("mini_valid_config"), + lazy_fixture("mini_valid_uns_config"), + lazy_fixture("mini_valid_trafo_config"), + ], + ) + def test_prepare_data(self, config): + genetic_plugin = self.cls(**config) + genetic_plugin.prepare_data() + del genetic_plugin + + @pytest.mark.parametrize( + "config", + [ + pytest.param(lazy_fixture("geno_debug_config"), marks=SKIPIF_NOT_BIH_HPC), + lazy_fixture("mini_train_config"), + lazy_fixture("mini_train_str_config"), + lazy_fixture("mini_train_uns_config"), + lazy_fixture("mini_train_trafo_config"), + lazy_fixture("mini_valid_config"), + lazy_fixture("mini_valid_uns_config"), + lazy_fixture("mini_valid_trafo_config"), + ], + ) + def test_setup(self, config): + genetic_plugin = self.cls(**config) + genetic_plugin.prepare_data() + genetic_plugin.setup() + del genetic_plugin + + @pytest.mark.parametrize( + "config, setup, eid, features, exp_item", + [ + (lazy_fixture("mini_train_valid_config"), False, 0, None, RuntimeError), + *[ + (lazy_fixture("mini_train_config"), True, eid, None, exp_item) + for eid, exp_item in zip( + [0, 1, 2], + [ + [0, 1, 0], + [0, 0, 1], + [1, 0, 0], + ], + ) + ], + *[ + (lazy_fixture("mini_train_str_config"), True, eid, None, exp_item) + for eid, exp_item in zip( + ["a1", "b2", "c3"], + [ + [0, 1, 0], + [0, 0, 1], + [1, 0, 0], + ], + ) + ], + *[ + (lazy_fixture("mini_train_valid_config"), True, eid, None, exp_item) + for eid, exp_item in zip( + [0, 1, 2, 3, 4, 6], + [ + [0, 1, 0], + [0, 0, 1], + [1, 0, 0], + [1, 0, 0], + [1, 0, 1], + [1, 1, 0], + ], + ) + ], + ( + lazy_fixture("mini_train_valid_config"), + True, + torch.tensor([0, 2], dtype=torch.int64), + None, + np.array([[0, 1, 0], [1, 0, 0]]), + ), + ( + lazy_fixture("mini_train_valid_config"), + True, + torch.tensor([0, 2], dtype=torch.int64), + {"genetic": (torch.tensor([True, True, False], dtype=torch.bool),)}, + np.array([[0, 1], [1, 0]]), + ), + ( + lazy_fixture("mini_train_valid_config"), + True, + torch.tensor([0, 2], dtype=torch.int64), + {"genetic": (["rs1", "rs2"],)}, + np.array([[0, 1], [1, 0]]), + ), + ( + lazy_fixture("mini_train_valid_config"), + True, + torch.tensor([1, 4], dtype=torch.int64), + None, + np.array( + [ + [0, 0, 1], + [1, 0, 1], + ] + ), + ), + ( + lazy_fixture("mini_train_valid_config"), + True, + torch.tensor([1, 4], dtype=torch.int64), + {"genetic": (torch.tensor([True, False, True], dtype=torch.bool),)}, + np.array( + [ + [0, 1], + [1, 1], + ] + ), + ), + ( + lazy_fixture("mini_train_valid_config"), + True, + torch.tensor([1, 4], dtype=torch.int64), + {"genetic": (["rs1", "rs3"],)}, + np.array( + [ + [0, 1], + [1, 1], + ] + ), + ), + *[ + (lazy_fixture("mini_train_valid_map_config"), True, eid, None, exp_item) + for eid, exp_item in zip( + [5, 6, 7, 8, 9, 10], + [ + [0, 1, 0], + [0, 0, 1], + [1, 0, 0], + [1, 0, 0], + [1, 0, 1], + [1, 1, 0], + ], + ) + ], + ], + ) + def test__getitem__(self, config, setup, eid, features, exp_item): + # genetic_plugin = self.cls(**config) + genetic_plugin = self.cls.from_config(config) + if setup: + genetic_plugin.prepare_data() + genetic_plugin.setup() + if inspect.isclass(exp_item) and issubclass(exp_item, Exception): + with pytest.raises(RuntimeError): + genetic_plugin[eid, features] + else: + item = copy.deepcopy(genetic_plugin[eid, features]) + # .clone() is required because otherweise a view of the dataset is returned + assert isinstance(item, dict) + assert list(item.keys()) == ["genetic"] + assert np.all(item["genetic"].numpy() == exp_item) + del genetic_plugin + + @pytest.mark.parametrize( + "config, prepare_data, setup, exp_len", + [ + (lazy_fixture("mini_train_config"), True, True, 3), + (lazy_fixture("mini_train_str_config"), True, True, 3), + pytest.param( + lazy_fixture("geno_debug_config"), + False, + False, + 0, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_debug_config"), + True, + False, + 0, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_debug_config"), + True, + True, + None, + marks=SKIPIF_NOT_BIH_HPC, + ), + ], + ) + def test__len__(self, config, prepare_data, setup, exp_len): + genetic_plugin = self.cls(**config) + if exp_len is None: + obs_lens = [] + for s_file in config.src: + obs_train_path = os.path.join(config.dataset_path, f"{s_file}.obs.csv") + obs_df = pd.read_csv(obs_train_path) + obs_lens += [len(obs_df)] + # obs_valid_path = os.path.join( + # config.dataset_path, "genetic_valid.obs.csv" + # ) + # obs_valid = pd.read_csv(obs_valid_path) + # obs_test_path = os.path.join( + # config.dataset_path, "genetic_test.obs.csv" + # ) + # obs_test = pd.read_csv(obs_test_path) + exp_len = sum(obs_lens) + if prepare_data: + genetic_plugin.prepare_data() + if setup: + genetic_plugin.setup() + assert len(genetic_plugin) == exp_len + + @pytest.mark.parametrize( + "config, prepare_data, setup, exp_eids", + [ + pytest.param( + lazy_fixture("geno_debug_config"), + False, + False, + [], + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_debug_config"), + True, + False, + [], + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_debug_config"), + True, + True, + None, + marks=SKIPIF_NOT_BIH_HPC, + ), + (lazy_fixture("mini_train_config"), True, True, [0, 1, 2]), + (lazy_fixture("mini_train_str_config"), True, True, ["a1", "b2", "c3"]), + (lazy_fixture("mini_train_valid_config"), True, True, [0, 1, 2, 3, 4, 6]), + ( + lazy_fixture("mini_train_valid_map_config"), + True, + True, + [0, 1, 2, 3, 4, 6], + ), + ], + ) + def test_get_native_eids(self, config, prepare_data, setup, exp_eids): + if exp_eids is None: + eids_train = pd.read_csv( + f"{config.dataset_path}/genetic_extended_tr.obs.csv" + )["index"].to_list() + eids_valid = pd.read_csv(f"{config.dataset_path}/genetic_valid.obs.csv")[ + "index" + ].to_list() + eids_test = pd.read_csv(f"{config.dataset_path}/genetic_test.obs.csv")[ + "index" + ].to_list() + exp_eids = [*eids_train, *eids_valid, *eids_test] + genetic_plugin = self.cls(**config) + if prepare_data: + genetic_plugin.prepare_data() + if setup: + genetic_plugin.setup() + eids = genetic_plugin.get_native_eids() + assert isinstance(eids, list) + assert set(eids) == set(exp_eids) + assert eids == exp_eids + del genetic_plugin + + @pytest.mark.parametrize( + "config, prepare_data, setup, exp_eids", + [ + pytest.param( + lazy_fixture("geno_debug_config"), + False, + False, + {}, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_debug_config"), + True, + False, + {}, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_debug_config"), + True, + True, + None, + marks=SKIPIF_NOT_BIH_HPC, + ), + ( + lazy_fixture("mini_train_valid_config"), + True, + True, + {"train": [0, 1, 2], "valid": [3, 4, 6]}, + ), + ( + lazy_fixture("mini_train_valid_map_config"), + True, + True, + {"train": [0, 1, 2], "valid": [3, 4, 6]}, + ), + ], + ) + def test_get_native_eids_as_struct(self, config, prepare_data, setup, exp_eids): + if exp_eids is None: + eids_train = pd.read_csv( + f"{config.dataset_path}/genetic_extended_tr.obs.csv" + )["index"].to_list() + eids_valid = pd.read_csv(f"{config.dataset_path}/genetic_valid.obs.csv")[ + "index" + ].to_list() + eids_test = pd.read_csv(f"{config.dataset_path}/genetic_test.obs.csv")[ + "index" + ].to_list() + exp_eids = { + "train": eids_train, + "valid": eids_valid, + "test": eids_test, + } + genetic_plugin = self.cls(**config) + if prepare_data: + genetic_plugin.prepare_data() + if setup: + genetic_plugin.setup() + eids = genetic_plugin.get_native_eids_as_struct() + assert isinstance(eids, dict) + assert set(eids.keys()) == set(exp_eids.keys()) + for key in eids: + assert isinstance(eids[key], list) + assert set(eids[key]) == set(exp_eids[key]) + assert eids[key] == exp_eids[key] + assert eids == exp_eids + del genetic_plugin + + @pytest.mark.parametrize( + "config, prepare_data, setup, exp_eids", + [ + (lazy_fixture("mini_train_config"), True, True, [0, 1, 2]), + (lazy_fixture("mini_train_str_config"), True, True, ["a1", "b2", "c3"]), + pytest.param( + lazy_fixture("geno_debug_config"), + False, + False, + [], + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_debug_config"), + True, + False, + [], + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("geno_debug_config"), + True, + True, + None, + marks=SKIPIF_NOT_BIH_HPC, + ), + (lazy_fixture("geno_debug_nomap_config"), True, True, None), + ], + ) + def test_get_eids(self, config, prepare_data, setup, exp_eids): + genetic_plugin = self.cls(**config) + if prepare_data: + genetic_plugin.prepare_data() + if setup: + genetic_plugin.setup() + eids = genetic_plugin.get_eids() + if exp_eids is None: + if config.eid_map_path is None: + exp_eids = genetic_plugin.get_native_eids() + else: + eid_map = pd.read_csv(config.eid_map_path, sep="\t") + eid_map = dict(zip(eid_map["EID.49966"], eid_map["EID.51157"])) + exp_eids = [eid_map[eid] for eid in genetic_plugin.get_native_eids()] + assert isinstance(eids, list) + assert set(eids) == set(exp_eids) + assert eids == exp_eids + + @pytest.mark.parametrize( + "config, exp_keys", + [ + pytest.param( + lazy_fixture("geno_debug_config"), + ["eids", "features", "feature_types"], + marks=SKIPIF_NOT_BIH_HPC, + ) + ], + ) + def test_get_metadata(self, config, exp_keys): + eid_map_path = "data/analysis/UKBB.application.link.file.20210608.txt" + eid_map = pd.read_csv(eid_map_path, sep="\t") + eid_map = dict(zip(eid_map["EID.49966"], eid_map["EID.51157"])) + genetic_plugin = self.cls(**config) + genetic_plugin.prepare_data() + genetic_plugin.setup() + metadata = genetic_plugin.get_metadata() + METADATA_KEYS = set(exp_keys) + assert isinstance(metadata, dict) + assert set(metadata.keys()).intersection(METADATA_KEYS) == METADATA_KEYS diff --git a/test/plugins/genetic/genetic_ts_plugin_test.py b/test/plugins/genetic/genetic_ts_plugin_test.py new file mode 100644 index 0000000..d17ed03 --- /dev/null +++ b/test/plugins/genetic/genetic_ts_plugin_test.py @@ -0,0 +1,734 @@ +import os +import pytest +from typing import Tuple, List +import json +from inspect import isclass +from addict import Dict +from pytest_lazyfixture import lazy_fixture +import numpy as np +import torch +import tensorstore as ts +from omegaconf import DictConfig, OmegaConf +from tempfile import TemporaryDirectory + +from udm.plugins.genetic.genetic_ts_plugin import ( + GeneticTSPlugin, + compress_mask, + decompress_mask, + compress_uint4, + compress_uint4_rot, + probs_to_int, +) + + +def get_ts_write_config( + dimensions: Tuple[int], + block_size: Tuple[int], + path: str = ".", + cname: str = "lz4", + clevel: int = 9, + dtype: str = "uint8", + **kwargs, +) -> Dict: + assert len(dimensions) == len(block_size) + config = Dict( + { + "driver": "n5", + "kvstore": { + "driver": "file", + "path": path, + }, + "metadata": { + "compression": { + "type": "blosc", + "cname": cname, + "clevel": clevel, + "shuffle": 0, + }, + "dataType": dtype, + "dimensions": dimensions, + "blockSize": block_size, + }, + "create": True, + "delete_existing": True, + } + ) + return config + + +@pytest.fixture +def temp_dir(): + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +def generate_dataset( + src_path, + config, + data, + eids: List[int] = None, + variants: List[str] = None, + dtype: str = None, +): + variants = variants or [f"var{i+1}" for i in range(data.shape[1])] + eids = eids or [*range(data.shape[0])] + dtype = dtype or str(data.dtype) + store = ts.open(config).result() + store[:].write(data).result() + meta_path = os.path.join(src_path, "attributes.json") + with open(meta_path, "r") as f: + metadata = json.load(f) + metadata["custom"] = {"eids": eids, "variants": variants, "dtype": dtype} + with open(meta_path, "w+") as f: + json.dump(metadata, f) + + +@pytest.fixture +def base_config(temp_dir): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + "as_dosages": True, + } + ) + data = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]], dtype=np.float32) + block_size = (1, 1) + ts_config = get_ts_write_config( + data.shape, block_size, src_path, dtype=str(data.dtype) + ) + generate_dataset(src_path, ts_config, data) + yield config + + +@pytest.fixture +def probs_data(): + data = np.array( + [ + [[0.7, 0.3], [0.5, 0.2], [0.8, 0.1]], + [[0.4, 0.4], [0.6, 0.3], [0.55, 0.23]], + [[0.66, 0.33], [0.8, 0.2], [0.7, 0.3]], + ], + dtype=np.float32, + ) + return data + + +@pytest.fixture +def dosage_data(): + data = np.array( + [[0.5, 1.8, 0.2], [0.2, 0.3, 0.1], [1.2, 1.1, 0.9]], dtype=np.float32 + ) + return data + + +@pytest.fixture +def probs_config(temp_dir, probs_data): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + } + ) + block_size = (1, 2, 1) + ts_config = get_ts_write_config( + probs_data.shape, block_size, src_path, dtype=str(probs_data.dtype) + ) + generate_dataset(src_path, ts_config, probs_data) + yield config + + +@pytest.fixture +def probs_str_config(temp_dir, probs_data): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + } + ) + block_size = (1, 2, 1) + ts_config = get_ts_write_config( + probs_data.shape, block_size, src_path, dtype=str(probs_data.dtype) + ) + eids = ["a1", "b2", "c3"] + generate_dataset(src_path, ts_config, probs_data, eids=eids) + yield config + + +@pytest.fixture +def probs3_config(temp_dir, probs_data): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + } + ) + probs3_data = np.empty([*probs_data.shape[:2], 3], dtype=probs_data.dtype) + probs3_data[:, :, :2] = probs_data + probs3_data[:, :, 2] = 1 - probs3_data[:, :, 0] - probs3_data[:, :, 1] + block_size = (1, 2, 1) + ts_config = get_ts_write_config( + probs3_data.shape, block_size, src_path, dtype=str(probs3_data.dtype) + ) + generate_dataset(src_path, ts_config, probs3_data) + yield config + + +@pytest.fixture +def probs3_str_config(temp_dir, probs_data): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + } + ) + probs3_data = np.empty([*probs_data.shape[:2], 3], dtype=probs_data.dtype) + probs3_data[:, :, :2] = probs_data + probs3_data[:, :, 2] = 1 - probs3_data[:, :, 0] - probs3_data[:, :, 1] + block_size = (1, 2, 1) + ts_config = get_ts_write_config( + probs3_data.shape, block_size, src_path, dtype=str(probs3_data.dtype) + ) + eids = ["a1", "b2", "c3"] + generate_dataset(src_path, ts_config, probs3_data, eids=eids) + yield config + + +@pytest.fixture +def dosage_uint8_config(temp_dir, dosage_data): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + "as_dosages": True, + } + ) + uint8_data = probs_to_int(dosage_data / 2, "uint8") + block_size = (1, 2) + ts_config = get_ts_write_config( + uint8_data.shape, block_size, src_path, dtype=str(uint8_data.dtype) + ) + generate_dataset( + src_path, + ts_config, + uint8_data, + variants=[f"var{i+1}" for i in range(dosage_data.shape[1])], + dtype="uint8", + ) + yield config + + +@pytest.fixture +def probs3_uint8_config(temp_dir, probs_data): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + "as_dosages": True, + } + ) + probs3_data = np.empty([*probs_data.shape[:2], 3], dtype=probs_data.dtype) + probs3_data[:, :, :2] = probs_data + probs3_data[:, :, 2] = 1 - probs3_data[:, :, 0] - probs3_data[:, :, 1] + probs3_data = probs_to_int(probs3_data, "uint8") + block_size = (1, 2, 1) + ts_config = get_ts_write_config( + probs3_data.shape, block_size, src_path, dtype=str(probs3_data.dtype) + ) + generate_dataset(src_path, ts_config, probs3_data) + yield config + + +@pytest.fixture +def dosage_uint4_config(temp_dir, dosage_data): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + "as_dosages": True, + } + ) + dosage_data = probs_to_int(dosage_data / 2, "uint4") + uint4_data, remainder = compress_uint4_rot(dosage_data) + block_size = (1, 2) + ts_config = get_ts_write_config( + uint4_data.shape, block_size, src_path, dtype=str(uint4_data.dtype) + ) + generate_dataset( + src_path, + ts_config, + uint4_data, + variants=[f"var{i+1}" for i in range(dosage_data.shape[1])], + dtype="uint4", + ) + yield config + + +@pytest.fixture +def probs_uint4_config(temp_dir, probs_data): + with TemporaryDirectory(dir=temp_dir) as src_path: + config = DictConfig( + { + "src": os.path.basename(src_path), + "dataset_path": temp_dir, + "as_dosages": True, + } + ) + probs_data = probs_to_int(probs_data, "uint4") + uint4_data = compress_uint4(probs_data[:, :, :1], probs_data[:, :, 1:]) + block_size = (1, 2, 1) + ts_config = get_ts_write_config( + uint4_data.shape, block_size, src_path, dtype=str(uint4_data.dtype) + ) + generate_dataset( + src_path, + ts_config, + uint4_data, + variants=[f"var{i}" for i in range(probs_data.shape[1])], + dtype="uint4", + ) + yield config + + +@pytest.mark.parametrize( + "mask, exp_mask", + [ + (np.array([0], dtype=bool), np.array([0], dtype=bool)), + (np.array([1], dtype=bool), np.array([1], dtype=bool)), + (np.array([0, 0], dtype=bool), np.array([0], dtype=bool)), + (np.array([0, 1], dtype=bool), np.array([1], dtype=bool)), + (np.array([1, 0], dtype=bool), np.array([1], dtype=bool)), + (np.array([1, 1], dtype=bool), np.array([1], dtype=bool)), + (np.array([0, 0, 0], dtype=bool), np.array([0, 0], dtype=bool)), + (np.array([0, 1, 0], dtype=bool), np.array([1, 0], dtype=bool)), + (np.array([1, 0, 0], dtype=bool), np.array([1, 0], dtype=bool)), + (np.array([1, 1, 0], dtype=bool), np.array([1, 0], dtype=bool)), + (np.array([0, 0, 1], dtype=bool), np.array([0, 1], dtype=bool)), + (np.array([0, 1, 1], dtype=bool), np.array([1, 1], dtype=bool)), + (np.array([1, 0, 1], dtype=bool), np.array([1, 1], dtype=bool)), + (np.array([1, 1, 1], dtype=bool), np.array([1, 1], dtype=bool)), + ( + np.array([0, 0, 0, 1, 1, 0, 1, 1, 1], dtype=bool), + np.array([0, 1, 1, 1, 1], dtype=bool), + ), + ], +) +def test_compress_mask(mask, exp_mask): + mask_, remainder = compress_mask(mask) + assert np.array_equal(mask_, exp_mask) + + +@pytest.mark.parametrize( + "mask, remainder, exp_mask", + [ + (np.array([0], dtype=bool), 0, np.array([0, 0], dtype=bool)), + (np.array([1], dtype=bool), 0, np.array([1, 1], dtype=bool)), + (np.array([0], dtype=bool), 1, np.array([0], dtype=bool)), + (np.array([1], dtype=bool), 1, np.array([1], dtype=bool)), + (np.array([0, 0], dtype=bool), 0, np.array([0, 0, 0, 0], dtype=bool)), + (np.array([0, 1], dtype=bool), 0, np.array([0, 0, 1, 1], dtype=bool)), + (np.array([1, 0], dtype=bool), 0, np.array([1, 1, 0, 0], dtype=bool)), + (np.array([1, 1], dtype=bool), 0, np.array([1, 1, 1, 1], dtype=bool)), + (np.array([0, 0], dtype=bool), 1, np.array([0, 0, 0], dtype=bool)), + (np.array([0, 1], dtype=bool), 1, np.array([0, 0, 1], dtype=bool)), + (np.array([1, 0], dtype=bool), 1, np.array([1, 1, 0], dtype=bool)), + (np.array([1, 1], dtype=bool), 1, np.array([1, 1, 1], dtype=bool)), + ( + np.array([0, 1, 0, 0, 1, 1], dtype=bool), + 0, + np.array([0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1], dtype=bool), + ), + ( + np.array([0, 1, 0, 0, 1, 1], dtype=bool), + 1, + np.array([0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1], dtype=bool), + ), + ], +) +def test_decompress_mask(mask, remainder, exp_mask): + mask_ = decompress_mask(mask, remainder) + assert np.array_equal(mask_, exp_mask) + + +class TestGeneticTSPlugin: + @pytest.mark.parametrize( + "config, overrides, error", + [ + (lazy_fixture("base_config"), {}, None), + (lazy_fixture("base_config"), {"format": "probs"}, ValueError), + (lazy_fixture("base_config"), {"format": "probs3"}, ValueError), + (lazy_fixture("probs_config"), {}, None), + (lazy_fixture("probs_config"), {"format": "probs"}, None), + (lazy_fixture("probs_config"), {"format": "probs3"}, None), + (lazy_fixture("probs_str_config"), {}, None), + (lazy_fixture("probs3_config"), {}, None), + (lazy_fixture("probs3_config"), {"format": "probs"}, None), + (lazy_fixture("probs3_config"), {"format": "probs3"}, None), + ], + ) + def test__init__(self, config, overrides, error): + if overrides: + overrides = DictConfig(overrides) + config = OmegaConf.merge(config, overrides) + + if error: + if issubclass(error, Warning): + with pytest.warns(error): + GeneticTSPlugin(**config) + elif issubclass(error, Exception): + with pytest.raises(error): + GeneticTSPlugin(**config) + else: + plugin = GeneticTSPlugin(**config) + + @pytest.mark.parametrize( + "config, overrides, error", + [ + (lazy_fixture("base_config"), {}, None), + (lazy_fixture("probs_config"), {}, None), + (lazy_fixture("probs_config"), {"format": "probs"}, None), + (lazy_fixture("probs_config"), {"format": "probs3"}, None), + (lazy_fixture("probs_str_config"), {}, None), + (lazy_fixture("probs3_config"), {}, None), + (lazy_fixture("probs3_config"), {"format": "probs"}, None), + (lazy_fixture("probs3_config"), {"format": "probs3"}, None), + ], + ) + def test_prepare_data(self, config, overrides, error): + if overrides: + overrides = DictConfig(overrides) + config = OmegaConf.merge(config, overrides) + plugin = GeneticTSPlugin(**config) + if error and issubclass(error, Exception): + with pytest.raises(error): + plugin.prepare_data() + else: + plugin.prepare_data() + + @pytest.mark.parametrize( + "config, overrides, error", + [ + (lazy_fixture("base_config"), {}, None), + (lazy_fixture("probs_config"), {}, None), + (lazy_fixture("probs_config"), {"format": "probs"}, None), + (lazy_fixture("probs_config"), {"format": "probs3"}, None), + (lazy_fixture("probs_str_config"), {}, None), + (lazy_fixture("probs3_config"), {}, None), + (lazy_fixture("probs3_config"), {"format": "probs"}, None), + (lazy_fixture("probs3_config"), {"format": "probs3"}, None), + ], + ) + def test_setup(self, config, overrides, error): + if overrides: + overrides = DictConfig(overrides) + config = OmegaConf.merge(config, overrides) + plugin = GeneticTSPlugin(**config) + plugin.prepare_data() + if error and issubclass(error, Exception): + with pytest.raises(error): + plugin.setup() + else: + plugin.setup() + + @pytest.mark.parametrize( + "config, overrides, exp_features", + [ + (lazy_fixture("base_config"), {}, {"genetic": (["var1", "var2", "var3"],)}), + ( + lazy_fixture("probs_config"), + {"format": "probs"}, + {"genetic": (["var1", "var2", "var3"], ["p0", "p1"])}, + ), + ( + lazy_fixture("probs_str_config"), + {"format": "probs"}, + {"genetic": (["var1", "var2", "var3"], ["p0", "p1"])}, + ), + ( + lazy_fixture("probs3_config"), + {"format": "probs3"}, + {"genetic": (["var1", "var2", "var3"], ["p0", "p1", "p2"])}, + ), + ], + ) + def test_get_features(self, config, overrides, exp_features): + if overrides: + overrides = DictConfig(overrides) + config = OmegaConf.merge(config, overrides) + plugin = GeneticTSPlugin(**config) + plugin.prepare_data() + plugin.setup() + features = plugin.get_features() + assert set(features.keys()) == set(exp_features.keys()) + assert features["genetic"] == exp_features["genetic"] + + @pytest.mark.parametrize( + "config, overrides, eids, features, exp_result", + [ + ( + lazy_fixture("base_config"), + {}, + 0, + None, + {"genetic": torch.tensor([0, 1, 0], dtype=torch.float32)}, + ), + ( + lazy_fixture("base_config"), + {}, + 1, + None, + { + "genetic": torch.tensor([0, 0, 1], dtype=torch.float32), + }, + ), + ( + lazy_fixture("base_config"), + {}, + 2, + None, + {"genetic": torch.tensor([1, 0, 0], dtype=torch.float32)}, + ), + ( + lazy_fixture("base_config"), + {}, + [0, 2], + None, + {"genetic": torch.tensor([[0, 1, 0], [1, 0, 0]], dtype=torch.float32)}, + ), + ( + lazy_fixture("base_config"), + {}, + 0, + {"genetic": (["var1", "var3"],)}, + {"genetic": torch.tensor([[0, 0]], dtype=torch.float32)}, + ), + ( + lazy_fixture("base_config"), + {}, + 1, + {"genetic": (["var1", "var3"],)}, + {"genetic": torch.tensor([[0, 1]], dtype=torch.float32)}, + ), + ( + lazy_fixture("base_config"), + {}, + 1, + {"genetic": (torch.tensor([1, 0, 1], dtype=torch.bool),)}, + {"genetic": torch.tensor([[0, 1]], dtype=torch.float32)}, + ), + ( + lazy_fixture("base_config"), + {}, + [0, 2], + {"genetic": (["var1", "var3"],)}, + {"genetic": torch.tensor([[0, 0], [1, 0]], dtype=torch.float32)}, + ), + ( + lazy_fixture("probs_config"), + {}, + 0, + {"genetic": (["var1", "var3"],)}, + {"genetic": torch.tensor([0.3, 0.3])}, + ), + ( + lazy_fixture("probs_config"), + {"format": "dosages"}, + 0, + None, + {"genetic": torch.tensor([0.3, 0.8, 0.3])}, + ), + ( + lazy_fixture("probs_config"), + {"format": "probs"}, + 0, + None, + {"genetic": torch.tensor([[0.7, 0.3], [0.5, 0.2], [0.8, 0.1]])}, + ), + ( + lazy_fixture("probs_config"), + {"format": "probs"}, + 0, + {"genetic": (["var1", "var3"], slice(None))}, + {"genetic": torch.tensor([[0.7, 0.3], [0.8, 0.1]])}, + ), + ( + lazy_fixture("probs_config"), + {"format": "probs3"}, + 0, + None, + { + "genetic": torch.tensor( + [[0.7, 0.3, 0.0], [0.5, 0.2, 0.3], [0.8, 0.1, 0.1]] + ) + }, + ), + ( + lazy_fixture("probs_config"), + {"format": "probs3"}, + 0, + {"genetic": (["var1", "var3"], slice(None))}, + {"genetic": torch.tensor([[0.7, 0.3, 0.0], [0.8, 0.1, 0.1]])}, + ), + ( + lazy_fixture("probs_str_config"), + {}, + "a1", + {"genetic": (["var1", "var3"],)}, + {"genetic": torch.tensor([0.3, 0.3])}, + ), + ( + lazy_fixture("probs3_config"), + {"format": "dosages"}, + 0, + None, + {"genetic": torch.tensor([0.3, 0.8, 0.3])}, + ), + ( + lazy_fixture("probs3_str_config"), + {"format": "dosages"}, + "a1", + None, + {"genetic": torch.tensor([0.3, 0.8, 0.3])}, + ), + ( + lazy_fixture("probs3_config"), + {"format": "probs3"}, + 0, + None, + { + "genetic": torch.tensor( + [[0.7, 0.3, 0.0], [0.5, 0.2, 0.3], [0.8, 0.1, 0.1]] + ) + }, + ), + ( + lazy_fixture("probs3_str_config"), + {"format": "probs3"}, + "a1", + None, + { + "genetic": torch.tensor( + [[0.7, 0.3, 0.0], [0.5, 0.2, 0.3], [0.8, 0.1, 0.1]] + ) + }, + ), + ( + lazy_fixture("probs3_config"), + {"format": "probs3"}, + [0, 2], + None, + { + "genetic": torch.tensor( + [ + [[0.7, 0.3, 0.0], [0.5, 0.2, 0.3], [0.8, 0.1, 0.1]], + [[0.66, 0.33, 0.01], [0.8, 0.2, 0.0], [0.7, 0.3, 0.0]], + ] + ) + }, + ), + ( + lazy_fixture("probs3_str_config"), + {"format": "probs3"}, + ["a1", "c3"], + None, + { + "genetic": torch.tensor( + [ + [[0.7, 0.3, 0.0], [0.5, 0.2, 0.3], [0.8, 0.1, 0.1]], + [[0.66, 0.33, 0.01], [0.8, 0.2, 0.0], [0.7, 0.3, 0.0]], + ] + ) + }, + ), + ( + lazy_fixture("probs3_uint8_config"), + {"format": "probs3"}, + 0, + None, + { + "genetic": torch.tensor( + [[0.7, 0.3, 0.0], [0.5, 0.2, 0.3], [0.8, 0.1, 0.1]] + ) + }, + ), + ( + lazy_fixture("probs_uint4_config"), + {"format": "probs"}, + 0, + None, + {"genetic": torch.tensor([[0.7, 0.3], [0.5, 0.2], [0.8, 0.1]])}, + ), + ( + lazy_fixture("dosage_uint8_config"), + {}, + 0, + None, + {"genetic": torch.tensor([0.5, 1.8, 0.2])}, + ), + ( + lazy_fixture("dosage_uint8_config"), + {}, + [0, 2], + None, + {"genetic": torch.tensor([[0.5, 1.8, 0.2], [1.2, 1.1, 0.9]])}, + ), + ( + lazy_fixture("dosage_uint8_config"), + {}, + [0, 2], + {"genetic": (["var1", "var3"],)}, + {"genetic": torch.tensor([[0.5, 0.2], [1.2, 0.9]])}, + ), + ( + lazy_fixture("dosage_uint4_config"), + {}, + 0, + None, + {"genetic": torch.tensor([0.5, 1.8, 0.2])}, + ), + ( + lazy_fixture("dosage_uint4_config"), + {}, + [0, 2], + None, + {"genetic": torch.tensor([[0.5, 1.8, 0.2], [1.2, 1.1, 0.9]])}, + ), + ( + lazy_fixture("dosage_uint4_config"), + {}, + [0, 2], + {"genetic": (["var1", "var3"],)}, + {"genetic": torch.tensor([[0.5, 0.2], [1.2, 0.9]])}, + ), + ], + ) + def test__getitem__(self, config, overrides, eids, features, exp_result): + if overrides: + overrides = DictConfig(overrides) + config = OmegaConf.merge(config, overrides) + plugin = GeneticTSPlugin(**config) + plugin.prepare_data() + plugin.setup() + if isclass(exp_result) and issubclass(exp_result, Exception): + with pytest.raises(error): + plugin[eids, features] + else: + result = plugin[eids, features] + assert set(result.keys()) == set(exp_result.keys()) + if plugin.dtype.startswith("uint"): + dtype_bits = int(plugin.dtype[4]) + atol = 1 / 2**dtype_bits + elif plugin.dtype.startswith("float"): + dtype_bits = int(plugin.dtype[5:]) + atol = 1 / 2**dtype_bits + assert torch.allclose( + result["genetic"], exp_result["genetic"], atol=max(2 * atol, 1e-7) + ) diff --git a/test/plugins/h5ad/__init__.py b/test/plugins/h5ad/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/plugins/h5ad/h5ad_plugin_test.py b/test/plugins/h5ad/h5ad_plugin_test.py new file mode 100644 index 0000000..2063c16 --- /dev/null +++ b/test/plugins/h5ad/h5ad_plugin_test.py @@ -0,0 +1,755 @@ +from inspect import isclass +from copy import deepcopy +import os +from os.path import dirname, abspath +from omegaconf import OmegaConf +from tempfile import TemporaryDirectory +import numpy as np +import hydra +import pytest +from pytest_lazyfixture import lazy_fixture +import pandas as pd +import torch + +from udm.plugins.h5ad.h5ad_plugin import H5adPlugin +from test.utils import try_clear_hydra + + +UDM_HOME = "../../.." +UDM_HOME_ABSPATH = abspath(os.path.join(dirname(__file__), UDM_HOME)) + + +@pytest.fixture +def temp_dir(): + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +@pytest.fixture +def mini1_data(): + data = np.array([[60, 1], [50, 1], [71, 0]], dtype=np.uint8) + obs = pd.DataFrame({"index": [0, 1, 2]}) + var = pd.DataFrame( + { + "ValueType": ["uint8", "uint8"], + "ID": ["age", "sex"], + } + ) + return data, obs, var + + +@pytest.fixture +def mini1_str_data(mini1_data): + data, obs, var = mini1_data + obs = pd.DataFrame({"index": ["a1", "b2", "c3"]}) + return data, obs, var + + +@pytest.fixture +def mini2_data(): + data = np.array([[81, 0], [64, 0]], dtype=np.uint8) + obs = pd.DataFrame({"index": [3, 4]}) + var = pd.DataFrame( + { + "ValueType": ["uint8", "uint8"], + "ID": ["age", "sex"], + } + ) + return data, obs, var + + +@pytest.fixture +def mini2_str_data(mini2_data): + data, obs, var = mini2_data + obs = pd.DataFrame({"index": ["d4", "e5"]}) + return data, obs, var + + +def prepare_data(temp_path, name, X, obs, var): + basename = os.path.join(temp_path, name) + with open(f"{basename}.X.npy", "wb+") as f: + np.save(f, X, allow_pickle=True) + obs.to_csv(f"{basename}.obs.csv", index=False) + var.to_csv(f"{basename}.var.csv", index=False) + return basename + + +@pytest.fixture +def prepare_mini1(temp_dir, mini1_data): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + data, obs, var = mini1_data + yield prepare_data(temp_dir, "mini1_config", data, obs, var) + + +@pytest.fixture +def prepare_mini1_str(temp_dir, mini1_str_data): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + data, obs, var = mini1_str_data + yield prepare_data(temp_dir, "mini1_config", data, obs, var) + + +@pytest.fixture +def prepare_mini2(temp_dir, mini2_data): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + data, obs, var = mini2_data + yield prepare_data(temp_dir, "mini2_config", data, obs, var) + + +@pytest.fixture +def prepare_mini2_str(temp_dir, mini2_str_data): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + data, obs, var = mini2_str_data + yield prepare_data(temp_dir, "mini2_config", data, obs, var) + + +@pytest.fixture +def prepare_mini_eid_map(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + eid_map = pd.DataFrame({"from": [0, 1, 2, 3, 4], "to": [5, 6, 7, 8, 9]}) + eid_map_path = f"{temp_dir}/eid_map.csv" + eid_map.to_csv(eid_map_path, sep="\t") + yield eid_map_path + + +@pytest.fixture +def mini1_config(prepare_mini1): + basename = prepare_mini1 + yield OmegaConf.create( + {"src": [basename], "src_names": ["mini1_config"], "eid_map_path": None} + ) + + +@pytest.fixture +def mini1_str_config(prepare_mini1_str): + basename = prepare_mini1_str + yield OmegaConf.create( + {"src": [basename], "src_names": ["mini1_config"], "eid_map_path": None} + ) + + +@pytest.fixture +def mini1_map_config(mini1_config, prepare_mini_eid_map): + eid_map_path = prepare_mini_eid_map + mini1_config.update( + { + "eid_map_path": eid_map_path, + "eid_map_from": "from", + "eid_map_to": "to", + } + ) + yield mini1_config + + +@pytest.fixture +def mini2_config(prepare_mini2): + basename = prepare_mini2 + yield OmegaConf.create( + {"src": [basename], "src_names": ["mini2_config"], "eid_map_path": None} + ) + + +@pytest.fixture +def mini2_str_config(prepare_mini2_str): + basename = prepare_mini2_str + yield OmegaConf.create( + {"src": [basename], "src_names": ["mini2_config"], "eid_map_path": None} + ) + + +@pytest.fixture +def mini2_map_config(mini2_config, prepare_mini_eid_map): + eid_map_path = prepare_mini_eid_map + mini2_config.update( + { + "eid_map_path": eid_map_path, + "eid_map_from": "from", + "eid_map_to": "to", + } + ) + yield mini2_config + + +@pytest.fixture +def mini_config(prepare_mini1, prepare_mini2): + basename1, basename2 = prepare_mini1, prepare_mini2 + yield OmegaConf.create( + { + "src": [basename1, basename2], + "src_names": ["mini1_config", "mini2_config"], + "eid_map_path": None, + } + ) + + +@pytest.fixture +def mini_map_config(mini_config, prepare_mini_eid_map): + eid_map_path = prepare_mini_eid_map + mini_config.update( + { + "eid_map_path": eid_map_path, + "eid_map_from": "from", + "eid_map_to": "to", + } + ) + yield mini_config + + +@pytest.fixture +def geno_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "h5ad") + hydra.initialize(version_base=None, config_path=config_path) + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/geno/") + overrides_config = OmegaConf.create( + { + "src": [ + "genetic_extended_tr", + "genetic_valid", + "genetic_test", + ], + "src_names": ["train", "valid", "test"], + "dataset_path": FAKE_DATA_PATH, + } + ) + config = hydra.compose(config_name="debug.yaml", overrides=[]) + yield OmegaConf.merge(config, overrides_config) + + +@pytest.fixture +def cov_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "h5ad") + hydra.initialize(version_base=None, config_path=config_path) + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/covariate/") + overrides_config = OmegaConf.create( + { + "src": [ + "covariates_new_base_extended_tr.h5ad", + "covariates_new_base_valid.h5ad", + "covariates_new_base_test.h5ad", + ], + "src_names": ["train", "valid", "test"], + "dataset_path": FAKE_DATA_PATH, + } + ) + config = hydra.compose(config_name="debug.yaml", overrides=[]) + yield OmegaConf.merge(config, overrides_config) + + +@pytest.fixture +def metabol_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "h5ad") + hydra.initialize(version_base=None, config_path=config_path) + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/metabolomics/") + overrides_config = OmegaConf.create( + { + "src": [ + "metabolomics_extended_tr.h5ad", + "metabolomics_valid.h5ad", + "metabolomics_test.h5ad", + ], + "src_names": ["train", "valid", "test"], + "dataset_path": FAKE_DATA_PATH, + } + ) + config = hydra.compose(config_name="debug.yaml", overrides=[]) + yield OmegaConf.merge(config, overrides_config) + + +@pytest.fixture +def pheno_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "h5ad") + hydra.initialize(version_base=None, config_path=config_path) + FAKE_DATA_PATH = os.path.join(UDM_HOME_ABSPATH, "res/datasets/fake/pheno/") + overrides_config = OmegaConf.create( + { + "src": [ + "cad_extended_tr.h5ad", + "cad_valid.h5ad", + "cad_test.h5ad", + ], + "src_names": ["train", "valid", "test"], + "dataset_path": FAKE_DATA_PATH, + } + ) + config = hydra.compose(config_name="debug.yaml", overrides=[]) + yield OmegaConf.merge(config, overrides_config) + + +@pytest.fixture +def h5ad_eids(): + yield [ + 5797, + 5099, + 1791, + 7810, + 1972, + 3055, + 9086, + 5933, + 1103, + 4152, + ] + + +@pytest.fixture +def h5ad_eids_head(h5ad_eids): + yield h5ad_eids + + +@pytest.fixture +def h5ad_eid(h5ad_eids_head): + yield h5ad_eids_head[0] + + +class TestH5adPlugin(object): + @pytest.mark.parametrize( + "config", + [ + lazy_fixture("mini1_config"), + lazy_fixture("mini1_str_config"), + lazy_fixture("mini2_config"), + lazy_fixture("mini2_str_config"), + lazy_fixture("mini_config"), + lazy_fixture("geno_config"), + lazy_fixture("cov_config"), + lazy_fixture("metabol_config"), + lazy_fixture("pheno_config"), + ], + ) + def test__init__(self, config): + plugin = H5adPlugin.from_config(config) + + @pytest.mark.parametrize( + "config", + [ + lazy_fixture("mini1_config"), + lazy_fixture("mini1_str_config"), + lazy_fixture("mini2_config"), + lazy_fixture("mini2_str_config"), + lazy_fixture("mini_config"), + lazy_fixture("geno_config"), + lazy_fixture("cov_config"), + lazy_fixture("metabol_config"), + lazy_fixture("pheno_config"), + ], + ) + def test_prepare_data(self, config): + plugin = H5adPlugin.from_config(config) + plugin.prepare_data() + + @pytest.mark.parametrize( + "config", + [ + lazy_fixture("mini1_config"), + lazy_fixture("mini1_str_config"), + lazy_fixture("mini2_config"), + lazy_fixture("mini2_str_config"), + lazy_fixture("mini_config"), + lazy_fixture("geno_config"), + lazy_fixture("cov_config"), + lazy_fixture("metabol_config"), + lazy_fixture("pheno_config"), + ], + ) + def test_setup(self, config): + plugin = H5adPlugin.from_config(config) + plugin.prepare_data() + plugin.setup() + + @pytest.mark.parametrize( + "config, prepare_data, setup, exp_len", + [ + (lazy_fixture("mini1_config"), False, False, 0), + (lazy_fixture("mini1_config"), True, False, 0), + (lazy_fixture("mini1_config"), True, True, 3), + (lazy_fixture("mini1_str_config"), True, True, 3), + (lazy_fixture("mini2_config"), True, True, 2), + (lazy_fixture("mini2_str_config"), True, True, 2), + (lazy_fixture("mini_config"), True, True, 5), + (lazy_fixture("geno_config"), False, False, 0), + (lazy_fixture("geno_config"), True, False, 0), + (lazy_fixture("geno_config"), True, True, 4096), + (lazy_fixture("cov_config"), True, True, 4096), + (lazy_fixture("metabol_config"), True, True, 4096), + (lazy_fixture("pheno_config"), True, True, 4096), + ], + ) + def test__len__(self, config, prepare_data, setup, exp_len): + plugin = H5adPlugin.from_config(config) + if prepare_data: + plugin.prepare_data() + if setup: + plugin.setup() + assert len(plugin) == exp_len + + @pytest.mark.parametrize( + "config, prepare_data, setup, eids, features, shape_only, exp_item", + [ + (lazy_fixture("mini1_config"), False, False, 0, None, False, RuntimeError), + (lazy_fixture("mini1_config"), True, False, 0, None, False, RuntimeError), + *[ + ( + lazy_fixture("mini1_config"), + True, + True, + eid, + None, + False, + {"tabular_data": item}, + ) + for eid, item in zip( + [0, 1, 2], + torch.tensor([[60, 1], [50, 1], [71, 0]], dtype=torch.float32), + ) + ], + *[ + ( + lazy_fixture("mini1_str_config"), + True, + True, + eid, + None, + False, + {"tabular_data": item}, + ) + for eid, item in zip( + ["a1", "b2", "c3"], + torch.tensor([[60, 1], [50, 1], [71, 0]], dtype=torch.float32), + ) + ], + ( + lazy_fixture("mini1_config"), + True, + True, + [0, 1, 2], + {"tabular_data": (torch.tensor([True, False], dtype=torch.bool),)}, + False, + {"tabular_data": torch.tensor([[60], [50], [71]], dtype=torch.float32)}, + ), + ( + lazy_fixture("mini1_config"), + True, + True, + [0, 1, 2], + { + "tabular_data": (["age"],), + }, + False, + {"tabular_data": torch.tensor([[60], [50], [71]], dtype=torch.float32)}, + ), + ( + lazy_fixture("mini1_config"), + True, + True, + [0, 1, 2], + {"tabular_data": (torch.tensor([False, True], dtype=torch.bool),)}, + False, + {"tabular_data": torch.tensor([[1], [1], [0]], dtype=torch.float32)}, + ), + ( + lazy_fixture("mini1_config"), + True, + True, + [0, 1, 2], + {"tabular_data": (["sex"],)}, + False, + {"tabular_data": torch.tensor([[1], [1], [0]], dtype=torch.float32)}, + ), + (lazy_fixture("mini2_config"), True, True, 0, None, False, KeyError), + *[ + ( + lazy_fixture("mini2_config"), + True, + True, + eid, + None, + False, + {"tabular_data": item}, + ) + for eid, item in zip( + [3, 4], torch.tensor([[81, 0], [64, 0]], dtype=torch.float32) + ) + ], + *[ + ( + lazy_fixture("mini2_str_config"), + True, + True, + eid, + None, + False, + {"tabular_data": item}, + ) + for eid, item in zip( + ["d4", "e5"], torch.tensor([[81, 0], [64, 0]], dtype=torch.float32) + ) + ], + ( + lazy_fixture("mini_config"), + True, + True, + 4, + None, + False, + {"tabular_data": torch.tensor([64, 0], dtype=torch.float32)}, + ), + ( + lazy_fixture("geno_config"), + True, + True, + lazy_fixture("h5ad_eid"), + None, + True, + {"tabular_data": [10]}, + ), + ( + lazy_fixture("metabol_config"), + True, + True, + lazy_fixture("h5ad_eid"), + None, + True, + {"tabular_data": [168]}, + ), + ( + lazy_fixture("cov_config"), + True, + True, + lazy_fixture("h5ad_eid"), + None, + True, + {"tabular_data": [9]}, + ), + ( + lazy_fixture("pheno_config"), + True, + True, + lazy_fixture("h5ad_eid"), + None, + True, + {"tabular_data": [1]}, + ), + ], + ) + def test__getitem__( + self, config, prepare_data, setup, eids, features, shape_only, exp_item + ): + plugin = H5adPlugin.from_config(config) + if prepare_data: + plugin.prepare_data() + if setup: + plugin.setup() + if isclass(exp_item) and issubclass(exp_item, Exception): + with pytest.raises(exp_item): + plugin[eids, features] + else: + item = deepcopy(plugin[eids, features]) + assert isinstance(item, dict) + assert set(item.keys()) == set(exp_item.keys()) + for key in item: + if shape_only: + assert list(item[key].shape) == exp_item[key] + else: + assert torch.all(item[key] == exp_item[key]) + del plugin + + @pytest.mark.parametrize( + "config, prepare_data, setup, head_only, exp_eids", + [ + (lazy_fixture("mini1_config"), False, False, False, []), + (lazy_fixture("mini1_config"), True, False, False, []), + (lazy_fixture("mini1_config"), True, True, False, [0, 1, 2]), + (lazy_fixture("mini1_str_config"), True, True, False, ["a1", "b2", "c3"]), + (lazy_fixture("mini1_map_config"), True, True, False, [0, 1, 2]), + (lazy_fixture("mini2_config"), True, True, False, [3, 4]), + (lazy_fixture("mini2_str_config"), True, True, False, ["d4", "e5"]), + (lazy_fixture("mini2_map_config"), True, True, False, [3, 4]), + (lazy_fixture("mini_config"), True, True, False, [0, 1, 2, 3, 4]), + ( + lazy_fixture("geno_config"), + True, + True, + True, + lazy_fixture("h5ad_eids_head"), + ), + ( + lazy_fixture("metabol_config"), + True, + True, + True, + lazy_fixture("h5ad_eids_head"), + ), + ( + lazy_fixture("cov_config"), + True, + True, + True, + lazy_fixture("h5ad_eids_head"), + ), + ( + lazy_fixture("pheno_config"), + True, + True, + True, + lazy_fixture("h5ad_eids_head"), + ), + ], + ) + def test_get_native_eids(self, config, prepare_data, setup, head_only, exp_eids): + self._test_get_eids( + config, prepare_data, setup, head_only, exp_eids, native=True + ) + + @pytest.mark.parametrize( + "config, prepare_data, setup, head_only, exp_eids", + [ + (lazy_fixture("mini1_config"), False, False, False, []), + (lazy_fixture("mini1_config"), True, False, False, []), + (lazy_fixture("mini1_config"), True, True, False, [0, 1, 2]), + (lazy_fixture("mini1_str_config"), True, True, False, ["a1", "b2", "c3"]), + (lazy_fixture("mini1_map_config"), True, True, False, [5, 6, 7]), + (lazy_fixture("mini2_config"), True, True, False, [3, 4]), + (lazy_fixture("mini2_str_config"), True, True, False, ["d4", "e5"]), + (lazy_fixture("mini2_map_config"), True, True, False, [8, 9]), + (lazy_fixture("mini_config"), True, True, False, [0, 1, 2, 3, 4]), + ( + lazy_fixture("geno_config"), + True, + True, + True, + lazy_fixture("h5ad_eids_head"), + ), + ( + lazy_fixture("metabol_config"), + True, + True, + True, + lazy_fixture("h5ad_eids_head"), + ), + ( + lazy_fixture("cov_config"), + True, + True, + True, + lazy_fixture("h5ad_eids_head"), + ), + ( + lazy_fixture("pheno_config"), + True, + True, + True, + lazy_fixture("h5ad_eids_head"), + ), + ], + ) + def test_get_eids(self, config, prepare_data, setup, head_only, exp_eids): + self._test_get_eids(config, prepare_data, setup, head_only, exp_eids) + + def _test_get_eids( + self, config, prepare_data, setup, head_only, exp_eids, native=False + ): + plugin = H5adPlugin.from_config(config) + if prepare_data: + plugin.prepare_data() + if setup: + plugin.setup() + if native: + eids = plugin.get_native_eids() + else: + eids = plugin.get_eids() + if head_only: + eids = eids[:10] + assert eids == exp_eids + del plugin + + @pytest.mark.parametrize( + "config, prepare_data, setup, head_only, exp_meta", + [ + ( + lazy_fixture("mini1_config"), + False, + False, + False, + { + "tags": [], + "eids": [], + "features": {}, + "feature_types": {}, + "variables": {}, + }, + ), + ( + lazy_fixture("mini1_config"), + True, + False, + False, + { + "tags": [], + "eids": [], + "features": {}, + "feature_types": {}, + "variables": {}, + }, + ), + ( + lazy_fixture("mini1_config"), + True, + True, + False, + { + "tags": [], + "eids": [0, 1, 2], + "features": {"tabular_data": (["age", "sex"],)}, + "feature_types": {"tabular_data": np.float32}, + "variables": { + "0": {"ValueType": "uint8", "ID": "age"}, + "1": {"ValueType": "uint8", "ID": "sex"}, + }, + }, + ), + ( + lazy_fixture("mini2_config"), + True, + True, + False, + { + "tags": [], + "eids": [3, 4], + "features": {"tabular_data": (["age", "sex"],)}, + "feature_types": {"tabular_data": np.float32}, + "variables": { + "0": {"ValueType": "uint8", "ID": "age"}, + "1": {"ValueType": "uint8", "ID": "sex"}, + }, + }, + ), + ( + lazy_fixture("mini_config"), + True, + True, + False, + { + "tags": [], + "eids": [0, 1, 2, 3, 4], + "features": {"tabular_data": (["age", "sex"],)}, + "feature_types": {"tabular_data": np.float32}, + "variables": { + "0": {"ValueType": "uint8", "ID": "age"}, + "1": {"ValueType": "uint8", "ID": "sex"}, + }, + }, + ), + ], + ) + def test_get_metadata(self, config, prepare_data, setup, head_only, exp_meta): + plugin = H5adPlugin.from_config(config) + if prepare_data: + plugin.prepare_data() + if setup: + plugin.setup() + meta = plugin.get_metadata() + assert set(meta.keys()) == set(exp_meta.keys()) + assert meta == exp_meta diff --git a/test/plugins/tabular/__init__.py b/test/plugins/tabular/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/plugins/tabular/tabular_plugin_test.py b/test/plugins/tabular/tabular_plugin_test.py new file mode 100644 index 0000000..d5a2362 --- /dev/null +++ b/test/plugins/tabular/tabular_plugin_test.py @@ -0,0 +1,900 @@ +import os +from typing import Callable +import hydra +import pandas as pd +import yaml +import pyarrow as pa +import pyarrow.feather as feather +import pytest +from pytest_lazyfixture import lazy_fixture +import torch +import json +from tempfile import TemporaryDirectory +from hydra.core.global_hydra import GlobalHydra +import json + +from udm.plugins.tabular.tabular_plugin import TabularPlugin +from test.utils import SKIPIF_NOT_BIH_HPC, try_clear_hydra + +UDM_HOME = "../../.." + + +@pytest.fixture +def temp_dir(): + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +@pytest.fixture +def mini1_data(): + return { + "eid": [0, 1, 2], + "age": [-0.7, -1.59, 0.9], + "sex_female": [1, 0, 0], + "sex_male": [0, 1, 1], + "midi-chlorian-level": [0.5, 0.3, -1.3], # may the force be with you + "home_planet_tatooine": [1, 0, 0], + } + + +@pytest.fixture +def mini1_str_data(mini1_data): + mini1_data.update({"eid": ["a1", "b2", "c3"]}) + return mini1_data + + +def prepare_feather(temp_path, name, data): + TMP_PATH = os.path.join(temp_path, f"{name}.feather") + tabular_pa = pa.Table.from_pydict(data) + feather.write_feather(tabular_pa, TMP_PATH) + return TMP_PATH + + +@pytest.fixture +def prepare_mini1(temp_dir, mini1_data): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_feather(temp_dir, "tabular1", mini1_data) + + +@pytest.fixture +def prepare_mini1_str(temp_dir, mini1_str_data): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_feather(temp_dir, "tabular1", mini1_str_data) + + +@pytest.fixture +def mini2_data(): + return { + "eid": [3, 4, 5], + "age": [-0.9, -1.380, 1.22], + "sex_female": [0, 0, 1], # female,male,female + "sex_male": [1, 1, 0], + "midi-chlorian-level": [-0.6, 0.353, 2.46], + "home_planet_tatooine": [0, 1, 0], + } + + +@pytest.fixture +def mini2_str_data(mini2_data): + mini2_data.update({"eid": ["d4", "e5", "f6"]}) + return mini2_data + + +@pytest.fixture +def prepare_mini2(temp_dir, mini2_data): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_feather(temp_dir, "tabular2", mini2_data) + + +@pytest.fixture +def prepare_mini2_str(temp_dir, mini2_str_data): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + yield prepare_feather(temp_dir, "tabular2", mini2_str_data) + + +@pytest.fixture +def prepare_variable_annotations(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + var_ann_data = { + "index": [ + "eid", + "age", + "sex_female", + "sex_male", + "midi-chlorian-level", + "home_planet_tatooine", + ], + "description": [ + "the electronic identifier", + "individual age", + "female", + "male", + "the amount of force sensitive microbes", + "who wants to live on tatooine?", + ], + } + VAR_ANNO_PATH = os.path.join(temp_dir, "var_annotations.csv") + var_ann_df = pd.DataFrame(var_ann_data) + var_ann_df.to_csv(VAR_ANNO_PATH) # TODO fix weird double index column + yield VAR_ANNO_PATH # TODO create a var anno anno debug dataset + + +@pytest.fixture +def prepare_phecode_dataset(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + phecode_data = { + "eid": [ + "0", + "1", + "2", + "3", + "4", + "5", + ], + "phecode_089": [ + 0, + 1, + 1, + 0, + 1, + 0, + ], + "phecode_460": [ + 1, + 1, + 0, + 0, + 0, + 1, + ], + "phecode_713": [ + 0, + 1, + 0, + 0, + 1, + 0, + ], + "phecode_713.3": [ + 1, + 1, + 1, + 0, + 0, + 1, + ], + "phecode_718": [ + 0, + 0, + 1, + 1, + 0, + 1, + ], + "phecode_283": [ + 0, + 0, + 0, + 1, + 0, + 0, + ], + "phecode_460.1": [ + 0, + 1, + 0, + 0, + 1, + 1, + ], + "phecode_660": [ + 0, + 1, + 1, + 0, + 1, + 1, + ], + "phecode_401": [ + 0, + 0, + 1, + 0, + 1, + 1, + ], + "phecode_401.1": [ + 1, + 1, + 0, + 0, + 0, + 1, + ], + } + PHECODE_DS_PATH = os.path.join(temp_dir, "phecode_ds.csv") + phecode_ds_df = pd.DataFrame(phecode_data) + phecode_ds_df.to_feather(PHECODE_DS_PATH) + yield PHECODE_DS_PATH + + +@pytest.fixture +def prepare_columns_subset_csv(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + subset = {"columns_to_pick": ["age", "sex_female", "home_planet_tatooine"]} + COL_SUBSET_PATH = os.path.join(temp_dir, "column_subset.csv") + col_subset_df = pd.DataFrame.from_dict(subset) + col_subset_df.to_csv(COL_SUBSET_PATH, index=False) + yield COL_SUBSET_PATH + + +@pytest.fixture +def prepare_composite_features1(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + subset = {"cad_endpoint": ["phecode_401", "phecode_401.1", "phecode_713.3"]} + COMP_FEATURES_PATH = os.path.join(temp_dir, "composite_features_test1.json") + with open(COMP_FEATURES_PATH, "w") as f: + json.dump(subset, f) + yield COMP_FEATURES_PATH + + +@pytest.fixture +def prepare_composite_features2(temp_dir): + subset = { + "cad_endpoint": ["phecode_401", "phecode_401.1", "phecode_713.3"], + "infection_endpoints": ["phecode_089", "phecode_713", "phecode_283"], + } + COMP_FEATURES_PATH = os.path.join(temp_dir, "composite_features_test2.json") + with open(COMP_FEATURES_PATH, "w") as f: + json.dump(subset, f) + yield COMP_FEATURES_PATH + + +@pytest.fixture +def prepare_intersection_features1(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + subset = {"cad_endpoint": ["phecode_401", "phecode_401.1", "phecode_713.3"]} + INTER_FEATURES_PATH = os.path.join(temp_dir, "intersection_features_test1.json") + with open(INTER_FEATURES_PATH, "w") as f: + json.dump(subset, f) + yield INTER_FEATURES_PATH + + +@pytest.fixture +def prepare_intersection_features2(temp_dir): + with TemporaryDirectory(dir=temp_dir) as temp_dir: + subset = { + "cad_endpoint": ["phecode_401", "phecode_401.1", "phecode_713.3"], + "infection_endpoints": ["phecode_089", "phecode_713", "phecode_283"], + } + INTER_FEATURES_PATH = os.path.join(temp_dir, "intersection_features_test2.json") + with open(INTER_FEATURES_PATH, "w") as f: + json.dump(subset, f) + yield INTER_FEATURES_PATH + + +@pytest.fixture +def tabular_debug_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose(config_name="default") + data_root = f"{config.data_paths}/{config.debug_data_paths}" + path = os.path.join( + data_root, + "covariates_new_base_outliers_te_debug.feather", + ) + config.data_paths = path + yield config + + +@pytest.fixture +def tabular_debug_usecols_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose(config_name="debug", overrides=["dropcols=null"]) + data_root = f"{config.data_paths}/{config.debug_data_paths}" + path = os.path.join( + data_root, + "covariates_new_base_outliers_te_debug.feather", + ) + config.data_paths = path + yield config + + +@pytest.fixture +def tabular_debug_usecols_csv_config(prepare_mini1, prepare_columns_subset_csv): + try_clear_hydra() + mini1_path, col_subset_path = prepare_mini1, prepare_columns_subset_csv + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="debug", + overrides=[ + f"data_paths={mini1_path}", + f"usecols={col_subset_path}", + f"dropcols=null", + ], + ) + yield config + + +@pytest.fixture +def tabular_debug_dropcols_config(): + try_clear_hydra() + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose(config_name="default", overrides=["usecols=null"]) + data_root = f"{config.data_paths}/{config.debug_data_paths}" + path = os.path.join( + data_root, + "covariates_new_base_outliers_te_debug.feather", + ) + config.data_paths = path + yield config + + +@pytest.fixture +def tabular_debug_dropcols_csv_config(prepare_mini1, prepare_columns_subset_csv): + try_clear_hydra() + mini1_path, col_subset_path = prepare_mini1, prepare_columns_subset_csv + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="debug", + overrides=[ + f"data_paths={mini1_path}", + f"usecols=null", + f"dropcols={col_subset_path}", + ], + ) + yield config + + +@pytest.fixture +def tabular_mini_config(prepare_mini1, prepare_mini2): + try_clear_hydra() + mini1_path, mini2_path = prepare_mini1, prepare_mini2 + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + "default.yaml", overrides=[f"data_paths=[{mini1_path}, {mini2_path}]"] + ) + yield config + + +@pytest.fixture +def tabular_mini1_config(prepare_mini1, prepare_variable_annotations): + try_clear_hydra() + mini1_path, var_anno_path = prepare_mini1, prepare_variable_annotations + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + "default.yaml", + overrides=[ + f"data_paths={mini1_path}", + f"variable_annotations_path={var_anno_path}", + ], + ) + yield config + + +@pytest.fixture +def tabular_mini1_str_config(prepare_mini1_str, prepare_variable_annotations): + try_clear_hydra() + mini1_path, var_anno_path = prepare_mini1_str, prepare_variable_annotations + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + "default.yaml", + overrides=[ + f"data_paths={mini1_path}", + f"variable_annotations_path={var_anno_path}", + ], + ) + yield config + + +@pytest.fixture +def tabular_mini2_config(prepare_mini2): + try_clear_hydra() + mini2_path = prepare_mini2 + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose("default.yaml", overrides=[f"data_paths={mini2_path}"]) + return config + + +@pytest.fixture +def tabular_mini2_str_config(prepare_mini2_str): + try_clear_hydra() + mini2_path = prepare_mini2_str + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose("default.yaml", overrides=[f"data_paths={mini2_path}"]) + return config + + +@pytest.fixture +def tabular_debug_composite_features1_config( + prepare_phecode_dataset, prepare_composite_features1 +): + try_clear_hydra() + phecode_ds_path, comp_feature_path = ( + prepare_phecode_dataset, + prepare_composite_features1, + ) + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="debug", + overrides=[ + f"data_paths={phecode_ds_path}", + f"composite_features={comp_feature_path}", + f"only_keep_composite_features=True", + f"usecols=null", + f"dropcols=null", + ], + ) + yield config + + +@pytest.fixture +def tabular_debug_composite_features2_config( + prepare_phecode_dataset, prepare_composite_features2 +): + try_clear_hydra() + phecode_ds_path, comp_feature_path = ( + prepare_phecode_dataset, + prepare_composite_features2, + ) + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="debug", + overrides=[ + f"data_paths={phecode_ds_path}", + f"composite_features={comp_feature_path}", + f"drop_individual_cols_of_composite_features=True", + f"usecols=null", + f"dropcols=null", + ], + ) + yield config + + +@pytest.fixture +def tabular_debug_intersection_features1_config( + prepare_phecode_dataset, prepare_intersection_features1 +): + try_clear_hydra() + phecode_ds_path, inter_feature_path = ( + prepare_phecode_dataset, + prepare_intersection_features1, + ) + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="debug", + overrides=[ + f"data_paths={phecode_ds_path}", + f"intersection_features={inter_feature_path}", + f"only_keep_intersection_features=True", + f"usecols=null", + f"dropcols=null", + ], + ) + yield config + + +@pytest.fixture +def tabular_debug_intersection_features2_config( + prepare_phecode_dataset, prepare_intersection_features2 +): + try_clear_hydra() + phecode_ds_path, inter_feature_path = ( + prepare_phecode_dataset, + prepare_intersection_features2, + ) + config_path = os.path.join(UDM_HOME, "config", "plugins", "tabular") + hydra.initialize(version_base=None, config_path=config_path) + config = hydra.compose( + config_name="debug", + overrides=[ + f"data_paths={phecode_ds_path}", + f"intersection_features={inter_feature_path}", + f"drop_individual_cols_of_intersection_features=True", + f"usecols=null", + f"dropcols=null", + ], + ) + yield config + + +@pytest.fixture() +def tabular_debug_eids(): + TABULAR_DEBUG_EIDS_PATH = os.path.join( + UDM_HOME, "data/analysis/results/genopheno/datasets_by_pheno_feather/eids.yaml" + ) + eids = yaml.load(TABULAR_DEBUG_EIDS_PATH, Loader=yaml.CSafeLoader) + yield list(eids) + + +@pytest.fixture() +def tabular_debug_eid(tabular_debug_eids): + yield tabular_debug_eids[0] + + +class TestTabularPlugin(object): + def setUp(self) -> None: + if GlobalHydra.instance().is_initialized(): + GlobalHydra().instance().clear() + + @pytest.mark.parametrize( + "config", + [ + lazy_fixture("tabular_debug_config"), + lazy_fixture("tabular_mini1_config"), + lazy_fixture("tabular_mini1_str_config"), + lazy_fixture("tabular_mini2_config"), + lazy_fixture("tabular_mini2_str_config"), + ], + ) + def test__init__(self, config): + tabular_plugin = TabularPlugin.from_config(config) + + @pytest.mark.parametrize( + "config", + [ + pytest.param( + lazy_fixture("tabular_debug_config"), marks=SKIPIF_NOT_BIH_HPC + ), + lazy_fixture("tabular_mini1_config"), + lazy_fixture("tabular_mini1_str_config"), + lazy_fixture("tabular_mini2_config"), + lazy_fixture("tabular_mini2_str_config"), + ], + ) + def test_prepare_data(self, config): + tabular_plugin = TabularPlugin.from_config(config) + tabular_plugin.prepare_data() + + @pytest.mark.parametrize( + "config", + [ + pytest.param( + lazy_fixture("tabular_debug_config"), marks=SKIPIF_NOT_BIH_HPC + ), + lazy_fixture("tabular_mini1_config"), + lazy_fixture("tabular_mini1_str_config"), + lazy_fixture("tabular_mini2_config"), + lazy_fixture("tabular_mini2_str_config"), + ], + ) + def test_setup(self, config): + tabular_plugin = TabularPlugin.from_config(config) + tabular_plugin.prepare_data() + tabular_plugin.setup() + + @pytest.mark.parametrize( + "config, eid, features, exp_item, check_equal", + [ + pytest.param( + lazy_fixture("tabular_debug_config"), + lazy_fixture("tabular_debug_eid"), + None, + {"tabular_data": None}, + False, + marks=SKIPIF_NOT_BIH_HPC, + ), + ( + lazy_fixture("tabular_mini1_config"), + 0, + None, + { + "tabular_data": torch.tensor( + [-0.7, 1, 0, 0.5, 1], dtype=torch.float32 + ) + }, + True, + ), + ( + lazy_fixture("tabular_mini1_str_config"), + "a1", + None, + { + "tabular_data": torch.tensor( + [-0.7, 1, 0, 0.5, 1], dtype=torch.float32 + ) + }, + True, + ), + ( + lazy_fixture("tabular_mini1_config"), + [0, 2], + None, + { + "tabular_data": torch.tensor( + [ + [-0.7, 0.9], # age + [1, 0], # sex_female + [0, 1], # sex_male + [0.5, -1.3], # midi-chlorian-level + [1, 0], # home_planet_tatooine + ], + dtype=torch.float32, + ).T + }, + True, + ), + ( + lazy_fixture("tabular_mini1_str_config"), + ["a1", "c3"], + None, + { + "tabular_data": torch.tensor( + [ + [-0.7, 0.9], # age + [1, 0], # sex_female + [0, 1], # sex_male + [0.5, -1.3], # midi-chlorian-level + [1, 0], # home_planet_tatooine + ], + dtype=torch.float32, + ).T + }, + True, + ), + ( + lazy_fixture("tabular_mini1_config"), + [0, 2], + { + "tabular_data": ( + torch.tensor( + [True, False, False, True, True], dtype=torch.bool + ), + ) + }, + { + "tabular_data": torch.tensor( + [ + [-0.7, 0.9], # age + [0.5, -1.3], # midi-chlorian-level + [1, 0], # home_planet_tatooine + ], + dtype=torch.float32, + ).T + }, + True, + ), + ( + lazy_fixture("tabular_mini1_config"), + [0, 2], + {"tabular_data": (["sex_female", "sex_male", "midi-chlorian-level"],)}, + { + "tabular_data": torch.tensor( + [ + [1, 0], # sex_female + [0, 1], # sex_male + [0.5, -1.3], # midi-chlorian-level + ], + dtype=torch.float32, + ).T + }, + True, + ), + *[ + ( + lazy_fixture("tabular_mini2_str_config"), + eid, + None, + { + "tabular_data": torch.tensor( + [ + [-0.9, -1.380, 1.22], + [0, 0, 1], + [1, 1, 0], + [-0.6, 0.353, 2.46], + [0, 1, 0], + ] + ).T[i, :] + }, + True, + ) + for i, eid in enumerate(["d4", "e5", "f6"]) + ], + ( + lazy_fixture("tabular_mini2_str_config"), + ["d4", "f6"], + None, + { + "tabular_data": torch.tensor( + [[-0.9, 1.22], [0, 1], [1, 0], [-0.6, 2.46], [0, 0]] + ).T + }, + True, + ), + ], + ) + def test__getitem__(self, config, eid, features, exp_item, check_equal): + # usecols = ["sex_female", "home_planet_tatooine"] + tabular_plugin = TabularPlugin.from_config(config) + tabular_plugin.prepare_data() + tabular_plugin.setup() + item = tabular_plugin[eid, features] + assert isinstance(item, dict) + assert list(item.keys()) == ["tabular_data"] + if check_equal: + assert torch.all(item["tabular_data"] == exp_item["tabular_data"]) + + @pytest.mark.parametrize( + "config, exp_meta_keys", + [ + pytest.param( + lazy_fixture("tabular_debug_config"), + {"eids", "features", "feature_types"}, + marks=SKIPIF_NOT_BIH_HPC, + ), + ( + lazy_fixture("tabular_mini1_config"), + {"eids", "features", "feature_types", "variable_annotations"}, + ), + ], + ) + def test_get_metadata(self, config, exp_meta_keys): + tabular_plugin = TabularPlugin.from_config(config) + tabular_plugin.prepare_data() + tabular_plugin.setup() + metadata = tabular_plugin.get_metadata() + assert isinstance(metadata, dict) + assert set(metadata.keys()).intersection(exp_meta_keys) == exp_meta_keys + + def _get_plugin_eids(tabular_plugin): + return list(tabular_plugin.tabular_df.index) + + @pytest.mark.parametrize( + "config, prepare_data, setup, exp_eids", + [ + (lazy_fixture("tabular_debug_config"), False, False, []), + pytest.param( + lazy_fixture("tabular_debug_config"), + True, + False, + _get_plugin_eids, + marks=SKIPIF_NOT_BIH_HPC, + ), + pytest.param( + lazy_fixture("tabular_debug_config"), + True, + True, + _get_plugin_eids, + marks=SKIPIF_NOT_BIH_HPC, + ), # TODO change when we have proper debug files + (lazy_fixture("tabular_mini1_config"), True, True, [0, 1, 2]), + (lazy_fixture("tabular_mini1_str_config"), True, True, ["a1", "b2", "c3"]), + (lazy_fixture("tabular_mini2_config"), True, True, [3, 4, 5]), + (lazy_fixture("tabular_mini2_str_config"), True, True, ["d4", "e5", "f6"]), + ], + ) + def test_get_eids(self, config, prepare_data, setup, exp_eids): + tabular_plugin = TabularPlugin.from_config(config) + if prepare_data: + tabular_plugin.prepare_data() + if setup: + tabular_plugin.setup() + eids = tabular_plugin.get_eids() + assert isinstance(eids, list) + if isinstance(exp_eids, Callable): + exp_eids = exp_eids(tabular_plugin) + assert eids == exp_eids + + @SKIPIF_NOT_BIH_HPC + @pytest.mark.parametrize( + "config, exp_features", + [ + ( + lazy_fixture("tabular_debug_usecols_config"), + [ + "genetic_principal_components_f22009_0_1", + "genetic_principal_components_f22009_0_2", + "genetic_principal_components_f22009_0_3", + "genetic_principal_components_f22009_0_4", + "genetic_principal_components_f22009_0_5", + "age", + "sex_f31_0_0_Female", + ], + ), + ( + lazy_fixture("tabular_debug_usecols_csv_config"), + ["age", "sex_female", "home_planet_tatooine"], + ), + ], + ) + def test_usecols(self, config, exp_features): + tabular_plugin = TabularPlugin.from_config(config) + tabular_plugin.prepare_data() + tabular_plugin.setup() + assert list(tabular_plugin.tabular_data_after_setup.columns) == exp_features + + @SKIPIF_NOT_BIH_HPC + @pytest.mark.parametrize( + "config, exp_features", + [ + ( + lazy_fixture("tabular_debug_dropcols_config"), + [ + "genetic_principal_components_f22009_0_1", + "genetic_principal_components_f22009_0_2", + "genetic_principal_components_f22009_0_3", + "genetic_principal_components_f22009_0_4", + "genetic_principal_components_f22009_0_5", + "age", + "batch", + "sex_f31_0_0_Female", + "sex_f31_0_0_Male", + ], + ), + ( + lazy_fixture("tabular_debug_dropcols_csv_config"), + ["sex_male", "midi-chlorian-level"], + ), + ], + ) + def test_dropcols(self, config, exp_features): + tabular_plugin = TabularPlugin.from_config(config) + tabular_plugin.prepare_data() + tabular_plugin.setup() + assert list(tabular_plugin.tabular_data_after_setup.columns) == exp_features + + @pytest.mark.parametrize( + "config, exp_features", + [ + ( + lazy_fixture("tabular_debug_composite_features1_config"), + ["cad_endpoint"], + ), + ( + lazy_fixture("tabular_debug_composite_features2_config"), + [ + "phecode_460", + "phecode_718", + "phecode_460.1", + "phecode_660", + "cad_endpoint", + "infection_endpoints", + ], + ), + ], + ) + def test_composite_features(self, config, exp_features): + tabular_plugin = TabularPlugin.from_config(config) + tabular_plugin.prepare_data() + tabular_plugin.setup() + assert list(tabular_plugin.tabular_data_after_setup.columns) == exp_features + + @pytest.mark.parametrize( + "config, exp_features", + [ + ( + lazy_fixture("tabular_debug_intersection_features1_config"), + ["cad_endpoint"], + ), + ( + lazy_fixture("tabular_debug_intersection_features2_config"), + [ + "phecode_460", + "phecode_718", + "phecode_460.1", + "phecode_660", + "cad_endpoint", + "infection_endpoints", + ], + ), + ], + ) + def test_intersection_features(self, config, exp_features): + tabular_plugin = TabularPlugin.from_config(config) + tabular_plugin.prepare_data() + tabular_plugin.setup() + assert list(tabular_plugin.tabular_data_after_setup.columns) == exp_features diff --git a/test/pytest.ini b/test/pytest.ini new file mode 100644 index 0000000..a4071d6 --- /dev/null +++ b/test/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +addopts = --strict-markers +markers = + slow: marks tests as slow (deselect with '-m "not slow"') diff --git a/test/transforms/__init__.py b/test/transforms/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/transforms/dict_transforms_test.py b/test/transforms/dict_transforms_test.py new file mode 100644 index 0000000..03d0ce6 --- /dev/null +++ b/test/transforms/dict_transforms_test.py @@ -0,0 +1,339 @@ +import pytest +import torch +from typing import Mapping +from omegaconf import DictConfig + +from udm.transforms.tensor_transforms import ListTransform +from udm.transforms.dict_transforms import ( + DictTransform, + PluginTransform, + DatasetTransform, +) +from test.transforms.tensor_transforms.factory_test import ScaleTrafo + + +def nestedTensorDictEqual(item1, item2): + assert type(item1) == type(item2) + if isinstance(item1, torch.Tensor): + assert torch.all(item1 == item2) + elif isinstance(item1, Mapping): + assert set(item1.keys()) == set(item2.keys()) + for key in item1.keys(): + _item1, _item2 = item1[key], item2[key] + nestedTensorDictEqual(_item1, _item2) + + +@pytest.fixture +def scale_trafo_cfg(): + return DictConfig( + { + "name": "DictTransform", + "transforms": { + "data": {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2} + }, + } + ) + + +@pytest.fixture +def composed_trafo_cfg(): + return DictConfig( + { + "name": "DictTransform", + "transforms": { + "data": { + "name": "ListTransform", + "transforms": [ + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + ], + } + }, + } + ) + + +@pytest.fixture +def nested_trafo_cfg(): + return DictConfig( + { + "name": "DictTransform", + "transforms": { + "data": { + "name": "DictTransform", + "transforms": { + "a": {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + "b": {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 3}, + }, + } + }, + } + ) + + +class TestDictTransform(object): + @pytest.mark.parametrize( + "transforms", + [ + {"data": ScaleTrafo(2.0)}, + {"data": ListTransform([ScaleTrafo(2.0), ScaleTrafo(2.0)])}, + {"data": DictTransform({"data": ScaleTrafo(2.0)})}, + {"data": DictTransform({"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)})}, + ], + ) + def test__init__(self, transforms): + trafo = DictTransform(transforms) + + @pytest.mark.parametrize( + "config,custom", + [ + (pytest.lazy_fixture("scale_trafo_cfg"), {"ScaleTrafo": ScaleTrafo}), + (pytest.lazy_fixture("composed_trafo_cfg"), {"ScaleTrafo": ScaleTrafo}), + (pytest.lazy_fixture("nested_trafo_cfg"), {"ScaleTrafo": ScaleTrafo}), + ], + ) + def test_from_config(self, config, custom): + trafo = DictTransform.from_config(config, custom=custom) + + @pytest.mark.parametrize( + "transforms,custom,input,exp_result,warning", + [ + ( + {"data": ScaleTrafo(2.0)}, + {}, + {"data": torch.tensor([1.0, 2.0, 3.0])}, + {"data": torch.tensor([2.0, 4.0, 6.0])}, + None, + ), + ( + {"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)}, + {}, + {"a": torch.tensor([1.0, 2.0, 3.0])}, + {"a": torch.tensor([2.0, 4.0, 6.0])}, + UserWarning, + ), + ( + {"a": ScaleTrafo(2.0)}, + {}, + { + "a": torch.tensor([1.0, 2.0, 3.0]), + "b": torch.tensor([4.0, 5.0, 6.0]), + }, + { + "a": torch.tensor([2.0, 4.0, 6.0]), + "b": torch.tensor([4.0, 5.0, 6.0]), + }, + UserWarning, + ), + ( + {"data": DictTransform({"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)})}, + {}, + { + "data": { + "a": torch.tensor([1.0, 2.0, 3.0]), + "b": torch.tensor([4.0, 5.0, 6.0]), + } + }, + { + "data": { + "a": torch.tensor([2.0, 4.0, 6.0]), + "b": torch.tensor([12.0, 15.0, 18.0]), + } + }, + None, + ), + ( + pytest.lazy_fixture("scale_trafo_cfg"), + {"ScaleTrafo": ScaleTrafo}, + {"data": torch.tensor([1.0, 2.0, 3.0])}, + {"data": torch.tensor([2.0, 4.0, 6.0])}, + None, + ), + ( + pytest.lazy_fixture("nested_trafo_cfg"), + {"ScaleTrafo": ScaleTrafo}, + { + "data": { + "a": torch.tensor([1.0, 2.0, 3.0]), + "b": torch.tensor([4.0, 5.0, 6.0]), + } + }, + { + "data": { + "a": torch.tensor([2.0, 4.0, 6.0]), + "b": torch.tensor([12.0, 15.0, 18.0]), + } + }, + None, + ), + ], + ) + def test__call__(self, transforms, custom, input, exp_result, warning): + if isinstance(transforms, DictConfig): + trafo = DictTransform.from_config(transforms, custom=custom) + else: + trafo = DictTransform(transforms, custom=custom) + if warning is not None: + with pytest.warns(warning): + result = trafo(input) + else: + result = trafo(input) + nestedTensorDictEqual(result, exp_result) + + +class TestPluginTransform(object): + @pytest.mark.parametrize( + "transforms,error", + [ + ({"data": ScaleTrafo(2.0)}, None), + ({"data": ListTransform([ScaleTrafo(2.0), ScaleTrafo(2.0)])}, None), + ({"data": DictTransform({"data": ScaleTrafo(2.0)})}, AssertionError), + ({"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)}, None), + ], + ) + def test__init__(self, transforms, error): + if error is not None: + with pytest.raises(error): + PluginTransform(transforms) + else: + trafo = PluginTransform(transforms) + + @pytest.mark.parametrize( + "transforms,custom,input,exp_result", + [ + ( + {"data": ScaleTrafo(2.0)}, + {}, + {"data": torch.tensor([1.0, 2.0, 3.0])}, + {"data": torch.tensor([2.0, 4.0, 6.0])}, + ), + ( + {"data": ListTransform([ScaleTrafo(2.0), ScaleTrafo(2.0)])}, + {}, + {"data": torch.tensor([1.0, 2.0, 3.0])}, + {"data": torch.tensor([4.0, 8.0, 12.0])}, + ), + ( + {"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)}, + {}, + { + "a": torch.tensor([1.0, 2.0, 3.0]), + "b": torch.tensor([4.0, 5.0, 6.0]), + }, + { + "a": torch.tensor([2.0, 4.0, 6.0]), + "b": torch.tensor([12.0, 15.0, 18.0]), + }, + ), + ], + ) + def test__call__(self, transforms, custom, input, exp_result): + if isinstance(transforms, DictConfig): + trafo = PluginTransform.from_config(transforms, custom=custom) + else: + trafo = PluginTransform(transforms, custom=custom) + result = trafo(input) + nestedTensorDictEqual(result, exp_result) + + +class TestDatasetTransform(object): + @pytest.mark.parametrize( + "transforms,error", + [ + ({"data": ScaleTrafo(2.0)}, AssertionError), + ( + {"data": ListTransform([ScaleTrafo(2.0), ScaleTrafo(2.0)])}, + AssertionError, + ), + ({"data": DictTransform({"data": ScaleTrafo(2.0)})}, AssertionError), + ( + { + "plugin1": PluginTransform( + {"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)} + ) + }, + None, + ), + ( + { + "plugin1": PluginTransform( + {"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)} + ), + "plugin2": PluginTransform( + {"c": ScaleTrafo(4.0), "d": ScaleTrafo(5.0)} + ), + }, + None, + ), + ], + ) + def test__init__(self, transforms, error): + if error is not None: + with pytest.raises(error): + DatasetTransform(transforms) + else: + trafo = DatasetTransform(transforms) + + @pytest.mark.parametrize( + "transforms,custom,input,exp_result", + [ + ( + { + "plugin1": PluginTransform( + {"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)} + ) + }, + {}, + { + "plugin1": { + "a": torch.tensor([1.0, 2.0, 3.0]), + "b": torch.tensor([2.0, 3.0, 4.0]), + } + }, + { + "plugin1": { + "a": torch.tensor([2.0, 4.0, 6.0]), + "b": torch.tensor([6.0, 9.0, 12.0]), + } + }, + ), + ( + { + "plugin1": PluginTransform( + {"a": ScaleTrafo(2.0), "b": ScaleTrafo(3.0)} + ), + "plugin2": PluginTransform( + {"c": ScaleTrafo(4.0), "d": ScaleTrafo(5.0)} + ), + }, + {}, + { + "plugin1": { + "a": torch.tensor([1.0, 2.0, 3.0]), + "b": torch.tensor([2.0, 3.0, 4.0]), + }, + "plugin2": { + "c": torch.tensor([3.0, 4.0, 5.0]), + "d": torch.tensor([4.0, 5.0, 6.0]), + }, + }, + { + "plugin1": { + "a": torch.tensor([2.0, 4.0, 6.0]), + "b": torch.tensor([6.0, 9.0, 12.0]), + }, + "plugin2": { + "c": torch.tensor([12.0, 16.0, 20.0]), + "d": torch.tensor([20.0, 25.0, 30.0]), + }, + }, + ), + ], + ) + def test__call__(self, transforms, custom, input, exp_result): + if isinstance(transforms, DictConfig): + trafo = DatasetTransform.from_config(transforms, custom=custom) + else: + trafo = DatasetTransform(transforms, custom=custom) + result = trafo(input) + nestedTensorDictEqual(result, exp_result) diff --git a/test/transforms/tensor_transforms/__init__.py b/test/transforms/tensor_transforms/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/transforms/tensor_transforms/factory_test.py b/test/transforms/tensor_transforms/factory_test.py new file mode 100644 index 0000000..601fb92 --- /dev/null +++ b/test/transforms/tensor_transforms/factory_test.py @@ -0,0 +1,98 @@ +import pytest +from omegaconf import DictConfig +from typing import Callable, Optional +import torch + +from udm.transforms.tensor_transforms.factory import get_transform + + +def scale_trafo(x: torch.Tensor, alpha: float): + return alpha * x + + +class ScaleTrafo(Callable): + def __init__(self, alpha: float): + self.alpha = alpha + + def __call__(self, x: torch.Tensor): + return self.alpha * x + + +class TestTransforms(object): + @pytest.mark.parametrize( + "config,custom,data,exp_data,error", + [ + ( + DictConfig( + { + "name": "torch.nn.functional.pad", + "pad": (1, 1), + "mode": "constant", + "value": 0, + } + ), + {}, + torch.tensor([1.0, 2.0, 3.0], dtype=torch.float), + torch.tensor([0.0, 1.0, 2.0, 3.0, 0.0], dtype=torch.float), + None, + ), + ( + DictConfig( + { + "name": "ConstantPad1d", + "__init__": "__init__", + "padding": (1, 1), + "value": 0, + } + ), + {}, + torch.tensor([1.0, 2.0, 3.0], dtype=torch.float), + torch.tensor([0.0, 1.0, 2.0, 3.0, 0.0], dtype=torch.float), + KeyError, + ), + ( + DictConfig({"name": "scale_trafo", "alpha": 2}), + {"scale_trafo": scale_trafo}, + torch.tensor([0.0, 1.0, 2.0, 3.0], dtype=torch.float), + torch.tensor([0.0, 2.0, 4.0, 6.0], dtype=torch.float), + None, + ), + ( + DictConfig({"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}), + {"ScaleTrafo": ScaleTrafo}, + torch.tensor([0.0, 1.0, 2.0, 3.0], dtype=torch.float), + torch.tensor([0.0, 2.0, 4.0, 6.0], dtype=torch.float), + None, + ), + ( + DictConfig( + { + "name": "ListTransform", + "transforms": [ + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + {"name": "ScaleTrafo", "__init__": "__init__", "alpha": 2}, + ], + } + ), + {"ScaleTrafo": ScaleTrafo}, + torch.tensor([0.0, 1.0, 2.0, 3.0], dtype=torch.float), + torch.tensor([0.0, 4.0, 8.0, 12.0], dtype=torch.float), + None, + ), + ], + ) + def test_get_transform( + self, + config, + custom, + data: torch.Tensor, + exp_data: torch.Tensor, + error: Optional[Exception], + ): + if error is not None: + with pytest.raises(error): + get_transform(config, custom) + else: + trafo = get_transform(config, custom=custom) + trafo_data = trafo(data) + assert torch.allclose(trafo_data, exp_data) diff --git a/test/utils.py b/test/utils.py new file mode 100644 index 0000000..bac4325 --- /dev/null +++ b/test/utils.py @@ -0,0 +1,71 @@ +import gc +import os +from contextlib import ExitStack +from os.path import abspath, dirname +from tempfile import TemporaryDirectory +from typing import Callable, Mapping + +import pytest +import torch +from hydra.core.global_hydra import GlobalHydra + + +UDM_HOME = ".." +SKIPIF_NOT_BIH_HPC = pytest.mark.skipif( + os.environ.get("BIH_HPC", "0") == "0", + reason="Run this test only on BIH-HPC environment.", +) + + +def try_clear_hydra(): + if GlobalHydra.instance().is_initialized(): + GlobalHydra.instance().clear() + + +def assertBatchEqual(batch, expected_batch, shapes_only=False): + # TODO: Extend test to also check eids + assert isinstance(batch, dict) + assert set(batch.keys()) == set([*expected_batch.keys(), "eids"]) + for p_key in set(batch.keys()) - set(["eids"]): + assert set(batch[p_key].keys()) == set(expected_batch[p_key].keys()) + for t_key in batch[p_key].keys(): + if shapes_only: + shape = [*batch[p_key][t_key].shape] + exp_shape = expected_batch[p_key][t_key] + assert shape == exp_shape + else: + assert torch.all(batch[p_key][t_key] == expected_batch[p_key][t_key]) + + +class BaseConfigHandler(ExitStack): + + name: str + # The name of the config to initialize + _configs: Mapping[str, Callable] + # A mapping of config name to a callable returning the config + + def __init__(self, name: str): + super().__init__() + self.name = name + self._configs = {} + + def __enter__(self): + DIR = abspath(os.path.join(dirname(__file__), UDM_HOME)) + self.TMP_DIR = TemporaryDirectory(dir=DIR) + if GlobalHydra.instance().is_initialized(): + GlobalHydra.instance().clear() + return self._configs[self.name]() + + def __exit__(self, exc_type, exc_val, exc_tb): + super().__exit__(exc_type, exc_val, exc_tb) + gc.collect() + self.TMP_DIR.cleanup() + + +class Head(list): + def __init__(self, *elements): + if len(elements) == 1 and isinstance(elements[0], list): + seq = elements[0] + else: + seq = [*elements] + super().__init__(seq[:10]) From 8d74b8330caca9a405cdd4cb07ab5c4662cdf45b Mon Sep 17 00:00:00 2001 From: Luis Herrmann Date: Sat, 1 Aug 2026 04:32:12 +0200 Subject: [PATCH 8/9] Add verification script to ensure git history is clean. --- .gitignore | 1 + scripts/verify_no_eids_in_history.py | 346 +++++++++++++++++++++++++++ 2 files changed, 347 insertions(+) create mode 100644 scripts/verify_no_eids_in_history.py diff --git a/.gitignore b/.gitignore index d38d21d..b98547c 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,4 @@ dist/ data/* !*.gitkeep +docs/build/ diff --git a/scripts/verify_no_eids_in_history.py b/scripts/verify_no_eids_in_history.py new file mode 100644 index 0000000..463d144 --- /dev/null +++ b/scripts/verify_no_eids_in_history.py @@ -0,0 +1,346 @@ +"""Scan git history for eid-shaped numeric literals (6-8 digit numbers). + +Meant to be run before/after a history rewrite to confirm no UKB-eid-shaped +literals remain reachable from any ref. This is a heuristic scan for review, +not a formal guarantee: it filters out common false positives (dates, float +fragments, hex/hash strings) but surviving candidates should still be checked +manually. + +Every path is scanned by default, regardless of extension - use --exclude to +opt specific files/folders back out (e.g. known-noisy vendored assets). + +Two scan modes (--mode): + diff (default) walks `git log -p`, i.e. every commit's diff against its + parent(s). Fast, but inherently tied to git's diff-generation logic + (merge commits, rename detection, etc). + blobs reads every distinct blob object reachable from the given refs + directly via `git cat-file`, bypassing diff/log generation + entirely. Slower, but independent of any diff-engine blind spots - + useful as a cross-check against the diff mode. Binary (non-UTF-8) + blobs are skipped in this mode too; it only text-scans decodable + content. + +Usage: + python scripts/verify_no_eids_in_history.py + python scripts/verify_no_eids_in_history.py -- test/ res/datasets/fake/ + python scripts/verify_no_eids_in_history.py --known-fake fake_eids.txt + python scripts/verify_no_eids_in_history.py --exclude "poetry.lock" "docs/build/*" "*.svg" + python scripts/verify_no_eids_in_history.py --mode blobs +""" +import argparse +import fnmatch +import re +import subprocess +import sys +from collections import defaultdict + +DIGIT_RE = re.compile(r"(? bool: + if len(value) == 8: + mm, dd = value[4:6], value[6:8] + elif len(value) == 6: + mm, dd = value[2:4], value[4:6] + else: + return False + try: + m, d = int(mm), int(dd) + except ValueError: + return False + return 1 <= m <= 12 and 1 <= d <= 31 + + +def is_float_fragment(line: str, start: int, end: int) -> bool: + before = line[start - 1] if start > 0 else "" + after = line[end] if end < len(line) else "" + return before == "." or after == "." + + +def is_hex_fragment(line: str, start: int, end: int) -> bool: + before = line[start - 1] if start > 0 else "" + after = line[end] if end < len(line) else "" + if before in HEX_CHARS or after in HEX_CHARS: + return True + if start > 0 and line[start - 1] == "#": + return True + return False + + +def should_scan_path(path: str, exclude_patterns) -> bool: + basename = path.rsplit("/", 1)[-1] + return not any( + fnmatch.fnmatch(path, pattern) or fnmatch.fnmatch(basename, pattern) + for pattern in exclude_patterns + ) + + +def iter_diff_blocks(refs, pathspecs): + cmd = ["git", "log", *refs, "-p", "--"] + cmd += pathspecs if pathspecs else ["."] + result = subprocess.run(cmd, capture_output=True, text=True, check=True) + return re.split(r"^diff --git ", result.stdout, flags=re.M)[1:] + + +# Ordered pipeline: each stage is (name, predicate). A candidate is dropped +# at the first stage whose predicate returns True (i.e. "this stage rejects it"). +STAGES = [ + ("known_fake", lambda value, line, start, end, known_fake: value in known_fake), + ("date", lambda value, line, start, end, known_fake: looks_like_date(value)), + ( + "float_fragment", + lambda value, line, start, end, known_fake: is_float_fragment(line, start, end), + ), + ( + "hex_fragment", + lambda value, line, start, end, known_fake: is_hex_fragment(line, start, end), + ), +] + + +HUNK_RE = re.compile(r"^@@ -\d+(?:,\d+)? \+(\d+)(?:,\d+)? @@") + + +def new_stage_hits(): + # stage_hits[stage_name][value][path] = (line_no, line_content) of the first + # occurrence of `value` in `path` that was still alive *after* that stage ran. + # stage_hits["raw"] holds every match before any filtering; stage_hits[] holds the final survivors. + stage_names = ["raw"] + [name for name, _ in STAGES] + return {name: defaultdict(dict) for name in stage_names} + + +def record_match(stage_hits, path, line_no, line, value, start, end, known_fake): + record = (line_no, line) + + # Always record at the "raw" (unfiltered) stage first... + if path not in stage_hits["raw"][value]: + stage_hits["raw"][value][path] = record + + # ...then run the filter pipeline. Each stage's predicate returns True to + # REJECT the candidate; on rejection we stop immediately (so it's absent + # from this and all later stages). Otherwise we record it as a survivor + # of this stage and move on to the next filter. + for name, predicate in STAGES: + if predicate(value, line, start, end, known_fake): + break + if path not in stage_hits[name][value]: + stage_hits[name][value][path] = record + + +def scan(refs, pathspecs, known_fake, exclude_patterns): + stage_hits = new_stage_hits() + + # `git log --all -p` emits one unified diff per commit. Splitting on + # "diff --git " gives us one block per touched file per commit, each + # starting with its own "a/ b/" header. + for block in iter_diff_blocks(refs, pathspecs): + header = block[:200] + m = re.search(r"a/(\S+) b/(\S+)", header) + if not m: + continue + path = m.group(2) + if not should_scan_path(path, exclude_patterns): + continue # user-supplied --exclude pattern matched this path/basename + + # Track the line number in the *post-change* file by walking the diff's + # hunk headers ("@@ -a,b +c,d @@") and advancing new_line_no as we go: + # "+" lines exist only in the new file (count, then advance), "-" lines + # exist only in the old file (don't advance new_line_no), and unprefixed + # context lines exist in both (advance without recording a match). + new_line_no = None + for line in block.splitlines(): + hunk_m = HUNK_RE.match(line) + if hunk_m: + new_line_no = int(hunk_m.group(1)) + continue + if new_line_no is None: + continue # haven't hit a hunk header yet (still in the diff preamble) + + if line.startswith("+") and not line.startswith("+++"): + content = line[1:] # strip the diff "+" marker + for dm in DIGIT_RE.finditer(content): + record_match( + stage_hits, + path, + new_line_no, + content, + dm.group(), + dm.start(), + dm.end(), + known_fake, + ) + new_line_no += 1 + elif line.startswith("-") and not line.startswith("---"): + pass # removed line, doesn't exist in the new file, no line number to advance + else: + new_line_no += 1 # context line, present in both old and new + + return stage_hits + + +def blob_paths(refs, pathspecs): + # `git rev-list --objects` walks every reachable commit's *full tree* + # (not just diffs) and prints " " for every blob/tree found, + # deduplicated by sha. This is how we get one representative path per + # distinct blob content, independent of any diff/log machinery. + cmd = ["git", "rev-list", "--objects", *refs, "--"] + cmd += pathspecs if pathspecs else ["."] + result = subprocess.run(cmd, capture_output=True, text=True, check=True) + paths = {} + for line in result.stdout.splitlines(): + parts = line.split(" ", 1) + if len(parts) == 2: + sha, path = parts + paths.setdefault(sha, path) # keep first-seen path per blob + return paths + + +def filter_blob_shas(shas): + # rev-list --objects also lists commit/tree objects; keep blobs only. + if not shas: + return [] + result = subprocess.run( + ["git", "cat-file", "--batch-check=%(objectname) %(objecttype)"], + input="\n".join(shas) + "\n", + capture_output=True, + text=True, + check=True, + ) + return [ + line.split()[0] for line in result.stdout.splitlines() if line.endswith(" blob") + ] + + +def iter_blob_contents(shas): + # `git cat-file --batch` streams every requested object in one process: + # each is " \n\n". Reading this + # ourselves (rather than one `cat-file -p` subprocess per blob) is what + # makes scanning ~1000 blobs fast. + if not shas: + return + proc = subprocess.run( + ["git", "cat-file", "--batch"], + input=("\n".join(shas) + "\n").encode(), + capture_output=True, + check=True, + ) + data = proc.stdout + pos, n = 0, len(data) + while pos < n: + header_end = data.index(b"\n", pos) + parts = data[pos:header_end].decode().split() + if len(parts) != 3: + break + sha, _objtype, size = parts[0], parts[1], int(parts[2]) + content_start = header_end + 1 + yield sha, data[content_start : content_start + size] + pos = content_start + size + 1 + + +def scan_blobs(refs, pathspecs, known_fake, exclude_patterns): + stage_hits = new_stage_hits() + + paths = blob_paths(refs, pathspecs) + blob_shas = filter_blob_shas(sorted(paths)) + + for sha, content in iter_blob_contents(blob_shas): + path = paths.get(sha, sha) + if not should_scan_path(path, exclude_patterns): + continue + try: + text = content.decode("utf-8") + except UnicodeDecodeError: + continue # binary blob; out of scope for text-based scanning + + for line_no, line in enumerate(text.splitlines(), start=1): + for dm in DIGIT_RE.finditer(line): + record_match( + stage_hits, + path, + line_no, + line, + dm.group(), + dm.start(), + dm.end(), + known_fake, + ) + + return stage_hits + + +def load_known_fake(path): + if path is None: + return set() + with open(path) as f: + return {line.strip() for line in f if line.strip()} + + +def print_candidates(hits): + for value in sorted(hits): + print(f" {value}") + for path in sorted(hits[value]): + line_no, content = hits[value][path] + print(f" {path}:{line_no}: {content.strip()}") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("pathspecs", nargs="*", help="restrict scan to these paths") + parser.add_argument( + "--refs", nargs="*", default=["--all"], help="git log refs (default: --all)" + ) + parser.add_argument( + "--known-fake", help="file of newline-separated values to ignore" + ) + parser.add_argument( + "--exclude", + nargs="*", + default=[], + help="glob patterns (fnmatch-style, '*' allowed) matched against the full " + "path or basename; matching files are skipped, e.g. 'poetry.lock' " + "'docs/build/*' '*.svg'", + ) + parser.add_argument( + "--mode", + choices=["diff", "blobs"], + default="diff", + help="'diff' (default) walks git log -p; 'blobs' reads every reachable " + "blob object directly via git cat-file, bypassing diff/log " + "generation entirely - slower, but independent of diff-engine " + "blind spots (merge commits, rename detection, etc)", + ) + args = parser.parse_args() + + known_fake = load_known_fake(args.known_fake) + scan_fn = scan if args.mode == "diff" else scan_blobs + stage_hits = scan_fn(args.refs, args.pathspecs, known_fake, args.exclude) + final_stage = STAGES[-1][0] + hits = stage_hits[final_stage] + + print("Candidates remaining after each filter stage:") + print(f" raw (shape match, excludes applied): {len(stage_hits['raw'])}") + for name, _ in STAGES: + print(f" after {name}: {len(stage_hits[name])}") + print() + + print( + f"=== Stage 0: raw ({len(stage_hits['raw'])} distinct candidates, unfiltered) ===\n" + ) + print_candidates(stage_hits["raw"]) + print() + + if not hits: + print("No eid-shaped literals survive all filter stages.") + return 0 + + print( + f"=== Final stage: after {final_stage} ({len(hits)} distinct candidates) ===\n" + ) + print_candidates(hits) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) From 521b56d02d4b2d0a62772d060850836e4234b03e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 3 Aug 2026 15:47:03 +0000 Subject: [PATCH 9/9] Bump pytorch-lightning from 1.8.3.post1 to 2.3.3 Bumps [pytorch-lightning](https://github.com/Lightning-AI/pytorch-lightning) from 1.8.3.post1 to 2.3.3. - [Release notes](https://github.com/Lightning-AI/pytorch-lightning/releases) - [Commits](https://github.com/Lightning-AI/pytorch-lightning/compare/1.8.3.post1...2.3.3) --- updated-dependencies: - dependency-name: pytorch-lightning dependency-version: 2.3.3 dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- poetry.lock | 372 +++++++++++++++++++++++++++++++++++-------------- pyproject.toml | 2 +- 2 files changed, 270 insertions(+), 104 deletions(-) diff --git a/poetry.lock b/poetry.lock index a61edd5..9ee9067 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. [[package]] name = "addict" @@ -6,6 +6,7 @@ version = "2.4.0" description = "Addict is a dictionary whose items can be set using both attribute and item syntax." optional = false python-versions = "*" +groups = ["main"] files = [ {file = "addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc"}, {file = "addict-2.4.0.tar.gz", hash = "sha256:b3b2210e0e067a281f5646c8c5db92e99b7231ea8b0eb5f74dbdf9e259d4e494"}, @@ -17,6 +18,7 @@ version = "3.8.4" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "aiohttp-3.8.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:5ce45967538fb747370308d3145aa68a074bdecb4f3a300869590f725ced69c1"}, {file = "aiohttp-3.8.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b744c33b6f14ca26b7544e8d8aadff6b765a80ad6164fb1a430bbadd593dfb1a"}, @@ -117,7 +119,7 @@ multidict = ">=4.5,<7.0" yarl = ">=1.0,<2.0" [package.extras] -speedups = ["Brotli", "aiodns", "cchardet"] +speedups = ["Brotli", "aiodns", "cchardet ; python_version < \"3.10\""] [[package]] name = "aiosignal" @@ -125,6 +127,7 @@ version = "1.3.1" description = "aiosignal: a list of registered asynchronous callbacks" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"}, {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"}, @@ -139,6 +142,7 @@ version = "0.7.13" description = "A configurable sidebar-enabled Sphinx theme" optional = false python-versions = ">=3.6" +groups = ["docs"] files = [ {file = "alabaster-0.7.13-py3-none-any.whl", hash = "sha256:1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3"}, {file = "alabaster-0.7.13.tar.gz", hash = "sha256:a27a4a084d5e690e16e01e03ad2b2e552c61a65469419b907243193de1a84ae2"}, @@ -150,6 +154,7 @@ version = "4.9.3" description = "ANTLR 4.9.3 runtime for Python 3.7" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, ] @@ -160,6 +165,7 @@ version = "1.4.4" description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." optional = false python-versions = "*" +groups = ["main"] files = [ {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"}, {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, @@ -171,6 +177,7 @@ version = "4.0.2" description = "Timeout context manager for asyncio programs" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "async-timeout-4.0.2.tar.gz", hash = "sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15"}, {file = "async_timeout-4.0.2-py3-none-any.whl", hash = "sha256:8ca1e4fcf50d07413d66d1a5e416e42cfdf5851c981d679a09851a6853383b3c"}, @@ -182,6 +189,7 @@ version = "22.2.0" description = "Classes Without Boilerplate" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "attrs-22.2.0-py3-none-any.whl", hash = "sha256:29e95c7f6778868dbd49170f98f8818f78f3dc5e0e37c0b1f474e3561b240836"}, {file = "attrs-22.2.0.tar.gz", hash = "sha256:c9227bfc2f01993c03f68db37d1d15c9690188323c067c641f1a35ca58185f99"}, @@ -192,7 +200,7 @@ cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] dev = ["attrs[docs,tests]"] docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] tests = ["attrs[tests-no-zope]", "zope.interface"] -tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] +tests-no-zope = ["cloudpickle ; platform_python_implementation == \"CPython\"", "cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990) ; platform_python_implementation == \"CPython\"", "mypy (>=0.971,<0.990) ; platform_python_implementation == \"CPython\"", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version < \"3.11\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version < \"3.11\"", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] [[package]] name = "babel" @@ -200,6 +208,7 @@ version = "2.12.1" description = "Internationalization utilities" optional = false python-versions = ">=3.7" +groups = ["docs"] files = [ {file = "Babel-2.12.1-py3-none-any.whl", hash = "sha256:b4246fb7677d3b98f501a39d43396d3cafdc8eadb045f4a31be01863f655c610"}, {file = "Babel-2.12.1.tar.gz", hash = "sha256:cc2d99999cd01d44420ae725a21c9e3711b3aadc7976d6147f622d8581963455"}, @@ -214,6 +223,7 @@ version = "2022.12.7" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" +groups = ["main", "docs"] files = [ {file = "certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"}, {file = "certifi-2022.12.7.tar.gz", hash = "sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3"}, @@ -225,6 +235,8 @@ version = "1.15.1" description = "Foreign Function Interface for Python calling C code." optional = false python-versions = "*" +groups = ["main"] +markers = "platform_python_implementation == \"PyPy\"" files = [ {file = "cffi-1.15.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:a66d3508133af6e8548451b25058d5812812ec3798c886bf38ed24a98216fab2"}, {file = "cffi-1.15.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:470c103ae716238bbe698d67ad020e1db9d9dba34fa5a899b5e21577e6d52ed2"}, @@ -301,6 +313,7 @@ version = "3.3.1" description = "Validate configuration and produce human readable error messages." optional = false python-versions = ">=3.6.1" +groups = ["dev"] files = [ {file = "cfgv-3.3.1-py2.py3-none-any.whl", hash = "sha256:c6a0883f3917a037485059700b9e75da2464e6c27051014ad85ba6aaa5884426"}, {file = "cfgv-3.3.1.tar.gz", hash = "sha256:f5a830efb9ce7a445376bb66ec94c638a9787422f96264c98edc6bdeed8ab736"}, @@ -312,6 +325,7 @@ version = "3.1.0" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." optional = false python-versions = ">=3.7.0" +groups = ["main", "docs"] files = [ {file = "charset-normalizer-3.1.0.tar.gz", hash = "sha256:34e0a2f9c370eb95597aae63bf85eb5e96826d81e3dcf88b8886012906f509b5"}, {file = "charset_normalizer-3.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e0ac8959c929593fee38da1c2b64ee9778733cdf03c482c9ff1d508b6b593b2b"}, @@ -396,6 +410,7 @@ version = "8.1.3" description = "Composable command line interface toolkit" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "click-8.1.3-py3-none-any.whl", hash = "sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48"}, {file = "click-8.1.3.tar.gz", hash = "sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e"}, @@ -410,6 +425,7 @@ version = "2.2.1" description = "Extended pickling support for Python objects" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "cloudpickle-2.2.1-py3-none-any.whl", hash = "sha256:61f594d1f4c295fa5cd9014ceb3a1fc4a70b0de1164b94fbc2d854ccba056f9f"}, {file = "cloudpickle-2.2.1.tar.gz", hash = "sha256:d89684b8de9e34a2a43b3460fbca07d09d6e25ce858df4d5a44240403b6178f5"}, @@ -421,6 +437,8 @@ version = "3.26.3" description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software" optional = false python-versions = "*" +groups = ["main"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" files = [ {file = "cmake-3.26.3-py2.py3-none-macosx_10_10_universal2.macosx_10_10_x86_64.macosx_11_0_arm64.macosx_11_0_universal2.whl", hash = "sha256:9d38ea5b4999f8f042a071bea3e213f085bac26d7ab54cb5a4c6a193c4baf132"}, {file = "cmake-3.26.3-py2.py3-none-manylinux2010_i686.manylinux_2_12_i686.whl", hash = "sha256:6e5fcd1cfaac33d015e2709e0dd1b7ad352a315367012ac359c9adc062cf075b"}, @@ -450,10 +468,12 @@ version = "0.4.6" description = "Cross-platform colored terminal text." optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" +groups = ["main", "dev", "docs"] files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] +markers = {main = "platform_system == \"Windows\" or sys_platform == \"win32\"", docs = "sys_platform == \"win32\""} [[package]] name = "coverage" @@ -461,6 +481,7 @@ version = "7.2.7" description = "Code coverage measurement for Python" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "coverage-7.2.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d39b5b4f2a66ccae8b7263ac3c8170994b65266797fb96cbbfd3fb5b23921db8"}, {file = "coverage-7.2.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6d040ef7c9859bb11dfeb056ff5b3872436e3b5e401817d87a31e1750b9ae2fb"}, @@ -525,7 +546,7 @@ files = [ ] [package.extras] -toml = ["tomli"] +toml = ["tomli ; python_full_version <= \"3.11.0a6\""] [[package]] name = "dask" @@ -533,6 +554,7 @@ version = "2023.3.2" description = "Parallel PyData with Task Scheduling" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "dask-2023.3.2-py3-none-any.whl", hash = "sha256:5e64763d62feb18afd3ad66f364e0b4f456f7ac92e894fcc87950af75029ecdf"}, {file = "dask-2023.3.2.tar.gz", hash = "sha256:51009e92ba9a280bd417633d1ae84f3ed23a8940f0a19594a4b7797ef226fff4"}, @@ -543,7 +565,7 @@ click = ">=7.0" cloudpickle = ">=1.1.1" fsspec = ">=0.6.0" importlib-metadata = ">=4.13.0" -numpy = {version = ">=1.21", optional = true, markers = "extra == \"array\""} +numpy = {version = ">=1.21", optional = true, markers = "extra == \"array\" or extra == \"dataframe\""} packaging = ">=20.0" pandas = {version = ">=1.3", optional = true, markers = "extra == \"dataframe\""} partd = ">=1.2.0" @@ -564,6 +586,7 @@ version = "0.2.0" description = "Generalized Linear Models with Dask" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "dask-glm-0.2.0.tar.gz", hash = "sha256:58b86cebf04fe5b9e58092e1c467e32e60d01e11b71fdc628baaa9fc6d1adee5"}, {file = "dask_glm-0.2.0-py2.py3-none-any.whl", hash = "sha256:a116c36a830cc20660c0815c4c8d67239814931952e8695d784604cb4839ea51"}, @@ -585,6 +608,7 @@ version = "2023.3.24" description = "A library for distributed and parallel machine learning" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "dask-ml-2023.3.24.tar.gz", hash = "sha256:96c090db6d32836536e3f09ca64deb599e864587aa8e3d4d2c6b4af588733307"}, {file = "dask_ml-2023.3.24-py3-none-any.whl", hash = "sha256:ac34d556cdf3c4b25096d3f3806bbb2bed1ff7937f0c04e14c71d11a6c925055"}, @@ -615,6 +639,7 @@ version = "0.3.6" description = "serialize all of python" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "dill-0.3.6-py3-none-any.whl", hash = "sha256:a07ffd2351b8c678dfc4a856a3005f8067aea51d6ba6c700796a4d9e280f39f0"}, {file = "dill-0.3.6.tar.gz", hash = "sha256:e5db55f3687856d8fbdab002ed78544e1c4559a130302693d839dfe8f93f2373"}, @@ -629,6 +654,7 @@ version = "0.3.6" description = "Distribution utilities" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "distlib-0.3.6-py2.py3-none-any.whl", hash = "sha256:f35c4b692542ca110de7ef0bea44d73981caeb34ca0b9b6b2e6d7790dda8f80e"}, {file = "distlib-0.3.6.tar.gz", hash = "sha256:14bad2d9b04d3a36127ac97f30b12a19268f211063d8f8ee4f47108896e11b46"}, @@ -640,6 +666,7 @@ version = "2023.3.2.1" description = "Distributed scheduler for Dask" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "distributed-2023.3.2.1-py3-none-any.whl", hash = "sha256:a7756a4b952ec5a7fd3163e93aef99aaf8b0000568fa9ee7c000113a470d7f8e"}, {file = "distributed-2023.3.2.1.tar.gz", hash = "sha256:f40c4578622a15261bb59676ca8d7024f7d108aecc58406a5482bdfb7e69ce99"}, @@ -668,6 +695,7 @@ version = "0.4.0" description = "Python bindings for the docker credentials store API" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "docker-pycreds-0.4.0.tar.gz", hash = "sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4"}, {file = "docker_pycreds-0.4.0-py2.py3-none-any.whl", hash = "sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49"}, @@ -682,6 +710,7 @@ version = "0.6.2" description = "Pythonic argument parser, that will make you smile" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "docopt-0.6.2.tar.gz", hash = "sha256:49b3a825280bd66b3aa83585ef59c4a8c82f2c8a522dbe754a8bc8d08c85c491"}, ] @@ -692,6 +721,7 @@ version = "0.18.1" description = "Docutils -- Python Documentation Utilities" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +groups = ["docs"] files = [ {file = "docutils-0.18.1-py2.py3-none-any.whl", hash = "sha256:23010f129180089fbcd3bc08cfefccb3b890b0050e1ca00c867036e9d161b98c"}, {file = "docutils-0.18.1.tar.gz", hash = "sha256:679987caf361a7539d76e584cbeddc311e3aee937877c87346f31debc63e9d06"}, @@ -703,6 +733,7 @@ version = "1.1.0" description = "An implementation of lxml.xmlfile for the standard library" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "et_xmlfile-1.1.0-py3-none-any.whl", hash = "sha256:a2ba85d1d6a74ef63837eed693bcb89c3f752169b0e3e7ae5b16ca5e1b3deada"}, {file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"}, @@ -714,6 +745,8 @@ version = "1.1.1" description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" +groups = ["main", "dev"] +markers = "python_version <= \"3.10\"" files = [ {file = "exceptiongroup-1.1.1-py3-none-any.whl", hash = "sha256:232c37c63e4f682982c8b6459f33a8981039e5fb8756b2074364e5055c498c9e"}, {file = "exceptiongroup-1.1.1.tar.gz", hash = "sha256:d484c3090ba2889ae2928419117447a14daf3c1231d5e30d0aae34f354f01785"}, @@ -728,6 +761,7 @@ version = "3.12.2" description = "A platform independent file lock." optional = false python-versions = ">=3.7" +groups = ["main", "dev"] files = [ {file = "filelock-3.12.2-py3-none-any.whl", hash = "sha256:cbb791cdea2a72f23da6ac5b5269ab0a0d161e9ef0100e653b69049a7706d1ec"}, {file = "filelock-3.12.2.tar.gz", hash = "sha256:002740518d8aa59a26b0c76e10fb8c6e15eae825d34b6fdf670333fd7b938d81"}, @@ -743,6 +777,7 @@ version = "1.3.3" description = "A list-like structure which implements collections.abc.MutableSequence" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "frozenlist-1.3.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ff8bf625fe85e119553b5383ba0fb6aa3d0ec2ae980295aaefa552374926b3f4"}, {file = "frozenlist-1.3.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dfbac4c2dfcc082fcf8d942d1e49b6aa0766c19d3358bd86e2000bf0fa4a9cf0"}, @@ -826,6 +861,7 @@ version = "2023.4.0" description = "File-system specification" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "fsspec-2023.4.0-py3-none-any.whl", hash = "sha256:f398de9b49b14e9d84d2c2d11b7b67121bc072fe97b930c4e5668ac3917d8307"}, {file = "fsspec-2023.4.0.tar.gz", hash = "sha256:bf064186cd8808f0b2f6517273339ba0a0c8fb1b7048991c28bc67f58b8b67cd"}, @@ -865,6 +901,7 @@ version = "4.0.10" description = "Git Object Database" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "gitdb-4.0.10-py3-none-any.whl", hash = "sha256:c286cf298426064079ed96a9e4a9d39e7f3e9bf15ba60701e95f5492f28415c7"}, {file = "gitdb-4.0.10.tar.gz", hash = "sha256:6eb990b69df4e15bad899ea868dc46572c3f75339735663b81de79b06f17eb9a"}, @@ -879,6 +916,7 @@ version = "3.1.31" description = "GitPython is a Python library used to interact with Git repositories" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "GitPython-3.1.31-py3-none-any.whl", hash = "sha256:f04893614f6aa713a60cbbe1e6a97403ef633103cdd0ef5eb6efe0deb98dbe8d"}, {file = "GitPython-3.1.31.tar.gz", hash = "sha256:8ce3bcf69adfdf7c7d503e78fd3b1c492af782d58893b650adb2ac8912ddd573"}, @@ -893,6 +931,7 @@ version = "1.3.1" description = "Python scripts to find enrichment of GO terms" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "goatools-1.3.1-py3-none-any.whl", hash = "sha256:eb87f7293fbe7b84482b46b1f8f9471c1389bbd39a088a2deae510ef9d77e77f"}, {file = "goatools-1.3.1.tar.gz", hash = "sha256:c25cb7b6044308d359a010fc55a9b9f2f58dd64e4e2afd181d22d665d781a3dc"}, @@ -915,6 +954,7 @@ version = "1.0.1" description = "a heap with decrease-key and increase-key operations" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "HeapDict-1.0.1-py3-none-any.whl", hash = "sha256:6065f90933ab1bb7e50db403b90cab653c853690c5992e69294c2de2b253fc92"}, {file = "HeapDict-1.0.1.tar.gz", hash = "sha256:8495f57b3e03d8e46d5f1b2cc62ca881aca392fd5cc048dc0aa2e1a6d23ecdb6"}, @@ -926,6 +966,7 @@ version = "1.3.2" description = "A framework for elegantly configuring complex applications" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "hydra-core-1.3.2.tar.gz", hash = "sha256:8a878ed67216997c3e9d88a8e72e7b4767e81af37afb4ea3334b269a4390a824"}, {file = "hydra_core-1.3.2-py3-none-any.whl", hash = "sha256:fa0238a9e31df3373b35b0bfb672c34cc92718d21f81311d8996a16de1141d8b"}, @@ -943,6 +984,7 @@ version = "2.5.22" description = "File identification library for Python" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "identify-2.5.22-py2.py3-none-any.whl", hash = "sha256:f0faad595a4687053669c112004178149f6c326db71ee999ae4636685753ad2f"}, {file = "identify-2.5.22.tar.gz", hash = "sha256:f7a93d6cf98e29bd07663c60728e7a4057615068d7a639d132dc883b2d54d31e"}, @@ -957,6 +999,7 @@ version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" optional = false python-versions = ">=3.5" +groups = ["main", "docs"] files = [ {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"}, {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"}, @@ -968,6 +1011,7 @@ version = "1.4.1" description = "Getting image size from png/jpeg/jpeg2000/gif file" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +groups = ["docs"] files = [ {file = "imagesize-1.4.1-py2.py3-none-any.whl", hash = "sha256:0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b"}, {file = "imagesize-1.4.1.tar.gz", hash = "sha256:69150444affb9cb0d5cc5a92b3676f0b2fb7cd9ae39e947a5e11a36b4497cd4a"}, @@ -979,10 +1023,12 @@ version = "6.3.0" description = "Read metadata from Python packages" optional = false python-versions = ">=3.7" +groups = ["main", "docs"] files = [ {file = "importlib_metadata-6.3.0-py3-none-any.whl", hash = "sha256:8f8bd2af397cf33bd344d35cfe7f489219b7d14fc79a3f854b75b8417e9226b0"}, {file = "importlib_metadata-6.3.0.tar.gz", hash = "sha256:23c2bcae4762dfb0bbe072d358faec24957901d75b6c4ab11172c0c982532402"}, ] +markers = {docs = "python_version < \"3.10\""} [package.dependencies] zipp = ">=0.5" @@ -990,7 +1036,7 @@ zipp = ">=0.5" [package.extras] docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] perf = ["ipython"] -testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] +testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7) ; platform_python_implementation != \"PyPy\"", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8 ; python_version < \"3.12\"", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\"", "pytest-perf (>=0.9.2)"] [[package]] name = "importlib-resources" @@ -998,6 +1044,8 @@ version = "5.12.0" description = "Read resources from Python packages" optional = false python-versions = ">=3.7" +groups = ["main"] +markers = "python_version == \"3.8\"" files = [ {file = "importlib_resources-5.12.0-py3-none-any.whl", hash = "sha256:7b1deeebbf351c7578e09bf2f63fa2ce8b5ffec296e0d349139d43cca061a81a"}, {file = "importlib_resources-5.12.0.tar.gz", hash = "sha256:4be82589bf5c1d7999aedf2a45159d10cb3ca4f19b2271f8792bc8e6da7b22f6"}, @@ -1008,7 +1056,7 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7) ; platform_python_implementation != \"PyPy\"", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8 ; python_version < \"3.12\"", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\""] [[package]] name = "iniconfig" @@ -1016,6 +1064,7 @@ version = "2.0.0" description = "brain-dead simple config-ini parsing" optional = false python-versions = ">=3.7" +groups = ["main", "dev"] files = [ {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"}, {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, @@ -1027,6 +1076,7 @@ version = "3.1.2" description = "A very fast and expressive template engine." optional = false python-versions = ">=3.7" +groups = ["main", "docs"] files = [ {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"}, {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"}, @@ -1044,6 +1094,7 @@ version = "1.2.0" description = "Lightweight pipelining with Python functions" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "joblib-1.2.0-py3-none-any.whl", hash = "sha256:091138ed78f800342968c523bdde947e7a305b8594b910a0fea2ab83c3c6d385"}, {file = "joblib-1.2.0.tar.gz", hash = "sha256:e1cee4a79e4af22881164f218d4311f60074197fb707e082e803b61f6d137018"}, @@ -1051,24 +1102,71 @@ files = [ [[package]] name = "lightning-utilities" -version = "0.8.0" -description = "PyTorch Lightning Sample project." +version = "0.11.9" +description = "Lightning toolbox for across the our ecosystem." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +groups = ["main"] +markers = "python_version == \"3.8\" or sys_platform != \"linux\" and python_version < \"3.10\"" files = [ - {file = "lightning-utilities-0.8.0.tar.gz", hash = "sha256:8e5d95c7c57f026cdfed7c154303e88c93a7a5e868c9944cb02cf71f1db29720"}, - {file = "lightning_utilities-0.8.0-py3-none-any.whl", hash = "sha256:22aa107b51c8f50ccef54d08885eb370903eb04148cddb2891b9c65c59de2a6e"}, + {file = "lightning_utilities-0.11.9-py3-none-any.whl", hash = "sha256:ac6d4e9e28faf3ff4be997876750fee10dc604753dbc429bf3848a95c5d7e0d2"}, + {file = "lightning_utilities-0.11.9.tar.gz", hash = "sha256:f5052b81344cc2684aa9afd74b7ce8819a8f49a858184ec04548a5a109dfd053"}, ] [package.dependencies] packaging = ">=17.1" +setuptools = "*" typing-extensions = "*" [package.extras] cli = ["fire"] -docs = ["sphinx (>=4.0,<5.0)"] -test = ["coverage (==6.5.0)"] -typing = ["mypy (>=1.0.0)"] +docs = ["requests (>=2.0.0)"] +typing = ["mypy (>=1.0.0)", "types-setuptools"] + +[[package]] +name = "lightning-utilities" +version = "0.15.2" +description = "Lightning toolbox for across the our ecosystem." +optional = false +python-versions = ">=3.9" +groups = ["main"] +markers = "python_version == \"3.9\" and sys_platform == \"linux\"" +files = [ + {file = "lightning_utilities-0.15.2-py3-none-any.whl", hash = "sha256:ad3ab1703775044bbf880dbf7ddaaac899396c96315f3aa1779cec9d618a9841"}, + {file = "lightning_utilities-0.15.2.tar.gz", hash = "sha256:cdf12f530214a63dacefd713f180d1ecf5d165338101617b4742e8f22c032e24"}, +] + +[package.dependencies] +packaging = ">=17.1" +setuptools = "*" +typing_extensions = "*" + +[package.extras] +cli = ["jsonargparse[signatures] (>=4.38.0)", "tomlkit"] +docs = ["requests (>=2.0.0)"] +typing = ["mypy (>=1.0.0)", "types-setuptools"] + +[[package]] +name = "lightning-utilities" +version = "0.15.3" +description = "Lightning toolbox for across the our ecosystem." +optional = false +python-versions = ">=3.10" +groups = ["main"] +markers = "python_version >= \"3.10\"" +files = [ + {file = "lightning_utilities-0.15.3-py3-none-any.whl", hash = "sha256:6c55f1bee70084a1cbeaa41ada96e4b3a0fea5909e844dd335bd80f5a73c5f91"}, + {file = "lightning_utilities-0.15.3.tar.gz", hash = "sha256:792ae0204c79f6859721ac7f386c237a33b0ed06ba775009cb894e010a842033"}, +] + +[package.dependencies] +packaging = ">=22" +typing_extensions = "*" + +[package.extras] +cli = ["jsonargparse[signatures] (>=4.38.0)", "tomlkit"] +docs = ["requests (>=2.0.0)"] +typing = ["mypy (>=1.0.0)", "types-setuptools"] [[package]] name = "lit" @@ -1076,6 +1174,8 @@ version = "16.0.1" description = "A Software Testing Tool" optional = false python-versions = "*" +groups = ["main"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" files = [ {file = "lit-16.0.1.tar.gz", hash = "sha256:630a47291b714cb115015df23ab04267c24fe59aec7ecd7e637d5c75cdb45c91"}, ] @@ -1086,6 +1186,7 @@ version = "0.40.1" description = "lightweight wrapper around basic LLVM functionality" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "llvmlite-0.40.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:84ce9b1c7a59936382ffde7871978cddcda14098e5a76d961e204523e5c372fb"}, {file = "llvmlite-0.40.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3673c53cb21c65d2ff3704962b5958e967c6fc0bd0cff772998face199e8d87b"}, @@ -1119,6 +1220,7 @@ version = "1.0.0" description = "File-based locks for Python on Linux and Windows" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +groups = ["main"] files = [ {file = "locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3"}, {file = "locket-1.0.0.tar.gz", hash = "sha256:5c0d4c052a8bbbf750e056a8e65ccd309086f4f0f18a2eac306a8dfa4112a632"}, @@ -1130,6 +1232,7 @@ version = "3.0.0" description = "Python port of markdown-it. Markdown parsing, done right!" optional = false python-versions = ">=3.8" +groups = ["docs"] files = [ {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"}, {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"}, @@ -1154,6 +1257,7 @@ version = "2.1.2" description = "Safely add untrusted strings to HTML/XML markup." optional = false python-versions = ">=3.7" +groups = ["main", "docs"] files = [ {file = "MarkupSafe-2.1.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:665a36ae6f8f20a4676b53224e33d456a6f5a72657d9c83c2aa00765072f31f7"}, {file = "MarkupSafe-2.1.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:340bea174e9761308703ae988e982005aedf427de816d1afe98147668cc03036"}, @@ -1213,6 +1317,7 @@ version = "0.4.0" description = "Collection of plugins for markdown-it-py" optional = false python-versions = ">=3.8" +groups = ["docs"] files = [ {file = "mdit_py_plugins-0.4.0-py3-none-any.whl", hash = "sha256:b51b3bb70691f57f974e257e367107857a93b36f322a9e6d44ca5bf28ec2def9"}, {file = "mdit_py_plugins-0.4.0.tar.gz", hash = "sha256:d8ab27e9aed6c38aa716819fedfde15ca275715955f8a185a8e1cf90fb1d2c1b"}, @@ -1232,6 +1337,7 @@ version = "0.1.2" description = "Markdown URL utilities" optional = false python-versions = ">=3.7" +groups = ["docs"] files = [ {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"}, {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"}, @@ -1243,6 +1349,7 @@ version = "1.3.0" description = "Python library for arbitrary-precision floating-point arithmetic" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, {file = "mpmath-1.3.0.tar.gz", 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hash = "sha256:c4d316f17c745ec9c728e30409612eaf77a8404c3733cdf6c9c1569634d1ca03"}, ] @@ -1661,6 +1792,8 @@ version = "10.2.10.91" description = "CURAND native runtime libraries" optional = false python-versions = ">=3" +groups = ["main"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" files = [ {file = "nvidia_curand_cu11-10.2.10.91-py3-none-manylinux1_x86_64.whl", hash = "sha256:eecb269c970fa599a2660c9232fa46aaccbf90d9170b96c462e13bcb4d129e2c"}, {file = "nvidia_curand_cu11-10.2.10.91-py3-none-win_amd64.whl", hash = "sha256:f742052af0e1e75523bde18895a9ed016ecf1e5aa0ecddfcc3658fd11a1ff417"}, @@ -1676,6 +1809,8 @@ version = "11.4.0.1" description = "CUDA solver native runtime libraries" optional = false python-versions = ">=3" +groups = ["main"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" files = [ {file = "nvidia_cusolver_cu11-11.4.0.1-2-py3-none-manylinux1_x86_64.whl", hash = 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platform_machine == \"x86_64\"" files = [ {file = "nvidia_nccl_cu11-2.14.3-py3-none-manylinux1_x86_64.whl", hash = "sha256:5e5534257d1284b8e825bc3a182c6f06acd6eb405e9f89d49340e98cd8f136eb"}, ] @@ -1717,6 +1856,8 @@ version = "11.7.91" description = "NVIDIA Tools Extension" optional = false python-versions = ">=3" +groups = ["main"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" files = [ {file = "nvidia_nvtx_cu11-11.7.91-py3-none-manylinux1_x86_64.whl", hash = "sha256:b22c64eee426a62fc00952b507d6d29cf62b4c9df7a480fcc417e540e05fd5ac"}, {file = "nvidia_nvtx_cu11-11.7.91-py3-none-win_amd64.whl", hash = "sha256:dfd7fcb2a91742513027d63a26b757f38dd8b07fecac282c4d132a9d373ff064"}, @@ -1732,6 +1873,7 @@ version = "2.3.0" description = "A flexible configuration library" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "omegaconf-2.3.0-py3-none-any.whl", hash = "sha256:7b4df175cdb08ba400f45cae3bdcae7ba8365db4d165fc65fd04b050ab63b46b"}, 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description = "Python Imaging Library (Fork)" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "Pillow-9.5.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:ace6ca218308447b9077c14ea4ef381ba0b67ee78d64046b3f19cf4e1139ad16"}, {file = "Pillow-9.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d3d403753c9d5adc04d4694d35cf0391f0f3d57c8e0030aac09d7678fa8030aa"}, @@ -1945,6 +2094,7 @@ version = "3.8.1" description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "platformdirs-3.8.1-py3-none-any.whl", hash = "sha256:cec7b889196b9144d088e4c57d9ceef7374f6c39694ad1577a0aab50d27ea28c"}, {file = "platformdirs-3.8.1.tar.gz", hash = "sha256:f87ca4fcff7d2b0f81c6a748a77973d7af0f4d526f98f308477c3c436c74d528"}, @@ -1960,6 +2110,7 @@ version = "0.13.1" description = "plugin and hook calling mechanisms for python" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +groups = ["main", "dev"] files = [ {file = "pluggy-0.13.1-py2.py3-none-any.whl", hash = "sha256:966c145cd83c96502c3c3868f50408687b38434af77734af1e9ca461a4081d2d"}, {file = "pluggy-0.13.1.tar.gz", hash = "sha256:15b2acde666561e1298d71b523007ed7364de07029219b604cf808bfa1c765b0"}, @@ -1974,6 +2125,7 @@ version = "0.9.1" description = "A collection of helpful Python tools!" optional = false python-versions = "*" +groups = ["docs"] files = [ {file = "pockets-0.9.1-py2.py3-none-any.whl", hash = "sha256:68597934193c08a08eb2bf6a1d85593f627c22f9b065cc727a4f03f669d96d86"}, {file = "pockets-0.9.1.tar.gz", hash = "sha256:9320f1a3c6f7a9133fe3b571f283bcf3353cd70249025ae8d618e40e9f7e92b3"}, @@ -1988,6 +2140,7 @@ version = "2.21.0" description = "A framework for managing and maintaining multi-language pre-commit hooks." optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "pre_commit-2.21.0-py2.py3-none-any.whl", hash = "sha256:e2f91727039fc39a92f58a588a25b87f936de6567eed4f0e673e0507edc75bad"}, {file = "pre_commit-2.21.0.tar.gz", hash = "sha256:31ef31af7e474a8d8995027fefdfcf509b5c913ff31f2015b4ec4beb26a6f658"}, @@ -2006,6 +2159,7 @@ version = "3.7.0" description = "A simple Python library for easily displaying tabular data in a visually appealing ASCII table format" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "prettytable-3.7.0-py3-none-any.whl", hash = "sha256:f4aaf2ed6e6062a82fd2e6e5289bbbe705ec2788fe401a3a1f62a1cea55526d2"}, {file = "prettytable-3.7.0.tar.gz", hash = "sha256:ef8334ee40b7ec721651fc4d37ecc7bb2ef55fde5098d994438f0dfdaa385c0c"}, @@ -2023,6 +2177,7 @@ version = "4.22.3" description = "" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "protobuf-4.22.3-cp310-abi3-win32.whl", hash = "sha256:8b54f56d13ae4a3ec140076c9d937221f887c8f64954673d46f63751209e839a"}, {file = "protobuf-4.22.3-cp310-abi3-win_amd64.whl", hash = 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for Apache Arrow" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "pyarrow-10.0.1-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:e00174764a8b4e9d8d5909b6d19ee0c217a6cf0232c5682e31fdfbd5a9f0ae52"}, {file = "pyarrow-10.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6f7a7dbe2f7f65ac1d0bd3163f756deb478a9e9afc2269557ed75b1b25ab3610"}, @@ -2108,6 +2265,8 @@ version = "2.21" description = "C parser in Python" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +groups = ["main"] +markers = "platform_python_implementation == \"PyPy\"" files = [ {file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"}, {file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"}, @@ -2119,6 +2278,7 @@ version = "1.4.2" description = "Python interface to Graphviz's Dot" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +groups = ["main"] files = [ {file = "pydot-1.4.2-py2.py3-none-any.whl", hash = "sha256:66c98190c65b8d2e2382a441b4c0edfdb4f4c025ef9cb9874de478fb0793a451"}, {file = "pydot-1.4.2.tar.gz", hash = "sha256:248081a39bcb56784deb018977e428605c1c758f10897a339fce1dd728ff007d"}, @@ -2133,13 +2293,14 @@ version = "2.16.1" description = "Pygments is a syntax highlighting package written in Python." optional = false python-versions = ">=3.7" +groups = ["docs"] files = [ {file = "Pygments-2.16.1-py3-none-any.whl", hash = "sha256:13fc09fa63bc8d8671a6d247e1eb303c4b343eaee81d861f3404db2935653692"}, {file = "Pygments-2.16.1.tar.gz", hash = "sha256:1daff0494820c69bc8941e407aa20f577374ee88364ee10a98fdbe0aece96e29"}, ] [package.extras] -plugins = ["importlib-metadata"] +plugins = ["importlib-metadata ; python_version < \"3.8\""] [[package]] name = "pyparsing" @@ -2147,6 +2308,7 @@ version = "3.0.9" description = "pyparsing module - Classes and methods to define and execute parsing grammars" optional = false python-versions = ">=3.6.8" +groups = ["main"] files = [ {file = "pyparsing-3.0.9-py3-none-any.whl", hash = "sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc"}, {file = "pyparsing-3.0.9.tar.gz", hash = "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb"}, @@ -2161,6 +2323,7 @@ version = "7.3.0" description = "pytest: simple powerful testing with Python" optional = false python-versions = ">=3.7" +groups = ["main", "dev"] files = [ {file = "pytest-7.3.0-py3-none-any.whl", hash = "sha256:933051fa1bfbd38a21e73c3960cebdad4cf59483ddba7696c48509727e17f201"}, {file = "pytest-7.3.0.tar.gz", hash = "sha256:58ecc27ebf0ea643ebfdf7fb1249335da761a00c9f955bcd922349bcb68ee57d"}, @@ -2183,6 +2346,7 @@ version = "0.6.3" description = "It helps to use fixtures in pytest.mark.parametrize" optional = false python-versions = "*" +groups = ["dev"] files = [ {file = "pytest-lazy-fixture-0.6.3.tar.gz", hash = "sha256:0e7d0c7f74ba33e6e80905e9bfd81f9d15ef9a790de97993e34213deb5ad10ac"}, {file = "pytest_lazy_fixture-0.6.3-py3-none-any.whl", hash = "sha256:e0b379f38299ff27a653f03eaa69b08a6fd4484e46fd1c9907d984b9f9daeda6"}, @@ -2197,6 +2361,7 @@ version = "2.8.2" description = "Extensions to the standard Python datetime module" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" +groups = ["main"] files = [ {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"}, {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"}, @@ -2207,38 +2372,35 @@ six = ">=1.5" [[package]] name = "pytorch-lightning" -version = "1.9.5" +version = "2.3.3" description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" +groups = ["main"] files = [ - {file = "pytorch-lightning-1.9.5.tar.gz", hash = "sha256:925fe7b80ddf04859fa385aa493b260be4000b11a2f22447afb4a932d1f07d26"}, - {file = "pytorch_lightning-1.9.5-py3-none-any.whl", hash = "sha256:06821558158623c5d2ecf5d3d0374dc8bd661e0acd3acf54a6d6f71737c156c5"}, + {file = "pytorch-lightning-2.3.3.tar.gz", hash = "sha256:5f974015425af6873b5689246c5495ca12686b446751479273c154b73aeea843"}, + {file = "pytorch_lightning-2.3.3-py3-none-any.whl", hash = "sha256:4365e3f2874e223e63cb42628d24c88c2bdc8d1794453cac38c0619b31115fba"}, ] [package.dependencies] -fsspec = {version = ">2021.06.0", extras = ["http"]} -lightning-utilities = ">=0.6.0.post0" +fsspec = {version = ">=2022.5.0", extras = ["http"]} +lightning-utilities = ">=0.10.0" numpy = ">=1.17.2" -packaging = ">=17.1" +packaging = ">=20.0" PyYAML = ">=5.4" -torch = ">=1.10.0" +torch = ">=2.0.0" torchmetrics = ">=0.7.0" tqdm = ">=4.57.0" -typing-extensions = ">=4.0.0" +typing-extensions = ">=4.4.0" [package.extras] -all = ["colossalai (>=0.2.0)", "deepspeed (>=0.6.0)", "fairscale (>=0.4.5)", "gym[classic-control] (>=0.17.0)", "hivemind (==1.1.5)", "horovod (>=0.21.2,!=0.24.0)", "hydra-core (>=1.0.5)", "ipython[all] (<8.7.1)", "jsonargparse[signatures] (>=4.18.0)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "rich (>=10.14.0,!=10.15.0.a)", "tensorboardX (>=2.2)", "torchmetrics (>=0.10.0)", "torchvision (>=0.11.1)"] -colossalai = ["colossalai (>=0.2.0)"] -deepspeed = ["deepspeed (>=0.6.0)"] -dev = ["cloudpickle (>=1.3)", "codecov (==2.1.12)", "colossalai (>=0.2.0)", "coverage (==6.5.0)", "deepspeed (>=0.6.0)", "fairscale (>=0.4.5)", "fastapi (<0.87.0)", "gym[classic-control] (>=0.17.0)", "hivemind (==1.1.5)", "horovod (>=0.21.2,!=0.24.0)", "hydra-core (>=1.0.5)", "ipython[all] (<8.7.1)", "jsonargparse[signatures] (>=4.18.0)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "onnx (<1.14.0)", 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["deepspeed (>=0.8.2,<=0.9.3) ; platform_system != \"Windows\" and platform_system != \"Darwin\""] +test = ["cloudpickle (>=1.3)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0)", "onnxruntime (>=0.15.0)", "pandas (>1.0)", "psutil (<5.9.6)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "uvicorn"] [[package]] name = "pytz" @@ -2246,10 +2408,12 @@ version = "2023.3" description = "World timezone definitions, modern and historical" optional = false python-versions = "*" +groups = ["main", "docs"] files = [ {file = "pytz-2023.3-py2.py3-none-any.whl", hash = "sha256:a151b3abb88eda1d4e34a9814df37de2a80e301e68ba0fd856fb9b46bfbbbffb"}, {file = "pytz-2023.3.tar.gz", hash = "sha256:1d8ce29db189191fb55338ee6d0387d82ab59f3d00eac103412d64e0ebd0c588"}, ] +markers = {docs = "python_version == \"3.8\""} [[package]] name = "pyyaml" @@ -2257,6 +2421,7 @@ version = "6.0" description = "YAML parser and emitter for Python" optional = false python-versions = ">=3.6" +groups = ["main", "dev", "docs"] files = [ {file = "PyYAML-6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53"}, {file = "PyYAML-6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c"}, @@ -2306,6 +2471,7 @@ version = "2.28.2" description = "Python HTTP for Humans." optional = false python-versions = ">=3.7, <4" +groups = ["main", "docs"] files = [ {file = "requests-2.28.2-py3-none-any.whl", hash = "sha256:64299f4909223da747622c030b781c0d7811e359c37124b4bd368fb8c6518baa"}, {file = "requests-2.28.2.tar.gz", hash = "sha256:98b1b2782e3c6c4904938b84c0eb932721069dfdb9134313beff7c83c2df24bf"}, @@ -2327,6 +2493,7 @@ version = "1.2.2" description = "A set of python modules for machine learning and data mining" optional = false python-versions = 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platform_python_implementation == \"CPython\""] [[package]] name = "wandb" @@ -3323,6 +3479,7 @@ version = "0.13.11" description = "A CLI and library for interacting with the Weights and Biases API." optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "wandb-0.13.11-py3-none-any.whl", hash = "sha256:e584ac7fb0097fba8a030d5aedc5fea157286aba69a2e6e6e689b794155fcc8f"}, {file = "wandb-0.13.11.tar.gz", hash = "sha256:f2b065d1c732e8e1828fabe720dc82f12e6109d0d2bbfbcccc17eaefae0fd7c9"}, @@ -3364,6 +3521,7 @@ version = "0.2.6" description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "wcwidth-0.2.6-py2.py3-none-any.whl", hash = "sha256:795b138f6875577cd91bba52baf9e445cd5118fd32723b460e30a0af30ea230e"}, {file = "wcwidth-0.2.6.tar.gz", hash = "sha256:a5220780a404dbe3353789870978e472cfe477761f06ee55077256e509b156d0"}, @@ -3375,6 +3533,8 @@ version = "0.40.0" description = "A built-package format for Python" optional = false python-versions = ">=3.7" +groups = ["main"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" files = [ {file = "wheel-0.40.0-py3-none-any.whl", hash = "sha256:d236b20e7cb522daf2390fa84c55eea81c5c30190f90f29ae2ca1ad8355bf247"}, {file = "wheel-0.40.0.tar.gz", hash = "sha256:cd1196f3faee2b31968d626e1731c94f99cbdb67cf5a46e4f5656cbee7738873"}, @@ -3389,6 +3549,7 @@ version = "3.1.0" description = "A Python module for creating Excel XLSX files." optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "XlsxWriter-3.1.0-py3-none-any.whl", hash = "sha256:b70a147d36235d1ee835cfd037396f789db1f76740a0e5c917d54137169341de"}, {file = "XlsxWriter-3.1.0.tar.gz", hash = "sha256:02913b50b74c00f165933d5da3e3a02cab4204cb4932722a1b342c5c71034122"}, @@ -3400,6 +3561,7 @@ version = "1.8.2" description = "Yet another URL library" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "yarl-1.8.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:bb81f753c815f6b8e2ddd2eef3c855cf7da193b82396ac013c661aaa6cc6b0a5"}, {file = "yarl-1.8.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:47d49ac96156f0928f002e2424299b2c91d9db73e08c4cd6742923a086f1c863"}, @@ -3487,6 +3649,7 @@ version = "2.2.0" description = "Mutable mapping tools" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "zict-2.2.0-py2.py3-none-any.whl", hash = "sha256:dabcc8c8b6833aa3b6602daad50f03da068322c1a90999ff78aed9eecc8fa92c"}, {file = "zict-2.2.0.tar.gz", hash = "sha256:d7366c2e2293314112dcf2432108428a67b927b00005619feefc310d12d833f3"}, @@ -3501,14 +3664,16 @@ version = "3.15.0" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.7" +groups = ["main", "docs"] files = [ {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"}, {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"}, ] +markers = {docs = "python_version < \"3.10\""} [package.extras] docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7) ; platform_python_implementation != \"PyPy\"", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8 ; python_version < \"3.12\"", "pytest-mypy (>=0.9.1) ; platform_python_implementation != \"PyPy\""] [[package]] name = "zstandard" @@ -3516,6 +3681,7 @@ version = "0.19.0" description = "Zstandard bindings for Python" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "zstandard-0.19.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a65e0119ad39e855427520f7829618f78eb2824aa05e63ff19b466080cd99210"}, {file = "zstandard-0.19.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4fa496d2d674c6e9cffc561639d17009d29adee84a27cf1e12d3c9be14aa8feb"}, @@ -3577,6 +3743,6 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\ cffi = ["cffi (>=1.11)"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = "^3.8" -content-hash = "279566202222450bbd6667dd6188e5b783a17ea9f69ba070400b71405db008cc" +content-hash = "d5636486f7dd86a5bd8046dc6a7256d455cab2d62751ba06062bd3de465c5f0a" diff --git a/pyproject.toml b/pyproject.toml index 9b61868..a680997 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,7 +48,7 @@ scipy = {version = "^1.9.3", source = "pypi"} tqdm = {version = "^4.64.1", source = "pypi"} wandb = {version = "^0.13.6", source = "pypi"} zstandard = {version = "^0.19.0", source = "pypi"} -pytorch-lightning = {version = "^1.8.3.post1", source = "pypi"} +pytorch-lightning = {version = "^2.3.3.0", source = "pypi"} pytest = "^7.2.1" dill = "^0.3.6" tensorstore = "^0.1.33"

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