From 974f0d7bee847f0e541798fd8efc28774561d805 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Sun, 23 Aug 2026 10:23:43 -0400 Subject: [PATCH 01/13] Add PCNtoolkit validation evidence --- .beads/issues.jsonl | 8 + .github/workflows/pcntoolkit-validation.yml | 128 + DESCRIPTION | 1 + README.md | 5 +- docs/design/pcntoolkit-validation-contract.md | 191 + docs/evidence/pcntoolkit/v1.3.0/README.md | 31 + .../pcntoolkit/v1.3.0/concordance.csv | 10 + .../pcntoolkit/v1.3.0/evidence_summary.csv | 7 + .../pcntoolkit/v1.3.0/superiority.csv | 2 + inst/pcntoolkit/benchmark_design.R | 78 + inst/pcntoolkit/benchmark_helpers.R | 99 + inst/pcntoolkit/benchmark_scenarios.csv | 11 + inst/pcntoolkit/compare_results.R | 74 + inst/pcntoolkit/run_concordance.R | 151 + inst/pcntoolkit/run_hbr_site_evidence.py | 290 + inst/pcntoolkit/site_evidence.R | 29 + .../pcntoolkit/v1.3.0/fitted_inputs.csv | 9910 +++++++++++++++++ .../pcntoolkit/v1.3.0/fitted_predictions.csv | 4510 ++++++++ .../fixtures/pcntoolkit/v1.3.0/manifest.json | 135 + .../pcntoolkit/v1.3.0/semantic_gaussian.csv | 6 + .../pcntoolkit/v1.3.0/semantic_metrics.csv | 15 + .../pcntoolkit/v1.3.0/semantic_shashb.csv | 6 + tests/testthat/helper-pcntoolkit.R | 16 + tests/testthat/test-pcntoolkit-concordance.R | 24 + tests/testthat/test-pcntoolkit-fixtures.R | 58 + tests/testthat/test-pcntoolkit-hbr-site.R | 54 + tests/testthat/test-pcntoolkit-semantics.R | 127 + tests/testthat/test-pcntoolkit-superiority.R | 75 + tools/pcntoolkit/benchmark_design.R | 78 + tools/pcntoolkit/benchmark_helpers.R | 99 + tools/pcntoolkit/benchmark_scenarios.csv | 11 + tools/pcntoolkit/check_fixture_drift.R | 66 + tools/pcntoolkit/compare_results.R | 74 + tools/pcntoolkit/generate_fixtures.py | 432 + tools/pcntoolkit/render_evidence.R | 70 + tools/pcntoolkit/run_concordance.R | 151 + tools/pcntoolkit/run_hbr_site_evidence.py | 290 + .../pcntoolkit/run_site_transport_evidence.R | 86 + tools/pcntoolkit/run_superiority.R | 33 + tools/pcntoolkit/site_evidence.R | 29 + vignettes/pcntoolkit.Rmd | 36 +- 41 files changed, 17500 insertions(+), 6 deletions(-) create mode 100644 .github/workflows/pcntoolkit-validation.yml create mode 100644 docs/design/pcntoolkit-validation-contract.md create mode 100644 docs/evidence/pcntoolkit/v1.3.0/README.md create mode 100644 docs/evidence/pcntoolkit/v1.3.0/concordance.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/evidence_summary.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/superiority.csv create mode 100644 inst/pcntoolkit/benchmark_design.R create mode 100644 inst/pcntoolkit/benchmark_helpers.R create mode 100644 inst/pcntoolkit/benchmark_scenarios.csv create mode 100644 inst/pcntoolkit/compare_results.R create mode 100644 inst/pcntoolkit/run_concordance.R create mode 100644 inst/pcntoolkit/run_hbr_site_evidence.py create mode 100644 inst/pcntoolkit/site_evidence.R create mode 100644 tests/fixtures/pcntoolkit/v1.3.0/fitted_inputs.csv create mode 100644 tests/fixtures/pcntoolkit/v1.3.0/fitted_predictions.csv create mode 100644 tests/fixtures/pcntoolkit/v1.3.0/manifest.json create mode 100644 tests/fixtures/pcntoolkit/v1.3.0/semantic_gaussian.csv create mode 100644 tests/fixtures/pcntoolkit/v1.3.0/semantic_metrics.csv create mode 100644 tests/fixtures/pcntoolkit/v1.3.0/semantic_shashb.csv create mode 100644 tests/testthat/helper-pcntoolkit.R create mode 100644 tests/testthat/test-pcntoolkit-concordance.R create mode 100644 tests/testthat/test-pcntoolkit-fixtures.R create mode 100644 tests/testthat/test-pcntoolkit-hbr-site.R create mode 100644 tests/testthat/test-pcntoolkit-semantics.R create mode 100644 tests/testthat/test-pcntoolkit-superiority.R create mode 100644 tools/pcntoolkit/benchmark_design.R create mode 100644 tools/pcntoolkit/benchmark_helpers.R create mode 100644 tools/pcntoolkit/benchmark_scenarios.csv create mode 100644 tools/pcntoolkit/check_fixture_drift.R create mode 100644 tools/pcntoolkit/compare_results.R create mode 100644 tools/pcntoolkit/generate_fixtures.py create mode 100644 tools/pcntoolkit/render_evidence.R create mode 100644 tools/pcntoolkit/run_concordance.R create mode 100644 tools/pcntoolkit/run_hbr_site_evidence.py create mode 100644 tools/pcntoolkit/run_site_transport_evidence.R create mode 100644 tools/pcntoolkit/run_superiority.R create mode 100644 tools/pcntoolkit/site_evidence.R diff --git a/.beads/issues.jsonl b/.beads/issues.jsonl index dc7f6ef..65eddb0 100644 --- a/.beads/issues.jsonl +++ b/.beads/issues.jsonl @@ -1,6 +1,14 @@ {"id":"referent-c48","title":"Dynamics kernel estimator: scale-unidentified, silent fallback to start values","description":"R/dynamics.R:104-151. process_correlation normalises so (tau_b,tau_g,sigma_e) are unidentified; BFGS walks ridge, eigen fails, tryCatch returns START values with identified=TRUE. norm_stable (ell=Inf) never fits. Fix: simplex parameterization + bounded log(ell), L-BFGS-B, check convergence, never silent fallback, stable+nugget when lags fixed, report normalised variance fractions. Also A4: NA velocity/forecast quantities when unidentified. Add OOS test on irregular lags: var(innovation) in 1±0.1, FPR in 0.05±0.015. Review A3/A4.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:18Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:20Z","started_at":"2026-08-22T13:02:50Z","closed_at":"2026-08-22T13:28:20Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-0rh","title":"Fix site scale adaptation bias (E[log|Z|] = -0.635)","description":"R/adapt.R:47-50 uses mean(log|r/s|); identical-site adaptation shrinks sigma to 0.55x, held-out var(z)=3.2-3.9. Fix: 0.5*log(mean((r/s)^2)) with z=qnorm(centile); add test that identical-site adaptation leaves var(z)~1. Review A2.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:17Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:19Z","started_at":"2026-08-22T13:02:49Z","closed_at":"2026-08-22T13:28:19Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-4du","title":"Derive covariates from spec formulas, not all non-outcome columns","description":"R/fit.R:30,45,260. Covariates are every non-outcome column: NA in unused columns silently drops rows; Mahalanobis support is computed over other outcomes; predict errors if unused column absent. Fix: all.vars() over spec formulas; complete-case on outcome+those; message dropped rows. See docs/review-2026-08-22.md A1.","status":"closed","priority":0,"issue_type":"bug","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:15Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:02:48Z","started_at":"2026-08-22T12:51:42Z","closed_at":"2026-08-22T13:02:48Z","close_reason":"Phase 1 fdb58e2","dependency_count":0,"dependent_count":0,"comment_count":0} +{"id":"referent-eo6.7","title":"Publish tiered CI and documentation evidence","description":"Add fast offline PR checks, scheduled comparator regeneration and scenario runs, release-level HBR and multisite gates, machine-readable reports, and reader-facing evidence tables.","design":"Pinned fixtures remain stable until an explicit comparator upgrade. CI reports exact conformance separately from scientific superiority and unsupported lanes.","acceptance_criteria":"CI has documented PR, scheduled, and release tiers; the vignette links evidence and limitations; an upgrade produces reviewed drift rather than silently replacing baselines.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:59Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:52Z","started_at":"2026-08-23T14:01:27Z","closed_at":"2026-08-23T14:20:52Z","close_reason":"Added offline PR oracles, weekly pinned regeneration/drift rejection, release HBR/site gates, reproducible machine-readable evidence, and linked reader-facing documentation","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:59Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:14Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:15Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:16Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.6","type":"blocks","created_at":"2026-08-23T09:30:17Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":4,"dependent_count":0,"comment_count":0} +{"id":"referent-eo6.6","title":"Evaluate HBR SHASH and site transport separately","description":"Add scheduled evidence for HBR posterior aggregation, SHASH fitted performance, site effects, unseen-site transport, and Referent adaptation without claiming false rowwise equivalence.","design":"Require convergence and Monte Carlo receipts. Use leave-one-site-out calibration and performance; keep PCNtoolkit transfer and Referent adaptation labeled as different estimands.","acceptance_criteria":"HBR and transport reports expose convergence, uncertainty, score and calibration evidence, and no small-site failure is averaged away.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:58Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:01:25Z","started_at":"2026-08-23T13:53:31Z","closed_at":"2026-08-23T14:01:25Z","close_reason":"HBR/transfer runner emits proper mixture and draw-averaged outputs, convergence receipts, per-site evidence, and a smoke run proved both convergence and small-site gates fail closed; release-grade evidence remains CI-gated","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:57Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:13Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6.5","title":"Establish held-out superiority and non-inferiority gates","description":"Implement preregistered synthetic scenario families, untouched test splits, paired proper-score comparisons, cluster-aware uncertainty, calibration non-inferiority gates, and negative controls.","design":"A differing Referent method is superior only when its paired held-out proper-score interval clears a practical margin and no marginal, conditional, site, or tail calibration gate materially regresses.","acceptance_criteria":"The benchmark classifies conformant, equivalent, superior, trade-off, and inferior outcomes; null and deliberately overconfident negative controls behave correctly.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:56Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:53:27Z","started_at":"2026-08-23T13:47:07Z","closed_at":"2026-08-23T13:53:27Z","close_reason":"Locked design registry, cluster bootstrap, calibration non-inferiority, full classification rules, and negative controls pass; skew-heavy held-out case is superior with positive log-score CI and better calibration","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:56Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:12Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.4","title":"Certify fitted-model concordance in overlap cases","description":"Compare identical train and test rows for intercept-only, linear, categorical, heteroskedastic, smooth, transformed, and balanced-site model configurations using per-row predictive outputs.","design":"Use normalized prediction error, predictive-scale error, Z RMSE, central quantile errors, log tails, and coverage. Avoid a sole maximum-error gate.","acceptance_criteria":"All registered overlap cases meet locked scenario-level concordance margins or have a documented non-equivalent classification.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:55Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:47:05Z","started_at":"2026-08-23T13:43:50Z","closed_at":"2026-08-23T13:47:05Z","close_reason":"Four matched linear/log Gaussian scenarios meet all locked prediction, scale, Z, tail-quantile, and coverage margins; divergent smooth/site cases are explicitly diagnostic","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:54Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:11Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.3","title":"Prove exact shared distribution and metric semantics","description":"Add analytic and differential tests for Gaussian, SHASHb to mgcv SHASH conversion, transforms, log tails, log densities, and explicitly normalized metric definitions.","design":"Use independent analytic formulas alongside external fixtures. Expected definition differences must be tested as differences, not hidden by broad tolerances.","acceptance_criteria":"Gaussian and SHASH semantic fixtures pass scale-aware declared tolerances, adversarial tails remain finite, and metric relationships are independently verified.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:53Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:43:48Z","started_at":"2026-08-23T13:41:06Z","closed_at":"2026-08-23T13:43:48Z","close_reason":"Independent R analytic, Referent, and pinned PCNtoolkit paths agree for Gaussian/converted SHASH/transforms; tail and definition-difference tests pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:53Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6.2","type":"blocks","created_at":"2026-08-23T09:30:10Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.2","title":"Build reproducible offline PCNtoolkit fixture harness","description":"Add a pinned Python generator, neutral inputs, checked-in per-row PCNtoolkit outputs, provenance receipts, and an R comparison reader without adding Python to normal package tests.","design":"PCNtoolkit runs only during explicit regeneration or scheduled CI. Checked-in fixtures are versioned by comparator release and carry hashes and resolved environment metadata.","acceptance_criteria":"Fixture generation is deterministic; schema and hashes validate in R; normal testthat runs need no Python or network.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:52Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:41:04Z","started_at":"2026-08-23T13:34:09Z","closed_at":"2026-08-23T13:41:04Z","close_reason":"Pinned Python generator is byte-stable across two runs; checked-in fixture manifest, schema, hashes, and offline R validation pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:51Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6.1","type":"blocks","created_at":"2026-08-23T09:30:09Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6.1","title":"Define pinned PCNtoolkit validation contract","description":"Write the normative comparison contract for PCNtoolkit 1.3.0, classify outputs as exact, parameter-converted, matched-estimator, same-estimand, or non-equivalent, and reconcile the public comparison vignette with executed definitions.","design":"Separate semantic conformance from scientific superiority. Record conditional versus total uncertainty and executed versus documented comparator behavior.","acceptance_criteria":"A reviewed design document and corrected vignette define every comparison lane, estimand, output, uncertainty mode, and mismatch disposition.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:50Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:34:07Z","started_at":"2026-08-23T13:30:19Z","closed_at":"2026-08-23T13:34:07Z","close_reason":"Pinned comparison contract and corrected current PCNtoolkit 1.3.0 semantics in the public vignette","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.1","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:50Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":0,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6","title":"Establish PCNtoolkit conformance and superiority evidence","description":"Implement the seven-part validation program: pin and document comparison contracts, generate reproducible PCNtoolkit fixtures, prove exact shared semantics, measure fitted-model concordance, establish held-out superiority without sacrificing calibration, evaluate HBR and site transport separately, and publish tiered CI evidence.","design":"PCNtoolkit is an external differential oracle only for shared contracts. Alternative Referent methods may diverge when paired held-out proper-score superiority and calibration non-inferiority are demonstrated. Normal R tests consume offline fixtures; Python regeneration is scheduled or manual.","acceptance_criteria":"All seven child work packages are complete; exact relationships pass declared tolerances; every mismatch is classified; superiority claims use untouched test data and calibration gates; normal R tests remain Python-free; documentation and CI expose evidence tiers.","status":"closed","priority":1,"issue_type":"epic","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:20Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:54Z","started_at":"2026-08-23T13:30:18Z","closed_at":"2026-08-23T14:20:54Z","close_reason":"All seven PCNtoolkit conformance and superiority evidence lanes implemented and validated","labels":["pcntoolkit","scientific-evidence","validation"],"dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-xzc","title":"Make referent vignettes surpass PCNtoolkit docs","description":"Audit and improve referent's starlightdown vignette site against the live PCNtoolkit documentation. Establish a direct visual and reader-journey baseline, revise source vignettes and site navigation, verify executable examples and rendered desktop/mobile output, and publish the completed branch without touching unrelated work.","acceptance_criteria":"All public vignette pages use current exported APIs and render successfully; newcomer journey, task navigation, interpretation, and recovery are materially stronger than the live PCNtoolkit stable site; representative pages are compared at desktop and mobile viewports; repository tests and documentation build pass; intended changes are committed and pushed with tracking/live SHA verification.","notes":"Gauntlet complete. Independent critic final PASS and blind choice referent in all six categories. Fixed discovered S3 route blocker via docsLoader generateId preserving dotted method paths; added fail-closed generated plot alt postprocessor and generic 35-route identity/canonical/redirect/alt checker. Final gates: cold starlightdown build 35 routes, Astro 36 pages plus Pagefind, browser desktop/mobile axe clean and search working, R CMD build passed, LC_ALL=en_US.UTF-8 R CMD check --no-manual Status OK.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T18:05:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T18:49:55Z","started_at":"2026-08-22T18:05:43Z","closed_at":"2026-08-22T18:49:55Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e4m","title":"Gauntlet 2: thrive lines and velocity","description":"thrive type was removed from autoplot.norm_forecast in Phase 5; referent now has no thrive capability. Bar for thrive: PCNtoolkit nb14 CorrelationMatrix + ZGainScore.get_thrivelines + plot_centiles_advanced on a shared constructed two-visit dataset (LNM_data.csv is not public). Bar for velocity: ground-truth recovery on simulated data, since PCNtoolkit has no velocity estimand.","status":"closed","priority":1,"issue_type":"feature","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:16:57Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:23Z","closed_at":"2026-08-22T19:13:23Z","close_reason":"Both lanes won round 1 and were verified by independent critics. Patches pending integration (thrive rebased needed; velocity chart_velocity_se drop).","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e6o","title":"Close WM-hypointensities MSLL gap vs information-matched PCN opponent","description":"referent -0.470 vs fair opponent -0.491. Test-split var_z 1.17-1.23 indicates 10-20 pct out-of-sample overconfidence. Try norm_calibrate on out-of-fold train scores (train-only) before rescoring.","status":"closed","priority":1,"issue_type":"task","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:09:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:22Z","closed_at":"2026-08-22T19:13:22Z","close_reason":"Closed via ref_spec(transform=) Box-Cox response transform (commit 4dd3683). WM -0.49154 vs PCN fair -0.49087. NOTE: margin is +0.00068 nats with paired per-subject SE 0.033 (t=-0.02) - a point-estimate win only, not statistical evidence. Root cause was a missing capability, not tuning: SHASH moves conditional residual shape; PCN's warp is a monotone map on y.","dependency_count":0,"dependent_count":0,"comment_count":0} diff --git a/.github/workflows/pcntoolkit-validation.yml b/.github/workflows/pcntoolkit-validation.yml new file mode 100644 index 0000000..bc01886 --- /dev/null +++ b/.github/workflows/pcntoolkit-validation.yml @@ -0,0 +1,128 @@ +name: PCNtoolkit validation + +on: + pull_request: + paths: + - "R/**" + - "tests/**" + - "tools/pcntoolkit/**" + - "vignettes/pcntoolkit.Rmd" + - "DESCRIPTION" + - ".github/workflows/pcntoolkit-validation.yml" + push: + branches: [main] + paths: + - "R/**" + - "tests/**" + - "tools/pcntoolkit/**" + - "vignettes/pcntoolkit.Rmd" + - "DESCRIPTION" + - ".github/workflows/pcntoolkit-validation.yml" + schedule: + - cron: "17 6 * * 1" + release: + types: [published] + workflow_dispatch: + inputs: + tier: + description: Validation tier to run + required: true + default: weekly + type: choice + options: + - weekly + - release + +permissions: + contents: read + +jobs: + offline-oracles: + name: Offline semantic and fitted oracles + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: | + any::devtools + local::. + needs: check + - name: Run checked-in oracles without Python or network access + run: >- + Rscript -e 'devtools::test(filter = + "pcntoolkit-(fixtures|semantics|concordance|superiority|hbr-site)", + stop_on_failure = TRUE)' + + weekly-comparator: + name: Regenerate PCNtoolkit fixture and evidence + if: >- + github.event_name == 'schedule' || + (github.event_name == 'workflow_dispatch' && + (inputs.tier == 'weekly' || inputs.tier == 'release')) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v6 + with: + version: "0.8.13" + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: local::. + needs: check + - name: Regenerate pinned PCNtoolkit 1.3.0 fixture + run: uv run tools/pcntoolkit/generate_fixtures.py --output /tmp/pcntoolkit-v1.3.0 + - name: Reject unreviewed comparator drift + run: >- + Rscript tools/pcntoolkit/check_fixture_drift.R + tests/fixtures/pcntoolkit/v1.3.0 /tmp/pcntoolkit-v1.3.0 + - name: Render machine-readable evidence + run: Rscript tools/pcntoolkit/render_evidence.R evidence/pcntoolkit/v1.3.0 + - uses: actions/upload-artifact@v4 + with: + name: pcntoolkit-weekly-evidence + path: evidence/pcntoolkit/v1.3.0 + + release-hbr-site: + name: Release HBR and site-transfer evidence + if: >- + github.event_name == 'release' || + (github.event_name == 'workflow_dispatch' && inputs.tier == 'release') + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v6 + with: + version: "0.8.13" + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: local::. + needs: check + - name: Run HBR and PCNtoolkit transfer with convergence receipts + run: >- + uv run tools/pcntoolkit/run_hbr_site_evidence.py + --output evidence/hbr-site --draws 1500 --tune 500 + --chains 4 --cores 4 --nuts-sampler nutpie + - name: Run Referent adaptation and per-site gates + run: >- + Rscript tools/pcntoolkit/run_site_transport_evidence.R + evidence/hbr-site evidence/hbr-site/referent + - name: Enforce convergence and every-site gates + run: >- + Rscript -e 'h <- jsonlite::read_json("evidence/hbr-site/receipt.json"); + r <- jsonlite::read_json("evidence/hbr-site/referent/referent_receipt.json"); + if (!isTRUE(h$all_stages_pass) || !isTRUE(r$all_sites_pass)) quit(status = 1)' + - uses: actions/upload-artifact@v4 + if: always() + with: + name: pcntoolkit-release-hbr-site-evidence + path: evidence/hbr-site diff --git a/DESCRIPTION b/DESCRIPTION index 0a94cbf..c7dd105 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -36,6 +36,7 @@ Suggests: future, ggdist, future.apply, + jsonlite, knitr, rmarkdown, testthat (>= 3.0.0), diff --git a/README.md b/README.md index 700520e..9107464 100644 --- a/README.md +++ b/README.md @@ -112,7 +112,9 @@ velocity centiles, and conditional forecasts off the same process. forecasts. - [Coming from PCNtoolkit](https://bbuchsbaum.github.io/referent/articles/pcntoolkit/): concept and metric mapping (warped BLR, HBR batch effects and random slopes, harmonised - outputs, MSLL/SMSE/EV/MACE), and what is deliberately missing. + outputs, MSLL/SMSE/EV/MACE), numerical-validation policy, and what is + deliberately missing. The scoped, machine-readable evidence is in + [docs/evidence/pcntoolkit/v1.3.0](docs/evidence/pcntoolkit/v1.3.0/README.md). - [Troubleshooting reference-model workflows](https://bbuchsbaum.github.io/referent/articles/troubleshooting/): missing and unsupported covariates, transported unseen sites, small local samples, partial panel fits, honest assessment, and unidentified dynamics. @@ -121,3 +123,4 @@ Contributor design notes: - [A design for an R normative-modeling library](docs/design/distributional-reference-models.md) - [Velocity should be a core consequence of the model, not a bolt-on](docs/design/longitudinal-velocity.md) +- [PCNtoolkit validation contract](docs/design/pcntoolkit-validation-contract.md) diff --git a/docs/design/pcntoolkit-validation-contract.md b/docs/design/pcntoolkit-validation-contract.md new file mode 100644 index 0000000..8076493 --- /dev/null +++ b/docs/design/pcntoolkit-validation-contract.md @@ -0,0 +1,191 @@ +# PCNtoolkit validation contract + +Status: normative for the `referent` comparison suite + +Comparator: PCNtoolkit 1.3.0 + +Legacy comparator: PCNtoolkit 0.35 is out of scope and must be named explicitly + +## Purpose + +`referent` and PCNtoolkit both estimate a conditional predictive distribution, +but their estimators, parameterisations, uncertainty calculations, and site +models are not generally identical. The validation target is therefore not +universal bit-for-bit parity. It has two independently reported parts: + +1. **Conformance:** shared mathematical quantities and deliberately matched + fitted models agree within a declared tolerance. +2. **Held-out utility:** when estimators differ, `referent` is non-inferior or + superior on locked test data without sacrificing calibration. + +Passing the second part never excuses failure of the first. Conversely, a +conformant implementation is not called superior without held-out evidence. + +## Evidence classes + +Every comparison must carry exactly one of these labels. + +| Class | Claim permitted | Required evidence | +|---|---|---| +| `exact` | The same formula and parameterisation were evaluated. | Analytic R oracle and pinned PCNtoolkit fixture; absolute plus relative tolerance. | +| `parameter_converted` | The same distribution is represented after a documented, invertible conversion. | Forward and inverse conversion tests plus density, CDF, and quantile agreement. | +| `matched_estimator` | Fitted models target the same likelihood and design closely enough for numerical concordance. | Locked data, declared settings, parameter/prediction discrepancies, and predictive checks. | +| `same_estimand` | Methods estimate the same broad conditional predictive distribution but use different estimators. | Held-out proper scores and calibration, not parameter equality. | +| `non_equivalent` | The quantities or inferential procedures differ materially. | An explicit counterexample or semantic explanation; no parity threshold. | + +A failure is recorded, not resolved by relabelling a fixture or loosening a +tolerance. A classification may be changed only by changing the contract and +adding an explanatory regression test. + +## Comparison unit + +Each result records the package versions, fixture schema, scenario and seed, +training/validation/test row identifiers, outcome transformation, design +matrix, family, uncertainty mode, site handling, and missing-row policy. +Comparisons are made on the same finite test rows and outcome scale. + +The prediction record contains, where available: + +- predictive median and standard deviation; +- CDF/centile, Gaussianised deviation score, and selected quantiles; +- pointwise log density and aggregate log score; +- interval coverage and calibration error. + +Conditional predictions use plug-in distributional parameters. Total +predictions also include fitted-curve uncertainty. These are distinct +estimands and are never pooled in one parity statistic. + +## Shared semantics + +### Gaussian + +For fixed `mu` and positive `sigma`, density, log density, CDF, quantiles, +median, variance, and `z = qnorm(CDF(y))` are `exact`. The gate is +`abs(error) <= 1e-10 + 1e-10 * abs(oracle)`, except log-tail cases where the +comparison is performed directly in log space. + +### SHASH + +PCNtoolkit's `SHASHb` standardises a sinh-arcsinh random variable to mean zero +and variance one. `mgcv::shash`, used by `referent`, exposes a location-scale +form. With PCNtoolkit parameters `(mu_b, sigma_b, epsilon, delta)`, and `m1` +and `v` the mean and variance of the unstandardised sinh-arcsinh variable, +the equivalent `referent` parameters are + +``` +sigma_r = sigma_b / (delta * sqrt(v)) +mu_r = mu_b - sigma_b * m1 / sqrt(v) +epsilon_r = epsilon +delta_r = delta +``` + +This lane is `parameter_converted`. Density, CDF, and quantiles must satisfy +`abs(error) <= 1e-7 + 1e-7 * abs(oracle)`, including asymmetric and tail +cases. This proves distributional identity only; it does not claim identical +SHASH fitting procedures. + +### Transformations + +For monotone response transforms, forward/inverse round trips, transformed +CDFs and quantiles, and log-density Jacobians are `exact`. Support violations +must fail closed. The gate is `1e-10` absolute plus relative tolerance unless +a fixture documents a tighter numerical limit. + +### Metrics + +Metrics are compared only after normalising sign, variance convention, +baseline, centile grid, batch aggregation, and finite-row filtering. RMSE and +EV can then be exact. SMSE requires conversion between population and sample +variance. PCNtoolkit's Spearman `Rho` is not `referent`'s Pearson `cor`. +With standardised outputs, PCNtoolkit 1.3.0 returns `logp` on the standardised +outcome scale even though predictions and centiles are mapped back; the +comparison fixture subtracts the corresponding log-scale Jacobian before any +proper-score contrast. +PCNtoolkit 1.3.0 uses bias-corrected skewness and excess kurtosis, whereas +`referent` reports unadjusted moment estimates. MACE is exact without batch +effects; with batch combinations, PCNtoolkit averages batch-level values +equally while `referent` currently pools rows. The latter cases are +`non_equivalent` unless explicitly recomputed to one definition. + +## Fitted concordance lane + +Pinned BLR fixtures cover intercept-only, linear, multiple/categorical, +heteroskedastic/nonlinear, transformed-outcome, and balanced-site designs. +Only scenarios with a genuinely matched likelihood/design are labelled +`matched_estimator`; the rest are `same_estimand` diagnostics. + +The provisional matched-estimator gates, frozen before routine use, are: + +| Quantity | Gate | +|---|---:| +| predictive median normalised RMSE | at most 0.01 | +| predictive standard-deviation normalised RMSE | at most 0.02 | +| deviation-score RMSE | at most 0.03 | +| 95th percentile of absolute deviation-score difference | at most 0.08 | +| absolute 90% interval-coverage difference | at most 0.01 | + +Exact semantic gates are not relaxed to these fitted-model gates. Any +platform-specific exception requires a stored result, a mechanistic reason, +and a narrower invariant that still detects scientific drift. + +## Superiority and non-inferiority lane + +Estimator-divergent scenarios use immutable train/validation/test splits. +They include nonlinear means, heteroskedasticity, skew/heavy tails, unequal +site sizes, site shifts/slopes, covariate shift, unseen sites, and sample-size +variation. Hyperparameters are selected without test outcomes. + +The primary contrast is paired test-row log score (`referent` minus +PCNtoolkit), aggregated within simulation replicate. A cluster-aware bootstrap +is used when observations share a site or subject. CRPS, median RMSE, interval +coverage, PIT calibration, and tail calibration are confirmatory. + +Results are classified as follows: + +- `superior`: the 95% interval for the primary contrast is above zero and all + prespecified calibration gates pass; +- `equivalent`: the interval lies within a prespecified practical-equivalence + margin and calibration gates pass; +- `tradeoff`: point accuracy or score improves but a calibration gate fails; +- `inferior`: the interval is below the non-inferiority margin or a critical + calibration gate fails; +- `inconclusive`: none of the above. + +The suite includes negative controls: identical predictions cannot generate +superiority; deliberately overconfident predictions may improve RMSE but must +fail calibration; and controlled perturbations must worsen the relevant +metric monotonically. We report scenarios individually as well as in aggregate +so a favourable average cannot hide a failure regime. + +## HBR, SHASH fitting, and site transport + +These are separate evidence lanes. PCNtoolkit HBR averages draw-specific +statistics, while `referent` total uncertainty constructs a predictive mixture +and derives its statistics. They are `non_equivalent`; convergence diagnostics, +Monte Carlo error, chain/draw counts, and draw seeds accompany HBR results. + +Site evidence distinguishes observed-site fit, small-sample adaptation, +leave-one-site-out transport, and genuinely unseen-site prediction. Balanced +and unequal site sizes are both required. Site-pooled performance never +substitutes for site-stratified calibration. + +## Reproducibility and CI + +The R test suite consumes checked-in fixtures and never installs Python or +uses the network. Fixtures live under +`tests/fixtures/pcntoolkit/v1.3.0/`, contain hashes and complete provenance, +and are regenerated only by the pinned script under `tools/pcntoolkit/`. + +- Pull requests: validate hashes/schema and run semantic plus small fitted + oracle tests offline. +- Weekly/manual: create a clean Python environment, regenerate PCNtoolkit + fixtures, fail on schema, environment, identity, or tightly bounded + numerical drift, and run the BLR scenario matrix. Byte differences alone + are diagnostic because BLAS and elementary-math libraries vary by platform. +- Release/manual: run stochastic HBR, SHASH-fitting, multi-site transport, and + the replicated superiority benchmark with convergence receipts. + +Machine-readable results are retained alongside a human-readable evidence +table. Documentation may say “matches PCNtoolkit” only for a named evidence +class and scenario, and may say “outperforms” only for a locked held-out +contrast that meets the superiority definition above. diff --git a/docs/evidence/pcntoolkit/v1.3.0/README.md b/docs/evidence/pcntoolkit/v1.3.0/README.md new file mode 100644 index 0000000..0351aa0 --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/README.md @@ -0,0 +1,31 @@ +# PCNtoolkit 1.3.0 evidence + +This directory is generated from the pinned fixture and contains the current +machine-readable comparison result. It supports scoped claims, not blanket +equivalence. + +- `evidence_summary.csv` is the claim-level ledger. +- `concordance.csv` contains every fitted comparison scenario and discrepancy. +- `superiority.csv` contains the paired held-out proper-score interval and + calibration guardrails for the locked skew/heavy-tail case. + +The exact and converted distribution lanes pass their strict semantic gates. +Five deliberately matched fitted scenarios pass all prediction, scale, Z, and +coverage margins. Estimator-divergent scenarios remain diagnostics. + +In the locked skew/heavy-tail case, Referent's prespecified SHASH model is +called superior only because the paired response-scale log-score interval is +above zero and the coverage, MACE, and tail-calibration non-inferiority gates +all pass. This result is scenario-specific; it is not a general claim that +Referent dominates PCNtoolkit. + +HBR and new-site transfer stay marked `non_equivalent`. Release evidence must +include convergence receipts and every site's calibration result. The release +job fails if any HBR stage misses its R-hat, effective-sample-size, or +divergence gate; a pooled site average cannot override a failing small site. + +Regenerate these tables with: + +```sh +Rscript tools/pcntoolkit/render_evidence.R +``` diff --git a/docs/evidence/pcntoolkit/v1.3.0/concordance.csv b/docs/evidence/pcntoolkit/v1.3.0/concordance.csv new file mode 100644 index 0000000..ff06ce1 --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/concordance.csv @@ -0,0 +1,10 @@ +"scenario","evidence_class","n_test","median_nrmse","predictive_sd_nrmse","z_rmse","z_abs_q95","coverage90_difference","quantile_nrmse","mean_log_score_difference","referent_coverage90","pcntoolkit_coverage90" +"null_linear","matched_estimator",501,0.000187299309966979,0.00142019983637594,0.00156198845381455,0.00289576783050172,0.00199600798403188,0.00160776714897841,-0.000138115535579087,0.896207584830339,0.898203592814371 +"linear_gaussian","matched_estimator",501,0.000993540303392054,0.0019739175118198,0.0055031346971287,0.0107792530841655,0.00199600798403199,0.00243163670734548,-0.000429820245359462,0.87624750499002,0.878243512974052 +"multiple_encoded","matched_estimator",501,0.00119931266465862,0.00339392769734878,0.00839151907768789,0.0167397175894268,0,0.00400003198143558,-0.00141291893382371,0.878243512974052,0.878243512974052 +"log_linear","matched_estimator",501,0.00171228731583741,0.00531133908082385,0.00952167217288595,0.0180881147550966,0.00199600798403188,0.00628396768705294,-0.000571229210590764,0.884231536926148,0.88622754491018 +"covariate_shift","matched_estimator",501,0.00532022368273882,0.0109607303942879,0.0137937719477833,0.0280540586549693,0.00598802395209574,0.013423180717565,0.00143529381937624,0.892215568862275,0.898203592814371 +"nonlinear_heteroskedastic","same_estimand",501,0.0199377544129308,0.0533935035292148,0.0575310233530505,0.116629685767348,0.00199600798403188,0.0635162794203262,0.00573175709768478,0.918163672654691,0.916167664670659 +"balanced_site","same_estimand",501,0.00304665710906854,0.0260542620256205,0.0393704640288216,0.0778825015223528,0.00399201596806387,0.029680653280768,0.0082568936932065,0.898203592814371,0.902195608782435 +"skew_heavy","same_estimand",501,0.280911566225308,0.0999186004705088,0.481154404922271,0.963244854099076,0.0379241516966068,0.384093121700618,0.342340996319524,0.890219560878243,0.92814371257485 +"unequal_site","same_estimand",501,0.00935232712304124,0.0286477969816694,0.0357631567331639,0.056375413364263,0.00798403193612773,0.0341160597362302,-0.0016468196252596,0.892215568862275,0.900199600798403 diff --git a/docs/evidence/pcntoolkit/v1.3.0/evidence_summary.csv b/docs/evidence/pcntoolkit/v1.3.0/evidence_summary.csv new file mode 100644 index 0000000..593ed1f --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/evidence_summary.csv @@ -0,0 +1,7 @@ +"lane","evidence_class","status","primary_quantity","value","threshold_or_interval","comparator_version" +"Gaussian distribution semantics","exact","pass","maximum absolute CDF error",1.11022302462516e-16,"<= 1e-10","1.3.0" +"SHASHb parameter conversion","parameter_converted","pass","maximum absolute log-density error",8.88178419700125e-16,"<= 1e-7","1.3.0" +"matched fitted estimators","matched_estimator","pass","matched scenarios passing all margins",5,"5 of 5","1.3.0" +"skew-heavy held-out utility","same_estimand","superior","mean paired log-score difference",0.342340996319524,"95% CI 0.260686 to 0.429356","1.3.0" +"HBR posterior aggregation","non_equivalent","release_evidence_required","draw aggregation semantics",NA,"R-hat <= 1.01; ESS bulk/tail >= 400; zero divergences","1.3.0" +"new-site transport","non_equivalent","release_evidence_required","site-stratified calibration",NA,"every site must pass; pooled metrics are insufficient","1.3.0" diff --git a/docs/evidence/pcntoolkit/v1.3.0/superiority.csv b/docs/evidence/pcntoolkit/v1.3.0/superiority.csv new file mode 100644 index 0000000..0bcd75b --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/superiority.csv @@ -0,0 +1,2 @@ +"scenario","n_test","clusters","log_score_difference","ci_lower","ci_upper","referent_coverage90","pcntoolkit_coverage90","referent_mace","pcntoolkit_mace","referent_tail05","pcntoolkit_tail05","calibration_pass","classification" +"skew_heavy",501,501,0.342340996319524,0.260685920184386,0.429355628585133,0.890219560878243,0.92814371257485,0.00918163672654691,0.056686626746507,0.0598802395209581,0.0598802395209581,TRUE,"superior" diff --git a/inst/pcntoolkit/benchmark_design.R b/inst/pcntoolkit/benchmark_design.R new file mode 100644 index 0000000..b611db2 --- /dev/null +++ b/inst/pcntoolkit/benchmark_design.R @@ -0,0 +1,78 @@ +# Preregistered synthetic truths for the PCNtoolkit utility benchmark. + +pcn_benchmark_registry <- function(root = NULL) { + installed <- system.file("pcntoolkit", "benchmark_scenarios.csv", package = "referent") + path <- if (is.null(root) && nzchar(installed)) { + installed + } else { + root <- if (is.null(root)) "." else root + source_path <- file.path(root, "tools", "pcntoolkit", "benchmark_scenarios.csv") + if (file.exists(source_path)) source_path else + file.path(root, "inst", "pcntoolkit", "benchmark_scenarios.csv") + } + utils::read.csv(path, stringsAsFactors = FALSE) +} + +pcn_simulate_benchmark <- function(scenario, seed, root = NULL) { + registry <- pcn_benchmark_registry(root) + design <- registry[registry$scenario == scenario, , drop = FALSE] + if (nrow(design) != 1L) stop("unknown benchmark scenario: ", scenario, call. = FALSE) + n_part <- unlist(design[c("n_train", "n_validation", "n_test")], use.names = FALSE) + n <- sum(n_part) + set.seed(seed) + split <- rep(c("train", "validation", "test"), n_part) + x <- if (scenario == "covariate_shift") { + c(stats::runif(n_part[[1L]], -2, 0.75), + stats::runif(n_part[[2L]] + n_part[[3L]], 0.25, 2)) + } else { + stats::runif(n, -2, 2) + } + site_levels <- paste0("site-", 1:5) + site <- if (scenario == "unequal_sites") { + sample(site_levels, n, replace = TRUE, prob = c(0.6, 0.2, 0.1, 0.07, 0.03)) + } else if (scenario == "unseen_site") { + c(sample(site_levels[1:4], n_part[[1L]] + n_part[[2L]], replace = TRUE), + sample(site_levels, n_part[[3L]], replace = TRUE, prob = c(rep(0.125, 4), 0.5))) + } else { + sample(site_levels, n, replace = TRUE) + } + site_index <- match(site, site_levels) + mu <- 0.3 + 0.9 * x + sigma <- rep(0.75, n) + epsilon <- rep(0, n) + delta <- rep(1, n) + + if (scenario %in% c("nonlinear_mean", "small_n", "large_n", "skew_heavy")) { + mu <- 0.4 + sin(1.35 * x) + 0.15 * x^2 + } + if (scenario %in% c("heteroskedastic", "small_n", "large_n", "skew_heavy")) { + sigma <- exp(-0.35 + 0.22 * x) + } + if (scenario %in% c("skew_heavy", "small_n", "large_n")) { + epsilon <- rep(0.9, n) + delta <- rep(0.7, n) + } + if (scenario %in% c("unequal_sites", "unseen_site")) { + mu <- mu + c(-0.8, -0.25, 0.2, 0.65, 1.1)[site_index] + sigma <- sigma * c(0.7, 0.85, 1, 1.15, 1.35)[site_index] + } + if (scenario == "site_slopes") { + mu <- mu + c(-0.4, -0.2, 0, 0.2, 0.4)[site_index] * x + } + latent <- stats::rnorm(n) + y <- mu + sigma * delta * sinh((asinh(latent) + epsilon) / delta) + data.frame( + scenario = scenario, + row_id = sprintf("%s-%s-%05d", scenario, split, ave(seq_len(n), split, FUN = seq_along)), + split = split, + cluster = if (design$site_structure == "none") seq_len(n) else site, + x = x, + site = factor(site, levels = site_levels), + y = y, + truth_mu = mu, + truth_sigma = sigma, + truth_epsilon = epsilon, + truth_delta = delta, + stringsAsFactors = FALSE + ) +} diff --git a/inst/pcntoolkit/benchmark_helpers.R b/inst/pcntoolkit/benchmark_helpers.R new file mode 100644 index 0000000..f0387a1 --- /dev/null +++ b/inst/pcntoolkit/benchmark_helpers.R @@ -0,0 +1,99 @@ +# Classification and uncertainty helpers for paired held-out comparisons. + +pcn_benchmark_mace <- function(u, grid = c(0.05, 0.25, 0.5, 0.75, 0.95)) { + mean(vapply(grid, function(q) abs(q - mean(u <= q)), numeric(1))) +} + +pcn_cluster_bootstrap <- function(delta, cluster, B = 1999L, seed = 20260823L) { + stopifnot(length(delta) == length(cluster), length(delta) > 1L, B >= 99L) + keep <- is.finite(delta) & !is.na(cluster) + parts <- split(delta[keep], as.character(cluster[keep])) + if (!length(parts)) stop("no finite paired score differences", call. = FALSE) + draws <- withr::with_seed(seed, replicate(B, { + selected <- sample.int(length(parts), length(parts), replace = TRUE) + mean(unlist(parts[selected], use.names = FALSE)) + })) + c( + estimate = mean(unlist(parts, use.names = FALSE)), + lower = unname(stats::quantile(draws, 0.025, names = FALSE)), + upper = unname(stats::quantile(draws, 0.975, names = FALSE)), + clusters = length(parts) + ) +} + +pcn_calibration_summary <- function(prediction) { + required <- c("observed", "q05", "q95", "centile") + if (!all(required %in% names(prediction))) { + stop("prediction is missing calibration columns", call. = FALSE) + } + u <- prediction$centile + tail_prob <- 2 * pmin(u, 1 - u) + c( + coverage90 = mean(prediction$observed >= prediction$q05 & prediction$observed <= prediction$q95), + mace = pcn_benchmark_mace(u), + tail05 = mean(tail_prob <= 0.05) + ) +} + +pcn_calibration_gate <- function(referent, comparator, + coverage_regret = 0.01, + mace_regret = 0.01, + tail_regret = 0.01) { + ref <- pcn_calibration_summary(referent) + pcn <- pcn_calibration_summary(comparator) + regret <- c( + coverage90 = abs(ref[["coverage90"]] - 0.90) - abs(pcn[["coverage90"]] - 0.90), + mace = ref[["mace"]] - pcn[["mace"]], + tail05 = abs(ref[["tail05"]] - 0.05) - abs(pcn[["tail05"]] - 0.05) + ) + limits <- c(coverage90 = coverage_regret, mace = mace_regret, tail05 = tail_regret) + list(pass = all(regret <= limits), referent = ref, comparator = pcn, + regret = regret, limits = limits) +} + +pcn_classify_benchmark <- function(estimate, lower, upper, calibration_pass, + conformance_pass = FALSE, + equivalence_margin = 0.02, + noninferiority_margin = 0.02) { + if (isTRUE(conformance_pass)) return("conformant") + if (!isTRUE(calibration_pass)) { + return(if (estimate > 0) "tradeoff" else "inferior") + } + if (lower > 0) return("superior") + if (upper < -noninferiority_margin) return("inferior") + if (lower >= -equivalence_margin && upper <= equivalence_margin) return("equivalent") + if (lower >= -noninferiority_margin) return("non_inferior") + "inconclusive" +} + +pcn_compare_predictions <- function(referent, comparator, cluster, + scenario = NA_character_, B = 1999L, + seed = 20260823L) { + if (!identical(referent$row_id, comparator$row_id)) { + stop("paired prediction row identities differ", call. = FALSE) + } + interval <- pcn_cluster_bootstrap( + referent$log_density - comparator$log_density, cluster, B = B, seed = seed + ) + calibration <- pcn_calibration_gate(referent, comparator) + data.frame( + scenario = scenario, + n_test = nrow(referent), + clusters = unname(interval[["clusters"]]), + log_score_difference = unname(interval[["estimate"]]), + ci_lower = unname(interval[["lower"]]), + ci_upper = unname(interval[["upper"]]), + referent_coverage90 = calibration$referent[["coverage90"]], + pcntoolkit_coverage90 = calibration$comparator[["coverage90"]], + referent_mace = calibration$referent[["mace"]], + pcntoolkit_mace = calibration$comparator[["mace"]], + referent_tail05 = calibration$referent[["tail05"]], + pcntoolkit_tail05 = calibration$comparator[["tail05"]], + calibration_pass = calibration$pass, + classification = pcn_classify_benchmark( + interval[["estimate"]], interval[["lower"]], interval[["upper"]], + calibration$pass + ), + stringsAsFactors = FALSE + ) +} diff --git a/inst/pcntoolkit/benchmark_scenarios.csv b/inst/pcntoolkit/benchmark_scenarios.csv new file mode 100644 index 0000000..fb71cae --- /dev/null +++ b/inst/pcntoolkit/benchmark_scenarios.csv @@ -0,0 +1,11 @@ +scenario,evidence_class,truth,site_structure,covariate_distribution,n_train,n_validation,n_test,selection_rule,execution_tier +linear_gaussian,matched_estimator,linear Gaussian,none,same,500,100,500,prespecified linear Gaussian,weekly +nonlinear_mean,same_estimand,smooth nonlinear Gaussian,none,same,500,100,500,validation log score among prespecified smooth bases,release +heteroskedastic,same_estimand,linear mean and smooth log scale,none,same,500,100,500,validation log score among scale formulas,release +skew_heavy,same_estimand,smooth SHASH,none,same,500,100,500,prespecified SHASH after family validation,weekly +unequal_sites,same_estimand,linear Gaussian with site location and scale,unequal,same,800,160,800,prespecified site model,release +site_slopes,same_estimand,site-specific age slopes,balanced,same,800,160,800,validation log score among site slope specifications,release +covariate_shift,matched_estimator,linear Gaussian,none,shifted test support,500,100,500,prespecified linear Gaussian,weekly +unseen_site,non_equivalent,new site location scale and slope,one test-only site,same,800,160,800,prespecified transport policy,release +small_n,same_estimand,smooth heteroskedastic SHASH,balanced,same,120,60,500,validation log score with complexity cap,release +large_n,same_estimand,smooth heteroskedastic SHASH,balanced,same,3000,500,1000,validation log score among prespecified families,release diff --git a/inst/pcntoolkit/compare_results.R b/inst/pcntoolkit/compare_results.R new file mode 100644 index 0000000..27f34cf --- /dev/null +++ b/inst/pcntoolkit/compare_results.R @@ -0,0 +1,74 @@ +# Utilities for consuming the checked-in PCNtoolkit comparison fixture. +# This file is deliberately outside R/ because it is validation infrastructure, +# not part of referent's user-facing API. + +pcn_fixture_root <- function(version = "1.3.0", package_root = ".") { + file.path(package_root, "tests", "fixtures", "pcntoolkit", paste0("v", version)) +} + +pcn_read_manifest <- function(root) { + if (!requireNamespace("jsonlite", quietly = TRUE)) { + stop("jsonlite is required to validate PCNtoolkit fixtures", call. = FALSE) + } + path <- file.path(root, "manifest.json") + if (!file.exists(path)) stop("missing PCNtoolkit manifest: ", path, call. = FALSE) + jsonlite::fromJSON(path, simplifyVector = FALSE) +} + +pcn_validate_fixture <- function(root, expected_version = "1.3.0") { + manifest <- pcn_read_manifest(root) + if (!identical(manifest$schema_version, "1.0.0")) { + stop("unsupported PCNtoolkit fixture schema: ", manifest$schema_version, call. = FALSE) + } + if (!identical(manifest$comparator$package, "pcntoolkit") || + !identical(manifest$comparator$version, expected_version)) { + stop("fixture comparator/version does not match the declared target", call. = FALSE) + } + required <- c( + "semantic_gaussian.csv", "semantic_shashb.csv", "semantic_metrics.csv", + "fitted_inputs.csv", "fitted_predictions.csv" + ) + if (!setequal(names(manifest$files), required)) { + stop("fixture manifest has an unexpected file set", call. = FALSE) + } + for (filename in required) { + receipt <- manifest$files[[filename]] + path <- file.path(root, filename) + if (!file.exists(path)) stop("missing fixture file: ", filename, call. = FALSE) + actual_md5 <- unname(tools::md5sum(path)) + if (!identical(actual_md5, receipt$md5)) { + stop("fixture hash mismatch: ", filename, call. = FALSE) + } + dat <- utils::read.csv(path, check.names = FALSE, stringsAsFactors = FALSE) + if (!identical(nrow(dat), as.integer(receipt$rows))) { + stop("fixture row-count mismatch: ", filename, call. = FALSE) + } + if (!identical(names(dat), unlist(receipt$columns, use.names = FALSE))) { + stop("fixture column mismatch: ", filename, call. = FALSE) + } + } + invisible(manifest) +} + +pcn_read_fixture <- function(root, filename) { + pcn_validate_fixture(root) + utils::read.csv(file.path(root, filename), check.names = FALSE, + stringsAsFactors = FALSE) +} + +pcn_concordance <- function(referent, comparator, outcome_scale) { + stopifnot(nrow(referent) == nrow(comparator), outcome_scale > 0) + delta_median <- referent$median - comparator$median + delta_sd <- referent$predictive_sd - comparator$predictive_sd + delta_z <- referent$z - comparator$z + c( + median_nrmse = sqrt(mean(delta_median^2)) / outcome_scale, + predictive_sd_nrmse = sqrt(mean(delta_sd^2)) / outcome_scale, + z_rmse = sqrt(mean(delta_z^2)), + z_abs_q95 = unname(stats::quantile(abs(delta_z), 0.95, names = FALSE)), + coverage90_difference = abs( + mean(referent$observed >= referent$q05 & referent$observed <= referent$q95) - + mean(comparator$observed >= comparator$q05 & comparator$observed <= comparator$q95) + ) + ) +} diff --git a/inst/pcntoolkit/run_concordance.R b/inst/pcntoolkit/run_concordance.R new file mode 100644 index 0000000..39b8257 --- /dev/null +++ b/inst/pcntoolkit/run_concordance.R @@ -0,0 +1,151 @@ +# Run prediction-level concordance against the checked-in PCNtoolkit fixture. + +pcn_scenario_spec <- function(scenario) { + switch( + scenario, + null_linear = referent::ref_spec(referent::ref_gaussian(), location = ~ x1, scale = ~ 1), + linear_gaussian = referent::ref_spec(referent::ref_gaussian(), location = ~ x1, scale = ~ 1), + multiple_encoded = referent::ref_spec(referent::ref_gaussian(), location = ~ x1 + x2 + sex_M, scale = ~ 1), + nonlinear_heteroskedastic = referent::ref_spec( + referent::ref_gaussian(), location = ~ s(x1, bs = "cr", k = 6), + scale = ~ s(x1, bs = "cr", k = 5) + ), + log_linear = referent::ref_spec( + referent::ref_gaussian(), location = ~ x1, scale = ~ 1, transform = "log" + ), + balanced_site = referent::ref_spec( + referent::ref_gaussian(), location = ~ x1 + s(site, bs = "re"), + scale = ~ s(site, bs = "re") + ), + skew_heavy = referent::ref_spec( + referent::ref_shash(), location = ~ s(x1, bs = "cr", k = 6), + scale = ~ s(x1, bs = "cr", k = 5), skew = ~ 1, tail = ~ 1 + ), + unequal_site = referent::ref_spec( + referent::ref_gaussian(), location = ~ x1 + s(site, bs = "re"), + scale = ~ s(site, bs = "re") + ), + covariate_shift = referent::ref_spec( + referent::ref_gaussian(), location = ~ x1, scale = ~ 1 + ), + stop("unknown PCNtoolkit scenario: ", scenario, call. = FALSE) + ) +} + +pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L) { + dat <- inputs[inputs$scenario == scenario, , drop = FALSE] + dat$site <- factor(dat$site) + train <- dat[dat$split == "train", , drop = FALSE] + test <- dat[dat$split == "test", , drop = FALSE] + fit <- referent::ref_fit(pcn_scenario_spec(scenario), train, outcomes = "y") + score <- stats::predict( + fit, newdata = test, uncertainty = "total", n_draw = n_draw, + allow_extrapolation = TRUE + ) + dist <- stats::predict( + fit, newdata = test, type = "distribution", uncertainty = "total", + n_draw = n_draw, allow_extrapolation = TRUE + )$y + probs <- c(0.05, 0.25, 0.5, 0.75, 0.95) + quantiles <- vapply( + probs, function(p) referent:::dist_quantile(dist, p), numeric(nrow(test)) + ) + colnames(quantiles) <- paste0("q", c("05", "25", "50", "75", "95")) + data.frame( + scenario = scenario, + row_id = test$row_id, + observed = test$y, + median = score$median, + predictive_sd = (quantiles[, "q95"] - quantiles[, "q05"]) / + (2 * stats::qnorm(0.95)), + centile = score$centile, + z = score$z, + log_density = score$log_density, + quantiles, + check.names = FALSE + ) +} + +pcn_concordance_registry <- function() { + data.frame( + scenario = c( + "null_linear", "linear_gaussian", "multiple_encoded", "log_linear", + "covariate_shift", "nonlinear_heteroskedastic", "balanced_site", + "skew_heavy", "unequal_site" + ), + evidence_class = c( + rep("matched_estimator", 5), rep("same_estimand", 4) + ), + reason = c( + rep("Gaussian BLR and linear mgcv model on the same design", 3), + "Gaussian linear model on log(y), scored back on the response scale", + "Gaussian linear model under a locked covariate-shifted test distribution", + "B-spline and penalised smooth plus variance estimators differ", + "PCN fixed batch effects and Referent penalised site effects differ", + "Gaussian BLR is compared with a selected SHASH distributional model", + "Fixed batch effects and penalised site effects differ under unequal site sizes" + ), + stringsAsFactors = FALSE + ) +} + +pcn_run_concordance <- function(root, n_draw = 2000L) { + inputs <- utils::read.csv(file.path(root, "fitted_inputs.csv"), stringsAsFactors = FALSE) + comparator <- utils::read.csv( + file.path(root, "fitted_predictions.csv"), stringsAsFactors = FALSE + ) + registry <- pcn_concordance_registry() + rows <- lapply(registry$scenario, function(scenario) { + referent <- pcn_referent_predictions(inputs, scenario, n_draw = n_draw) + pcn <- comparator[comparator$scenario == scenario, , drop = FALSE] + pcn <- pcn[match(referent$row_id, pcn$row_id), , drop = FALSE] + if (anyNA(pcn$row_id) || !identical(referent$row_id, pcn$row_id)) { + stop("row identity mismatch in scenario ", scenario, call. = FALSE) + } + core <- pcn_concordance(referent, pcn, stats::sd(referent$observed)) + q_delta <- as.matrix(referent[paste0("q", c("05", "25", "50", "75", "95"))]) - + as.matrix(pcn[paste0("q", c("05", "25", "50", "75", "95"))]) + data.frame( + scenario = scenario, + evidence_class = registry$evidence_class[registry$scenario == scenario], + n_test = nrow(referent), + as.list(core), + quantile_nrmse = sqrt(mean(q_delta^2)) / stats::sd(referent$observed), + mean_log_score_difference = mean(referent$log_density - pcn$log_density), + referent_coverage90 = mean( + referent$observed >= referent$q05 & referent$observed <= referent$q95 + ), + pcntoolkit_coverage90 = mean(pcn$observed >= pcn$q05 & pcn$observed <= pcn$q95), + stringsAsFactors = FALSE + ) + }) + do.call(rbind, rows) +} + +pcn_concordance_pass <- function(results) { + matched <- results$evidence_class == "matched_estimator" + checks <- cbind( + median_nrmse = results$median_nrmse <= 0.01, + predictive_sd_nrmse = results$predictive_sd_nrmse <= 0.02, + z_rmse = results$z_rmse <= 0.03, + z_abs_q95 = results$z_abs_q95 <= 0.08, + coverage90_difference = results$coverage90_difference <= 0.01 + ) + rowSums(!checks[matched, , drop = FALSE]) == 0L +} + +if (sys.nframe() == 0L) { + source("tools/pcntoolkit/compare_results.R") + root <- pcn_fixture_root() + pcn_validate_fixture(root) + result <- pcn_run_concordance(root) + print(result, row.names = FALSE) + args <- commandArgs(trailingOnly = TRUE) + if (length(args) == 2L && identical(args[[1L]], "--output")) { + dir.create(dirname(args[[2L]]), recursive = TRUE, showWarnings = FALSE) + utils::write.csv(result, args[[2L]], row.names = FALSE) + } else if (length(args)) { + stop("usage: run_concordance.R [--output FILE]") + } + if (!all(pcn_concordance_pass(result))) quit(status = 1L) +} diff --git a/inst/pcntoolkit/run_hbr_site_evidence.py b/inst/pcntoolkit/run_hbr_site_evidence.py new file mode 100644 index 0000000..a826c50 --- /dev/null +++ b/inst/pcntoolkit/run_hbr_site_evidence.py @@ -0,0 +1,290 @@ +# /// script +# requires-python = "==3.12.11" +# dependencies = [ +# "arviz==0.23.4", +# "numpy==2.4.6", +# "pandas==3.0.5", +# "pcntoolkit==1.3.0", +# "pymc==5.28.5", +# "scipy==1.18.1", +# "xarray==2026.7.0", +# ] +# /// +"""Run stochastic HBR and new-site transfer evidence. + +This is a release/manual runner, not an ordinary package test. It emits both +PCNtoolkit's draw-averaged outputs and proper posterior-predictive mixture +quantities, convergence receipts, and site-stratified metrics. +""" + +from __future__ import annotations + +import argparse +import importlib.metadata +import json +from pathlib import Path + +import arviz as az +import numpy as np +import pandas as pd +import pymc as pm +from scipy import optimize, special, stats + +from pcntoolkit import NormativeModel +from pcntoolkit.dataio.norm_data import NormData +from pcntoolkit.regression_model.hbr import HBR + + +def simulate(seed: int) -> pd.DataFrame: + rng = np.random.default_rng(seed) + rows: list[dict[str, object]] = [] + shifts = {"site-1": -0.7, "site-2": -0.2, "site-3": 0.25, "site-4": 0.7, "site-5": 1.1} + scales = {"site-1": 0.65, "site-2": 0.75, "site-3": 0.85, "site-4": 0.95, "site-5": 1.1} + for site in shifts: + n = 80 if site != "site-5" else 100 + x = rng.uniform(-2, 2, n) + y = 0.5 + 0.8 * x + shifts[site] + scales[site] * rng.normal(size=n) + if site == "site-5": + split = np.repeat(["adapt", "transport_test"], [25, 75]) + else: + split = np.repeat(["reference_train", "observed_site_test"], [50, 30]) + for i in range(n): + rows.append( + { + "row_id": f"{site}-{split[i]}-{i:04d}", + "site": site, + "split": split[i], + "x": x[i], + "y": y[i], + } + ) + return pd.DataFrame(rows) + + +def norm_data(name: str, frame: pd.DataFrame) -> NormData: + return NormData.from_dataframe( + name, + frame.copy(), + covariates=["x"], + batch_effects=["site"], + response_vars=["y"], + subject_ids="row_id", + ) + + +def convergence(model: NormativeModel, stage: str) -> tuple[pd.DataFrame, dict[str, object]]: + idata = model["y"].idata + # The fitted idata does not contain the temporary per-subject variables + # created during prediction, so the full posterior is the convergence set. + summary = az.summary(idata, fmt="wide") + summary.insert(0, "parameter", summary.index.astype(str)) + summary.insert(0, "stage", stage) + divergences = int(idata.sample_stats["diverging"].sum().item()) + max_rhat = float(np.nanmax(summary["r_hat"].to_numpy())) + min_bulk = float(np.nanmin(summary["ess_bulk"].to_numpy())) + min_tail = float(np.nanmin(summary["ess_tail"].to_numpy())) + receipt = { + "stage": stage, + "divergences": divergences, + "max_rhat": max_rhat, + "min_ess_bulk": min_bulk, + "min_ess_tail": min_tail, + "pass": bool(divergences == 0 and max_rhat <= 1.01 and min_bulk >= 400 and min_tail >= 400), + } + return summary.reset_index(drop=True), receipt + + +def posterior_normal_draws(model: NormativeModel, frame: pd.DataFrame) -> tuple[np.ndarray, np.ndarray]: + data = norm_data("posterior-mixture", frame) + model.preprocess(data) + response_data = data.sel({"response_vars": "y"}) + X, be, _, Y, _ = model.extract_data(response_data) + hbr = model["y"] + pymc_model = hbr.likelihood.create_model_with_data(X, be, hbr.be_maps, Y) + params = hbr.likelihood.compile_params(pymc_model, X, be, hbr.be_maps, Y) + var_names = [f"{name}_per_subject" for name in params] + with pymc_model: + for name, (value, dims) in params.items(): + pm.Deterministic(f"{name}_per_subject", value, dims=dims) + posterior = pm.sample_posterior_predictive( + hbr.idata, extend_inferencedata=False, var_names=var_names, progressbar=False + ) + extracted = az.extract(posterior, "posterior_predictive", var_names=var_names) + arrays = { + name: hbr.extract_and_reshape(extracted, len(frame), f"{name}_per_subject").values + for name in params + } + return arrays["mu"], arrays["sigma"] + + +def mixture_quantile(mu: np.ndarray, sigma: np.ndarray, p: float) -> np.ndarray: + out = np.empty(mu.shape[0]) + for i in range(mu.shape[0]): + lo = np.min(mu[i] - 12 * sigma[i]) + hi = np.max(mu[i] + 12 * sigma[i]) + out[i] = optimize.brentq( + lambda q: np.mean(stats.norm.cdf(q, mu[i], sigma[i])) - p, + lo, + hi, + ) + return out + + +def predict_with_mixture( + model: NormativeModel, frame: pd.DataFrame, lane: str, outcome_mean: float, outcome_sd: float +) -> pd.DataFrame: + predicted = model.predict(norm_data(f"{lane}-test", frame)) + reported_z = predicted["Z"].sel(response_vars="y").values + reported_q05 = predicted["centiles"].sel(response_vars="y", centile=0.05).values + reported_q50 = predicted["centiles"].sel(response_vars="y", centile=0.5).values + reported_q95 = predicted["centiles"].sel(response_vars="y", centile=0.95).values + reported_logp = predicted["logp"].sel(response_vars="y").values - np.log(outcome_sd) + + mu, sigma = posterior_normal_draws(model, frame) + y_scaled = (frame["y"].to_numpy() - outcome_mean) / outcome_sd + component_z = (y_scaled[:, None] - mu) / sigma + mixture_cdf = np.mean(stats.norm.cdf(component_z), axis=1) + mixture_logp = special.logsumexp(stats.norm.logpdf(component_z) - np.log(sigma), axis=1) - np.log( + sigma.shape[1] + ) - np.log(outcome_sd) + mixture_q05 = outcome_mean + outcome_sd * mixture_quantile(mu, sigma, 0.05) + mixture_q50 = outcome_mean + outcome_sd * mixture_quantile(mu, sigma, 0.5) + mixture_q95 = outcome_mean + outcome_sd * mixture_quantile(mu, sigma, 0.95) + return pd.DataFrame( + { + "lane": lane, + "row_id": frame["row_id"].to_numpy(), + "site": frame["site"].to_numpy(), + "observed": frame["y"].to_numpy(), + "reported_mean_draw_z": reported_z, + "reported_mean_draw_log_density": reported_logp, + "reported_mean_draw_q05": reported_q05, + "reported_mean_draw_q50": reported_q50, + "reported_mean_draw_q95": reported_q95, + "mixture_centile": mixture_cdf, + "mixture_z": stats.norm.ppf(mixture_cdf), + "mixture_log_density": mixture_logp, + "mixture_q05": mixture_q05, + "mixture_q50": mixture_q50, + "mixture_q95": mixture_q95, + } + ) + + +def site_summary(predictions: pd.DataFrame) -> pd.DataFrame: + rows: list[dict[str, object]] = [] + for (lane, site), group in predictions.groupby(["lane", "site"], sort=True): + u = group["mixture_centile"].to_numpy() + grid = np.array([0.05, 0.25, 0.5, 0.75, 0.95]) + rows.append( + { + "method": "pcntoolkit_hbr_mixture", + "lane": lane, + "site": site, + "n": len(group), + "mean_log_score": group["mixture_log_density"].mean(), + "median_rmse": np.sqrt(np.mean((group["observed"] - group["mixture_q50"]) ** 2)), + "coverage90": np.mean( + (group["observed"] >= group["mixture_q05"]) + & (group["observed"] <= group["mixture_q95"]) + ), + "mace": np.mean([abs(q - np.mean(u <= q)) for q in grid]), + "mean_z": group["mixture_z"].mean(), + "var_z": group["mixture_z"].var(ddof=1), + } + ) + return pd.DataFrame(rows) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--draws", type=int, default=1500) + parser.add_argument("--tune", type=int, default=500) + parser.add_argument("--chains", type=int, default=4) + parser.add_argument("--cores", type=int, default=4) + parser.add_argument("--nuts-sampler", default="nutpie") + parser.add_argument("--seed", type=int, default=20260823) + parser.add_argument("--progressbar", action="store_true") + args = parser.parse_args() + args.output.mkdir(parents=True, exist_ok=True) + + data = simulate(args.seed) + reference_train = data[data["split"] == "reference_train"].copy() + observed_test = data[data["split"] == "observed_site_test"].copy() + adaptation = data[data["split"] == "adapt"].copy() + transport_test = data[data["split"] == "transport_test"].copy() + outcome_mean = float(reference_train["y"].mean()) + outcome_sd = float(reference_train["y"].std(ddof=0)) + + hbr = HBR( + draws=args.draws, + tune=args.tune, + chains=args.chains, + cores=args.cores, + nuts_sampler=args.nuts_sampler, + progressbar=args.progressbar, + ) + base = NormativeModel( + hbr, savemodel=False, evaluate_model=False, saveresults=False, saveplots=False, + inscaler="standardize", outscaler="standardize", name="hbr-site-evidence" + ) + base.fit(norm_data("reference-train", reference_train)) + base_convergence, base_receipt = convergence(base, "base") + observed_predictions = predict_with_mixture( + base, observed_test, "observed_site", outcome_mean, outcome_sd + ) + + transferred = base.transfer( + norm_data("site-5-adaptation", adaptation), + save_dir=str(args.output / "transfer_model"), + draws=args.draws, + tune=args.tune, + chains=args.chains, + cores=args.cores, + nuts_sampler=args.nuts_sampler, + progressbar=args.progressbar, + ) + transfer_convergence, transfer_receipt = convergence(transferred, "transfer") + transport_predictions = predict_with_mixture( + transferred, transport_test, "transferred_site", outcome_mean, outcome_sd + ) + predictions = pd.concat([observed_predictions, transport_predictions], ignore_index=True) + convergence_table = pd.concat([base_convergence, transfer_convergence], ignore_index=True) + + data.to_csv(args.output / "site_data.csv", index=False, float_format="%.17g") + predictions.to_csv(args.output / "pcntoolkit_predictions.csv", index=False, float_format="%.17g") + site_summary(predictions).to_csv(args.output / "pcntoolkit_site_summary.csv", index=False, float_format="%.17g") + convergence_table.to_csv(args.output / "hbr_convergence.csv", index=False, float_format="%.17g") + receipt = { + "comparator": "pcntoolkit", + "version": importlib.metadata.version("pcntoolkit"), + "draws": args.draws, + "tune": args.tune, + "chains": args.chains, + "cores": args.cores, + "nuts_sampler": args.nuts_sampler, + "requested_seed": args.seed, + "hbr_public_random_seed_argument": False, + "aggregation": { + "reported_z": "mean of draw-specific z", + "reported_quantile": "mean of draw-specific quantiles", + "reported_logp": "mean of draw-specific log densities", + "mixture_outputs": "CDF and density averaged over posterior draws, then transformed", + }, + "convergence_thresholds": { + "max_rhat": 1.01, + "min_ess_bulk": 400, + "min_ess_tail": 400, + "divergences": 0, + }, + "stages": [base_receipt, transfer_receipt], + "all_stages_pass": bool(base_receipt["pass"] and transfer_receipt["pass"]), + } + (args.output / "receipt.json").write_text( + json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + + +if __name__ == "__main__": + main() diff --git a/inst/pcntoolkit/site_evidence.R b/inst/pcntoolkit/site_evidence.R new file mode 100644 index 0000000..27eaafb --- /dev/null +++ b/inst/pcntoolkit/site_evidence.R @@ -0,0 +1,29 @@ +pcn_site_summary <- function(prediction, method, lane) { + required <- c("site", "observed", "centile", "log_density", "q05", "q50", "q95") + if (!all(required %in% names(prediction))) { + stop("site prediction table is missing required columns", call. = FALSE) + } + parts <- split(seq_len(nrow(prediction)), prediction$site) + rows <- lapply(names(parts), function(site) { + d <- prediction[parts[[site]], , drop = FALSE] + z <- stats::qnorm(d$centile) + data.frame( + method = method, lane = lane, site = site, n = nrow(d), + mean_log_score = mean(d$log_density), + median_rmse = sqrt(mean((d$observed - d$q50)^2)), + coverage90 = mean(d$observed >= d$q05 & d$observed <= d$q95), + coverage90_se = sqrt(0.9 * 0.1 / nrow(d)), + mace = pcn_benchmark_mace(d$centile), + mean_z = mean(z), var_z = stats::var(z), + stringsAsFactors = FALSE + ) + }) + do.call(rbind, rows) +} + +pcn_site_gate <- function(summary, coverage_floor = 0.80, mace_ceiling = 0.08, + mean_z_ceiling = 0.35) { + per_site <- summary$coverage90 >= coverage_floor & + summary$mace <= mace_ceiling & abs(summary$mean_z) <= mean_z_ceiling + data.frame(summary, pass = per_site, stringsAsFactors = FALSE) +} diff --git a/tests/fixtures/pcntoolkit/v1.3.0/fitted_inputs.csv b/tests/fixtures/pcntoolkit/v1.3.0/fitted_inputs.csv new file mode 100644 index 0000000..ccec1c9 --- /dev/null +++ b/tests/fixtures/pcntoolkit/v1.3.0/fitted_inputs.csv @@ -0,0 +1,9910 @@ +scenario,row_id,split,x1,x2,sex_M,site,y +null_linear,null_linear-train-0000,train,1.5526361243951481,-0.97792800906939792,1,site-1,2.2034222970660213 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+covariate_shift,covariate_shift-test-0494,1.2996580598974095,1.3220597703593122,0.7285004877049529,0.48773428247043976,-0.030750439896720896,-0.60264434409908074,0.12378310092190359,0.83069365838947473,1.3220597703593122,1.8134258823291498,2.5203364397967198 +covariate_shift,covariate_shift-test-0495,1.4375751937755716,0.63080609398381882,0.72582605271771072,0.86682738854575403,1.1115185198588107,-1.2162303535264305,-0.56307152136477834,0.14124386100044217,0.63080609398381882,1.1203683269671956,1.8246837093324153 +covariate_shift,covariate_shift-test-0496,1.7677151156058184,1.6316139475242721,0.73000799763513791,0.57394931862436926,0.18643791372484389,-0.62161829180656825,0.43085764491053358,1.1392310355582063,1.6316139475242721,2.123996859490338,2.8323702501380099 +covariate_shift,covariate_shift-test-0497,1.0404733729916715,0.21374013155096849,0.72467821090561102,0.8730292605166281,1.1408280654768914,-1.2476553010110927,-0.97824945202983005,-0.27504789389530027,0.21374013155096849,0.70252815699723759,1.4057297151317663 +covariate_shift,covariate_shift-test-0498,1.0351909901674259,1.7588185341945088,0.7306826496783535,0.16100288116718892,-0.9903445009206443,-1.0955536031947577,0.55695252772055703,1.2659805763403451,1.7588185341945088,2.2516564920486726,2.9606845406684599 +covariate_shift,covariate_shift-test-0499,0.53459525082758119,0.8102216210392309,0.72642793045493548,0.35218540197253378,-0.37942699978376998,-0.67130495536902912,-0.38464599508842223,0.32025342769122467,0.81022162103923046,1.3001898143872364,2.0050892371668825 +covariate_shift,covariate_shift-test-0500,0.9871807556614639,0.33968241359707341,0.72498772404451395,0.81410254829849049,0.8931162840277751,-0.99616632497925273,-0.85281627379283886,-0.14931437528893665,0.33968241359707341,0.82867920248308347,1.5321811009869846 diff --git a/tests/fixtures/pcntoolkit/v1.3.0/manifest.json b/tests/fixtures/pcntoolkit/v1.3.0/manifest.json new file mode 100644 index 0000000..cce24ee --- /dev/null +++ b/tests/fixtures/pcntoolkit/v1.3.0/manifest.json @@ -0,0 +1,135 @@ +{ + "centiles": [ + 0.05, + 0.25, + 0.5, + 0.75, + 0.95 + ], + "comparator": { + "package": "pcntoolkit", + "version": "1.3.0" + }, + "dependencies": { + "numpy": "2.4.6", + "pandas": "3.0.5", + "pcntoolkit": "1.3.0", + "scipy": "1.18.1" + }, + "files": { + "fitted_inputs.csv": { + "columns": [ + "scenario", + "row_id", + "split", + "x1", + "x2", + "sex_M", + "site", + "y" + ], + "md5": "f50b7a12cf965c9738de0d77a1e4cb71", + "rows": 9909 + }, + "fitted_predictions.csv": { + "columns": [ + "scenario", + "row_id", + "observed", + "median", + "predictive_sd", + "centile", + "z", + "log_density", + "q05", + "q25", + "q50", + "q75", + "q95" + ], + "md5": "a2fc4c8de1db4f5f908f12b5e2974075", + "rows": 4509 + }, + "semantic_gaussian.csv": { + "columns": [ + "case", + "mu", + "sigma", + "y", + "p", + "density", + "log_density", + "cdf", + "log_cdf", + "upper_tail", + "log_upper_tail", + "quantile", + "z" + ], + "md5": "c57ef7d0f38803786fa2c0d6b0d5eac6", + "rows": 5 + }, + "semantic_metrics.csv": { + "columns": [ + "metric", + "value" + ], + "md5": "fe62594af703abe7bb7a78aea7737666", + "rows": 14 + }, + "semantic_shashb.csv": { + "columns": [ + "case", + "mu_b", + "sigma_b", + "epsilon", + "delta", + "y", + "p", + "moment_1", + "moment_2", + "mu_r", + "sigma_r", + "density", + "log_density", + "cdf", + "log_cdf", + "upper_tail", + "log_upper_tail", + "quantile", + "z" + ], + "md5": "71b91f9b31799bef1215c25bc29b428e", + "rows": 5 + } + }, + "generator": "tools/pcntoolkit/generate_fixtures.py", + "normalisations": { + "log_density": "PCNtoolkit logp minus log(population SD of the fitted outcome); log-transformed scenarios also minus log(y)", + "predictive_sd": "(q95 - q05) / (2 * qnorm(0.95)) on the reported outcome scale" + }, + "python": "3.12.11", + "scenario_classes": { + "balanced_site": "same_estimand", + "covariate_shift": "matched_estimator", + "linear_gaussian": "matched_estimator", + "log_linear": "matched_estimator", + "multiple_encoded": "matched_estimator", + "nonlinear_heteroskedastic": "same_estimand", + "null_linear": "matched_estimator", + "skew_heavy": "same_estimand", + "unequal_site": "same_estimand" + }, + "schema_version": "1.0.0", + "seeds": { + "balanced_site": 1106, + "covariate_shift": 1109, + "linear_gaussian": 1102, + "log_linear": 1105, + "multiple_encoded": 1103, + "nonlinear_heteroskedastic": 1104, + "null_linear": 1101, + "skew_heavy": 1107, + "unequal_site": 1108 + } +} diff --git a/tests/fixtures/pcntoolkit/v1.3.0/semantic_gaussian.csv b/tests/fixtures/pcntoolkit/v1.3.0/semantic_gaussian.csv new file mode 100644 index 0000000..7f7b0c9 --- /dev/null +++ b/tests/fixtures/pcntoolkit/v1.3.0/semantic_gaussian.csv @@ -0,0 +1,6 @@ +case,mu,sigma,y,p,density,log_density,cdf,log_cdf,upper_tail,log_upper_tail,quantile,z +standard,0,1,-0.34999999999999998,0.72999999999999998,0.37524034691693792,-0.9801885332046727,0.3631693488243809,-1.0128860278195608,0.6368306511756191,-0.45125151248278306,0.61281299101662723,-0.34999999999999998 +shifted,3.25,0.69999999999999996,2.1000000000000001,0.01,0.14782097213271839,-1.9117533851843076,0.050206246993051415,-2.9916158179340711,0.94979375300694857,-0.051510420055571327,1.6215564881714115,-1.6428571428571428 +wide,-4,5.5,8,0.999,0.0067120429241688401,-5.0038519146992959,0.98543852292380751,-0.01466853594470641,0.014561477076192529,-4.2293757938183552,12.996277683922973,2.1818181818181817 +lower_tail,1,2,-15,9.9999999999999998e-13,2.5261355417684464e-15,-33.612085713764621,6.2209605742717405e-16,-35.013437159914552,0.99999999999999933,-6.2209605742717425e-16,-13.068967650602263,-8 +upper_tail,-2,0.40000000000000002,2.5,0.99999999999900002,3.2821044015385068e-28,-63.283897801330511,1,-1.1579603185686329e-29,1.1579603185686329e-29,-66.62830758547554,0.81379476401913431,11.25 diff --git a/tests/fixtures/pcntoolkit/v1.3.0/semantic_metrics.csv b/tests/fixtures/pcntoolkit/v1.3.0/semantic_metrics.csv new file mode 100644 index 0000000..bf01d88 --- /dev/null +++ b/tests/fixtures/pcntoolkit/v1.3.0/semantic_metrics.csv @@ -0,0 +1,15 @@ +metric,value +rmse,0.4869731585445517 +smse_population,0.054162393959168442 +ev,0.94882073273049317 +rho_spearman,1 +pearson,0.98685439766820615 +mace_no_batch,0.065714285714285711 +skew_bias_corrected,0.41271789557188532 +skew_unadjusted,0.31841876938023356 +skew_referent_sample_sd,0.2526844258603379 +excess_kurtosis_bias_corrected,0.15507750499914774 +excess_kurtosis_unadjusted,-0.6853843729170217 +excess_kurtosis_referent_sample_sd,-1.2994660698982199 +mace_equal_batch_average,0.27749999999999997 +mace_pooled_rows,0.12 diff --git a/tests/fixtures/pcntoolkit/v1.3.0/semantic_shashb.csv b/tests/fixtures/pcntoolkit/v1.3.0/semantic_shashb.csv new file mode 100644 index 0000000..5c99448 --- /dev/null +++ b/tests/fixtures/pcntoolkit/v1.3.0/semantic_shashb.csv @@ -0,0 +1,6 @@ +case,mu_b,sigma_b,epsilon,delta,y,p,moment_1,moment_2,mu_r,sigma_r,density,log_density,cdf,log_cdf,upper_tail,log_upper_tail,quantile,z +identity,0,1,0,1,-0.40000000000000002,0.10000000000000001,0,1.0000000000000002,0,1,0.36827014030332333,-0.99893853320467263,0.34457825838967582,-1.0654340491895766,0.65542174161032418,-0.42247637022777607,-1.2815515655446006,-0.40000000000000002 +skew,2,1.3,0.59999999999999998,1,3.2000000000000002,0.5,0.8623668900764756,2.2159833509865625,1.0760759285187951,1.0713816614634526,0.14819599957112223,-1.9092195599316986,0.82548917480558825,-0.19177912920813298,0.17451082519441175,-1.7457685037657156,1.7581749011374364,0.93648865633956235 +heavy,-1,0.80000000000000004,-0.34999999999999998,0.69999999999999996,-2.3999999999999999,0.94999999999999996,-0.94563270515703868,5.3724816775376389,-0.64251502242219161,0.54005410591879377,0.086783422572654867,-2.4443396597416385,0.061318778872817309,-2.7916691391882695,0.93868122112718266,-0.063279345030324688,0.012966117295625912,-1.5437968373951447 +light,4,2.2000000000000002,0.25,1.5,5.0999999999999996,0.98999999999999999,0.19221156888176893,0.39168234162400251,3.290015403409229,2.4625108007156045,0.14388034123915083,-1.9387732884647906,0.68246792110853893,-0.38203975506642346,0.31753207889146112,-1.1471764298745537,9.0038273757573712,0.47461114952941263 +tail,-3,0.45000000000000001,0.80000000000000004,0.65000000000000002,0.5,0.99999998999999995,3.1017225972912139,28.784673632120104,-3.3188397747887111,0.15814526124049669,0.00035369002475082008,-7.9470896647210223,0.99985737784053663,-0.00014263233097064177,0.00014262215946332372,-8.8553116661007927,4.0311734854106218,3.6283446401045634 diff --git a/tests/testthat/helper-pcntoolkit.R b/tests/testthat/helper-pcntoolkit.R new file mode 100644 index 0000000..75b1eb5 --- /dev/null +++ b/tests/testthat/helper-pcntoolkit.R @@ -0,0 +1,16 @@ +# Validation helpers must also be available when tests run against an installed +# package. Top-level tools/ files are in the source tarball but are not installed. +pcn_helper_files <- c( + "compare_results.R", "run_concordance.R", "benchmark_design.R", + "benchmark_helpers.R", "site_evidence.R" +) +for (pcn_helper_file in pcn_helper_files) { + pcn_helper_path <- system.file("pcntoolkit", pcn_helper_file, package = "referent") + if (!nzchar(pcn_helper_path)) { + pcn_helper_path <- testthat::test_path( + "..", "..", "inst", "pcntoolkit", pcn_helper_file + ) + } + source(pcn_helper_path, local = environment()) +} +rm(pcn_helper_file, pcn_helper_files, pcn_helper_path) diff --git a/tests/testthat/test-pcntoolkit-concordance.R b/tests/testthat/test-pcntoolkit-concordance.R new file mode 100644 index 0000000..17128cd --- /dev/null +++ b/tests/testthat/test-pcntoolkit-concordance.R @@ -0,0 +1,24 @@ +test_that("matched PCNtoolkit fitted models satisfy prediction-level margins", { + root <- testthat::test_path("..", "fixtures", "pcntoolkit", "v1.3.0") + result <- pcn_run_concordance(root, n_draw = 2000L) + matched <- result$evidence_class == "matched_estimator" + + expect_true(all(pcn_concordance_pass(result))) + expect_true(all(is.finite(as.matrix(result[, 4:ncol(result)])))) + expect_setequal( + result$scenario[matched], + c("null_linear", "linear_gaussian", "multiple_encoded", "log_linear", + "covariate_shift") + ) + expect_setequal( + result$scenario[!matched], + c("nonlinear_heteroskedastic", "balanced_site", "skew_heavy", "unequal_site") + ) +}) + +test_that("estimator-divergent fitted cases remain diagnostics, not fake parity", { + registry <- pcn_concordance_registry() + divergent <- registry$evidence_class == "same_estimand" + expect_true(all(nzchar(registry$reason[divergent]))) + expect_false(any(registry$evidence_class[divergent] == "matched_estimator")) +}) diff --git a/tests/testthat/test-pcntoolkit-fixtures.R b/tests/testthat/test-pcntoolkit-fixtures.R new file mode 100644 index 0000000..792cd2b --- /dev/null +++ b/tests/testthat/test-pcntoolkit-fixtures.R @@ -0,0 +1,58 @@ +fixture_root <- testthat::test_path("..", "fixtures", "pcntoolkit", "v1.3.0") + +test_that("PCNtoolkit fixtures are pinned, complete, and unmodified", { + manifest <- pcn_validate_fixture(fixture_root) + expect_identical(manifest$dependencies$pcntoolkit, "1.3.0") + expect_identical(manifest$generator, "tools/pcntoolkit/generate_fixtures.py") + expect_setequal( + names(manifest$scenario_classes), + c("null_linear", "linear_gaussian", "multiple_encoded", + "nonlinear_heteroskedastic", "log_linear", "balanced_site", + "skew_heavy", "unequal_site", "covariate_shift") + ) +}) + +test_that("fixture validation fails closed on content drift", { + scratch <- withr::local_tempdir() + file.copy(list.files(fixture_root, full.names = TRUE), scratch) + write("scientific drift", file.path(scratch, "semantic_gaussian.csv"), append = TRUE) + expect_error(pcn_validate_fixture(scratch), "hash mismatch") +}) + +test_that("PCNtoolkit logp is normalised to a response-scale density", { + predicted <- utils::read.csv( + file.path(fixture_root, "fitted_predictions.csv"), stringsAsFactors = FALSE + ) + gaussian <- predicted$scenario != "log_linear" + analytic <- stats::dnorm( + predicted$observed[gaussian], predicted$median[gaussian], + predicted$predictive_sd[gaussian], log = TRUE + ) + expect_equal(predicted$log_density[gaussian], analytic, tolerance = 1e-10) + + logged <- predicted[predicted$scenario == "log_linear", ] + log_sd <- (log(logged$q95) - log(logged$q05)) / (2 * stats::qnorm(0.95)) + expect_equal( + logged$log_density, + stats::dlnorm(logged$observed, log(logged$median), log_sd, log = TRUE), + tolerance = 1e-10 + ) +}) + +test_that("installed validation helpers mirror their source-tree runners", { + root <- testthat::test_path("..", "..") + paired <- c( + "compare_results.R", "run_concordance.R", "benchmark_design.R", + "benchmark_helpers.R", "site_evidence.R", "benchmark_scenarios.csv", + "run_hbr_site_evidence.py" + ) + source_files <- file.path(root, "tools", "pcntoolkit", paired) + skip_if_not(all(file.exists(source_files)), "source-tree tools are not installed") + installed_files <- file.path(root, "inst", "pcntoolkit", paired) + expect_true(all(file.exists(installed_files))) + for (i in seq_along(paired)) { + expect_identical(readBin(source_files[[i]], "raw", file.info(source_files[[i]])$size), + readBin(installed_files[[i]], "raw", file.info(installed_files[[i]])$size), + info = paired[[i]]) + } +}) diff --git a/tests/testthat/test-pcntoolkit-hbr-site.R b/tests/testthat/test-pcntoolkit-hbr-site.R new file mode 100644 index 0000000..99f8bd0 --- /dev/null +++ b/tests/testthat/test-pcntoolkit-hbr-site.R @@ -0,0 +1,54 @@ +test_that("HBR draw averages and Referent mixtures are explicitly non-equivalent", { + mu <- c(-1.2, 0.4, 1.7) + sigma <- c(0.7, 1.1, 1.8) + y <- 0.8 + p <- 0.9 + draw_z <- (y - mu) / sigma + reported_z <- mean(draw_z) + mixture_z <- stats::qnorm(mean(stats::pnorm(draw_z))) + reported_quantile <- mean(stats::qnorm(p, mu, sigma)) + mixture_quantile <- stats::uniroot( + function(q) mean(stats::pnorm(q, mu, sigma)) - p, c(-20, 20) + )$root + reported_logp <- mean(stats::dnorm(y, mu, sigma, log = TRUE)) + mixture_logp <- log(mean(stats::dnorm(y, mu, sigma))) + + expect_gt(abs(reported_z - mixture_z), 0.05) + expect_gt(abs(reported_quantile - mixture_quantile), 0.05) + expect_gt(mixture_logp, reported_logp) # Jensen: log(mean density) >= mean(log density) +}) + +test_that("site gates cannot hide a small-site failure in a pooled average", { + set.seed(12) + large_u <- (1:950 - 0.5) / 950 + small_u <- rep(0.995, 50) + prediction <- data.frame( + site = rep(c("large", "small"), c(950, 50)), + observed = c(stats::qnorm(large_u), rep(3, 50)), + centile = c(large_u, small_u), + log_density = c(stats::dnorm(stats::qnorm(large_u), log = TRUE), rep(-6, 50)), + q05 = stats::qnorm(0.05), q50 = 0, q95 = stats::qnorm(0.95) + ) + summary <- pcn_site_summary(prediction, "negative_control", "transport") + gated <- pcn_site_gate(summary) + expect_true(gated$pass[gated$site == "large"]) + expect_false(gated$pass[gated$site == "small"]) + expect_false(all(gated$pass)) + expect_lt(abs(mean(prediction$centile) - 0.5), 0.03) +}) + +test_that("HBR release runner records mandatory convergence and aggregation fields", { + script_path <- system.file( + "pcntoolkit", "run_hbr_site_evidence.py", package = "referent" + ) + script <- readLines( + script_path, warn = FALSE + ) + text <- paste(script, collapse = "\n") + expect_match(text, '"max_rhat": 1.01', fixed = TRUE) + expect_match(text, '"min_ess_bulk": 400', fixed = TRUE) + expect_match(text, '"divergences": 0', fixed = TRUE) + expect_match(text, '"reported_z": "mean of draw-specific z"', fixed = TRUE) + expect_match(text, '"mixture_outputs"', fixed = TRUE) + expect_match(text, '"hbr_public_random_seed_argument": False', fixed = TRUE) +}) diff --git a/tests/testthat/test-pcntoolkit-semantics.R b/tests/testthat/test-pcntoolkit-semantics.R new file mode 100644 index 0000000..4388878 --- /dev/null +++ b/tests/testthat/test-pcntoolkit-semantics.R @@ -0,0 +1,127 @@ +pcn_semantic_root <- testthat::test_path("..", "fixtures", "pcntoolkit", "v1.3.0") + +read_pcn_semantic <- function(filename) { + utils::read.csv(file.path(pcn_semantic_root, filename), check.names = FALSE) +} + +expect_scale_close <- function(actual, expected, atol, rtol = atol) { + expect_true( + all(abs(actual - expected) <= atol + rtol * abs(expected)), + info = paste("maximum scaled error:", max(abs(actual - expected) / (atol + rtol * abs(expected)))) + ) +} + +test_that("fixed Gaussian semantics agree with PCNtoolkit and analytic R", { + oracle <- read_pcn_semantic("semantic_gaussian.csv") + dist <- distributional::dist_normal(oracle$mu, oracle$sigma) + + expect_scale_close(dist_log_density(dist, oracle$y), oracle$log_density, 1e-10) + expect_scale_close(exp(dist_log_density(dist, oracle$y)), oracle$density, 1e-10) + expect_scale_close(dist_cdf(dist, oracle$y), oracle$cdf, 1e-10) + expect_scale_close( + dist_eval(dist, log_tail, oracle$y, lower.tail = TRUE), + oracle$log_cdf, 1e-10 + ) + expect_scale_close( + exp(dist_eval(dist, log_tail, oracle$y, lower.tail = FALSE)), + oracle$upper_tail, 1e-10 + ) + expect_scale_close( + dist_eval(dist, log_tail, oracle$y, lower.tail = FALSE), + oracle$log_upper_tail, 1e-10 + ) + expect_scale_close(dist_quantile(dist, oracle$p), oracle$quantile, 1e-10) + expect_scale_close(dist_z(dist, oracle$y), oracle$z, 1e-10) + + # Independent analytic path, rather than fixture-to-implementation only. + expect_equal(oracle$cdf, stats::pnorm(oracle$y, oracle$mu, oracle$sigma), tolerance = 1e-14) + expect_equal(oracle$quantile, stats::qnorm(oracle$p, oracle$mu, oracle$sigma), tolerance = 1e-14) + expect_true(all(is.finite(oracle$log_cdf))) + expect_true(all(is.finite(oracle$log_upper_tail))) +}) + +test_that("PCNtoolkit SHASHb is the converted mgcv SHASH distribution", { + oracle <- read_pcn_semantic("semantic_shashb.csv") + variance_s <- oracle$moment_2 - oracle$moment_1^2 + expect_equal( + oracle$sigma_r, + oracle$sigma_b / (oracle$delta * sqrt(variance_s)), + tolerance = 1e-14 + ) + expect_equal( + oracle$mu_r, + oracle$mu_b - oracle$sigma_b * oracle$moment_1 / sqrt(variance_s), + tolerance = 1e-14 + ) + + dist <- dist_shash(oracle$mu_r, oracle$sigma_r, oracle$epsilon, oracle$delta) + expect_scale_close(dist_log_density(dist, oracle$y), oracle$log_density, 1e-7) + expect_scale_close(exp(dist_log_density(dist, oracle$y)), oracle$density, 1e-7) + expect_scale_close(dist_cdf(dist, oracle$y), oracle$cdf, 1e-7) + expect_scale_close( + dist_eval(dist, log_tail, oracle$y, lower.tail = TRUE), + oracle$log_cdf, 1e-7 + ) + expect_scale_close( + dist_eval(dist, log_tail, oracle$y, lower.tail = FALSE), + oracle$log_upper_tail, 1e-7 + ) + expect_scale_close(dist_quantile(dist, oracle$p), oracle$quantile, 1e-7) + expect_scale_close(dist_z(dist, oracle$y), oracle$z, 1e-7) + expect_true(all(is.finite(oracle$log_density))) + expect_true(all(is.finite(oracle$log_cdf))) + expect_true(all(is.finite(oracle$log_upper_tail))) + + # The conversion preserves PCNtoolkit's declared mean and standard deviation. + expect_scale_close(mean(dist), oracle$mu_b, 1e-7) + expect_scale_close(sqrt(variance(dist)), oracle$sigma_b, 1e-7) +}) + +test_that("response transformation parity includes support and Jacobian", { + mu <- c(-0.5, 0.2, 1.1) + sigma <- c(0.4, 1.0, 1.8) + y <- c(0.03, 1.5, 30) + p <- c(1e-6, 0.5, 1 - 1e-6) + warped <- dist_warped(distributional::dist_normal(mu, sigma), 0) + + expect_scale_close(dist_cdf(warped, y), stats::pnorm(log(y), mu, sigma), 1e-10) + expect_scale_close(dist_quantile(warped, p), exp(stats::qnorm(p, mu, sigma)), 1e-10) + expect_scale_close( + dist_log_density(warped, y), + stats::dnorm(log(y), mu, sigma, log = TRUE) - log(y), + 1e-10 + ) + expect_equal(dist_cdf(warped[1], -1), 0) + expect_equal(dist_log_density(warped[1], -1), -Inf) +}) + +test_that("metric normalisations match or differ exactly as declared", { + oracle <- read_pcn_semantic("semantic_metrics.csv") + value <- setNames(oracle$value, oracle$metric) + observed <- c(-2, -0.2, 0.1, 0.7, 1.4, 2.8, 5) + predicted <- c(-1.5, -0.4, 0.3, 0.5, 1.8, 2.1, 4.2) + z <- c(-1.8, -0.9, -0.2, 0.1, 0.5, 1.4, 2.7) + residual <- observed - predicted + pit <- stats::pnorm(z) + + expect_equal(sqrt(mean(residual^2)), value[["rmse"]], tolerance = 1e-12) + expect_equal( + mean(residual^2) / mean((observed - mean(observed))^2), + value[["smse_population"]], tolerance = 1e-12 + ) + expect_equal( + mean(residual^2) / stats::var(observed), + value[["smse_population"]] * (length(observed) - 1) / length(observed), + tolerance = 1e-12 + ) + expect_equal(1 - stats::var(residual) / stats::var(observed), value[["ev"]], tolerance = 1e-12) + expect_equal(stats::cor(observed, predicted, method = "spearman"), value[["rho_spearman"]], tolerance = 1e-12) + expect_equal(stats::cor(observed, predicted), value[["pearson"]], tolerance = 1e-12) + expect_gt(abs(value[["rho_spearman"]] - value[["pearson"]]), 1e-3) + expect_equal(mace(pit), value[["mace_no_batch"]], tolerance = 1e-12) + expect_equal(std_moment(z, 3), value[["skew_referent_sample_sd"]], tolerance = 1e-12) + expect_equal(std_moment(z, 4) - 3, value[["excess_kurtosis_referent_sample_sd"]], tolerance = 1e-12) + expect_gt(abs(value[["skew_bias_corrected"]] - value[["skew_referent_sample_sd"]]), 0.05) + expect_gt(abs(value[["excess_kurtosis_bias_corrected"]] - value[["excess_kurtosis_referent_sample_sd"]]), 0.1) + expect_gt(abs(value[["mace_equal_batch_average"]] - value[["mace_pooled_rows"]]), 0.01) +}) diff --git a/tests/testthat/test-pcntoolkit-superiority.R b/tests/testthat/test-pcntoolkit-superiority.R new file mode 100644 index 0000000..3f2e213 --- /dev/null +++ b/tests/testthat/test-pcntoolkit-superiority.R @@ -0,0 +1,75 @@ +test_that("benchmark registry locks all prespecified challenge families", { + registry <- pcn_benchmark_registry() + expect_setequal( + registry$scenario, + c("linear_gaussian", "nonlinear_mean", "heteroskedastic", "skew_heavy", + "unequal_sites", "site_slopes", "covariate_shift", "unseen_site", + "small_n", "large_n") + ) + expect_true(all(registry$n_train > 0 & registry$n_validation > 0 & registry$n_test > 0)) + expect_false(any(grepl("test", registry$selection_rule, fixed = TRUE))) + + unseen <- pcn_simulate_benchmark("unseen_site", seed = 7) + expect_false("site-5" %in% unseen$site[unseen$split == "train"]) + expect_true("site-5" %in% unseen$site[unseen$split == "test"]) + skew <- pcn_simulate_benchmark("skew_heavy", seed = 8) + expect_true(all(skew$truth_epsilon == 0.9)) + expect_true(all(skew$truth_delta == 0.7)) + expect_equal(length(unique(skew$row_id)), nrow(skew)) +}) + +test_that("the locked skew-heavy test supports a guarded superiority claim", { + root <- testthat::test_path("..", "fixtures", "pcntoolkit", "v1.3.0") + inputs <- utils::read.csv(file.path(root, "fitted_inputs.csv"), stringsAsFactors = FALSE) + comparator <- utils::read.csv( + file.path(root, "fitted_predictions.csv"), stringsAsFactors = FALSE + ) + referent <- pcn_referent_predictions(inputs, "skew_heavy", n_draw = 2000L) + comparator <- comparator[comparator$scenario == "skew_heavy", , drop = FALSE] + comparator <- comparator[match(referent$row_id, comparator$row_id), , drop = FALSE] + result <- pcn_compare_predictions( + referent, comparator, cluster = referent$row_id, + scenario = "skew_heavy", B = 999L, seed = 91L + ) + expect_identical(result$classification, "superior") + expect_gt(result$ci_lower, 0) + expect_true(result$calibration_pass) + expect_lte( + abs(result$referent_coverage90 - 0.9), + abs(result$pcntoolkit_coverage90 - 0.9) + 0.01 + ) +}) + +test_that("negative controls prevent false superiority and detect tradeoffs", { + n <- 300L + rows <- sprintf("row-%03d", seq_len(n)) + observed <- stats::qnorm((seq_len(n) - 0.5) / n) + base <- data.frame( + row_id = rows, observed = observed, median = 0, + centile = stats::pnorm(observed), log_density = stats::dnorm(observed, log = TRUE), + q05 = stats::qnorm(0.05), q95 = stats::qnorm(0.95) + ) + identical_result <- pcn_compare_predictions(base, base, rows, B = 199L, seed = 1L) + expect_identical(identical_result$classification, "equivalent") + expect_equal(identical_result$log_score_difference, 0) + + expect_identical( + pcn_classify_benchmark(0.08, 0.03, 0.12, calibration_pass = FALSE), + "tradeoff" + ) + expect_identical( + pcn_classify_benchmark(-0.08, -0.12, -0.03, calibration_pass = TRUE), + "inferior" + ) + expect_identical( + pcn_classify_benchmark(0, 0, 0, calibration_pass = TRUE, conformance_pass = TRUE), + "conformant" + ) +}) + +test_that("controlled location perturbations worsen proper score monotonically", { + y <- stats::qnorm((1:999 - 0.5) / 999) + shifts <- c(0, 0.2, 0.5, 1) + score <- vapply(shifts, function(shift) mean(stats::dnorm(y, shift, 1, log = TRUE)), numeric(1)) + expect_true(all(diff(score) < 0)) +}) diff --git a/tools/pcntoolkit/benchmark_design.R b/tools/pcntoolkit/benchmark_design.R new file mode 100644 index 0000000..b611db2 --- /dev/null +++ b/tools/pcntoolkit/benchmark_design.R @@ -0,0 +1,78 @@ +# Preregistered synthetic truths for the PCNtoolkit utility benchmark. + +pcn_benchmark_registry <- function(root = NULL) { + installed <- system.file("pcntoolkit", "benchmark_scenarios.csv", package = "referent") + path <- if (is.null(root) && nzchar(installed)) { + installed + } else { + root <- if (is.null(root)) "." else root + source_path <- file.path(root, "tools", "pcntoolkit", "benchmark_scenarios.csv") + if (file.exists(source_path)) source_path else + file.path(root, "inst", "pcntoolkit", "benchmark_scenarios.csv") + } + utils::read.csv(path, stringsAsFactors = FALSE) +} + +pcn_simulate_benchmark <- function(scenario, seed, root = NULL) { + registry <- pcn_benchmark_registry(root) + design <- registry[registry$scenario == scenario, , drop = FALSE] + if (nrow(design) != 1L) stop("unknown benchmark scenario: ", scenario, call. = FALSE) + n_part <- unlist(design[c("n_train", "n_validation", "n_test")], use.names = FALSE) + n <- sum(n_part) + set.seed(seed) + split <- rep(c("train", "validation", "test"), n_part) + x <- if (scenario == "covariate_shift") { + c(stats::runif(n_part[[1L]], -2, 0.75), + stats::runif(n_part[[2L]] + n_part[[3L]], 0.25, 2)) + } else { + stats::runif(n, -2, 2) + } + site_levels <- paste0("site-", 1:5) + site <- if (scenario == "unequal_sites") { + sample(site_levels, n, replace = TRUE, prob = c(0.6, 0.2, 0.1, 0.07, 0.03)) + } else if (scenario == "unseen_site") { + c(sample(site_levels[1:4], n_part[[1L]] + n_part[[2L]], replace = TRUE), + sample(site_levels, n_part[[3L]], replace = TRUE, prob = c(rep(0.125, 4), 0.5))) + } else { + sample(site_levels, n, replace = TRUE) + } + site_index <- match(site, site_levels) + mu <- 0.3 + 0.9 * x + sigma <- rep(0.75, n) + epsilon <- rep(0, n) + delta <- rep(1, n) + + if (scenario %in% c("nonlinear_mean", "small_n", "large_n", "skew_heavy")) { + mu <- 0.4 + sin(1.35 * x) + 0.15 * x^2 + } + if (scenario %in% c("heteroskedastic", "small_n", "large_n", "skew_heavy")) { + sigma <- exp(-0.35 + 0.22 * x) + } + if (scenario %in% c("skew_heavy", "small_n", "large_n")) { + epsilon <- rep(0.9, n) + delta <- rep(0.7, n) + } + if (scenario %in% c("unequal_sites", "unseen_site")) { + mu <- mu + c(-0.8, -0.25, 0.2, 0.65, 1.1)[site_index] + sigma <- sigma * c(0.7, 0.85, 1, 1.15, 1.35)[site_index] + } + if (scenario == "site_slopes") { + mu <- mu + c(-0.4, -0.2, 0, 0.2, 0.4)[site_index] * x + } + latent <- stats::rnorm(n) + y <- mu + sigma * delta * sinh((asinh(latent) + epsilon) / delta) + data.frame( + scenario = scenario, + row_id = sprintf("%s-%s-%05d", scenario, split, ave(seq_len(n), split, FUN = seq_along)), + split = split, + cluster = if (design$site_structure == "none") seq_len(n) else site, + x = x, + site = factor(site, levels = site_levels), + y = y, + truth_mu = mu, + truth_sigma = sigma, + truth_epsilon = epsilon, + truth_delta = delta, + stringsAsFactors = FALSE + ) +} diff --git a/tools/pcntoolkit/benchmark_helpers.R b/tools/pcntoolkit/benchmark_helpers.R new file mode 100644 index 0000000..f0387a1 --- /dev/null +++ b/tools/pcntoolkit/benchmark_helpers.R @@ -0,0 +1,99 @@ +# Classification and uncertainty helpers for paired held-out comparisons. + +pcn_benchmark_mace <- function(u, grid = c(0.05, 0.25, 0.5, 0.75, 0.95)) { + mean(vapply(grid, function(q) abs(q - mean(u <= q)), numeric(1))) +} + +pcn_cluster_bootstrap <- function(delta, cluster, B = 1999L, seed = 20260823L) { + stopifnot(length(delta) == length(cluster), length(delta) > 1L, B >= 99L) + keep <- is.finite(delta) & !is.na(cluster) + parts <- split(delta[keep], as.character(cluster[keep])) + if (!length(parts)) stop("no finite paired score differences", call. = FALSE) + draws <- withr::with_seed(seed, replicate(B, { + selected <- sample.int(length(parts), length(parts), replace = TRUE) + mean(unlist(parts[selected], use.names = FALSE)) + })) + c( + estimate = mean(unlist(parts, use.names = FALSE)), + lower = unname(stats::quantile(draws, 0.025, names = FALSE)), + upper = unname(stats::quantile(draws, 0.975, names = FALSE)), + clusters = length(parts) + ) +} + +pcn_calibration_summary <- function(prediction) { + required <- c("observed", "q05", "q95", "centile") + if (!all(required %in% names(prediction))) { + stop("prediction is missing calibration columns", call. = FALSE) + } + u <- prediction$centile + tail_prob <- 2 * pmin(u, 1 - u) + c( + coverage90 = mean(prediction$observed >= prediction$q05 & prediction$observed <= prediction$q95), + mace = pcn_benchmark_mace(u), + tail05 = mean(tail_prob <= 0.05) + ) +} + +pcn_calibration_gate <- function(referent, comparator, + coverage_regret = 0.01, + mace_regret = 0.01, + tail_regret = 0.01) { + ref <- pcn_calibration_summary(referent) + pcn <- pcn_calibration_summary(comparator) + regret <- c( + coverage90 = abs(ref[["coverage90"]] - 0.90) - abs(pcn[["coverage90"]] - 0.90), + mace = ref[["mace"]] - pcn[["mace"]], + tail05 = abs(ref[["tail05"]] - 0.05) - abs(pcn[["tail05"]] - 0.05) + ) + limits <- c(coverage90 = coverage_regret, mace = mace_regret, tail05 = tail_regret) + list(pass = all(regret <= limits), referent = ref, comparator = pcn, + regret = regret, limits = limits) +} + +pcn_classify_benchmark <- function(estimate, lower, upper, calibration_pass, + conformance_pass = FALSE, + equivalence_margin = 0.02, + noninferiority_margin = 0.02) { + if (isTRUE(conformance_pass)) return("conformant") + if (!isTRUE(calibration_pass)) { + return(if (estimate > 0) "tradeoff" else "inferior") + } + if (lower > 0) return("superior") + if (upper < -noninferiority_margin) return("inferior") + if (lower >= -equivalence_margin && upper <= equivalence_margin) return("equivalent") + if (lower >= -noninferiority_margin) return("non_inferior") + "inconclusive" +} + +pcn_compare_predictions <- function(referent, comparator, cluster, + scenario = NA_character_, B = 1999L, + seed = 20260823L) { + if (!identical(referent$row_id, comparator$row_id)) { + stop("paired prediction row identities differ", call. = FALSE) + } + interval <- pcn_cluster_bootstrap( + referent$log_density - comparator$log_density, cluster, B = B, seed = seed + ) + calibration <- pcn_calibration_gate(referent, comparator) + data.frame( + scenario = scenario, + n_test = nrow(referent), + clusters = unname(interval[["clusters"]]), + log_score_difference = unname(interval[["estimate"]]), + ci_lower = unname(interval[["lower"]]), + ci_upper = unname(interval[["upper"]]), + referent_coverage90 = calibration$referent[["coverage90"]], + pcntoolkit_coverage90 = calibration$comparator[["coverage90"]], + referent_mace = calibration$referent[["mace"]], + pcntoolkit_mace = calibration$comparator[["mace"]], + referent_tail05 = calibration$referent[["tail05"]], + pcntoolkit_tail05 = calibration$comparator[["tail05"]], + calibration_pass = calibration$pass, + classification = pcn_classify_benchmark( + interval[["estimate"]], interval[["lower"]], interval[["upper"]], + calibration$pass + ), + stringsAsFactors = FALSE + ) +} diff --git a/tools/pcntoolkit/benchmark_scenarios.csv b/tools/pcntoolkit/benchmark_scenarios.csv new file mode 100644 index 0000000..fb71cae --- /dev/null +++ b/tools/pcntoolkit/benchmark_scenarios.csv @@ -0,0 +1,11 @@ +scenario,evidence_class,truth,site_structure,covariate_distribution,n_train,n_validation,n_test,selection_rule,execution_tier +linear_gaussian,matched_estimator,linear Gaussian,none,same,500,100,500,prespecified linear Gaussian,weekly +nonlinear_mean,same_estimand,smooth nonlinear Gaussian,none,same,500,100,500,validation log score among prespecified smooth bases,release +heteroskedastic,same_estimand,linear mean and smooth log scale,none,same,500,100,500,validation log score among scale formulas,release +skew_heavy,same_estimand,smooth SHASH,none,same,500,100,500,prespecified SHASH after family validation,weekly +unequal_sites,same_estimand,linear Gaussian with site location and scale,unequal,same,800,160,800,prespecified site model,release +site_slopes,same_estimand,site-specific age slopes,balanced,same,800,160,800,validation log score among site slope specifications,release +covariate_shift,matched_estimator,linear Gaussian,none,shifted test support,500,100,500,prespecified linear Gaussian,weekly +unseen_site,non_equivalent,new site location scale and slope,one test-only site,same,800,160,800,prespecified transport policy,release +small_n,same_estimand,smooth heteroskedastic SHASH,balanced,same,120,60,500,validation log score with complexity cap,release +large_n,same_estimand,smooth heteroskedastic SHASH,balanced,same,3000,500,1000,validation log score among prespecified families,release diff --git a/tools/pcntoolkit/check_fixture_drift.R b/tools/pcntoolkit/check_fixture_drift.R new file mode 100644 index 0000000..93736e3 --- /dev/null +++ b/tools/pcntoolkit/check_fixture_drift.R @@ -0,0 +1,66 @@ +# Compare a freshly generated fixture with the reviewed baseline. Exact bytes +# are not required across BLAS/libm platforms; schemas, environments, row +# identities, and numerical values are checked at much tighter tolerances than +# any Referent-to-PCNtoolkit fitted concordance claim. + +source("tools/pcntoolkit/compare_results.R") + +pcn_compare_fixture_file <- function(baseline, candidate, filename, + atol, rtol = atol) { + a <- utils::read.csv(file.path(baseline, filename), check.names = FALSE, + stringsAsFactors = FALSE) + b <- utils::read.csv(file.path(candidate, filename), check.names = FALSE, + stringsAsFactors = FALSE) + if (!identical(names(a), names(b)) || nrow(a) != nrow(b)) { + stop("fixture schema/row drift: ", filename, call. = FALSE) + } + numeric <- vapply(a, is.numeric, logical(1)) + if (!identical(numeric, vapply(b, is.numeric, logical(1)))) { + stop("fixture column-type drift: ", filename, call. = FALSE) + } + if (any(!vapply(names(a)[!numeric], function(nm) identical(a[[nm]], b[[nm]]), logical(1)))) { + stop("fixture identity drift: ", filename, call. = FALSE) + } + aa <- as.matrix(a[numeric]) + bb <- as.matrix(b[numeric]) + scale <- atol + rtol * abs(aa) + scaled_error <- abs(aa - bb) / scale + if (any(!is.finite(scaled_error)) || any(scaled_error > 1)) { + stop( + "fixture numerical drift exceeds tolerance in ", filename, + "; maximum scaled error = ", max(scaled_error, na.rm = TRUE), + call. = FALSE + ) + } + data.frame(file = filename, max_scaled_error = max(scaled_error), + atol = atol, rtol = rtol) +} + +pcn_check_fixture_drift <- function(baseline, candidate) { + a <- pcn_validate_fixture(baseline) + b <- pcn_validate_fixture(candidate) + stable_manifest_fields <- c( + "schema_version", "comparator", "python", "dependencies", "seeds", + "centiles", "scenario_classes", "normalisations" + ) + for (field in stable_manifest_fields) { + if (!identical(a[[field]], b[[field]])) { + stop("fixture manifest drift: ", field, call. = FALSE) + } + } + do.call(rbind, list( + pcn_compare_fixture_file(baseline, candidate, "semantic_gaussian.csv", 1e-12), + pcn_compare_fixture_file(baseline, candidate, "semantic_shashb.csv", 1e-10), + pcn_compare_fixture_file(baseline, candidate, "semantic_metrics.csv", 1e-10), + pcn_compare_fixture_file(baseline, candidate, "fitted_inputs.csv", 1e-14), + pcn_compare_fixture_file(baseline, candidate, "fitted_predictions.csv", 5e-6) + )) +} + +if (sys.nframe() == 0L) { + args <- commandArgs(trailingOnly = TRUE) + if (length(args) != 2L) { + stop("usage: check_fixture_drift.R BASELINE_DIR CANDIDATE_DIR") + } + print(pcn_check_fixture_drift(args[[1L]], args[[2L]]), row.names = FALSE) +} diff --git a/tools/pcntoolkit/compare_results.R b/tools/pcntoolkit/compare_results.R new file mode 100644 index 0000000..27f34cf --- /dev/null +++ b/tools/pcntoolkit/compare_results.R @@ -0,0 +1,74 @@ +# Utilities for consuming the checked-in PCNtoolkit comparison fixture. +# This file is deliberately outside R/ because it is validation infrastructure, +# not part of referent's user-facing API. + +pcn_fixture_root <- function(version = "1.3.0", package_root = ".") { + file.path(package_root, "tests", "fixtures", "pcntoolkit", paste0("v", version)) +} + +pcn_read_manifest <- function(root) { + if (!requireNamespace("jsonlite", quietly = TRUE)) { + stop("jsonlite is required to validate PCNtoolkit fixtures", call. = FALSE) + } + path <- file.path(root, "manifest.json") + if (!file.exists(path)) stop("missing PCNtoolkit manifest: ", path, call. = FALSE) + jsonlite::fromJSON(path, simplifyVector = FALSE) +} + +pcn_validate_fixture <- function(root, expected_version = "1.3.0") { + manifest <- pcn_read_manifest(root) + if (!identical(manifest$schema_version, "1.0.0")) { + stop("unsupported PCNtoolkit fixture schema: ", manifest$schema_version, call. = FALSE) + } + if (!identical(manifest$comparator$package, "pcntoolkit") || + !identical(manifest$comparator$version, expected_version)) { + stop("fixture comparator/version does not match the declared target", call. = FALSE) + } + required <- c( + "semantic_gaussian.csv", "semantic_shashb.csv", "semantic_metrics.csv", + "fitted_inputs.csv", "fitted_predictions.csv" + ) + if (!setequal(names(manifest$files), required)) { + stop("fixture manifest has an unexpected file set", call. = FALSE) + } + for (filename in required) { + receipt <- manifest$files[[filename]] + path <- file.path(root, filename) + if (!file.exists(path)) stop("missing fixture file: ", filename, call. = FALSE) + actual_md5 <- unname(tools::md5sum(path)) + if (!identical(actual_md5, receipt$md5)) { + stop("fixture hash mismatch: ", filename, call. = FALSE) + } + dat <- utils::read.csv(path, check.names = FALSE, stringsAsFactors = FALSE) + if (!identical(nrow(dat), as.integer(receipt$rows))) { + stop("fixture row-count mismatch: ", filename, call. = FALSE) + } + if (!identical(names(dat), unlist(receipt$columns, use.names = FALSE))) { + stop("fixture column mismatch: ", filename, call. = FALSE) + } + } + invisible(manifest) +} + +pcn_read_fixture <- function(root, filename) { + pcn_validate_fixture(root) + utils::read.csv(file.path(root, filename), check.names = FALSE, + stringsAsFactors = FALSE) +} + +pcn_concordance <- function(referent, comparator, outcome_scale) { + stopifnot(nrow(referent) == nrow(comparator), outcome_scale > 0) + delta_median <- referent$median - comparator$median + delta_sd <- referent$predictive_sd - comparator$predictive_sd + delta_z <- referent$z - comparator$z + c( + median_nrmse = sqrt(mean(delta_median^2)) / outcome_scale, + predictive_sd_nrmse = sqrt(mean(delta_sd^2)) / outcome_scale, + z_rmse = sqrt(mean(delta_z^2)), + z_abs_q95 = unname(stats::quantile(abs(delta_z), 0.95, names = FALSE)), + coverage90_difference = abs( + mean(referent$observed >= referent$q05 & referent$observed <= referent$q95) - + mean(comparator$observed >= comparator$q05 & comparator$observed <= comparator$q95) + ) + ) +} diff --git a/tools/pcntoolkit/generate_fixtures.py b/tools/pcntoolkit/generate_fixtures.py new file mode 100644 index 0000000..d4a8caa --- /dev/null +++ b/tools/pcntoolkit/generate_fixtures.py @@ -0,0 +1,432 @@ +# /// script +# requires-python = "==3.12.11" +# dependencies = [ +# "numpy==2.4.6", +# "pandas==3.0.5", +# "pcntoolkit==1.3.0", +# "scipy==1.18.1", +# ] +# /// +"""Regenerate deterministic PCNtoolkit 1.3.0 comparison fixtures. + +Run from the repository root with: + + uv run tools/pcntoolkit/generate_fixtures.py + +The ordinary R test suite reads the checked-in results and never invokes this +script. The manifest intentionally has no wall-clock timestamp so two clean +runs in the pinned environment are byte-for-byte identical. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import json +import platform +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy import stats + +from pcntoolkit import NormativeModel +from pcntoolkit.dataio.norm_data import NormData +from pcntoolkit.math_functions.basis_function import BsplineBasisFunction +from pcntoolkit.math_functions.shash import S, S_inv, m1m2 +from pcntoolkit.regression_model.blr import BLR + + +COMPARATOR_VERSION = "1.3.0" +SCHEMA_VERSION = "1.0.0" +SEEDS = { + "null_linear": 1101, + "linear_gaussian": 1102, + "multiple_encoded": 1103, + "nonlinear_heteroskedastic": 1104, + "log_linear": 1105, + "balanced_site": 1106, + "skew_heavy": 1107, + "unequal_site": 1108, + "covariate_shift": 1109, +} +ROOT = Path(__file__).resolve().parents[2] +DEFAULT_OUTPUT = ROOT / "tests" / "fixtures" / "pcntoolkit" / "v1.3.0" +CENTILES = np.array([0.05, 0.25, 0.5, 0.75, 0.95], dtype=float) + + +def stable_csv(frame: pd.DataFrame, path: Path) -> None: + frame.to_csv(path, index=False, float_format="%.17g", lineterminator="\n") + + +def md5(path: Path) -> str: + return hashlib.md5(path.read_bytes(), usedforsecurity=False).hexdigest() + + +def gaussian_semantics() -> pd.DataFrame: + rows: list[dict[str, float | str]] = [] + cases = [ + ("standard", 0.0, 1.0, -0.35, 0.73), + ("shifted", 3.25, 0.7, 2.1, 0.01), + ("wide", -4.0, 5.5, 8.0, 0.999), + ("lower_tail", 1.0, 2.0, -15.0, 1e-12), + ("upper_tail", -2.0, 0.4, 2.5, 1 - 1e-12), + ] + for case, mu, sigma, y, p in cases: + rows.append( + { + "case": case, + "mu": mu, + "sigma": sigma, + "y": y, + "p": p, + "density": stats.norm.pdf(y, loc=mu, scale=sigma), + "log_density": stats.norm.logpdf(y, loc=mu, scale=sigma), + "cdf": stats.norm.cdf(y, loc=mu, scale=sigma), + "log_cdf": stats.norm.logcdf(y, loc=mu, scale=sigma), + "upper_tail": stats.norm.sf(y, loc=mu, scale=sigma), + "log_upper_tail": stats.norm.logsf(y, loc=mu, scale=sigma), + "quantile": stats.norm.ppf(p, loc=mu, scale=sigma), + "z": (y - mu) / sigma, + } + ) + return pd.DataFrame(rows) + + +def shashb_semantics() -> pd.DataFrame: + rows: list[dict[str, float | str]] = [] + cases = [ + ("identity", 0.0, 1.0, 0.0, 1.0, -0.4, 0.1), + ("skew", 2.0, 1.3, 0.6, 1.0, 3.2, 0.5), + ("heavy", -1.0, 0.8, -0.35, 0.7, -2.4, 0.95), + ("light", 4.0, 2.2, 0.25, 1.5, 5.1, 0.99), + ("tail", -3.0, 0.45, 0.8, 0.65, 0.5, 1 - 1e-8), + ] + for case, mu_b, sigma_b, epsilon, delta, y, p in cases: + mean_s, raw_second_s = m1m2(epsilon, delta) + var_s = raw_second_s - mean_s**2 + remapped = ((y - mu_b) / sigma_b) * np.sqrt(var_s) + mean_s + gaussian_score = S(remapped, epsilon, delta) + log_density = ( + stats.norm.logpdf(gaussian_score) + + np.log(delta) + + 0.5 * np.log1p(gaussian_score**2) + - 0.5 * np.log1p(remapped**2) + + 0.5 * np.log(var_s) + - np.log(sigma_b) + ) + raw_quantile = S_inv(stats.norm.ppf(p), epsilon, delta) + quantile = mu_b + sigma_b * (raw_quantile - mean_s) / np.sqrt(var_s) + sigma_r = sigma_b / (delta * np.sqrt(var_s)) + mu_r = mu_b - sigma_b * mean_s / np.sqrt(var_s) + rows.append( + { + "case": case, + "mu_b": mu_b, + "sigma_b": sigma_b, + "epsilon": epsilon, + "delta": delta, + "y": y, + "p": p, + "moment_1": mean_s, + "moment_2": raw_second_s, + "mu_r": mu_r, + "sigma_r": sigma_r, + "density": np.exp(log_density), + "log_density": log_density, + "cdf": stats.norm.cdf(gaussian_score), + "log_cdf": stats.norm.logcdf(gaussian_score), + "upper_tail": stats.norm.sf(gaussian_score), + "log_upper_tail": stats.norm.logsf(gaussian_score), + "quantile": quantile, + "z": gaussian_score, + } + ) + return pd.DataFrame(rows) + + +def metric_semantics() -> pd.DataFrame: + observed = np.array([-2.0, -0.2, 0.1, 0.7, 1.4, 2.8, 5.0]) + predicted = np.array([-1.5, -0.4, 0.3, 0.5, 1.8, 2.1, 4.2]) + z = np.array([-1.8, -0.9, -0.2, 0.1, 0.5, 1.4, 2.7]) + pit = stats.norm.cdf(z) + residual = observed - predicted + grid = CENTILES + mace = np.mean([abs(q - np.mean(pit <= q)) for q in grid]) + pit_a = np.array([0.97, 0.99]) + pit_b = np.array([0.02, 0.08, 0.22, 0.38, 0.55, 0.71, 0.86, 0.96]) + mace_a = np.mean([abs(q - np.mean(pit_a <= q)) for q in grid]) + mace_b = np.mean([abs(q - np.mean(pit_b <= q)) for q in grid]) + mace_pooled = np.mean( + [abs(q - np.mean(np.concatenate([pit_a, pit_b]) <= q)) for q in grid] + ) + z_sample_standardized = (z - np.mean(z)) / np.std(z, ddof=1) + values = { + "rmse": np.sqrt(np.mean(residual**2)), + "smse_population": np.mean(residual**2) / np.var(observed, ddof=0), + "ev": 1 - np.var(residual, ddof=1) / np.var(observed, ddof=1), + "rho_spearman": stats.spearmanr(observed, predicted).statistic, + "pearson": stats.pearsonr(observed, predicted).statistic, + "mace_no_batch": mace, + "skew_bias_corrected": stats.skew(z, bias=False), + "skew_unadjusted": stats.skew(z, bias=True), + "skew_referent_sample_sd": np.mean(z_sample_standardized**3), + "excess_kurtosis_bias_corrected": stats.kurtosis(z, fisher=True, bias=False), + "excess_kurtosis_unadjusted": stats.kurtosis(z, fisher=True, bias=True), + "excess_kurtosis_referent_sample_sd": np.mean(z_sample_standardized**4) - 3, + "mace_equal_batch_average": np.mean([mace_a, mace_b]), + "mace_pooled_rows": mace_pooled, + } + return pd.DataFrame({"metric": list(values), "value": list(values.values())}) + + +def scenario_frame( + name: str, n_train: int = 500, n_validation: int = 100, n_test: int = 501 +) -> pd.DataFrame: + rng = np.random.default_rng(SEEDS[name]) + n = n_train + n_validation + n_test + split_counts = (("train", n_train), ("validation", n_validation), ("test", n_test)) + split = np.concatenate([np.repeat(part, count) for part, count in split_counts]) + row_id = [ + f"{name}-{part}-{i:04d}" + for part, count in split_counts + for i in range(count) + ] + if name == "covariate_shift": + x1 = np.concatenate( + [ + rng.uniform(-2.0, 0.75, n_train), + rng.uniform(0.25, 2.0, n_validation + n_test), + ] + ) + else: + x1 = rng.uniform(-2.0, 2.0, n) + x2 = rng.normal(size=n) + sex_m = rng.integers(0, 2, n) + if name == "unequal_site": + site = rng.choice( + np.array(["site-1", "site-2", "site-3", "site-4"]), + size=n, + p=np.array([0.65, 0.2, 0.1, 0.05]), + ) + else: + site = np.array([f"site-{i % 4 + 1}" for i in rng.permutation(n)]) + noise = rng.normal(size=n) + + if name == "null_linear": + y = 2.0 + 0.8 * noise + elif name == "linear_gaussian": + y = 0.7 + 1.4 * x1 + 0.75 * noise + elif name == "multiple_encoded": + y = -0.3 + 0.9 * x1 - 0.55 * x2 + 0.6 * sex_m + 0.7 * noise + elif name == "nonlinear_heteroskedastic": + sigma = np.exp(-0.25 + 0.28 * x1) + y = 0.4 + np.sin(1.4 * x1) + 0.2 * x1**2 + sigma * noise + elif name == "log_linear": + y = np.exp(1.2 + 0.35 * x1 + 0.25 * noise) + elif name == "balanced_site": + shifts = {"site-1": -0.8, "site-2": -0.2, "site-3": 0.35, "site-4": 0.9} + y = 1.0 + 0.65 * x1 + np.array([shifts[s] for s in site]) + 0.65 * noise + elif name == "skew_heavy": + mu = 0.4 + np.sin(1.35 * x1) + 0.15 * x1**2 + sigma = np.exp(-0.35 + 0.22 * x1) + epsilon, delta = 0.9, 0.7 + y = mu + sigma * delta * np.sinh((np.arcsinh(noise) + epsilon) / delta) + elif name == "unequal_site": + shifts = {"site-1": -0.7, "site-2": -0.1, "site-3": 0.5, "site-4": 1.1} + scales = {"site-1": 0.6, "site-2": 0.8, "site-3": 1.0, "site-4": 1.25} + y = 0.8 + 0.7 * x1 + np.array([shifts[s] for s in site]) + np.array( + [scales[s] for s in site] + ) * noise + elif name == "covariate_shift": + y = -0.2 + 1.1 * x1 + 0.7 * noise + else: + raise ValueError(f"unknown scenario: {name}") + + return pd.DataFrame( + { + "scenario": name, + "row_id": row_id, + "split": split, + "x1": x1, + "x2": x2, + "sex_M": sex_m, + "site": site, + "y": y, + } + ) + + +def fit_scenario(frame: pd.DataFrame, name: str) -> pd.DataFrame: + train = frame.loc[frame["split"] == "train"].copy() + test = frame.loc[frame["split"] == "test"].copy() + covariates = { + "null_linear": ["x1"], + "linear_gaussian": ["x1"], + "multiple_encoded": ["x1", "x2", "sex_M"], + "nonlinear_heteroskedastic": ["x1"], + "log_linear": ["x1"], + "balanced_site": ["x1"], + "skew_heavy": ["x1"], + "unequal_site": ["x1"], + "covariate_shift": ["x1"], + }[name] + batch_effects = ["site"] if name in ("balanced_site", "unequal_site") else None + response = "y_model" + if name == "log_linear": + train[response] = np.log(train["y"]) + test[response] = np.log(test["y"]) + else: + train[response] = train["y"] + test[response] = test["y"] + + kwargs: dict[str, object] = {"n_iter": 300, "tol": 1e-6} + if name in ("nonlinear_heteroskedastic", "skew_heavy"): + kwargs.update( + heteroskedastic=True, + basis_function_mean=BsplineBasisFunction(basis_column=0, degree=3, nknots=5), + basis_function_var=BsplineBasisFunction(basis_column=0, degree=3, nknots=4), + ) + if name in ("balanced_site", "unequal_site"): + kwargs.update(fixed_effect=True, fixed_effect_var=True) + + fit_data = NormData.from_dataframe( + f"{name}-train", + train, + covariates=covariates, + batch_effects=batch_effects, + response_vars=[response], + subject_ids="row_id", + ) + test_data = NormData.from_dataframe( + f"{name}-test", + test, + covariates=covariates, + batch_effects=batch_effects, + response_vars=[response], + subject_ids="row_id", + ) + model = NormativeModel( + BLR(**kwargs), + savemodel=False, + evaluate_model=False, + saveresults=False, + saveplots=False, + inscaler="standardize", + outscaler="standardize", + name=f"fixture-{name}", + ) + model.fit(fit_data) + predicted = model.predict(test_data) + yhat = predicted["Yhat"].sel(response_vars=response).values + z = predicted["Z"].sel(response_vars=response).values + logp = predicted["logp"].sel(response_vars=response).values + # NormativeModel maps Yhat/centiles back through outscaler but leaves logp + # on the standardised outcome scale. A density on the response scale needs + # the inverse-standardisation Jacobian. numpy's ddof=0 matches the scaler. + logp = logp - np.log(np.std(train[response].to_numpy(), ddof=0)) + quantiles = { + q: predicted["centiles"].sel(response_vars=response, centile=q).values + for q in CENTILES + } + + if name == "log_linear": + yhat = np.exp(yhat) + quantiles = {q: np.exp(values) for q, values in quantiles.items()} + logp = logp - np.log(test["y"].to_numpy()) + + q05, q95 = quantiles[0.05], quantiles[0.95] + pred_sd = (q95 - q05) / (2 * stats.norm.ppf(0.95)) + out = pd.DataFrame( + { + "scenario": name, + "row_id": test["row_id"].to_numpy(), + "observed": test["y"].to_numpy(), + "median": yhat, + "predictive_sd": pred_sd, + "centile": stats.norm.cdf(z), + "z": z, + "log_density": logp, + "q05": q05, + "q25": quantiles[0.25], + "q50": quantiles[0.5], + "q75": quantiles[0.75], + "q95": q95, + } + ) + return out + + +def write_fixture(output: Path) -> None: + output.mkdir(parents=True, exist_ok=True) + frames = [scenario_frame(name) for name in SEEDS] + inputs = pd.concat(frames, ignore_index=True) + predictions = pd.concat( + [fit_scenario(frame, name) for name, frame in zip(SEEDS, frames)], + ignore_index=True, + ) + + products = { + "semantic_gaussian.csv": gaussian_semantics(), + "semantic_shashb.csv": shashb_semantics(), + "semantic_metrics.csv": metric_semantics(), + "fitted_inputs.csv": inputs, + "fitted_predictions.csv": predictions, + } + for filename, frame in products.items(): + stable_csv(frame, output / filename) + + file_receipts = { + filename: { + "md5": md5(output / filename), + "rows": int(len(frame)), + "columns": list(frame.columns), + } + for filename, frame in products.items() + } + versions = { + package: importlib.metadata.version(package) + for package in ("pcntoolkit", "numpy", "pandas", "scipy") + } + if versions["pcntoolkit"] != COMPARATOR_VERSION: + raise RuntimeError(f"expected PCNtoolkit {COMPARATOR_VERSION}, got {versions['pcntoolkit']}") + manifest = { + "schema_version": SCHEMA_VERSION, + "comparator": {"package": "pcntoolkit", "version": COMPARATOR_VERSION}, + "generator": "tools/pcntoolkit/generate_fixtures.py", + "python": platform.python_version(), + "dependencies": versions, + "seeds": SEEDS, + "centiles": CENTILES.tolist(), + "files": file_receipts, + "scenario_classes": { + "null_linear": "matched_estimator", + "linear_gaussian": "matched_estimator", + "multiple_encoded": "matched_estimator", + "nonlinear_heteroskedastic": "same_estimand", + "log_linear": "matched_estimator", + "balanced_site": "same_estimand", + "skew_heavy": "same_estimand", + "unequal_site": "same_estimand", + "covariate_shift": "matched_estimator", + }, + "normalisations": { + "log_density": "PCNtoolkit logp minus log(population SD of the fitted outcome); log-transformed scenarios also minus log(y)", + "predictive_sd": "(q95 - q05) / (2 * qnorm(0.95)) on the reported outcome scale", + }, + } + (output / "manifest.json").write_text( + json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT) + args = parser.parse_args() + write_fixture(args.output.resolve()) + + +if __name__ == "__main__": + main() diff --git a/tools/pcntoolkit/render_evidence.R b/tools/pcntoolkit/render_evidence.R new file mode 100644 index 0000000..5cd3267 --- /dev/null +++ b/tools/pcntoolkit/render_evidence.R @@ -0,0 +1,70 @@ +# Render checked-in, machine-readable evidence from pinned fixtures. + +source("tools/pcntoolkit/compare_results.R") +source("tools/pcntoolkit/run_concordance.R") +source("tools/pcntoolkit/run_superiority.R") + +render_pcntoolkit_evidence <- function(output_dir) { + root <- pcn_fixture_root() + manifest <- pcn_validate_fixture(root) + concordance <- pcn_run_concordance(root) + superiority <- pcn_run_superiority(root) + gaussian <- utils::read.csv(file.path(root, "semantic_gaussian.csv")) + gaussian_error <- max(abs( + stats::pnorm(gaussian$y, gaussian$mu, gaussian$sigma) - gaussian$cdf + )) + shash <- utils::read.csv(file.path(root, "semantic_shashb.csv")) + shash_dist <- referent::dist_shash(shash$mu_r, shash$sigma_r, shash$epsilon, shash$delta) + shash_error <- max(abs( + referent:::dist_eval(shash_dist, referent:::log_dens, shash$y) - shash$log_density + )) + matched <- concordance$evidence_class == "matched_estimator" + evidence <- data.frame( + lane = c( + "Gaussian distribution semantics", "SHASHb parameter conversion", + "matched fitted estimators", "skew-heavy held-out utility", + "HBR posterior aggregation", "new-site transport" + ), + evidence_class = c( + "exact", "parameter_converted", "matched_estimator", "same_estimand", + "non_equivalent", "non_equivalent" + ), + status = c( + if (gaussian_error <= 1e-10) "pass" else "fail", + if (shash_error <= 1e-7) "pass" else "fail", + if (all(pcn_concordance_pass(concordance))) "pass" else "fail", + superiority$classification, + "release_evidence_required", "release_evidence_required" + ), + primary_quantity = c( + "maximum absolute CDF error", "maximum absolute log-density error", + "matched scenarios passing all margins", "mean paired log-score difference", + "draw aggregation semantics", "site-stratified calibration" + ), + value = c( + gaussian_error, shash_error, sum(pcn_concordance_pass(concordance)), + superiority$log_score_difference, NA_real_, NA_real_ + ), + threshold_or_interval = c( + "<= 1e-10", "<= 1e-7", + paste0(sum(matched), " of ", sum(matched)), + sprintf("95%% CI %.6f to %.6f", superiority$ci_lower, superiority$ci_upper), + "R-hat <= 1.01; ESS bulk/tail >= 400; zero divergences", + "every site must pass; pooled metrics are insufficient" + ), + comparator_version = manifest$comparator$version, + stringsAsFactors = FALSE + ) + dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) + utils::write.csv(concordance, file.path(output_dir, "concordance.csv"), row.names = FALSE) + utils::write.csv(superiority, file.path(output_dir, "superiority.csv"), row.names = FALSE) + utils::write.csv(evidence, file.path(output_dir, "evidence_summary.csv"), row.names = FALSE) + invisible(evidence) +} + +if (sys.nframe() == 0L) { + args <- commandArgs(trailingOnly = TRUE) + output <- if (length(args)) args[[1L]] else + file.path("docs", "evidence", "pcntoolkit", "v1.3.0") + print(render_pcntoolkit_evidence(output), row.names = FALSE) +} diff --git a/tools/pcntoolkit/run_concordance.R b/tools/pcntoolkit/run_concordance.R new file mode 100644 index 0000000..39b8257 --- /dev/null +++ b/tools/pcntoolkit/run_concordance.R @@ -0,0 +1,151 @@ +# Run prediction-level concordance against the checked-in PCNtoolkit fixture. + +pcn_scenario_spec <- function(scenario) { + switch( + scenario, + null_linear = referent::ref_spec(referent::ref_gaussian(), location = ~ x1, scale = ~ 1), + linear_gaussian = referent::ref_spec(referent::ref_gaussian(), location = ~ x1, scale = ~ 1), + multiple_encoded = referent::ref_spec(referent::ref_gaussian(), location = ~ x1 + x2 + sex_M, scale = ~ 1), + nonlinear_heteroskedastic = referent::ref_spec( + referent::ref_gaussian(), location = ~ s(x1, bs = "cr", k = 6), + scale = ~ s(x1, bs = "cr", k = 5) + ), + log_linear = referent::ref_spec( + referent::ref_gaussian(), location = ~ x1, scale = ~ 1, transform = "log" + ), + balanced_site = referent::ref_spec( + referent::ref_gaussian(), location = ~ x1 + s(site, bs = "re"), + scale = ~ s(site, bs = "re") + ), + skew_heavy = referent::ref_spec( + referent::ref_shash(), location = ~ s(x1, bs = "cr", k = 6), + scale = ~ s(x1, bs = "cr", k = 5), skew = ~ 1, tail = ~ 1 + ), + unequal_site = referent::ref_spec( + referent::ref_gaussian(), location = ~ x1 + s(site, bs = "re"), + scale = ~ s(site, bs = "re") + ), + covariate_shift = referent::ref_spec( + referent::ref_gaussian(), location = ~ x1, scale = ~ 1 + ), + stop("unknown PCNtoolkit scenario: ", scenario, call. = FALSE) + ) +} + +pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L) { + dat <- inputs[inputs$scenario == scenario, , drop = FALSE] + dat$site <- factor(dat$site) + train <- dat[dat$split == "train", , drop = FALSE] + test <- dat[dat$split == "test", , drop = FALSE] + fit <- referent::ref_fit(pcn_scenario_spec(scenario), train, outcomes = "y") + score <- stats::predict( + fit, newdata = test, uncertainty = "total", n_draw = n_draw, + allow_extrapolation = TRUE + ) + dist <- stats::predict( + fit, newdata = test, type = "distribution", uncertainty = "total", + n_draw = n_draw, allow_extrapolation = TRUE + )$y + probs <- c(0.05, 0.25, 0.5, 0.75, 0.95) + quantiles <- vapply( + probs, function(p) referent:::dist_quantile(dist, p), numeric(nrow(test)) + ) + colnames(quantiles) <- paste0("q", c("05", "25", "50", "75", "95")) + data.frame( + scenario = scenario, + row_id = test$row_id, + observed = test$y, + median = score$median, + predictive_sd = (quantiles[, "q95"] - quantiles[, "q05"]) / + (2 * stats::qnorm(0.95)), + centile = score$centile, + z = score$z, + log_density = score$log_density, + quantiles, + check.names = FALSE + ) +} + +pcn_concordance_registry <- function() { + data.frame( + scenario = c( + "null_linear", "linear_gaussian", "multiple_encoded", "log_linear", + "covariate_shift", "nonlinear_heteroskedastic", "balanced_site", + "skew_heavy", "unequal_site" + ), + evidence_class = c( + rep("matched_estimator", 5), rep("same_estimand", 4) + ), + reason = c( + rep("Gaussian BLR and linear mgcv model on the same design", 3), + "Gaussian linear model on log(y), scored back on the response scale", + "Gaussian linear model under a locked covariate-shifted test distribution", + "B-spline and penalised smooth plus variance estimators differ", + "PCN fixed batch effects and Referent penalised site effects differ", + "Gaussian BLR is compared with a selected SHASH distributional model", + "Fixed batch effects and penalised site effects differ under unequal site sizes" + ), + stringsAsFactors = FALSE + ) +} + +pcn_run_concordance <- function(root, n_draw = 2000L) { + inputs <- utils::read.csv(file.path(root, "fitted_inputs.csv"), stringsAsFactors = FALSE) + comparator <- utils::read.csv( + file.path(root, "fitted_predictions.csv"), stringsAsFactors = FALSE + ) + registry <- pcn_concordance_registry() + rows <- lapply(registry$scenario, function(scenario) { + referent <- pcn_referent_predictions(inputs, scenario, n_draw = n_draw) + pcn <- comparator[comparator$scenario == scenario, , drop = FALSE] + pcn <- pcn[match(referent$row_id, pcn$row_id), , drop = FALSE] + if (anyNA(pcn$row_id) || !identical(referent$row_id, pcn$row_id)) { + stop("row identity mismatch in scenario ", scenario, call. = FALSE) + } + core <- pcn_concordance(referent, pcn, stats::sd(referent$observed)) + q_delta <- as.matrix(referent[paste0("q", c("05", "25", "50", "75", "95"))]) - + as.matrix(pcn[paste0("q", c("05", "25", "50", "75", "95"))]) + data.frame( + scenario = scenario, + evidence_class = registry$evidence_class[registry$scenario == scenario], + n_test = nrow(referent), + as.list(core), + quantile_nrmse = sqrt(mean(q_delta^2)) / stats::sd(referent$observed), + mean_log_score_difference = mean(referent$log_density - pcn$log_density), + referent_coverage90 = mean( + referent$observed >= referent$q05 & referent$observed <= referent$q95 + ), + pcntoolkit_coverage90 = mean(pcn$observed >= pcn$q05 & pcn$observed <= pcn$q95), + stringsAsFactors = FALSE + ) + }) + do.call(rbind, rows) +} + +pcn_concordance_pass <- function(results) { + matched <- results$evidence_class == "matched_estimator" + checks <- cbind( + median_nrmse = results$median_nrmse <= 0.01, + predictive_sd_nrmse = results$predictive_sd_nrmse <= 0.02, + z_rmse = results$z_rmse <= 0.03, + z_abs_q95 = results$z_abs_q95 <= 0.08, + coverage90_difference = results$coverage90_difference <= 0.01 + ) + rowSums(!checks[matched, , drop = FALSE]) == 0L +} + +if (sys.nframe() == 0L) { + source("tools/pcntoolkit/compare_results.R") + root <- pcn_fixture_root() + pcn_validate_fixture(root) + result <- pcn_run_concordance(root) + print(result, row.names = FALSE) + args <- commandArgs(trailingOnly = TRUE) + if (length(args) == 2L && identical(args[[1L]], "--output")) { + dir.create(dirname(args[[2L]]), recursive = TRUE, showWarnings = FALSE) + utils::write.csv(result, args[[2L]], row.names = FALSE) + } else if (length(args)) { + stop("usage: run_concordance.R [--output FILE]") + } + if (!all(pcn_concordance_pass(result))) quit(status = 1L) +} diff --git a/tools/pcntoolkit/run_hbr_site_evidence.py b/tools/pcntoolkit/run_hbr_site_evidence.py new file mode 100644 index 0000000..a826c50 --- /dev/null +++ b/tools/pcntoolkit/run_hbr_site_evidence.py @@ -0,0 +1,290 @@ +# /// script +# requires-python = "==3.12.11" +# dependencies = [ +# "arviz==0.23.4", +# "numpy==2.4.6", +# "pandas==3.0.5", +# "pcntoolkit==1.3.0", +# "pymc==5.28.5", +# "scipy==1.18.1", +# "xarray==2026.7.0", +# ] +# /// +"""Run stochastic HBR and new-site transfer evidence. + +This is a release/manual runner, not an ordinary package test. It emits both +PCNtoolkit's draw-averaged outputs and proper posterior-predictive mixture +quantities, convergence receipts, and site-stratified metrics. +""" + +from __future__ import annotations + +import argparse +import importlib.metadata +import json +from pathlib import Path + +import arviz as az +import numpy as np +import pandas as pd +import pymc as pm +from scipy import optimize, special, stats + +from pcntoolkit import NormativeModel +from pcntoolkit.dataio.norm_data import NormData +from pcntoolkit.regression_model.hbr import HBR + + +def simulate(seed: int) -> pd.DataFrame: + rng = np.random.default_rng(seed) + rows: list[dict[str, object]] = [] + shifts = {"site-1": -0.7, "site-2": -0.2, "site-3": 0.25, "site-4": 0.7, "site-5": 1.1} + scales = {"site-1": 0.65, "site-2": 0.75, "site-3": 0.85, "site-4": 0.95, "site-5": 1.1} + for site in shifts: + n = 80 if site != "site-5" else 100 + x = rng.uniform(-2, 2, n) + y = 0.5 + 0.8 * x + shifts[site] + scales[site] * rng.normal(size=n) + if site == "site-5": + split = np.repeat(["adapt", "transport_test"], [25, 75]) + else: + split = np.repeat(["reference_train", "observed_site_test"], [50, 30]) + for i in range(n): + rows.append( + { + "row_id": f"{site}-{split[i]}-{i:04d}", + "site": site, + "split": split[i], + "x": x[i], + "y": y[i], + } + ) + return pd.DataFrame(rows) + + +def norm_data(name: str, frame: pd.DataFrame) -> NormData: + return NormData.from_dataframe( + name, + frame.copy(), + covariates=["x"], + batch_effects=["site"], + response_vars=["y"], + subject_ids="row_id", + ) + + +def convergence(model: NormativeModel, stage: str) -> tuple[pd.DataFrame, dict[str, object]]: + idata = model["y"].idata + # The fitted idata does not contain the temporary per-subject variables + # created during prediction, so the full posterior is the convergence set. + summary = az.summary(idata, fmt="wide") + summary.insert(0, "parameter", summary.index.astype(str)) + summary.insert(0, "stage", stage) + divergences = int(idata.sample_stats["diverging"].sum().item()) + max_rhat = float(np.nanmax(summary["r_hat"].to_numpy())) + min_bulk = float(np.nanmin(summary["ess_bulk"].to_numpy())) + min_tail = float(np.nanmin(summary["ess_tail"].to_numpy())) + receipt = { + "stage": stage, + "divergences": divergences, + "max_rhat": max_rhat, + "min_ess_bulk": min_bulk, + "min_ess_tail": min_tail, + "pass": bool(divergences == 0 and max_rhat <= 1.01 and min_bulk >= 400 and min_tail >= 400), + } + return summary.reset_index(drop=True), receipt + + +def posterior_normal_draws(model: NormativeModel, frame: pd.DataFrame) -> tuple[np.ndarray, np.ndarray]: + data = norm_data("posterior-mixture", frame) + model.preprocess(data) + response_data = data.sel({"response_vars": "y"}) + X, be, _, Y, _ = model.extract_data(response_data) + hbr = model["y"] + pymc_model = hbr.likelihood.create_model_with_data(X, be, hbr.be_maps, Y) + params = hbr.likelihood.compile_params(pymc_model, X, be, hbr.be_maps, Y) + var_names = [f"{name}_per_subject" for name in params] + with pymc_model: + for name, (value, dims) in params.items(): + pm.Deterministic(f"{name}_per_subject", value, dims=dims) + posterior = pm.sample_posterior_predictive( + hbr.idata, extend_inferencedata=False, var_names=var_names, progressbar=False + ) + extracted = az.extract(posterior, "posterior_predictive", var_names=var_names) + arrays = { + name: hbr.extract_and_reshape(extracted, len(frame), f"{name}_per_subject").values + for name in params + } + return arrays["mu"], arrays["sigma"] + + +def mixture_quantile(mu: np.ndarray, sigma: np.ndarray, p: float) -> np.ndarray: + out = np.empty(mu.shape[0]) + for i in range(mu.shape[0]): + lo = np.min(mu[i] - 12 * sigma[i]) + hi = np.max(mu[i] + 12 * sigma[i]) + out[i] = optimize.brentq( + lambda q: np.mean(stats.norm.cdf(q, mu[i], sigma[i])) - p, + lo, + hi, + ) + return out + + +def predict_with_mixture( + model: NormativeModel, frame: pd.DataFrame, lane: str, outcome_mean: float, outcome_sd: float +) -> pd.DataFrame: + predicted = model.predict(norm_data(f"{lane}-test", frame)) + reported_z = predicted["Z"].sel(response_vars="y").values + reported_q05 = predicted["centiles"].sel(response_vars="y", centile=0.05).values + reported_q50 = predicted["centiles"].sel(response_vars="y", centile=0.5).values + reported_q95 = predicted["centiles"].sel(response_vars="y", centile=0.95).values + reported_logp = predicted["logp"].sel(response_vars="y").values - np.log(outcome_sd) + + mu, sigma = posterior_normal_draws(model, frame) + y_scaled = (frame["y"].to_numpy() - outcome_mean) / outcome_sd + component_z = (y_scaled[:, None] - mu) / sigma + mixture_cdf = np.mean(stats.norm.cdf(component_z), axis=1) + mixture_logp = special.logsumexp(stats.norm.logpdf(component_z) - np.log(sigma), axis=1) - np.log( + sigma.shape[1] + ) - np.log(outcome_sd) + mixture_q05 = outcome_mean + outcome_sd * mixture_quantile(mu, sigma, 0.05) + mixture_q50 = outcome_mean + outcome_sd * mixture_quantile(mu, sigma, 0.5) + mixture_q95 = outcome_mean + outcome_sd * mixture_quantile(mu, sigma, 0.95) + return pd.DataFrame( + { + "lane": lane, + "row_id": frame["row_id"].to_numpy(), + "site": frame["site"].to_numpy(), + "observed": frame["y"].to_numpy(), + "reported_mean_draw_z": reported_z, + "reported_mean_draw_log_density": reported_logp, + "reported_mean_draw_q05": reported_q05, + "reported_mean_draw_q50": reported_q50, + "reported_mean_draw_q95": reported_q95, + "mixture_centile": mixture_cdf, + "mixture_z": stats.norm.ppf(mixture_cdf), + "mixture_log_density": mixture_logp, + "mixture_q05": mixture_q05, + "mixture_q50": mixture_q50, + "mixture_q95": mixture_q95, + } + ) + + +def site_summary(predictions: pd.DataFrame) -> pd.DataFrame: + rows: list[dict[str, object]] = [] + for (lane, site), group in predictions.groupby(["lane", "site"], sort=True): + u = group["mixture_centile"].to_numpy() + grid = np.array([0.05, 0.25, 0.5, 0.75, 0.95]) + rows.append( + { + "method": "pcntoolkit_hbr_mixture", + "lane": lane, + "site": site, + "n": len(group), + "mean_log_score": group["mixture_log_density"].mean(), + "median_rmse": np.sqrt(np.mean((group["observed"] - group["mixture_q50"]) ** 2)), + "coverage90": np.mean( + (group["observed"] >= group["mixture_q05"]) + & (group["observed"] <= group["mixture_q95"]) + ), + "mace": np.mean([abs(q - np.mean(u <= q)) for q in grid]), + "mean_z": group["mixture_z"].mean(), + "var_z": group["mixture_z"].var(ddof=1), + } + ) + return pd.DataFrame(rows) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--draws", type=int, default=1500) + parser.add_argument("--tune", type=int, default=500) + parser.add_argument("--chains", type=int, default=4) + parser.add_argument("--cores", type=int, default=4) + parser.add_argument("--nuts-sampler", default="nutpie") + parser.add_argument("--seed", type=int, default=20260823) + parser.add_argument("--progressbar", action="store_true") + args = parser.parse_args() + args.output.mkdir(parents=True, exist_ok=True) + + data = simulate(args.seed) + reference_train = data[data["split"] == "reference_train"].copy() + observed_test = data[data["split"] == "observed_site_test"].copy() + adaptation = data[data["split"] == "adapt"].copy() + transport_test = data[data["split"] == "transport_test"].copy() + outcome_mean = float(reference_train["y"].mean()) + outcome_sd = float(reference_train["y"].std(ddof=0)) + + hbr = HBR( + draws=args.draws, + tune=args.tune, + chains=args.chains, + cores=args.cores, + nuts_sampler=args.nuts_sampler, + progressbar=args.progressbar, + ) + base = NormativeModel( + hbr, savemodel=False, evaluate_model=False, saveresults=False, saveplots=False, + inscaler="standardize", outscaler="standardize", name="hbr-site-evidence" + ) + base.fit(norm_data("reference-train", reference_train)) + base_convergence, base_receipt = convergence(base, "base") + observed_predictions = predict_with_mixture( + base, observed_test, "observed_site", outcome_mean, outcome_sd + ) + + transferred = base.transfer( + norm_data("site-5-adaptation", adaptation), + save_dir=str(args.output / "transfer_model"), + draws=args.draws, + tune=args.tune, + chains=args.chains, + cores=args.cores, + nuts_sampler=args.nuts_sampler, + progressbar=args.progressbar, + ) + transfer_convergence, transfer_receipt = convergence(transferred, "transfer") + transport_predictions = predict_with_mixture( + transferred, transport_test, "transferred_site", outcome_mean, outcome_sd + ) + predictions = pd.concat([observed_predictions, transport_predictions], ignore_index=True) + convergence_table = pd.concat([base_convergence, transfer_convergence], ignore_index=True) + + data.to_csv(args.output / "site_data.csv", index=False, float_format="%.17g") + predictions.to_csv(args.output / "pcntoolkit_predictions.csv", index=False, float_format="%.17g") + site_summary(predictions).to_csv(args.output / "pcntoolkit_site_summary.csv", index=False, float_format="%.17g") + convergence_table.to_csv(args.output / "hbr_convergence.csv", index=False, float_format="%.17g") + receipt = { + "comparator": "pcntoolkit", + "version": importlib.metadata.version("pcntoolkit"), + "draws": args.draws, + "tune": args.tune, + "chains": args.chains, + "cores": args.cores, + "nuts_sampler": args.nuts_sampler, + "requested_seed": args.seed, + "hbr_public_random_seed_argument": False, + "aggregation": { + "reported_z": "mean of draw-specific z", + "reported_quantile": "mean of draw-specific quantiles", + "reported_logp": "mean of draw-specific log densities", + "mixture_outputs": "CDF and density averaged over posterior draws, then transformed", + }, + "convergence_thresholds": { + "max_rhat": 1.01, + "min_ess_bulk": 400, + "min_ess_tail": 400, + "divergences": 0, + }, + "stages": [base_receipt, transfer_receipt], + "all_stages_pass": bool(base_receipt["pass"] and transfer_receipt["pass"]), + } + (args.output / "receipt.json").write_text( + json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + + +if __name__ == "__main__": + main() diff --git a/tools/pcntoolkit/run_site_transport_evidence.R b/tools/pcntoolkit/run_site_transport_evidence.R new file mode 100644 index 0000000..cee3f51 --- /dev/null +++ b/tools/pcntoolkit/run_site_transport_evidence.R @@ -0,0 +1,86 @@ +# Compare Referent adaptation with the separately defined PCNtoolkit HBR +# transfer evidence. Usage: +# Rscript tools/pcntoolkit/run_site_transport_evidence.R INPUT_DIR OUTPUT_DIR + +source("tools/pcntoolkit/benchmark_helpers.R") +source("tools/pcntoolkit/site_evidence.R") + +run_referent_site_evidence <- function(input_dir, output_dir) { + data <- utils::read.csv(file.path(input_dir, "site_data.csv"), stringsAsFactors = FALSE) + train <- data[data$split == "reference_train", , drop = FALSE] + observed <- data[data$split == "observed_site_test", , drop = FALSE] + adaptation <- data[data$split == "adapt", , drop = FALSE] + transported <- data[data$split == "transport_test", , drop = FALSE] + train$site <- factor(train$site) + observed$site <- factor(observed$site, levels = levels(train$site)) + adaptation$site <- factor(adaptation$site) + transported$site <- factor(transported$site) + + fit <- referent::ref_fit( + referent::ref_spec( + referent::ref_gaussian(), location = ~ s(x, k = 6) + s(site, bs = "re"), + scale = ~ s(site, bs = "re") + ), + train, outcomes = "y" + ) + adapted <- referent::ref_adapt( + fit, adaptation, by = site, parameters = c("location", "scale") + ) + + score_rows <- function(model, frame, lane) { + score <- stats::predict( + model, frame, uncertainty = "total", allow_extrapolation = TRUE + ) + dist <- stats::predict( + model, frame, type = "distribution", uncertainty = "total", + allow_extrapolation = TRUE + )$y + data.frame( + method = "referent_adaptation", lane = lane, row_id = frame$row_id, + site = as.character(frame$site), observed = frame$y, + centile = score$centile, log_density = score$log_density, + q05 = referent:::dist_quantile(dist, 0.05), + q50 = referent:::dist_quantile(dist, 0.5), + q95 = referent:::dist_quantile(dist, 0.95), + stringsAsFactors = FALSE + ) + } + predictions <- rbind( + score_rows(fit, observed, "observed_site"), + score_rows(adapted, transported, "transferred_site") + ) + ref_summary <- do.call(rbind, lapply(split(predictions, predictions$lane), function(d) { + pcn_site_summary(d, "referent_adaptation", unique(d$lane)) + })) + pcn_summary <- utils::read.csv( + file.path(input_dir, "pcntoolkit_site_summary.csv"), stringsAsFactors = FALSE + ) + if (!"coverage90_se" %in% names(pcn_summary)) { + pcn_summary$coverage90_se <- sqrt(0.9 * 0.1 / pcn_summary$n) + } + summary <- rbind( + ref_summary[, names(pcn_summary), drop = FALSE], + pcn_summary + ) + gated <- pcn_site_gate(summary) + dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) + utils::write.csv(predictions, file.path(output_dir, "referent_predictions.csv"), row.names = FALSE) + utils::write.csv(gated, file.path(output_dir, "site_comparison.csv"), row.names = FALSE) + receipt <- list( + estimand = "separate adaptation and transfer policies; no rowwise parity claim", + all_sites_pass = all(gated$pass), + failed_sites = split(gated$site[!gated$pass], gated$method[!gated$pass]), + referent_adaptation = unclass(adapted$adaptation) + ) + jsonlite::write_json( + receipt, file.path(output_dir, "referent_receipt.json"), + auto_unbox = TRUE, pretty = TRUE, null = "null" + ) + invisible(gated) +} + +if (sys.nframe() == 0L) { + args <- commandArgs(trailingOnly = TRUE) + if (length(args) != 2L) stop("usage: run_site_transport_evidence.R INPUT_DIR OUTPUT_DIR") + run_referent_site_evidence(args[[1L]], args[[2L]]) +} diff --git a/tools/pcntoolkit/run_superiority.R b/tools/pcntoolkit/run_superiority.R new file mode 100644 index 0000000..8674fd4 --- /dev/null +++ b/tools/pcntoolkit/run_superiority.R @@ -0,0 +1,33 @@ +# Run the locked skew/heavy-tail superiority benchmark. + +source("tools/pcntoolkit/run_concordance.R") +source("tools/pcntoolkit/benchmark_helpers.R") + +pcn_run_superiority <- function(root, B = 4999L, n_draw = 2000L, + seed = 91L) { + inputs <- utils::read.csv(file.path(root, "fitted_inputs.csv"), stringsAsFactors = FALSE) + comparator <- utils::read.csv( + file.path(root, "fitted_predictions.csv"), stringsAsFactors = FALSE + ) + referent <- pcn_referent_predictions(inputs, "skew_heavy", n_draw = n_draw) + comparator <- comparator[comparator$scenario == "skew_heavy", , drop = FALSE] + comparator <- comparator[match(referent$row_id, comparator$row_id), , drop = FALSE] + pcn_compare_predictions( + referent, comparator, cluster = referent$row_id, + scenario = "skew_heavy", B = B, seed = seed + ) +} + +if (sys.nframe() == 0L) { + root <- file.path("tests", "fixtures", "pcntoolkit", "v1.3.0") + result <- pcn_run_superiority(root) + print(result, row.names = FALSE) + args <- commandArgs(trailingOnly = TRUE) + if (length(args) == 2L && identical(args[[1L]], "--output")) { + dir.create(dirname(args[[2L]]), recursive = TRUE, showWarnings = FALSE) + utils::write.csv(result, args[[2L]], row.names = FALSE) + } else if (length(args)) { + stop("usage: run_superiority.R [--output FILE]") + } + if (!identical(result$classification, "superior")) quit(status = 1L) +} diff --git a/tools/pcntoolkit/site_evidence.R b/tools/pcntoolkit/site_evidence.R new file mode 100644 index 0000000..27eaafb --- /dev/null +++ b/tools/pcntoolkit/site_evidence.R @@ -0,0 +1,29 @@ +pcn_site_summary <- function(prediction, method, lane) { + required <- c("site", "observed", "centile", "log_density", "q05", "q50", "q95") + if (!all(required %in% names(prediction))) { + stop("site prediction table is missing required columns", call. = FALSE) + } + parts <- split(seq_len(nrow(prediction)), prediction$site) + rows <- lapply(names(parts), function(site) { + d <- prediction[parts[[site]], , drop = FALSE] + z <- stats::qnorm(d$centile) + data.frame( + method = method, lane = lane, site = site, n = nrow(d), + mean_log_score = mean(d$log_density), + median_rmse = sqrt(mean((d$observed - d$q50)^2)), + coverage90 = mean(d$observed >= d$q05 & d$observed <= d$q95), + coverage90_se = sqrt(0.9 * 0.1 / nrow(d)), + mace = pcn_benchmark_mace(d$centile), + mean_z = mean(z), var_z = stats::var(z), + stringsAsFactors = FALSE + ) + }) + do.call(rbind, rows) +} + +pcn_site_gate <- function(summary, coverage_floor = 0.80, mace_ceiling = 0.08, + mean_z_ceiling = 0.35) { + per_site <- summary$coverage90 >= coverage_floor & + summary$mace <= mace_ceiling & abs(summary$mean_z) <= mean_z_ceiling + data.frame(summary, pass = per_site, stringsAsFactors = FALSE) +} diff --git a/vignettes/pcntoolkit.Rmd b/vignettes/pcntoolkit.Rmd index 345fc17..6228aaa 100644 --- a/vignettes/pcntoolkit.Rmd +++ b/vignettes/pcntoolkit.Rmd @@ -34,7 +34,7 @@ assessment <- ref_assess(fit, newdata = val) | PCNtoolkit | referent | Notes | |---|---|---| -| normative model (`BLR`, `HBR`, `GPR`) | [`ref_spec()`](https://bbuchsbaum.github.io/referent/reference/ref_spec/) + [`ref_fit()`](https://bbuchsbaum.github.io/referent/reference/ref_fit/) | one `mgcv` model per outcome; Gaussian or SHASH families with formulas per parameter | +| normative model (`BLR`, `HBR`) | [`ref_spec()`](https://bbuchsbaum.github.io/referent/reference/ref_spec/) + [`ref_fit()`](https://bbuchsbaum.github.io/referent/reference/ref_fit/) | one `mgcv` model per outcome; Gaussian or SHASH families with formulas per parameter; GPR belongs to the legacy PCNtoolkit interface, not 1.x | | response variable | `outcomes =` | several outcomes fit as a panel, kept wide | | covariates, batch effects | formula terms | `s(site, bs = "re")` plays the role of a batch random effect | | `predict()` / `estimate()` | `predict(fit, newdata)` | returns scores or `` columns | @@ -119,11 +119,18 @@ not the legacy 0.35 interface. | `EV` (`EXPV`) | `ev` | Algebraically equivalent on nonconstant complete data | Both compute `1 - var(residual) / var(y)`. Constant outcomes and filtering of non-finite rows can produce different edge behaviour. | | `Rho` | no exact column | Different statistic | PCNtoolkit 1.x reports Spearman rank correlation. `cor` is Pearson correlation of the observed value and predictive median. | | `RMSE` | `rmse` | Exact on the same included rows | Missing and non-finite row handling must also match. | -| `MACE` | `mace` | Same definition | Mean over the centiles 0.05, 0.25, 0.5, 0.75, 0.95 of the absolute gap between the centile and the fraction of observations at or below its curve. | +| `MACE` | `mace` | Exact only without batch effects | Both use the mean absolute calibration gap on a centile grid. With batch combinations, PCNtoolkit 1.3.0 averages the batch-level values equally; `referent` currently pools rows. | | `ShapiroW` | `shapiro_w` | Same statistic | Shapiro-Wilk W of Z; `referent` subsamples 5000 scores with a fixed seed when n is larger, because `shapiro.test()` accepts no more. | -| `Skew`, `Kurtosis` | `skew_z`, `excess_kurtosis_z` | Exact up to the kurtosis convention | Both are standardised moments of Z. PCNtoolkit reports kurtosis; `referent` reports excess kurtosis (kurtosis minus 3). | +| `Skew`, `Kurtosis` | `skew_z`, `excess_kurtosis_z` | Related, not exact | PCNtoolkit 1.3.0 uses SciPy's bias-corrected skewness and excess kurtosis. `referent` reports unadjusted standardised moments (and also uses excess kurtosis). The difference is material for small samples. | | (none) | `mean_log_score`, `crps` | referent additions | Proper scores of the full predictive distribution; larger log score and smaller CRPS are better. | +One implementation detail matters for external rescoring: with outcome +standardisation enabled, PCNtoolkit 1.3.0 maps predictions and centiles back to +the outcome scale but returns `logp` on the standardised scale. Subtract +`log(sd(y_train))` (using the population standard deviation used by the +scaler), plus any response-transform Jacobian, before comparing response-scale +log scores. + ```{r metrics} assessment$overall[, c("standardized_log_score", "smse", "ev", "cor", "rmse", "crps")] ``` @@ -190,8 +197,27 @@ data.frame( `Z` here includes uncertainty in the fitted curves (`uncertainty = "total"`, the default). That is closest to legacy BLR's coefficient-aware predictive variance, but it is not a general claim of numerical equivalence across -PCNtoolkit model families. `uncertainty = "conditional"` gives the plug-in -score. +PCNtoolkit model families. In particular, PCNtoolkit HBR averages +draw-specific Z scores and quantiles, while `referent` derives them from a +predictive mixture. Those are different estimands even with identical draws. +`uncertainty = "conditional"` gives the plug-in score. + +## What numerical agreement means + +The executable comparison target is PCNtoolkit 1.3.0. Fixed Gaussian +distributions and transformed-distribution algebra are checked as exact; +SHASH distributions are checked after an explicit parameter conversion; +deliberately matched fitted models use prediction-level tolerances. When the +estimators differ, agreement is assessed by held-out proper scores and +calibration rather than by forcing their parameters or predictions to match. + +These are independent claims: better held-out performance cannot excuse a +failure of shared distribution algebra, and numerical agreement does not by +itself establish superiority. The complete classifications, tolerances, and +CI tiers are recorded in the repository's +`docs/design/pcntoolkit-validation-contract.md`, with current machine-readable +results under `docs/evidence/pcntoolkit/v1.3.0/`. Both paths are linked from +the repository README. # Site adaptation From 9f1d47c1963c73244dac2a52c5b08e90d7cc199f Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Sun, 23 Aug 2026 14:42:19 -0400 Subject: [PATCH 02/13] Remove stale Beads tracking records From f1395bf34ea29fbc9684aa50026912d19b671c40 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Sun, 23 Aug 2026 14:43:02 -0400 Subject: [PATCH 03/13] Exclude stale Beads tracking records --- .beads/issues.jsonl | 8 -------- 1 file changed, 8 deletions(-) diff --git a/.beads/issues.jsonl b/.beads/issues.jsonl index 65eddb0..dc7f6ef 100644 --- a/.beads/issues.jsonl +++ b/.beads/issues.jsonl @@ -1,14 +1,6 @@ {"id":"referent-c48","title":"Dynamics kernel estimator: scale-unidentified, silent fallback to start values","description":"R/dynamics.R:104-151. process_correlation normalises so (tau_b,tau_g,sigma_e) are unidentified; BFGS walks ridge, eigen fails, tryCatch returns START values with identified=TRUE. norm_stable (ell=Inf) never fits. Fix: simplex parameterization + bounded log(ell), L-BFGS-B, check convergence, never silent fallback, stable+nugget when lags fixed, report normalised variance fractions. Also A4: NA velocity/forecast quantities when unidentified. Add OOS test on irregular lags: var(innovation) in 1±0.1, FPR in 0.05±0.015. Review A3/A4.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:18Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:20Z","started_at":"2026-08-22T13:02:50Z","closed_at":"2026-08-22T13:28:20Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-0rh","title":"Fix site scale adaptation bias (E[log|Z|] = -0.635)","description":"R/adapt.R:47-50 uses mean(log|r/s|); identical-site adaptation shrinks sigma to 0.55x, held-out var(z)=3.2-3.9. Fix: 0.5*log(mean((r/s)^2)) with z=qnorm(centile); add test that identical-site adaptation leaves var(z)~1. Review A2.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:17Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:19Z","started_at":"2026-08-22T13:02:49Z","closed_at":"2026-08-22T13:28:19Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-4du","title":"Derive covariates from spec formulas, not all non-outcome columns","description":"R/fit.R:30,45,260. Covariates are every non-outcome column: NA in unused columns silently drops rows; Mahalanobis support is computed over other outcomes; predict errors if unused column absent. Fix: all.vars() over spec formulas; complete-case on outcome+those; message dropped rows. See docs/review-2026-08-22.md A1.","status":"closed","priority":0,"issue_type":"bug","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:15Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:02:48Z","started_at":"2026-08-22T12:51:42Z","closed_at":"2026-08-22T13:02:48Z","close_reason":"Phase 1 fdb58e2","dependency_count":0,"dependent_count":0,"comment_count":0} -{"id":"referent-eo6.7","title":"Publish tiered CI and documentation evidence","description":"Add fast offline PR checks, scheduled comparator regeneration and scenario runs, release-level HBR and multisite gates, machine-readable reports, and reader-facing evidence tables.","design":"Pinned fixtures remain stable until an explicit comparator upgrade. CI reports exact conformance separately from scientific superiority and unsupported lanes.","acceptance_criteria":"CI has documented PR, scheduled, and release tiers; the vignette links evidence and limitations; an upgrade produces reviewed drift rather than silently replacing baselines.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:59Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:52Z","started_at":"2026-08-23T14:01:27Z","closed_at":"2026-08-23T14:20:52Z","close_reason":"Added offline PR oracles, weekly pinned regeneration/drift rejection, release HBR/site gates, reproducible machine-readable evidence, and linked reader-facing documentation","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:59Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:14Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:15Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:16Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.6","type":"blocks","created_at":"2026-08-23T09:30:17Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":4,"dependent_count":0,"comment_count":0} -{"id":"referent-eo6.6","title":"Evaluate HBR SHASH and site transport separately","description":"Add scheduled evidence for HBR posterior aggregation, SHASH fitted performance, site effects, unseen-site transport, and Referent adaptation without claiming false rowwise equivalence.","design":"Require convergence and Monte Carlo receipts. Use leave-one-site-out calibration and performance; keep PCNtoolkit transfer and Referent adaptation labeled as different estimands.","acceptance_criteria":"HBR and transport reports expose convergence, uncertainty, score and calibration evidence, and no small-site failure is averaged away.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:58Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:01:25Z","started_at":"2026-08-23T13:53:31Z","closed_at":"2026-08-23T14:01:25Z","close_reason":"HBR/transfer runner emits proper mixture and draw-averaged outputs, convergence receipts, per-site evidence, and a smoke run proved both convergence and small-site gates fail closed; release-grade evidence remains CI-gated","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:57Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:13Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} -{"id":"referent-eo6.5","title":"Establish held-out superiority and non-inferiority gates","description":"Implement preregistered synthetic scenario families, untouched test splits, paired proper-score comparisons, cluster-aware uncertainty, calibration non-inferiority gates, and negative controls.","design":"A differing Referent method is superior only when its paired held-out proper-score interval clears a practical margin and no marginal, conditional, site, or tail calibration gate materially regresses.","acceptance_criteria":"The benchmark classifies conformant, equivalent, superior, trade-off, and inferior outcomes; null and deliberately overconfident negative controls behave correctly.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:56Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:53:27Z","started_at":"2026-08-23T13:47:07Z","closed_at":"2026-08-23T13:53:27Z","close_reason":"Locked design registry, cluster bootstrap, calibration non-inferiority, full classification rules, and negative controls pass; skew-heavy held-out case is superior with positive log-score CI and better calibration","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:56Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:12Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} -{"id":"referent-eo6.4","title":"Certify fitted-model concordance in overlap cases","description":"Compare identical train and test rows for intercept-only, linear, categorical, heteroskedastic, smooth, transformed, and balanced-site model configurations using per-row predictive outputs.","design":"Use normalized prediction error, predictive-scale error, Z RMSE, central quantile errors, log tails, and coverage. Avoid a sole maximum-error gate.","acceptance_criteria":"All registered overlap cases meet locked scenario-level concordance margins or have a documented non-equivalent classification.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:55Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:47:05Z","started_at":"2026-08-23T13:43:50Z","closed_at":"2026-08-23T13:47:05Z","close_reason":"Four matched linear/log Gaussian scenarios meet all locked prediction, scale, Z, tail-quantile, and coverage margins; divergent smooth/site cases are explicitly diagnostic","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:54Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:11Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} -{"id":"referent-eo6.3","title":"Prove exact shared distribution and metric semantics","description":"Add analytic and differential tests for Gaussian, SHASHb to mgcv SHASH conversion, transforms, log tails, log densities, and explicitly normalized metric definitions.","design":"Use independent analytic formulas alongside external fixtures. Expected definition differences must be tested as differences, not hidden by broad tolerances.","acceptance_criteria":"Gaussian and SHASH semantic fixtures pass scale-aware declared tolerances, adversarial tails remain finite, and metric relationships are independently verified.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:53Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:43:48Z","started_at":"2026-08-23T13:41:06Z","closed_at":"2026-08-23T13:43:48Z","close_reason":"Independent R analytic, Referent, and pinned PCNtoolkit paths agree for Gaussian/converted SHASH/transforms; tail and definition-difference tests pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:53Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6.2","type":"blocks","created_at":"2026-08-23T09:30:10Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} -{"id":"referent-eo6.2","title":"Build reproducible offline PCNtoolkit fixture harness","description":"Add a pinned Python generator, neutral inputs, checked-in per-row PCNtoolkit outputs, provenance receipts, and an R comparison reader without adding Python to normal package tests.","design":"PCNtoolkit runs only during explicit regeneration or scheduled CI. Checked-in fixtures are versioned by comparator release and carry hashes and resolved environment metadata.","acceptance_criteria":"Fixture generation is deterministic; schema and hashes validate in R; normal testthat runs need no Python or network.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:52Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:41:04Z","started_at":"2026-08-23T13:34:09Z","closed_at":"2026-08-23T13:41:04Z","close_reason":"Pinned Python generator is byte-stable across two runs; checked-in fixture manifest, schema, hashes, and offline R validation pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:51Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6.1","type":"blocks","created_at":"2026-08-23T09:30:09Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} -{"id":"referent-eo6.1","title":"Define pinned PCNtoolkit validation contract","description":"Write the normative comparison contract for PCNtoolkit 1.3.0, classify outputs as exact, parameter-converted, matched-estimator, same-estimand, or non-equivalent, and reconcile the public comparison vignette with executed definitions.","design":"Separate semantic conformance from scientific superiority. Record conditional versus total uncertainty and executed versus documented comparator behavior.","acceptance_criteria":"A reviewed design document and corrected vignette define every comparison lane, estimand, output, uncertainty mode, and mismatch disposition.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:50Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:34:07Z","started_at":"2026-08-23T13:30:19Z","closed_at":"2026-08-23T13:34:07Z","close_reason":"Pinned comparison contract and corrected current PCNtoolkit 1.3.0 semantics in the public vignette","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.1","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:50Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":0,"dependent_count":1,"comment_count":0} -{"id":"referent-eo6","title":"Establish PCNtoolkit conformance and superiority evidence","description":"Implement the seven-part validation program: pin and document comparison contracts, generate reproducible PCNtoolkit fixtures, prove exact shared semantics, measure fitted-model concordance, establish held-out superiority without sacrificing calibration, evaluate HBR and site transport separately, and publish tiered CI evidence.","design":"PCNtoolkit is an external differential oracle only for shared contracts. Alternative Referent methods may diverge when paired held-out proper-score superiority and calibration non-inferiority are demonstrated. Normal R tests consume offline fixtures; Python regeneration is scheduled or manual.","acceptance_criteria":"All seven child work packages are complete; exact relationships pass declared tolerances; every mismatch is classified; superiority claims use untouched test data and calibration gates; normal R tests remain Python-free; documentation and CI expose evidence tiers.","status":"closed","priority":1,"issue_type":"epic","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:20Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:54Z","started_at":"2026-08-23T13:30:18Z","closed_at":"2026-08-23T14:20:54Z","close_reason":"All seven PCNtoolkit conformance and superiority evidence lanes implemented and validated","labels":["pcntoolkit","scientific-evidence","validation"],"dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-xzc","title":"Make referent vignettes surpass PCNtoolkit docs","description":"Audit and improve referent's starlightdown vignette site against the live PCNtoolkit documentation. Establish a direct visual and reader-journey baseline, revise source vignettes and site navigation, verify executable examples and rendered desktop/mobile output, and publish the completed branch without touching unrelated work.","acceptance_criteria":"All public vignette pages use current exported APIs and render successfully; newcomer journey, task navigation, interpretation, and recovery are materially stronger than the live PCNtoolkit stable site; representative pages are compared at desktop and mobile viewports; repository tests and documentation build pass; intended changes are committed and pushed with tracking/live SHA verification.","notes":"Gauntlet complete. Independent critic final PASS and blind choice referent in all six categories. Fixed discovered S3 route blocker via docsLoader generateId preserving dotted method paths; added fail-closed generated plot alt postprocessor and generic 35-route identity/canonical/redirect/alt checker. Final gates: cold starlightdown build 35 routes, Astro 36 pages plus Pagefind, browser desktop/mobile axe clean and search working, R CMD build passed, LC_ALL=en_US.UTF-8 R CMD check --no-manual Status OK.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T18:05:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T18:49:55Z","started_at":"2026-08-22T18:05:43Z","closed_at":"2026-08-22T18:49:55Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e4m","title":"Gauntlet 2: thrive lines and velocity","description":"thrive type was removed from autoplot.norm_forecast in Phase 5; referent now has no thrive capability. Bar for thrive: PCNtoolkit nb14 CorrelationMatrix + ZGainScore.get_thrivelines + plot_centiles_advanced on a shared constructed two-visit dataset (LNM_data.csv is not public). Bar for velocity: ground-truth recovery on simulated data, since PCNtoolkit has no velocity estimand.","status":"closed","priority":1,"issue_type":"feature","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:16:57Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:23Z","closed_at":"2026-08-22T19:13:23Z","close_reason":"Both lanes won round 1 and were verified by independent critics. Patches pending integration (thrive rebased needed; velocity chart_velocity_se drop).","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e6o","title":"Close WM-hypointensities MSLL gap vs information-matched PCN opponent","description":"referent -0.470 vs fair opponent -0.491. Test-split var_z 1.17-1.23 indicates 10-20 pct out-of-sample overconfidence. Try norm_calibrate on out-of-fold train scores (train-only) before rescoring.","status":"closed","priority":1,"issue_type":"task","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:09:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:22Z","closed_at":"2026-08-22T19:13:22Z","close_reason":"Closed via ref_spec(transform=) Box-Cox response transform (commit 4dd3683). WM -0.49154 vs PCN fair -0.49087. NOTE: margin is +0.00068 nats with paired per-subject SE 0.033 (t=-0.02) - a point-estimate win only, not statistical evidence. Root cause was a missing capability, not tuning: SHASH moves conditional residual shape; PCN's warp is a monotone map on y.","dependency_count":0,"dependent_count":0,"comment_count":0} From 4ce5c87501ebd49392e14c11a6e5e3712269e511 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Sun, 23 Aug 2026 19:33:52 -0400 Subject: [PATCH 04/13] Address PCNtoolkit validation review --- .github/workflows/pcntoolkit-validation.yml | 40 +++- .gitignore | 2 + docs/design/pcntoolkit-validation-contract.md | 9 + docs/evidence/pcntoolkit/v1.3.0/README.md | 5 + inst/pcntoolkit/benchmark_helpers.R | 73 +++++++ inst/pcntoolkit/run_concordance.R | 9 +- inst/pcntoolkit/run_hbr_site_evidence.py | 1 + tests/testthat/test-pcntoolkit-hbr-site.R | 1 + tests/testthat/test-pcntoolkit-superiority.R | 46 ++++- tests/testthat/test-pcntoolkit-workflow.R | 38 ++++ tools/pcntoolkit/benchmark_helpers.R | 73 +++++++ tools/pcntoolkit/generate_fixtures.py | 8 +- .../generate_release_superiority.py | 129 ++++++++++++ tools/pcntoolkit/render_evidence.R | 21 +- tools/pcntoolkit/run_concordance.R | 9 +- tools/pcntoolkit/run_hbr_site_evidence.py | 1 + tools/pcntoolkit/run_release_superiority.R | 190 ++++++++++++++++++ 17 files changed, 640 insertions(+), 15 deletions(-) create mode 100644 tests/testthat/test-pcntoolkit-workflow.R create mode 100644 tools/pcntoolkit/generate_release_superiority.py create mode 100644 tools/pcntoolkit/run_release_superiority.R diff --git a/.github/workflows/pcntoolkit-validation.yml b/.github/workflows/pcntoolkit-validation.yml index bc01886..8af930d 100644 --- a/.github/workflows/pcntoolkit-validation.yml +++ b/.github/workflows/pcntoolkit-validation.yml @@ -61,6 +61,7 @@ jobs: name: Regenerate PCNtoolkit fixture and evidence if: >- github.event_name == 'schedule' || + github.event_name == 'release' || (github.event_name == 'workflow_dispatch' && (inputs.tier == 'weekly' || inputs.tier == 'release')) runs-on: ubuntu-latest @@ -83,7 +84,10 @@ jobs: Rscript tools/pcntoolkit/check_fixture_drift.R tests/fixtures/pcntoolkit/v1.3.0 /tmp/pcntoolkit-v1.3.0 - name: Render machine-readable evidence - run: Rscript tools/pcntoolkit/render_evidence.R evidence/pcntoolkit/v1.3.0 + run: >- + Rscript tools/pcntoolkit/render_evidence.R + evidence/pcntoolkit/v1.3.0 + --fixture-root /tmp/pcntoolkit-v1.3.0 - uses: actions/upload-artifact@v4 with: name: pcntoolkit-weekly-evidence @@ -126,3 +130,37 @@ jobs: with: name: pcntoolkit-release-hbr-site-evidence path: evidence/hbr-site + + release-shash-superiority: + name: Release SHASH fitting and replicated superiority evidence + if: >- + github.event_name == 'release' || + (github.event_name == 'workflow_dispatch' && inputs.tier == 'release') + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v6 + with: + version: "0.8.13" + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: local::. + needs: check + - name: Generate independent PCNtoolkit comparison replicates + run: >- + uv run tools/pcntoolkit/generate_release_superiority.py + --output evidence/release-superiority --replicates 20 + --seed-start 20260824 + - name: Fit SHASH models and enforce replicated held-out gates + run: >- + Rscript tools/pcntoolkit/run_release_superiority.R + evidence/release-superiority evidence/release-superiority + 499 4999 2000 + - uses: actions/upload-artifact@v4 + if: always() + with: + name: pcntoolkit-release-shash-superiority-evidence + path: evidence/release-superiority diff --git a/.gitignore b/.gitignore index ed0c3b0..45acd0f 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,8 @@ .omc/ .claude/ .mote/ +__pycache__/ +*.py[cod] tests/testthat/testthat-problems.rds tests/testthat/_problems/ tmp/ diff --git a/docs/design/pcntoolkit-validation-contract.md b/docs/design/pcntoolkit-validation-contract.md index 8076493..3299d65 100644 --- a/docs/design/pcntoolkit-validation-contract.md +++ b/docs/design/pcntoolkit-validation-contract.md @@ -185,6 +185,15 @@ and are regenerated only by the pinned script under `tools/pcntoolkit/`. - Release/manual: run stochastic HBR, SHASH-fitting, multi-site transport, and the replicated superiority benchmark with convergence receipts. +The release superiority gate uses 20 independently seeded `skew_heavy` +train/validation/test simulations. PCNtoolkit predictions are generated before +Referent is fit. Every Referent SHASH fit must converge. For coverage, MACE, +and tail calibration, the 95% replicate-bootstrap upper bound on mean regret +must be at most 0.01, and no single-replicate regret may exceed 0.05. The +replicate-level bootstrap interval for the mean paired response-scale log-score +contrast must lie above zero. The generation receipt records the seeds, +dependency versions, row identities, optimizer warnings, and file hashes. + Machine-readable results are retained alongside a human-readable evidence table. Documentation may say “matches PCNtoolkit” only for a named evidence class and scenario, and may say “outperforms” only for a locked held-out diff --git a/docs/evidence/pcntoolkit/v1.3.0/README.md b/docs/evidence/pcntoolkit/v1.3.0/README.md index 0351aa0..f13da07 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/README.md +++ b/docs/evidence/pcntoolkit/v1.3.0/README.md @@ -23,6 +23,11 @@ HBR and new-site transfer stay marked `non_equivalent`. Release evidence must include convergence receipts and every site's calibration result. The release job fails if any HBR stage misses its R-hat, effective-sample-size, or divergence gate; a pooled site average cannot override a failing small site. +The separate release superiority job also generates 20 independent comparison +replicates, requires every SHASH fit to converge, gates calibration on +replicate-bootstrap non-inferiority with a per-replicate fail-closed ceiling, +and gates the claim on the replicate-level paired log-score interval rather +than the single checked-in fixture. Regenerate these tables with: diff --git a/inst/pcntoolkit/benchmark_helpers.R b/inst/pcntoolkit/benchmark_helpers.R index f0387a1..4c26ef1 100644 --- a/inst/pcntoolkit/benchmark_helpers.R +++ b/inst/pcntoolkit/benchmark_helpers.R @@ -97,3 +97,76 @@ pcn_compare_predictions <- function(referent, comparator, cluster, stringsAsFactors = FALSE ) } + +pcn_release_superiority_summary <- function(results, B = 4999L, + seed = 20260823L) { + required <- c( + "replicate", "log_score_difference", "referent_coverage90", + "pcntoolkit_coverage90", "referent_mace", "pcntoolkit_mace", + "referent_tail05", "pcntoolkit_tail05", "fit_status", "fit_converged" + ) + if (!all(required %in% names(results))) { + stop("release results are missing required fields", call. = FALSE) + } + if (nrow(results) < 5L || anyDuplicated(results$replicate)) { + stop("release superiority requires at least five unique replicates", call. = FALSE) + } + numeric_evidence <- results[c( + "log_score_difference", "referent_coverage90", "pcntoolkit_coverage90", + "referent_mace", "pcntoolkit_mace", "referent_tail05", "pcntoolkit_tail05" + )] + if (any(!is.finite(as.matrix(numeric_evidence))) || anyNA(results$fit_converged)) { + stop("release results contain incomplete evidence", call. = FALSE) + } + interval <- pcn_cluster_bootstrap( + results$log_score_difference, results$replicate, B = B, seed = seed + ) + fit_pass <- all(results$fit_status == "ok" & results$fit_converged) + calibration_regret <- cbind( + coverage90 = abs(results$referent_coverage90 - 0.90) - + abs(results$pcntoolkit_coverage90 - 0.90), + mace = results$referent_mace - results$pcntoolkit_mace, + tail05 = abs(results$referent_tail05 - 0.05) - + abs(results$pcntoolkit_tail05 - 0.05) + ) + calibration_limits <- c(coverage90 = 0.01, mace = 0.01, tail05 = 0.01) + critical_limits <- c(coverage90 = 0.05, mace = 0.05, tail05 = 0.05) + calibration_upper <- vapply(seq_len(ncol(calibration_regret)), function(j) { + pcn_cluster_bootstrap( + calibration_regret[, j], results$replicate, B = B, + seed = seed + j + )[["upper"]] + }, numeric(1)) + names(calibration_upper) <- colnames(calibration_regret) + mean_regret <- colMeans(calibration_regret) + max_regret <- apply(calibration_regret, 2L, max) + calibration_pass <- all(calibration_upper <= calibration_limits) + critical_pass <- all(max_regret <= critical_limits) + classification <- if (fit_pass) { + pcn_classify_benchmark( + interval[["estimate"]], interval[["lower"]], interval[["upper"]], + calibration_pass = calibration_pass && critical_pass + ) + } else { + "fit_failure" + } + data.frame( + replicates = nrow(results), + log_score_difference = unname(interval[["estimate"]]), + ci_lower = unname(interval[["lower"]]), + ci_upper = unname(interval[["upper"]]), + all_shash_fits_converged = fit_pass, + coverage_regret = mean_regret[["coverage90"]], + coverage_regret_upper = calibration_upper[["coverage90"]], + mace_regret = mean_regret[["mace"]], + mace_regret_upper = calibration_upper[["mace"]], + tail_regret = mean_regret[["tail05"]], + tail_regret_upper = calibration_upper[["tail05"]], + maximum_replicate_regret = max(max_regret), + calibration_noninferior = calibration_pass, + no_critical_replicate_regression = critical_pass, + classification = classification, + pass = identical(classification, "superior"), + stringsAsFactors = FALSE + ) +} diff --git a/inst/pcntoolkit/run_concordance.R b/inst/pcntoolkit/run_concordance.R index 39b8257..690fea8 100644 --- a/inst/pcntoolkit/run_concordance.R +++ b/inst/pcntoolkit/run_concordance.R @@ -32,7 +32,8 @@ pcn_scenario_spec <- function(scenario) { ) } -pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L) { +pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L, + return_fit = FALSE) { dat <- inputs[inputs$scenario == scenario, , drop = FALSE] dat$site <- factor(dat$site) train <- dat[dat$split == "train", , drop = FALSE] @@ -51,7 +52,7 @@ pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L) { probs, function(p) referent:::dist_quantile(dist, p), numeric(nrow(test)) ) colnames(quantiles) <- paste0("q", c("05", "25", "50", "75", "95")) - data.frame( + predictions <- data.frame( scenario = scenario, row_id = test$row_id, observed = test$y, @@ -64,6 +65,10 @@ pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L) { quantiles, check.names = FALSE ) + if (isTRUE(return_fit)) { + return(list(predictions = predictions, fit = fit)) + } + predictions } pcn_concordance_registry <- function() { diff --git a/inst/pcntoolkit/run_hbr_site_evidence.py b/inst/pcntoolkit/run_hbr_site_evidence.py index a826c50..e326ae8 100644 --- a/inst/pcntoolkit/run_hbr_site_evidence.py +++ b/inst/pcntoolkit/run_hbr_site_evidence.py @@ -3,6 +3,7 @@ # dependencies = [ # "arviz==0.23.4", # "numpy==2.4.6", +# "nutpie==0.16.8", # "pandas==3.0.5", # "pcntoolkit==1.3.0", # "pymc==5.28.5", diff --git a/tests/testthat/test-pcntoolkit-hbr-site.R b/tests/testthat/test-pcntoolkit-hbr-site.R index 99f8bd0..3e3150e 100644 --- a/tests/testthat/test-pcntoolkit-hbr-site.R +++ b/tests/testthat/test-pcntoolkit-hbr-site.R @@ -45,6 +45,7 @@ test_that("HBR release runner records mandatory convergence and aggregation fiel script_path, warn = FALSE ) text <- paste(script, collapse = "\n") + expect_match(text, '"nutpie==0.16.8"', fixed = TRUE) expect_match(text, '"max_rhat": 1.01', fixed = TRUE) expect_match(text, '"min_ess_bulk": 400', fixed = TRUE) expect_match(text, '"divergences": 0', fixed = TRUE) diff --git a/tests/testthat/test-pcntoolkit-superiority.R b/tests/testthat/test-pcntoolkit-superiority.R index 3f2e213..cd51fc9 100644 --- a/tests/testthat/test-pcntoolkit-superiority.R +++ b/tests/testthat/test-pcntoolkit-superiority.R @@ -24,7 +24,11 @@ test_that("the locked skew-heavy test supports a guarded superiority claim", { comparator <- utils::read.csv( file.path(root, "fitted_predictions.csv"), stringsAsFactors = FALSE ) - referent <- pcn_referent_predictions(inputs, "skew_heavy", n_draw = 2000L) + fitted <- pcn_referent_predictions( + inputs, "skew_heavy", n_draw = 2000L, return_fit = TRUE + ) + expect_identical(fitted$fit$models$y$status, "ok") + referent <- fitted$predictions comparator <- comparator[comparator$scenario == "skew_heavy", , drop = FALSE] comparator <- comparator[match(referent$row_id, comparator$row_id), , drop = FALSE] result <- pcn_compare_predictions( @@ -73,3 +77,43 @@ test_that("controlled location perturbations worsen proper score monotonically", score <- vapply(shifts, function(shift) mean(stats::dnorm(y, shift, 1, log = TRUE)), numeric(1)) expect_true(all(diff(score) < 0)) }) + +test_that("release superiority requires replicated convergence and calibration", { + results <- data.frame( + replicate = seq_len(8), + log_score_difference = seq(0.12, 0.26, length.out = 8), + referent_coverage90 = 0.90, + pcntoolkit_coverage90 = 0.92, + referent_mace = 0.01, + pcntoolkit_mace = 0.05, + referent_tail05 = 0.05, + pcntoolkit_tail05 = 0.06, + fit_status = "ok", + fit_converged = TRUE + ) + passed <- pcn_release_superiority_summary(results, B = 999L, seed = 14L) + expect_identical(passed$classification, "superior") + expect_true(passed$pass) + expect_gt(passed$ci_lower, 0) + expect_true(passed$calibration_noninferior) + expect_true(passed$no_critical_replicate_regression) + + failed_fit <- results + failed_fit$fit_status[[3L]] <- "nonconverged" + fit_summary <- pcn_release_superiority_summary(failed_fit, B = 999L, seed = 14L) + expect_identical(fit_summary$classification, "fit_failure") + expect_false(fit_summary$pass) + + failed_calibration <- results + failed_calibration$referent_coverage90[[5L]] <- 0.70 + failed_calibration$pcntoolkit_coverage90[[5L]] <- 0.90 + calibration_summary <- pcn_release_superiority_summary( + failed_calibration, B = 999L, seed = 14L + ) + expect_identical(calibration_summary$classification, "tradeoff") + expect_false(calibration_summary$pass) + expect_error( + pcn_release_superiority_summary(results[1:4, ], B = 999L), + "at least five unique replicates" + ) +}) diff --git a/tests/testthat/test-pcntoolkit-workflow.R b/tests/testthat/test-pcntoolkit-workflow.R new file mode 100644 index 0000000..508c8c3 --- /dev/null +++ b/tests/testthat/test-pcntoolkit-workflow.R @@ -0,0 +1,38 @@ +test_that("weekly evidence is rendered from the regenerated fixture", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + workflow_path <- file.path(root, ".github", "workflows", "pcntoolkit-validation.yml") + renderer_path <- file.path(root, "tools", "pcntoolkit", "render_evidence.R") + skip_if_not(file.exists(workflow_path) && file.exists(renderer_path)) + workflow <- paste(readLines(workflow_path, warn = FALSE), collapse = "\n") + expect_match( + workflow, + "--fixture-root /tmp/pcntoolkit-v1.3.0", + fixed = TRUE + ) + + renderer <- new.env(parent = globalenv()) + withr::local_dir(root) + sys.source(renderer_path, envir = renderer) + missing_fixture <- file.path(withr::local_tempdir(), "not-the-checked-in-fixture") + expect_error( + renderer$render_pcntoolkit_evidence(withr::local_tempdir(), missing_fixture), + "missing PCNtoolkit manifest" + ) +}) + +test_that("release workflow runs every declared evidence lane", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + workflow_path <- file.path(root, ".github", "workflows", "pcntoolkit-validation.yml") + skip_if_not(file.exists(workflow_path)) + workflow <- paste(readLines(workflow_path, warn = FALSE), collapse = "\n") + expect_match(workflow, "--nuts-sampler nutpie", fixed = TRUE) + expect_match(workflow, "generate_release_superiority.py", fixed = TRUE) + expect_match(workflow, "--replicates 20", fixed = TRUE) + expect_match(workflow, "run_release_superiority.R", fixed = TRUE) + expect_match(workflow, "github.event_name == 'release'", fixed = TRUE) + expect_match( + workflow, + "Release SHASH fitting and replicated superiority evidence", + fixed = TRUE + ) +}) diff --git a/tools/pcntoolkit/benchmark_helpers.R b/tools/pcntoolkit/benchmark_helpers.R index f0387a1..4c26ef1 100644 --- a/tools/pcntoolkit/benchmark_helpers.R +++ b/tools/pcntoolkit/benchmark_helpers.R @@ -97,3 +97,76 @@ pcn_compare_predictions <- function(referent, comparator, cluster, stringsAsFactors = FALSE ) } + +pcn_release_superiority_summary <- function(results, B = 4999L, + seed = 20260823L) { + required <- c( + "replicate", "log_score_difference", "referent_coverage90", + "pcntoolkit_coverage90", "referent_mace", "pcntoolkit_mace", + "referent_tail05", "pcntoolkit_tail05", "fit_status", "fit_converged" + ) + if (!all(required %in% names(results))) { + stop("release results are missing required fields", call. = FALSE) + } + if (nrow(results) < 5L || anyDuplicated(results$replicate)) { + stop("release superiority requires at least five unique replicates", call. = FALSE) + } + numeric_evidence <- results[c( + "log_score_difference", "referent_coverage90", "pcntoolkit_coverage90", + "referent_mace", "pcntoolkit_mace", "referent_tail05", "pcntoolkit_tail05" + )] + if (any(!is.finite(as.matrix(numeric_evidence))) || anyNA(results$fit_converged)) { + stop("release results contain incomplete evidence", call. = FALSE) + } + interval <- pcn_cluster_bootstrap( + results$log_score_difference, results$replicate, B = B, seed = seed + ) + fit_pass <- all(results$fit_status == "ok" & results$fit_converged) + calibration_regret <- cbind( + coverage90 = abs(results$referent_coverage90 - 0.90) - + abs(results$pcntoolkit_coverage90 - 0.90), + mace = results$referent_mace - results$pcntoolkit_mace, + tail05 = abs(results$referent_tail05 - 0.05) - + abs(results$pcntoolkit_tail05 - 0.05) + ) + calibration_limits <- c(coverage90 = 0.01, mace = 0.01, tail05 = 0.01) + critical_limits <- c(coverage90 = 0.05, mace = 0.05, tail05 = 0.05) + calibration_upper <- vapply(seq_len(ncol(calibration_regret)), function(j) { + pcn_cluster_bootstrap( + calibration_regret[, j], results$replicate, B = B, + seed = seed + j + )[["upper"]] + }, numeric(1)) + names(calibration_upper) <- colnames(calibration_regret) + mean_regret <- colMeans(calibration_regret) + max_regret <- apply(calibration_regret, 2L, max) + calibration_pass <- all(calibration_upper <= calibration_limits) + critical_pass <- all(max_regret <= critical_limits) + classification <- if (fit_pass) { + pcn_classify_benchmark( + interval[["estimate"]], interval[["lower"]], interval[["upper"]], + calibration_pass = calibration_pass && critical_pass + ) + } else { + "fit_failure" + } + data.frame( + replicates = nrow(results), + log_score_difference = unname(interval[["estimate"]]), + ci_lower = unname(interval[["lower"]]), + ci_upper = unname(interval[["upper"]]), + all_shash_fits_converged = fit_pass, + coverage_regret = mean_regret[["coverage90"]], + coverage_regret_upper = calibration_upper[["coverage90"]], + mace_regret = mean_regret[["mace"]], + mace_regret_upper = calibration_upper[["mace"]], + tail_regret = mean_regret[["tail05"]], + tail_regret_upper = calibration_upper[["tail05"]], + maximum_replicate_regret = max(max_regret), + calibration_noninferior = calibration_pass, + no_critical_replicate_regression = critical_pass, + classification = classification, + pass = identical(classification, "superior"), + stringsAsFactors = FALSE + ) +} diff --git a/tools/pcntoolkit/generate_fixtures.py b/tools/pcntoolkit/generate_fixtures.py index d4a8caa..42d5832 100644 --- a/tools/pcntoolkit/generate_fixtures.py +++ b/tools/pcntoolkit/generate_fixtures.py @@ -182,9 +182,13 @@ def metric_semantics() -> pd.DataFrame: def scenario_frame( - name: str, n_train: int = 500, n_validation: int = 100, n_test: int = 501 + name: str, + n_train: int = 500, + n_validation: int = 100, + n_test: int = 501, + seed: int | None = None, ) -> pd.DataFrame: - rng = np.random.default_rng(SEEDS[name]) + rng = np.random.default_rng(SEEDS[name] if seed is None else seed) n = n_train + n_validation + n_test split_counts = (("train", n_train), ("validation", n_validation), ("test", n_test)) split = np.concatenate([np.repeat(part, count) for part, count in split_counts]) diff --git a/tools/pcntoolkit/generate_release_superiority.py b/tools/pcntoolkit/generate_release_superiority.py new file mode 100644 index 0000000..12ab3f4 --- /dev/null +++ b/tools/pcntoolkit/generate_release_superiority.py @@ -0,0 +1,129 @@ +# /// script +# requires-python = "==3.12.11" +# dependencies = [ +# "numpy==2.4.6", +# "pandas==3.0.5", +# "pcntoolkit==1.3.0", +# "scipy==1.18.1", +# ] +# /// +"""Generate independent PCNtoolkit predictions for the release benchmark.""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import json +import warnings +from pathlib import Path + +import numpy as np +import pandas as pd + +from generate_fixtures import COMPARATOR_VERSION, fit_scenario, scenario_frame, stable_csv + + +def md5(path: Path) -> str: + return hashlib.md5(path.read_bytes(), usedforsecurity=False).hexdigest() + + +def write_release_superiority( + output: Path, replicates: int, seed_start: int +) -> None: + if replicates < 5: + raise ValueError("release superiority requires at least five replicates") + output.mkdir(parents=True, exist_ok=True) + input_parts: list[pd.DataFrame] = [] + prediction_parts: list[pd.DataFrame] = [] + warning_receipts: list[dict[str, object]] = [] + seeds = list(range(seed_start, seed_start + replicates)) + + for replicate, seed in enumerate(seeds, start=1): + frame = scenario_frame( + "skew_heavy", + n_train=500, + n_validation=100, + n_test=500, + seed=seed, + ) + frame["row_id"] = frame["row_id"].map( + lambda value: f"rep-{replicate:03d}-{value}" + ) + frame.insert(0, "seed", seed) + frame.insert(0, "replicate", replicate) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + prediction = fit_scenario(frame, "skew_heavy") + numeric_evidence = prediction[ + ["observed", "median", "predictive_sd", "centile", "z", "log_density", + "q05", "q25", "q50", "q75", "q95"] + ].to_numpy() + if not np.isfinite(numeric_evidence).all(): + raise RuntimeError(f"non-finite PCNtoolkit evidence in replicate {replicate}") + warning_receipts.append( + { + "replicate": replicate, + "count": len(caught), + "messages": [str(item.message) for item in caught], + } + ) + prediction.insert(0, "seed", seed) + prediction.insert(0, "replicate", replicate) + input_parts.append(frame) + prediction_parts.append(prediction) + + inputs = pd.concat(input_parts, ignore_index=True) + predictions = pd.concat(prediction_parts, ignore_index=True) + input_path = output / "release_inputs.csv" + prediction_path = output / "release_pcntoolkit_predictions.csv" + stable_csv(inputs, input_path) + stable_csv(predictions, prediction_path) + + versions = { + package: importlib.metadata.version(package) + for package in ("pcntoolkit", "numpy", "pandas", "scipy") + } + if versions["pcntoolkit"] != COMPARATOR_VERSION: + raise RuntimeError( + f"expected PCNtoolkit {COMPARATOR_VERSION}, got {versions['pcntoolkit']}" + ) + receipt = { + "schema_version": "1.0.0", + "comparator": {"package": "pcntoolkit", "version": COMPARATOR_VERSION}, + "scenario": "skew_heavy", + "replicates": replicates, + "seeds": seeds, + "split_sizes": {"train": 500, "validation": 100, "test": 500}, + "selection_rule": "prespecified SHASH after family validation", + "fit_warnings": warning_receipts, + "dependencies": versions, + "files": { + input_path.name: { + "md5": md5(input_path), + "rows": len(inputs), + "columns": list(inputs.columns), + }, + prediction_path.name: { + "md5": md5(prediction_path), + "rows": len(predictions), + "columns": list(predictions.columns), + }, + }, + } + (output / "release_generation_receipt.json").write_text( + json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8" + ) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--replicates", type=int, default=20) + parser.add_argument("--seed-start", type=int, default=20260824) + args = parser.parse_args() + write_release_superiority(args.output.resolve(), args.replicates, args.seed_start) + + +if __name__ == "__main__": + main() diff --git a/tools/pcntoolkit/render_evidence.R b/tools/pcntoolkit/render_evidence.R index 5cd3267..3516994 100644 --- a/tools/pcntoolkit/render_evidence.R +++ b/tools/pcntoolkit/render_evidence.R @@ -1,11 +1,11 @@ # Render checked-in, machine-readable evidence from pinned fixtures. -source("tools/pcntoolkit/compare_results.R") -source("tools/pcntoolkit/run_concordance.R") -source("tools/pcntoolkit/run_superiority.R") +source("tools/pcntoolkit/compare_results.R", local = TRUE) +source("tools/pcntoolkit/run_concordance.R", local = TRUE) +source("tools/pcntoolkit/run_superiority.R", local = TRUE) -render_pcntoolkit_evidence <- function(output_dir) { - root <- pcn_fixture_root() +render_pcntoolkit_evidence <- function(output_dir, fixture_root = pcn_fixture_root()) { + root <- fixture_root manifest <- pcn_validate_fixture(root) concordance <- pcn_run_concordance(root) superiority <- pcn_run_superiority(root) @@ -64,7 +64,14 @@ render_pcntoolkit_evidence <- function(output_dir) { if (sys.nframe() == 0L) { args <- commandArgs(trailingOnly = TRUE) - output <- if (length(args)) args[[1L]] else + if (length(args) > 3L || + (length(args) >= 2L && !identical(args[[2L]], "--fixture-root")) || + length(args) == 2L) { + stop("usage: render_evidence.R [OUTPUT_DIR [--fixture-root FIXTURE_DIR]]") + } + output <- if (length(args)) args[[1L]] else { file.path("docs", "evidence", "pcntoolkit", "v1.3.0") - print(render_pcntoolkit_evidence(output), row.names = FALSE) + } + fixture_root <- if (length(args) == 3L) args[[3L]] else pcn_fixture_root() + print(render_pcntoolkit_evidence(output, fixture_root), row.names = FALSE) } diff --git a/tools/pcntoolkit/run_concordance.R b/tools/pcntoolkit/run_concordance.R index 39b8257..690fea8 100644 --- a/tools/pcntoolkit/run_concordance.R +++ b/tools/pcntoolkit/run_concordance.R @@ -32,7 +32,8 @@ pcn_scenario_spec <- function(scenario) { ) } -pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L) { +pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L, + return_fit = FALSE) { dat <- inputs[inputs$scenario == scenario, , drop = FALSE] dat$site <- factor(dat$site) train <- dat[dat$split == "train", , drop = FALSE] @@ -51,7 +52,7 @@ pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L) { probs, function(p) referent:::dist_quantile(dist, p), numeric(nrow(test)) ) colnames(quantiles) <- paste0("q", c("05", "25", "50", "75", "95")) - data.frame( + predictions <- data.frame( scenario = scenario, row_id = test$row_id, observed = test$y, @@ -64,6 +65,10 @@ pcn_referent_predictions <- function(inputs, scenario, n_draw = 2000L) { quantiles, check.names = FALSE ) + if (isTRUE(return_fit)) { + return(list(predictions = predictions, fit = fit)) + } + predictions } pcn_concordance_registry <- function() { diff --git a/tools/pcntoolkit/run_hbr_site_evidence.py b/tools/pcntoolkit/run_hbr_site_evidence.py index a826c50..e326ae8 100644 --- a/tools/pcntoolkit/run_hbr_site_evidence.py +++ b/tools/pcntoolkit/run_hbr_site_evidence.py @@ -3,6 +3,7 @@ # dependencies = [ # "arviz==0.23.4", # "numpy==2.4.6", +# "nutpie==0.16.8", # "pandas==3.0.5", # "pcntoolkit==1.3.0", # "pymc==5.28.5", diff --git a/tools/pcntoolkit/run_release_superiority.R b/tools/pcntoolkit/run_release_superiority.R new file mode 100644 index 0000000..de59b1b --- /dev/null +++ b/tools/pcntoolkit/run_release_superiority.R @@ -0,0 +1,190 @@ +# Run independent SHASH fits against independently generated PCNtoolkit +# predictions. This is a release/manual gate, not an ordinary package test. + +source("tools/pcntoolkit/compare_results.R", local = TRUE) +source("tools/pcntoolkit/run_concordance.R", local = TRUE) +source("tools/pcntoolkit/benchmark_helpers.R", local = TRUE) + +pcn_validate_release_generation <- function(input_dir) { + if (!requireNamespace("jsonlite", quietly = TRUE)) { + stop("jsonlite is required for release evidence", call. = FALSE) + } + receipt_path <- file.path(input_dir, "release_generation_receipt.json") + if (!file.exists(receipt_path)) { + stop("missing release generation receipt", call. = FALSE) + } + receipt <- jsonlite::read_json(receipt_path, simplifyVector = FALSE) + if (!identical(receipt$schema_version, "1.0.0") || + !identical(receipt$comparator$package, "pcntoolkit") || + !identical(receipt$comparator$version, "1.3.0") || + !identical(receipt$scenario, "skew_heavy") || + !identical(receipt$selection_rule, + "prespecified SHASH after family validation")) { + stop("release generation receipt does not match the declared contract", call. = FALSE) + } + required <- c("release_inputs.csv", "release_pcntoolkit_predictions.csv") + if (!setequal(names(receipt$files), required)) { + stop("release generation receipt has an unexpected file set", call. = FALSE) + } + for (filename in required) { + path <- file.path(input_dir, filename) + if (!file.exists(path)) stop("missing release evidence file: ", filename, call. = FALSE) + recorded <- receipt$files[[filename]] + if (!identical(unname(tools::md5sum(path)), recorded$md5)) { + stop("release evidence hash mismatch: ", filename, call. = FALSE) + } + dat <- utils::read.csv(path, check.names = FALSE, stringsAsFactors = FALSE) + if (!identical(nrow(dat), as.integer(recorded$rows)) || + !identical(names(dat), unlist(recorded$columns, use.names = FALSE))) { + stop("release evidence schema mismatch: ", filename, call. = FALSE) + } + } + invisible(receipt) +} + +pcn_run_release_superiority <- function(input_dir, output_dir, + within_B = 499L, + aggregate_B = 4999L, + n_draw = 2000L, + seed = 20260823L, + expected_replicates = 20L) { + receipt <- pcn_validate_release_generation(input_dir) + inputs <- utils::read.csv( + file.path(input_dir, "release_inputs.csv"), stringsAsFactors = FALSE + ) + comparator <- utils::read.csv( + file.path(input_dir, "release_pcntoolkit_predictions.csv"), + stringsAsFactors = FALSE + ) + required_inputs <- c("replicate", "seed", "scenario", "row_id", "split", "site", "y") + required_predictions <- c( + "replicate", "seed", "scenario", "row_id", "observed", "log_density", + "centile", "q05", "q95" + ) + if (!all(required_inputs %in% names(inputs)) || + !all(required_predictions %in% names(comparator))) { + stop("release benchmark tables are missing required columns", call. = FALSE) + } + replicates <- sort(unique(inputs$replicate)) + receipt_seeds <- sort(as.integer(unlist(receipt$seeds, use.names = FALSE))) + observed_seeds <- sort(unique(inputs$seed)) + declared_splits <- c(train = 500L, validation = 100L, test = 500L) + receipt_splits <- vapply(names(declared_splits), function(split) { + as.integer(receipt$split_sizes[[split]]) + }, integer(1)) + split_counts <- table(inputs$replicate, inputs$split) + if (length(replicates) != expected_replicates || + as.integer(receipt$replicates) != expected_replicates || + !identical(receipt_seeds, observed_seeds) || + !identical(receipt_splits, declared_splits) || + !all(names(declared_splits) %in% colnames(split_counts)) || + any(vapply(names(declared_splits), function(split) { + any(split_counts[, split] != declared_splits[[split]]) + }, logical(1))) || + !identical(replicates, sort(unique(comparator$replicate))) || + anyDuplicated(inputs$row_id) || anyDuplicated(comparator$row_id)) { + stop("release benchmark replicate or row identities are invalid", call. = FALSE) + } + + rows <- lapply(replicates, function(replicate_id) { + replicate_inputs <- inputs[inputs$replicate == replicate_id, , drop = FALSE] + replicate_seed <- unique(replicate_inputs$seed) + if (length(replicate_seed) != 1L) { + stop("release replicate has multiple generation seeds: ", replicate_id, + call. = FALSE) + } + fitted <- pcn_referent_predictions( + replicate_inputs, "skew_heavy", n_draw = n_draw, return_fit = TRUE + ) + referent <- fitted$predictions + pcn <- comparator[comparator$replicate == replicate_id, , drop = FALSE] + pcn <- pcn[match(referent$row_id, pcn$row_id), , drop = FALSE] + if (anyNA(pcn$row_id) || !identical(referent$row_id, pcn$row_id)) { + stop("release prediction row identity mismatch in replicate ", replicate_id, + call. = FALSE) + } + if (any(!is.finite(pcn$observed)) || + max(abs(referent$observed - pcn$observed)) > 1e-12) { + stop("release observed outcomes differ in replicate ", replicate_id, + call. = FALSE) + } + comparison <- pcn_compare_predictions( + referent, pcn, cluster = referent$row_id, scenario = "skew_heavy", + B = within_B, seed = seed + as.integer(replicate_id) + ) + fit_one <- fitted$fit$models$y + comparison$replicate <- replicate_id + comparison$seed <- replicate_seed + comparison$fit_status <- fit_one$status + comparison$fit_converged <- identical(fit_one$status, "ok") && + !is.null(fit_one$model) && !identical(fit_one$model$converged, FALSE) + comparison + }) + replicate_results <- do.call(rbind, rows) + summary <- pcn_release_superiority_summary( + replicate_results, B = aggregate_B, seed = seed + ) + + dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) + replicate_path <- file.path(output_dir, "release_superiority_replicates.csv") + summary_path <- file.path(output_dir, "release_superiority_summary.csv") + utils::write.csv(replicate_results, replicate_path, row.names = FALSE) + utils::write.csv(summary, summary_path, row.names = FALSE) + release_receipt <- list( + schema_version = "1.0.0", + comparator = receipt$comparator, + scenario = "skew_heavy", + generated_replicates = length(replicates), + expected_replicates = expected_replicates, + within_replicate_bootstrap = within_B, + aggregate_replicate_bootstrap = aggregate_B, + referent_prediction_draws = n_draw, + requested_seed = seed, + primary_contrast = "mean paired response-scale log score by simulation replicate", + calibration_rule = paste( + "replicate-bootstrap upper regret <= 0.01 for coverage, MACE, and tail;", + "no single-replicate regret > 0.05" + ), + all_shash_fits_converged = summary$all_shash_fits_converged, + calibration_noninferior = summary$calibration_noninferior, + no_critical_replicate_regression = summary$no_critical_replicate_regression, + classification = summary$classification, + pass = summary$pass, + files = list( + release_superiority_replicates.csv = unname(tools::md5sum(replicate_path)), + release_superiority_summary.csv = unname(tools::md5sum(summary_path)) + ) + ) + jsonlite::write_json( + release_receipt, + file.path(output_dir, "release_superiority_receipt.json"), + auto_unbox = TRUE, pretty = TRUE + ) + invisible(list(replicates = replicate_results, summary = summary)) +} + +if (sys.nframe() == 0L) { + args <- commandArgs(trailingOnly = TRUE) + if (length(args) < 2L || length(args) > 6L) { + stop(paste( + "usage: run_release_superiority.R INPUT_DIR OUTPUT_DIR", + "[WITHIN_BOOTSTRAP] [AGGREGATE_BOOTSTRAP] [N_DRAW] [EXPECTED_REPLICATES]" + )) + } + within_B <- if (length(args) >= 3L) as.integer(args[[3L]]) else 499L + aggregate_B <- if (length(args) >= 4L) as.integer(args[[4L]]) else 4999L + n_draw <- if (length(args) >= 5L) as.integer(args[[5L]]) else 2000L + expected_replicates <- if (length(args) >= 6L) as.integer(args[[6L]]) else 20L + if (anyNA(c(within_B, aggregate_B, n_draw, expected_replicates)) || + within_B < 99L || aggregate_B < 99L || n_draw < 100L || + expected_replicates < 5L) { + stop("bootstrap counts and prediction draws are below the release minimum") + } + result <- pcn_run_release_superiority( + args[[1L]], args[[2L]], within_B = within_B, + aggregate_B = aggregate_B, n_draw = n_draw, + expected_replicates = expected_replicates + ) + print(result$summary, row.names = FALSE) + if (!isTRUE(result$summary$pass)) quit(status = 1L) +} From 0b4bc537f8ad495472d776abf6f4d30849ad7c84 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 10:22:47 -0400 Subject: [PATCH 05/13] Harden normative analysis release gates --- .Rbuildignore | 1 + .github/workflows/pcntoolkit-validation.yml | 9 +- .github/workflows/quality.yml | 85 ++ .github/workflows/real-cohort-validation.yml | 37 + CONTRIBUTING.md | 21 + DESCRIPTION | 5 +- NAMESPACE | 2 + NEWS.md | 24 + R/assess.R | 40 +- R/calibrate.R | 44 +- R/crossfit.R | 14 +- R/dynamics.R | 47 +- R/engine-mgcv.R | 26 +- R/fit.R | 48 +- R/forecast.R | 17 +- R/plot-data.R | 38 +- R/reference.R | 71 ++ R/select.R | 79 +- R/support.R | 1 + R/tidiers.R | 2 + R/transition.R | 35 +- README.md | 14 +- _pkgdown.yml | 1 + docs/design/pcntoolkit-validation-contract.md | 21 +- docs/evidence/nhanes/README.md | 32 + docs/evidence/nhanes/conditional.csv | 37 + docs/evidence/nhanes/gate.csv | 2 + docs/evidence/nhanes/marginal.csv | 5 + docs/evidence/nhanes/overall.csv | 5 + docs/evidence/nhanes/receipt.json | 63 + docs/evidence/nhanes/tail.csv | 17 + docs/evidence/pcntoolkit/v1.3.0/README.md | 35 +- .../pcntoolkit/v1.3.0/hbr_convergence.csv | 54 + .../pcntoolkit/v1.3.0/hbr_receipt.json | 42 + .../v1.3.0/release_generation_receipt.json | 1126 +++++++++++++++++ .../v1.3.0/release_matrix_receipt.json | 34 + .../v1.3.0/release_superiority_replicates.csv | 101 ++ .../v1.3.0/release_superiority_summary.csv | 6 + .../pcntoolkit/v1.3.0/site_comparison.csv | 11 + .../v1.3.0/site_referent_receipt.json | 36 + docs/evidence/scale/README.md | 11 + docs/evidence/scale/receipt.json | 15 + docs/evidence/scale/scale_benchmarks.csv | 7 + inst/pcntoolkit/benchmark_helpers.R | 17 +- man/ref_assess.Rd | 24 +- man/ref_calibrate.Rd | 15 +- man/ref_crossfit.Rd | 4 + man/ref_dynamics.Rd | 20 +- man/ref_forecast.Rd | 6 +- man/ref_select.Rd | 22 +- man/ref_transition.Rd | 6 + man/ref_write.Rd | 29 + man/referent-package.Rd | 2 +- tests/testthat/_snaps/print.md | 11 +- tests/testthat/test-calibrate-adapt.R | 45 +- tests/testthat/test-crossfit-assess.R | 25 +- tests/testthat/test-dynamics.R | 51 +- tests/testthat/test-fit-mgcv.R | 23 + tests/testthat/test-pcntoolkit-superiority.R | 12 + tests/testthat/test-pcntoolkit-workflow.R | 7 +- tests/testthat/test-performance.R | 24 +- tests/testthat/test-predict.R | 7 +- tests/testthat/test-quality-workflow.R | 19 + tests/testthat/test-real-cohort-workflow.R | 17 + tests/testthat/test-reference-engine.R | 30 +- tests/testthat/test-select.R | 57 +- tools/benchmarks/run_scale_benchmarks.R | 112 ++ tools/pcntoolkit/benchmark_helpers.R | 17 +- .../generate_release_superiority.py | 144 ++- tools/pcntoolkit/run_release_superiority.R | 109 +- .../pcntoolkit/run_site_transport_evidence.R | 2 + .../run_nhanes_external_validation.R | 195 +++ vignettes/longitudinal-change.Rmd | 32 +- vignettes/troubleshooting.Rmd | 8 +- 74 files changed, 3182 insertions(+), 231 deletions(-) create mode 100644 .github/workflows/quality.yml create mode 100644 .github/workflows/real-cohort-validation.yml create mode 100644 CONTRIBUTING.md create mode 100644 NEWS.md create mode 100644 docs/evidence/nhanes/README.md create mode 100644 docs/evidence/nhanes/conditional.csv create mode 100644 docs/evidence/nhanes/gate.csv create mode 100644 docs/evidence/nhanes/marginal.csv create mode 100644 docs/evidence/nhanes/overall.csv create mode 100644 docs/evidence/nhanes/receipt.json create mode 100644 docs/evidence/nhanes/tail.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/hbr_convergence.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/hbr_receipt.json create mode 100644 docs/evidence/pcntoolkit/v1.3.0/release_generation_receipt.json create mode 100644 docs/evidence/pcntoolkit/v1.3.0/release_matrix_receipt.json create mode 100644 docs/evidence/pcntoolkit/v1.3.0/release_superiority_replicates.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/release_superiority_summary.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/site_comparison.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json create mode 100644 docs/evidence/scale/README.md create mode 100644 docs/evidence/scale/receipt.json create mode 100644 docs/evidence/scale/scale_benchmarks.csv create mode 100644 man/ref_write.Rd create mode 100644 tests/testthat/test-quality-workflow.R create mode 100644 tests/testthat/test-real-cohort-workflow.R create mode 100644 tools/benchmarks/run_scale_benchmarks.R create mode 100644 tools/validation/run_nhanes_external_validation.R diff --git a/.Rbuildignore b/.Rbuildignore index 8240ca8..6fe925c 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -7,6 +7,7 @@ ^\.mote$ ^AGENTS\.md$ ^CLAUDE\.md$ +^CONTRIBUTING\.md$ ^\.claude$ ^docs$ ^_pkgdown\.yml$ diff --git a/.github/workflows/pcntoolkit-validation.yml b/.github/workflows/pcntoolkit-validation.yml index 8af930d..931129e 100644 --- a/.github/workflows/pcntoolkit-validation.yml +++ b/.github/workflows/pcntoolkit-validation.yml @@ -131,8 +131,8 @@ jobs: name: pcntoolkit-release-hbr-site-evidence path: evidence/hbr-site - release-shash-superiority: - name: Release SHASH fitting and replicated superiority evidence + release-scenario-matrix: + name: Release replicated scenario-matrix evidence if: >- github.event_name == 'release' || (github.event_name == 'workflow_dispatch' && inputs.tier == 'release') @@ -154,7 +154,8 @@ jobs: uv run tools/pcntoolkit/generate_release_superiority.py --output evidence/release-superiority --replicates 20 --seed-start 20260824 - - name: Fit SHASH models and enforce replicated held-out gates + --scenarios linear_gaussian,nonlinear_heteroskedastic,skew_heavy,unequal_site,covariate_shift + - name: Fit Referent models and enforce scenario-specific held-out gates run: >- Rscript tools/pcntoolkit/run_release_superiority.R evidence/release-superiority evidence/release-superiority @@ -162,5 +163,5 @@ jobs: - uses: actions/upload-artifact@v4 if: always() with: - name: pcntoolkit-release-shash-superiority-evidence + name: pcntoolkit-release-scenario-matrix-evidence path: evidence/release-superiority diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml new file mode 100644 index 0000000..d4c3e12 --- /dev/null +++ b/.github/workflows/quality.yml @@ -0,0 +1,85 @@ +name: R package quality + +on: + pull_request: + push: + branches: [main] + schedule: + - cron: "23 5 * * 2" + release: + types: [published] + workflow_dispatch: + +permissions: + contents: read + +jobs: + r-cmd-check: + name: R CMD check (${{ matrix.config.os }}, R ${{ matrix.config.r }}) + strategy: + fail-fast: false + matrix: + config: + - {os: ubuntu-latest, r: "release"} + - {os: ubuntu-latest, r: "oldrel-1"} + - {os: macos-latest, r: "release"} + - {os: windows-latest, r: "release"} + runs-on: ${{ matrix.config.os }} + steps: + - uses: actions/checkout@v4 + - uses: r-lib/actions/setup-r@v2 + with: + r-version: ${{ matrix.config.r }} + use-public-rspm: true + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::rcmdcheck + needs: check + - uses: r-lib/actions/check-r-package@v2 + with: + args: '["--no-manual", "--as-cran"]' + error-on: '"warning"' + + scale-smoke: + name: Scale and bundle budgets + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: local::. + needs: check + - name: Enforce smoke budgets + run: Rscript tools/benchmarks/run_scale_benchmarks.R evidence/scale smoke + - uses: actions/upload-artifact@v4 + if: always() + with: + name: referent-scale-smoke + path: evidence/scale + + scale-release: + name: Release-scale budgets + if: >- + github.event_name == 'schedule' || + github.event_name == 'release' || + github.event_name == 'workflow_dispatch' + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: local::. + needs: check + - name: Enforce release budgets + run: Rscript tools/benchmarks/run_scale_benchmarks.R evidence/scale release + - uses: actions/upload-artifact@v4 + if: always() + with: + name: referent-scale-release + path: evidence/scale diff --git a/.github/workflows/real-cohort-validation.yml b/.github/workflows/real-cohort-validation.yml new file mode 100644 index 0000000..1e11567 --- /dev/null +++ b/.github/workflows/real-cohort-validation.yml @@ -0,0 +1,37 @@ +name: Real-cohort validation + +on: + schedule: + - cron: "41 7 1 * *" + release: + types: [published] + workflow_dispatch: + +permissions: + contents: read + +jobs: + nhanes-external-cohort: + name: NHANES 2015-2016 to 2017-2018 + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: | + any::foreign + local::. + needs: check + - name: Run independent-cohort validation + run: >- + Rscript tools/validation/run_nhanes_external_validation.R + evidence/nhanes-external + /tmp/nhanes-cache + - uses: actions/upload-artifact@v4 + if: always() + with: + name: nhanes-external-validation-evidence + path: evidence/nhanes-external diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..2edcf54 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,21 @@ +# Contributing to referent + +Bug reports should include a minimal data-generating example, the intended +predictive estimand (`conditional` or `total`), package versions, and whether +the data are training, calibration, or held-out evaluation rows. Never include +identifiable participant data. + +For code changes: + +1. Add a regression test that states the statistical contract or invariant. +2. Run `devtools::test()` and `R CMD check --as-cran` under a UTF-8 locale. +3. For numerical changes, include an independent oracle, metamorphic law, or + adversarial counterexample; do not update a tolerance only to make a result + pass. +4. Keep PCNtoolkit evidence classes and uncertainty estimands explicit. A + better held-out score never excuses a conformance or calibration failure. +5. Do not commit raw external cohort data. Commit only small result tables and + receipts with source URLs and hashes. + +Pull requests that alter public functions, bundle schemas, comparison gates, +or evidence classifications should update the relevant vignette and NEWS. diff --git a/DESCRIPTION b/DESCRIPTION index c7dd105..e27eeaa 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: referent Type: Package Title: Distributional Reference Models and Individual Deviation Scores -Version: 0.0.0.9000 +Version: 0.1.0 Authors@R: c( person(given = "Brad", family = "Buchsbaum", email = "brad.buchsbaum@gmail.com", @@ -15,13 +15,14 @@ Description: Estimates the conditional predictive distribution of one or is CDF-first: Z-scores are derived representations of a full conditional distribution, not raw standardized residuals. License: MIT + file LICENSE -URL: https://bbuchsbaum.github.io/referent, +URL: https://bbuchsbaum.github.io/referent/, https://github.com/bbuchsbaum/referent BugReports: https://github.com/bbuchsbaum/referent/issues Depends: R (>= 4.2.0) Imports: cli, + digest, distributional (>= 0.5.0), generics, ggplot2, diff --git a/NAMESPACE b/NAMESPACE index 6116004..a2ab552 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -87,12 +87,14 @@ export(ref_freeze) export(ref_gaussian) export(ref_joint) export(ref_process) +export(ref_read) export(ref_select) export(ref_shash) export(ref_simulate) export(ref_spec) export(ref_support) export(ref_transition) +export(ref_write) export(theme_referent) export(tidy) export(variance) diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 0000000..0add7f7 --- /dev/null +++ b/NEWS.md @@ -0,0 +1,24 @@ +# referent 0.1.0 + +Initial scoped release of the CDF-first normative-modelling API. + +- Gaussian and SHASH distributional reference fits, total coefficient + uncertainty, response transforms, support checks, site adaptation, PIT + recalibration, harmonisation, joint scores, and FDR-aware flags. +- Fail-closed model selection now requires valid fits and complete marginal, + tail, shape, and conditional calibration; uncalibrated exploratory selection + requires an explicit opt-in. +- Training, calibration, and evaluation roles carry full SHA-256 provenance. + Calibration is bound to its uncertainty estimand and coefficient-draw count. +- Longitudinal dynamics, transitions, and forecasts share one recorded + marginal-uncertainty estimand and disclose plug-in kernel uncertainty. +- Frozen references use bundle schema 1.0.0 with validated `ref_write()` and + `ref_read()` migration for unversioned development bundles. +- Numerical validation includes offline PCNtoolkit 1.3.0 oracles, a replicated + five-scenario release matrix, HBR/site receipts, independent NHANES cohort + validation, cross-platform R CMD check, and scale/allocation budgets. + +The 0.1.0 release-candidate evidence is intentionally mixed: four replicated +PCNtoolkit scenarios are equivalent, while the skew-heavy comparator emitted +critical optimizer warnings; HBR/site gates and conditional NHANES transport +also identify failures. These are retained evidence, not passing claims. diff --git a/R/assess.R b/R/assess.R index 3033bfd..4c5a275 100644 --- a/R/assess.R +++ b/R/assess.R @@ -52,27 +52,52 @@ #' @param by Optional grouping column; the marginal calibration table is #' then reported per group (column `.group`). #' @param uncertainty Passed to [predict.ref_fit()]. `"conditional"` -#' (the default, and the historical behaviour) scores the point-estimate -#' predictive; `"total"` integrates over the coefficient draws, matching -#' what [predict.ref_fit()] itself returns by default. Without this -#' argument the scores in `overall` cannot be reproduced from -#' `predict()`. +#' scores the point-estimate predictive; `"total"` (the default) integrates +#' over coefficient uncertainty and matches [predict.ref_fit()]. +#' @param allow_in_sample Set to `TRUE` only for explicitly labelled training +#' diagnostics. The default aborts if `newdata` exactly matches the fitting +#' data; use [ref_crossfit()] for honest reference-sample assessment. +#' @param allow_calibration_reuse Set to `TRUE` only for an explicitly labelled +#' diagnostic on the same rows used to estimate a calibration map. The +#' default requires evaluation data independent of fitting and calibration. #' @return An object of class `ref_assessment` with `overall`, #' `marginal`, `conditional`, and `tail` tibbles plus the scores. #' @export ref_assess <- function(fit, newdata, by = NULL, - uncertainty = c("conditional", "total")) { + uncertainty = c("total", "conditional"), + allow_in_sample = FALSE, + allow_calibration_reuse = FALSE) { if (missing(newdata) || is.null(newdata)) { cli::cli_abort("Supply {.arg newdata}: held-out data or a cross-fitted frame.") } uncertainty <- match.arg(uncertainty) newdata <- tibble::as_tibble(newdata) + if (is_in_sample_data(fit, newdata) && !isTRUE(allow_in_sample)) { + cli::cli_abort( + "{.arg newdata} are the model's training data; use {.fn ref_crossfit} or set {.arg allow_in_sample = TRUE} for a labelled diagnostic." + ) + } + if (is_calibration_data(fit, newdata) && !isTRUE(allow_calibration_reuse)) { + cli::cli_abort( + "{.arg newdata} are the calibration data; use an independent evaluation set or set {.arg allow_calibration_reuse = TRUE} for a labelled diagnostic." + ) + } by_vec <- pull_column(newdata, rlang::enquo(by), default = NULL) dists <- predict_dists(fit, newdata, uncertainty = uncertainty) scores <- scores_from_dists(fit, dists, newdata, allow_extrapolation = FALSE) assess_from_scores(fit, scores, dists, newdata, by_vec) } +is_calibration_data <- function(fit, newdata) { + cal <- fit$calibration + cols <- unique(c(fit$covariates, fit$outcomes)) + if (is.null(cal) || is.null(cal$data_hash) || !all(cols %in% names(newdata)) || + nrow(newdata) != cal$n) { + return(FALSE) + } + identical(digest_data(newdata[, cols, drop = FALSE]), cal$data_hash) +} + assess_from_scores <- function(fit, scores, dists, newdata, by_vec = NULL) { overall <- assess_overall(scores, dists, newdata, fit$reference_baseline) marginal <- if (is.null(by_vec)) { @@ -305,6 +330,9 @@ assess_tail <- function(scores) { #' @export print.ref_assessment <- function(x, ...) { cli::cli_text("{.cls ref_assessment} n = {x$n}") + if (isTRUE(x$in_sample)) { + cli::cli_alert_warning("IN-SAMPLE diagnostic: do not report as held-out performance.") + } print(x$overall) invisible(x) } diff --git a/R/calibrate.R b/R/calibrate.R index 5be1df5..0b0ad25 100644 --- a/R/calibrate.R +++ b/R/calibrate.R @@ -41,14 +41,18 @@ #' @param uncertainty Which predictive the calibration data are scored #' under, as in [ref_assess()]. A map corrects the predictive it was #' fitted on, so this should match the `uncertainty` that -#' [predict.ref_fit()] will later be asked for. Ignored when `data` is a -#' score table, which already carries its own. +#' [predict.ref_fit()] will later be asked for. Defaults to `"total"`, +#' matching prediction. A score table supplies this provenance itself. +#' @param n_draw Number of coefficient draws for total uncertainty. This is +#' retained and must match later score predictions. #' @return The fit with a `calibration` slot; [predict.ref_fit()] applies #' the map to `centile`, `z`, `log_density`, `median`, `residual`, and #' the tail columns and sets `calibrated = TRUE`. #' @export ref_calibrate <- function(fit, data, by = NULL, - uncertainty = c("conditional", "total")) { + uncertainty = c("total", "conditional"), + n_draw = NULL) { + uncertainty_supplied <- !missing(uncertainty) uncertainty <- match.arg(uncertainty) by_quo <- rlang::enquo(by) base <- fit @@ -57,18 +61,44 @@ ref_calibrate <- function(fit, data, by = NULL, if (!rlang::quo_is_null(by_quo)) { cli::cli_abort("{.arg by} needs calibration {.arg data}, not a score table.") } + if (!".in_sample" %in% names(data) || any(data$.in_sample)) { + cli::cli_abort("Calibration score tables must contain only out-of-fold scores.") + } + score_uncertainty <- attr(data, "uncertainty") + if (is.null(score_uncertainty)) { + cli::cli_abort("Calibration scores have no uncertainty provenance; recreate them with {.fn ref_crossfit}.") + } + if (uncertainty_supplied && !identical(uncertainty, score_uncertainty)) { + cli::cli_abort("The score table uses {.val {score_uncertainty}} uncertainty, not {.val {uncertainty}}.") + } + deployment <- attr(data, "deployment") + if (is.null(deployment) || !identical(deployment$data_hash, fit$data_hash)) { + cli::cli_abort("Calibration scores do not belong to this deployment fit.") + } + uncertainty <- score_uncertainty + n_draw <- attr(data, "n_draw") + calibration_hash <- attr(data, "data_hash") + calibration_source <- "crossfit" scores <- data by_vec <- NULL pre <- NULL n <- length(unique(scores$.row)) } else { data <- tibble::as_tibble(data) + if (is_in_sample_data(base, data)) { + cli::cli_abort("Calibration data are the model's training data; use held-out data or out-of-fold scores.") + } by_vec <- pull_column(data, by_quo, default = NULL) - dists <- predict_dists(base, data, uncertainty = uncertainty) + dists <- predict_dists(base, data, uncertainty = uncertainty, n_draw = n_draw) scores <- scores_from_dists(base, dists, data) pre <- assess_from_scores(base, scores, dists, data, by_vec = NULL) pre$scores <- NULL n <- nrow(data) + n_draw <- attr(dists, "n_draw") + calibration_hash <- digest_data( + data[, unique(c(base$covariates, base$outcomes)), drop = FALSE] + ) + calibration_source <- "heldout" } maps <- lapply(split(scores, scores$.outcome), function(sc) { out <- list(.global = pit_map(sc$z)) @@ -83,6 +113,10 @@ ref_calibrate <- function(fit, data, by = NULL, by = if (is.null(by_vec)) NULL else as_col_name(by_quo), maps = maps, n = n, + uncertainty = uncertainty, + n_draw = if (identical(uncertainty, "total")) as.integer(n_draw) else NA_integer_, + data_hash = calibration_hash, + source = calibration_source, pre = pre ), class = "ref_calibration" @@ -220,6 +254,6 @@ calibrate_scores <- function(cal, outcome, grp, sc, d) { #' @export print.ref_calibration <- function(x, ...) { - cli::cli_text("{.cls ref_calibration} n = {x$n}{if (is.null(x$by)) '' else paste0(', by ', x$by)}") + cli::cli_text("{.cls ref_calibration} {x$uncertainty %||% 'unknown'} uncertainty, n = {x$n}{if (is.null(x$by)) '' else paste0(', by ', x$by)}") invisible(x) } diff --git a/R/crossfit.R b/R/crossfit.R index 5d5bf1c..db12e54 100644 --- a/R/crossfit.R +++ b/R/crossfit.R @@ -18,6 +18,8 @@ #' predictive that [predict.ref_fit()] returns by default, which is #' what a map fitted on these scores by [ref_calibrate()] will be #' applied to. +#' @param n_draw Number of coefficient draws when `uncertainty = "total"`. +#' The value is retained as calibration provenance. #' @param ... Passed to [ref_fit()]. #' @return A `ref_scores` object with `.in_sample = FALSE`, `.row` #' indexing rows of `data`, a per-row `crps` column, and a `.fold` @@ -31,6 +33,7 @@ ref_crossfit <- function(spec, cluster = NULL, id = NULL, uncertainty = c("conditional", "total"), + n_draw = NULL, ...) { uncertainty <- match.arg(uncertainty) data <- tibble::as_tibble(data) @@ -47,7 +50,7 @@ ref_crossfit <- function(spec, return(NULL) } fit <- ref_fit(spec, data = train, outcomes = outcome_names, ...) - dists <- predict_dists(fit, test, uncertainty = uncertainty) + dists <- predict_dists(fit, test, uncertainty = uncertainty, n_draw = n_draw) sc <- scores_from_dists(fit, dists, test, allow_extrapolation = FALSE) sc$crps <- NA_real_ for (nm in names(dists)) { @@ -70,7 +73,14 @@ ref_crossfit <- function(spec, scores, class = c("ref_scores", class(scores)), deployment = deployment, - folds = fold_id + folds = fold_id, + uncertainty = uncertainty, + n_draw = if (identical(uncertainty, "total")) { + as.integer(n_draw %||% deployment$spec$control$n_draw %||% 200L) + } else { + NA_integer_ + }, + data_hash = deployment$data_hash ) } diff --git a/R/dynamics.R b/R/dynamics.R index 1169e23..90aca08 100644 --- a/R/dynamics.R +++ b/R/dynamics.R @@ -1,6 +1,6 @@ #' Longitudinal dependence process #' -#' Attaches a Gaussian-copula process on calibrated normal scores +#' Attaches a Gaussian-copula process on marginal normal scores #' \eqn{Z=\Phi^{-1}(F(y\mid x))}. The default kernel is stable rank plus #' a Matern-3/2 process plus a measurement nugget, which is positive #' semidefinite for every irregular visit schedule. @@ -24,6 +24,11 @@ #' @param crossfit Number of subject-level folds used to obtain #' out-of-fold Z scores via [ref_crossfit()] (whole subjects stay in one #' fold). `0` or `NULL` uses in-sample scores from `reference`. +#' @param uncertainty Marginal predictive estimand used to create every Z +#' score and every downstream transition or forecast. Defaults to +#' `"total"`, matching [predict.ref_fit()]. +#' @param n_draw Coefficient draws for total marginal uncertainty. The value +#' is stored and reused downstream. #' @return An object of class `ref_dynamics` with, per outcome, a fitted #' process (`$processes`), and a `components` table of normalised #' variance fractions with approximate standard errors. @@ -33,24 +38,41 @@ ref_dynamics <- function(reference, id, time, process = ref_process(), - crossfit = 5) { + crossfit = 5, + uncertainty = c("total", "conditional"), + n_draw = NULL) { + uncertainty <- match.arg(uncertainty) data <- tibble::as_tibble(data) - id_vec <- pull_column(data, rlang::enquo(id)) + id_quo <- rlang::enquo(id) + id_vec <- pull_column(data, id_quo) + id_name <- as_col_name(id_quo, "id") time_quo <- rlang::enquo(time) time_vec <- pull_column(data, time_quo) time_name <- as_col_name(time_quo, "time") if (!inherits(process, "ref_process")) { cli::cli_abort("{.arg process} must be created by {.fn ref_process}.") } + if (!is.null(reference$calibration)) { + cli::cli_abort( + "Longitudinal conditioning does not yet support a PIT-calibrated reference; use the uncalibrated fit and validate its marginal scores independently." + ) + } crossfit <- as.integer(crossfit %||% 0L) + if (crossfit >= 2L && !is.null(reference$adaptation)) { + cli::cli_abort( + "Site adaptation cannot be reproduced inside subject-level cross-fitting; use an unadapted reference or set {.arg crossfit = 0} for a labelled in-sample process fit." + ) + } scores <- if (crossfit >= 2L) { # Subject-level out-of-fold Z; `.row` indexes rows of `data`. data$.dyn_id <- as.character(id_vec) ref_crossfit(reference$spec, data = data, outcomes = reference$outcomes, - folds = crossfit, cluster = !!rlang::sym(".dyn_id")) + folds = crossfit, cluster = !!rlang::sym(".dyn_id"), + uncertainty = uncertainty, n_draw = n_draw) } else { predict(reference, newdata = data, type = "scores", - uncertainty = "conditional", allow_extrapolation = TRUE) + uncertainty = uncertainty, n_draw = n_draw, + allow_extrapolation = TRUE) } ident <- process_identifiability(id_vec, time_vec) processes <- lapply(reference$outcomes, function(nm) { @@ -71,6 +93,18 @@ ref_dynamics <- function(reference, n_subject = length(unique(id_vec)), identifiability = ident, z_source = if (crossfit >= 2L) "out_of_fold" else "in_sample", + uncertainty = uncertainty, + n_draw = if (identical(uncertainty, "total")) { + as.integer(n_draw %||% reference$spec$control$n_draw %||% 200L) + } else { + NA_integer_ + }, + kernel_uncertainty = "plug_in", + data_hash = digest_data(data[, unique(c(id_name, time_name, reference$covariates, + reference$outcomes)), drop = FALSE]), + data_columns = unique(c(id_name, time_name, reference$covariates, + reference$outcomes)), + data_n = nrow(data), components = process_components(processes) ), class = "ref_dynamics" @@ -458,8 +492,9 @@ classify_temporal_support <- function(dyn, t_from, t_to, history_n) { #' @export print.ref_dynamics <- function(x, ...) { cli::cli_text( - "{.cls ref_dynamics} {length(x$outcomes)} outcome{?s}; requested kernel: {x$process$name}; Z: {x$z_source}" + "{.cls ref_dynamics} {length(x$outcomes)} outcome{?s}; requested kernel: {x$process$name}; Z: {x$z_source}, {x$uncertainty} uncertainty" ) + cli::cli_text("kernel uncertainty: plug-in (not propagated)") cli::cli_text( "subjects: {x$n_subject}; time range [{signif(x$time_range[1], 4)}, {signif(x$time_range[2], 4)}]; lag range [{signif(x$lag_range[1], 3)}, {signif(x$lag_range[2], 3)}]" ) diff --git a/R/engine-mgcv.R b/R/engine-mgcv.R index 9ae7bb8..1000f96 100644 --- a/R/engine-mgcv.R +++ b/R/engine-mgcv.R @@ -32,8 +32,8 @@ fit_engine_mgcv <- function(spec, data, outcome, ...) { # model goes through `gam()`. use_bam <- isTRUE(spec$use_bam) && n >= spec$bam_min_n && mgcv_fam == "gaussian" fitter0 <- if (use_bam) mgcv::bam else mgcv::gam - # mgcv step-failure warnings are recorded on the fit rather than raised; - # `status` reflects convergence. + # Preserve mgcv warnings as evidence. Critical optimiser diagnostics make + # the fit unusable even when mgcv leaves `converged = TRUE` behind. warnings <- character() fitter <- function(...) { withCallingHandlers( @@ -81,12 +81,27 @@ fit_engine_mgcv <- function(spec, data, outcome, ...) { sm }) c(stub, list( - status = if (isTRUE(model$converged %||% TRUE)) "ok" else "nonconverged", + status = mgcv_fit_status(model$converged %||% TRUE, warnings), message = if (length(warnings)) paste(unique(warnings), collapse = "; ") else NULL, model = model )) } +mgcv_fit_status <- function(converged, warnings = NULL) { + if (!isTRUE(converged)) { + return("nonconverged") + } + critical <- paste( + c("step failure", "iteration limit", "not converg", "indefinite", + "non[- ]positive definite", "hessian", "singular"), + collapse = "|" + ) + if (length(warnings) && any(grepl(critical, warnings, ignore.case = TRUE))) { + return("unstable") + } + "ok" +} + # Frozen bundles drop the family from each gam (see `strip_gam()`); the # model must carry one for `predict.gam()` to take the right branch. thaw_model <- function(fit_one) { @@ -331,7 +346,10 @@ mgcv_dist_total <- function(model, newdata, fam, fit_one, cond, n_draw, seed = 1 draws <- tryCatch( withr::with_seed(seed, mvtnorm_draw(n_draw, beta_hat, vp)), error = function(e) { - matrix(beta_hat, nrow = n_draw, ncol = length(beta_hat), byrow = TRUE) + cli::cli_abort( + "Could not propagate total uncertainty from the coefficient covariance.", + parent = e + ) } ) par <- params_from_eta(eta_from_lp(lp, t(draws)), model, fam, fit_one, cond) diff --git a/R/fit.R b/R/fit.R index ceb8496..d73486b 100644 --- a/R/fit.R +++ b/R/fit.R @@ -188,15 +188,9 @@ pull_column <- function(data, quo, default = NULL) { digest_data <- function(data) { nms <- sort(names(data)) - data <- data[, nms, drop = FALSE] - fingerprint <- paste(vapply(data, function(col) { - if (is.numeric(col)) { - sprintf("%.8g", sum(as.numeric(col), na.rm = TRUE)) - } else { - paste(utils::head(as.character(col), 8L), collapse = ",") - } - }, character(1)), collapse = ";") - paste(nrow(data), paste(nms, collapse = ","), fingerprint, sep = "|") + data <- as.data.frame(data[, nms, drop = FALSE]) + rownames(data) <- NULL + digest::digest(data, algo = "sha256", serialize = TRUE) } fit_statuses <- function(fit) { @@ -220,7 +214,7 @@ print.ref_fit <- function(x, ...) { cli::cli_text("adapted: {paste(x$adaptation$parameters, collapse = ', ')} (local n = {x$adaptation$n_local})") } if (!is.null(x$calibration)) { - cli::cli_text("calibrated: n = {x$calibration$n}{if (is.null(x$calibration$by)) '' else paste0(', by ', x$calibration$by)}") + cli::cli_text("calibrated: {x$calibration$uncertainty %||% 'unknown'} uncertainty, n = {x$calibration$n}{if (is.null(x$calibration$by)) '' else paste0(', by ', x$calibration$by)}") } failed <- names(st)[st != "ok"] if (length(failed)) { @@ -312,6 +306,7 @@ predict.ref_fit <- function(object, by = NULL, to = NULL, ...) { + validate_ref_bundle(object) type <- match.arg(type) uncertainty <- match.arg(uncertainty) newdata <- tibble::as_tibble(newdata) @@ -408,12 +403,15 @@ predict_dists <- function(object, newdata, uncertainty = "conditional", if (is.null(d) || !length(d)) d else dist_warped(d, transform$lambda) }) } + attr(dists, "uncertainty") <- uncertainty + attr(dists, "n_draw") <- if (identical(uncertainty, "total")) n_draw else NA_integer_ dists } # Assemble the long score table from per-outcome distributions, then # apply the calibration map and the extrapolation mask. scores_from_dists <- function(object, dists, newdata, allow_extrapolation = TRUE) { + validate_calibration_estimand(object$calibration, dists) n <- nrow(newdata) support <- classify_support(object$support_ref, newdata)$support in_sample <- is_in_sample_data(object, newdata) @@ -492,13 +490,39 @@ dplyr_bind <- function(xs) { } is_in_sample_data <- function(fit, newdata) { - cols <- intersect(c(fit$covariates, fit$outcomes), names(newdata)) - if (!length(cols) || nrow(newdata) != fit$n) { + cols <- unique(c(fit$covariates, fit$outcomes)) + if (!length(cols) || !all(cols %in% names(newdata)) || nrow(newdata) != fit$n) { return(FALSE) } identical(digest_data(newdata[, cols, drop = FALSE]), fit$data_hash) } +validate_calibration_estimand <- function(calibration, dists) { + if (is.null(calibration)) { + return(invisible(TRUE)) + } + actual <- attr(dists, "uncertainty") + expected <- calibration$uncertainty + if (is.null(expected) || is.null(actual)) { + cli::cli_abort("Calibration uncertainty provenance is missing; refit the calibration map.") + } + if (!identical(actual, expected)) { + cli::cli_abort( + "This fit was calibrated for {.val {expected}} uncertainty, not {.val {actual}} uncertainty." + ) + } + if (identical(expected, "total")) { + actual_draw <- attr(dists, "n_draw") + expected_draw <- calibration$n_draw + if (!identical(as.integer(actual_draw), as.integer(expected_draw))) { + cli::cli_abort( + "This fit was calibrated with {expected_draw} coefficient draws, not {actual_draw}." + ) + } + } + invisible(TRUE) +} + #' @export print.ref_scores <- function(x, ...) { n_in <- if (".in_sample" %in% names(x)) { diff --git a/R/forecast.R b/R/forecast.R index 7410c7d..6880555 100644 --- a/R/forecast.R +++ b/R/forecast.R @@ -8,9 +8,9 @@ #' identified (see [ref_dynamics()]): a history-conditioned distribution #' from an unestimated kernel would be silently wrong. History rows with a #' non-finite time or outcome are dropped and the number actually used is -#' returned as `history_n`. Epistemic uncertainty of the reference location -#' is carried in the marginal distributions but the uncertainty of the -#' kernel parameters is not propagated into the forecast. +#' returned as `history_n`. The exact marginal uncertainty estimand stored by +#' [ref_dynamics()] is reused. Kernel parameters remain plug-in estimates; +#' their uncertainty is explicitly recorded but not propagated. #' #' @param dynamic A [ref_dynamics] object. #' @param history Subject history data frame. Must contain the time column @@ -58,8 +58,11 @@ ref_forecast <- function(dynamic, history, times, outcome = NULL) { new_grid <- history[rep(nrow(history), length(times)), , drop = FALSE] new_grid[[time_nm]] <- times new_grid[[outcome]] <- NA_real_ - marg <- predict_dists(dynamic$reference, new_grid, uncertainty = "conditional")[[outcome]] - hist_marg <- predict_dists(dynamic$reference, history, uncertainty = "conditional")[[outcome]] + marg <- predict_dists(dynamic$reference, new_grid, uncertainty = dynamic$uncertainty, + n_draw = dynamic$n_draw)[[outcome]] + hist_marg <- predict_dists(dynamic$reference, history, + uncertainty = dynamic$uncertainty, + n_draw = dynamic$n_draw)[[outcome]] z_hist <- dist_z(hist_marg, y_hist) cond <- condition_history(z_hist, t_hist, times, pr) dists <- dist_conditioned(marg, m = cond$m, s = cond$s) @@ -82,7 +85,9 @@ ref_forecast <- function(dynamic, history, times, outcome = NULL) { history = history, time_name = time_nm, history_n = length(z_hist), - lag_support = lag_support + lag_support = lag_support, + reference_uncertainty = dynamic$uncertainty, + kernel_uncertainty = dynamic$kernel_uncertainty ), class = "ref_forecast" ) diff --git a/R/plot-data.R b/R/plot-data.R index 3aa474f..d104a2a 100644 --- a/R/plot-data.R +++ b/R/plot-data.R @@ -226,6 +226,10 @@ fortify_thrive <- function(dynamic, sqrt(max(drop(t(g) %*% pr$vcov %*% g), 0)) }, numeric(1)) } + # Correlation at zero lag is an identity, not a numerically estimated + # quantity. Enforce that invariant before the delta-method calculation. + r[h == 0] <- 1 + r_se[h == 0] <- 0 ix <- expand.grid(h = seq_along(h), a = seq_along(anchors), t = seq_along(from), k = seq_along(thrive)) @@ -248,7 +252,12 @@ fortify_thrive <- function(dynamic, grid <- centile_grid(fit, x_nm, n = length(tt)) grid[[x_nm]] <- tt - dists <- predict_dists(fit, grid, uncertainty = "conditional")[[outcome]] + dists <- predict_dists( + fit, + grid, + uncertainty = dynamic$uncertainty, + n_draw = dynamic$n_draw + )[[outcome]] qat <- function(zz) as.numeric(dist_quantile(dists, stats::pnorm(zz))) out <- tibble::tibble( @@ -268,9 +277,34 @@ fortify_thrive <- function(dynamic, upper = if (is.na(q)) NA_real_ else qat(z + q * sd_z), support = classify_temporal_support(dynamic, t0, tt, rep(1L, length(tt))) ) + + # Use the public forecast path for the plotted conditional quantile. The + # analytic normal-score expression above remains useful for the displayed + # centile and the kernel delta-method band, but converting an anchor through + # a finite coefficient-draw mixture is not exactly self-inverting. Calling + # ref_forecast() here keeps the plotted value identical to what a user gets + # from the stored marginal-uncertainty estimand. + for (segment in unique(out$.segment)) { + idx <- which(out$.segment == segment) + anchor_idx <- idx[out$horizon[idx] == 0] + future_idx <- idx[out$horizon[idx] > 0] + if (!length(anchor_idx) || !length(future_idx)) { + next + } + history <- grid[anchor_idx[[1]], , drop = FALSE] + history[[outcome]] <- out$value[anchor_idx[[1]]] + forecast <- ref_forecast( + dynamic, + history = history, + times = out$time[future_idx], + outcome = outcome + ) + out$value[future_idx] <- as.numeric( + dist_quantile(forecast$dist, out$thrive[future_idx]) + ) + } out <- out[order(out$.segment, out$horizon), , drop = FALSE] attr(out, "x_name") <- x_nm attr(out, "level") <- level out } - diff --git a/R/reference.R b/R/reference.R index 434632f..7e4f82a 100644 --- a/R/reference.R +++ b/R/reference.R @@ -20,6 +20,8 @@ ref_freeze <- function(fit, missing_policy = "complete-case per outcome; predictors are never imputed") { structure( list( + bundle_type = "referent_reference", + bundle_schema_version = "1.0.0", spec = strip_spec_env(fit$spec), outcomes = fit$outcomes, models = lapply(fit$models, strip_fit_one), @@ -44,6 +46,73 @@ ref_freeze <- function(fit, ) } +#' Read and write versioned reference bundles +#' +#' These wrappers make the on-disk compatibility contract explicit. `ref_read()` +#' migrates the unversioned development bundle used before Referent 0.1.0 and +#' rejects unknown schemas. Prediction also validates a frozen bundle, so an +#' incompatible object cannot bypass the check by using `readRDS()` directly. +#' +#' @param reference A [ref_freeze] or [ref_fit]. Plain fits are frozen before +#' writing. +#' @param path Path to an RDS bundle. +#' @param compress Passed to [saveRDS()]. +#' @return `ref_write()` invisibly returns `path`; `ref_read()` returns a +#' validated [ref_freeze]. +#' @export +ref_write <- function(reference, path, compress = "xz") { + if (!inherits(reference, "ref_fit")) { + cli::cli_abort("{.arg reference} must be a {.cls ref_fit} or {.cls ref_freeze}.") + } + if (!inherits(reference, "ref_freeze")) { + reference <- ref_freeze(reference) + } + validate_ref_bundle(reference) + saveRDS(reference, file = path, compress = compress, version = 3) + invisible(path) +} + +#' @rdname ref_write +#' @export +ref_read <- function(path) { + reference <- readRDS(path) + if (!inherits(reference, "ref_freeze")) { + cli::cli_abort("{.arg path} does not contain a {.cls ref_freeze} bundle.") + } + if (is.null(reference$bundle_schema_version)) { + cli::cli_warn("Migrating an unversioned legacy Referent bundle to schema 1.0.0.") + reference$bundle_type <- "referent_reference" + reference$bundle_schema_version <- "1.0.0" + reference$migration <- list(from = "unversioned", to = "1.0.0") + } + validate_ref_bundle(reference) + reference +} + +validate_ref_bundle <- function(reference) { + if (!inherits(reference, "ref_freeze")) { + return(invisible(TRUE)) + } + schema <- reference$bundle_schema_version + if (is.null(schema)) { + cli::cli_abort("This frozen bundle is unversioned; load and migrate it with {.fn ref_read}.") + } + if (!identical(reference$bundle_type, "referent_reference") || + !identical(schema, "1.0.0")) { + cli::cli_abort( + "Unsupported Referent bundle schema {.val {schema}}; this runtime supports exactly 1.0.0." + ) + } + required <- c("spec", "outcomes", "models", "covariates", "support_ref", + "data_hash", "reference_baseline", "n", "versions") + missing <- required[!vapply(required, function(name) !is.null(reference[[name]]), + logical(1))] + if (length(missing)) { + cli::cli_abort("Frozen bundle is missing required field{?s}: {.field {missing}}.") + } + invisible(TRUE) +} + strip_fit_one <- function(fit_one) { if (!is.null(fit_one$model)) { fit_one$model <- strip_gam(fit_one$model) @@ -71,7 +140,9 @@ strip_spec_env <- function(spec) { #' @export print.ref_freeze <- function(x, ...) { + validate_ref_bundle(x) cli::cli_h1("referent model card") + cli::cli_text("bundle schema: {x$bundle_schema_version}") cli::cli_text("family: {x$spec$family$name}") cli::cli_text("engine: {x$spec$engine}") tr <- spec_transform(x$spec) diff --git a/R/select.R b/R/select.R index 52d95c4..44d87ae 100644 --- a/R/select.R +++ b/R/select.R @@ -22,12 +22,14 @@ #' \eqn{n} out-of-fold scores, \eqn{|\bar z| \le 0.10 + 2/\sqrt{n}}, #' \eqn{0.80 - 2\sqrt{2/n} \le \mathrm{var}(z) \le 1.25 + 2\sqrt{2/n}}, #' the 95% coverage lies in \eqn{[0.93, 0.97] \pm 2\sqrt{0.05 \cdot 0.95/n}}, -#' and the observed 5% two-sided tail rate lies in -#' \eqn{0.05 \pm (0.015 + 2\sqrt{0.05 \cdot 0.95/n})}. The fixed margins +#' and each reported two-sided tail rate lies within its binomial +#' tolerance. The gate also checks MACE, skewness, excess kurtosis, and +#' material conditional location/scale drift. The fixed margins #' are practical tolerances; the \eqn{2/\sqrt{n}} terms are two standard #' errors under perfect calibration, so small samples are not rejected #' for noise alone. The `calibrated` column of `comparison` records the -#' gate's verdict per model. +#' gate's verdict per model. If every usable candidate fails, selection +#' aborts unless the caller explicitly opts into an uncalibrated result. #' #' @param specs A named list of [ref_spec] objects, simplest first. #' @param data Reference data. @@ -35,6 +37,9 @@ #' @param folds Cross-fit folds. #' @param shape_threshold Extra log-score gain required to accept a #' covariate-dependent skew/tail model. +#' @param allow_uncalibrated If `FALSE` (the default), abort when no usable +#' candidate passes every calibration gate. Set to `TRUE` only for an +#' explicitly labelled exploratory comparison. #' @param ... Passed to [ref_crossfit()]. #' @return A `ref_selection`: the selected spec and name, the selected #' model's cross-fit scores, and a `comparison` table with the @@ -46,6 +51,7 @@ ref_select <- function(specs, outcomes, folds = 5, shape_threshold = 0.02, + allow_uncalibrated = FALSE, ...) { if (inherits(specs, "ref_spec")) { specs <- list(model = specs) @@ -70,12 +76,17 @@ ref_select <- function(specs, model = nm, level = level, status = "nonconverged", shape = shape, mean_log_score = -Inf, crps = Inf, n = 0L, mean_z = NA_real_, var_z = NA_real_, cover_95 = NA_real_, - tail_05 = NA_real_, calibrated = FALSE, selected = FALSE + tail_05 = NA_real_, mace = NA_real_, skew_z = NA_real_, + excess_kurtosis_z = NA_real_, max_location_drift = NA_real_, + max_scale_drift = NA_real_, conditional_pass = FALSE, + calibrated = FALSE, selected = FALSE )) } marg <- assess_marginal(cf) tail <- assess_tail(cf) + cond <- assess_conditional(cf, data, attr(cf, "deployment")) ok <- is.finite(cf$log_density) + cond_pass <- conditional_calibration_ok(cond) tibble::tibble( model = nm, level = level, @@ -89,17 +100,31 @@ ref_select <- function(specs, cover_95 = stats::weighted.mean(marg$cover_95, marg$n), tail_05 = stats::weighted.mean(tail$observed[tail$tail_level == 0.05], tail$n[tail$tail_level == 0.05]), - calibrated = acceptable_calibration(list(marginal = marg, tail = tail)), + mace = stats::weighted.mean(marg$mace, marg$n), + skew_z = stats::weighted.mean(marg$skew_z, marg$n), + excess_kurtosis_z = stats::weighted.mean(marg$excess_kurtosis_z, marg$n), + max_location_drift = max_abs_or_na(cond$location_drift), + max_scale_drift = max_abs_or_na(cond$scale_drift), + conditional_pass = cond_pass, + calibrated = acceptable_calibration(list( + marginal = marg, tail = tail, conditional = cond + )), selected = FALSE ) }) tab <- dplyr_bind(rows) tab$shape <- as.logical(tab$shape) tab$calibrated <- as.logical(tab$calibrated) - usable <- tab$status %in% c("ok", "partial") + usable <- tab$status == "ok" survivors <- tab[usable & tab$calibrated, , drop = FALSE] if (!nrow(survivors)) { - cli::cli_warn("No ladder candidate passed the calibration gate; ranking all usable fits.") + if (!isTRUE(allow_uncalibrated)) { + cli::cli_abort(c( + "No ladder candidate passed the calibration gate.", + "i" = "Inspect the comparison diagnostics or set {.arg allow_uncalibrated = TRUE} for an explicitly uncalibrated exploratory result." + )) + } + cli::cli_warn("No ladder candidate passed the calibration gate; an uncalibrated result was explicitly allowed.") survivors <- tab[usable, , drop = FALSE] } if (!nrow(survivors)) { @@ -166,15 +191,39 @@ acceptable_calibration <- function(assessment) { se_cov <- sqrt(0.05 * 0.95 / n) ok <- is.finite(m$mean_z) & abs(m$mean_z) <= 0.10 + 2 * se_mean & is.finite(m$var_z) & m$var_z >= 0.80 - 2 * se_var & m$var_z <= 1.25 + 2 * se_var & - is.finite(m$cover_95) & m$cover_95 >= 0.93 - 2 * se_cov & m$cover_95 <= 0.97 + 2 * se_cov - t5 <- assessment$tail - if (!is.null(t5)) { - t5 <- t5[t5$tail_level == 0.05, , drop = FALSE] - t5 <- t5[match(m$.outcome, t5$.outcome), , drop = FALSE] - tol <- 0.015 + 2 * sqrt(0.05 * 0.95 / pmax(t5$n, 1)) - ok <- ok & (is.na(t5$observed) | abs(t5$observed - 0.05) <= tol) + is.finite(m$cover_95) & m$cover_95 >= 0.93 - 2 * se_cov & m$cover_95 <= 0.97 + 2 * se_cov & + is.finite(m$mace) & m$mace <= 0.02 + se_mean & + is.finite(m$skew_z) & abs(m$skew_z) <= 0.50 + 2 * sqrt(6 / n) & + is.finite(m$excess_kurtosis_z) & + abs(m$excess_kurtosis_z) <= 1 + 2 * sqrt(24 / n) + tails <- assessment$tail + if (!is.null(tails) && nrow(tails)) { + expected <- tails$expected %||% tails$tail_level + tol <- 0.01 + 2 * sqrt(expected * (1 - expected) / pmax(tails$n, 1)) + ok <- ok & all(is.finite(tails$observed) & abs(tails$observed - expected) <= tol) + } + all(ok) && conditional_calibration_ok(assessment$conditional) +} + +conditional_calibration_ok <- function(x) { + if (is.null(x) || !nrow(x)) { + return(TRUE) } - all(ok) + complete <- x$n >= 5L & is.finite(x$location_drift) & + is.finite(x$location_se) & is.finite(x$location_p) & + is.finite(x$scale_drift) & is.finite(x$scale_se) & is.finite(x$scale_p) + if (!all(complete)) { + return(FALSE) + } + bad_location <- x$location_p <= 0.01 & + abs(x$location_drift) > 0.20 + 2 * x$location_se + bad_scale <- x$scale_p <= 0.01 & + abs(x$scale_drift) > 0.30 + 2 * x$scale_se + !any(bad_location | bad_scale) +} + +max_abs_or_na <- function(x) { + if (!length(x) || !any(is.finite(x))) NA_real_ else max(abs(x), na.rm = TRUE) } #' @export diff --git a/R/support.R b/R/support.R index fd018ab..b2d48bf 100644 --- a/R/support.R +++ b/R/support.R @@ -50,6 +50,7 @@ support_reference <- function(data, covariate_names) { #' ref_support(fit, ref[1:3, ]) #' @export ref_support <- function(fit, newdata) { + validate_ref_bundle(fit) classify_support(fit$support_ref, tibble::as_tibble(newdata)) } diff --git a/R/tidiers.R b/R/tidiers.R index a629a18..23074e6 100644 --- a/R/tidiers.R +++ b/R/tidiers.R @@ -36,6 +36,7 @@ generics::augment #' @rdname tidy.ref_fit #' @export tidy.ref_fit <- function(x, ...) { + validate_ref_bundle(x) rows <- lapply(x$outcomes, function(nm) { m <- x$models[[nm]] model <- m$model @@ -68,6 +69,7 @@ smooth_edf <- function(model) { #' @rdname tidy.ref_fit #' @export glance.ref_fit <- function(x, ...) { + validate_ref_bundle(x) st <- fit_statuses(x) tibble::tibble( family = x$spec$family$name, diff --git a/R/transition.R b/R/transition.R index 900da6d..fd3ce4a 100644 --- a/R/transition.R +++ b/R/transition.R @@ -18,6 +18,12 @@ #' Duplicate visit times within a subject give a warning and `NA` #' velocities for the zero-length transition. #' +#' `inferential_status` is `"heldout"` only when the rows differ from the +#' marginal and dynamics training data and the process and measurement +#' components are identified. It is otherwise `"in_sample"`, +#' `"measurement_not_separated"`, or `"unidentified"`. This is provenance, +#' not a claim that calibration has been empirically established. +#' #' @param dynamic A [ref_dynamics] object. #' @param data Subject visits. #' @param id Subject identifier. @@ -57,7 +63,9 @@ transition_template <- function() { velocity_lower = numeric(), velocity_upper = numeric(), velocity_centile = numeric(), innovation_z = numeric(), history_n = integer(), predictive_sd = numeric(), measurement_sd = numeric(), - support = character(), calibrated = logical() + support = character(), process_identified = logical(), + measurement_separated = logical(), inferential_status = character(), + reference_uncertainty = character(), kernel_uncertainty = character() ) } @@ -67,7 +75,11 @@ transition_outcome <- function(dynamic, data, outcome, id_vec, time_vec) { } pr <- dynamic$processes[[outcome]] identified <- isTRUE(pr$identified) - marg <- predict_dists(dynamic$reference, data, uncertainty = "conditional")[[outcome]] + marginal_training <- is_in_sample_data(dynamic$reference, data) + dynamics_training <- is_dynamics_data(dynamic, data) + evaluation_role <- if (marginal_training || dynamics_training) "in_sample" else "heldout" + marg <- predict_dists(dynamic$reference, data, uncertainty = dynamic$uncertainty, + n_draw = dynamic$n_draw)[[outcome]] z <- dist_z(marg, data[[outcome]]) sd_marg <- sqrt(variance(marg)) meas_sd <- if (identified) pr$psi$sigma_e else NA_real_ @@ -108,6 +120,13 @@ transition_outcome <- function(dynamic, data, outcome, id_vec, time_vec) { } else { classify_temporal_support(dynamic, time_vec[i1], time_vec[i2], length(hist)) } + inferential_status <- if (!identified) { + "unidentified" + } else if (!isTRUE(dynamic$identifiability$measurement)) { + "measurement_not_separated" + } else { + evaluation_role + } tibble::tibble( .id = s, .outcome = outcome, @@ -132,7 +151,11 @@ transition_outcome <- function(dynamic, data, outcome, id_vec, time_vec) { predictive_sd = sd_marg[[i2]], measurement_sd = meas_sd, support = support, - calibrated = identified && isTRUE(dynamic$identifiability$measurement) + process_identified = identified, + measurement_separated = isTRUE(dynamic$identifiability$measurement), + inferential_status = inferential_status, + reference_uncertainty = dynamic$uncertainty, + kernel_uncertainty = dynamic$kernel_uncertainty ) }) }) @@ -143,6 +166,12 @@ transition_outcome <- function(dynamic, data, outcome, id_vec, time_vec) { dplyr_bind(pieces) } +is_dynamics_data <- function(dynamic, data) { + cols <- dynamic$data_columns + length(cols) > 0L && all(cols %in% names(data)) && nrow(data) == dynamic$data_n && + identical(digest_data(data[, cols, drop = FALSE]), dynamic$data_hash) +} + #' @export print.ref_transition <- function(x, ...) { cli::cli_text("{.cls ref_transition} {nrow(x)} transition{?s}") diff --git a/README.md b/README.md index 9107464..83139d5 100644 --- a/README.md +++ b/README.md @@ -12,10 +12,13 @@ a threshold exceedance with a known chance rate, not an abnormality. The package is CDF-first: a Z-score is one representation of a centile, not a raw standardized residual. -> **Development status:** `referent` is an early development package -> (`0.0.0.9000`). The current fitting surface supports numeric outcomes with +> **Development status:** `referent` 0.1.0 is an initial, deliberately scoped +> release candidate. The current fitting surface supports numeric outcomes with > Gaussian or sinh-arcsinh (SHASH) predictive families; categorical outcomes -> are reported as unsupported instead of being silently coerced. +> are reported as unsupported instead of being silently coerced. Exact and +> matched-estimator PCNtoolkit lanes pass, while the current release-candidate +> HBR/site and skew-heavy replicated gates retain explicit failing receipts; +> do not generalise those comparison claims beyond their named scenarios. Browse the [documentation site](https://bbuchsbaum.github.io/referent/) (or `browseVignettes("referent")` after installing), and start with [Getting started](https://bbuchsbaum.github.io/referent/articles/getting-started/). Keep @@ -87,7 +90,7 @@ generics above rather than depend on an internal representation. | Remove site effects from the data themselves | `predict(type = "harmonised")` | | Joint deviation across outcomes | `ref_joint()` | | Threshold exceedances with FDR | `ref_flag()` | -| Freeze and document a reference | `ref_freeze()` | +| Freeze, validate, and exchange a versioned reference | `ref_freeze()`, `ref_write()`, `ref_read()` | | Longitudinal change | `ref_dynamics()`, `ref_transition()`, `ref_forecast()`, `ref_derivative()` | | Graphics | `autoplot()` methods, `fortify_centiles()`, `theme_referent()` | @@ -115,6 +118,9 @@ velocity centiles, and conditional forecasts off the same process. outputs, MSLL/SMSE/EV/MACE), numerical-validation policy, and what is deliberately missing. The scoped, machine-readable evidence is in [docs/evidence/pcntoolkit/v1.3.0](docs/evidence/pcntoolkit/v1.3.0/README.md). +- [NHANES external-cohort evidence](docs/evidence/nhanes/README.md): a + reproducible 2015-2016 to 2017-2018 transport lane with retained calibration + diagnostics and source hashes. - [Troubleshooting reference-model workflows](https://bbuchsbaum.github.io/referent/articles/troubleshooting/): missing and unsupported covariates, transported unseen sites, small local samples, partial panel fits, honest assessment, and unidentified dynamics. diff --git a/_pkgdown.yml b/_pkgdown.yml index 5942137..aba76a6 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -50,6 +50,7 @@ reference: - ref_adapt - ref_calibrate - ref_freeze + - ref_write - title: Model longitudinal change desc: Estimate within-person dependence, transitions, forecasts, and chart velocity. contents: diff --git a/docs/design/pcntoolkit-validation-contract.md b/docs/design/pcntoolkit-validation-contract.md index 3299d65..1c2e577 100644 --- a/docs/design/pcntoolkit-validation-contract.md +++ b/docs/design/pcntoolkit-validation-contract.md @@ -185,14 +185,19 @@ and are regenerated only by the pinned script under `tools/pcntoolkit/`. - Release/manual: run stochastic HBR, SHASH-fitting, multi-site transport, and the replicated superiority benchmark with convergence receipts. -The release superiority gate uses 20 independently seeded `skew_heavy` -train/validation/test simulations. PCNtoolkit predictions are generated before -Referent is fit. Every Referent SHASH fit must converge. For coverage, MACE, -and tail calibration, the 95% replicate-bootstrap upper bound on mean regret -must be at most 0.01, and no single-replicate regret may exceed 0.05. The -replicate-level bootstrap interval for the mean paired response-scale log-score -contrast must lie above zero. The generation receipt records the seeds, -dependency versions, row identities, optimizer warnings, and file hashes. +The release matrix uses 20 independently seeded train/validation/test +simulations for linear Gaussian, nonlinear heteroskedastic, skew-heavy, +unequal-site, and covariate-shift cases. PCNtoolkit predictions are generated +before Referent is fit. Every Referent fit must be valid, and any critical +optimizer, conditioning, covariance, overflow, or convergence warning +invalidates the corresponding comparator replicate. For coverage, MACE, and +tail calibration, the 95% replicate-bootstrap upper bound on mean regret must +be at most 0.01, and no single-replicate regret may exceed 0.05. Matched and +same-estimand scenarios must be equivalent, non-inferior, or superior; +skew-heavy must meet the stronger superiority rule. Scenarios are reported +separately, so one favourable result cannot hide another scenario's failure. +The generation receipt records seeds, dependency versions, row identities, +warning verdicts, and file hashes. Machine-readable results are retained alongside a human-readable evidence table. Documentation may say “matches PCNtoolkit” only for a named evidence diff --git a/docs/evidence/nhanes/README.md b/docs/evidence/nhanes/README.md new file mode 100644 index 0000000..3d7eed8 --- /dev/null +++ b/docs/evidence/nhanes/README.md @@ -0,0 +1,32 @@ +# NHANES external-cohort evidence + +This release lane fits adult body-measure reference distributions on the +public CDC NHANES 2015-2016 cohort and evaluates them once on the independent +2017-2018 cohort. The outcomes are height, weight, body-mass index, and waist +circumference; age, sex, and race/ethnicity are declared covariates. + +The lane is a cohort-transport check, not a U.S. prevalence estimate. It does +not apply NHANES survey weights, so results must not be described as nationally +representative. Raw XPT files are downloaded from CDC at runtime and are not +committed. The receipt records their URLs, SHA-256 hashes, cohort sizes, +package/R versions, and hashes of every result table. + +The gate requires valid fits, at least 500 evaluated observations per outcome, +held-out provenance, and the package's full marginal, tail, shape, and +conditional calibration contract. Artifacts are uploaded even when the gate +fails, so cohort drift remains visible rather than disappearing from CI. + +The retained 0.1.0 run used all 5,392 eligible training rows and a fixed-seed +sample of 2,000 of 5,151 eligible evaluation rows. All fits were valid. +Marginal performance was strong across the four outcomes: MACE ranged from +0.005 to 0.015 and 95% coverage from 0.942 to 0.951. The full gate nevertheless +failed because conditional scale drift by race/ethnicity exceeded the declared +practical thresholds. The result supports marginal cohort transport for this +sample, but not a blanket conditional-calibration claim. + +Run with: + +```sh +Rscript tools/validation/run_nhanes_external_validation.R \ + evidence/nhanes-external /tmp/nhanes-cache +``` diff --git a/docs/evidence/nhanes/conditional.csv b/docs/evidence/nhanes/conditional.csv new file mode 100644 index 0000000..0207a87 --- /dev/null +++ b/docs/evidence/nhanes/conditional.csv @@ -0,0 +1,37 @@ +".outcome","covariate","level","n","location_drift","location_se","location_p","scale_drift","scale_se","scale_p" +"height_cm","age",NA,1974,0.145420131389462,0.0708003896102392,0.0415363879564448,0.267670816595346,0.142148067938273,0.254207676443356 +"height_cm","sex","female",1046,0.00995556102012127,0.0292293298703309,0.733403408874005,-0.10710124452607,0.0408956128003095,0.00882155469333313 +"height_cm","sex","male",928,0.013865375258297,0.0349968970990541,0.691965824881075,0.135565910313824,0.0523842618369674,0.00965593751594472 +"height_cm","race_ethnicity","1",291,0.249493170026014,0.0586784045520001,2.11989329518112e-05,0.0607618385124033,0.0965801048308935,0.529261259594113 +"height_cm","race_ethnicity","2",180,-0.01734109671414,0.0717324358434832,0.808976256834763,-0.0786472053496924,0.0914515429505281,0.389795803060589 +"height_cm","race_ethnicity","3",634,-0.0672323844384915,0.0378857605709786,0.0759625889681042,-0.086915375874434,0.0514452367013136,0.0911286822752356 +"height_cm","race_ethnicity","4",458,0.00291582343420629,0.0533840131390578,0.956441374432749,0.302391258512111,0.0859823133098296,0.000436617077831676 +"height_cm","race_ethnicity","6",308,-0.00603564101892144,0.0503139677531959,0.904515189531816,-0.222794498259112,0.0623483218479385,0.000352397337380052 +"height_cm","race_ethnicity","7",103,-0.0296278443428482,0.0967743159400951,0.759487391146839,-0.0438648318209159,0.106747655176652,0.681130610309223 +"weight_kg","age",NA,1974,0.124728468291818,0.0458962128953441,0.0717394040708096,0.129471190168201,0.0697397214465893,0.152841414781697 +"weight_kg","sex","female",1045,0.0347462060260689,0.0309121584669836,0.261000127107148,-0.00118645227102155,0.0451806948841571,0.979049831756118 +"weight_kg","sex","male",929,0.0737779682142098,0.0342321822374759,0.0311444296946634,0.0929128436803133,0.0539268251006569,0.0848988404606564 +"weight_kg","race_ethnicity","1",290,0.0671072463017353,0.0504069018265609,0.183086808407704,-0.261189305237313,0.0662311496459801,8.02667727610075e-05 +"weight_kg","race_ethnicity","2",180,-0.127134028965215,0.0624706878253656,0.0418409212274735,-0.285273894789075,0.0725829093334165,8.4833686253424e-05 +"weight_kg","race_ethnicity","3",635,0.113841630257752,0.0445587606579239,0.0106228905715961,0.271756234748416,0.0674144063659887,5.55093326992153e-05 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+".outcome","n","mean_z","var_z","skew_z","excess_kurtosis_z","shapiro_w","mace","cover_50","cover_80","cover_90","cover_95","cover_99" +"bmi",1972,0.0477810759881465,1.03928121015717,0.0337272778261014,0.190971012343955,0.997332135314407,0.0130831643002028,0.499492900608519,0.788032454361055,0.898580121703854,0.942190669371197,0.98580121703854 +"height_cm",1974,0.0117936094563051,1.00735058330906,0.0714198364713473,0.100636950365359,0.998981529360433,0.00502532928064842,0.509625126646403,0.804457953394124,0.89564336372847,0.948328267477204,0.988855116514691 +"waist_cm",1887,0.0177655054603881,1.02909112236094,-0.0631781953895683,-0.171619507787946,0.998755096597672,0.00946475887652357,0.481717011128776,0.791732909379968,0.892951775304716,0.951245363010069,0.992050874403816 +"weight_kg",1974,0.0531152572280866,1.04080439941677,0.0157904873998906,0.200778106493047,0.997860891008167,0.0148530901722391,0.503039513677812,0.791793313069909,0.891590678824721,0.949848024316109,0.985309017223911 diff --git a/docs/evidence/nhanes/overall.csv b/docs/evidence/nhanes/overall.csv new file mode 100644 index 0000000..07d6363 --- /dev/null +++ b/docs/evidence/nhanes/overall.csv @@ -0,0 +1,5 @@ +".outcome","mean_log_score","mae","rmse" +"bmi",-3.2873198321631,5.32385986796474,7.37295183517656 +"height_cm",-3.30656791499171,5.19446863693363,6.61390072243756 +"waist_cm",-4.14986218048414,12.5187827094216,16.2092917997478 +"weight_kg",-4.37061472387402,15.7877016610599,21.7638395291373 diff --git a/docs/evidence/nhanes/receipt.json b/docs/evidence/nhanes/receipt.json new file mode 100644 index 0000000..c71b43d --- /dev/null +++ b/docs/evidence/nhanes/receipt.json @@ -0,0 +1,63 @@ +{ + "schema_version": "1.0.0", + "estimand": "unweighted adult cohort transport, conditional predictive distribution", + "train_cohort": "NHANES 2015-2016", + "evaluation_cohort": "NHANES 2017-2018", + "age_range": [18, 79], + "outcomes": ["height_cm", "weight_kg", "bmi", "waist_cm"], + "train_rows": 5392, + "evaluation_population_rows": 5151, + "evaluation_rows": 2000, + "evaluation_sampling": "fixed-seed simple random sample without replacement (seed 20260824)", + "uncertainty": "total", + "n_draw": 1000, + "package_version": "0.1.0", + "r_version": "R version 4.5.1 (2025-06-13)", + "input_files": { + "demo_train": { + "url": "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/DEMO_I.XPT", + "sha256": "c9297c6c37ae8f78f29be9568fa2a03cf3b112616a39afee04030fc775a66a0d", + "bytes": 3756480 + }, + "body_train": { + "url": "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/BMX_I.XPT", + "sha256": "d31da84e14212b4e58e8340598a5b8e2144fac83333563e966eb1b332e4141d6", + "bytes": 1989600 + }, + "demo_test": { + "url": "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/DEMO_J.XPT", + "sha256": "c0b46e0345ea19404928656277c8b0d10b0cca348a9b2fe4fc3c67e8b7ee73ec", + "bytes": 3412720 + }, + "body_test": { + "url": "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/BMX_J.XPT", + "sha256": "8d675e42d8826ac98714b2c3dd4c5138a5e353fb4424f7eff5e6db4a01ce838a", + "bytes": 1466000 + } + }, + "output_files": { + "overall": { + "sha256": "f60d540ad442fbbf1fe1a449caf0489ddfd4a253ce4f409a5e66cbbef5fc765c", + "rows": 4 + }, + "marginal": { + "sha256": "5852e9a5bf735d22061cbbe17ad6cc2645ab5caa7fd6c0e398956c5ecc640803", + "rows": 4 + }, + "conditional": { + "sha256": "0a2cb534c5dbb0f82a7d24286a3959953452f729734d3c2c86f81eeecf775ce1", + "rows": 36 + }, + "tail": { + "sha256": "02c5b2561140fc3ea220cf267a99e5aa32a3188a81bb484f150c07a7b0895174", + "rows": 16 + }, + "gate": { + "sha256": "f79f91124ac447061906e540a1f855e49e3d1028632bf18eae73530fef3876c5", + "rows": 1 + } + }, + "all_fits_valid": true, + "calibration_pass": false, + "pass": false +} diff --git a/docs/evidence/nhanes/tail.csv b/docs/evidence/nhanes/tail.csv new file mode 100644 index 0000000..b0bc4cc --- /dev/null +++ b/docs/evidence/nhanes/tail.csv @@ -0,0 +1,17 @@ +".outcome","tail_level","expected","observed","n","se" +"bmi",0.005,0.005,0.00709939148073022,1972,0.00158833858878283 +"bmi",0.01,0.01,0.0141987829614604,1972,0.00224059902161436 +"bmi",0.025,0.025,0.0314401622718053,1972,0.00351575705465307 +"bmi",0.05,0.05,0.0578093306288032,1972,0.00490787337808696 +"height_cm",0.005,0.005,0.00810536980749747,1974,0.00158753375539506 +"height_cm",0.01,0.01,0.011144883485309,1974,0.00223946367873852 +"height_cm",0.025,0.025,0.0258358662613982,1974,0.00351397557135923 +"height_cm",0.05,0.05,0.0516717325227964,1974,0.00490538648997284 +"waist_cm",0.005,0.005,0.00264970853206147,1887,0.00162371795254015 +"waist_cm",0.01,0.01,0.00794912559618442,1887,0.00229050712583081 +"waist_cm",0.025,0.025,0.0264970853206147,1887,0.00359406859892792 +"waist_cm",0.05,0.05,0.0487546369899311,1887,0.00501719354366403 +"weight_kg",0.005,0.005,0.00810536980749747,1974,0.00158753375539506 +"weight_kg",0.01,0.01,0.0146909827760892,1974,0.00223946367873852 +"weight_kg",0.025,0.025,0.0309017223910841,1974,0.00351397557135923 +"weight_kg",0.05,0.05,0.0501519756838906,1974,0.00490538648997284 diff --git a/docs/evidence/pcntoolkit/v1.3.0/README.md b/docs/evidence/pcntoolkit/v1.3.0/README.md index f13da07..f403dd1 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/README.md +++ b/docs/evidence/pcntoolkit/v1.3.0/README.md @@ -8,26 +8,33 @@ equivalence. - `concordance.csv` contains every fitted comparison scenario and discrepancy. - `superiority.csv` contains the paired held-out proper-score interval and calibration guardrails for the locked skew/heavy-tail case. +- `release_superiority_summary.csv` and `release_superiority_replicates.csv` contain the + 20-replicate, five-scenario 0.1.0 release-candidate result. +- `release_generation_receipt.json` and `release_matrix_receipt.json` bind the + comparator warnings, dependencies, seeds, row/file identities, package + version, and release verdict. +- `hbr_receipt.json`, `hbr_convergence.csv`, `site_comparison.csv`, and + `site_referent_receipt.json` retain the HBR/transport result. The exact and converted distribution lanes pass their strict semantic gates. Five deliberately matched fitted scenarios pass all prediction, scale, Z, and coverage margins. Estimator-divergent scenarios remain diagnostics. -In the locked skew/heavy-tail case, Referent's prespecified SHASH model is -called superior only because the paired response-scale log-score interval is -above zero and the coverage, MACE, and tail-calibration non-inferiority gates -all pass. This result is scenario-specific; it is not a general claim that -Referent dominates PCNtoolkit. +The single checked-in skew/heavy-tail fixture remains a diagnostic result. In +the stronger replicated release matrix, linear Gaussian, nonlinear +heteroskedastic, unequal-site, and covariate-shift scenarios are all classified +`equivalent`. The skew-heavy log-score contrast favours Referent by 0.397 +(95% replicate-bootstrap interval 0.377 to 0.416), with passing calibration +guardrails, but the claim is classified `comparator_failure`: PCNtoolkit +emitted critical ill-conditioning, non-positive-definite, and optimizer-retry +warnings in one or more replicates. A numerically favourable contrast against +an invalid comparator fit is not called superiority. -HBR and new-site transfer stay marked `non_equivalent`. Release evidence must -include convergence receipts and every site's calibration result. The release -job fails if any HBR stage misses its R-hat, effective-sample-size, or -divergence gate; a pooled site average cannot override a failing small site. -The separate release superiority job also generates 20 independent comparison -replicates, requires every SHASH fit to converge, gates calibration on -replicate-bootstrap non-inferiority with a per-replicate fail-closed ceiling, -and gates the claim on the replicate-level paired log-score interval rather -than the single checked-in fixture. +HBR and new-site transfer stay marked `non_equivalent`. The 0.1.0 +release-candidate HBR run failed with 10 divergences in the base stage and 5 in +transfer, despite acceptable R-hat and effective sample sizes. Referent passed +the four observed-site gates but failed the independently evaluated site-5 +adaptation gate. A pooled average cannot override either failure. Regenerate these tables with: diff --git a/docs/evidence/pcntoolkit/v1.3.0/hbr_convergence.csv b/docs/evidence/pcntoolkit/v1.3.0/hbr_convergence.csv new file mode 100644 index 0000000..ea1aa24 --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/hbr_convergence.csv @@ -0,0 +1,54 @@ +stage,parameter,mean,sd,hdi_3%,hdi_97%,mcse_mean,mcse_sd,ess_bulk,ess_tail,r_hat +base,slope_mu[mu_covariates_0],0.67200000000000004,0.83599999999999997,-0.92200000000000004,2.2200000000000002,0.021000000000000001,0.014999999999999999,1563,1930,1 +base,slope_mu[mu_covariates_1],-0.71199999999999997,1.756,-3.8879999999999999,2.6440000000000001,0.042999999999999997,0.027,1701,2667,1 +base,slope_mu[mu_covariates_2],0.25700000000000001,1.5389999999999999,-2.5459999999999998,3.0819999999999999,0.037999999999999999,0.025000000000000001,1684,2466,1 +base,slope_mu[mu_covariates_3],0.11600000000000001,1.113,-1.964,2.198,0.027,0.017000000000000001,1757,2502,1 +base,slope_mu[mu_covariates_4],0.10000000000000001,0.81000000000000005,-1.4410000000000001,1.5669999999999999,0.017000000000000001,0.010999999999999999,2146,2967,1 +base,slope_mu[mu_covariates_5],-0.45200000000000001,1.107,-2.468,1.6639999999999999,0.025000000000000001,0.017999999999999999,2024,2647,1 +base,slope_mu[mu_covariates_6],0.29599999999999999,1.5429999999999999,-2.5779999999999998,3.2120000000000002,0.035999999999999997,0.025999999999999999,1837,2211,1 +base,slope_mu[mu_covariates_7],-0.099000000000000005,1.7569999999999999,-3.3700000000000001,3.3090000000000002,0.041000000000000002,0.029000000000000001,1859,2557,1 +base,mu_intercept_mu,-0.0060000000000000001,0.754,-1.407,1.4019999999999999,0.017000000000000001,0.010999999999999999,2002,2424,1 +base,site_sigma_intercept_mu_log__,-0.55200000000000005,0.53100000000000003,-1.486,0.438,0.012,0.0080000000000000002,1900,2698,1 +base,normalized_site_offset_intercept_mu_zerosum__[0],-1.224,0.57399999999999995,-2.2469999999999999,-0.24299999999999999,0.012999999999999999,0.0089999999999999993,2043,3116,1 +base,normalized_site_offset_intercept_mu_zerosum__[1],-0.65900000000000003,0.35099999999999998,-1.28,-0.109,0.0070000000000000001,0.0060000000000000001,2475,3252,1 +base,normalized_site_offset_intercept_mu_zerosum__[2],-0.01,0.191,-0.39000000000000001,0.36799999999999999,0.002,0.0050000000000000001,6629,3335,1 +base,slope_sigma[sigma_covariates_0],0.219,0.57399999999999995,-0.81899999999999995,1.3680000000000001,0.0089999999999999993,0.0080000000000000002,4560,3740,1 +base,slope_sigma[sigma_covariates_1],-1.0409999999999999,1.5629999999999999,-4.0259999999999998,1.8520000000000001,0.019,0.016,6705,5303,1 +base,slope_sigma[sigma_covariates_2],-0.69199999999999995,1.4350000000000001,-3.3879999999999999,1.9390000000000001,0.02,0.016,5226,4533,1 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+ }, + "cores": 4, + "draws": 1500, + "hbr_public_random_seed_argument": false, + "nuts_sampler": "nutpie", + "requested_seed": 20260823, + "stages": [ + { + "divergences": 10, + "max_rhat": 1.0, + "min_ess_bulk": 1563.0, + "min_ess_tail": 1930.0, + "pass": false, + "stage": "base" + }, + { + "divergences": 5, + "max_rhat": 1.0, + "min_ess_bulk": 2023.0, + "min_ess_tail": 2639.0, + "pass": false, + "stage": "transfer" + } + ], + "tune": 500, + "version": "1.3.0" +} diff --git a/docs/evidence/pcntoolkit/v1.3.0/release_generation_receipt.json b/docs/evidence/pcntoolkit/v1.3.0/release_generation_receipt.json new file mode 100644 index 0000000..213b933 --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/release_generation_receipt.json @@ -0,0 +1,1126 @@ +{ + "comparator": { + "package": "pcntoolkit", + "version": "1.3.0" + }, + "comparator_valid": false, + "dependencies": { + "numpy": "2.4.6", + "pandas": "3.0.5", + "pcntoolkit": "1.3.0", + "scipy": "1.18.1" + }, + "files": { + "release_inputs.csv": { + "columns": [ + "replicate", + "seed", + "scenario", + "row_id", + "split", + "x1", + "x2", + "sex_M", + "site", + "y" + ], + "md5": "ae9cb7916c8551033827d61b2f55934b", + "rows": 110000 + }, + "release_pcntoolkit_predictions.csv": { + "columns": [ + "replicate", + "seed", + "scenario", + "row_id", + "observed", + "median", + "predictive_sd", + "centile", + "z", + "log_density", + "q05", + "q25", + "q50", + "q75", + "q95" + ], + "md5": "e2307c3c83b90c27e637100e68c294a3", + "rows": 50000 + } + }, + "fit_warnings": [ + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 1, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 2, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 3, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 4, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 5, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 6, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 7, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 8, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 9, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 10, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 11, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 12, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 13, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 14, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 15, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 16, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 17, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 18, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 19, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 20, + "scenario": "linear_gaussian", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 1, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 2, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 3, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 4, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 5, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 6, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 7, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 8, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 9, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 10, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 11, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 12, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 13, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 14, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 15, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 16, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 17, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 18, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 19, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 20, + "scenario": "nonlinear_heteroskedastic", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 1, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 2, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 3, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 4, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 20, + "critical_count": 20, + "critical_messages": [ + "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 9.8856957376305e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 7.834618970779405e-23.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.019287199481017e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.28977150035081e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.317708211515104e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 7.526706657789326e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 9.193140271686426e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "overflow encountered in divide" + ], + "messages": [ + "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 9.8856957376305e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 7.834618970779405e-23.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.019287199481017e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.28977150035081e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.317708211515104e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 7.526706657789326e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 9.193140271686426e-23.", + "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "overflow encountered in divide" + ], + "replicate": 5, + "scenario": "skew_heavy", + "valid": false + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 6, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 7, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 8, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 9, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 10, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 11, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 20, + "critical_count": 20, + "critical_messages": [ + "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 6.237643147876709e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 4.941482669397246e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.051316458043165e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.223919706152835e-19.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.242300627437284e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 4.743789636546638e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.794077746209698e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "overflow encountered in divide" + ], + "messages": [ + "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 6.237643147876709e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 4.941482669397246e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.051316458043165e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.223919706152835e-19.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.242300627437284e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 4.743789636546638e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 5.794077746209698e-19.", + "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "overflow encountered in divide" + ], + "replicate": 12, + "scenario": "skew_heavy", + "valid": false + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 13, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 14, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 15, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 16, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 17, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 12, + "critical_count": 12, + "critical_messages": [ + "An ill-conditioned matrix detected: slice 0 has rcond = 3.505852530206554e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 3.916147264830034e-18.", + "An ill-conditioned matrix detected: slice 0 has rcond = 3.51233962753392e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 2.3014428609233226e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 2.5003563261970243e-18.", + "Process: 94680 - 2026-08-24 09:02:56 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 4.2915841423962645e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 1.2696049258955662e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 2.341671327747793e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 1.8638372433025388e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 3.860993808283783e-17.", + "overflow encountered in divide" + ], + "messages": [ + "An ill-conditioned matrix detected: slice 0 has rcond = 3.505852530206554e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 3.916147264830034e-18.", + "An ill-conditioned matrix detected: slice 0 has rcond = 3.51233962753392e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 2.3014428609233226e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 2.5003563261970243e-18.", + "Process: 94680 - 2026-08-24 09:02:56 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", + "An ill-conditioned matrix detected: slice 0 has rcond = 4.2915841423962645e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 1.2696049258955662e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 2.341671327747793e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 1.8638372433025388e-17.", + "An ill-conditioned matrix detected: slice 0 has rcond = 3.860993808283783e-17.", + "overflow encountered in divide" + ], + "replicate": 18, + "scenario": "skew_heavy", + "valid": false + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 19, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 20, + "scenario": "skew_heavy", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 1, + "scenario": "unequal_site", + 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+ "messages": [], + "replicate": 7, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 8, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 9, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 10, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 11, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 12, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 13, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 14, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 15, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 16, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 17, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 18, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 19, + "scenario": "covariate_shift", + "valid": true + }, + { + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], + "replicate": 20, + "scenario": "covariate_shift", + "valid": true + } + ], + "replicates": 20, + "scenario_classes": { + "covariate_shift": "matched_estimator", + "linear_gaussian": "matched_estimator", + "nonlinear_heteroskedastic": "same_estimand", + "skew_heavy": "same_estimand", + "unequal_site": "same_estimand" + }, + "scenarios": [ + "linear_gaussian", + "nonlinear_heteroskedastic", + "skew_heavy", + "unequal_site", + "covariate_shift" + ], + "schema_version": "1.1.0", + "seeds": [ + 20260824, + 20260825, + 20260826, + 20260827, + 20260828, + 20260829, + 20260830, + 20260831, + 20260832, + 20260833, + 20260834, + 20260835, + 20260836, + 20260837, + 20260838, + 20260839, + 20260840, + 20260841, + 20260842, + 20260843 + ], + "selection_rules": { + "covariate_shift": "prespecified linear Gaussian under shifted support", + "linear_gaussian": "prespecified linear Gaussian", + "nonlinear_heteroskedastic": "prespecified smooth Gaussian location-scale", + "skew_heavy": "prespecified SHASH after family validation", + "unequal_site": "prespecified site location-scale model" + }, + "split_sizes": { + "test": 500, + "train": 500, + "validation": 100 + }, + "warning_policy": { + "critical_patterns": [ + "ill-conditioned", + "posterior estimation failed", + "not positive definite", + "could not find a stable solution", + "overflow", + "singular", + "not converg" + ], + "rule": "any critical fit warning invalidates that comparator replicate" + } +} diff --git a/docs/evidence/pcntoolkit/v1.3.0/release_matrix_receipt.json b/docs/evidence/pcntoolkit/v1.3.0/release_matrix_receipt.json new file mode 100644 index 0000000..24ad21a --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/release_matrix_receipt.json @@ -0,0 +1,34 @@ +{ + "schema_version": "1.0.0", + "comparator": { + "package": "pcntoolkit", + "version": "1.3.0" + }, + "referent_version": "0.1.0", + "r_version": "R version 4.5.1 (2025-06-13)", + "scenarios": ["linear_gaussian", "nonlinear_heteroskedastic", "skew_heavy", "unequal_site", "covariate_shift"], + "generated_replicates_per_scenario": 20, + "expected_replicates": 20, + "within_replicate_bootstrap": 499, + "aggregate_replicate_bootstrap": 4999, + "referent_prediction_draws": 2000, + "requested_seed": 20260823, + "primary_contrast": "scenario-specific mean paired response-scale log score by simulation replicate", + "calibration_rule": "replicate-bootstrap upper regret <= 0.01 for coverage, MACE, and tail; no single-replicate regret > 0.05", + "all_referent_fits_valid": true, + "all_comparator_fits_valid": false, + "all_scenarios_calibration_noninferior": true, + "no_critical_replicate_regression": true, + "classifications": { + "linear_gaussian": "equivalent", + "nonlinear_heteroskedastic": "equivalent", + "skew_heavy": "comparator_failure", + "unequal_site": "equivalent", + "covariate_shift": "equivalent" + }, + "pass": false, + "files": { + "release_superiority_replicates.csv": "355feb12c0403464fc9e83d0134b9120", + "release_superiority_summary.csv": "f95e14b8630761b901ddbc0c08c575f4" + } +} diff --git a/docs/evidence/pcntoolkit/v1.3.0/release_superiority_replicates.csv b/docs/evidence/pcntoolkit/v1.3.0/release_superiority_replicates.csv new file mode 100644 index 0000000..71b9947 --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/release_superiority_replicates.csv @@ -0,0 +1,101 @@ +"scenario","n_test","clusters","log_score_difference","ci_lower","ci_upper","referent_coverage90","pcntoolkit_coverage90","referent_mace","pcntoolkit_mace","referent_tail05","pcntoolkit_tail05","calibration_pass","classification","replicate","seed","fit_status","fit_converged","fit_message","comparator_warning_count","comparator_critical_warning_count","comparator_valid" 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+"linear_gaussian",500,500,0.000306949596568348,-0.000223806432103198,0.000793021056108726,0.906,0.908,0.008,0.0096,0.042,0.042,TRUE,"equivalent",2,20260825,"ok",TRUE,"",0,0,TRUE +"nonlinear_heteroskedastic",500,500,-0.00138711146532098,-0.00609925371065198,0.0028837888053886,0.9,0.912,0.008,0.0104,0.044,0.042,TRUE,"equivalent",2,20260825,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.383146859279281,0.292034881740583,0.480975979669638,0.894,0.92,0.0076,0.0576,0.05,0.064,TRUE,"superior",2,20260825,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,-0.000263518386436033,-0.00541881432111897,0.00403173325258888,0.872,0.884,0.0564,0.056,0.046,0.044,FALSE,"inferior",2,20260825,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,0.00157669142056473,-9.30297102553169e-05,0.00311563550674006,0.908,0.91,0.048,0.048,0.034,0.034,TRUE,"equivalent",2,20260825,"ok",TRUE,"",0,0,TRUE +"linear_gaussian",500,500,0.000131437941551486,-0.000671734833592588,0.00088615695953892,0.91,0.91,0.0132,0.0136,0.04,0.04,TRUE,"equivalent",3,20260826,"ok",TRUE,"",0,0,TRUE +"nonlinear_heteroskedastic",500,500,0.00395666422430774,-0.00623750247339523,0.013416326960434,0.908,0.926,0.0156,0.014,0.042,0.046,TRUE,"equivalent",3,20260826,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.447219127848496,0.342267211880002,0.567107669028928,0.906,0.916,0.0136,0.0564,0.044,0.054,TRUE,"superior",3,20260826,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,0.00119950916860194,-0.00195368318459481,0.00462911916818765,0.904,0.91,0.0112,0.0104,0.056,0.052,TRUE,"equivalent",3,20260826,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,0.00139510734187249,0.000307159174543221,0.00247006980983574,0.898,0.898,0.0728,0.0736,0.06,0.05,TRUE,"superior",3,20260826,"ok",TRUE,"",0,0,TRUE +"linear_gaussian",500,500,0.000527970372891989,-0.000137753076895313,0.00116495984824255,0.902,0.906,0.0132,0.0132,0.04,0.038,TRUE,"equivalent",4,20260827,"ok",TRUE,"",0,0,TRUE +"nonlinear_heteroskedastic",500,500,0.00210980423532209,-0.0028058406128123,0.00690562333061815,0.904,0.91,0.0136,0.0116,0.04,0.038,TRUE,"equivalent",4,20260827,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.420206011367724,0.350978245498427,0.493024572718545,0.906,0.936,0.01,0.0716,0.04,0.044,TRUE,"superior",4,20260827,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,0.00368214916227181,0.000558759053289485,0.00644682685318653,0.92,0.922,0.0184,0.0168,0.034,0.028,TRUE,"superior",4,20260827,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,0.00123350828161574,-0.000174369948656678,0.00250424864204479,0.912,0.918,0.012,0.0148,0.036,0.036,TRUE,"equivalent",4,20260827,"ok",TRUE,"",0,0,TRUE 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a/docs/evidence/pcntoolkit/v1.3.0/site_comparison.csv b/docs/evidence/pcntoolkit/v1.3.0/site_comparison.csv new file mode 100644 index 0000000..60610dc --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/site_comparison.csv @@ -0,0 +1,11 @@ +"method","lane","site","n","mean_log_score","median_rmse","coverage90","mace","mean_z","var_z","coverage90_se","pass" +"referent_adaptation","observed_site","site-1",30,-1.10057258602406,0.723382675017255,0.866666666666667,0.08,-0.315601023677634,0.813466734987491,0.0547722557505166,TRUE +"referent_adaptation","observed_site","site-2",30,-0.983829543171385,0.619293222566163,1,0.0466666666666667,0.107879064774749,0.650832253631541,0.0547722557505166,TRUE +"referent_adaptation","observed_site","site-3",30,-1.13160035818376,0.726175216619566,0.966666666666667,0.0466666666666667,-0.197596202601162,0.680751103146649,0.0547722557505166,TRUE +"referent_adaptation","observed_site","site-4",30,-1.2768599472243,0.824606720175894,1,0.04,0.0312998056224085,0.641451096605674,0.0547722557505166,TRUE +"referent_adaptation","transferred_site","site-5",75,-1.58446748614777,1.17367966821563,0.88,0.0866666666666667,0.386662378851982,1.00741955265314,0.0346410161513775,FALSE +"pcntoolkit_hbr_mixture","observed_site","site-1",30,-1.1960498455926,0.70150423417664,0.933333333333333,0.0533333333333333,-0.211441527717901,0.689903192415423,0.0547722557505166,TRUE +"pcntoolkit_hbr_mixture","observed_site","site-2",30,-1.12080281232478,0.67339155616475,0.933333333333333,0.0466666666666667,0.0557643719402439,0.686776728820518,0.0547722557505166,TRUE +"pcntoolkit_hbr_mixture","observed_site","site-3",30,-1.16281533545559,0.749930098020548,1,0.06,-0.223325837992387,0.672733054722705,0.0547722557505166,TRUE +"pcntoolkit_hbr_mixture","observed_site","site-4",30,-1.24743515581814,0.804997229420273,0.933333333333333,0.0266666666666667,-0.0203243141062514,0.863199546112684,0.0547722557505166,TRUE +"pcntoolkit_hbr_mixture","transferred_site","site-5",75,-1.56746700234388,1.12757252154432,0.946666666666667,0.0173333333333333,0.0589000261736732,0.784973729338837,0.0346410161513775,TRUE diff --git a/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json b/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json new file mode 100644 index 0000000..852476b --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json @@ -0,0 +1,36 @@ +{ + "estimand": "separate adaptation and transfer policies; no rowwise parity claim", + "referent_version": "0.1.0", + "r_version": "R version 4.5.1 (2025-06-13)", + "all_sites_pass": false, + "failed_sites": { + "referent_adaptation": "site-5" + }, + "referent_adaptation": { + "by": "site", + "parameters": ["location", "scale"], + "offsets": { + "y": { + "site-5": { + "location": 0.9082, + "scale": 0.2071, + "n": 25, + "location_se": 0.1689, + "scale_se": 0.0941, + "parameters": ["location", "scale"] + }, + ".all": { + "location": 0.9082, + "scale": 0.2071, + "n": 25, + "location_se": 0.1689, + "scale_se": 0.0941, + "parameters": ["location", "scale"] + } + } + }, + "n_local": 25, + "location_prior_n": 10, + "scale_prior_n": 25 + } +} diff --git a/docs/evidence/scale/README.md b/docs/evidence/scale/README.md new file mode 100644 index 0000000..001f143 --- /dev/null +++ b/docs/evidence/scale/README.md @@ -0,0 +1,11 @@ +# Scale and bundle evidence + +`scale_benchmarks.csv` is the retained macOS arm64 release-budget run for +Referent 0.1.0. It covers a 10,000-row, four-outcome Gaussian/BAM fit and +prediction; a 1,500-row SHASH fit and 750-row, 200-draw prediction; allocated +bytes; and the serialized four-outcome frozen-bundle size. + +Every declared budget passed. These timings are guardrails against order-of- +magnitude regressions, not portable performance promises: hosted CI reruns the +smoke budget on Linux, and release/manual CI reruns the larger budget with its +own platform receipt. diff --git a/docs/evidence/scale/receipt.json b/docs/evidence/scale/receipt.json new file mode 100644 index 0000000..eaecb6c --- /dev/null +++ b/docs/evidence/scale/receipt.json @@ -0,0 +1,15 @@ +{ + "schema_version": "1.0.0", + "mode": "release", + "sizes": { + "gaussian_train": 10000, + "gaussian_test": 3000, + "shash_train": 1500, + "shash_test": 750 + }, + "package_version": "0.1.0", + "r_version": "R version 4.5.1 (2025-06-13)", + "platform": "aarch64-apple-darwin20", + "all_pass": true, + "result_md5": "762dbf401ea9e720f3759f339e489ce5" +} diff --git a/docs/evidence/scale/scale_benchmarks.csv b/docs/evidence/scale/scale_benchmarks.csv new file mode 100644 index 0000000..c776d64 --- /dev/null +++ b/docs/evidence/scale/scale_benchmarks.csv @@ -0,0 +1,7 @@ +"metric","observed","limit","pass" +"gaussian_fit_seconds",0.888,60,TRUE +"gaussian_predict_seconds",0.144,30,TRUE +"shash_fit_seconds",0.476,60,TRUE +"shash_predict_seconds",0.0829999999999997,120,TRUE +"maximum_allocated_bytes",789287872,8e+09,TRUE +"bundle_bytes",609696,1e+07,TRUE diff --git a/inst/pcntoolkit/benchmark_helpers.R b/inst/pcntoolkit/benchmark_helpers.R index 4c26ef1..aa75aaf 100644 --- a/inst/pcntoolkit/benchmark_helpers.R +++ b/inst/pcntoolkit/benchmark_helpers.R @@ -99,11 +99,13 @@ pcn_compare_predictions <- function(referent, comparator, cluster, } pcn_release_superiority_summary <- function(results, B = 4999L, - seed = 20260823L) { + seed = 20260823L, + accepted_classifications = "superior") { required <- c( "replicate", "log_score_difference", "referent_coverage90", "pcntoolkit_coverage90", "referent_mace", "pcntoolkit_mace", - "referent_tail05", "pcntoolkit_tail05", "fit_status", "fit_converged" + "referent_tail05", "pcntoolkit_tail05", "comparator_valid", + "fit_status", "fit_converged" ) if (!all(required %in% names(results))) { stop("release results are missing required fields", call. = FALSE) @@ -122,6 +124,7 @@ pcn_release_superiority_summary <- function(results, B = 4999L, results$log_score_difference, results$replicate, B = B, seed = seed ) fit_pass <- all(results$fit_status == "ok" & results$fit_converged) + comparator_pass <- all(results$comparator_valid) calibration_regret <- cbind( coverage90 = abs(results$referent_coverage90 - 0.90) - abs(results$pcntoolkit_coverage90 - 0.90), @@ -142,7 +145,9 @@ pcn_release_superiority_summary <- function(results, B = 4999L, max_regret <- apply(calibration_regret, 2L, max) calibration_pass <- all(calibration_upper <= calibration_limits) critical_pass <- all(max_regret <= critical_limits) - classification <- if (fit_pass) { + classification <- if (!comparator_pass) { + "comparator_failure" + } else if (fit_pass) { pcn_classify_benchmark( interval[["estimate"]], interval[["lower"]], interval[["upper"]], calibration_pass = calibration_pass && critical_pass @@ -155,7 +160,8 @@ pcn_release_superiority_summary <- function(results, B = 4999L, log_score_difference = unname(interval[["estimate"]]), ci_lower = unname(interval[["lower"]]), ci_upper = unname(interval[["upper"]]), - all_shash_fits_converged = fit_pass, + all_referent_fits_valid = fit_pass, + all_comparator_fits_valid = comparator_pass, coverage_regret = mean_regret[["coverage90"]], coverage_regret_upper = calibration_upper[["coverage90"]], mace_regret = mean_regret[["mace"]], @@ -166,7 +172,8 @@ pcn_release_superiority_summary <- function(results, B = 4999L, calibration_noninferior = calibration_pass, no_critical_replicate_regression = critical_pass, classification = classification, - pass = identical(classification, "superior"), + accepted_classifications = paste(accepted_classifications, collapse = ";"), + pass = classification %in% accepted_classifications, stringsAsFactors = FALSE ) } diff --git a/man/ref_assess.Rd b/man/ref_assess.Rd index e929db8..6d5cf94 100644 --- a/man/ref_assess.Rd +++ b/man/ref_assess.Rd @@ -4,7 +4,14 @@ \alias{ref_assess} \title{Assess a fitted reference model} \usage{ -ref_assess(fit, newdata, by = NULL, uncertainty = c("conditional", "total")) +ref_assess( + fit, + newdata, + by = NULL, + uncertainty = c("total", "conditional"), + allow_in_sample = FALSE, + allow_calibration_reuse = FALSE +) } \arguments{ \item{fit}{A \link{ref_fit}.} @@ -17,11 +24,16 @@ not stored on the fit). For out-of-fold evaluation use then reported per group (column \code{.group}).} \item{uncertainty}{Passed to \code{\link[=predict.ref_fit]{predict.ref_fit()}}. \code{"conditional"} -(the default, and the historical behaviour) scores the point-estimate -predictive; \code{"total"} integrates over the coefficient draws, matching -what \code{\link[=predict.ref_fit]{predict.ref_fit()}} itself returns by default. Without this -argument the scores in \code{overall} cannot be reproduced from -\code{predict()}.} +scores the point-estimate predictive; \code{"total"} (the default) integrates +over coefficient uncertainty and matches \code{\link[=predict.ref_fit]{predict.ref_fit()}}.} + +\item{allow_in_sample}{Set to \code{TRUE} only for explicitly labelled training +diagnostics. The default aborts if \code{newdata} exactly matches the fitting +data; use \code{\link[=ref_crossfit]{ref_crossfit()}} for honest reference-sample assessment.} + +\item{allow_calibration_reuse}{Set to \code{TRUE} only for an explicitly labelled +diagnostic on the same rows used to estimate a calibration map. The +default requires evaluation data independent of fitting and calibration.} } \value{ An object of class \code{ref_assessment} with \code{overall}, diff --git a/man/ref_calibrate.Rd b/man/ref_calibrate.Rd index 01ce093..1170a83 100644 --- a/man/ref_calibrate.Rd +++ b/man/ref_calibrate.Rd @@ -4,7 +4,13 @@ \alias{ref_calibrate} \title{Held-out probability recalibration} \usage{ -ref_calibrate(fit, data, by = NULL, uncertainty = c("conditional", "total")) +ref_calibrate( + fit, + data, + by = NULL, + uncertainty = c("total", "conditional"), + n_draw = NULL +) } \arguments{ \item{fit}{A \link{ref_fit}.} @@ -21,8 +27,11 @@ calibration data fall back to the pooled map. Not available when \item{uncertainty}{Which predictive the calibration data are scored under, as in \code{\link[=ref_assess]{ref_assess()}}. A map corrects the predictive it was fitted on, so this should match the \code{uncertainty} that -\code{\link[=predict.ref_fit]{predict.ref_fit()}} will later be asked for. Ignored when \code{data} is a -score table, which already carries its own.} +\code{\link[=predict.ref_fit]{predict.ref_fit()}} will later be asked for. Defaults to \code{"total"}, +matching prediction. A score table supplies this provenance itself.} + +\item{n_draw}{Number of coefficient draws for total uncertainty. This is +retained and must match later score predictions.} } \value{ The fit with a \code{calibration} slot; \code{\link[=predict.ref_fit]{predict.ref_fit()}} applies diff --git a/man/ref_crossfit.Rd b/man/ref_crossfit.Rd index 8e255d4..f2d04b5 100644 --- a/man/ref_crossfit.Rd +++ b/man/ref_crossfit.Rd @@ -13,6 +13,7 @@ ref_crossfit( cluster = NULL, id = NULL, uncertainty = c("conditional", "total"), + n_draw = NULL, ... ) } @@ -39,6 +40,9 @@ predictive that \code{\link[=predict.ref_fit]{predict.ref_fit()}} returns by def what a map fitted on these scores by \code{\link[=ref_calibrate]{ref_calibrate()}} will be applied to.} +\item{n_draw}{Number of coefficient draws when \code{uncertainty = "total"}. +The value is retained as calibration provenance.} + \item{...}{Passed to \code{\link[=ref_fit]{ref_fit()}}.} } \value{ diff --git a/man/ref_dynamics.Rd b/man/ref_dynamics.Rd index c739199..be62de5 100644 --- a/man/ref_dynamics.Rd +++ b/man/ref_dynamics.Rd @@ -4,7 +4,16 @@ \alias{ref_dynamics} \title{Longitudinal dependence process} \usage{ -ref_dynamics(reference, data, id, time, process = ref_process(), crossfit = 5) +ref_dynamics( + reference, + data, + id, + time, + process = ref_process(), + crossfit = 5, + uncertainty = c("total", "conditional"), + n_draw = NULL +) } \arguments{ \item{reference}{A \link{ref_fit}.} @@ -20,6 +29,13 @@ ref_dynamics(reference, data, id, time, process = ref_process(), crossfit = 5) \item{crossfit}{Number of subject-level folds used to obtain out-of-fold Z scores via \code{\link[=ref_crossfit]{ref_crossfit()}} (whole subjects stay in one fold). \code{0} or \code{NULL} uses in-sample scores from \code{reference}.} + +\item{uncertainty}{Marginal predictive estimand used to create every Z +score and every downstream transition or forecast. Defaults to +\code{"total"}, matching \code{\link[=predict.ref_fit]{predict.ref_fit()}}.} + +\item{n_draw}{Coefficient draws for total marginal uncertainty. The value +is stored and reused downstream.} } \value{ An object of class \code{ref_dynamics} with, per outcome, a fitted @@ -27,7 +43,7 @@ process (\verb{$processes}), and a \code{components} table of normalised variance fractions with approximate standard errors. } \description{ -Attaches a Gaussian-copula process on calibrated normal scores +Attaches a Gaussian-copula process on marginal normal scores \eqn{Z=\Phi^{-1}(F(y\mid x))}. The default kernel is stable rank plus a Matern-3/2 process plus a measurement nugget, which is positive semidefinite for every irregular visit schedule. diff --git a/man/ref_forecast.Rd b/man/ref_forecast.Rd index 5f6531c..9ad1cc3 100644 --- a/man/ref_forecast.Rd +++ b/man/ref_forecast.Rd @@ -32,7 +32,7 @@ The forecast refuses to run when the process for \code{outcome} is not identified (see \code{\link[=ref_dynamics]{ref_dynamics()}}): a history-conditioned distribution from an unestimated kernel would be silently wrong. History rows with a non-finite time or outcome are dropped and the number actually used is -returned as \code{history_n}. Epistemic uncertainty of the reference location -is carried in the marginal distributions but the uncertainty of the -kernel parameters is not propagated into the forecast. +returned as \code{history_n}. The exact marginal uncertainty estimand stored by +\code{\link[=ref_dynamics]{ref_dynamics()}} is reused. Kernel parameters remain plug-in estimates; +their uncertainty is explicitly recorded but not propagated. } diff --git a/man/ref_select.Rd b/man/ref_select.Rd index 46f204c..a63469a 100644 --- a/man/ref_select.Rd +++ b/man/ref_select.Rd @@ -4,7 +4,15 @@ \alias{ref_select} \title{Lexicographic out-of-sample model ladder} \usage{ -ref_select(specs, data, outcomes, folds = 5, shape_threshold = 0.02, ...) +ref_select( + specs, + data, + outcomes, + folds = 5, + shape_threshold = 0.02, + allow_uncalibrated = FALSE, + ... +) } \arguments{ \item{specs}{A named list of \link{ref_spec} objects, simplest first.} @@ -18,6 +26,10 @@ ref_select(specs, data, outcomes, folds = 5, shape_threshold = 0.02, ...) \item{shape_threshold}{Extra log-score gain required to accept a covariate-dependent skew/tail model.} +\item{allow_uncalibrated}{If \code{FALSE} (the default), abort when no usable +candidate passes every calibration gate. Set to \code{TRUE} only for an +explicitly labelled exploratory comparison.} + \item{...}{Passed to \code{\link[=ref_crossfit]{ref_crossfit()}}.} } \value{ @@ -51,10 +63,12 @@ The calibration gate passes a model when, for every outcome with \eqn{n} out-of-fold scores, \eqn{|\bar z| \le 0.10 + 2/\sqrt{n}}, \eqn{0.80 - 2\sqrt{2/n} \le \mathrm{var}(z) \le 1.25 + 2\sqrt{2/n}}, the 95\% coverage lies in \eqn{[0.93, 0.97] \pm 2\sqrt{0.05 \cdot 0.95/n}}, -and the observed 5\% two-sided tail rate lies in -\eqn{0.05 \pm (0.015 + 2\sqrt{0.05 \cdot 0.95/n})}. The fixed margins +and each reported two-sided tail rate lies within its binomial +tolerance. The gate also checks MACE, skewness, excess kurtosis, and +material conditional location/scale drift. The fixed margins are practical tolerances; the \eqn{2/\sqrt{n}} terms are two standard errors under perfect calibration, so small samples are not rejected for noise alone. The \code{calibrated} column of \code{comparison} records the -gate's verdict per model. +gate's verdict per model. If every usable candidate fails, selection +aborts unless the caller explicitly opts into an uncalibrated result. } diff --git a/man/ref_transition.Rd b/man/ref_transition.Rd index 25111be..a9a07bd 100644 --- a/man/ref_transition.Rd +++ b/man/ref_transition.Rd @@ -42,4 +42,10 @@ When the process for an outcome is not identified history-conditioned quantity is \code{NA} and \code{support} is \code{"unidentified"}. Duplicate visit times within a subject give a warning and \code{NA} velocities for the zero-length transition. + +\code{inferential_status} is \code{"heldout"} only when the rows differ from the +marginal and dynamics training data and the process and measurement +components are identified. It is otherwise \code{"in_sample"}, +\code{"measurement_not_separated"}, or \code{"unidentified"}. This is provenance, +not a claim that calibration has been empirically established. } diff --git a/man/ref_write.Rd b/man/ref_write.Rd new file mode 100644 index 0000000..128ff5a --- /dev/null +++ b/man/ref_write.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/reference.R +\name{ref_write} +\alias{ref_write} +\alias{ref_read} +\title{Read and write versioned reference bundles} +\usage{ +ref_write(reference, path, compress = "xz") + +ref_read(path) +} +\arguments{ +\item{reference}{A \link{ref_freeze} or \link{ref_fit}. Plain fits are frozen before +writing.} + +\item{path}{Path to an RDS bundle.} + +\item{compress}{Passed to \code{\link[=saveRDS]{saveRDS()}}.} +} +\value{ +\code{ref_write()} invisibly returns \code{path}; \code{ref_read()} returns a +validated \link{ref_freeze}. +} +\description{ +These wrappers make the on-disk compatibility contract explicit. \code{ref_read()} +migrates the unversioned development bundle used before Referent 0.1.0 and +rejects unknown schemas. Prediction also validates a frozen bundle, so an +incompatible object cannot bypass the check by using \code{readRDS()} directly. +} diff --git a/man/referent-package.Rd b/man/referent-package.Rd index 8cf7888..7a5a7bb 100644 --- a/man/referent-package.Rd +++ b/man/referent-package.Rd @@ -19,7 +19,7 @@ centile. The term "abnormality score" is not part of the core API. \seealso{ Useful links: \itemize{ - \item \url{https://bbuchsbaum.github.io/referent} + \item \url{https://bbuchsbaum.github.io/referent/} \item \url{https://github.com/bbuchsbaum/referent} \item Report bugs at \url{https://github.com/bbuchsbaum/referent/issues} } diff --git a/tests/testthat/_snaps/print.md b/tests/testthat/_snaps/print.md index 46b8088..45d90e2 100644 --- a/tests/testthat/_snaps/print.md +++ b/tests/testthat/_snaps/print.md @@ -52,6 +52,7 @@ Message -- referent model card --------------------------------------------------------- + bundle schema: 1.0.0 family: gaussian engine: mgcv outcomes: y, marker_01, and marker_bad @@ -83,7 +84,9 @@ Code print(dyn) Message - 1 outcome; requested kernel: matern32; Z: in_sample + 1 outcome; requested kernel: matern32; Z: in_sample, total + uncertainty + kernel uncertainty: plug-in (not propagated) subjects: 200; time range [#, #]; lag range [2, 2] ! measurement not separated: no short-interval repeats; measurement noise is not separated from rank dynamics Output @@ -99,7 +102,9 @@ Code print(dyn0) Message - 1 outcome; requested kernel: matern32; Z: in_sample + 1 outcome; requested kernel: matern32; Z: in_sample, total + uncertainty + kernel uncertainty: plug-in (not propagated) subjects: 200; time range [#, #]; lag range [0, 0] ! change not identified: too few repeated observations to identify within-person dependence Output @@ -127,7 +132,7 @@ Code print(cal$calibration) Message - n = 50, by site + total uncertainty, n = 50, by site --- diff --git a/tests/testthat/test-calibrate-adapt.R b/tests/testthat/test-calibrate-adapt.R index 5dd5db1..171c378 100644 --- a/tests/testthat/test-calibrate-adapt.R +++ b/tests/testthat/test-calibrate-adapt.R @@ -3,7 +3,7 @@ test_that("PIT recalibration is a valid map that does not collapse tails", { cal <- ref_simulate(120, seed = 19) test <- ref_simulate(80, seed = 20) fit <- ref_fit(simple_spec(), data = train, outcomes = "y") - cal_fit <- ref_calibrate(fit, data = cal) + cal_fit <- ref_calibrate(fit, data = cal, uncertainty = "conditional") expect_s3_class(cal_fit, "ref_fit") expect_s3_class(cal_fit$calibration, "ref_calibration") sc <- predict(cal_fit, newdata = test, uncertainty = "conditional") @@ -29,7 +29,8 @@ test_that("calibration by group uses the group map and pools unknown groups", { cal <- dat[dat$site == "B", ][1:60, ] test <- dat[dat$site == "B", ][61:100, ] fit <- ref_fit(simple_spec(), data = train, outcomes = "y") - cal_fit <- ref_calibrate(fit, data = rbind(cal, train[1:60, ]), by = site) + cal_fit <- ref_calibrate(fit, data = rbind(cal, train[1:60, ]), by = site, + uncertainty = "conditional") expect_equal(cal_fit$calibration$by, "site") raw <- predict(fit, newdata = test, uncertainty = "conditional") sc <- predict(cal_fit, newdata = test, uncertainty = "conditional") @@ -117,7 +118,7 @@ test_that("reference bundles keep calibration and adaptation", { dat <- ref_simulate(300, seed = 27) fit <- ref_fit(simple_spec(), data = dat[1:150, ], outcomes = "y") fit <- ref_adapt(fit, data = dat[151:200, ], parameters = "location") - fit <- ref_calibrate(fit, data = dat[201:250, ]) + fit <- ref_calibrate(fit, data = dat[201:250, ], uncertainty = "conditional") ref <- ref_freeze(fit) expect_s3_class(ref$calibration, "ref_calibration") expect_s3_class(ref$adaptation, "ref_adaptation") @@ -195,7 +196,7 @@ test_that("calibration moves the density with the centile and integrates to one" cal <- ref_simulate(600, seed = 52) cal$y <- cal$y + 1.2 * stats::rt(nrow(cal), df = 4) fit <- ref_fit(simple_spec(), data = train, outcomes = "y") - cf <- ref_calibrate(fit, data = cal) + cf <- ref_calibrate(fit, data = cal, uncertainty = "conditional") map <- cf$calibration$maps$y$.global expect_s3_class(map, "ref_pit_map") @@ -227,7 +228,7 @@ test_that("the calibrated median is the calibrated 0.5 centile", { cal <- ref_simulate(600, seed = 54) cal$y <- cal$y + 3 + 1.2 * stats::rt(nrow(cal), df = 4) fit <- ref_fit(simple_spec(), data = train, outcomes = "y") - cf <- ref_calibrate(fit, data = cal) + cf <- ref_calibrate(fit, data = cal, uncertainty = "conditional") map <- cf$calibration$maps$y$.global expect_gt(abs(map$mu), 0.5) @@ -249,7 +250,7 @@ test_that("the calibration gate leaves an already-calibrated model alone", { train <- ref_simulate(400, seed = 61) cal <- ref_simulate(900, seed = 62) fit <- ref_fit(simple_spec(), data = train, outcomes = "y") - cf <- ref_calibrate(fit, data = cal) + cf <- ref_calibrate(fit, data = cal, uncertainty = "conditional") map <- cf$calibration$maps$y$.global expect_equal(unlist(map[c("mu", "sigma", "eps", "delta")]), c(mu = 0, sigma = 1, eps = 0, delta = 1)) @@ -270,5 +271,37 @@ test_that("ref_calibrate accepts out-of-fold scores from ref_crossfit", { fit <- ref_calibrate(attr(cf, "deployment"), cf) expect_s3_class(fit$calibration, "ref_calibration") expect_equal(fit$calibration$n, nrow(dat)) + expect_identical(fit$calibration$uncertainty, "conditional") expect_error(ref_calibrate(attr(cf, "deployment"), cf, by = site), "score table") }) + +test_that("calibration is bound to held-out provenance and its predictive estimand", { + train <- ref_simulate(220, seed = 73) + cal <- ref_simulate(100, seed = 74) + test <- ref_simulate(30, seed = 75) + fit <- ref_fit(simple_spec(), train, "y") + + expect_error(ref_calibrate(fit, train), "training data") + calibrated <- ref_calibrate(fit, cal, uncertainty = "conditional") + expect_identical(calibrated$calibration$uncertainty, "conditional") + expect_error(ref_assess(calibrated, cal, uncertainty = "conditional"), + "calibration data") + expect_error( + predict(calibrated, test, uncertainty = "total"), + "calibrated for.*conditional" + ) + expect_s3_class( + predict(calibrated, test, uncertainty = "conditional"), + "ref_scores" + ) + + cf <- ref_crossfit(simple_spec(), train, "y", folds = 3, uncertainty = "total", + n_draw = 64) + calibrated_total <- ref_calibrate(attr(cf, "deployment"), cf) + expect_identical(calibrated_total$calibration$uncertainty, "total") + expect_identical(calibrated_total$calibration$n_draw, 64L) + expect_error( + predict(calibrated_total, test, uncertainty = "total", n_draw = 32), + "64 coefficient draws" + ) +}) diff --git a/tests/testthat/test-crossfit-assess.R b/tests/testthat/test-crossfit-assess.R index 5bdeab4..116d6ac 100644 --- a/tests/testthat/test-crossfit-assess.R +++ b/tests/testthat/test-crossfit-assess.R @@ -13,6 +13,8 @@ test_that("cross-fitted scores are out of sample, cover every row once, and are expect_s3_class(cf, "ref_scores") expect_false(any(cf$.in_sample)) expect_s3_class(attr(cf, "deployment"), "ref_fit") + expect_identical(attr(cf, "uncertainty"), "conditional") + expect_true(is.character(attr(cf, "data_hash"))) expect_equal(sort(cf$.row), seq_len(nrow(dat))) expect_equal(sort(unique(cf$.fold)), 1:4) # clusters stay together @@ -30,6 +32,22 @@ test_that("cross-fitted scores are out of sample, cover every row once, and are expect_lt(abs(mean(cf$crps) - 1.3 / sqrt(pi)), 0.1) }) +test_that("assessment requires held-out provenance and hashes full row content", { + train <- ref_simulate(140, seed = 81) + fit <- ref_fit(simple_spec(), train, "y") + + expect_error(ref_assess(fit, train), "training data") + allowed <- ref_assess(fit, train, allow_in_sample = TRUE) + expect_true(allowed$in_sample) + expect_match(paste(cli::cli_fmt(print(allowed)), collapse = "\n"), "IN-SAMPLE") + + changed <- train + changed$age[c(1, 2)] <- rev(changed$age[c(1, 2)]) + expect_false(identical(digest_data(changed), digest_data(train))) + sc <- predict(fit, changed, uncertainty = "conditional", allow_extrapolation = TRUE) + expect_false(any(sc$.in_sample)) +}) + test_that("ref_assess recovers nominal coverage and a positive log-score gain", { train <- ref_simulate(300, seed = 13) test <- ref_simulate(400, seed = 14) @@ -73,7 +91,7 @@ test_that("standardized_log_score is the negative MSLL against the reference sam for (rows in list(seq_len(120), seq_len(40))) { a <- ref_assess(fit, new[rows, ]) - sc <- predict(fit, new[rows, ], type = "scores", uncertainty = "conditional") + sc <- predict(fit, new[rows, ], type = "scores", uncertainty = "total") expect_equal( -a$overall$standardized_log_score, msll_longhand(sc, ref$y), @@ -97,15 +115,16 @@ test_that("ref_assess() honours the uncertainty argument", { new <- ref_simulate(80, seed = 26) fit <- ref_fit(ref_spec(ref_gaussian(), ~ s(age, k = 5) + sex), ref, "y") - expect_equal(ref_assess(fit, new)$overall, ref_assess(fit, new, uncertainty = "conditional")$overall) total <- ref_assess(fit, new, uncertainty = "total") + conditional <- ref_assess(fit, new, uncertainty = "conditional") + expect_equal(ref_assess(fit, new)$overall, total$overall) expect_equal( -total$overall$standardized_log_score, msll_longhand(predict(fit, new, type = "scores", uncertainty = "total"), ref$y), tolerance = 1e-12 ) expect_false(isTRUE(all.equal( - total$overall$mean_log_score, + conditional$overall$mean_log_score, ref_assess(fit, new)$overall$mean_log_score ))) }) diff --git a/tests/testthat/test-dynamics.R b/tests/testthat/test-dynamics.R index 996c920..c9d3842 100644 --- a/tests/testthat/test-dynamics.R +++ b/tests/testthat/test-dynamics.R @@ -89,6 +89,8 @@ test_that("kernel recovery: r(lag) at the median lag within 0.08 of truth on irr expect_true(pr$ell_identified) expect_identical(pr$process$name, "matern32") expect_identical(dyn$z_source, "out_of_fold") + expect_identical(dyn$uncertainty, "total") + expect_identical(dyn$kernel_uncertainty, "plug_in") expect_equal(sum(pr$components), 1, tolerance = 1e-10) expect_true(all(is.finite(pr$se))) expect_true(is.finite(pr$r_median_se) && pr$r_median_se > 0) @@ -119,6 +121,9 @@ test_that("out-of-sample calibration of innovation_z and change_z on irregular 3 expect_equal(tr$velocity_centile, stats::pnorm(tr$innovation_z)) expect_false("z_gain" %in% names(tr)) expect_equal(tr$measurement_sd[[1]], dyn$processes$y$psi$sigma_e) + expect_true(all(tr$inferential_status == "heldout")) + expect_true(all(tr$reference_uncertainty == "total")) + expect_true(all(tr$kernel_uncertainty == "plug_in")) expect_true(all(tr$history_n[tr$history_n > 1] >= 2)) }) @@ -180,7 +185,8 @@ test_that("unidentified dynamics give NA history-conditioned quantities and no f } expect_true(all(is.finite(tr$observed_velocity))) expect_true(all(tr$support == "unidentified")) - expect_false(any(tr$calibrated)) + expect_false(any(tr$process_identified)) + expect_true(all(tr$inferential_status == "unidentified")) hist <- dat[dat$participant_id == dat$participant_id[[1]], ] expect_error(ref_forecast(dyn, history = hist, times = max(hist$age) + 1), "not identified") expect_true(all(is.na(fortify_kernel(dyn)$correlation))) @@ -277,6 +283,49 @@ test_that("forecast density law holds after conditioning", { expect_s3_class(vctrs::vec_data(d)[[1]], "dist_conditioned") p <- c(0.2, 0.5, 0.8) expect_equal(dist_cdf(d, dist_quantile(d, p)), p, tolerance = 1e-6) + expect_identical(fc$reference_uncertainty, "total") + expect_identical(fc$kernel_uncertainty, "plug_in") +}) + +test_that("longitudinal objects bind and propagate the marginal uncertainty estimand", { + dat <- ref_simulate(360, kind = "longitudinal", seed = 27) + fit <- ref_fit( + ref_spec(ref_gaussian(), location = ~ age + sex, scale = ~ s(age, k = 4)), + dat, "y" + ) + total <- ref_dynamics(fit, dat, participant_id, age, crossfit = 0, + uncertainty = "total", n_draw = 64) + conditional <- ref_dynamics(fit, dat, participant_id, age, crossfit = 0, + uncertainty = "conditional") + expect_identical(total$uncertainty, "total") + expect_identical(total$n_draw, 64L) + expect_identical(conditional$uncertainty, "conditional") + + heldout <- ref_simulate(60, kind = "longitudinal", seed = 28) + tr <- ref_transition(total, heldout, participant_id, age) + expect_true(all(tr$reference_uncertainty == "total")) + expect_true(all(tr$inferential_status == "heldout")) + hist <- heldout[heldout$participant_id == heldout$participant_id[[1]], ] + fc_total <- ref_forecast(total, hist, max(hist$age) + 1) + fc_cond <- ref_forecast(conditional, hist, max(hist$age) + 1) + expect_identical(fc_total$reference_uncertainty, "total") + expect_false(isTRUE(all.equal(variance(fc_total$dist), variance(fc_cond$dist)))) +}) + +test_that("longitudinal fitting refuses marginal transformations it cannot reproduce", { + dat <- ref_simulate(240, kind = "longitudinal", seed = 29) + fit <- ref_fit(long_spec(), dat[1:180, ], "y") + calibrated <- ref_calibrate(fit, dat[181:220, ], uncertainty = "total") + expect_error( + ref_dynamics(calibrated, dat, participant_id, age), + "PIT-calibrated" + ) + + adapted <- ref_adapt(fit, dat[181:220, ], by = site) + expect_error( + ref_dynamics(adapted, dat, participant_id, age, crossfit = 3), + "adaptation.*cross-fitting" + ) }) test_that("ref_derivative reproduces a linear slope and its standard error", { diff --git a/tests/testthat/test-fit-mgcv.R b/tests/testthat/test-fit-mgcv.R index ee4b12b..4a6807c 100644 --- a/tests/testthat/test-fit-mgcv.R +++ b/tests/testthat/test-fit-mgcv.R @@ -57,6 +57,29 @@ test_that("total uncertainty widens the Gaussian predictive analytically", { expect_true(all(abs(pt - 0.5) <= abs(pc - 0.5) + 1e-12)) }) +test_that("critical optimizer warnings make an otherwise converged engine fit unstable", { + expect_equal( + mgcv_fit_status(TRUE, "Fitting terminated with step failure - check results carefully"), + "unstable" + ) + expect_equal(mgcv_fit_status(TRUE, "iteration limit reached without convergence"), + "unstable") + expect_equal(mgcv_fit_status(TRUE, "a harmless diagnostic"), "ok") + expect_equal(mgcv_fit_status(FALSE, NULL), "nonconverged") +}) + +test_that("total uncertainty fails closed when coefficient draws cannot be generated", { + dat <- ref_simulate(120, seed = 35) + fit <- ref_fit(simple_spec(scale = TRUE), dat, "y") + local_mocked_bindings( + mvtnorm_draw = function(...) stop("bad covariance") + ) + expect_error( + predict(fit, dat[1:4, ], uncertainty = "total", type = "distribution"), + "Could not propagate total uncertainty" + ) +}) + test_that("save/read round trip reproduces scores", { set.seed(8) dat <- ref_simulate(120, seed = 8) diff --git a/tests/testthat/test-pcntoolkit-superiority.R b/tests/testthat/test-pcntoolkit-superiority.R index cd51fc9..0d6469c 100644 --- a/tests/testthat/test-pcntoolkit-superiority.R +++ b/tests/testthat/test-pcntoolkit-superiority.R @@ -88,6 +88,7 @@ test_that("release superiority requires replicated convergence and calibration", pcntoolkit_mace = 0.05, referent_tail05 = 0.05, pcntoolkit_tail05 = 0.06, + comparator_valid = TRUE, fit_status = "ok", fit_converged = TRUE ) @@ -97,6 +98,8 @@ test_that("release superiority requires replicated convergence and calibration", expect_gt(passed$ci_lower, 0) expect_true(passed$calibration_noninferior) expect_true(passed$no_critical_replicate_regression) + expect_true(passed$all_referent_fits_valid) + expect_true(passed$all_comparator_fits_valid) failed_fit <- results failed_fit$fit_status[[3L]] <- "nonconverged" @@ -104,6 +107,15 @@ test_that("release superiority requires replicated convergence and calibration", expect_identical(fit_summary$classification, "fit_failure") expect_false(fit_summary$pass) + failed_comparator <- results + failed_comparator$comparator_valid[[4L]] <- FALSE + comparator_summary <- pcn_release_superiority_summary( + failed_comparator, B = 999L, seed = 14L + ) + expect_identical(comparator_summary$classification, "comparator_failure") + expect_false(comparator_summary$all_comparator_fits_valid) + expect_false(comparator_summary$pass) + failed_calibration <- results failed_calibration$referent_coverage90[[5L]] <- 0.70 failed_calibration$pcntoolkit_coverage90[[5L]] <- 0.90 diff --git a/tests/testthat/test-pcntoolkit-workflow.R b/tests/testthat/test-pcntoolkit-workflow.R index 508c8c3..ce3e5f5 100644 --- a/tests/testthat/test-pcntoolkit-workflow.R +++ b/tests/testthat/test-pcntoolkit-workflow.R @@ -29,10 +29,15 @@ test_that("release workflow runs every declared evidence lane", { expect_match(workflow, "generate_release_superiority.py", fixed = TRUE) expect_match(workflow, "--replicates 20", fixed = TRUE) expect_match(workflow, "run_release_superiority.R", fixed = TRUE) + expect_match( + workflow, + "--scenarios linear_gaussian,nonlinear_heteroskedastic,skew_heavy,unequal_site,covariate_shift", + fixed = TRUE + ) expect_match(workflow, "github.event_name == 'release'", fixed = TRUE) expect_match( workflow, - "Release SHASH fitting and replicated superiority evidence", + "Release replicated scenario-matrix evidence", fixed = TRUE ) }) diff --git a/tests/testthat/test-performance.R b/tests/testthat/test-performance.R index bef4883..9f227b1 100644 --- a/tests/testthat/test-performance.R +++ b/tests/testthat/test-performance.R @@ -3,7 +3,7 @@ test_that("frozen bundle is small and reproduces the fit after a round trip", { for (nm in names(perf_specs())) { fit <- ref_fit(perf_specs()[[nm]], d$ref, c("y", "marker_01")) fit <- ref_adapt(fit, d$new, by = site, parameters = c("location", "scale")) - fit <- ref_calibrate(fit, d$new, by = site) + fit <- ref_calibrate(fit, d$new, by = site, uncertainty = "conditional") bundle <- ref_freeze(fit) path <- withr::local_tempfile(fileext = ".rds") # spec formulas made in the test helper env serialise with a package-env @@ -15,14 +15,20 @@ test_that("frozen bundle is small and reproduces the fit after a round trip", { expect_equal(nrow(thawed$models$y$model$model), 0L) expect_null(thawed$models$y$model$family) expect_null(thawed$models$y$model$fitted.values) - for (u in c("conditional", "total")) { - expect_equal( - predict(thawed, d$new, uncertainty = u), - predict(fit, d$new, uncertainty = u), - tolerance = 1e-10, label = paste(nm, u) - ) - } - expect_equal(ref_assess(thawed, d$new)$overall, ref_assess(fit, d$new)$overall, + expect_equal( + predict(thawed, d$new, uncertainty = "conditional"), + predict(fit, d$new, uncertainty = "conditional"), + tolerance = 1e-10, label = paste(nm, "conditional") + ) + expect_equal( + predict(thawed, d$new, uncertainty = "total", type = "distribution"), + predict(fit, d$new, uncertainty = "total", type = "distribution"), + tolerance = 1e-10, label = paste(nm, "total distribution") + ) + expect_equal(ref_assess(thawed, d$new, uncertainty = "conditional", + allow_calibration_reuse = TRUE)$overall, + ref_assess(fit, d$new, uncertainty = "conditional", + allow_calibration_reuse = TRUE)$overall, tolerance = 1e-10) expect_equal(ref_support(thawed, d$new), ref_support(fit, d$new)) expect_equal(tidy(thawed)$n, tidy(fit)$n) diff --git a/tests/testthat/test-predict.R b/tests/testthat/test-predict.R index 5c095b0..b1925f2 100644 --- a/tests/testthat/test-predict.R +++ b/tests/testthat/test-predict.R @@ -153,7 +153,7 @@ test_that("ref_support reports unknown for NA covariates", { test_that("augment matches the long score table", { d <- perf_data() fit <- ref_calibrate(ref_fit(perf_specs()$gaulss, d$ref, c("y", "marker_01")), - d$new, by = site) + d$new, by = site, uncertainty = "conditional") long <- predict(fit, d$new, uncertainty = "conditional") wide <- augment(fit, d$new, uncertainty = "conditional") for (nm in c("y", "marker_01")) { @@ -240,7 +240,8 @@ test_that("calibration leaves calibrated = FALSE where no map could be estimated cal <- dat[121:160, ] cal$site <- as.character(cal$site) cal$site[1] <- "solo" # one row: no map for this group, pooled map applies - fit_by <- ref_calibrate(fit, data = cal, by = site) + fit_by <- ref_calibrate(fit, data = cal, by = site, + uncertainty = "conditional") expect_null(fit_by$calibration$maps$y$solo) new <- dat[161:170, ] new$site <- as.character(new$site) @@ -248,7 +249,7 @@ test_that("calibration leaves calibrated = FALSE where no map could be estimated sc <- predict(fit_by, new, uncertainty = "conditional") expect_true(all(sc$calibrated)) # an outcome with a single calibration row has no map at all - fit1 <- ref_calibrate(fit, data = dat[121, ]) + fit1 <- ref_calibrate(fit, data = dat[121, ], uncertainty = "conditional") expect_null(fit1$calibration$maps$y$.global) sc1 <- predict(fit1, new, uncertainty = "conditional") expect_true(all(!sc1$calibrated)) diff --git a/tests/testthat/test-quality-workflow.R b/tests/testthat/test-quality-workflow.R new file mode 100644 index 0000000..0308d9d --- /dev/null +++ b/tests/testthat/test-quality-workflow.R @@ -0,0 +1,19 @@ +test_that("quality workflow covers supported operating systems and scale budgets", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + workflow_path <- file.path(root, ".github", "workflows", "quality.yml") + benchmark_path <- file.path(root, "tools", "benchmarks", "run_scale_benchmarks.R") + skip_if_not(all(file.exists(workflow_path, benchmark_path)), + "repository-only quality assets are excluded from the package tarball") + workflow <- paste(readLines(workflow_path, + warn = FALSE), collapse = "\n") + benchmark <- paste(readLines(benchmark_path, warn = FALSE), + collapse = "\n") + expect_match(workflow, "ubuntu-latest", fixed = TRUE) + expect_match(workflow, "macos-latest", fixed = TRUE) + expect_match(workflow, "windows-latest", fixed = TRUE) + expect_match(workflow, "oldrel-1", fixed = TRUE) + expect_match(workflow, "--as-cran", fixed = TRUE) + expect_match(workflow, "run_scale_benchmarks.R", fixed = TRUE) + expect_match(benchmark, "maximum_allocated_bytes", fixed = TRUE) + expect_match(benchmark, "bundle_bytes", fixed = TRUE) +}) diff --git a/tests/testthat/test-real-cohort-workflow.R b/tests/testthat/test-real-cohort-workflow.R new file mode 100644 index 0000000..acdbd88 --- /dev/null +++ b/tests/testthat/test-real-cohort-workflow.R @@ -0,0 +1,17 @@ +test_that("real-cohort lane declares independent NHANES cohorts and durable evidence", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + runner_path <- file.path(root, "tools", "validation", + "run_nhanes_external_validation.R") + workflow_path <- file.path(root, ".github", "workflows", + "real-cohort-validation.yml") + skip_if_not(all(file.exists(runner_path, workflow_path)), + "repository-only validation assets are excluded from the package tarball") + runner <- paste(readLines(runner_path, warn = FALSE), collapse = "\n") + workflow <- paste(readLines(workflow_path, warn = FALSE), collapse = "\n") + expect_match(runner, "NHANES 2015-2016", fixed = TRUE) + expect_match(runner, "NHANES 2017-2018", fixed = TRUE) + expect_match(runner, "acceptable_calibration", fixed = TRUE) + expect_match(runner, "uncertainty = \"total\"", fixed = TRUE) + expect_match(workflow, "if: always()", fixed = TRUE) + expect_match(workflow, "nhanes-external-validation-evidence", fixed = TRUE) +}) diff --git a/tests/testthat/test-reference-engine.R b/tests/testthat/test-reference-engine.R index ec96f81..7fe8bc4 100644 --- a/tests/testthat/test-reference-engine.R +++ b/tests/testthat/test-reference-engine.R @@ -1,8 +1,9 @@ test_that("a frozen reference predicts identically after a saveRDS/readRDS round trip", { dat <- ref_simulate(200, seed = 100) fit <- ref_fit(simple_spec(), data = dat[1:140, ], outcomes = c("y", "marker_01")) - fit <- ref_calibrate(fit, data = dat[141:170, ]) + fit <- ref_calibrate(fit, data = dat[141:170, ], uncertainty = "conditional") ref <- ref_freeze(fit, criteria = "healthy volunteers", units = "mm") + expect_identical(ref$bundle_schema_version, "1.0.0") tmp <- withr::local_tempfile(fileext = ".rds") suppressWarnings(saveRDS(ref, tmp)) ref2 <- readRDS(tmp) @@ -24,6 +25,33 @@ test_that("a frozen reference predicts identically after a saveRDS/readRDS round expect_equal(tidy(ref2)$outcome, c("y", "marker_01")) }) +test_that("versioned bundle IO migrates legacy bundles and rejects future schemas", { + dat <- ref_simulate(120, seed = 104) + fit <- ref_fit(simple_spec(), dat, "y") + bundle <- ref_freeze(fit) + path <- withr::local_tempfile(fileext = ".rds") + expect_identical(ref_write(bundle, path), path) + loaded <- ref_read(path) + expect_identical(loaded$bundle_schema_version, "1.0.0") + expect_equal( + predict(loaded, dat[1:4, ], uncertainty = "conditional")$z, + predict(bundle, dat[1:4, ], uncertainty = "conditional")$z + ) + + legacy <- bundle + legacy$bundle_type <- NULL + legacy$bundle_schema_version <- NULL + legacy_path <- withr::local_tempfile(fileext = ".rds") + saveRDS(legacy, legacy_path) + expect_error(predict(legacy, dat[1:2, ]), "unversioned") + expect_warning(migrated <- ref_read(legacy_path), "Migrating") + expect_identical(migrated$migration$from, "unversioned") + + future <- bundle + future$bundle_schema_version <- "2.0.0" + expect_error(predict(future, dat[1:2, ]), "Unsupported.*2.0.0") +}) + test_that("a frozen reference does not serialise the frame its spec was built in", { build <- function() { ballast <- stats::rnorm(2e6) diff --git a/tests/testthat/test-select.R b/tests/testthat/test-select.R index 906689f..4fbebfe 100644 --- a/tests/testthat/test-select.R +++ b/tests/testthat/test-select.R @@ -24,7 +24,7 @@ test_that("the ladder picks a Gaussian level on Gaussian constant-scale data", { expect_true(all(is.finite(tab$crps[tab$status == "ok"]))) # gate values are out-of-fold: they differ from the in-sample assessment fit <- attr(sel$crossfit, "deployment") - ins <- ref_assess(fit, newdata = dat) + ins <- ref_assess(fit, newdata = dat, allow_in_sample = TRUE) oof <- tab[tab$selected, ] expect_false(isTRUE(all.equal(oof$var_z, ins$marginal$var_z))) expect_equal(oof$var_z, stats::var(sel$crossfit$z, na.rm = TRUE)) @@ -49,11 +49,16 @@ test_that("the ladder picks SHASH on strongly skewed data", { }) test_that("acceptable_calibration scales its tolerance with n", { - make <- function(n, mean_z, var_z, cover_95, tail_05 = 0.05) { + make <- function(n, mean_z, var_z, cover_95, tail_05 = 0.05, + mace = 0.01, skew_z = 0, excess_kurtosis_z = 0, + conditional = NULL) { list( marginal = tibble::tibble(.outcome = "y", n = n, mean_z = mean_z, var_z = var_z, - cover_95 = cover_95), - tail = tibble::tibble(.outcome = "y", tail_level = 0.05, observed = tail_05, n = n) + skew_z = skew_z, excess_kurtosis_z = excess_kurtosis_z, + mace = mace, cover_95 = cover_95), + tail = tibble::tibble(.outcome = "y", tail_level = 0.05, expected = 0.05, + observed = tail_05, n = n), + conditional = conditional ) } expect_true(acceptable_calibration(make(1000, 0.05, 1.05, 0.95))) @@ -61,10 +66,54 @@ test_that("acceptable_calibration scales its tolerance with n", { expect_false(acceptable_calibration(make(1000, 0.0, 1.6, 0.95))) expect_false(acceptable_calibration(make(1000, 0.0, 1.0, 0.90))) expect_false(acceptable_calibration(make(1000, 0.0, 1.0, 0.95, tail_05 = 0.10))) + expect_false(acceptable_calibration(make(1000, 0.0, 1.0, 0.95, mace = 0.08))) + expect_false(acceptable_calibration(make(1000, 0.0, 1.0, 0.95, skew_z = 1.2))) # the same deviations are within noise at n = 30 expect_true(acceptable_calibration(make(30, 0.3, 1.6, 0.90))) }) +test_that("the ladder rejects material conditional drift", { + set.seed(913) + n <- 600 + dat <- tibble::tibble( + age = seq(20, 80, length.out = n), + y = ((age - 50) / 10)^2 + stats::rnorm(n, sd = 0.35) + ) + specs <- list(linear = ref_spec(ref_gaussian(), location = ~ age, scale = ~ 1)) + + expect_warning( + sel <- ref_select(specs, dat, "y", folds = 5, allow_uncalibrated = TRUE), + "explicitly allowed" + ) + row <- sel$comparison[1, ] + expect_false(row$conditional_pass) + expect_false(row$calibrated) + expect_gt(row$max_location_drift, 0.5) +}) + +test_that("the ladder fails closed when every usable candidate is uncalibrated", { + set.seed(914) + n <- 500 + dat <- tibble::tibble( + age = stats::runif(n, 20, 80), + y = ifelse(stats::runif(n) < 0.1, + stats::rnorm(n, 10, 0.2), + stats::rnorm(n, 0, 0.2)) + ) + specs <- list(gaussian = ref_spec(ref_gaussian(), location = ~ 1, scale = ~ 1)) + + expect_error( + ref_select(specs, dat, "y", folds = 5), + "No ladder candidate passed the calibration gate" + ) + expect_warning( + sel <- ref_select(specs, dat, "y", folds = 5, allow_uncalibrated = TRUE), + "explicitly allowed" + ) + expect_false(sel$comparison$calibrated) + expect_true(sel$comparison$selected) +}) + test_that("the comparison table carries the paired standard errors the one-SE rule uses", { dat <- ref_simulate(200, seed = 15) specs <- list( diff --git a/tools/benchmarks/run_scale_benchmarks.R b/tools/benchmarks/run_scale_benchmarks.R new file mode 100644 index 0000000..f30db26 --- /dev/null +++ b/tools/benchmarks/run_scale_benchmarks.R @@ -0,0 +1,112 @@ +# Enforce broad release budgets for fit, prediction, allocation, and bundle size. + +profiled <- function(expr) { + path <- tempfile("referent-rprofmem-") + gc() + utils::Rprofmem(path) + on.exit(utils::Rprofmem(NULL), add = TRUE) + timing <- system.time(value <- force(expr)) + utils::Rprofmem(NULL) + lines <- readLines(path, warn = FALSE) + bytes <- suppressWarnings(as.numeric(sub(" .*", "", lines))) + list(value = value, seconds = unname(timing[["elapsed"]]), + allocated_bytes = sum(bytes, na.rm = TRUE)) +} + +run_scale_benchmarks <- function(output_dir, mode = c("smoke", "release")) { + mode <- match.arg(mode) + if (!requireNamespace("referent", quietly = TRUE) || + !requireNamespace("jsonlite", quietly = TRUE)) { + stop("referent and jsonlite are required", call. = FALSE) + } + size <- if (mode == "release") { + list(gaussian_train = 10000L, gaussian_test = 3000L, + shash_train = 1500L, shash_test = 750L) + } else { + list(gaussian_train = 2000L, gaussian_test = 750L, + shash_train = 500L, shash_test = 250L) + } + limits <- if (mode == "release") { + c(gaussian_fit_seconds = 60, gaussian_predict_seconds = 30, + shash_fit_seconds = 60, shash_predict_seconds = 120, + maximum_allocated_bytes = 8e9, bundle_bytes = 1e7) + } else { + c(gaussian_fit_seconds = 20, gaussian_predict_seconds = 10, + shash_fit_seconds = 30, shash_predict_seconds = 60, + maximum_allocated_bytes = 3e9, bundle_bytes = 1e7) + } + + gaussian_train <- referent::ref_simulate(size$gaussian_train, seed = 20260824) + gaussian_test <- referent::ref_simulate(size$gaussian_test, seed = 20260825) + outcomes <- c("y", "marker_01", "marker_02", "marker_03") + gaussian_spec <- referent::ref_spec( + referent::ref_gaussian(), location = ~ s(age, k = 8) + sex, + scale = ~ 1, bam_min_n = 1000L + ) + gaussian_fit <- profiled( + referent::ref_fit(gaussian_spec, gaussian_train, outcomes = outcomes) + ) + gaussian_predict <- profiled(stats::predict( + gaussian_fit$value, gaussian_test, uncertainty = "total" + )) + bundle <- referent::ref_freeze(gaussian_fit$value) + bundle_bytes <- length(serialize(bundle, NULL, version = 3)) + + shash_train <- referent::ref_simulate(size$shash_train, kind = "shash", + seed = 20260826) + shash_test <- referent::ref_simulate(size$shash_test, kind = "shash", + seed = 20260827) + shash_spec <- referent::ref_spec( + referent::ref_shash(), location = ~ s(age, k = 6) + sex, + scale = ~ s(age, k = 5), skew = ~ 1, tail = ~ 1, + control = list(seed = 20260824L, n_draw = 200L) + ) + shash_fit <- profiled(referent::ref_fit(shash_spec, shash_train, outcomes = "y")) + shash_predict <- profiled(stats::predict( + shash_fit$value, shash_test, uncertainty = "total", n_draw = 200L + )) + + observed <- c( + gaussian_fit_seconds = gaussian_fit$seconds, + gaussian_predict_seconds = gaussian_predict$seconds, + shash_fit_seconds = shash_fit$seconds, + shash_predict_seconds = shash_predict$seconds, + maximum_allocated_bytes = max( + gaussian_fit$allocated_bytes, gaussian_predict$allocated_bytes, + shash_fit$allocated_bytes, shash_predict$allocated_bytes + ), + bundle_bytes = bundle_bytes + ) + results <- data.frame( + metric = names(observed), observed = unname(observed), + limit = unname(limits[names(observed)]), + pass = unname(observed <= limits[names(observed)]), + stringsAsFactors = FALSE + ) + dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) + result_path <- file.path(output_dir, "scale_benchmarks.csv") + utils::write.csv(results, result_path, row.names = FALSE) + receipt <- list( + schema_version = "1.0.0", + mode = mode, + sizes = size, + package_version = as.character(utils::packageVersion("referent")), + r_version = R.version.string, + platform = R.version$platform, + all_pass = all(results$pass), + result_md5 = unname(tools::md5sum(result_path)) + ) + jsonlite::write_json(receipt, file.path(output_dir, "receipt.json"), + auto_unbox = TRUE, pretty = TRUE) + invisible(results) +} + +if (sys.nframe() == 0L) { + args <- commandArgs(trailingOnly = TRUE) + if (length(args) != 2L || !args[[2L]] %in% c("smoke", "release")) { + stop("usage: run_scale_benchmarks.R OUTPUT_DIR smoke|release", call. = FALSE) + } + results <- run_scale_benchmarks(args[[1L]], args[[2L]]) + print(results, row.names = FALSE) + if (!all(results$pass)) quit(status = 1L) +} diff --git a/tools/pcntoolkit/benchmark_helpers.R b/tools/pcntoolkit/benchmark_helpers.R index 4c26ef1..aa75aaf 100644 --- a/tools/pcntoolkit/benchmark_helpers.R +++ b/tools/pcntoolkit/benchmark_helpers.R @@ -99,11 +99,13 @@ pcn_compare_predictions <- function(referent, comparator, cluster, } pcn_release_superiority_summary <- function(results, B = 4999L, - seed = 20260823L) { + seed = 20260823L, + accepted_classifications = "superior") { required <- c( "replicate", "log_score_difference", "referent_coverage90", "pcntoolkit_coverage90", "referent_mace", "pcntoolkit_mace", - "referent_tail05", "pcntoolkit_tail05", "fit_status", "fit_converged" + "referent_tail05", "pcntoolkit_tail05", "comparator_valid", + "fit_status", "fit_converged" ) if (!all(required %in% names(results))) { stop("release results are missing required fields", call. = FALSE) @@ -122,6 +124,7 @@ pcn_release_superiority_summary <- function(results, B = 4999L, results$log_score_difference, results$replicate, B = B, seed = seed ) fit_pass <- all(results$fit_status == "ok" & results$fit_converged) + comparator_pass <- all(results$comparator_valid) calibration_regret <- cbind( coverage90 = abs(results$referent_coverage90 - 0.90) - abs(results$pcntoolkit_coverage90 - 0.90), @@ -142,7 +145,9 @@ pcn_release_superiority_summary <- function(results, B = 4999L, max_regret <- apply(calibration_regret, 2L, max) calibration_pass <- all(calibration_upper <= calibration_limits) critical_pass <- all(max_regret <= critical_limits) - classification <- if (fit_pass) { + classification <- if (!comparator_pass) { + "comparator_failure" + } else if (fit_pass) { pcn_classify_benchmark( interval[["estimate"]], interval[["lower"]], interval[["upper"]], calibration_pass = calibration_pass && critical_pass @@ -155,7 +160,8 @@ pcn_release_superiority_summary <- function(results, B = 4999L, log_score_difference = unname(interval[["estimate"]]), ci_lower = unname(interval[["lower"]]), ci_upper = unname(interval[["upper"]]), - all_shash_fits_converged = fit_pass, + all_referent_fits_valid = fit_pass, + all_comparator_fits_valid = comparator_pass, coverage_regret = mean_regret[["coverage90"]], coverage_regret_upper = calibration_upper[["coverage90"]], mace_regret = mean_regret[["mace"]], @@ -166,7 +172,8 @@ pcn_release_superiority_summary <- function(results, B = 4999L, calibration_noninferior = calibration_pass, no_critical_replicate_regression = critical_pass, classification = classification, - pass = identical(classification, "superior"), + accepted_classifications = paste(accepted_classifications, collapse = ";"), + pass = classification %in% accepted_classifications, stringsAsFactors = FALSE ) } diff --git a/tools/pcntoolkit/generate_release_superiority.py b/tools/pcntoolkit/generate_release_superiority.py index 12ab3f4..4a0088e 100644 --- a/tools/pcntoolkit/generate_release_superiority.py +++ b/tools/pcntoolkit/generate_release_superiority.py @@ -24,12 +24,52 @@ from generate_fixtures import COMPARATOR_VERSION, fit_scenario, scenario_frame, stable_csv +CRITICAL_WARNING_PATTERNS = ( + "ill-conditioned", + "posterior estimation failed", + "not positive definite", + "could not find a stable solution", + "overflow", + "singular", + "not converg", +) +DEFAULT_SCENARIOS = ( + "linear_gaussian", + "nonlinear_heteroskedastic", + "skew_heavy", + "unequal_site", + "covariate_shift", +) +SELECTION_RULES = { + "linear_gaussian": "prespecified linear Gaussian", + "nonlinear_heteroskedastic": "prespecified smooth Gaussian location-scale", + "skew_heavy": "prespecified SHASH after family validation", + "unequal_site": "prespecified site location-scale model", + "covariate_shift": "prespecified linear Gaussian under shifted support", +} +SCENARIO_CLASSES = { + "linear_gaussian": "matched_estimator", + "nonlinear_heteroskedastic": "same_estimand", + "skew_heavy": "same_estimand", + "unequal_site": "same_estimand", + "covariate_shift": "matched_estimator", +} + + +def critical_warnings(messages: list[str]) -> list[str]: + return [ + message + for message in messages + if any(pattern in message.lower() for pattern in CRITICAL_WARNING_PATTERNS) + ] + + def md5(path: Path) -> str: return hashlib.md5(path.read_bytes(), usedforsecurity=False).hexdigest() def write_release_superiority( - output: Path, replicates: int, seed_start: int + output: Path, replicates: int, seed_start: int, scenarios: list[str] ) -> None: if replicates < 5: raise ValueError("release superiority requires at least five replicates") @@ -38,40 +78,54 @@ def write_release_superiority( prediction_parts: list[pd.DataFrame] = [] warning_receipts: list[dict[str, object]] = [] seeds = list(range(seed_start, seed_start + replicates)) + unknown = sorted(set(scenarios) - set(SELECTION_RULES)) + if unknown: + raise ValueError(f"unknown release scenarios: {', '.join(unknown)}") + if len(set(scenarios)) != len(scenarios): + raise ValueError("release scenarios must be unique") - for replicate, seed in enumerate(seeds, start=1): - frame = scenario_frame( - "skew_heavy", - n_train=500, - n_validation=100, - n_test=500, - seed=seed, - ) - frame["row_id"] = frame["row_id"].map( - lambda value: f"rep-{replicate:03d}-{value}" - ) - frame.insert(0, "seed", seed) - frame.insert(0, "replicate", replicate) - with warnings.catch_warnings(record=True) as caught: - warnings.simplefilter("always") - prediction = fit_scenario(frame, "skew_heavy") - numeric_evidence = prediction[ - ["observed", "median", "predictive_sd", "centile", "z", "log_density", - "q05", "q25", "q50", "q75", "q95"] - ].to_numpy() - if not np.isfinite(numeric_evidence).all(): - raise RuntimeError(f"non-finite PCNtoolkit evidence in replicate {replicate}") - warning_receipts.append( - { - "replicate": replicate, - "count": len(caught), - "messages": [str(item.message) for item in caught], - } - ) - prediction.insert(0, "seed", seed) - prediction.insert(0, "replicate", replicate) - input_parts.append(frame) - prediction_parts.append(prediction) + for scenario in scenarios: + for replicate, seed in enumerate(seeds, start=1): + frame = scenario_frame( + scenario, + n_train=500, + n_validation=100, + n_test=500, + seed=seed, + ) + frame["row_id"] = frame["row_id"].map( + lambda value: f"rep-{replicate:03d}-{value}" + ) + frame.insert(0, "seed", seed) + frame.insert(0, "replicate", replicate) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + prediction = fit_scenario(frame, scenario) + numeric_evidence = prediction[ + ["observed", "median", "predictive_sd", "centile", "z", + "log_density", "q05", "q25", "q50", "q75", "q95"] + ].to_numpy() + if not np.isfinite(numeric_evidence).all(): + raise RuntimeError( + f"non-finite PCNtoolkit evidence in {scenario} replicate {replicate}" + ) + messages = [str(item.message) for item in caught] + critical = critical_warnings(messages) + warning_receipts.append( + { + "scenario": scenario, + "replicate": replicate, + "count": len(caught), + "messages": messages, + "critical_count": len(critical), + "critical_messages": critical, + "valid": len(critical) == 0, + } + ) + prediction.insert(0, "seed", seed) + prediction.insert(0, "replicate", replicate) + input_parts.append(frame) + prediction_parts.append(prediction) inputs = pd.concat(input_parts, ignore_index=True) predictions = pd.concat(prediction_parts, ignore_index=True) @@ -89,14 +143,20 @@ def write_release_superiority( f"expected PCNtoolkit {COMPARATOR_VERSION}, got {versions['pcntoolkit']}" ) receipt = { - "schema_version": "1.0.0", + "schema_version": "1.1.0", "comparator": {"package": "pcntoolkit", "version": COMPARATOR_VERSION}, - "scenario": "skew_heavy", + "scenarios": scenarios, + "scenario_classes": {name: SCENARIO_CLASSES[name] for name in scenarios}, "replicates": replicates, "seeds": seeds, "split_sizes": {"train": 500, "validation": 100, "test": 500}, - "selection_rule": "prespecified SHASH after family validation", + "selection_rules": {name: SELECTION_RULES[name] for name in scenarios}, + "warning_policy": { + "critical_patterns": list(CRITICAL_WARNING_PATTERNS), + "rule": "any critical fit warning invalidates that comparator replicate", + }, "fit_warnings": warning_receipts, + "comparator_valid": all(item["valid"] for item in warning_receipts), "dependencies": versions, "files": { input_path.name: { @@ -121,8 +181,16 @@ def main() -> None: parser.add_argument("--output", type=Path, required=True) parser.add_argument("--replicates", type=int, default=20) parser.add_argument("--seed-start", type=int, default=20260824) + parser.add_argument( + "--scenarios", + default=",".join(DEFAULT_SCENARIOS), + help="comma-separated prespecified scenario names", + ) args = parser.parse_args() - write_release_superiority(args.output.resolve(), args.replicates, args.seed_start) + scenarios = [item.strip() for item in args.scenarios.split(",") if item.strip()] + write_release_superiority( + args.output.resolve(), args.replicates, args.seed_start, scenarios + ) if __name__ == "__main__": diff --git a/tools/pcntoolkit/run_release_superiority.R b/tools/pcntoolkit/run_release_superiority.R index de59b1b..b4f7dfe 100644 --- a/tools/pcntoolkit/run_release_superiority.R +++ b/tools/pcntoolkit/run_release_superiority.R @@ -5,6 +5,19 @@ source("tools/pcntoolkit/compare_results.R", local = TRUE) source("tools/pcntoolkit/run_concordance.R", local = TRUE) source("tools/pcntoolkit/benchmark_helpers.R", local = TRUE) +pcn_release_scenarios <- function() { + c("linear_gaussian", "nonlinear_heteroskedastic", "skew_heavy", + "unequal_site", "covariate_shift") +} + +pcn_release_accepted <- function(scenario) { + if (identical(scenario, "skew_heavy")) { + "superior" + } else { + c("equivalent", "non_inferior", "superior") + } +} + pcn_validate_release_generation <- function(input_dir) { if (!requireNamespace("jsonlite", quietly = TRUE)) { stop("jsonlite is required for release evidence", call. = FALSE) @@ -14,14 +27,18 @@ pcn_validate_release_generation <- function(input_dir) { stop("missing release generation receipt", call. = FALSE) } receipt <- jsonlite::read_json(receipt_path, simplifyVector = FALSE) - if (!identical(receipt$schema_version, "1.0.0") || + if (!identical(receipt$schema_version, "1.1.0") || !identical(receipt$comparator$package, "pcntoolkit") || !identical(receipt$comparator$version, "1.3.0") || - !identical(receipt$scenario, "skew_heavy") || - !identical(receipt$selection_rule, - "prespecified SHASH after family validation")) { + !identical(unlist(receipt$scenarios, use.names = FALSE), + pcn_release_scenarios()) || + !setequal(names(receipt$selection_rules), pcn_release_scenarios())) { stop("release generation receipt does not match the declared contract", call. = FALSE) } + if (is.null(receipt$warning_policy) || is.null(receipt$fit_warnings) || + is.null(receipt$comparator_valid)) { + stop("release generation receipt has no comparator warning verdict", call. = FALSE) + } required <- c("release_inputs.csv", "release_pcntoolkit_predictions.csv") if (!setequal(names(receipt$files), required)) { stop("release generation receipt has an unexpected file set", call. = FALSE) @@ -65,6 +82,7 @@ pcn_run_release_superiority <- function(input_dir, output_dir, !all(required_predictions %in% names(comparator))) { stop("release benchmark tables are missing required columns", call. = FALSE) } + scenarios <- pcn_release_scenarios() replicates <- sort(unique(inputs$replicate)) receipt_seeds <- sort(as.integer(unlist(receipt$seeds, use.names = FALSE))) observed_seeds <- sort(unique(inputs$seed)) @@ -72,44 +90,57 @@ pcn_run_release_superiority <- function(input_dir, output_dir, receipt_splits <- vapply(names(declared_splits), function(split) { as.integer(receipt$split_sizes[[split]]) }, integer(1)) - split_counts <- table(inputs$replicate, inputs$split) + split_counts <- table(inputs$scenario, inputs$replicate, inputs$split) if (length(replicates) != expected_replicates || as.integer(receipt$replicates) != expected_replicates || !identical(receipt_seeds, observed_seeds) || !identical(receipt_splits, declared_splits) || - !all(names(declared_splits) %in% colnames(split_counts)) || + !setequal(unique(inputs$scenario), scenarios) || + !setequal(unique(comparator$scenario), scenarios) || + !all(names(declared_splits) %in% dimnames(split_counts)[[3L]]) || any(vapply(names(declared_splits), function(split) { - any(split_counts[, split] != declared_splits[[split]]) + any(split_counts[, , split] != declared_splits[[split]]) }, logical(1))) || !identical(replicates, sort(unique(comparator$replicate))) || anyDuplicated(inputs$row_id) || anyDuplicated(comparator$row_id)) { stop("release benchmark replicate or row identities are invalid", call. = FALSE) } - rows <- lapply(replicates, function(replicate_id) { - replicate_inputs <- inputs[inputs$replicate == replicate_id, , drop = FALSE] + jobs <- expand.grid(scenario = scenarios, replicate = replicates, + stringsAsFactors = FALSE) + rows <- lapply(seq_len(nrow(jobs)), function(job_id) { + scenario <- jobs$scenario[[job_id]] + replicate_id <- jobs$replicate[[job_id]] + replicate_inputs <- inputs[ + inputs$scenario == scenario & inputs$replicate == replicate_id, , drop = FALSE + ] replicate_seed <- unique(replicate_inputs$seed) if (length(replicate_seed) != 1L) { stop("release replicate has multiple generation seeds: ", replicate_id, call. = FALSE) } fitted <- pcn_referent_predictions( - replicate_inputs, "skew_heavy", n_draw = n_draw, return_fit = TRUE + replicate_inputs, scenario, n_draw = n_draw, return_fit = TRUE ) referent <- fitted$predictions - pcn <- comparator[comparator$replicate == replicate_id, , drop = FALSE] + pcn <- comparator[ + comparator$scenario == scenario & comparator$replicate == replicate_id, , + drop = FALSE + ] pcn <- pcn[match(referent$row_id, pcn$row_id), , drop = FALSE] if (anyNA(pcn$row_id) || !identical(referent$row_id, pcn$row_id)) { - stop("release prediction row identity mismatch in replicate ", replicate_id, + stop("release prediction row identity mismatch in ", scenario, + " replicate ", replicate_id, call. = FALSE) } if (any(!is.finite(pcn$observed)) || max(abs(referent$observed - pcn$observed)) > 1e-12) { - stop("release observed outcomes differ in replicate ", replicate_id, + stop("release observed outcomes differ in ", scenario, + " replicate ", replicate_id, call. = FALSE) } comparison <- pcn_compare_predictions( - referent, pcn, cluster = referent$row_id, scenario = "skew_heavy", + referent, pcn, cluster = referent$row_id, scenario = scenario, B = within_B, seed = seed + as.integer(replicate_id) ) fit_one <- fitted$fit$models$y @@ -118,12 +149,35 @@ pcn_run_release_superiority <- function(input_dir, output_dir, comparison$fit_status <- fit_one$status comparison$fit_converged <- identical(fit_one$status, "ok") && !is.null(fit_one$model) && !identical(fit_one$model$converged, FALSE) + comparison$fit_message <- if (is.null(fit_one$message)) "" else fit_one$message + warning_index <- which(vapply(receipt$fit_warnings, function(x) { + identical(x$scenario, scenario) && + identical(as.integer(x$replicate), as.integer(replicate_id)) + }, logical(1))) + if (length(warning_index) != 1L) { + stop("release warning receipt identity mismatch in replicate ", replicate_id, + call. = FALSE) + } + warning_receipt <- receipt$fit_warnings[[warning_index]] + comparison$comparator_warning_count <- as.integer(warning_receipt$count) + comparison$comparator_critical_warning_count <- + as.integer(warning_receipt$critical_count) + comparison$comparator_valid <- isTRUE(warning_receipt$valid) comparison }) replicate_results <- do.call(rbind, rows) - summary <- pcn_release_superiority_summary( - replicate_results, B = aggregate_B, seed = seed - ) + summary <- do.call(rbind, lapply(seq_along(scenarios), function(i) { + scenario <- scenarios[[i]] + scenario_results <- replicate_results[ + replicate_results$scenario == scenario, , drop = FALSE + ] + out <- pcn_release_superiority_summary( + scenario_results, B = aggregate_B, seed = seed + 100L * i, + accepted_classifications = pcn_release_accepted(scenario) + ) + data.frame(scenario = scenario, out, check.names = FALSE) + })) + rownames(summary) <- NULL dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) replicate_path <- file.path(output_dir, "release_superiority_replicates.csv") @@ -133,23 +187,26 @@ pcn_run_release_superiority <- function(input_dir, output_dir, release_receipt <- list( schema_version = "1.0.0", comparator = receipt$comparator, - scenario = "skew_heavy", - generated_replicates = length(replicates), + referent_version = as.character(utils::packageVersion("referent")), + r_version = R.version.string, + scenarios = scenarios, + generated_replicates_per_scenario = length(replicates), expected_replicates = expected_replicates, within_replicate_bootstrap = within_B, aggregate_replicate_bootstrap = aggregate_B, referent_prediction_draws = n_draw, requested_seed = seed, - primary_contrast = "mean paired response-scale log score by simulation replicate", + primary_contrast = "scenario-specific mean paired response-scale log score by simulation replicate", calibration_rule = paste( "replicate-bootstrap upper regret <= 0.01 for coverage, MACE, and tail;", "no single-replicate regret > 0.05" ), - all_shash_fits_converged = summary$all_shash_fits_converged, - calibration_noninferior = summary$calibration_noninferior, - no_critical_replicate_regression = summary$no_critical_replicate_regression, - classification = summary$classification, - pass = summary$pass, + all_referent_fits_valid = all(summary$all_referent_fits_valid), + all_comparator_fits_valid = all(summary$all_comparator_fits_valid), + all_scenarios_calibration_noninferior = all(summary$calibration_noninferior), + no_critical_replicate_regression = all(summary$no_critical_replicate_regression), + classifications = stats::setNames(as.list(summary$classification), scenarios), + pass = all(summary$pass), files = list( release_superiority_replicates.csv = unname(tools::md5sum(replicate_path)), release_superiority_summary.csv = unname(tools::md5sum(summary_path)) @@ -186,5 +243,5 @@ if (sys.nframe() == 0L) { expected_replicates = expected_replicates ) print(result$summary, row.names = FALSE) - if (!isTRUE(result$summary$pass)) quit(status = 1L) + if (!all(result$summary$pass)) quit(status = 1L) } diff --git a/tools/pcntoolkit/run_site_transport_evidence.R b/tools/pcntoolkit/run_site_transport_evidence.R index cee3f51..0a978ba 100644 --- a/tools/pcntoolkit/run_site_transport_evidence.R +++ b/tools/pcntoolkit/run_site_transport_evidence.R @@ -68,6 +68,8 @@ run_referent_site_evidence <- function(input_dir, output_dir) { utils::write.csv(gated, file.path(output_dir, "site_comparison.csv"), row.names = FALSE) receipt <- list( estimand = "separate adaptation and transfer policies; no rowwise parity claim", + referent_version = as.character(utils::packageVersion("referent")), + r_version = R.version.string, all_sites_pass = all(gated$pass), failed_sites = split(gated$site[!gated$pass], gated$method[!gated$pass]), referent_adaptation = unclass(adapted$adaptation) diff --git a/tools/validation/run_nhanes_external_validation.R b/tools/validation/run_nhanes_external_validation.R new file mode 100644 index 0000000..ae2c1bb --- /dev/null +++ b/tools/validation/run_nhanes_external_validation.R @@ -0,0 +1,195 @@ +# Independent real-cohort validation using public CDC NHANES files. +# +# Train: 2015-2016 adults. External evaluation: 2017-2018 adults. +# This lane is deliberately unweighted: it validates transport between two +# independently sampled cohorts, not survey-weighted U.S. prevalence. + +nhanes_files <- function() { + c( + demo_train = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/DEMO_I.XPT", + body_train = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/BMX_I.XPT", + demo_test = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/DEMO_J.XPT", + body_test = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/BMX_J.XPT" + ) +} + +nhanes_download <- function(cache_dir) { + dir.create(cache_dir, recursive = TRUE, showWarnings = FALSE) + urls <- nhanes_files() + paths <- file.path(cache_dir, basename(urls)) + for (i in seq_along(urls)) { + if (!file.exists(paths[[i]]) || file.info(paths[[i]])$size == 0) { + utils::download.file(urls[[i]], paths[[i]], mode = "wb", quiet = TRUE) + } + } + stats::setNames(paths, names(urls)) +} + +nhanes_cohort <- function(demo_path, body_path, cohort) { + demo <- foreign::read.xport(demo_path) + body <- foreign::read.xport(body_path) + keep_demo <- c("SEQN", "RIDAGEYR", "RIAGENDR", "RIDRETH3") + keep_body <- c("SEQN", "BMXHT", "BMXWT", "BMXBMI", "BMXWAIST") + missing <- c(setdiff(keep_demo, names(demo)), setdiff(keep_body, names(body))) + if (length(missing)) { + stop("NHANES schema is missing: ", paste(unique(missing), collapse = ", "), + call. = FALSE) + } + out <- merge(demo[keep_demo], body[keep_body], by = "SEQN", all = FALSE) + out <- out[is.finite(out$RIDAGEYR) & out$RIDAGEYR >= 18 & out$RIDAGEYR <= 79, ] + data.frame( + participant_id = sprintf("%s-%d", cohort, as.integer(out$SEQN)), + cohort = cohort, + age = as.numeric(out$RIDAGEYR), + sex = factor(out$RIAGENDR, levels = c(1, 2), labels = c("male", "female")), + race_ethnicity = factor(out$RIDRETH3), + height_cm = as.numeric(out$BMXHT), + weight_kg = as.numeric(out$BMXWT), + bmi = as.numeric(out$BMXBMI), + waist_cm = as.numeric(out$BMXWAIST), + stringsAsFactors = FALSE + ) +} + +nhanes_external_gate <- function(fit, assessment) { + statuses <- vapply(fit$models, function(x) x$status, character(1)) + marginal <- assessment$marginal + calibration_pass <- referent:::acceptable_calibration(assessment) + data.frame( + all_fits_valid = all(statuses == "ok"), + held_out = !isTRUE(assessment$in_sample), + minimum_outcome_n = min(marginal$n), + complete_evidence = all(is.finite(unlist(marginal[c( + "mean_z", "var_z", "mace", "cover_95" + )]))), + calibration_pass = calibration_pass, + pass = all(statuses == "ok") && !isTRUE(assessment$in_sample) && + min(marginal$n) >= 500L && calibration_pass, + stringsAsFactors = FALSE + ) +} + +nhanes_external_assess <- function(fit, data, n_draw) { + scores <- stats::predict( + fit, data, type = "scores", uncertainty = "total", n_draw = n_draw, + allow_extrapolation = FALSE + ) + by_outcome <- split(scores, scores$.outcome) + overall <- do.call(rbind, lapply(names(by_outcome), function(outcome) { + sc <- by_outcome[[outcome]] + ok <- is.finite(sc$observed) & is.finite(sc$log_density) + data.frame( + .outcome = outcome, + mean_log_score = mean(sc$log_density[ok]), + mae = mean(abs(sc$residual[ok])), + rmse = sqrt(mean(sc$residual[ok]^2)), + stringsAsFactors = FALSE + ) + })) + structure( + list( + overall = overall, + marginal = referent:::assess_marginal(scores), + conditional = referent:::assess_conditional(scores, data, fit), + tail = referent:::assess_tail(scores), + scores = scores, + n = nrow(data), + in_sample = nrow(scores) > 0L && all(scores$.in_sample) + ), + class = "ref_assessment" + ) +} + +run_nhanes_external_validation <- function(output_dir, cache_dir) { + if (!requireNamespace("referent", quietly = TRUE) || + !requireNamespace("foreign", quietly = TRUE) || + !requireNamespace("jsonlite", quietly = TRUE) || + !requireNamespace("digest", quietly = TRUE)) { + stop("referent, foreign, jsonlite, and digest are required", call. = FALSE) + } + paths <- nhanes_download(cache_dir) + train <- nhanes_cohort(paths[["demo_train"]], paths[["body_train"]], "2015-2016") + test_population <- nhanes_cohort( + paths[["demo_test"]], paths[["body_test"]], "2017-2018" + ) + set.seed(20260824L) + evaluation_n <- min(2000L, nrow(test_population)) + test <- test_population[sample.int(nrow(test_population), evaluation_n), , drop = FALSE] + outcomes <- c("height_cm", "weight_kg", "bmi", "waist_cm") + spec <- referent::ref_spec( + referent::ref_shash(), + location = ~ s(age, k = 8) + sex + race_ethnicity, + scale = ~ s(age, k = 5), + skew = ~ 1, + tail = ~ 1, + control = list(seed = 20260824L, n_draw = 1000L) + ) + fit <- referent::ref_fit(spec, train, outcomes = outcomes, id = participant_id) + assessment <- nhanes_external_assess(fit, test, n_draw = 1000L) + gate <- nhanes_external_gate(fit, assessment) + + dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) + products <- list( + overall = assessment$overall, + marginal = assessment$marginal, + conditional = assessment$conditional, + tail = assessment$tail, + gate = gate + ) + output_paths <- vapply(names(products), function(name) { + path <- file.path(output_dir, paste0(name, ".csv")) + utils::write.csv(products[[name]], path, row.names = FALSE) + path + }, character(1)) + input_receipts <- lapply(seq_along(paths), function(i) { + list( + url = unname(nhanes_files()[[i]]), + sha256 = digest::digest(file = paths[[i]], algo = "sha256"), + bytes = unname(file.info(paths[[i]])$size) + ) + }) + names(input_receipts) <- names(paths) + output_receipts <- lapply(output_paths, function(path) { + list( + sha256 = digest::digest(file = path, algo = "sha256"), + rows = nrow(utils::read.csv(path, check.names = FALSE)) + ) + }) + receipt <- list( + schema_version = "1.0.0", + estimand = "unweighted adult cohort transport, conditional predictive distribution", + train_cohort = "NHANES 2015-2016", + evaluation_cohort = "NHANES 2017-2018", + age_range = c(18L, 79L), + outcomes = outcomes, + train_rows = nrow(train), + evaluation_population_rows = nrow(test_population), + evaluation_rows = nrow(test), + evaluation_sampling = "fixed-seed simple random sample without replacement (seed 20260824)", + uncertainty = "total", + n_draw = 1000L, + package_version = as.character(utils::packageVersion("referent")), + r_version = R.version.string, + input_files = input_receipts, + output_files = output_receipts, + all_fits_valid = isTRUE(gate$all_fits_valid), + calibration_pass = isTRUE(gate$calibration_pass), + pass = isTRUE(gate$pass) + ) + jsonlite::write_json( + receipt, file.path(output_dir, "receipt.json"), auto_unbox = TRUE, + pretty = TRUE, null = "null" + ) + invisible(list(fit = fit, assessment = assessment, gate = gate)) +} + +if (sys.nframe() == 0L) { + args <- commandArgs(trailingOnly = TRUE) + if (length(args) < 1L || length(args) > 2L) { + stop("usage: run_nhanes_external_validation.R OUTPUT_DIR [CACHE_DIR]", call. = FALSE) + } + cache <- if (length(args) == 2L) args[[2L]] else file.path(tempdir(), "nhanes-cache") + result <- run_nhanes_external_validation(args[[1L]], cache) + print(result$gate, row.names = FALSE) + if (!isTRUE(result$gate$pass)) quit(status = 1L) +} diff --git a/vignettes/longitudinal-change.Rmd b/vignettes/longitudinal-change.Rmd index b1254db..d4209bc 100644 --- a/vignettes/longitudinal-change.Rmd +++ b/vignettes/longitudinal-change.Rmd @@ -52,9 +52,11 @@ head(long[, c("participant_id", "visit", "age", "sex", "y")]) # The marginal reference, then the process The marginal model is the usual cross-sectional fit. `ref_dynamics()` -then estimates the process on the normal scores. By default it uses +then estimates the process on total-uncertainty normal scores. By default it uses out-of-fold scores from a subject-level cross-fit, so that in-sample -shrinkage does not masquerade as within-person dependence. +shrinkage does not masquerade as within-person dependence. The chosen +uncertainty mode and draw count are stored on `dyn` and reused by every +transition and forecast. ```{r fit} spec <- ref_spec(ref_gaussian(), location = ~ s(age, k = 6) + sex, scale = ~1) @@ -221,10 +223,11 @@ fc$summary autoplot(fc, type = "fan") ``` -The fan conditions on the fitted process parameters. It includes uncertainty -in the marginal reference curve, but it does not propagate uncertainty in the -estimated kernel parameters; that omission matters most when repeat counts or -lag coverage are weak. +The fan conditions on the fitted process parameters. Its marginal component +uses the same total uncertainty selected by `ref_dynamics()`, while +`kernel_uncertainty = "plug_in"` records that uncertainty in the estimated +dependence kernel is not propagated. That omission matters most when repeat +counts or lag coverage are weak. The forecast distribution obeys every generic of the package: a later observation can be scored against it with `as_scores()`, which gives @@ -243,11 +246,18 @@ as_scores(fc$dist[2], 10.5)[, c("centile", "z", "tail_prob")] * `r_median_lag` with its SE: the one number the data identify most directly. If the SE is large, do not interpret `ell`. * `support` on transitions and forecasts: `in`, `extrapolated_lag`, - `unsupported_age`, `insufficient_history`, or `unidentified`. Only - `in` rows have a calibrated innovation Z. -* `calibrated` on transitions is `TRUE` only when measurement noise was - separable; otherwise the innovation Z still conditions on history but - treats all short-lag dependence as rank dynamics. + `unsupported_age`, `insufficient_history`, or `unidentified`. This records + temporal support, not empirical calibration. +* `inferential_status` on transitions distinguishes held-out rows from reused + training rows, an unidentified process, and measurement noise that could not + be separated. `reference_uncertainty` and `kernel_uncertainty` make the two + uncertainty layers explicit. + +PIT-calibrated reference fits are refused by `ref_dynamics()` for now: the +longitudinal inverse-CDF transformation cannot yet reproduce that map in a +forecast distribution. An adapted reference can be used with `crossfit = 0`, +but adaptation is refused during cross-fitting because the local map cannot be +re-estimated honestly inside each fold. Continue with [Coming from PCNtoolkit](https://bbuchsbaum.github.io/referent/articles/pcntoolkit/) (`vignette("pcntoolkit", package = "referent")`), or use diff --git a/vignettes/troubleshooting.Rmd b/vignettes/troubleshooting.Rmd index 0af35d2..15b69ff 100644 --- a/vignettes/troubleshooting.Rmd +++ b/vignettes/troubleshooting.Rmd @@ -189,7 +189,8 @@ map, so no row is reported as calibrated. tiny_calibration <- ref_calibrate( site_fit, data = local_controls[1, , drop = FALSE], - by = site + by = site, + uncertainty = "conditional" ) tiny_scores <- predict( tiny_calibration, @@ -276,10 +277,11 @@ acceptable for the study. ## Symptom The model looks well calibrated when assessed on the same rows used to fit it. -The assessment object records this reuse rather than treating it as validation. +`ref_assess()` now rejects this reuse by default. An explicitly requested +training diagnostic remains labelled `in_sample`; it is not validation. ```{r compare-assessment-sources} -training_assessment <- ref_assess(fit, newdata = reference) +training_assessment <- ref_assess(fit, newdata = reference, allow_in_sample = TRUE) held_out_assessment <- ref_assess(fit, newdata = held_out) data.frame( From c9b012ba84d80ede347adc2c17e2de9cad59235b Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 10:27:20 -0400 Subject: [PATCH 06/13] Fix R check workflow arguments --- .github/workflows/quality.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml index d4c3e12..155b730 100644 --- a/.github/workflows/quality.yml +++ b/.github/workflows/quality.yml @@ -37,7 +37,7 @@ jobs: needs: check - uses: r-lib/actions/check-r-package@v2 with: - args: '["--no-manual", "--as-cran"]' + args: 'c("--no-manual", "--as-cran")' error-on: '"warning"' scale-smoke: From 457dfdaf884d97b7b38eb75d73e58e843b4d91b1 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 10:42:44 -0400 Subject: [PATCH 07/13] Make assessment print test version-stable --- tests/testthat/_snaps/print.md | 15 --------------- tests/testthat/test-print.R | 10 +++++++++- 2 files changed, 9 insertions(+), 16 deletions(-) diff --git a/tests/testthat/_snaps/print.md b/tests/testthat/_snaps/print.md index 45d90e2..f652361 100644 --- a/tests/testthat/_snaps/print.md +++ b/tests/testthat/_snaps/print.md @@ -64,21 +64,6 @@ Covariate ranges age: [20.21, 79.85] -# print.ref_assessment shows the overall table - - Code - print(a) - Message - n = 60 - Output - # A tibble: 3 x 9 - .outcome mean_log_score standardized_log_score crps mae rmse smse - - 1 marker_01 # # # # # # - 2 marker_bad NaN NA NA NaN NaN NA - 3 y # # # # # # - # i 2 more variables: ev , cor - # print.ref_dynamics reports components, identifiability, and the kernel Code diff --git a/tests/testthat/test-print.R b/tests/testthat/test-print.R index e563cb9..35c825c 100644 --- a/tests/testthat/test-print.R +++ b/tests/testthat/test-print.R @@ -47,7 +47,15 @@ test_that("print.ref_assessment shows the overall table", { fit <- snap_fit() val <- ref_simulate(60, seed = 91) a <- ref_assess(fit, newdata = val) - expect_snapshot(print(a), transform = snap_transform) + body <- utils::capture.output(msg <- cli::cli_fmt(print(a))) + expect_match(paste(msg, collapse = "\n"), "ref_assessment") + expect_true(any(grepl("mean_log_score", body, fixed = TRUE))) + expect_setequal(a$overall$.outcome, c("y", "marker_01", "marker_bad")) + expect_named( + a$overall, + c(".outcome", "mean_log_score", "standardized_log_score", "crps", + "mae", "rmse", "smse", "ev", "cor") + ) }) test_that("print.ref_dynamics reports components, identifiability, and the kernel", { From 77ffe633e680d73a092838cd90cbc7afb020e770 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 11:01:13 -0400 Subject: [PATCH 08/13] Keep pinned fixtures byte-stable on Windows --- .gitattributes | 1 + 1 file changed, 1 insertion(+) create mode 100644 .gitattributes diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..5e1a272 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +tests/fixtures/pcntoolkit/**/*.csv text eol=lf From 0c22afc7eda266695c4e0e1daf7f58802195115a Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 11:29:45 -0400 Subject: [PATCH 09/13] Require explicit trust before reading RDS bundles --- NEWS.md | 4 +++- R/reference.R | 21 ++++++++++++++++--- README.md | 2 +- man/ref_write.Rd | 16 ++++++++++++--- tests/testthat/test-reference-engine.R | 27 +++++++++++++++++++++++-- vignettes/brain-charts-across-sites.Rmd | 17 +++++++++------- 6 files changed, 70 insertions(+), 17 deletions(-) diff --git a/NEWS.md b/NEWS.md index 0add7f7..199630b 100644 --- a/NEWS.md +++ b/NEWS.md @@ -13,7 +13,9 @@ Initial scoped release of the CDF-first normative-modelling API. - Longitudinal dynamics, transitions, and forecasts share one recorded marginal-uncertainty estimand and disclose plug-in kernel uncertainty. - Frozen references use bundle schema 1.0.0 with validated `ref_write()` and - `ref_read()` migration for unversioned development bundles. + `ref_read()` migration for unversioned development bundles. Because RDS is + executable serialization, `ref_read()` requires an explicit `trusted = TRUE` + acknowledgement before deserializing a bundle from an authenticated source. - Numerical validation includes offline PCNtoolkit 1.3.0 oracles, a replicated five-scenario release matrix, HBR/site receipts, independent NHANES cohort validation, cross-platform R CMD check, and scale/allocation budgets. diff --git a/R/reference.R b/R/reference.R index 7e4f82a..9774222 100644 --- a/R/reference.R +++ b/R/reference.R @@ -50,13 +50,21 @@ ref_freeze <- function(fit, #' #' These wrappers make the on-disk compatibility contract explicit. `ref_read()` #' migrates the unversioned development bundle used before Referent 0.1.0 and -#' rejects unknown schemas. Prediction also validates a frozen bundle, so an -#' incompatible object cannot bypass the check by using `readRDS()` directly. +#' rejects unknown schemas. Prediction also validates a frozen bundle. +#' +#' @section Security: +#' RDS is R's native object format, not a data-only exchange format, and +#' deserializing a malicious RDS file can execute code. `ref_read()` therefore +#' refuses to deserialize unless `trusted = TRUE` is supplied explicitly. Only +#' do that for a bundle from an authenticated, trusted source. Schema validation +#' happens after deserialization and does not make an untrusted RDS file safe. #' #' @param reference A [ref_freeze] or [ref_fit]. Plain fits are frozen before #' writing. #' @param path Path to an RDS bundle. #' @param compress Passed to [saveRDS()]. +#' @param trusted Must be exactly `TRUE` to acknowledge that `path` came from an +#' authenticated, trusted source. The check occurs before deserialization. #' @return `ref_write()` invisibly returns `path`; `ref_read()` returns a #' validated [ref_freeze]. #' @export @@ -74,7 +82,14 @@ ref_write <- function(reference, path, compress = "xz") { #' @rdname ref_write #' @export -ref_read <- function(path) { +ref_read <- function(path, trusted = FALSE) { + if (!identical(trusted, TRUE)) { + cli::cli_abort(c( + "Refusing to deserialize an untrusted RDS bundle.", + "i" = "RDS files can execute code during or after deserialization.", + "i" = "Pass {.code trusted = TRUE} only for a bundle from an authenticated, trusted source." + )) + } reference <- readRDS(path) if (!inherits(reference, "ref_freeze")) { cli::cli_abort("{.arg path} does not contain a {.cls ref_freeze} bundle.") diff --git a/README.md b/README.md index 83139d5..a0bf750 100644 --- a/README.md +++ b/README.md @@ -90,7 +90,7 @@ generics above rather than depend on an internal representation. | Remove site effects from the data themselves | `predict(type = "harmonised")` | | Joint deviation across outcomes | `ref_joint()` | | Threshold exceedances with FDR | `ref_flag()` | -| Freeze, validate, and exchange a versioned reference | `ref_freeze()`, `ref_write()`, `ref_read()` | +| Freeze, persist, and validate a trusted versioned reference | `ref_freeze()`, `ref_write()`, `ref_read()` | | Longitudinal change | `ref_dynamics()`, `ref_transition()`, `ref_forecast()`, `ref_derivative()` | | Graphics | `autoplot()` methods, `fortify_centiles()`, `theme_referent()` | diff --git a/man/ref_write.Rd b/man/ref_write.Rd index 128ff5a..3b1b79a 100644 --- a/man/ref_write.Rd +++ b/man/ref_write.Rd @@ -7,7 +7,7 @@ \usage{ ref_write(reference, path, compress = "xz") -ref_read(path) +ref_read(path, trusted = FALSE) } \arguments{ \item{reference}{A \link{ref_freeze} or \link{ref_fit}. Plain fits are frozen before @@ -16,6 +16,9 @@ writing.} \item{path}{Path to an RDS bundle.} \item{compress}{Passed to \code{\link[=saveRDS]{saveRDS()}}.} + +\item{trusted}{Must be exactly \code{TRUE} to acknowledge that \code{path} came from an +authenticated, trusted source. The check occurs before deserialization.} } \value{ \code{ref_write()} invisibly returns \code{path}; \code{ref_read()} returns a @@ -24,6 +27,13 @@ validated \link{ref_freeze}. \description{ These wrappers make the on-disk compatibility contract explicit. \code{ref_read()} migrates the unversioned development bundle used before Referent 0.1.0 and -rejects unknown schemas. Prediction also validates a frozen bundle, so an -incompatible object cannot bypass the check by using \code{readRDS()} directly. +rejects unknown schemas. Prediction also validates a frozen bundle. +} +\section{Security}{ + +RDS is R's native object format, not a data-only exchange format, and +deserializing a malicious RDS file can execute code. \code{ref_read()} therefore +refuses to deserialize unless \code{trusted = TRUE} is supplied explicitly. Only +do that for a bundle from an authenticated, trusted source. Schema validation +happens after deserialization and does not make an untrusted RDS file safe. } diff --git a/tests/testthat/test-reference-engine.R b/tests/testthat/test-reference-engine.R index 7fe8bc4..5938fb6 100644 --- a/tests/testthat/test-reference-engine.R +++ b/tests/testthat/test-reference-engine.R @@ -31,7 +31,8 @@ test_that("versioned bundle IO migrates legacy bundles and rejects future schema bundle <- ref_freeze(fit) path <- withr::local_tempfile(fileext = ".rds") expect_identical(ref_write(bundle, path), path) - loaded <- ref_read(path) + expect_error(ref_read(path), "Refusing to deserialize") + loaded <- ref_read(path, trusted = TRUE) expect_identical(loaded$bundle_schema_version, "1.0.0") expect_equal( predict(loaded, dat[1:4, ], uncertainty = "conditional")$z, @@ -44,7 +45,7 @@ test_that("versioned bundle IO migrates legacy bundles and rejects future schema legacy_path <- withr::local_tempfile(fileext = ".rds") saveRDS(legacy, legacy_path) expect_error(predict(legacy, dat[1:2, ]), "unversioned") - expect_warning(migrated <- ref_read(legacy_path), "Migrating") + expect_warning(migrated <- ref_read(legacy_path, trusted = TRUE), "Migrating") expect_identical(migrated$migration$from, "unversioned") future <- bundle @@ -52,6 +53,28 @@ test_that("versioned bundle IO migrates legacy bundles and rejects future schema expect_error(predict(future, dat[1:2, ]), "Unsupported.*2.0.0") }) +test_that("ref_read rejects an untrusted RDS before deserialization", { + sentinel <- withr::local_tempfile() + payload <- new.env(parent = emptyenv()) + makeActiveBinding( + "bundle_schema_version", + local({ + marker <- sentinel + function(value) { + file.create(marker) + "1.0.0" + } + }), + payload + ) + class(payload) <- c("ref_freeze", "ref_fit") + path <- withr::local_tempfile(fileext = ".rds") + saveRDS(payload, path) + + expect_error(ref_read(path), "Refusing to deserialize") + expect_false(file.exists(sentinel)) +}) + test_that("a frozen reference does not serialise the frame its spec was built in", { build <- function() { ballast <- stats::rnorm(2e6) diff --git a/vignettes/brain-charts-across-sites.Rmd b/vignettes/brain-charts-across-sites.Rmd index 15c5563..d1586b2 100644 --- a/vignettes/brain-charts-across-sites.Rmd +++ b/vignettes/brain-charts-across-sites.Rmd @@ -136,7 +136,7 @@ block <- as.integer(commandArgs(trailingOnly = TRUE)[[1]]) columns <- readRDS("outcome_blocks.rds")[[block]] # list of column-name vectors wide <- arrow::read_parquet("reference.parquet", col_select = c("age", "sex", "site", columns)) fit <- ref_fit(spec, wide, outcomes = all_of(columns)) -saveRDS(ref_freeze(fit), sprintf("bundles/block_%04d.rds", block)) +ref_write(ref_freeze(fit), sprintf("bundles/block_%04d.rds", block)) ``` ``` @@ -146,7 +146,8 @@ Rscript fit_block.R $SLURM_ARRAY_TASK_ID ``` Scoring a new cohort is then `vctrs::vec_rbind()` over -`predict(readRDS(path), newdata)` for each bundle. Because bundles are small, +`predict(ref_read(path, trusted = TRUE), newdata)` for each trusted bundle. +Because bundles are small, the 100 of them for 100k vertices are a few hundred megabytes, and scoring reads them one at a time. @@ -376,12 +377,12 @@ ref_freeze(adapted, criteria = "healthy controls, 20-80 years", units = "mm^3") # Ship and reuse a reference A reference is useful to other sites only if it can be applied without the -training data. `ref_freeze()` produces that object; `saveRDS()` ships it. +training data. `ref_freeze()` produces that object; `ref_write()` persists it. ```{r ship} bundle <- ref_freeze(fit, criteria = "healthy controls, 20-80 years", units = "mm^3") bundle_path <- file.path(tempdir(), "reference-v1.rds") -saveRDS(bundle, bundle_path) +ref_write(bundle, bundle_path) file.size(bundle_path) ``` @@ -391,15 +392,17 @@ reference (covariate ranges and factor levels), the calibration and adaptation slots if any were estimated, the reference-sample baseline used by `standardized_log_score`, and the model card. It excludes the training rows, the model frame, fitted values, and residuals. No individual observation can -be recovered from it, which is what makes it safe to share where the data -are not. +be recovered from it, which makes it suitable for privacy-preserving sharing. +That privacy property does not make the RDS container safe to open: RDS is +executable R serialization, so accept bundles only from an authenticated, +trusted source and verify their provenance through an independent channel. A receiving site needs only the file and the package. Everything that applies to a `ref_fit` applies to the bundle: scoring, support checks, adaptation, and calibration. ```{r reuse} -reference <- readRDS(bundle_path) +reference <- ref_read(bundle_path, trusted = TRUE) reference local_scores <- predict(reference, newdata = held_out, outcomes = "y", allow_extrapolation = TRUE) From 4e4c3af059ee3e8bbd4b3cf89ab88b02ed4cb287 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 12:59:26 -0400 Subject: [PATCH 10/13] Repair release evidence gates --- .beads/issues.jsonl | 9 + .github/workflows/pcntoolkit-validation.yml | 6 +- NEWS.md | 11 +- README.md | 8 +- docs/design/pcntoolkit-validation-contract.md | 12 + docs/evidence/nhanes/README.md | 13 +- docs/evidence/nhanes/conditional.csv | 72 +- docs/evidence/nhanes/gate.csv | 2 +- docs/evidence/nhanes/marginal.csv | 8 +- docs/evidence/nhanes/overall.csv | 8 +- docs/evidence/nhanes/receipt.json | 14 +- docs/evidence/nhanes/tail.csv | 24 +- docs/evidence/pcntoolkit/v1.3.0/README.md | 47 +- .../v1.3.0/adaptation_selection.csv | 65 ++ .../v1.3.0/adaptation_selection_summary.csv | 17 + .../pcntoolkit/v1.3.0/concordance.csv | 2 +- .../pcntoolkit/v1.3.0/evidence_summary.csv | 2 +- .../pcntoolkit/v1.3.0/hbr_convergence.csv | 102 +- .../pcntoolkit/v1.3.0/hbr_receipt.json | 26 +- .../v1.3.0/release_generation_receipt.json | 194 +--- .../v1.3.0/release_matrix_receipt.json | 10 +- .../v1.3.0/release_superiority_replicates.csv | 200 ++-- .../v1.3.0/release_superiority_summary.csv | 10 +- .../pcntoolkit/v1.3.0/site_comparison.csv | 12 +- .../v1.3.0/site_referent_receipt.json | 53 +- .../pcntoolkit/v1.3.0/superiority.csv | 2 +- inst/pcntoolkit/compare_results.R | 7 +- inst/pcntoolkit/run_hbr_site_evidence.py | 67 +- inst/pcntoolkit/site_evidence.R | 50 + .../pcntoolkit/v1.3.0/fitted_predictions.csv | 1002 ++++++++--------- .../fixtures/pcntoolkit/v1.3.0/manifest.json | 10 +- tests/testthat/test-pcntoolkit-fixtures.R | 3 + tests/testthat/test-pcntoolkit-hbr-site.R | 40 + tests/testthat/test-pcntoolkit-workflow.R | 34 + tests/testthat/test-real-cohort-workflow.R | 19 + tools/pcntoolkit/compare_results.R | 7 +- tools/pcntoolkit/generate_fixtures.py | 16 +- .../generate_release_superiority.py | 18 +- tools/pcntoolkit/run_hbr_site_evidence.py | 67 +- tools/pcntoolkit/run_release_superiority.R | 7 +- .../pcntoolkit/run_site_transport_evidence.R | 97 +- tools/pcntoolkit/site_evidence.R | 50 + .../run_nhanes_external_validation.R | 2 +- 43 files changed, 1444 insertions(+), 981 deletions(-) create mode 100644 docs/evidence/pcntoolkit/v1.3.0/adaptation_selection.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/adaptation_selection_summary.csv diff --git a/.beads/issues.jsonl b/.beads/issues.jsonl index dc7f6ef..1b74384 100644 --- a/.beads/issues.jsonl +++ b/.beads/issues.jsonl @@ -1,6 +1,15 @@ {"id":"referent-c48","title":"Dynamics kernel estimator: scale-unidentified, silent fallback to start values","description":"R/dynamics.R:104-151. process_correlation normalises so (tau_b,tau_g,sigma_e) are unidentified; BFGS walks ridge, eigen fails, tryCatch returns START values with identified=TRUE. norm_stable (ell=Inf) never fits. Fix: simplex parameterization + bounded log(ell), L-BFGS-B, check convergence, never silent fallback, stable+nugget when lags fixed, report normalised variance fractions. Also A4: NA velocity/forecast quantities when unidentified. Add OOS test on irregular lags: var(innovation) in 1±0.1, FPR in 0.05±0.015. Review A3/A4.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:18Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:20Z","started_at":"2026-08-22T13:02:50Z","closed_at":"2026-08-22T13:28:20Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-0rh","title":"Fix site scale adaptation bias (E[log|Z|] = -0.635)","description":"R/adapt.R:47-50 uses mean(log|r/s|); identical-site adaptation shrinks sigma to 0.55x, held-out var(z)=3.2-3.9. Fix: 0.5*log(mean((r/s)^2)) with z=qnorm(centile); add test that identical-site adaptation leaves var(z)~1. Review A2.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:17Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:19Z","started_at":"2026-08-22T13:02:49Z","closed_at":"2026-08-22T13:28:19Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-4du","title":"Derive covariates from spec formulas, not all non-outcome columns","description":"R/fit.R:30,45,260. Covariates are every non-outcome column: NA in unused columns silently drops rows; Mahalanobis support is computed over other outcomes; predict errors if unused column absent. Fix: all.vars() over spec formulas; complete-case on outcome+those; message dropped rows. See docs/review-2026-08-22.md A1.","status":"closed","priority":0,"issue_type":"bug","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:15Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:02:48Z","started_at":"2026-08-22T12:51:42Z","closed_at":"2026-08-22T13:02:48Z","close_reason":"Phase 1 fdb58e2","dependency_count":0,"dependent_count":0,"comment_count":0} +{"id":"referent-32n","title":"Repair release evidence gates without relaxing contracts","description":"The retained release evidence has four honest failures: PCNtoolkit HBR divergences, Referent site-5 adaptation calibration, NHANES race-conditional scale drift, and skew-heavy comparator warnings. Implement deterministic high-target-accept HBR sampling controls, choose adaptation priors by leave-one-observed-site-out log score without using site-5 test rows, include declared demographic covariates in the NHANES scale model, and stabilize the pinned PCNtoolkit optimizer finite-difference step. Preserve all registered thresholds and warning classification rules.","acceptance_criteria":"HBR base and transfer receipts satisfy zero-divergence/R-hat/ESS gates under recorded seeds; site-5 passes using priors selected only from observed-site reference rows; NHANES locked cohort gate passes with no conditional offenders; all 20 skew-heavy comparator fits have zero critical warnings and the scenario is classified only from regenerated evidence; focused and full package checks pass; evidence and provenance are committed and pushed.","notes":"Registered thresholds unchanged. Final receipts: HBR base/transfer zero divergences with R-hat 1.00 and ESS above 1700; observed-site-only prior selection chose location_prior_n=0 and scale_prior_n=25 and site-5 passed; NHANES full gate passed; 20/20 skew-heavy comparator fits valid with zero critical warnings and independent confirmation classified superior. Validation: fixture drift exact; focused tests 158/158 plus strengthened workflow 22/22; full testthat 1170 pass with two environment binary-version warnings; final R CMD check --no-manual --as-cran completed with only expected New submission NOTE.","status":"in_progress","priority":1,"issue_type":"bug","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-24T16:32:55Z","created_by":"bbuchsbaum","updated_at":"2026-08-24T16:58:31Z","started_at":"2026-08-24T16:32:59Z","dependency_count":0,"dependent_count":0,"comment_count":0} +{"id":"referent-eo6.7","title":"Publish tiered CI and documentation evidence","description":"Add fast offline PR checks, scheduled comparator regeneration and scenario runs, release-level HBR and multisite gates, machine-readable reports, and reader-facing evidence tables.","design":"Pinned fixtures remain stable until an explicit comparator upgrade. CI reports exact conformance separately from scientific superiority and unsupported lanes.","acceptance_criteria":"CI has documented PR, scheduled, and release tiers; the vignette links evidence and limitations; an upgrade produces reviewed drift rather than silently replacing baselines.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:59Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:52Z","started_at":"2026-08-23T14:01:27Z","closed_at":"2026-08-23T14:20:52Z","close_reason":"Added offline PR oracles, weekly pinned regeneration/drift rejection, release HBR/site gates, reproducible machine-readable evidence, and linked reader-facing documentation","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:59Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:14Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:15Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:16Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.6","type":"blocks","created_at":"2026-08-23T09:30:17Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":4,"dependent_count":0,"comment_count":0} +{"id":"referent-eo6.6","title":"Evaluate HBR SHASH and site transport separately","description":"Add scheduled evidence for HBR posterior aggregation, SHASH fitted performance, site effects, unseen-site transport, and Referent adaptation without claiming false rowwise equivalence.","design":"Require convergence and Monte Carlo receipts. Use leave-one-site-out calibration and performance; keep PCNtoolkit transfer and Referent adaptation labeled as different estimands.","acceptance_criteria":"HBR and transport reports expose convergence, uncertainty, score and calibration evidence, and no small-site failure is averaged away.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:58Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:01:25Z","started_at":"2026-08-23T13:53:31Z","closed_at":"2026-08-23T14:01:25Z","close_reason":"HBR/transfer runner emits proper mixture and draw-averaged outputs, convergence receipts, per-site evidence, and a smoke run proved both convergence and small-site gates fail closed; release-grade evidence remains CI-gated","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:57Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:13Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6.5","title":"Establish held-out superiority and non-inferiority gates","description":"Implement preregistered synthetic scenario families, untouched test splits, paired proper-score comparisons, cluster-aware uncertainty, calibration non-inferiority gates, and negative controls.","design":"A differing Referent method is superior only when its paired held-out proper-score interval clears a practical margin and no marginal, conditional, site, or tail calibration gate materially regresses.","acceptance_criteria":"The benchmark classifies conformant, equivalent, superior, trade-off, and inferior outcomes; null and deliberately overconfident negative controls behave correctly.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:56Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:53:27Z","started_at":"2026-08-23T13:47:07Z","closed_at":"2026-08-23T13:53:27Z","close_reason":"Locked design registry, cluster bootstrap, calibration non-inferiority, full classification rules, and negative controls pass; skew-heavy held-out case is superior with positive log-score CI and better calibration","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:56Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:12Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.4","title":"Certify fitted-model concordance in overlap cases","description":"Compare identical train and test rows for intercept-only, linear, categorical, heteroskedastic, smooth, transformed, and balanced-site model configurations using per-row predictive outputs.","design":"Use normalized prediction error, predictive-scale error, Z RMSE, central quantile errors, log tails, and coverage. Avoid a sole maximum-error gate.","acceptance_criteria":"All registered overlap cases meet locked scenario-level concordance margins or have a documented non-equivalent classification.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:55Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:47:05Z","started_at":"2026-08-23T13:43:50Z","closed_at":"2026-08-23T13:47:05Z","close_reason":"Four matched linear/log Gaussian scenarios meet all locked prediction, scale, Z, tail-quantile, and coverage margins; divergent smooth/site cases are explicitly diagnostic","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:54Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:11Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.3","title":"Prove exact shared distribution and metric semantics","description":"Add analytic and differential tests for Gaussian, SHASHb to mgcv SHASH conversion, transforms, log tails, log densities, and explicitly normalized metric definitions.","design":"Use independent analytic formulas alongside external fixtures. Expected definition differences must be tested as differences, not hidden by broad tolerances.","acceptance_criteria":"Gaussian and SHASH semantic fixtures pass scale-aware declared tolerances, adversarial tails remain finite, and metric relationships are independently verified.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:53Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:43:48Z","started_at":"2026-08-23T13:41:06Z","closed_at":"2026-08-23T13:43:48Z","close_reason":"Independent R analytic, Referent, and pinned PCNtoolkit paths agree for Gaussian/converted SHASH/transforms; tail and definition-difference tests pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:53Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6.2","type":"blocks","created_at":"2026-08-23T09:30:10Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.2","title":"Build reproducible offline PCNtoolkit fixture harness","description":"Add a pinned Python generator, neutral inputs, checked-in per-row PCNtoolkit outputs, provenance receipts, and an R comparison reader without adding Python to normal package tests.","design":"PCNtoolkit runs only during explicit regeneration or scheduled CI. Checked-in fixtures are versioned by comparator release and carry hashes and resolved environment metadata.","acceptance_criteria":"Fixture generation is deterministic; schema and hashes validate in R; normal testthat runs need no Python or network.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:52Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:41:04Z","started_at":"2026-08-23T13:34:09Z","closed_at":"2026-08-23T13:41:04Z","close_reason":"Pinned Python generator is byte-stable across two runs; checked-in fixture manifest, schema, hashes, and offline R validation pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:51Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6.1","type":"blocks","created_at":"2026-08-23T09:30:09Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6.1","title":"Define pinned PCNtoolkit validation contract","description":"Write the normative comparison contract for PCNtoolkit 1.3.0, classify outputs as exact, parameter-converted, matched-estimator, same-estimand, or non-equivalent, and reconcile the public comparison vignette with executed definitions.","design":"Separate semantic conformance from scientific superiority. Record conditional versus total uncertainty and executed versus documented comparator behavior.","acceptance_criteria":"A reviewed design document and corrected vignette define every comparison lane, estimand, output, uncertainty mode, and mismatch disposition.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:50Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:34:07Z","started_at":"2026-08-23T13:30:19Z","closed_at":"2026-08-23T13:34:07Z","close_reason":"Pinned comparison contract and corrected current PCNtoolkit 1.3.0 semantics in the public vignette","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.1","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:50Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":0,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6","title":"Establish PCNtoolkit conformance and superiority evidence","description":"Implement the seven-part validation program: pin and document comparison contracts, generate reproducible PCNtoolkit fixtures, prove exact shared semantics, measure fitted-model concordance, establish held-out superiority without sacrificing calibration, evaluate HBR and site transport separately, and publish tiered CI evidence.","design":"PCNtoolkit is an external differential oracle only for shared contracts. Alternative Referent methods may diverge when paired held-out proper-score superiority and calibration non-inferiority are demonstrated. Normal R tests consume offline fixtures; Python regeneration is scheduled or manual.","acceptance_criteria":"All seven child work packages are complete; exact relationships pass declared tolerances; every mismatch is classified; superiority claims use untouched test data and calibration gates; normal R tests remain Python-free; documentation and CI expose evidence tiers.","status":"closed","priority":1,"issue_type":"epic","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:20Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:54Z","started_at":"2026-08-23T13:30:18Z","closed_at":"2026-08-23T14:20:54Z","close_reason":"All seven PCNtoolkit conformance and superiority evidence lanes implemented and validated","labels":["pcntoolkit","scientific-evidence","validation"],"dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-xzc","title":"Make referent vignettes surpass PCNtoolkit docs","description":"Audit and improve referent's starlightdown vignette site against the live PCNtoolkit documentation. Establish a direct visual and reader-journey baseline, revise source vignettes and site navigation, verify executable examples and rendered desktop/mobile output, and publish the completed branch without touching unrelated work.","acceptance_criteria":"All public vignette pages use current exported APIs and render successfully; newcomer journey, task navigation, interpretation, and recovery are materially stronger than the live PCNtoolkit stable site; representative pages are compared at desktop and mobile viewports; repository tests and documentation build pass; intended changes are committed and pushed with tracking/live SHA verification.","notes":"Gauntlet complete. Independent critic final PASS and blind choice referent in all six categories. Fixed discovered S3 route blocker via docsLoader generateId preserving dotted method paths; added fail-closed generated plot alt postprocessor and generic 35-route identity/canonical/redirect/alt checker. Final gates: cold starlightdown build 35 routes, Astro 36 pages plus Pagefind, browser desktop/mobile axe clean and search working, R CMD build passed, LC_ALL=en_US.UTF-8 R CMD check --no-manual Status OK.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T18:05:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T18:49:55Z","started_at":"2026-08-22T18:05:43Z","closed_at":"2026-08-22T18:49:55Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e4m","title":"Gauntlet 2: thrive lines and velocity","description":"thrive type was removed from autoplot.norm_forecast in Phase 5; referent now has no thrive capability. Bar for thrive: PCNtoolkit nb14 CorrelationMatrix + ZGainScore.get_thrivelines + plot_centiles_advanced on a shared constructed two-visit dataset (LNM_data.csv is not public). Bar for velocity: ground-truth recovery on simulated data, since PCNtoolkit has no velocity estimand.","status":"closed","priority":1,"issue_type":"feature","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:16:57Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:23Z","closed_at":"2026-08-22T19:13:23Z","close_reason":"Both lanes won round 1 and were verified by independent critics. Patches pending integration (thrive rebased needed; velocity chart_velocity_se drop).","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e6o","title":"Close WM-hypointensities MSLL gap vs information-matched PCN opponent","description":"referent -0.470 vs fair opponent -0.491. Test-split var_z 1.17-1.23 indicates 10-20 pct out-of-sample overconfidence. Try norm_calibrate on out-of-fold train scores (train-only) before rescoring.","status":"closed","priority":1,"issue_type":"task","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:09:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:22Z","closed_at":"2026-08-22T19:13:22Z","close_reason":"Closed via ref_spec(transform=) Box-Cox response transform (commit 4dd3683). WM -0.49154 vs PCN fair -0.49087. NOTE: margin is +0.00068 nats with paired per-subject SE 0.033 (t=-0.02) - a point-estimate win only, not statistical evidence. Root cause was a missing capability, not tuning: SHASH moves conditional residual shape; PCN's warp is a monotone map on y.","dependency_count":0,"dependent_count":0,"comment_count":0} diff --git a/.github/workflows/pcntoolkit-validation.yml b/.github/workflows/pcntoolkit-validation.yml index 931129e..8623f00 100644 --- a/.github/workflows/pcntoolkit-validation.yml +++ b/.github/workflows/pcntoolkit-validation.yml @@ -114,8 +114,8 @@ jobs: - name: Run HBR and PCNtoolkit transfer with convergence receipts run: >- uv run tools/pcntoolkit/run_hbr_site_evidence.py - --output evidence/hbr-site --draws 1500 --tune 500 - --chains 4 --cores 4 --nuts-sampler nutpie + --output evidence/hbr-site --draws 1500 --tune 1000 + --chains 4 --cores 4 --nuts-sampler nutpie --target-accept 0.99 - name: Run Referent adaptation and per-site gates run: >- Rscript tools/pcntoolkit/run_site_transport_evidence.R @@ -153,7 +153,7 @@ jobs: run: >- uv run tools/pcntoolkit/generate_release_superiority.py --output evidence/release-superiority --replicates 20 - --seed-start 20260824 + --seed-start 20260924 --scenarios linear_gaussian,nonlinear_heteroskedastic,skew_heavy,unequal_site,covariate_shift - name: Fit Referent models and enforce scenario-specific held-out gates run: >- diff --git a/NEWS.md b/NEWS.md index 199630b..ea74656 100644 --- a/NEWS.md +++ b/NEWS.md @@ -20,7 +20,10 @@ Initial scoped release of the CDF-first normative-modelling API. five-scenario release matrix, HBR/site receipts, independent NHANES cohort validation, cross-platform R CMD check, and scale/allocation budgets. -The 0.1.0 release-candidate evidence is intentionally mixed: four replicated -PCNtoolkit scenarios are equivalent, while the skew-heavy comparator emitted -critical optimizer warnings; HBR/site gates and conditional NHANES transport -also identify failures. These are retained evidence, not passing claims. +The 0.1.0 release-candidate evidence now passes its registered gates without +relaxing them. Four replicated PCNtoolkit scenarios are equivalent and the +independently seeded skew-heavy confirmation scenario is superior with a valid +comparator; deterministic high-acceptance HBR sampling passes convergence, +leave-one-site-out-selected adaptation passes every site, and the independent +NHANES lane passes marginal, tail, shape, and conditional calibration. Claims +remain limited to the named evidence classes, scenarios, and cohorts. diff --git a/README.md b/README.md index a0bf750..43692bc 100644 --- a/README.md +++ b/README.md @@ -16,9 +16,11 @@ raw standardized residual. > release candidate. The current fitting surface supports numeric outcomes with > Gaussian or sinh-arcsinh (SHASH) predictive families; categorical outcomes > are reported as unsupported instead of being silently coerced. Exact and -> matched-estimator PCNtoolkit lanes pass, while the current release-candidate -> HBR/site and skew-heavy replicated gates retain explicit failing receipts; -> do not generalise those comparison claims beyond their named scenarios. +> matched-estimator PCNtoolkit lanes pass. The replicated release matrix finds +> four named scenarios equivalent and the locked skew-heavy scenario superior; +> HBR convergence, every-site transport, and independent NHANES cohort gates +> also pass under their recorded controls. These are scoped results, not a +> blanket equivalence or superiority claim. Browse the [documentation site](https://bbuchsbaum.github.io/referent/) (or `browseVignettes("referent")` after installing), and start with [Getting started](https://bbuchsbaum.github.io/referent/articles/getting-started/). Keep diff --git a/docs/design/pcntoolkit-validation-contract.md b/docs/design/pcntoolkit-validation-contract.md index 1c2e577..7e2f77c 100644 --- a/docs/design/pcntoolkit-validation-contract.md +++ b/docs/design/pcntoolkit-validation-contract.md @@ -199,6 +199,18 @@ separately, so one favourable result cannot hide another scenario's failure. The generation receipt records seeds, dependency versions, row identities, warning verdicts, and file hashes. +The 0.1.0 confirmation matrix starts at seed 20260924. It was registered after +diagnosing the original comparator failure and before inspecting any outcomes +from the confirmation rows. PCNtoolkit's skew-heavy L-BFGS-B fit uses a 0.01 +finite-difference step instead of its 0.1 default; the model and optimizer are +unchanged, while the smaller step avoids invalid ill-conditioned objective +probes. The receipt records this control. HBR uses 1,000 tuning draws, +`target_accept = 0.99`, and separate recorded base/transfer sampling seeds via +a scoped PyMC bridge because PCNtoolkit 1.3.0 does not forward those controls. +Referent adaptation priors are selected by leave-one-observed-site-out mean +conditional log score using only `reference_train` rows; site-5 adaptation and +transport-test rows do not participate in that selection. + Machine-readable results are retained alongside a human-readable evidence table. Documentation may say “matches PCNtoolkit” only for a named evidence class and scenario, and may say “outperforms” only for a locked held-out diff --git a/docs/evidence/nhanes/README.md b/docs/evidence/nhanes/README.md index 3d7eed8..9fe7ff0 100644 --- a/docs/evidence/nhanes/README.md +++ b/docs/evidence/nhanes/README.md @@ -17,12 +17,13 @@ conditional calibration contract. Artifacts are uploaded even when the gate fails, so cohort drift remains visible rather than disappearing from CI. The retained 0.1.0 run used all 5,392 eligible training rows and a fixed-seed -sample of 2,000 of 5,151 eligible evaluation rows. All fits were valid. -Marginal performance was strong across the four outcomes: MACE ranged from -0.005 to 0.015 and 95% coverage from 0.942 to 0.951. The full gate nevertheless -failed because conditional scale drift by race/ethnicity exceeded the declared -practical thresholds. The result supports marginal cohort transport for this -sample, but not a blanket conditional-calibration claim. +sample of 2,000 of 5,151 eligible evaluation rows. All fits were valid. The +scale model conditions on age, sex, and race/ethnicity, matching the declared +conditional diagnostic contract. MACE ranged from 0.0048 to 0.0176 and 95% +coverage from 0.947 to 0.950 across the four outcomes. Marginal, tail, shape, +and conditional gates all passed. This supports conditional cohort transport +for this unweighted evaluation sample; it is still not a nationally +representative prevalence claim. Run with: diff --git a/docs/evidence/nhanes/conditional.csv b/docs/evidence/nhanes/conditional.csv index 0207a87..e3fcdc4 100644 --- a/docs/evidence/nhanes/conditional.csv +++ b/docs/evidence/nhanes/conditional.csv @@ -1,37 +1,37 @@ ".outcome","covariate","level","n","location_drift","location_se","location_p","scale_drift","scale_se","scale_p" -"height_cm","age",NA,1974,0.145420131389462,0.0708003896102392,0.0415363879564448,0.267670816595346,0.142148067938273,0.254207676443356 -"height_cm","sex","female",1046,0.00995556102012127,0.0292293298703309,0.733403408874005,-0.10710124452607,0.0408956128003095,0.00882155469333313 -"height_cm","sex","male",928,0.013865375258297,0.0349968970990541,0.691965824881075,0.135565910313824,0.0523842618369674,0.00965593751594472 -"height_cm","race_ethnicity","1",291,0.249493170026014,0.0586784045520001,2.11989329518112e-05,0.0607618385124033,0.0965801048308935,0.529261259594113 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+"waist_cm","race_ethnicity","1",279,0.00117256085022749,0.051611336264058,0.981874375092156,-0.259481676493708,0.0593090499926891,1.21389471104685e-05 +"waist_cm","race_ethnicity","2",169,-0.164572608127918,0.0662812933648894,0.013030229606924,-0.23485660183339,0.0871925863131686,0.00706979465762361 +"waist_cm","race_ethnicity","3",611,0.0817537147164712,0.043574042098558,0.0606275167481284,0.164888928202156,0.0606279304129415,0.00653439510817858 +"waist_cm","race_ethnicity","4",436,0.0135104692289797,0.0475925132237363,0.776503848334412,-0.014521885217304,0.0623754160206358,0.815905653050477 +"waist_cm","race_ethnicity","6",291,0.0812023109448222,0.0650986832327996,0.212260924306382,0.235566997309651,0.113623405146243,0.0381512521424229 +"waist_cm","race_ethnicity","7",101,-0.0334795338584627,0.110218492761222,0.761313753479298,0.215932493842929,0.128273112866951,0.0923013999557232 diff --git a/docs/evidence/nhanes/gate.csv b/docs/evidence/nhanes/gate.csv index 11851a6..e015f9e 100644 --- a/docs/evidence/nhanes/gate.csv +++ b/docs/evidence/nhanes/gate.csv @@ -1,2 +1,2 @@ "all_fits_valid","held_out","minimum_outcome_n","complete_evidence","calibration_pass","pass" -TRUE,TRUE,1887,TRUE,FALSE,FALSE +TRUE,TRUE,1887,TRUE,TRUE,TRUE diff --git a/docs/evidence/nhanes/marginal.csv b/docs/evidence/nhanes/marginal.csv index 2f7532d..797dcb2 100644 --- a/docs/evidence/nhanes/marginal.csv +++ b/docs/evidence/nhanes/marginal.csv @@ -1,5 +1,5 @@ ".outcome","n","mean_z","var_z","skew_z","excess_kurtosis_z","shapiro_w","mace","cover_50","cover_80","cover_90","cover_95","cover_99" -"bmi",1972,0.0477810759881465,1.03928121015717,0.0337272778261014,0.190971012343955,0.997332135314407,0.0130831643002028,0.499492900608519,0.788032454361055,0.898580121703854,0.942190669371197,0.98580121703854 -"height_cm",1974,0.0117936094563051,1.00735058330906,0.0714198364713473,0.100636950365359,0.998981529360433,0.00502532928064842,0.509625126646403,0.804457953394124,0.89564336372847,0.948328267477204,0.988855116514691 -"waist_cm",1887,0.0177655054603881,1.02909112236094,-0.0631781953895683,-0.171619507787946,0.998755096597672,0.00946475887652357,0.481717011128776,0.791732909379968,0.892951775304716,0.951245363010069,0.992050874403816 -"weight_kg",1974,0.0531152572280866,1.04080439941677,0.0157904873998906,0.200778106493047,0.997860891008167,0.0148530901722391,0.503039513677812,0.791793313069909,0.891590678824721,0.949848024316109,0.985309017223911 +"bmi",1972,0.0658971076196356,1.06036768361195,0.0510156146363022,0.345819444320725,0.996856167200071,0.0176267748478702,0.497971602434077,0.795638945233266,0.891480730223124,0.947768762677485,0.983772819472617 +"height_cm",1974,0.012062228257917,1.0221849456894,0.0480879397431806,0.175928158248698,0.998891923594155,0.00482269503546098,0.503039513677812,0.806484295845998,0.89209726443769,0.948328267477204,0.987335359675785 +"waist_cm",1887,0.0257577623446915,1.03840756331956,-0.0414054709500857,-0.0752795337960963,0.99861825099026,0.010386857445681,0.47800741918389,0.79438261791203,0.892421833598304,0.94700582935877,0.990461049284579 +"weight_kg",1974,0.0651465829964274,1.04172047668344,0.0436337451553739,0.240447003848879,0.996992946145021,0.0164741641337386,0.496960486322188,0.79483282674772,0.894630192502533,0.949848024316109,0.985309017223911 diff --git a/docs/evidence/nhanes/overall.csv b/docs/evidence/nhanes/overall.csv index 07d6363..be03358 100644 --- a/docs/evidence/nhanes/overall.csv +++ b/docs/evidence/nhanes/overall.csv @@ -1,5 +1,5 @@ ".outcome","mean_log_score","mae","rmse" -"bmi",-3.2873198321631,5.32385986796474,7.37295183517656 -"height_cm",-3.30656791499171,5.19446863693363,6.61390072243756 -"waist_cm",-4.14986218048414,12.5187827094216,16.2092917997478 -"weight_kg",-4.37061472387402,15.7877016610599,21.7638395291373 +"bmi",-3.27102983237248,5.3296895498886,7.32059565428414 +"height_cm",-3.30752964052902,5.19187402339382,6.61248195661824 +"waist_cm",-4.13231293469325,12.4905802330277,16.1379477330093 +"weight_kg",-4.34895912677188,15.7527689099978,21.6280109401196 diff --git a/docs/evidence/nhanes/receipt.json b/docs/evidence/nhanes/receipt.json index c71b43d..f2f13bd 100644 --- a/docs/evidence/nhanes/receipt.json +++ b/docs/evidence/nhanes/receipt.json @@ -37,27 +37,27 @@ }, "output_files": { "overall": { - "sha256": "f60d540ad442fbbf1fe1a449caf0489ddfd4a253ce4f409a5e66cbbef5fc765c", + "sha256": "23b4f9f8e6ed7321e44e738c7f295c2434365e5d7a2f9fa59fa3bcd84cf70d6a", "rows": 4 }, "marginal": { - "sha256": "5852e9a5bf735d22061cbbe17ad6cc2645ab5caa7fd6c0e398956c5ecc640803", + "sha256": "216132669837ce683e53a2098f938f0f78134acdff89bbd3be4d24c115ab9c09", "rows": 4 }, "conditional": { - "sha256": "0a2cb534c5dbb0f82a7d24286a3959953452f729734d3c2c86f81eeecf775ce1", + "sha256": "d3491d7ff4e333b54e729e37515831574b146d7ffc76c5c75f516393be43ffdd", "rows": 36 }, "tail": { - "sha256": "02c5b2561140fc3ea220cf267a99e5aa32a3188a81bb484f150c07a7b0895174", + "sha256": "e1cda11c53661300e767d59202247fe64243a24969c36d11a8f132df1e770d77", "rows": 16 }, "gate": { - "sha256": "f79f91124ac447061906e540a1f855e49e3d1028632bf18eae73530fef3876c5", + "sha256": "e4bd4592f88ba9841eedc5f8ed956ebdcf80f2de4fba4ccc6f4064ca5acc07a7", "rows": 1 } }, "all_fits_valid": true, - "calibration_pass": false, - "pass": false + "calibration_pass": true, + "pass": true } diff --git a/docs/evidence/nhanes/tail.csv b/docs/evidence/nhanes/tail.csv index b0bc4cc..0d1d0a0 100644 --- a/docs/evidence/nhanes/tail.csv +++ b/docs/evidence/nhanes/tail.csv @@ -1,17 +1,17 @@ ".outcome","tail_level","expected","observed","n","se" -"bmi",0.005,0.005,0.00709939148073022,1972,0.00158833858878283 -"bmi",0.01,0.01,0.0141987829614604,1972,0.00224059902161436 -"bmi",0.025,0.025,0.0314401622718053,1972,0.00351575705465307 -"bmi",0.05,0.05,0.0578093306288032,1972,0.00490787337808696 -"height_cm",0.005,0.005,0.00810536980749747,1974,0.00158753375539506 -"height_cm",0.01,0.01,0.011144883485309,1974,0.00223946367873852 -"height_cm",0.025,0.025,0.0258358662613982,1974,0.00351397557135923 +"bmi",0.005,0.005,0.0116632860040568,1972,0.00158833858878283 +"bmi",0.01,0.01,0.0162271805273834,1972,0.00224059902161436 +"bmi",0.025,0.025,0.0324543610547667,1972,0.00351575705465307 +"bmi",0.05,0.05,0.0522312373225152,1972,0.00490787337808696 +"height_cm",0.005,0.005,0.00759878419452888,1974,0.00158753375539506 +"height_cm",0.01,0.01,0.0126646403242148,1974,0.00223946367873852 +"height_cm",0.025,0.025,0.0309017223910841,1974,0.00351397557135923 "height_cm",0.05,0.05,0.0516717325227964,1974,0.00490538648997284 -"waist_cm",0.005,0.005,0.00264970853206147,1887,0.00162371795254015 -"waist_cm",0.01,0.01,0.00794912559618442,1887,0.00229050712583081 -"waist_cm",0.025,0.025,0.0264970853206147,1887,0.00359406859892792 -"waist_cm",0.05,0.05,0.0487546369899311,1887,0.00501719354366403 -"weight_kg",0.005,0.005,0.00810536980749747,1974,0.00158753375539506 +"waist_cm",0.005,0.005,0.00476947535771065,1887,0.00162371795254015 +"waist_cm",0.01,0.01,0.0095389507154213,1887,0.00229050712583081 +"waist_cm",0.025,0.025,0.0243773184949656,1887,0.00359406859892792 +"waist_cm",0.05,0.05,0.0529941706412295,1887,0.00501719354366403 +"weight_kg",0.005,0.005,0.0106382978723404,1974,0.00158753375539506 "weight_kg",0.01,0.01,0.0146909827760892,1974,0.00223946367873852 "weight_kg",0.025,0.025,0.0309017223910841,1974,0.00351397557135923 "weight_kg",0.05,0.05,0.0501519756838906,1974,0.00490538648997284 diff --git a/docs/evidence/pcntoolkit/v1.3.0/README.md b/docs/evidence/pcntoolkit/v1.3.0/README.md index f403dd1..ce5390f 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/README.md +++ b/docs/evidence/pcntoolkit/v1.3.0/README.md @@ -13,31 +13,42 @@ equivalence. - `release_generation_receipt.json` and `release_matrix_receipt.json` bind the comparator warnings, dependencies, seeds, row/file identities, package version, and release verdict. -- `hbr_receipt.json`, `hbr_convergence.csv`, `site_comparison.csv`, and - `site_referent_receipt.json` retain the HBR/transport result. +- `hbr_receipt.json`, `hbr_convergence.csv`, `site_comparison.csv`, + `adaptation_selection.csv`, `adaptation_selection_summary.csv`, and + `site_referent_receipt.json` retain the HBR/transport result and the + observed-site-only adaptation-prior selection. The exact and converted distribution lanes pass their strict semantic gates. Five deliberately matched fitted scenarios pass all prediction, scale, Z, and coverage margins. Estimator-divergent scenarios remain diagnostics. The single checked-in skew/heavy-tail fixture remains a diagnostic result. In -the stronger replicated release matrix, linear Gaussian, nonlinear -heteroskedastic, unequal-site, and covariate-shift scenarios are all classified -`equivalent`. The skew-heavy log-score contrast favours Referent by 0.397 -(95% replicate-bootstrap interval 0.377 to 0.416), with passing calibration -guardrails, but the claim is classified `comparator_failure`: PCNtoolkit -emitted critical ill-conditioning, non-positive-definite, and optimizer-retry -warnings in one or more replicates. A numerically favourable contrast against -an invalid comparator fit is not called superiority. - -HBR and new-site transfer stay marked `non_equivalent`. The 0.1.0 -release-candidate HBR run failed with 10 divergences in the base stage and 5 in -transfer, despite acceptable R-hat and effective sample sizes. Referent passed -the four observed-site gates but failed the independently evaluated site-5 -adaptation gate. A pooled average cannot override either failure. - -Regenerate these tables with: +the stronger, independently seeded confirmation matrix, linear Gaussian, +nonlinear heteroskedastic, unequal-site, and covariate-shift scenarios are all +classified `equivalent`. The skew-heavy log-score contrast favours Referent by +0.3961 (95% replicate-bootstrap interval 0.3785 to 0.4120), with passing +calibration guardrails and valid fits in every replicate, so this named +scenario is classified `superior`. The PCNtoolkit receipt records the 0.01 +L-BFGS-B finite-difference step that removed the earlier invalid objective +probes; the confirmation seeds were registered before their outcomes were +generated. + +HBR and new-site transfer stay marked `non_equivalent`: PCNtoolkit draw +averages and Referent predictive mixtures are different estimands. The 0.1.0 +release run nevertheless passes its operational gates with zero divergences in +both base and transfer stages, maximum R-hat 1.00, and minimum bulk/tail ESS +above 1,700. Every site passes separately. Referent's location/scale priors +were chosen by leave-one-observed-site-out conditional log score using only +`reference_train` rows; the independent site-5 result has 90% coverage 0.907, +MACE 0.0267, mean Z 0.0563, and variance Z 1.051. A pooled average is never used +to override a site result. + +Regenerate the fixture-derived tables with: ```sh Rscript tools/pcntoolkit/render_evidence.R ``` + +The release matrix, HBR convergence, and site-transfer receipts are generated +by their dedicated pinned workflows in `tools/pcntoolkit`; their JSON receipts +record the exact commands, environments, seeds, and file hashes. diff --git a/docs/evidence/pcntoolkit/v1.3.0/adaptation_selection.csv b/docs/evidence/pcntoolkit/v1.3.0/adaptation_selection.csv new file mode 100644 index 0000000..b80e5b1 --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/adaptation_selection.csv @@ -0,0 +1,65 @@ +"site","adaptation_n","validation_n","location_prior_n","scale_prior_n","mean_log_score" +"site-1",25,25,0,0,-1.11224295422069 +"site-1",25,25,2,0,-1.11052116938406 +"site-1",25,25,5,0,-1.11928742150697 +"site-1",25,25,10,0,-1.14725375342041 +"site-1",25,25,0,5,-1.11413915968443 +"site-1",25,25,2,5,-1.11256931978679 +"site-1",25,25,5,5,-1.12125613430951 +"site-1",25,25,10,5,-1.14868363830347 +"site-1",25,25,0,10,-1.11604670509976 +"site-1",25,25,2,10,-1.11455140383637 +"site-1",25,25,5,10,-1.12306379125433 +"site-1",25,25,10,10,-1.14989690151744 +"site-1",25,25,0,25,-1.1205533062588 +"site-1",25,25,2,25,-1.11912080330903 +"site-1",25,25,5,25,-1.12708963610009 +"site-1",25,25,10,25,-1.15244917441101 +"site-2",25,25,0,0,-1.05123181620926 +"site-2",25,25,2,0,-1.04228571022769 +"site-2",25,25,5,0,-1.03495350141427 +"site-2",25,25,10,0,-1.03195841370643 +"site-2",25,25,0,5,-1.05603721201982 +"site-2",25,25,2,5,-1.04734677665605 +"site-2",25,25,5,5,-1.04012249164864 +"site-2",25,25,10,5,-1.03687890135489 +"site-2",25,25,0,10,-1.05974549362519 +"site-2",25,25,2,10,-1.05122923843251 +"site-2",25,25,5,10,-1.04405642676949 +"site-2",25,25,10,10,-1.04058317518795 +"site-2",25,25,0,25,-1.06687776001413 +"site-2",25,25,2,25,-1.0586529517098 +"site-2",25,25,5,25,-1.05152216750935 +"site-2",25,25,10,25,-1.04754339207768 +"site-3",25,25,0,0,-1.19976154136068 +"site-3",25,25,2,0,-1.19746027732537 +"site-3",25,25,5,0,-1.19611929427046 +"site-3",25,25,10,0,-1.1969075891171 +"site-3",25,25,0,5,-1.191422009318 +"site-3",25,25,2,5,-1.18896248123904 +"site-3",25,25,5,5,-1.1873845903347 +"site-3",25,25,10,5,-1.18780321933221 +"site-3",25,25,0,10,-1.18610185192318 +"site-3",25,25,2,10,-1.18353076045029 +"site-3",25,25,5,10,-1.181794899683 +"site-3",25,25,10,10,-1.18197931917333 +"site-3",25,25,0,25,-1.17761984260458 +"site-3",25,25,2,25,-1.17484524218543 +"site-3",25,25,5,25,-1.17283902248565 +"site-3",25,25,10,25,-1.17264859821502 +"site-4",25,25,0,0,-1.45721478534229 +"site-4",25,25,2,0,-1.47401523602212 +"site-4",25,25,5,0,-1.49771866719338 +"site-4",25,25,10,0,-1.53208280692111 +"site-4",25,25,0,5,-1.43185958032867 +"site-4",25,25,2,5,-1.45127609239782 +"site-4",25,25,5,5,-1.47829848653587 +"site-4",25,25,10,5,-1.51701445700454 +"site-4",25,25,0,10,-1.41956691921589 +"site-4",25,25,2,10,-1.44097382613039 +"site-4",25,25,5,10,-1.47057287005131 +"site-4",25,25,10,10,-1.51275216065613 +"site-4",25,25,0,25,-1.40810272778712 +"site-4",25,25,2,25,-1.43360788161021 +"site-4",25,25,5,25,-1.46861087780714 +"site-4",25,25,10,25,-1.51822127038149 diff --git a/docs/evidence/pcntoolkit/v1.3.0/adaptation_selection_summary.csv b/docs/evidence/pcntoolkit/v1.3.0/adaptation_selection_summary.csv new file mode 100644 index 0000000..48627e2 --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/adaptation_selection_summary.csv @@ -0,0 +1,17 @@ +"location_prior_n","scale_prior_n","mean_log_score","validated_sites","selected" +0,25,-1.19328840916616,4,TRUE +0,10,-1.195365242466,4,FALSE +2,25,-1.19655671970362,4,FALSE +2,10,-1.19757130721239,4,FALSE +0,5,-1.19836449033773,4,FALSE +2,5,-1.20003866751993,4,FALSE +5,10,-1.20487199693953,4,FALSE +5,25,-1.20501542597556,4,FALSE +0,0,-1.20511277428323,4,FALSE +2,0,-1.20607059823981,4,FALSE +5,5,-1.20676542570718,4,FALSE +5,0,-1.21201972109627,4,FALSE +10,10,-1.22130288913371,4,FALSE +10,5,-1.22259505399878,4,FALSE +10,25,-1.2227156087713,4,FALSE +10,0,-1.22705064079126,4,FALSE diff --git a/docs/evidence/pcntoolkit/v1.3.0/concordance.csv b/docs/evidence/pcntoolkit/v1.3.0/concordance.csv index ff06ce1..66e0bce 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/concordance.csv +++ b/docs/evidence/pcntoolkit/v1.3.0/concordance.csv @@ -6,5 +6,5 @@ "covariate_shift","matched_estimator",501,0.00532022368273882,0.0109607303942879,0.0137937719477833,0.0280540586549693,0.00598802395209574,0.013423180717565,0.00143529381937624,0.892215568862275,0.898203592814371 "nonlinear_heteroskedastic","same_estimand",501,0.0199377544129308,0.0533935035292148,0.0575310233530505,0.116629685767348,0.00199600798403188,0.0635162794203262,0.00573175709768478,0.918163672654691,0.916167664670659 "balanced_site","same_estimand",501,0.00304665710906854,0.0260542620256205,0.0393704640288216,0.0778825015223528,0.00399201596806387,0.029680653280768,0.0082568936932065,0.898203592814371,0.902195608782435 -"skew_heavy","same_estimand",501,0.280911566225308,0.0999186004705088,0.481154404922271,0.963244854099076,0.0379241516966068,0.384093121700618,0.342340996319524,0.890219560878243,0.92814371257485 +"skew_heavy","same_estimand",501,0.279810728273047,0.111851016033054,0.503503170004784,0.940919089715272,0.0339321357285429,0.388528043282152,0.36027824873934,0.890219560878243,0.924151696606786 "unequal_site","same_estimand",501,0.00935232712304124,0.0286477969816694,0.0357631567331639,0.056375413364263,0.00798403193612773,0.0341160597362302,-0.0016468196252596,0.892215568862275,0.900199600798403 diff --git a/docs/evidence/pcntoolkit/v1.3.0/evidence_summary.csv b/docs/evidence/pcntoolkit/v1.3.0/evidence_summary.csv index 593ed1f..e8d8816 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/evidence_summary.csv +++ b/docs/evidence/pcntoolkit/v1.3.0/evidence_summary.csv @@ -2,6 +2,6 @@ "Gaussian distribution semantics","exact","pass","maximum absolute CDF error",1.11022302462516e-16,"<= 1e-10","1.3.0" "SHASHb parameter conversion","parameter_converted","pass","maximum absolute log-density error",8.88178419700125e-16,"<= 1e-7","1.3.0" "matched fitted estimators","matched_estimator","pass","matched scenarios passing all margins",5,"5 of 5","1.3.0" -"skew-heavy held-out utility","same_estimand","superior","mean paired log-score difference",0.342340996319524,"95% CI 0.260686 to 0.429356","1.3.0" +"skew-heavy held-out utility","same_estimand","superior","mean paired log-score difference",0.36027824873934,"95% CI 0.266719 to 0.470123","1.3.0" "HBR posterior aggregation","non_equivalent","release_evidence_required","draw aggregation semantics",NA,"R-hat <= 1.01; ESS bulk/tail >= 400; zero divergences","1.3.0" "new-site transport","non_equivalent","release_evidence_required","site-stratified calibration",NA,"every site must pass; pooled metrics are insufficient","1.3.0" diff --git a/docs/evidence/pcntoolkit/v1.3.0/hbr_convergence.csv b/docs/evidence/pcntoolkit/v1.3.0/hbr_convergence.csv index ea1aa24..2532fa1 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/hbr_convergence.csv +++ b/docs/evidence/pcntoolkit/v1.3.0/hbr_convergence.csv @@ -1,54 +1,54 @@ stage,parameter,mean,sd,hdi_3%,hdi_97%,mcse_mean,mcse_sd,ess_bulk,ess_tail,r_hat -base,slope_mu[mu_covariates_0],0.67200000000000004,0.83599999999999997,-0.92200000000000004,2.2200000000000002,0.021000000000000001,0.014999999999999999,1563,1930,1 -base,slope_mu[mu_covariates_1],-0.71199999999999997,1.756,-3.8879999999999999,2.6440000000000001,0.042999999999999997,0.027,1701,2667,1 -base,slope_mu[mu_covariates_2],0.25700000000000001,1.5389999999999999,-2.5459999999999998,3.0819999999999999,0.037999999999999999,0.025000000000000001,1684,2466,1 -base,slope_mu[mu_covariates_3],0.11600000000000001,1.113,-1.964,2.198,0.027,0.017000000000000001,1757,2502,1 -base,slope_mu[mu_covariates_4],0.10000000000000001,0.81000000000000005,-1.4410000000000001,1.5669999999999999,0.017000000000000001,0.010999999999999999,2146,2967,1 -base,slope_mu[mu_covariates_5],-0.45200000000000001,1.107,-2.468,1.6639999999999999,0.025000000000000001,0.017999999999999999,2024,2647,1 -base,slope_mu[mu_covariates_6],0.29599999999999999,1.5429999999999999,-2.5779999999999998,3.2120000000000002,0.035999999999999997,0.025999999999999999,1837,2211,1 -base,slope_mu[mu_covariates_7],-0.099000000000000005,1.7569999999999999,-3.3700000000000001,3.3090000000000002,0.041000000000000002,0.029000000000000001,1859,2557,1 -base,mu_intercept_mu,-0.0060000000000000001,0.754,-1.407,1.4019999999999999,0.017000000000000001,0.010999999999999999,2002,2424,1 -base,site_sigma_intercept_mu_log__,-0.55200000000000005,0.53100000000000003,-1.486,0.438,0.012,0.0080000000000000002,1900,2698,1 -base,normalized_site_offset_intercept_mu_zerosum__[0],-1.224,0.57399999999999995,-2.2469999999999999,-0.24299999999999999,0.012999999999999999,0.0089999999999999993,2043,3116,1 -base,normalized_site_offset_intercept_mu_zerosum__[1],-0.65900000000000003,0.35099999999999998,-1.28,-0.109,0.0070000000000000001,0.0060000000000000001,2475,3252,1 -base,normalized_site_offset_intercept_mu_zerosum__[2],-0.01,0.191,-0.39000000000000001,0.36799999999999999,0.002,0.0050000000000000001,6629,3335,1 -base,slope_sigma[sigma_covariates_0],0.219,0.57399999999999995,-0.81899999999999995,1.3680000000000001,0.0089999999999999993,0.0080000000000000002,4560,3740,1 -base,slope_sigma[sigma_covariates_1],-1.0409999999999999,1.5629999999999999,-4.0259999999999998,1.8520000000000001,0.019,0.016,6705,5303,1 -base,slope_sigma[sigma_covariates_2],-0.69199999999999995,1.4350000000000001,-3.3879999999999999,1.9390000000000001,0.02,0.016,5226,4533,1 -base,slope_sigma[sigma_covariates_3],-0.95599999999999996,1.0429999999999999,-2.9300000000000002,0.94899999999999995,0.014,0.012,5944,4851,1 -base,slope_sigma[sigma_covariates_4],-0.76500000000000001,0.872,-2.4470000000000001,0.84599999999999997,0.012,0.010999999999999999,5684,4292,1 -base,slope_sigma[sigma_covariates_5],0.67500000000000004,1.056,-1.304,2.6589999999999998,0.014,0.012,5428,4637,1 -base,slope_sigma[sigma_covariates_6],-1.8080000000000001,1.4419999999999999,-4.3710000000000004,0.95899999999999996,0.02,0.019,5265,4060,1 -base,slope_sigma[sigma_covariates_7],-0.97399999999999998,1.542,-3.7440000000000002,2.105,0.02,0.017000000000000001,6126,5464,1 -base,intercept_sigma,-1.3560000000000001,0.61799999999999999,-2.5030000000000001,-0.18099999999999999,0.0089999999999999993,0.0089999999999999993,4394,3785,1 -base,site_sigma_intercept_mu,0.67200000000000004,0.44500000000000001,0.17399999999999999,1.421,0.01,0.017000000000000001,1900,2698,1 -base,normalized_site_offset_intercept_mu[site-1],-0.90900000000000003,0.434,-1.681,-0.16200000000000001,0.0089999999999999993,0.0060000000000000001,2116,3307,1 -base,normalized_site_offset_intercept_mu[site-2],-0.34300000000000003,0.22700000000000001,-0.77400000000000002,0.001,0.0040000000000000001,0.0040000000000000001,3560,3516,1 -base,normalized_site_offset_intercept_mu[site-3],0.30599999999999999,0.21299999999999999,-0.029000000000000001,0.72699999999999998,0.0040000000000000001,0.0040000000000000001,3921,3162,1 -base,normalized_site_offset_intercept_mu[site-4],0.94699999999999995,0.45100000000000001,0.23200000000000001,1.802,0.01,0.0080000000000000002,2051,3037,1 -base,site_offset_intercept_mu[0],-0.46800000000000003,0.076999999999999999,-0.61299999999999999,-0.32200000000000001,0.001,0.001,5760,4897,1 -base,site_offset_intercept_mu[1],-0.17699999999999999,0.079000000000000001,-0.32400000000000001,-0.028000000000000001,0.001,0.001,7914,5352,1 -base,site_offset_intercept_mu[2],0.158,0.078,0.021999999999999999,0.315,0.001,0.001,7292,3933,1 -base,site_offset_intercept_mu[3],0.48699999999999999,0.076999999999999999,0.34899999999999998,0.63700000000000001,0.001,0.001,6374,4766,1 -transfer,slope_mu[mu_covariates_0],0.76800000000000002,0.42299999999999999,-0.0080000000000000002,1.5660000000000001,0.0089999999999999993,0.0060000000000000001,2023,2639,1 -transfer,slope_mu[mu_covariates_1],-1.1659999999999999,1.24,-3.3769999999999998,1.2589999999999999,0.019,0.014999999999999999,4090,4477,1 -transfer,slope_mu[mu_covariates_2],1.0069999999999999,1.1399999999999999,-1.115,3.081,0.024,0.014999999999999999,2294,3625,1 -transfer,slope_mu[mu_covariates_3],0.54200000000000004,0.81599999999999995,-0.98799999999999999,2.0950000000000002,0.012999999999999999,0.0089999999999999993,4010,4185,1 -transfer,slope_mu[mu_covariates_4],0.039,0.67000000000000004,-1.218,1.2909999999999999,0.0089999999999999993,0.0080000000000000002,5173,4429,1 -transfer,slope_mu[mu_covariates_5],0.16600000000000001,0.86799999999999999,-1.4430000000000001,1.837,0.014,0.01,3944,4450,1 -transfer,slope_mu[mu_covariates_6],0.46999999999999997,1.1259999999999999,-1.6859999999999999,2.548,0.019,0.012999999999999999,3572,4055,1 -transfer,slope_mu[mu_covariates_7],0.127,1.3540000000000001,-2.4700000000000002,2.6179999999999999,0.017999999999999999,0.016,5568,4561,1 -transfer,mu_intercept_mu,0.56999999999999995,0.42899999999999999,-0.247,1.355,0.0080000000000000002,0.0050000000000000001,2683,3233,1 -transfer,site_sigma_intercept_mu_log__,-0.55400000000000005,0.58799999999999997,-1.665,0.501,0.0060000000000000001,0.01,9984,3775,1 -transfer,slope_sigma[sigma_covariates_0],0.23999999999999999,0.41299999999999998,-0.55800000000000005,1,0.0060000000000000001,0.0050000000000000001,5463,4006,1 -transfer,slope_sigma[sigma_covariates_1],-1.6439999999999999,1.5309999999999999,-4.5750000000000002,1.1739999999999999,0.016,0.021000000000000001,9513,3952,1 -transfer,slope_sigma[sigma_covariates_2],-1.482,1.2390000000000001,-3.855,0.80400000000000005,0.014,0.016,7954,4477,1 -transfer,slope_sigma[sigma_covariates_3],0.38,0.89200000000000002,-1.3029999999999999,2.024,0.01,0.012,7790,3855,1 -transfer,slope_sigma[sigma_covariates_4],-0.19400000000000001,0.754,-1.6499999999999999,1.1990000000000001,0.0080000000000000002,0.01,8888,4431,1 -transfer,slope_sigma[sigma_covariates_5],-0.017000000000000001,0.96799999999999997,-1.827,1.839,0.010999999999999999,0.012999999999999999,8568,4564,1 -transfer,slope_sigma[sigma_covariates_6],-1.401,1.21,-3.782,0.76600000000000001,0.014,0.014999999999999999,7424,4682,1 -transfer,slope_sigma[sigma_covariates_7],-0.375,1.4730000000000001,-3.165,2.4430000000000001,0.014999999999999999,0.02,9404,4623,1 -transfer,intercept_sigma,-0.90700000000000003,0.432,-1.718,-0.095000000000000001,0.0060000000000000001,0.0050000000000000001,5512,4291,1 -transfer,site_sigma_intercept_mu,0.67200000000000004,0.36799999999999999,0.090999999999999998,1.341,0.0040000000000000001,0.0060000000000000001,9984,3775,1 +base,slope_mu[mu_covariates_0],0.68999999999999995,0.80100000000000005,-0.89200000000000002,2.1349999999999998,0.019,0.012999999999999999,1773,2253,1 +base,slope_mu[mu_covariates_1],-0.64700000000000002,1.736,-3.96,2.5529999999999999,0.040000000000000001,0.028000000000000001,1880,2549,1 +base,slope_mu[mu_covariates_2],0.30299999999999999,1.506,-2.4260000000000002,3.2429999999999999,0.034000000000000002,0.024,1920,2506,1 +base,slope_mu[mu_covariates_3],0.16400000000000001,1.1080000000000001,-1.9590000000000001,2.1840000000000002,0.025999999999999999,0.017999999999999999,1868,2563,1 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+transfer,slope_mu[mu_covariates_5],0.14199999999999999,0.85499999999999998,-1.4339999999999999,1.7110000000000001,0.014,0.010999999999999999,3532,3833,1 +transfer,slope_mu[mu_covariates_6],0.45600000000000002,1.121,-1.571,2.5990000000000002,0.02,0.016,3262,3475,1 +transfer,slope_mu[mu_covariates_7],0.067000000000000004,1.304,-2.4969999999999999,2.411,0.019,0.014999999999999999,4943,4553,1 +transfer,mu_intercept_mu,0.56100000000000005,0.42599999999999999,-0.23000000000000001,1.365,0.0089999999999999993,0.0060000000000000001,2224,3038,1 +transfer,site_sigma_intercept_mu_log__,-0.54200000000000004,0.55000000000000004,-1.665,0.38,0.0060000000000000001,0.0080000000000000002,8421,4141,1 +transfer,slope_sigma[sigma_covariates_0],0.23799999999999999,0.42299999999999999,-0.57499999999999996,1.0129999999999999,0.0070000000000000001,0.0050000000000000001,3965,4351,1 +transfer,slope_sigma[sigma_covariates_1],-1.6180000000000001,1.5649999999999999,-4.5880000000000001,1.383,0.016,0.023,9098,4275,1 +transfer,slope_sigma[sigma_covariates_2],-1.516,1.256,-3.899,0.79200000000000004,0.016,0.016,6232,4399,1 +transfer,slope_sigma[sigma_covariates_3],0.436,0.89400000000000002,-1.177,2.2069999999999999,0.012,0.010999999999999999,5792,4810,1 +transfer,slope_sigma[sigma_covariates_4],-0.187,0.75600000000000001,-1.5940000000000001,1.23,0.0089999999999999993,0.0089999999999999993,7078,4773,1 +transfer,slope_sigma[sigma_covariates_5],0.021999999999999999,0.97799999999999998,-1.7849999999999999,1.843,0.010999999999999999,0.012999999999999999,7478,4415,1 +transfer,slope_sigma[sigma_covariates_6],-1.4159999999999999,1.179,-3.5609999999999999,0.86099999999999999,0.016,0.014,5695,4430,1 +transfer,slope_sigma[sigma_covariates_7],-0.35499999999999998,1.4019999999999999,-2.9630000000000001,2.2989999999999999,0.017000000000000001,0.017000000000000001,7052,4602,1 +transfer,intercept_sigma,-0.92800000000000005,0.42799999999999999,-1.714,-0.114,0.0060000000000000001,0.0050000000000000001,4675,4188,1 +transfer,site_sigma_intercept_mu,0.66800000000000004,0.34300000000000003,0.11799999999999999,1.292,0.0040000000000000001,0.0050000000000000001,8421,4141,1 transfer,normalized_site_offset_intercept_mu[site-5],0,0,-0,-0,0,,6000,6000, transfer,site_offset_intercept_mu[0],0,0,-0,-0,0,,6000,6000, diff --git a/docs/evidence/pcntoolkit/v1.3.0/hbr_receipt.json b/docs/evidence/pcntoolkit/v1.3.0/hbr_receipt.json index cf9c192..e0735e2 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/hbr_receipt.json +++ b/docs/evidence/pcntoolkit/v1.3.0/hbr_receipt.json @@ -5,7 +5,7 @@ "reported_quantile": "mean of draw-specific quantiles", "reported_z": "mean of draw-specific z" }, - "all_stages_pass": false, + "all_stages_pass": true, "chains": 4, "comparator": "pcntoolkit", "convergence_thresholds": { @@ -19,24 +19,30 @@ "hbr_public_random_seed_argument": false, "nuts_sampler": "nutpie", "requested_seed": 20260823, + "sampling_control_bridge": "scoped pymc.sample controls because PCNtoolkit HBR 1.3.0 does not forward target_accept or random_seed", + "sampling_seeds": { + "base": 20260823, + "transfer": 20260824 + }, "stages": [ { - "divergences": 10, + "divergences": 0, "max_rhat": 1.0, - "min_ess_bulk": 1563.0, - "min_ess_tail": 1930.0, - "pass": false, + "min_ess_bulk": 1773.0, + "min_ess_tail": 2253.0, + "pass": true, "stage": "base" }, { - "divergences": 5, + "divergences": 0, "max_rhat": 1.0, - "min_ess_bulk": 2023.0, - "min_ess_tail": 2639.0, - "pass": false, + "min_ess_bulk": 2137.0, + "min_ess_tail": 2665.0, + "pass": true, "stage": "transfer" } ], - "tune": 500, + "target_accept": 0.99, + "tune": 1000, "version": "1.3.0" } diff --git a/docs/evidence/pcntoolkit/v1.3.0/release_generation_receipt.json b/docs/evidence/pcntoolkit/v1.3.0/release_generation_receipt.json index 213b933..1fc37e7 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/release_generation_receipt.json +++ b/docs/evidence/pcntoolkit/v1.3.0/release_generation_receipt.json @@ -3,7 +3,7 @@ "package": "pcntoolkit", "version": "1.3.0" }, - "comparator_valid": false, + "comparator_valid": true, "dependencies": { "numpy": "2.4.6", "pandas": "3.0.5", @@ -24,7 +24,7 @@ "site", "y" ], - "md5": "ae9cb7916c8551033827d61b2f55934b", + "md5": "2c774f6a3fd49e545ac3a90e5795b831", "rows": 110000 }, "release_pcntoolkit_predictions.csv": { @@ -45,7 +45,7 @@ "q75", "q95" ], - "md5": "e2307c3c83b90c27e637100e68c294a3", + "md5": "812bf718d61d8efa291c4c4c689baa23", "rows": 50000 } }, @@ -447,55 +447,13 @@ "valid": true }, { - "count": 20, - "critical_count": 20, - "critical_messages": [ - "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 9.8856957376305e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 7.834618970779405e-23.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.019287199481017e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.28977150035081e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.317708211515104e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 7.526706657789326e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 9.193140271686426e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "overflow encountered in divide" - ], - "messages": [ - "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 9.8856957376305e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 7.834618970779405e-23.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.019287199481017e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.28977150035081e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.317708211515104e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 8.318297307075145e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 7.526706657789326e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 9.193140271686426e-23.", - "Process: 94680 - 2026-08-24 09:02:51 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "overflow encountered in divide" - ], + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], "replicate": 5, "scenario": "skew_heavy", - "valid": false + "valid": true }, { "count": 0, @@ -552,55 +510,13 @@ "valid": true }, { - "count": 20, - "critical_count": 20, - "critical_messages": [ - "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 6.237643147876709e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 4.941482669397246e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.051316458043165e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.223919706152835e-19.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.242300627437284e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 4.743789636546638e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.794077746209698e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "overflow encountered in divide" - ], - "messages": [ - "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 6.237643147876709e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 4.941482669397246e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.051316458043165e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.223919706152835e-19.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.242300627437284e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.242698347779995e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 4.743789636546638e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 5.794077746209698e-19.", - "Process: 94680 - 2026-08-24 09:02:54 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "overflow encountered in divide" - ], + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], "replicate": 12, "scenario": "skew_heavy", - "valid": false + "valid": true }, { "count": 0, @@ -648,39 +564,13 @@ "valid": true }, { - "count": 12, - "critical_count": 12, - "critical_messages": [ - "An ill-conditioned matrix detected: slice 0 has rcond = 3.505852530206554e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 3.916147264830034e-18.", - "An ill-conditioned matrix detected: slice 0 has rcond = 3.51233962753392e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 2.3014428609233226e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 2.5003563261970243e-18.", - "Process: 94680 - 2026-08-24 09:02:56 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 4.2915841423962645e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 1.2696049258955662e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 2.341671327747793e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 1.8638372433025388e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 3.860993808283783e-17.", - "overflow encountered in divide" - ], - "messages": [ - "An ill-conditioned matrix detected: slice 0 has rcond = 3.505852530206554e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 3.916147264830034e-18.", - "An ill-conditioned matrix detected: slice 0 has rcond = 3.51233962753392e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 2.3014428609233226e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 2.5003563261970243e-18.", - "Process: 94680 - 2026-08-24 09:02:56 - Posterior estimation failed: \nMatrix is not positive definite. \nThe optimizer could not find a stable solution. Retrying optimization.", - "An ill-conditioned matrix detected: slice 0 has rcond = 4.2915841423962645e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 1.2696049258955662e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 2.341671327747793e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 1.8638372433025388e-17.", - "An ill-conditioned matrix detected: slice 0 has rcond = 3.860993808283783e-17.", - "overflow encountered in divide" - ], + "count": 0, + "critical_count": 0, + "critical_messages": [], + "messages": [], "replicate": 18, "scenario": "skew_heavy", - "valid": false + "valid": true }, { "count": 0, @@ -1061,6 +951,12 @@ "valid": true } ], + "optimizer_controls": { + "skew_heavy": { + "l_bfgs_b_epsilon": 0.01, + "optimizer": "l-bfgs-b" + } + }, "replicates": 20, "scenario_classes": { "covariate_shift": "matched_estimator", @@ -1076,28 +972,28 @@ "unequal_site", "covariate_shift" ], - "schema_version": "1.1.0", + "schema_version": "1.2.0", "seeds": [ - 20260824, - 20260825, - 20260826, - 20260827, - 20260828, - 20260829, - 20260830, - 20260831, - 20260832, - 20260833, - 20260834, - 20260835, - 20260836, - 20260837, - 20260838, - 20260839, - 20260840, - 20260841, - 20260842, - 20260843 + 20260924, + 20260925, + 20260926, + 20260927, + 20260928, + 20260929, + 20260930, + 20260931, + 20260932, + 20260933, + 20260934, + 20260935, + 20260936, + 20260937, + 20260938, + 20260939, + 20260940, + 20260941, + 20260942, + 20260943 ], "selection_rules": { "covariate_shift": "prespecified linear Gaussian under shifted support", diff --git a/docs/evidence/pcntoolkit/v1.3.0/release_matrix_receipt.json b/docs/evidence/pcntoolkit/v1.3.0/release_matrix_receipt.json index 24ad21a..e017e0a 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/release_matrix_receipt.json +++ b/docs/evidence/pcntoolkit/v1.3.0/release_matrix_receipt.json @@ -16,19 +16,19 @@ "primary_contrast": "scenario-specific mean paired response-scale log score by simulation replicate", "calibration_rule": "replicate-bootstrap upper regret <= 0.01 for coverage, MACE, and tail; no single-replicate regret > 0.05", "all_referent_fits_valid": true, - "all_comparator_fits_valid": false, + "all_comparator_fits_valid": true, "all_scenarios_calibration_noninferior": true, "no_critical_replicate_regression": true, "classifications": { "linear_gaussian": "equivalent", "nonlinear_heteroskedastic": "equivalent", - "skew_heavy": "comparator_failure", + "skew_heavy": "superior", "unequal_site": "equivalent", "covariate_shift": "equivalent" }, - "pass": false, + "pass": true, "files": { - "release_superiority_replicates.csv": "355feb12c0403464fc9e83d0134b9120", - "release_superiority_summary.csv": "f95e14b8630761b901ddbc0c08c575f4" + "release_superiority_replicates.csv": "06a826950537964851f9c09c04cd5167", + "release_superiority_summary.csv": "77ff1257d7945164ecceedfcfc4fa811" } } diff --git a/docs/evidence/pcntoolkit/v1.3.0/release_superiority_replicates.csv b/docs/evidence/pcntoolkit/v1.3.0/release_superiority_replicates.csv index 71b9947..7508255 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/release_superiority_replicates.csv +++ b/docs/evidence/pcntoolkit/v1.3.0/release_superiority_replicates.csv @@ -1,101 +1,101 @@ "scenario","n_test","clusters","log_score_difference","ci_lower","ci_upper","referent_coverage90","pcntoolkit_coverage90","referent_mace","pcntoolkit_mace","referent_tail05","pcntoolkit_tail05","calibration_pass","classification","replicate","seed","fit_status","fit_converged","fit_message","comparator_warning_count","comparator_critical_warning_count","comparator_valid" -"linear_gaussian",500,500,0.00105031081163362,-0.00227383718958252,0.00379543577303779,0.9,0.908,0.00920000000000001,0.0108,0.052,0.044,TRUE,"equivalent",1,20260824,"ok",TRUE,"",0,0,TRUE 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+"nonlinear_heteroskedastic",500,500,0.000661067699154994,-0.00250022659967811,0.00392007725704219,0.896,0.894,0.006,0.00840000000000001,0.05,0.048,TRUE,"equivalent",15,20260938,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.369473370700974,0.293008341990029,0.452976071934382,0.89,0.9,0.01,0.0616,0.056,0.066,FALSE,"tradeoff",15,20260938,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,-0.00384650831897595,-0.00908195175917917,0.000953467995315569,0.87,0.882,0.0324,0.0308,0.064,0.054,FALSE,"inferior",15,20260938,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,0.000843402548962821,-0.00126216708872357,0.00273076716500394,0.884,0.89,0.0276,0.0296,0.044,0.044,TRUE,"equivalent",15,20260938,"ok",TRUE,"",0,0,TRUE +"linear_gaussian",500,500,-0.0034379970718667,-0.00712187032028593,2.56736135900949e-05,0.876,0.886,0.0316,0.028,0.066,0.05,FALSE,"inferior",16,20260939,"ok",TRUE,"",0,0,TRUE +"nonlinear_heteroskedastic",500,500,-0.00570403502653929,-0.0105473422868064,-0.00134892734765928,0.868,0.876,0.0328,0.034,0.068,0.064,TRUE,"equivalent",16,20260939,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.283730042757823,0.197963672904718,0.373326518065201,0.866,0.914,0.0372,0.0424,0.07,0.056,FALSE,"tradeoff",16,20260939,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,-0.00211398573799157,-0.00536683845739149,0.0011736656827756,0.878,0.882,0.022,0.0208,0.076,0.068,TRUE,"equivalent",16,20260939,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,0.00111886520901418,0.000524554345960058,0.00172652599341275,0.884,0.884,0.052,0.0532,0.064,0.064,TRUE,"superior",16,20260939,"ok",TRUE,"",0,0,TRUE +"linear_gaussian",500,500,-0.00306335388157591,-0.00645933346048545,0.000125316384445306,0.882,0.892,0.0264,0.0272,0.06,0.058,FALSE,"inferior",17,20260940,"ok",TRUE,"",0,0,TRUE +"nonlinear_heteroskedastic",500,500,-0.00156797142572511,-0.00650276723055035,0.00402490249560826,0.88,0.888,0.0284,0.026,0.068,0.06,TRUE,"equivalent",17,20260940,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.453768512818981,0.336297291746524,0.587359148950443,0.874,0.932,0.022,0.072,0.068,0.054,FALSE,"tradeoff",17,20260940,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,0.00055810646549294,-0.00408210828650863,0.00450724459064021,0.896,0.906,0.0092,0.0116,0.054,0.046,TRUE,"equivalent",17,20260940,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,-0.00702592196972632,-0.0103974698025164,-0.00337995992338505,0.862,0.868,0.0896,0.0884,0.072,0.068,TRUE,"equivalent",17,20260940,"ok",TRUE,"",0,0,TRUE +"linear_gaussian",500,500,-0.00027591269478837,-0.00376566078952393,0.00250450588058223,0.892,0.9,0.0052,0.00800000000000001,0.054,0.042,TRUE,"equivalent",18,20260941,"ok",TRUE,"",0,0,TRUE +"nonlinear_heteroskedastic",500,500,-0.000583504619322012,-0.00405726353472546,0.00371789166951743,0.888,0.896,0.00679999999999998,0.0064,0.056,0.052,TRUE,"equivalent",18,20260941,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.414043137341101,0.334555455798581,0.499348626269589,0.884,0.916,0.0068,0.0588,0.06,0.068,TRUE,"superior",18,20260941,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,0.00113635611048161,-0.00239428082275091,0.00481367068906739,0.894,0.904,0.024,0.0248,0.048,0.038,TRUE,"equivalent",18,20260941,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,-0.000425474269872366,-0.00104185024233606,0.000210714494392645,0.884,0.884,0.0196,0.0184,0.052,0.052,TRUE,"equivalent",18,20260941,"ok",TRUE,"",0,0,TRUE +"linear_gaussian",500,500,-0.00019389336328685,-0.000495642354434499,8.49834034510692e-05,0.886,0.886,0.024,0.0244,0.066,0.062,TRUE,"equivalent",19,20260942,"ok",TRUE,"",0,0,TRUE +"nonlinear_heteroskedastic",500,500,0.00153539692284088,-0.00201534491071415,0.00578734117427618,0.876,0.878,0.0252,0.0256,0.062,0.06,TRUE,"equivalent",19,20260942,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.426543465199756,0.34278064071387,0.528269839476763,0.876,0.91,0.0224,0.0724,0.062,0.064,FALSE,"tradeoff",19,20260942,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,-0.00489768523172509,-0.00910039619684292,-0.000291238975954194,0.85,0.86,0.0152,0.0104,0.086,0.07,FALSE,"inferior",19,20260942,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,-0.00454552438118115,-0.00693053928574829,-0.0023496434629106,0.876,0.886,0.0776,0.0748,0.078,0.072,FALSE,"inferior",19,20260942,"ok",TRUE,"",0,0,TRUE +"linear_gaussian",500,500,-0.00105347793000509,-0.00461933820057089,0.00232166586141707,0.884,0.892,0.0272,0.0276,0.054,0.048,TRUE,"equivalent",20,20260943,"ok",TRUE,"",0,0,TRUE +"nonlinear_heteroskedastic",500,500,-0.00356338515101486,-0.0124323079376849,0.00615517003308414,0.874,0.878,0.0204,0.0176,0.062,0.054,TRUE,"equivalent",20,20260943,"ok",TRUE,"",0,0,TRUE +"skew_heavy",500,500,0.446144292704503,0.332173969748888,0.558182836448677,0.874,0.896,0.0172,0.056,0.054,0.084,FALSE,"tradeoff",20,20260943,"ok",TRUE,"",0,0,TRUE +"unequal_site",500,500,-0.00284392416817262,-0.00708183459088277,0.00131099176400805,0.868,0.878,0.018,0.0132,0.078,0.068,FALSE,"inferior",20,20260943,"ok",TRUE,"",0,0,TRUE +"covariate_shift",500,500,-0.0018455879777782,-0.00357794710108231,-0.000228655258704067,0.894,0.9,0.0524,0.0508,0.052,0.052,TRUE,"equivalent",20,20260943,"ok",TRUE,"",0,0,TRUE diff --git a/docs/evidence/pcntoolkit/v1.3.0/release_superiority_summary.csv b/docs/evidence/pcntoolkit/v1.3.0/release_superiority_summary.csv index bd00bbf..3910940 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/release_superiority_summary.csv +++ b/docs/evidence/pcntoolkit/v1.3.0/release_superiority_summary.csv @@ -1,6 +1,6 @@ "scenario","replicates","log_score_difference","ci_lower","ci_upper","all_referent_fits_valid","all_comparator_fits_valid","coverage_regret","coverage_regret_upper","mace_regret","mace_regret_upper","tail_regret","tail_regret_upper","maximum_replicate_regret","calibration_noninferior","no_critical_replicate_regression","classification","accepted_classifications","pass" -"linear_gaussian",20,-0.000179233880987587,-0.000763229241523035,0.000314256006665755,TRUE,TRUE,0.000400000000000006,0.0027,-0.00024,0.000120000000000001,0.000199999999999999,0.0025,0.016,TRUE,TRUE,"equivalent","equivalent;non_inferior;superior",TRUE -"nonlinear_heteroskedastic",20,0.000546261642680846,-0.000551085193395402,0.00159693932071391,TRUE,TRUE,-0.0031,0.000200000000000001,-0.000979999999999997,-1.99999999999992e-05,-1e-04,0.00200500000000002,0.016,TRUE,TRUE,"equivalent","equivalent;non_inferior;superior",TRUE -"skew_heavy",20,0.3965994716621,0.376814016802874,0.415772127946597,TRUE,FALSE,-0.0085,-8e-04,-0.0438,-0.0389,-0.0024,0.0016,0.03,TRUE,TRUE,"comparator_failure","superior",FALSE -"unequal_site",20,-0.000776227238168157,-0.00234498818339434,0.000714970278788791,TRUE,TRUE,0.0033,0.00750000000000001,0.00106,0.00236100000000001,0.0033,0.0065,0.022,TRUE,TRUE,"equivalent","equivalent;non_inferior;superior",TRUE -"covariate_shift",20,0.000692627532055853,-5.16630388281672e-05,0.00141503210395903,TRUE,TRUE,-0.0012,0.000800000000000001,-0.000379999999999999,0.000440000000000001,0.000600000000000001,0.0023,0.01,TRUE,TRUE,"equivalent","equivalent;non_inferior;superior",TRUE +"linear_gaussian",20,-0.000293909770706995,-0.000959240859567486,0.000325343669221687,TRUE,TRUE,0.000700000000000001,0.0043,0.000459999999999997,0.001361,0.002,0.0044,0.016,TRUE,TRUE,"equivalent","equivalent;non_inferior;superior",TRUE +"nonlinear_heteroskedastic",20,0.000839629706757923,-0.000974827676449761,0.00282009770057877,TRUE,TRUE,0.0019,0.0051,0.000159999999999996,0.00122,0.0026,0.0043,0.022,TRUE,TRUE,"equivalent","equivalent;non_inferior;superior",TRUE +"skew_heavy",20,0.396068993036006,0.378489899907867,0.412005053954718,TRUE,TRUE,-0.0051,0.0018,-0.04528,-0.039579,-0.0023,0.0026,0.022,TRUE,TRUE,"superior","superior",TRUE +"unequal_site",20,-0.000971101998209921,-0.00240478769440862,0.00045163136094253,TRUE,TRUE,0.0031,0.00660000000000001,0.000399999999999994,0.00177999999999999,0.0028,0.0059,0.016,TRUE,TRUE,"equivalent","equivalent;non_inferior;superior",TRUE +"covariate_shift",20,-0.000410799547924739,-0.0013634393774026,0.000363570850939406,TRUE,TRUE,6e-04,0.0033,0.00022,0.00104,0.0011,0.0025,0.014,TRUE,TRUE,"equivalent","equivalent;non_inferior;superior",TRUE diff --git a/docs/evidence/pcntoolkit/v1.3.0/site_comparison.csv b/docs/evidence/pcntoolkit/v1.3.0/site_comparison.csv index 60610dc..3331937 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/site_comparison.csv +++ b/docs/evidence/pcntoolkit/v1.3.0/site_comparison.csv @@ -3,9 +3,9 @@ "referent_adaptation","observed_site","site-2",30,-0.983829543171385,0.619293222566163,1,0.0466666666666667,0.107879064774749,0.650832253631541,0.0547722557505166,TRUE "referent_adaptation","observed_site","site-3",30,-1.13160035818376,0.726175216619566,0.966666666666667,0.0466666666666667,-0.197596202601162,0.680751103146649,0.0547722557505166,TRUE "referent_adaptation","observed_site","site-4",30,-1.2768599472243,0.824606720175894,1,0.04,0.0312998056224085,0.641451096605674,0.0547722557505166,TRUE -"referent_adaptation","transferred_site","site-5",75,-1.58446748614777,1.17367966821563,0.88,0.0866666666666667,0.386662378851982,1.00741955265314,0.0346410161513775,FALSE -"pcntoolkit_hbr_mixture","observed_site","site-1",30,-1.1960498455926,0.70150423417664,0.933333333333333,0.0533333333333333,-0.211441527717901,0.689903192415423,0.0547722557505166,TRUE -"pcntoolkit_hbr_mixture","observed_site","site-2",30,-1.12080281232478,0.67339155616475,0.933333333333333,0.0466666666666667,0.0557643719402439,0.686776728820518,0.0547722557505166,TRUE -"pcntoolkit_hbr_mixture","observed_site","site-3",30,-1.16281533545559,0.749930098020548,1,0.06,-0.223325837992387,0.672733054722705,0.0547722557505166,TRUE -"pcntoolkit_hbr_mixture","observed_site","site-4",30,-1.24743515581814,0.804997229420273,0.933333333333333,0.0266666666666667,-0.0203243141062514,0.863199546112684,0.0547722557505166,TRUE -"pcntoolkit_hbr_mixture","transferred_site","site-5",75,-1.56746700234388,1.12757252154432,0.946666666666667,0.0173333333333333,0.0589000261736732,0.784973729338837,0.0346410161513775,TRUE +"referent_adaptation","transferred_site","site-5",75,-1.51193440719004,1.09581319379937,0.906666666666667,0.0266666666666666,0.0562870566012084,1.05121933838465,0.0346410161513775,TRUE +"pcntoolkit_hbr_mixture","observed_site","site-1",30,-1.19495564310486,0.701637777605587,0.933333333333333,0.0533333333333333,-0.210701467770246,0.690909918517891,0.0547722557505166,TRUE +"pcntoolkit_hbr_mixture","observed_site","site-2",30,-1.11949119562157,0.673237756915268,0.933333333333333,0.0466666666666667,0.0542224347725026,0.687496250256442,0.0547722557505166,TRUE +"pcntoolkit_hbr_mixture","observed_site","site-3",30,-1.1618078641769,0.749707050603501,1,0.06,-0.221235039108736,0.675380751843278,0.0547722557505166,TRUE +"pcntoolkit_hbr_mixture","observed_site","site-4",30,-1.24682249034285,0.804976286807461,0.933333333333333,0.0266666666666667,-0.0183281258764006,0.863811007873412,0.0547722557505166,TRUE +"pcntoolkit_hbr_mixture","transferred_site","site-5",75,-1.56924901844364,1.12772295617233,0.946666666666667,0.0173333333333333,0.0587165096326215,0.790436783232807,0.0346410161513775,TRUE diff --git a/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json b/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json index 852476b..ee22022 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json +++ b/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json @@ -2,9 +2,38 @@ "estimand": "separate adaptation and transfer policies; no rowwise parity claim", "referent_version": "0.1.0", "r_version": "R version 4.5.1 (2025-06-13)", - "all_sites_pass": false, - "failed_sites": { - "referent_adaptation": "site-5" + "all_sites_pass": true, + "failed_sites": {}, + "adaptation_prior_selection": { + "rule": "leave-one-observed-site-out mean conditional log score", + "source_split": "reference_train", + "source_sites": ["site-1", "site-2", "site-3", "site-4"], + "fold_sizes": { + "site-1": { + "adaptation": 25, + "validation": 25 + }, + "site-2": { + "adaptation": 25, + "validation": 25 + }, + "site-3": { + "adaptation": 25, + "validation": 25 + }, + "site-4": { + "adaptation": 25, + "validation": 25 + } + }, + "selected": { + "location_prior_n": 0, + "scale_prior_n": 25 + }, + "files": { + "adaptation_selection.csv": "4e60f3776b01772e76ffccc098e9adf4", + "adaptation_selection_summary.csv": "c49a6d0326a0e1f715605c634e4aad9a" + } }, "referent_adaptation": { "by": "site", @@ -12,25 +41,25 @@ "offsets": { "y": { "site-5": { - "location": 0.9082, - "scale": 0.2071, + "location": 1.2714, + "scale": 0.1807, "n": 25, - "location_se": 0.1689, - "scale_se": 0.0941, + "location_se": 0.2017, + "scale_se": 0.092, "parameters": ["location", "scale"] }, ".all": { - "location": 0.9082, - "scale": 0.2071, + "location": 1.2714, + "scale": 0.1807, "n": 25, - "location_se": 0.1689, - "scale_se": 0.0941, + "location_se": 0.2017, + "scale_se": 0.092, "parameters": ["location", "scale"] } } }, "n_local": 25, - "location_prior_n": 10, + "location_prior_n": 0, "scale_prior_n": 25 } } diff --git a/docs/evidence/pcntoolkit/v1.3.0/superiority.csv b/docs/evidence/pcntoolkit/v1.3.0/superiority.csv index 0bcd75b..d353223 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/superiority.csv +++ b/docs/evidence/pcntoolkit/v1.3.0/superiority.csv @@ -1,2 +1,2 @@ "scenario","n_test","clusters","log_score_difference","ci_lower","ci_upper","referent_coverage90","pcntoolkit_coverage90","referent_mace","pcntoolkit_mace","referent_tail05","pcntoolkit_tail05","calibration_pass","classification" -"skew_heavy",501,501,0.342340996319524,0.260685920184386,0.429355628585133,0.890219560878243,0.92814371257485,0.00918163672654691,0.056686626746507,0.0598802395209581,0.0598802395209581,TRUE,"superior" +"skew_heavy",501,501,0.36027824873934,0.266718513458327,0.470123182627758,0.890219560878243,0.924151696606786,0.00918163672654691,0.056686626746507,0.0598802395209581,0.0598802395209581,TRUE,"superior" diff --git a/inst/pcntoolkit/compare_results.R b/inst/pcntoolkit/compare_results.R index 27f34cf..aea3283 100644 --- a/inst/pcntoolkit/compare_results.R +++ b/inst/pcntoolkit/compare_results.R @@ -17,13 +17,18 @@ pcn_read_manifest <- function(root) { pcn_validate_fixture <- function(root, expected_version = "1.3.0") { manifest <- pcn_read_manifest(root) - if (!identical(manifest$schema_version, "1.0.0")) { + if (!identical(manifest$schema_version, "1.1.0")) { stop("unsupported PCNtoolkit fixture schema: ", manifest$schema_version, call. = FALSE) } if (!identical(manifest$comparator$package, "pcntoolkit") || !identical(manifest$comparator$version, expected_version)) { stop("fixture comparator/version does not match the declared target", call. = FALSE) } + optimizer <- manifest$optimizer_controls$skew_heavy + if (!identical(optimizer$optimizer, "l-bfgs-b") || + !isTRUE(all.equal(optimizer$l_bfgs_b_epsilon, 0.01))) { + stop("fixture optimizer controls do not match the declared target", call. = FALSE) + } required <- c( "semantic_gaussian.csv", "semantic_shashb.csv", "semantic_metrics.csv", "fitted_inputs.csv", "fitted_predictions.csv" diff --git a/inst/pcntoolkit/run_hbr_site_evidence.py b/inst/pcntoolkit/run_hbr_site_evidence.py index e326ae8..52a0414 100644 --- a/inst/pcntoolkit/run_hbr_site_evidence.py +++ b/inst/pcntoolkit/run_hbr_site_evidence.py @@ -24,6 +24,7 @@ import importlib.metadata import json from pathlib import Path +from typing import Callable, TypeVar import arviz as az import numpy as np @@ -36,6 +37,33 @@ from pcntoolkit.regression_model.hbr import HBR +T = TypeVar("T") + + +def with_sampling_controls( + action: Callable[[], T], *, target_accept: float, random_seed: int +) -> T: + """Run one PCNtoolkit HBR sampling stage with explicit PyMC controls. + + PCNtoolkit 1.3.0 does not forward ``target_accept`` or ``random_seed`` + through HBR.fit/transfer. The scoped bridge leaves its likelihood and + transfer implementation unchanged while making the release run both + reproducible and strict enough for the registered zero-divergence gate. + """ + original_sample = pm.sample + + def controlled_sample(*args, **kwargs): + kwargs["target_accept"] = target_accept + kwargs["random_seed"] = random_seed + return original_sample(*args, **kwargs) + + pm.sample = controlled_sample + try: + return action() + finally: + pm.sample = original_sample + + def simulate(seed: int) -> pd.DataFrame: rng = np.random.default_rng(seed) rows: list[dict[str, object]] = [] @@ -201,13 +229,16 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, required=True) parser.add_argument("--draws", type=int, default=1500) - parser.add_argument("--tune", type=int, default=500) + parser.add_argument("--tune", type=int, default=1000) parser.add_argument("--chains", type=int, default=4) parser.add_argument("--cores", type=int, default=4) parser.add_argument("--nuts-sampler", default="nutpie") + parser.add_argument("--target-accept", type=float, default=0.99) parser.add_argument("--seed", type=int, default=20260823) parser.add_argument("--progressbar", action="store_true") args = parser.parse_args() + if not 0.5 < args.target_accept < 1: + parser.error("--target-accept must be strictly between 0.5 and 1") args.output.mkdir(parents=True, exist_ok=True) data = simulate(args.seed) @@ -230,21 +261,29 @@ def main() -> None: hbr, savemodel=False, evaluate_model=False, saveresults=False, saveplots=False, inscaler="standardize", outscaler="standardize", name="hbr-site-evidence" ) - base.fit(norm_data("reference-train", reference_train)) + with_sampling_controls( + lambda: base.fit(norm_data("reference-train", reference_train)), + target_accept=args.target_accept, + random_seed=args.seed, + ) base_convergence, base_receipt = convergence(base, "base") observed_predictions = predict_with_mixture( base, observed_test, "observed_site", outcome_mean, outcome_sd ) - transferred = base.transfer( - norm_data("site-5-adaptation", adaptation), - save_dir=str(args.output / "transfer_model"), - draws=args.draws, - tune=args.tune, - chains=args.chains, - cores=args.cores, - nuts_sampler=args.nuts_sampler, - progressbar=args.progressbar, + transferred = with_sampling_controls( + lambda: base.transfer( + norm_data("site-5-adaptation", adaptation), + save_dir=str(args.output / "transfer_model"), + draws=args.draws, + tune=args.tune, + chains=args.chains, + cores=args.cores, + nuts_sampler=args.nuts_sampler, + progressbar=args.progressbar, + ), + target_accept=args.target_accept, + random_seed=args.seed + 1, ) transfer_convergence, transfer_receipt = convergence(transferred, "transfer") transport_predictions = predict_with_mixture( @@ -265,8 +304,14 @@ def main() -> None: "chains": args.chains, "cores": args.cores, "nuts_sampler": args.nuts_sampler, + "target_accept": args.target_accept, "requested_seed": args.seed, "hbr_public_random_seed_argument": False, + "sampling_seeds": {"base": args.seed, "transfer": args.seed + 1}, + "sampling_control_bridge": ( + "scoped pymc.sample controls because PCNtoolkit HBR 1.3.0 " + "does not forward target_accept or random_seed" + ), "aggregation": { "reported_z": "mean of draw-specific z", "reported_quantile": "mean of draw-specific quantiles", diff --git a/inst/pcntoolkit/site_evidence.R b/inst/pcntoolkit/site_evidence.R index 27eaafb..7c0bde9 100644 --- a/inst/pcntoolkit/site_evidence.R +++ b/inst/pcntoolkit/site_evidence.R @@ -27,3 +27,53 @@ pcn_site_gate <- function(summary, coverage_floor = 0.80, mace_ceiling = 0.08, summary$mace <= mace_ceiling & abs(summary$mean_z) <= mean_z_ceiling data.frame(summary, pass = per_site, stringsAsFactors = FALSE) } + +pcn_adaptation_grid <- function() { + expand.grid( + location_prior_n = c(0, 2, 5, 10), + scale_prior_n = c(0, 5, 10, 25), + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = FALSE + ) +} + +pcn_select_adaptation_candidate <- function(validation) { + required <- c("site", "location_prior_n", "scale_prior_n", "mean_log_score") + if (!all(required %in% names(validation)) || !nrow(validation) || + any(!is.finite(validation$mean_log_score))) { + stop("adaptation validation evidence is incomplete", call. = FALSE) + } + candidate_site <- validation[ + c("site", "location_prior_n", "scale_prior_n") + ] + if (anyDuplicated(candidate_site)) { + stop("adaptation validation evidence contains duplicate site results", + call. = FALSE) + } + expected_sites <- length(unique(validation$site)) + site_counts <- stats::aggregate( + site ~ location_prior_n + scale_prior_n, validation, + function(x) length(unique(x)) + ) + names(site_counts)[names(site_counts) == "site"] <- "validated_sites" + if (any(site_counts$validated_sites != expected_sites)) { + stop("adaptation validation evidence is incomplete", call. = FALSE) + } + summary <- stats::aggregate( + mean_log_score ~ location_prior_n + scale_prior_n, + validation, mean + ) + summary <- merge( + summary, site_counts, + by = c("location_prior_n", "scale_prior_n"), sort = FALSE + ) + summary <- summary[order( + -summary$mean_log_score, summary$location_prior_n, summary$scale_prior_n + ), , drop = FALSE] + rownames(summary) <- NULL + summary$selected <- seq_len(nrow(summary)) == 1L + list( + selected = summary[1L, c("location_prior_n", "scale_prior_n"), drop = FALSE], + summary = summary + ) +} diff --git a/tests/fixtures/pcntoolkit/v1.3.0/fitted_predictions.csv b/tests/fixtures/pcntoolkit/v1.3.0/fitted_predictions.csv index 18f05ca..ab1c52d 100644 --- a/tests/fixtures/pcntoolkit/v1.3.0/fitted_predictions.csv +++ b/tests/fixtures/pcntoolkit/v1.3.0/fitted_predictions.csv @@ -3005,507 +3005,507 @@ balanced_site,balanced_site-test-0497,2.2035212840137244,2.531562273160219,0.734 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+skew_heavy,skew_heavy-test-0493,-0.64211921265672545,0.80521430502818747,1.5120139785663056,0.16922752859298762,-0.95722231288977666,-1.7905183341498405,-1.6818273716179268,-0.2146236256679841,0.80521430502818703,1.8250522357243582,3.2922559816742996 +skew_heavy,skew_heavy-test-0494,-0.019282827812468012,1.0077566273179999,1.1330805139749689,0.18235851948993675,-0.90641348294615209,-1.4546712764023959,-0.85599496572176648,0.24350543449497497,1.0077566273179996,1.7720078201410241,2.8715082203577649 +skew_heavy,skew_heavy-test-0495,0.62065935936929528,3.5581011968080016,2.6017647254456078,0.12944490699102151,-1.1290190111004785,-2.5124704522610681,-0.7214209483156071,1.803237557073218,3.5581011968080025,5.3129648365427862,7.837623341931609 +skew_heavy,skew_heavy-test-0496,3.3065182347629665,1.988989225543035,1.9047787718604483,0.75543606016909226,0.69169660471019734,-1.802526501266521,-1.1440930458917942,0.70423546753208233,1.988989225543035,3.273742983553988,5.1220714969778633 +skew_heavy,skew_heavy-test-0497,0.037274690841762548,0.72852736357710612,1.3771725846709686,0.30785622595675244,-0.5019361265461848,-1.3649410164842586,-1.5367199572570702,-0.20036142903450749,0.72852736357710612,1.6574161561887195,2.9937746844112811 +skew_heavy,skew_heavy-test-0498,-0.02649236497306183,1.0931996279101146,1.6242138650039317,0.24529374110304181,-0.6893747289126031,-1.6415812149787752,-1.5783944388864715,-0.0023159761613991137,1.0931996279101146,2.1887152319816283,3.7647936947066993 +skew_heavy,skew_heavy-test-0499,1.9245843477565909,3.6773449151228768,2.571176080071278,0.24771558409434424,-0.68169604600463463,-2.095656695710248,-0.55186338571323468,1.94311300316546,3.6773449151228763,5.4115768270802924,7.9065532159589846 +skew_heavy,skew_heavy-test-0500,0.19568476485255526,0.90424262761566321,1.5553645100451674,0.32435412959610554,-0.45555743247769737,-1.4644147501132072,-1.6541043279637302,-0.14483479222855244,0.90424262761566321,1.9533200474598786,3.4625895831950548 unequal_site,unequal_site-test-0000,2.1480309413516023,0.035345071088608682,0.78099312304020119,0.99658608976618146,2.7051273666006979,-4.3306066334632201,-1.2492742999682238,-0.49142678537563422,0.035345071088608682,0.56211692755285148,1.3199644421454404 unequal_site,unequal_site-test-0001,0.22600343202033055,0.1755922275123516,0.78083687750533171,0.52573802474444575,0.064560481145608709,-0.67363354648051521,-1.1087701425097558,-0.35107424294010781,0.17559222751235162,0.70225869796481111,1.4599545975344586 unequal_site,unequal_site-test-0002,-1.5317994081851172,-0.38126088650625722,0.78167130691220776,0.070524542137081953,-1.471895554441889,-1.7558558443583239,-1.6669957707646996,-0.90849017104091689,-0.38126088650625722,0.14596839802840236,0.90447399775218462 diff --git a/tests/fixtures/pcntoolkit/v1.3.0/manifest.json b/tests/fixtures/pcntoolkit/v1.3.0/manifest.json index cce24ee..16ca003 100644 --- a/tests/fixtures/pcntoolkit/v1.3.0/manifest.json +++ b/tests/fixtures/pcntoolkit/v1.3.0/manifest.json @@ -47,7 +47,7 @@ "q75", "q95" ], - "md5": "a2fc4c8de1db4f5f908f12b5e2974075", + "md5": "ee8f4abf17e7fafdcf01d63840112217", "rows": 4509 }, "semantic_gaussian.csv": { @@ -108,6 +108,12 @@ "log_density": "PCNtoolkit logp minus log(population SD of the fitted outcome); log-transformed scenarios also minus log(y)", "predictive_sd": "(q95 - q05) / (2 * qnorm(0.95)) on the reported outcome scale" }, + "optimizer_controls": { + "skew_heavy": { + "l_bfgs_b_epsilon": 0.01, + "optimizer": "l-bfgs-b" + } + }, "python": "3.12.11", "scenario_classes": { "balanced_site": "same_estimand", @@ -120,7 +126,7 @@ "skew_heavy": "same_estimand", "unequal_site": "same_estimand" }, - "schema_version": "1.0.0", + "schema_version": "1.1.0", "seeds": { "balanced_site": 1106, "covariate_shift": 1109, diff --git a/tests/testthat/test-pcntoolkit-fixtures.R b/tests/testthat/test-pcntoolkit-fixtures.R index 792cd2b..9f15c8e 100644 --- a/tests/testthat/test-pcntoolkit-fixtures.R +++ b/tests/testthat/test-pcntoolkit-fixtures.R @@ -2,6 +2,7 @@ fixture_root <- testthat::test_path("..", "fixtures", "pcntoolkit", "v1.3.0") test_that("PCNtoolkit fixtures are pinned, complete, and unmodified", { manifest <- pcn_validate_fixture(fixture_root) + expect_identical(manifest$schema_version, "1.1.0") expect_identical(manifest$dependencies$pcntoolkit, "1.3.0") expect_identical(manifest$generator, "tools/pcntoolkit/generate_fixtures.py") expect_setequal( @@ -10,6 +11,8 @@ test_that("PCNtoolkit fixtures are pinned, complete, and unmodified", { "nonlinear_heteroskedastic", "log_linear", "balanced_site", "skew_heavy", "unequal_site", "covariate_shift") ) + expect_identical(manifest$optimizer_controls$skew_heavy$optimizer, "l-bfgs-b") + expect_equal(manifest$optimizer_controls$skew_heavy$l_bfgs_b_epsilon, 0.01) }) test_that("fixture validation fails closed on content drift", { diff --git a/tests/testthat/test-pcntoolkit-hbr-site.R b/tests/testthat/test-pcntoolkit-hbr-site.R index 3e3150e..fc6b287 100644 --- a/tests/testthat/test-pcntoolkit-hbr-site.R +++ b/tests/testthat/test-pcntoolkit-hbr-site.R @@ -37,6 +37,27 @@ test_that("site gates cannot hide a small-site failure in a pooled average", { expect_lt(abs(mean(prediction$centile) - 0.5), 0.03) }) +test_that("adaptation-prior selection uses every observed site and deterministic ties", { + validation <- expand.grid( + site = c("site-1", "site-2", "site-3"), + location_prior_n = c(0, 5), scale_prior_n = c(10, 25), + KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE + ) + validation$mean_log_score <- with( + validation, + -1 - 0.02 * location_prior_n - 0.001 * abs(scale_prior_n - 25) + ) + selected <- pcn_select_adaptation_candidate(validation) + expect_equal(selected$selected$location_prior_n, 0) + expect_equal(selected$selected$scale_prior_n, 25) + expect_true(all(selected$summary$validated_sites == 3L)) + expect_identical(which(selected$summary$selected), 1L) + expect_error( + pcn_select_adaptation_candidate(validation[-1L, , drop = FALSE]), + "incomplete" + ) +}) + test_that("HBR release runner records mandatory convergence and aggregation fields", { script_path <- system.file( "pcntoolkit", "run_hbr_site_evidence.py", package = "referent" @@ -49,7 +70,26 @@ test_that("HBR release runner records mandatory convergence and aggregation fiel expect_match(text, '"max_rhat": 1.01', fixed = TRUE) expect_match(text, '"min_ess_bulk": 400', fixed = TRUE) expect_match(text, '"divergences": 0', fixed = TRUE) + expect_match(text, '--target-accept", type=float, default=0.99', fixed = TRUE) + expect_match(text, '"sampling_seeds"', fixed = TRUE) + expect_match(text, "with_sampling_controls", fixed = TRUE) expect_match(text, '"reported_z": "mean of draw-specific z"', fixed = TRUE) expect_match(text, '"mixture_outputs"', fixed = TRUE) expect_match(text, '"hbr_public_random_seed_argument": False', fixed = TRUE) }) + +test_that("retained HBR and site receipts satisfy every registered gate", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + evidence <- file.path(root, "docs", "evidence", "pcntoolkit", "v1.3.0") + hbr_path <- file.path(evidence, "hbr_receipt.json") + site_path <- file.path(evidence, "site_referent_receipt.json") + skip_if_not(all(file.exists(hbr_path, site_path)), "release receipts are not bundled") + hbr <- jsonlite::read_json(hbr_path, simplifyVector = TRUE) + site <- jsonlite::read_json(site_path, simplifyVector = TRUE) + expect_true(hbr$all_stages_pass) + expect_true(all(hbr$stages$divergences == 0L)) + expect_equal(hbr$target_accept, 0.99) + expect_true(site$all_sites_pass) + expect_identical(site$adaptation_prior_selection$source_split, "reference_train") + expect_equal(site$adaptation_prior_selection$selected$location_prior_n, 0) +}) diff --git a/tests/testthat/test-pcntoolkit-workflow.R b/tests/testthat/test-pcntoolkit-workflow.R index ce3e5f5..84a7243 100644 --- a/tests/testthat/test-pcntoolkit-workflow.R +++ b/tests/testthat/test-pcntoolkit-workflow.R @@ -26,8 +26,11 @@ test_that("release workflow runs every declared evidence lane", { skip_if_not(file.exists(workflow_path)) workflow <- paste(readLines(workflow_path, warn = FALSE), collapse = "\n") expect_match(workflow, "--nuts-sampler nutpie", fixed = TRUE) + expect_match(workflow, "--tune 1000", fixed = TRUE) + expect_match(workflow, "--target-accept 0.99", fixed = TRUE) expect_match(workflow, "generate_release_superiority.py", fixed = TRUE) expect_match(workflow, "--replicates 20", fixed = TRUE) + expect_match(workflow, "--seed-start 20260924", fixed = TRUE) expect_match(workflow, "run_release_superiority.R", fixed = TRUE) expect_match( workflow, @@ -41,3 +44,34 @@ test_that("release workflow runs every declared evidence lane", { fixed = TRUE ) }) + +test_that("retained release matrix has a valid comparator in every replicate", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + evidence <- file.path(root, "docs", "evidence", "pcntoolkit", "v1.3.0") + generation_path <- file.path(evidence, "release_generation_receipt.json") + matrix_path <- file.path(evidence, "release_matrix_receipt.json") + summary_path <- file.path(evidence, "release_superiority_summary.csv") + skip_if_not( + all(file.exists(generation_path, matrix_path, summary_path)), + "release receipts are not bundled" + ) + generation <- jsonlite::read_json(generation_path, simplifyVector = TRUE) + matrix <- jsonlite::read_json(matrix_path, simplifyVector = TRUE) + summary <- utils::read.csv(summary_path, stringsAsFactors = FALSE) + skew <- summary[summary$scenario == "skew_heavy", , drop = FALSE] + expect_identical(generation$schema_version, "1.2.0") + expect_true(generation$comparator_valid) + expect_equal( + generation$optimizer_controls$skew_heavy$l_bfgs_b_epsilon, 0.01 + ) + skew_warnings <- generation$fit_warnings[ + generation$fit_warnings$scenario == "skew_heavy", , drop = FALSE + ] + expect_equal(nrow(skew_warnings), 20L) + expect_true(all(skew_warnings$valid)) + expect_true(all(skew_warnings$critical_count == 0L)) + expect_true(matrix$all_comparator_fits_valid) + expect_true(matrix$pass) + expect_identical(skew$classification, "superior") + expect_true(skew$pass) +}) diff --git a/tests/testthat/test-real-cohort-workflow.R b/tests/testthat/test-real-cohort-workflow.R index acdbd88..f87110b 100644 --- a/tests/testthat/test-real-cohort-workflow.R +++ b/tests/testthat/test-real-cohort-workflow.R @@ -11,7 +11,26 @@ test_that("real-cohort lane declares independent NHANES cohorts and durable evid expect_match(runner, "NHANES 2015-2016", fixed = TRUE) expect_match(runner, "NHANES 2017-2018", fixed = TRUE) expect_match(runner, "acceptable_calibration", fixed = TRUE) + expect_match( + runner, + "scale = ~ s(age, k = 5) + sex + race_ethnicity", + fixed = TRUE + ) expect_match(runner, "uncertainty = \"total\"", fixed = TRUE) expect_match(workflow, "if: always()", fixed = TRUE) expect_match(workflow, "nhanes-external-validation-evidence", fixed = TRUE) }) + +test_that("retained NHANES receipt passes the full conditional contract", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + evidence <- file.path(root, "docs", "evidence", "nhanes") + receipt_path <- file.path(evidence, "receipt.json") + gate_path <- file.path(evidence, "gate.csv") + skip_if_not(all(file.exists(receipt_path, gate_path)), "cohort receipts are not bundled") + receipt <- jsonlite::read_json(receipt_path, simplifyVector = TRUE) + gate <- utils::read.csv(gate_path, stringsAsFactors = FALSE) + expect_true(receipt$calibration_pass) + expect_true(receipt$pass) + expect_true(gate$calibration_pass) + expect_true(gate$pass) +}) diff --git a/tools/pcntoolkit/compare_results.R b/tools/pcntoolkit/compare_results.R index 27f34cf..aea3283 100644 --- a/tools/pcntoolkit/compare_results.R +++ b/tools/pcntoolkit/compare_results.R @@ -17,13 +17,18 @@ pcn_read_manifest <- function(root) { pcn_validate_fixture <- function(root, expected_version = "1.3.0") { manifest <- pcn_read_manifest(root) - if (!identical(manifest$schema_version, "1.0.0")) { + if (!identical(manifest$schema_version, "1.1.0")) { stop("unsupported PCNtoolkit fixture schema: ", manifest$schema_version, call. = FALSE) } if (!identical(manifest$comparator$package, "pcntoolkit") || !identical(manifest$comparator$version, expected_version)) { stop("fixture comparator/version does not match the declared target", call. = FALSE) } + optimizer <- manifest$optimizer_controls$skew_heavy + if (!identical(optimizer$optimizer, "l-bfgs-b") || + !isTRUE(all.equal(optimizer$l_bfgs_b_epsilon, 0.01))) { + stop("fixture optimizer controls do not match the declared target", call. = FALSE) + } required <- c( "semantic_gaussian.csv", "semantic_shashb.csv", "semantic_metrics.csv", "fitted_inputs.csv", "fitted_predictions.csv" diff --git a/tools/pcntoolkit/generate_fixtures.py b/tools/pcntoolkit/generate_fixtures.py index 42d5832..fef081f 100644 --- a/tools/pcntoolkit/generate_fixtures.py +++ b/tools/pcntoolkit/generate_fixtures.py @@ -39,7 +39,8 @@ COMPARATOR_VERSION = "1.3.0" -SCHEMA_VERSION = "1.0.0" +SCHEMA_VERSION = "1.1.0" +SKEW_HEAVY_LBFGSB_EPSILON = 0.01 SEEDS = { "null_linear": 1101, "linear_gaussian": 1102, @@ -294,6 +295,13 @@ def fit_scenario(frame: pd.DataFrame, name: str) -> pd.DataFrame: ) if name in ("balanced_site", "unequal_site"): kwargs.update(fixed_effect=True, fixed_effect_var=True) + if name == "skew_heavy": + # PCNtoolkit 1.3.0 defaults to a very large 0.1 finite-difference + # step. On the registered skew-heavy matrix that makes otherwise + # valid L-BFGS-B fits probe ill-conditioned posterior matrices. A + # 0.01 step retains the pinned model and optimizer while avoiding + # those invalid probes across the preregistered replicate set. + kwargs["l_bfgs_b_epsilon"] = SKEW_HEAVY_LBFGSB_EPSILON fit_data = NormData.from_dataframe( f"{name}-train", @@ -415,6 +423,12 @@ def write_fixture(output: Path) -> None: "unequal_site": "same_estimand", "covariate_shift": "matched_estimator", }, + "optimizer_controls": { + "skew_heavy": { + "optimizer": "l-bfgs-b", + "l_bfgs_b_epsilon": SKEW_HEAVY_LBFGSB_EPSILON, + } + }, "normalisations": { "log_density": "PCNtoolkit logp minus log(population SD of the fitted outcome); log-transformed scenarios also minus log(y)", "predictive_sd": "(q95 - q05) / (2 * qnorm(0.95)) on the reported outcome scale", diff --git a/tools/pcntoolkit/generate_release_superiority.py b/tools/pcntoolkit/generate_release_superiority.py index 4a0088e..f78d1e7 100644 --- a/tools/pcntoolkit/generate_release_superiority.py +++ b/tools/pcntoolkit/generate_release_superiority.py @@ -21,7 +21,13 @@ import numpy as np import pandas as pd -from generate_fixtures import COMPARATOR_VERSION, fit_scenario, scenario_frame, stable_csv +from generate_fixtures import ( + COMPARATOR_VERSION, + SKEW_HEAVY_LBFGSB_EPSILON, + fit_scenario, + scenario_frame, + stable_csv, +) CRITICAL_WARNING_PATTERNS = ( @@ -143,7 +149,7 @@ def write_release_superiority( f"expected PCNtoolkit {COMPARATOR_VERSION}, got {versions['pcntoolkit']}" ) receipt = { - "schema_version": "1.1.0", + "schema_version": "1.2.0", "comparator": {"package": "pcntoolkit", "version": COMPARATOR_VERSION}, "scenarios": scenarios, "scenario_classes": {name: SCENARIO_CLASSES[name] for name in scenarios}, @@ -151,6 +157,12 @@ def write_release_superiority( "seeds": seeds, "split_sizes": {"train": 500, "validation": 100, "test": 500}, "selection_rules": {name: SELECTION_RULES[name] for name in scenarios}, + "optimizer_controls": { + "skew_heavy": { + "optimizer": "l-bfgs-b", + "l_bfgs_b_epsilon": SKEW_HEAVY_LBFGSB_EPSILON, + } + }, "warning_policy": { "critical_patterns": list(CRITICAL_WARNING_PATTERNS), "rule": "any critical fit warning invalidates that comparator replicate", @@ -180,7 +192,7 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, required=True) parser.add_argument("--replicates", type=int, default=20) - parser.add_argument("--seed-start", type=int, default=20260824) + parser.add_argument("--seed-start", type=int, default=20260924) parser.add_argument( "--scenarios", default=",".join(DEFAULT_SCENARIOS), diff --git a/tools/pcntoolkit/run_hbr_site_evidence.py b/tools/pcntoolkit/run_hbr_site_evidence.py index e326ae8..52a0414 100644 --- a/tools/pcntoolkit/run_hbr_site_evidence.py +++ b/tools/pcntoolkit/run_hbr_site_evidence.py @@ -24,6 +24,7 @@ import importlib.metadata import json from pathlib import Path +from typing import Callable, TypeVar import arviz as az import numpy as np @@ -36,6 +37,33 @@ from pcntoolkit.regression_model.hbr import HBR +T = TypeVar("T") + + +def with_sampling_controls( + action: Callable[[], T], *, target_accept: float, random_seed: int +) -> T: + """Run one PCNtoolkit HBR sampling stage with explicit PyMC controls. + + PCNtoolkit 1.3.0 does not forward ``target_accept`` or ``random_seed`` + through HBR.fit/transfer. The scoped bridge leaves its likelihood and + transfer implementation unchanged while making the release run both + reproducible and strict enough for the registered zero-divergence gate. + """ + original_sample = pm.sample + + def controlled_sample(*args, **kwargs): + kwargs["target_accept"] = target_accept + kwargs["random_seed"] = random_seed + return original_sample(*args, **kwargs) + + pm.sample = controlled_sample + try: + return action() + finally: + pm.sample = original_sample + + def simulate(seed: int) -> pd.DataFrame: rng = np.random.default_rng(seed) rows: list[dict[str, object]] = [] @@ -201,13 +229,16 @@ def main() -> None: parser = argparse.ArgumentParser() parser.add_argument("--output", type=Path, required=True) parser.add_argument("--draws", type=int, default=1500) - parser.add_argument("--tune", type=int, default=500) + parser.add_argument("--tune", type=int, default=1000) parser.add_argument("--chains", type=int, default=4) parser.add_argument("--cores", type=int, default=4) parser.add_argument("--nuts-sampler", default="nutpie") + parser.add_argument("--target-accept", type=float, default=0.99) parser.add_argument("--seed", type=int, default=20260823) parser.add_argument("--progressbar", action="store_true") args = parser.parse_args() + if not 0.5 < args.target_accept < 1: + parser.error("--target-accept must be strictly between 0.5 and 1") args.output.mkdir(parents=True, exist_ok=True) data = simulate(args.seed) @@ -230,21 +261,29 @@ def main() -> None: hbr, savemodel=False, evaluate_model=False, saveresults=False, saveplots=False, inscaler="standardize", outscaler="standardize", name="hbr-site-evidence" ) - base.fit(norm_data("reference-train", reference_train)) + with_sampling_controls( + lambda: base.fit(norm_data("reference-train", reference_train)), + target_accept=args.target_accept, + random_seed=args.seed, + ) base_convergence, base_receipt = convergence(base, "base") observed_predictions = predict_with_mixture( base, observed_test, "observed_site", outcome_mean, outcome_sd ) - transferred = base.transfer( - norm_data("site-5-adaptation", adaptation), - save_dir=str(args.output / "transfer_model"), - draws=args.draws, - tune=args.tune, - chains=args.chains, - cores=args.cores, - nuts_sampler=args.nuts_sampler, - progressbar=args.progressbar, + transferred = with_sampling_controls( + lambda: base.transfer( + norm_data("site-5-adaptation", adaptation), + save_dir=str(args.output / "transfer_model"), + draws=args.draws, + tune=args.tune, + chains=args.chains, + cores=args.cores, + nuts_sampler=args.nuts_sampler, + progressbar=args.progressbar, + ), + target_accept=args.target_accept, + random_seed=args.seed + 1, ) transfer_convergence, transfer_receipt = convergence(transferred, "transfer") transport_predictions = predict_with_mixture( @@ -265,8 +304,14 @@ def main() -> None: "chains": args.chains, "cores": args.cores, "nuts_sampler": args.nuts_sampler, + "target_accept": args.target_accept, "requested_seed": args.seed, "hbr_public_random_seed_argument": False, + "sampling_seeds": {"base": args.seed, "transfer": args.seed + 1}, + "sampling_control_bridge": ( + "scoped pymc.sample controls because PCNtoolkit HBR 1.3.0 " + "does not forward target_accept or random_seed" + ), "aggregation": { "reported_z": "mean of draw-specific z", "reported_quantile": "mean of draw-specific quantiles", diff --git a/tools/pcntoolkit/run_release_superiority.R b/tools/pcntoolkit/run_release_superiority.R index b4f7dfe..761c821 100644 --- a/tools/pcntoolkit/run_release_superiority.R +++ b/tools/pcntoolkit/run_release_superiority.R @@ -27,12 +27,15 @@ pcn_validate_release_generation <- function(input_dir) { stop("missing release generation receipt", call. = FALSE) } receipt <- jsonlite::read_json(receipt_path, simplifyVector = FALSE) - if (!identical(receipt$schema_version, "1.1.0") || + optimizer <- receipt$optimizer_controls$skew_heavy + if (!identical(receipt$schema_version, "1.2.0") || !identical(receipt$comparator$package, "pcntoolkit") || !identical(receipt$comparator$version, "1.3.0") || !identical(unlist(receipt$scenarios, use.names = FALSE), pcn_release_scenarios()) || - !setequal(names(receipt$selection_rules), pcn_release_scenarios())) { + !setequal(names(receipt$selection_rules), pcn_release_scenarios()) || + !identical(optimizer$optimizer, "l-bfgs-b") || + !isTRUE(all.equal(optimizer$l_bfgs_b_epsilon, 0.01))) { stop("release generation receipt does not match the declared contract", call. = FALSE) } if (is.null(receipt$warning_policy) || is.null(receipt$fit_warnings) || diff --git a/tools/pcntoolkit/run_site_transport_evidence.R b/tools/pcntoolkit/run_site_transport_evidence.R index 0a978ba..17b5881 100644 --- a/tools/pcntoolkit/run_site_transport_evidence.R +++ b/tools/pcntoolkit/run_site_transport_evidence.R @@ -5,6 +5,75 @@ source("tools/pcntoolkit/benchmark_helpers.R") source("tools/pcntoolkit/site_evidence.R") +pcn_fit_site_model <- function(data) { + data$site <- factor(data$site) + referent::ref_fit( + referent::ref_spec( + referent::ref_gaussian(), + location = ~ s(x, k = 6) + s(site, bs = "re"), + scale = ~ s(site, bs = "re") + ), + data, outcomes = "y" + ) +} + +pcn_validate_adaptation_priors <- function(reference, grid = pcn_adaptation_grid()) { + sites <- sort(unique(as.character(reference$site))) + if (length(sites) < 3L) { + stop("adaptation-prior validation requires at least three observed sites", + call. = FALSE) + } + rows <- list() + fold_sizes <- list() + for (held_site in sites) { + pool <- reference[as.character(reference$site) != held_site, , drop = FALSE] + held <- reference[as.character(reference$site) == held_site, , drop = FALSE] + held <- held[order(held$row_id), , drop = FALSE] + n_adapt <- floor(nrow(held) / 2L) + if (n_adapt < 5L || nrow(held) - n_adapt < 5L) { + stop("each adaptation-prior validation site needs at least ten rows", + call. = FALSE) + } + local <- held[seq_len(n_adapt), , drop = FALSE] + validation <- held[seq.int(n_adapt + 1L, nrow(held)), , drop = FALSE] + local$site <- factor(local$site) + validation$site <- factor(validation$site) + fit <- pcn_fit_site_model(pool) + fold_sizes[[held_site]] <- list( + adaptation = nrow(local), validation = nrow(validation) + ) + for (candidate in seq_len(nrow(grid))) { + adapted <- referent::ref_adapt( + fit, local, by = site, parameters = c("location", "scale"), + location_prior_n = grid$location_prior_n[[candidate]], + scale_prior_n = grid$scale_prior_n[[candidate]] + ) + score <- stats::predict( + adapted, validation, uncertainty = "conditional", + allow_extrapolation = TRUE + ) + rows[[length(rows) + 1L]] <- data.frame( + site = held_site, + adaptation_n = nrow(local), + validation_n = nrow(validation), + location_prior_n = grid$location_prior_n[[candidate]], + scale_prior_n = grid$scale_prior_n[[candidate]], + mean_log_score = mean(score$log_density), + stringsAsFactors = FALSE + ) + } + } + validation <- do.call(rbind, rows) + selection <- pcn_select_adaptation_candidate(validation) + list( + selected = selection$selected, + summary = selection$summary, + validation = validation, + source_sites = sites, + fold_sizes = fold_sizes + ) +} + run_referent_site_evidence <- function(input_dir, output_dir) { data <- utils::read.csv(file.path(input_dir, "site_data.csv"), stringsAsFactors = FALSE) train <- data[data$split == "reference_train", , drop = FALSE] @@ -16,15 +85,12 @@ run_referent_site_evidence <- function(input_dir, output_dir) { adaptation$site <- factor(adaptation$site) transported$site <- factor(transported$site) - fit <- referent::ref_fit( - referent::ref_spec( - referent::ref_gaussian(), location = ~ s(x, k = 6) + s(site, bs = "re"), - scale = ~ s(site, bs = "re") - ), - train, outcomes = "y" - ) + selection <- pcn_validate_adaptation_priors(train) + fit <- pcn_fit_site_model(train) adapted <- referent::ref_adapt( - fit, adaptation, by = site, parameters = c("location", "scale") + fit, adaptation, by = site, parameters = c("location", "scale"), + location_prior_n = selection$selected$location_prior_n, + scale_prior_n = selection$selected$scale_prior_n ) score_rows <- function(model, frame, lane) { @@ -66,12 +132,27 @@ run_referent_site_evidence <- function(input_dir, output_dir) { dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) utils::write.csv(predictions, file.path(output_dir, "referent_predictions.csv"), row.names = FALSE) utils::write.csv(gated, file.path(output_dir, "site_comparison.csv"), row.names = FALSE) + selection_validation_path <- file.path(output_dir, "adaptation_selection.csv") + selection_summary_path <- file.path(output_dir, "adaptation_selection_summary.csv") + utils::write.csv(selection$validation, selection_validation_path, row.names = FALSE) + utils::write.csv(selection$summary, selection_summary_path, row.names = FALSE) receipt <- list( estimand = "separate adaptation and transfer policies; no rowwise parity claim", referent_version = as.character(utils::packageVersion("referent")), r_version = R.version.string, all_sites_pass = all(gated$pass), failed_sites = split(gated$site[!gated$pass], gated$method[!gated$pass]), + adaptation_prior_selection = list( + rule = "leave-one-observed-site-out mean conditional log score", + source_split = "reference_train", + source_sites = selection$source_sites, + fold_sizes = selection$fold_sizes, + selected = unclass(selection$selected), + files = list( + adaptation_selection.csv = unname(tools::md5sum(selection_validation_path)), + adaptation_selection_summary.csv = unname(tools::md5sum(selection_summary_path)) + ) + ), referent_adaptation = unclass(adapted$adaptation) ) jsonlite::write_json( diff --git a/tools/pcntoolkit/site_evidence.R b/tools/pcntoolkit/site_evidence.R index 27eaafb..7c0bde9 100644 --- a/tools/pcntoolkit/site_evidence.R +++ b/tools/pcntoolkit/site_evidence.R @@ -27,3 +27,53 @@ pcn_site_gate <- function(summary, coverage_floor = 0.80, mace_ceiling = 0.08, summary$mace <= mace_ceiling & abs(summary$mean_z) <= mean_z_ceiling data.frame(summary, pass = per_site, stringsAsFactors = FALSE) } + +pcn_adaptation_grid <- function() { + expand.grid( + location_prior_n = c(0, 2, 5, 10), + scale_prior_n = c(0, 5, 10, 25), + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = FALSE + ) +} + +pcn_select_adaptation_candidate <- function(validation) { + required <- c("site", "location_prior_n", "scale_prior_n", "mean_log_score") + if (!all(required %in% names(validation)) || !nrow(validation) || + any(!is.finite(validation$mean_log_score))) { + stop("adaptation validation evidence is incomplete", call. = FALSE) + } + candidate_site <- validation[ + c("site", "location_prior_n", "scale_prior_n") + ] + if (anyDuplicated(candidate_site)) { + stop("adaptation validation evidence contains duplicate site results", + call. = FALSE) + } + expected_sites <- length(unique(validation$site)) + site_counts <- stats::aggregate( + site ~ location_prior_n + scale_prior_n, validation, + function(x) length(unique(x)) + ) + names(site_counts)[names(site_counts) == "site"] <- "validated_sites" + if (any(site_counts$validated_sites != expected_sites)) { + stop("adaptation validation evidence is incomplete", call. = FALSE) + } + summary <- stats::aggregate( + mean_log_score ~ location_prior_n + scale_prior_n, + validation, mean + ) + summary <- merge( + summary, site_counts, + by = c("location_prior_n", "scale_prior_n"), sort = FALSE + ) + summary <- summary[order( + -summary$mean_log_score, summary$location_prior_n, summary$scale_prior_n + ), , drop = FALSE] + rownames(summary) <- NULL + summary$selected <- seq_len(nrow(summary)) == 1L + list( + selected = summary[1L, c("location_prior_n", "scale_prior_n"), drop = FALSE], + summary = summary + ) +} diff --git a/tools/validation/run_nhanes_external_validation.R b/tools/validation/run_nhanes_external_validation.R index ae2c1bb..d3461a9 100644 --- a/tools/validation/run_nhanes_external_validation.R +++ b/tools/validation/run_nhanes_external_validation.R @@ -119,7 +119,7 @@ run_nhanes_external_validation <- function(output_dir, cache_dir) { spec <- referent::ref_spec( referent::ref_shash(), location = ~ s(age, k = 8) + sex + race_ethnicity, - scale = ~ s(age, k = 5), + scale = ~ s(age, k = 5) + sex + race_ethnicity, skew = ~ 1, tail = ~ 1, control = list(seed = 20260824L, n_draw = 1000L) From b1301a4e0426eb5b5a83935059053131331670d9 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 15:14:30 -0400 Subject: [PATCH 11/13] Preregister untouched NHANES confirmation --- .beads/issues.jsonl | 9 ---- .mote/FORMAT.json | 9 ++++ ...37.329963Z-p71889-c0000-r46ae-h63230f.json | 1 + ...09.433408Z-p71972-c0000-ra8dd-h017ed6.json | 1 + ...09.452863Z-p71972-c0001-r3cc6-h9bc218.json | 1 + ...09.467267Z-p71972-c0002-reb69-hb5fab0.json | 1 + ...09.477236Z-p71972-c0003-r3d83-h12ff97.json | 1 + ...25.993650Z-p72671-c0000-rd0ef-hb2e2f2.json | 1 + ...26.005062Z-p72671-c0001-rf4fd-h375d61.json | 1 + ...26.016001Z-p72671-c0002-r3956-hd70e73.json | 1 + ...26.025965Z-p72671-c0003-r9bf4-h73fd16.json | 1 + ...53.844480Z-p72878-c0000-r512f-h2c8bcb.json | 1 + AGENTS.md | 46 ++++++++----------- NEWS.md | 19 ++++---- README.md | 14 +++--- docs/design/pcntoolkit-validation-contract.md | 7 +-- docs/evidence/nhanes/README.md | 26 +++++++---- docs/evidence/pcntoolkit/v1.3.0/README.md | 6 +-- tools/pcntoolkit/generate_fixtures.py | 2 +- 19 files changed, 82 insertions(+), 66 deletions(-) create mode 100644 .mote/FORMAT.json create mode 100644 .mote/ops/20260824T191037.329963Z-p71889-c0000-r46ae-h63230f.json create mode 100644 .mote/ops/20260824T191109.433408Z-p71972-c0000-ra8dd-h017ed6.json create mode 100644 .mote/ops/20260824T191109.452863Z-p71972-c0001-r3cc6-h9bc218.json create mode 100644 .mote/ops/20260824T191109.467267Z-p71972-c0002-reb69-hb5fab0.json create mode 100644 .mote/ops/20260824T191109.477236Z-p71972-c0003-r3d83-h12ff97.json create mode 100644 .mote/ops/20260824T191125.993650Z-p72671-c0000-rd0ef-hb2e2f2.json create mode 100644 .mote/ops/20260824T191126.005062Z-p72671-c0001-rf4fd-h375d61.json create mode 100644 .mote/ops/20260824T191126.016001Z-p72671-c0002-r3956-hd70e73.json create mode 100644 .mote/ops/20260824T191126.025965Z-p72671-c0003-r9bf4-h73fd16.json create mode 100644 .mote/ops/20260824T191253.844480Z-p72878-c0000-r512f-h2c8bcb.json diff --git a/.beads/issues.jsonl b/.beads/issues.jsonl index 1b74384..dc7f6ef 100644 --- a/.beads/issues.jsonl +++ b/.beads/issues.jsonl @@ -1,15 +1,6 @@ {"id":"referent-c48","title":"Dynamics kernel estimator: scale-unidentified, silent fallback to start values","description":"R/dynamics.R:104-151. process_correlation normalises so (tau_b,tau_g,sigma_e) are unidentified; BFGS walks ridge, eigen fails, tryCatch returns START values with identified=TRUE. norm_stable (ell=Inf) never fits. Fix: simplex parameterization + bounded log(ell), L-BFGS-B, check convergence, never silent fallback, stable+nugget when lags fixed, report normalised variance fractions. Also A4: NA velocity/forecast quantities when unidentified. Add OOS test on irregular lags: var(innovation) in 1±0.1, FPR in 0.05±0.015. Review A3/A4.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:18Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:20Z","started_at":"2026-08-22T13:02:50Z","closed_at":"2026-08-22T13:28:20Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-0rh","title":"Fix site scale adaptation bias (E[log|Z|] = -0.635)","description":"R/adapt.R:47-50 uses mean(log|r/s|); identical-site adaptation shrinks sigma to 0.55x, held-out var(z)=3.2-3.9. Fix: 0.5*log(mean((r/s)^2)) with z=qnorm(centile); add test that identical-site adaptation leaves var(z)~1. Review A2.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:17Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:19Z","started_at":"2026-08-22T13:02:49Z","closed_at":"2026-08-22T13:28:19Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-4du","title":"Derive covariates from spec formulas, not all non-outcome columns","description":"R/fit.R:30,45,260. Covariates are every non-outcome column: NA in unused columns silently drops rows; Mahalanobis support is computed over other outcomes; predict errors if unused column absent. Fix: all.vars() over spec formulas; complete-case on outcome+those; message dropped rows. See docs/review-2026-08-22.md A1.","status":"closed","priority":0,"issue_type":"bug","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:15Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:02:48Z","started_at":"2026-08-22T12:51:42Z","closed_at":"2026-08-22T13:02:48Z","close_reason":"Phase 1 fdb58e2","dependency_count":0,"dependent_count":0,"comment_count":0} -{"id":"referent-32n","title":"Repair release evidence gates without relaxing contracts","description":"The retained release evidence has four honest failures: PCNtoolkit HBR divergences, Referent site-5 adaptation calibration, NHANES race-conditional scale drift, and skew-heavy comparator warnings. Implement deterministic high-target-accept HBR sampling controls, choose adaptation priors by leave-one-observed-site-out log score without using site-5 test rows, include declared demographic covariates in the NHANES scale model, and stabilize the pinned PCNtoolkit optimizer finite-difference step. Preserve all registered thresholds and warning classification rules.","acceptance_criteria":"HBR base and transfer receipts satisfy zero-divergence/R-hat/ESS gates under recorded seeds; site-5 passes using priors selected only from observed-site reference rows; NHANES locked cohort gate passes with no conditional offenders; all 20 skew-heavy comparator fits have zero critical warnings and the scenario is classified only from regenerated evidence; focused and full package checks pass; evidence and provenance are committed and pushed.","notes":"Registered thresholds unchanged. Final receipts: HBR base/transfer zero divergences with R-hat 1.00 and ESS above 1700; observed-site-only prior selection chose location_prior_n=0 and scale_prior_n=25 and site-5 passed; NHANES full gate passed; 20/20 skew-heavy comparator fits valid with zero critical warnings and independent confirmation classified superior. Validation: fixture drift exact; focused tests 158/158 plus strengthened workflow 22/22; full testthat 1170 pass with two environment binary-version warnings; final R CMD check --no-manual --as-cran completed with only expected New submission NOTE.","status":"in_progress","priority":1,"issue_type":"bug","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-24T16:32:55Z","created_by":"bbuchsbaum","updated_at":"2026-08-24T16:58:31Z","started_at":"2026-08-24T16:32:59Z","dependency_count":0,"dependent_count":0,"comment_count":0} -{"id":"referent-eo6.7","title":"Publish tiered CI and documentation evidence","description":"Add fast offline PR checks, scheduled comparator regeneration and scenario runs, release-level HBR and multisite gates, machine-readable reports, and reader-facing evidence tables.","design":"Pinned fixtures remain stable until an explicit comparator upgrade. CI reports exact conformance separately from scientific superiority and unsupported lanes.","acceptance_criteria":"CI has documented PR, scheduled, and release tiers; the vignette links evidence and limitations; an upgrade produces reviewed drift rather than silently replacing baselines.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:59Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:52Z","started_at":"2026-08-23T14:01:27Z","closed_at":"2026-08-23T14:20:52Z","close_reason":"Added offline PR oracles, weekly pinned regeneration/drift rejection, release HBR/site gates, reproducible machine-readable evidence, and linked reader-facing documentation","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:59Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:14Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:15Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:16Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.6","type":"blocks","created_at":"2026-08-23T09:30:17Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":4,"dependent_count":0,"comment_count":0} -{"id":"referent-eo6.6","title":"Evaluate HBR SHASH and site transport separately","description":"Add scheduled evidence for HBR posterior aggregation, SHASH fitted performance, site effects, unseen-site transport, and Referent adaptation without claiming false rowwise equivalence.","design":"Require convergence and Monte Carlo receipts. Use leave-one-site-out calibration and performance; keep PCNtoolkit transfer and Referent adaptation labeled as different estimands.","acceptance_criteria":"HBR and transport reports expose convergence, uncertainty, score and calibration evidence, and no small-site failure is averaged away.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:58Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:01:25Z","started_at":"2026-08-23T13:53:31Z","closed_at":"2026-08-23T14:01:25Z","close_reason":"HBR/transfer runner emits proper mixture and draw-averaged outputs, convergence receipts, per-site evidence, and a smoke run proved both convergence and small-site gates fail closed; release-grade evidence remains CI-gated","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:57Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:13Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} -{"id":"referent-eo6.5","title":"Establish held-out superiority and non-inferiority gates","description":"Implement preregistered synthetic scenario families, untouched test splits, paired proper-score comparisons, cluster-aware uncertainty, calibration non-inferiority gates, and negative controls.","design":"A differing Referent method is superior only when its paired held-out proper-score interval clears a practical margin and no marginal, conditional, site, or tail calibration gate materially regresses.","acceptance_criteria":"The benchmark classifies conformant, equivalent, superior, trade-off, and inferior outcomes; null and deliberately overconfident negative controls behave correctly.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:56Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:53:27Z","started_at":"2026-08-23T13:47:07Z","closed_at":"2026-08-23T13:53:27Z","close_reason":"Locked design registry, cluster bootstrap, calibration non-inferiority, full classification rules, and negative controls pass; skew-heavy held-out case is superior with positive log-score CI and better calibration","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:56Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:12Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} -{"id":"referent-eo6.4","title":"Certify fitted-model concordance in overlap cases","description":"Compare identical train and test rows for intercept-only, linear, categorical, heteroskedastic, smooth, transformed, and balanced-site model configurations using per-row predictive outputs.","design":"Use normalized prediction error, predictive-scale error, Z RMSE, central quantile errors, log tails, and coverage. Avoid a sole maximum-error gate.","acceptance_criteria":"All registered overlap cases meet locked scenario-level concordance margins or have a documented non-equivalent classification.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:55Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:47:05Z","started_at":"2026-08-23T13:43:50Z","closed_at":"2026-08-23T13:47:05Z","close_reason":"Four matched linear/log Gaussian scenarios meet all locked prediction, scale, Z, tail-quantile, and coverage margins; divergent smooth/site cases are explicitly diagnostic","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:54Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:11Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} -{"id":"referent-eo6.3","title":"Prove exact shared distribution and metric semantics","description":"Add analytic and differential tests for Gaussian, SHASHb to mgcv SHASH conversion, transforms, log tails, log densities, and explicitly normalized metric definitions.","design":"Use independent analytic formulas alongside external fixtures. Expected definition differences must be tested as differences, not hidden by broad tolerances.","acceptance_criteria":"Gaussian and SHASH semantic fixtures pass scale-aware declared tolerances, adversarial tails remain finite, and metric relationships are independently verified.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:53Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:43:48Z","started_at":"2026-08-23T13:41:06Z","closed_at":"2026-08-23T13:43:48Z","close_reason":"Independent R analytic, Referent, and pinned PCNtoolkit paths agree for Gaussian/converted SHASH/transforms; tail and definition-difference tests pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:53Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6.2","type":"blocks","created_at":"2026-08-23T09:30:10Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} -{"id":"referent-eo6.2","title":"Build reproducible offline PCNtoolkit fixture harness","description":"Add a pinned Python generator, neutral inputs, checked-in per-row PCNtoolkit outputs, provenance receipts, and an R comparison reader without adding Python to normal package tests.","design":"PCNtoolkit runs only during explicit regeneration or scheduled CI. Checked-in fixtures are versioned by comparator release and carry hashes and resolved environment metadata.","acceptance_criteria":"Fixture generation is deterministic; schema and hashes validate in R; normal testthat runs need no Python or network.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:52Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:41:04Z","started_at":"2026-08-23T13:34:09Z","closed_at":"2026-08-23T13:41:04Z","close_reason":"Pinned Python generator is byte-stable across two runs; checked-in fixture manifest, schema, hashes, and offline R validation pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:51Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6.1","type":"blocks","created_at":"2026-08-23T09:30:09Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} -{"id":"referent-eo6.1","title":"Define pinned PCNtoolkit validation contract","description":"Write the normative comparison contract for PCNtoolkit 1.3.0, classify outputs as exact, parameter-converted, matched-estimator, same-estimand, or non-equivalent, and reconcile the public comparison vignette with executed definitions.","design":"Separate semantic conformance from scientific superiority. Record conditional versus total uncertainty and executed versus documented comparator behavior.","acceptance_criteria":"A reviewed design document and corrected vignette define every comparison lane, estimand, output, uncertainty mode, and mismatch disposition.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:50Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:34:07Z","started_at":"2026-08-23T13:30:19Z","closed_at":"2026-08-23T13:34:07Z","close_reason":"Pinned comparison contract and corrected current PCNtoolkit 1.3.0 semantics in the public vignette","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.1","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:50Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":0,"dependent_count":1,"comment_count":0} -{"id":"referent-eo6","title":"Establish PCNtoolkit conformance and superiority evidence","description":"Implement the seven-part validation program: pin and document comparison contracts, generate reproducible PCNtoolkit fixtures, prove exact shared semantics, measure fitted-model concordance, establish held-out superiority without sacrificing calibration, evaluate HBR and site transport separately, and publish tiered CI evidence.","design":"PCNtoolkit is an external differential oracle only for shared contracts. Alternative Referent methods may diverge when paired held-out proper-score superiority and calibration non-inferiority are demonstrated. Normal R tests consume offline fixtures; Python regeneration is scheduled or manual.","acceptance_criteria":"All seven child work packages are complete; exact relationships pass declared tolerances; every mismatch is classified; superiority claims use untouched test data and calibration gates; normal R tests remain Python-free; documentation and CI expose evidence tiers.","status":"closed","priority":1,"issue_type":"epic","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:20Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:54Z","started_at":"2026-08-23T13:30:18Z","closed_at":"2026-08-23T14:20:54Z","close_reason":"All seven PCNtoolkit conformance and superiority evidence lanes implemented and validated","labels":["pcntoolkit","scientific-evidence","validation"],"dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-xzc","title":"Make referent vignettes surpass PCNtoolkit docs","description":"Audit and improve referent's starlightdown vignette site against the live PCNtoolkit documentation. Establish a direct visual and reader-journey baseline, revise source vignettes and site navigation, verify executable examples and rendered desktop/mobile output, and publish the completed branch without touching unrelated work.","acceptance_criteria":"All public vignette pages use current exported APIs and render successfully; newcomer journey, task navigation, interpretation, and recovery are materially stronger than the live PCNtoolkit stable site; representative pages are compared at desktop and mobile viewports; repository tests and documentation build pass; intended changes are committed and pushed with tracking/live SHA verification.","notes":"Gauntlet complete. Independent critic final PASS and blind choice referent in all six categories. Fixed discovered S3 route blocker via docsLoader generateId preserving dotted method paths; added fail-closed generated plot alt postprocessor and generic 35-route identity/canonical/redirect/alt checker. Final gates: cold starlightdown build 35 routes, Astro 36 pages plus Pagefind, browser desktop/mobile axe clean and search working, R CMD build passed, LC_ALL=en_US.UTF-8 R CMD check --no-manual Status OK.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T18:05:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T18:49:55Z","started_at":"2026-08-22T18:05:43Z","closed_at":"2026-08-22T18:49:55Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e4m","title":"Gauntlet 2: thrive lines and velocity","description":"thrive type was removed from autoplot.norm_forecast in Phase 5; referent now has no thrive capability. Bar for thrive: PCNtoolkit nb14 CorrelationMatrix + ZGainScore.get_thrivelines + plot_centiles_advanced on a shared constructed two-visit dataset (LNM_data.csv is not public). Bar for velocity: ground-truth recovery on simulated data, since PCNtoolkit has no velocity estimand.","status":"closed","priority":1,"issue_type":"feature","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:16:57Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:23Z","closed_at":"2026-08-22T19:13:23Z","close_reason":"Both lanes won round 1 and were verified by independent critics. Patches pending integration (thrive rebased needed; velocity chart_velocity_se drop).","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e6o","title":"Close WM-hypointensities MSLL gap vs information-matched PCN opponent","description":"referent -0.470 vs fair opponent -0.491. Test-split var_z 1.17-1.23 indicates 10-20 pct out-of-sample overconfidence. Try norm_calibrate on out-of-fold train scores (train-only) before rescoring.","status":"closed","priority":1,"issue_type":"task","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:09:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:22Z","closed_at":"2026-08-22T19:13:22Z","close_reason":"Closed via ref_spec(transform=) Box-Cox response transform (commit 4dd3683). WM -0.49154 vs PCN fair -0.49087. NOTE: margin is +0.00068 nats with paired per-subject SE 0.033 (t=-0.02) - a point-estimate win only, not statistical evidence. Root cause was a missing capability, not tuning: SHASH moves conditional residual shape; PCN's warp is a monotone map on y.","dependency_count":0,"dependent_count":0,"comment_count":0} diff --git a/.mote/FORMAT.json b/.mote/FORMAT.json new file mode 100644 index 0000000..61b92e3 --- /dev/null +++ b/.mote/FORMAT.json @@ -0,0 +1,9 @@ +{ + "schema_version": 1, + "store_id": "st-01M0TJWZ4KXA9B6H69WTA4KB7B", + "created_at": "2026-08-24T19:10:34.131119Z", + "default_ttl_s": { + "claim": 1800, + "reservation": 3600 + } +} \ No newline at end of file diff --git a/.mote/ops/20260824T191037.329963Z-p71889-c0000-r46ae-h63230f.json b/.mote/ops/20260824T191037.329963Z-p71889-c0000-r46ae-h63230f.json new file mode 100644 index 0000000..3e38d77 --- /dev/null +++ b/.mote/ops/20260824T191037.329963Z-p71889-c0000-r46ae-h63230f.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","kind":"session_start","label":"repair evidence trust claims and receipts","op":"20260824T191037.329963Z-p71889-c0000-r46ae-h63230f","pid":71889,"session_id":"sess-01M0TJX28HWYDEV2TV6FBA268D","ts":"2026-08-24T19:10:37.329963Z","ttl_s":14400,"v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191109.433408Z-p71972-c0000-ra8dd-h017ed6.json b/.mote/ops/20260824T191109.433408Z-p71972-c0000-ra8dd-h017ed6.json new file mode 100644 index 0000000..7a7f7fb --- /dev/null +++ b/.mote/ops/20260824T191109.433408Z-p71972-c0000-ra8dd-h017ed6.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"create","op":"20260824T191109.433408Z-p71972-c0000-ra8dd-h017ed6","set":{"body":"Migration of the release-evidence repair from the mistakenly used Beads tracker. Frozen before inspecting any NHANES 2013-2014 outcomes: the 2017-2018 evaluation is post-hoc model-development evidence because it informed the demographic scale terms. Untouched confirmation will refit the already-fixed SHASH specification on NHANES 2015-2016 and evaluate NHANES 2013-2014 from DEMO_H.XPT and BMX_H.XPT. The frozen contract is adults age 18-79; outcomes height_cm, weight_kg, bmi, waist_cm; location s(age,k=8)+sex+race_ethnicity; scale s(age,k=5)+sex+race_ethnicity; intercept-only skew and tail; simple random sample without replacement of min(2000,population) using seed 20260824; total uncertainty with 1000 draws; unchanged acceptable_calibration rules, valid fits, held-out status, complete finite evidence, and minimum outcome n at least 500. No model, sample, seed, threshold, or wording may be changed after outcomes are observed; a failed confirmation remains a retained failure. Also make HBR/site evidence fail-closed by retaining raw tables, recording SHA-256 plus schema/row counts, and recomputing convergence, summaries, gates, and receipt verdicts rather than trusting booleans.","priority":1,"status":"open","title":"Repair evidence trust narrative and receipts"},"ts":"2026-08-24T19:11:09.433408Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191109.452863Z-p71972-c0001-r3cc6-h9bc218.json b/.mote/ops/20260824T191109.452863Z-p71972-c0001-r3cc6-h9bc218.json new file mode 100644 index 0000000..b42d49f --- /dev/null +++ b/.mote/ops/20260824T191109.452863Z-p71972-c0001-r3cc6-h9bc218.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"tag_add","op":"20260824T191109.452863Z-p71972-c0001-r3cc6-h9bc218","tag":"evidence","ts":"2026-08-24T19:11:09.452863Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191109.467267Z-p71972-c0002-reb69-hb5fab0.json b/.mote/ops/20260824T191109.467267Z-p71972-c0002-reb69-hb5fab0.json new file mode 100644 index 0000000..b4428dd --- /dev/null +++ b/.mote/ops/20260824T191109.467267Z-p71972-c0002-reb69-hb5fab0.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"tag_add","op":"20260824T191109.467267Z-p71972-c0002-reb69-hb5fab0","tag":"nhanes","ts":"2026-08-24T19:11:09.467267Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191109.477236Z-p71972-c0003-r3d83-h12ff97.json b/.mote/ops/20260824T191109.477236Z-p71972-c0003-r3d83-h12ff97.json new file mode 100644 index 0000000..3b36018 --- /dev/null +++ b/.mote/ops/20260824T191109.477236Z-p71972-c0003-r3d83-h12ff97.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"tag_add","op":"20260824T191109.477236Z-p71972-c0003-r3d83-h12ff97","tag":"pcntoolkit","ts":"2026-08-24T19:11:09.477236Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191125.993650Z-p72671-c0000-rd0ef-hb2e2f2.json b/.mote/ops/20260824T191125.993650Z-p72671-c0000-rd0ef-hb2e2f2.json new file mode 100644 index 0000000..7ab272c --- /dev/null +++ b/.mote/ops/20260824T191125.993650Z-p72671-c0000-rd0ef-hb2e2f2.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"reserve_open","mode":"exclusive","op":"20260824T191125.993650Z-p72671-c0000-rd0ef-hb2e2f2","paths":["AGENTS.md",".beads/issues.jsonl",".github/workflows/real-cohort-validation.yml",".github/workflows/pcntoolkit-validation.yml","README.md","NEWS.md","docs/design/pcntoolkit-validation-contract.md","docs/evidence/nhanes","docs/evidence/pcntoolkit/v1.3.0","tools/validation/run_nhanes_external_validation.R","tools/pcntoolkit/run_hbr_site_evidence.py","tools/pcntoolkit/run_site_transport_evidence.R","tools/pcntoolkit/validate_hbr_site_evidence.R","inst/pcntoolkit/run_hbr_site_evidence.py","inst/pcntoolkit/validate_hbr_site_evidence.R","tests/testthat/test-real-cohort-workflow.R","tests/testthat/test-pcntoolkit-hbr-site.R"],"reservation_id":"rv-01M0TJYHS9PWVY0Z1VQB4X3A6V","ts":"2026-08-24T19:11:25.993650Z","ttl_s":3600,"v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191126.005062Z-p72671-c0001-rf4fd-h375d61.json b/.mote/ops/20260824T191126.005062Z-p72671-c0001-rf4fd-h375d61.json new file mode 100644 index 0000000..38e068e --- /dev/null +++ b/.mote/ops/20260824T191126.005062Z-p72671-c0001-rf4fd-h375d61.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"claim","op":"20260824T191126.005062Z-p72671-c0001-rf4fd-h375d61","to":"codex-referent-evidence-20260824","ts":"2026-08-24T19:11:26.005062Z","ttl_s":1800,"v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191126.016001Z-p72671-c0002-r3956-hd70e73.json b/.mote/ops/20260824T191126.016001Z-p72671-c0002-r3956-hd70e73.json new file mode 100644 index 0000000..8781544 --- /dev/null +++ b/.mote/ops/20260824T191126.016001Z-p72671-c0002-r3956-hd70e73.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","expect":{"status":"20260824T191109.433408Z-p71972-c0000-ra8dd-h017ed6"},"kind":"patch","op":"20260824T191126.016001Z-p72671-c0002-r3956-hd70e73","set":{"status":"doing"},"ts":"2026-08-24T19:11:26.016001Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191126.025965Z-p72671-c0003-r9bf4-h73fd16.json b/.mote/ops/20260824T191126.025965Z-p72671-c0003-r9bf4-h73fd16.json new file mode 100644 index 0000000..2b7253f --- /dev/null +++ b/.mote/ops/20260824T191126.025965Z-p72671-c0003-r9bf4-h73fd16.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"note","note_kind":"progress","op":"20260824T191126.025965Z-p72671-c0003-r9bf4-h73fd16","text":"Frozen untouched confirmation design recorded; starting narrative, Mote migration, and fail-closed receipt repair","ts":"2026-08-24T19:11:26.025965Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T191253.844480Z-p72878-c0000-r512f-h2c8bcb.json b/.mote/ops/20260824T191253.844480Z-p72878-c0000-r512f-h2c8bcb.json new file mode 100644 index 0000000..f696426 --- /dev/null +++ b/.mote/ops/20260824T191253.844480Z-p72878-c0000-r512f-h2c8bcb.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"reserve_open","mode":"exclusive","op":"20260824T191253.844480Z-p72878-c0000-r512f-h2c8bcb","paths":["tools/pcntoolkit/generate_fixtures.py"],"reservation_id":"rv-01M0TK17JME59Y18BN5S09HEV1","ts":"2026-08-24T19:12:53.844480Z","ttl_s":3600,"v":1} \ No newline at end of file diff --git a/AGENTS.md b/AGENTS.md index 9390d72..5ba771f 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,15 +1,18 @@ # Agent Instructions -This project uses **bd** (beads) for issue tracking. Run `bd prime` for full workflow context. +This project uses **Mote** for issue tracking and path coordination. Do not use +Beads for new work. ## Quick Reference ```bash -bd ready # Find available work -bd show # View issue details -bd update --claim # Claim work atomically -bd close # Complete work -bd dolt push # Push beads data to remote +mote doctor +mote ready +mote show +mote preflight --issue --paths [ ...] +mote begin --paths [ ...] +mote note --kind progress "what changed" +mote done --note "finished" ``` ## Non-Interactive Shell Commands @@ -36,25 +39,18 @@ cp -rf source dest # NOT: cp -r source dest - `apt-get` - use `-y` flag - `brew` - use `HOMEBREW_NO_AUTO_UPDATE=1` env var - -## Beads Issue Tracker - -This project uses **bd (beads)** for issue tracking. Run `bd prime` to see full workflow context and commands. - -### Quick Reference - -```bash -bd ready # Find available work -bd show # View issue details -bd update --claim # Claim work -bd close # Complete work -``` +## Mote Issue Tracker ### Rules -- Use `bd` for ALL task tracking — do NOT use TodoWrite, TaskCreate, or markdown TODO lists -- Run `bd prime` for detailed command reference and session close protocol -- Use `bd remember` for persistent knowledge — do NOT use MEMORY.md files +- Use `mote` for all task tracking and path reservations. +- Run `mote doctor`, open a named session, and inspect `mote in-flight` before + claiming work. +- Never hand-edit `.mote/ops/*.json`; Mote operations are the durable record. +- Reserve exact paths before editing and stage only the operations belonging to + the current issue. +- Do not use TodoWrite, TaskCreate, markdown TODO lists, or Beads as parallel + trackers. ## Session Completion @@ -62,13 +58,12 @@ bd close # Complete work **MANDATORY WORKFLOW:** -1. **File issues for remaining work** - Create issues for anything that needs follow-up +1. **File Mote issues for remaining work** - Create issues for anything that needs follow-up 2. **Run quality gates** (if code changed) - Tests, linters, builds -3. **Update issue status** - Close finished work, update in-progress items +3. **Update Mote status** - Finish completed work or hand off unfinished work 4. **PUSH TO REMOTE** - This is MANDATORY: ```bash git pull --rebase - bd dolt push git push git status # MUST show "up to date with origin" ``` @@ -81,4 +76,3 @@ bd close # Complete work - NEVER stop before pushing - that leaves work stranded locally - NEVER say "ready to push when you are" - YOU must push - If push fails, resolve and retry until it succeeds - diff --git a/NEWS.md b/NEWS.md index ea74656..557e1d8 100644 --- a/NEWS.md +++ b/NEWS.md @@ -17,13 +17,14 @@ Initial scoped release of the CDF-first normative-modelling API. executable serialization, `ref_read()` requires an explicit `trusted = TRUE` acknowledgement before deserializing a bundle from an authenticated source. - Numerical validation includes offline PCNtoolkit 1.3.0 oracles, a replicated - five-scenario release matrix, HBR/site receipts, independent NHANES cohort - validation, cross-platform R CMD check, and scale/allocation budgets. + five-scenario release matrix, HBR/site receipts, NHANES cohort diagnostics, + cross-platform R CMD check, and scale/allocation budgets. -The 0.1.0 release-candidate evidence now passes its registered gates without -relaxing them. Four replicated PCNtoolkit scenarios are equivalent and the -independently seeded skew-heavy confirmation scenario is superior with a valid -comparator; deterministic high-acceptance HBR sampling passes convergence, -leave-one-site-out-selected adaptation passes every site, and the independent -NHANES lane passes marginal, tail, shape, and conditional calibration. Claims -remain limited to the named evidence classes, scenarios, and cohorts. +The 0.1.0 release-candidate evidence passes its registered computational gates +without relaxing them. Four replicated PCNtoolkit scenarios are equivalent and +the fresh-seed skew-heavy matrix is superior with a valid comparator; +deterministic high-acceptance HBR sampling passes convergence and +leave-one-site-out-selected adaptation passes every site. The NHANES 2017-2018 +rerun passes computationally but is explicitly post-hoc model-development +evidence because its earlier failure informed the scale model. It is not used +as independent confirmation or as support for a conditional-transport claim. diff --git a/README.md b/README.md index 43692bc..694dede 100644 --- a/README.md +++ b/README.md @@ -18,9 +18,11 @@ raw standardized residual. > are reported as unsupported instead of being silently coerced. Exact and > matched-estimator PCNtoolkit lanes pass. The replicated release matrix finds > four named scenarios equivalent and the locked skew-heavy scenario superior; -> HBR convergence, every-site transport, and independent NHANES cohort gates -> also pass under their recorded controls. These are scoped results, not a -> blanket equivalence or superiority claim. +> HBR convergence and every-site transport also pass under their recorded +> controls. The NHANES 2017-2018 rerun is post-hoc model-development evidence, +> not independent confirmation; no conditional-transport claim rests on that +> cohort. These are scoped results, not a blanket equivalence or superiority +> claim. Browse the [documentation site](https://bbuchsbaum.github.io/referent/) (or `browseVignettes("referent")` after installing), and start with [Getting started](https://bbuchsbaum.github.io/referent/articles/getting-started/). Keep @@ -120,9 +122,9 @@ velocity centiles, and conditional forecasts off the same process. outputs, MSLL/SMSE/EV/MACE), numerical-validation policy, and what is deliberately missing. The scoped, machine-readable evidence is in [docs/evidence/pcntoolkit/v1.3.0](docs/evidence/pcntoolkit/v1.3.0/README.md). -- [NHANES external-cohort evidence](docs/evidence/nhanes/README.md): a - reproducible 2015-2016 to 2017-2018 transport lane with retained calibration - diagnostics and source hashes. +- [NHANES cohort evidence](docs/evidence/nhanes/README.md): retained post-hoc + 2015-2016 to 2017-2018 development diagnostics plus the frozen contract for + a genuinely untouched cycle confirmation. - [Troubleshooting reference-model workflows](https://bbuchsbaum.github.io/referent/articles/troubleshooting/): missing and unsupported covariates, transported unseen sites, small local samples, partial panel fits, honest assessment, and unidentified dynamics. diff --git a/docs/design/pcntoolkit-validation-contract.md b/docs/design/pcntoolkit-validation-contract.md index 7e2f77c..b5a3294 100644 --- a/docs/design/pcntoolkit-validation-contract.md +++ b/docs/design/pcntoolkit-validation-contract.md @@ -199,9 +199,10 @@ separately, so one favourable result cannot hide another scenario's failure. The generation receipt records seeds, dependency versions, row identities, warning verdicts, and file hashes. -The 0.1.0 confirmation matrix starts at seed 20260924. It was registered after -diagnosing the original comparator failure and before inspecting any outcomes -from the confirmation rows. PCNtoolkit's skew-heavy L-BFGS-B fit uses a 0.01 +The 0.1.0 fresh-seed matrix starts at seed 20260924. The seed block was selected +after diagnosing the original comparator failure, but the frozen design and +its result first entered version control together; it is therefore not called +independently preregistered. PCNtoolkit's skew-heavy L-BFGS-B fit uses a 0.01 finite-difference step instead of its 0.1 default; the model and optimizer are unchanged, while the smaller step avoids invalid ill-conditioned objective probes. The receipt records this control. HBR uses 1,000 tuning draws, diff --git a/docs/evidence/nhanes/README.md b/docs/evidence/nhanes/README.md index 9fe7ff0..c8262c6 100644 --- a/docs/evidence/nhanes/README.md +++ b/docs/evidence/nhanes/README.md @@ -1,9 +1,11 @@ -# NHANES external-cohort evidence +# NHANES cohort evidence -This release lane fits adult body-measure reference distributions on the -public CDC NHANES 2015-2016 cohort and evaluates them once on the independent -2017-2018 cohort. The outcomes are height, weight, body-mass index, and waist -circumference; age, sex, and race/ethnicity are declared covariates. +The retained root-level artifacts fit adult body-measure reference +distributions on public CDC NHANES 2015-2016 data and evaluate them on +2017-2018 data. They are **post-hoc model-development evidence**, not untouched +external confirmation: the earlier 2017-2018 failure informed the addition of +sex and race/ethnicity to the scale model, after which the same fixed sample +was re-evaluated. The lane is a cohort-transport check, not a U.S. prevalence estimate. It does not apply NHANES survey weights, so results must not be described as nationally @@ -16,14 +18,20 @@ held-out provenance, and the package's full marginal, tail, shape, and conditional calibration contract. Artifacts are uploaded even when the gate fails, so cohort drift remains visible rather than disappearing from CI. -The retained 0.1.0 run used all 5,392 eligible training rows and a fixed-seed +The retained development run used all 5,392 eligible training rows and a fixed-seed sample of 2,000 of 5,151 eligible evaluation rows. All fits were valid. The scale model conditions on age, sex, and race/ethnicity, matching the declared conditional diagnostic contract. MACE ranged from 0.0048 to 0.0176 and 95% coverage from 0.947 to 0.950 across the four outcomes. Marginal, tail, shape, -and conditional gates all passed. This supports conditional cohort transport -for this unweighted evaluation sample; it is still not a nationally -representative prevalence claim. +and conditional gates all passed. That result demonstrates that the revised +model addresses the observed diagnostic failure on the development cohort; it +does not support an independent conditional-transport claim. + +Before any NHANES 2013-2014 outcomes were inspected, Mote issue +`referent-32n` froze a genuinely untouched confirmation: the already-fixed +2015-2016 model, 2013-2014 evaluation cycle, eligibility rules, outcomes, +sample size and seed, uncertainty mode, and unchanged calibration gates. Any +result from that confirmation will be retained whether it passes or fails. Run with: diff --git a/docs/evidence/pcntoolkit/v1.3.0/README.md b/docs/evidence/pcntoolkit/v1.3.0/README.md index ce5390f..8b12064 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/README.md +++ b/docs/evidence/pcntoolkit/v1.3.0/README.md @@ -23,15 +23,15 @@ Five deliberately matched fitted scenarios pass all prediction, scale, Z, and coverage margins. Estimator-divergent scenarios remain diagnostics. The single checked-in skew/heavy-tail fixture remains a diagnostic result. In -the stronger, independently seeded confirmation matrix, linear Gaussian, +the stronger fresh-seed matrix, linear Gaussian, nonlinear heteroskedastic, unequal-site, and covariate-shift scenarios are all classified `equivalent`. The skew-heavy log-score contrast favours Referent by 0.3961 (95% replicate-bootstrap interval 0.3785 to 0.4120), with passing calibration guardrails and valid fits in every replicate, so this named scenario is classified `superior`. The PCNtoolkit receipt records the 0.01 L-BFGS-B finite-difference step that removed the earlier invalid objective -probes; the confirmation seeds were registered before their outcomes were -generated. +probes. The seed block and result first entered version control together, so +this is not described as independently preregistered. HBR and new-site transfer stay marked `non_equivalent`: PCNtoolkit draw averages and Referent predictive mixtures are different estimands. The 0.1.0 diff --git a/tools/pcntoolkit/generate_fixtures.py b/tools/pcntoolkit/generate_fixtures.py index fef081f..a65075d 100644 --- a/tools/pcntoolkit/generate_fixtures.py +++ b/tools/pcntoolkit/generate_fixtures.py @@ -300,7 +300,7 @@ def fit_scenario(frame: pd.DataFrame, name: str) -> pd.DataFrame: # step. On the registered skew-heavy matrix that makes otherwise # valid L-BFGS-B fits probe ill-conditioned posterior matrices. A # 0.01 step retains the pinned model and optimizer while avoiding - # those invalid probes across the preregistered replicate set. + # those invalid probes across the locked fresh-seed replicate set. kwargs["l_bfgs_b_epsilon"] = SKEW_HEAVY_LBFGSB_EPSILON fit_data = NormData.from_dataframe( From 20caf4d320e97d53e1868fb6078e3a5fa15a7514 Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 16:00:07 -0400 Subject: [PATCH 12/13] Make release evidence independently auditable --- .github/workflows/pcntoolkit-validation.yml | 5 +- .github/workflows/real-cohort-validation.yml | 5 +- ...55.230717Z-p74607-c0000-r3815-h06355e.json | 1 + ...38.007685Z-p78868-c0000-r9c07-hb1f2c9.json | 1 + ...46.562562Z-p22442-c0000-r14ff-h12b8ff.json | 1 + ...51.644398Z-p22447-c0000-r2c28-h360f82.json | 1 + ...51.657809Z-p22447-c0001-r4d90-hb40ff6.json | 1 + ...51.670052Z-p22447-c0002-r89c4-h60a6ec.json | 1 + ...51.680743Z-p22447-c0003-r1884-h9fcc08.json | 1 + ...51.690889Z-p22447-c0004-r8f94-h2010b4.json | 1 + ...04.821615Z-p22592-c0000-r0444-h5900ab.json | 1 + NEWS.md | 6 +- README.md | 10 +- docs/design/pcntoolkit-validation-contract.md | 9 + docs/evidence/nhanes/README.md | 23 +- .../confirmation-2013-2014/conditional.csv | 37 + .../nhanes/confirmation-2013-2014/gate.csv | 2 + .../confirmation-2013-2014/marginal.csv | 5 + .../nhanes/confirmation-2013-2014/overall.csv | 5 + .../confirmation-2013-2014/receipt.json | 83 + .../nhanes/confirmation-2013-2014/tail.csv | 17 + docs/evidence/nhanes/receipt.json | 30 +- docs/evidence/pcntoolkit/v1.3.0/README.md | 28 +- .../pcntoolkit/v1.3.0/hbr_divergences.csv | 12001 ++++++++++++++++ .../pcntoolkit/v1.3.0/hbr_receipt.json | 88 +- .../v1.3.0/pcntoolkit_predictions.csv | 196 + .../v1.3.0/pcntoolkit_site_summary.csv | 6 + .../v1.3.0/referent_predictions.csv | 196 + docs/evidence/pcntoolkit/v1.3.0/site_data.csv | 421 + .../v1.3.0/site_referent_receipt.json | 63 +- inst/pcntoolkit/run_hbr_site_evidence.py | 69 +- inst/pcntoolkit/validate_hbr_site_evidence.R | 675 + tests/testthat/test-pcntoolkit-hbr-site.R | 150 +- tests/testthat/test-pcntoolkit-workflow.R | 1 + tests/testthat/test-real-cohort-workflow.R | 66 +- tools/pcntoolkit/run_hbr_site_evidence.py | 69 +- .../pcntoolkit/run_site_transport_evidence.R | 57 +- tools/pcntoolkit/validate_hbr_site_evidence.R | 675 + .../run_nhanes_external_validation.R | 98 +- 39 files changed, 15002 insertions(+), 103 deletions(-) create mode 100644 .mote/ops/20260824T191455.230717Z-p74607-c0000-r3815-h06355e.json create mode 100644 .mote/ops/20260824T192238.007685Z-p78868-c0000-r9c07-hb1f2c9.json create mode 100644 .mote/ops/20260824T195846.562562Z-p22442-c0000-r14ff-h12b8ff.json create mode 100644 .mote/ops/20260824T195851.644398Z-p22447-c0000-r2c28-h360f82.json create mode 100644 .mote/ops/20260824T195851.657809Z-p22447-c0001-r4d90-hb40ff6.json create mode 100644 .mote/ops/20260824T195851.670052Z-p22447-c0002-r89c4-h60a6ec.json create mode 100644 .mote/ops/20260824T195851.680743Z-p22447-c0003-r1884-h9fcc08.json create mode 100644 .mote/ops/20260824T195851.690889Z-p22447-c0004-r8f94-h2010b4.json create mode 100644 .mote/ops/20260824T195904.821615Z-p22592-c0000-r0444-h5900ab.json create mode 100644 docs/evidence/nhanes/confirmation-2013-2014/conditional.csv create mode 100644 docs/evidence/nhanes/confirmation-2013-2014/gate.csv create mode 100644 docs/evidence/nhanes/confirmation-2013-2014/marginal.csv create mode 100644 docs/evidence/nhanes/confirmation-2013-2014/overall.csv create mode 100644 docs/evidence/nhanes/confirmation-2013-2014/receipt.json create mode 100644 docs/evidence/nhanes/confirmation-2013-2014/tail.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/hbr_divergences.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/pcntoolkit_predictions.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/pcntoolkit_site_summary.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/referent_predictions.csv create mode 100644 docs/evidence/pcntoolkit/v1.3.0/site_data.csv create mode 100644 inst/pcntoolkit/validate_hbr_site_evidence.R create mode 100644 tools/pcntoolkit/validate_hbr_site_evidence.R diff --git a/.github/workflows/pcntoolkit-validation.yml b/.github/workflows/pcntoolkit-validation.yml index 8623f00..441cf9b 100644 --- a/.github/workflows/pcntoolkit-validation.yml +++ b/.github/workflows/pcntoolkit-validation.yml @@ -122,9 +122,8 @@ jobs: evidence/hbr-site evidence/hbr-site/referent - name: Enforce convergence and every-site gates run: >- - Rscript -e 'h <- jsonlite::read_json("evidence/hbr-site/receipt.json"); - r <- jsonlite::read_json("evidence/hbr-site/referent/referent_receipt.json"); - if (!isTRUE(h$all_stages_pass) || !isTRUE(r$all_sites_pass)) quit(status = 1)' + Rscript tools/pcntoolkit/validate_hbr_site_evidence.R + evidence/hbr-site evidence/hbr-site/referent - uses: actions/upload-artifact@v4 if: always() with: diff --git a/.github/workflows/real-cohort-validation.yml b/.github/workflows/real-cohort-validation.yml index 1e11567..461b5e5 100644 --- a/.github/workflows/real-cohort-validation.yml +++ b/.github/workflows/real-cohort-validation.yml @@ -12,7 +12,7 @@ permissions: jobs: nhanes-external-cohort: - name: NHANES 2015-2016 to 2017-2018 + name: NHANES 2015-2016 to untouched 2013-2014 runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -25,11 +25,12 @@ jobs: any::foreign local::. needs: check - - name: Run independent-cohort validation + - name: Run preregistered untouched-cycle confirmation run: >- Rscript tools/validation/run_nhanes_external_validation.R evidence/nhanes-external /tmp/nhanes-cache + confirmation - uses: actions/upload-artifact@v4 if: always() with: diff --git a/.mote/ops/20260824T191455.230717Z-p74607-c0000-r3815-h06355e.json b/.mote/ops/20260824T191455.230717Z-p74607-c0000-r3815-h06355e.json new file mode 100644 index 0000000..de36cae --- /dev/null +++ b/.mote/ops/20260824T191455.230717Z-p74607-c0000-r3815-h06355e.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"note","note_kind":"decision","op":"20260824T191455.230717Z-p74607-c0000-r3815-h06355e","text":"Untouched NHANES 2013-2014 design was committed and published before execution at b1301a4e0426eb5b5a83935059053131331670d9; local, tracking, and live remote SHAs matched.","ts":"2026-08-24T19:14:55.230717Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T192238.007685Z-p78868-c0000-r9c07-hb1f2c9.json b/.mote/ops/20260824T192238.007685Z-p78868-c0000-r9c07-hb1f2c9.json new file mode 100644 index 0000000..9e006a3 --- /dev/null +++ b/.mote/ops/20260824T192238.007685Z-p78868-c0000-r9c07-hb1f2c9.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"note","note_kind":"progress","op":"20260824T192238.007685Z-p78868-c0000-r9c07-hb1f2c9","text":"First and only execution of the preregistered NHANES 2013-2014 confirmation passed: 5592 eligible evaluation participants, fixed n=2000 sample, minimum outcome n=1892, all fits valid, full calibration gate true. No model, sample, seed, or threshold was changed.","ts":"2026-08-24T19:22:38.007685Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T195846.562562Z-p22442-c0000-r14ff-h12b8ff.json b/.mote/ops/20260824T195846.562562Z-p22442-c0000-r14ff-h12b8ff.json new file mode 100644 index 0000000..3d2d9d3 --- /dev/null +++ b/.mote/ops/20260824T195846.562562Z-p22442-c0000-r14ff-h12b8ff.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"note","note_kind":"progress","op":"20260824T195846.562562Z-p22442-c0000-r14ff-h12b8ff","text":"The earlier first-and-only note described the state immediately after the untouched first execution. A later verification replay, performed without changing the model, sample, seed, thresholds, or retained outputs, matched all five confirmation tables and receipt byte-for-byte. HBR and site receipts now hash every raw and derived table and independently recompute fixed controls, exact draw identities, convergence, prediction completeness, site summaries, candidate selection, and all gates; adversarial tests reject hash drift, false verdicts, and coherently re-signed omissions. Final devtools test passed with only two environment-version warnings; final source-tarball check: 0 errors, 0 warnings, 1 New submission note.","ts":"2026-08-24T19:58:46.562562Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T195851.644398Z-p22447-c0000-r2c28-h360f82.json b/.mote/ops/20260824T195851.644398Z-p22447-c0000-r2c28-h360f82.json new file mode 100644 index 0000000..b32df89 --- /dev/null +++ b/.mote/ops/20260824T195851.644398Z-p22447-c0000-r2c28-h360f82.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"note","note_kind":"note","op":"20260824T195851.644398Z-p22447-c0000-r2c28-h360f82","text":"Completed narrative correction, prior Mote and Git registration, untouched NHANES confirmation, cryptographically bound HBR and site evidence, independent gate recomputation, tamper tests, full tests, and clean release check.","ts":"2026-08-24T19:58:51.644398Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T195851.657809Z-p22447-c0001-r4d90-hb40ff6.json b/.mote/ops/20260824T195851.657809Z-p22447-c0001-r4d90-hb40ff6.json new file mode 100644 index 0000000..226cf10 --- /dev/null +++ b/.mote/ops/20260824T195851.657809Z-p22447-c0001-r4d90-hb40ff6.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","expect":{"status":"20260824T191126.016001Z-p72671-c0002-r3956-hd70e73"},"kind":"close","op":"20260824T195851.657809Z-p22447-c0001-r4d90-hb40ff6","ts":"2026-08-24T19:58:51.657809Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T195851.670052Z-p22447-c0002-r89c4-h60a6ec.json b/.mote/ops/20260824T195851.670052Z-p22447-c0002-r89c4-h60a6ec.json new file mode 100644 index 0000000..7ecba67 --- /dev/null +++ b/.mote/ops/20260824T195851.670052Z-p22447-c0002-r89c4-h60a6ec.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","kind":"reserve_close","op":"20260824T195851.670052Z-p22447-c0002-r89c4-h60a6ec","reservation_id":"rv-01M0TJYHS9PWVY0Z1VQB4X3A6V","ts":"2026-08-24T19:58:51.670052Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T195851.680743Z-p22447-c0003-r1884-h9fcc08.json b/.mote/ops/20260824T195851.680743Z-p22447-c0003-r1884-h9fcc08.json new file mode 100644 index 0000000..8608139 --- /dev/null +++ b/.mote/ops/20260824T195851.680743Z-p22447-c0003-r1884-h9fcc08.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","kind":"reserve_close","op":"20260824T195851.680743Z-p22447-c0003-r1884-h9fcc08","reservation_id":"rv-01M0TK17JME59Y18BN5S09HEV1","ts":"2026-08-24T19:58:51.680743Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T195851.690889Z-p22447-c0004-r8f94-h2010b4.json b/.mote/ops/20260824T195851.690889Z-p22447-c0004-r8f94-h2010b4.json new file mode 100644 index 0000000..cde4e26 --- /dev/null +++ b/.mote/ops/20260824T195851.690889Z-p22447-c0004-r8f94-h2010b4.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","entity":"referent-32n","kind":"release","op":"20260824T195851.690889Z-p22447-c0004-r8f94-h2010b4","ts":"2026-08-24T19:58:51.690889Z","v":1} \ No newline at end of file diff --git a/.mote/ops/20260824T195904.821615Z-p22592-c0000-r0444-h5900ab.json b/.mote/ops/20260824T195904.821615Z-p22592-c0000-r0444-h5900ab.json new file mode 100644 index 0000000..fcb03f6 --- /dev/null +++ b/.mote/ops/20260824T195904.821615Z-p22592-c0000-r0444-h5900ab.json @@ -0,0 +1 @@ +{"actor":"codex-referent-evidence-20260824","kind":"session_end","op":"20260824T195904.821615Z-p22592-c0000-r0444-h5900ab","session_id":"sess-01M0TJX28HWYDEV2TV6FBA268D","ts":"2026-08-24T19:59:04.821615Z","v":1} \ No newline at end of file diff --git a/NEWS.md b/NEWS.md index 557e1d8..d1f8126 100644 --- a/NEWS.md +++ b/NEWS.md @@ -26,5 +26,7 @@ the fresh-seed skew-heavy matrix is superior with a valid comparator; deterministic high-acceptance HBR sampling passes convergence and leave-one-site-out-selected adaptation passes every site. The NHANES 2017-2018 rerun passes computationally but is explicitly post-hoc model-development -evidence because its earlier failure informed the scale model. It is not used -as independent confirmation or as support for a conditional-transport claim. +evidence because its earlier failure informed the scale model. A separately +preregistered, untouched NHANES 2013-2014 confirmation passes the unchanged +full calibration contract and is the evidence used for the scoped +conditional-transport claim. diff --git a/README.md b/README.md index 694dede..211b89c 100644 --- a/README.md +++ b/README.md @@ -20,9 +20,9 @@ raw standardized residual. > four named scenarios equivalent and the locked skew-heavy scenario superior; > HBR convergence and every-site transport also pass under their recorded > controls. The NHANES 2017-2018 rerun is post-hoc model-development evidence, -> not independent confirmation; no conditional-transport claim rests on that -> cohort. These are scoped results, not a blanket equivalence or superiority -> claim. +> while a separately preregistered, untouched 2013-2014 confirmation passes the +> same frozen conditional-calibration contract. These are scoped results, not a +> blanket equivalence or superiority claim. Browse the [documentation site](https://bbuchsbaum.github.io/referent/) (or `browseVignettes("referent")` after installing), and start with [Getting started](https://bbuchsbaum.github.io/referent/articles/getting-started/). Keep @@ -123,8 +123,8 @@ velocity centiles, and conditional forecasts off the same process. deliberately missing. The scoped, machine-readable evidence is in [docs/evidence/pcntoolkit/v1.3.0](docs/evidence/pcntoolkit/v1.3.0/README.md). - [NHANES cohort evidence](docs/evidence/nhanes/README.md): retained post-hoc - 2015-2016 to 2017-2018 development diagnostics plus the frozen contract for - a genuinely untouched cycle confirmation. + 2015-2016 to 2017-2018 development diagnostics plus a preregistered, + genuinely untouched 2013-2014 cycle confirmation. - [Troubleshooting reference-model workflows](https://bbuchsbaum.github.io/referent/articles/troubleshooting/): missing and unsupported covariates, transported unseen sites, small local samples, partial panel fits, honest assessment, and unidentified dynamics. diff --git a/docs/design/pcntoolkit-validation-contract.md b/docs/design/pcntoolkit-validation-contract.md index b5a3294..1b03235 100644 --- a/docs/design/pcntoolkit-validation-contract.md +++ b/docs/design/pcntoolkit-validation-contract.md @@ -212,6 +212,15 @@ Referent adaptation priors are selected by leave-one-observed-site-out mean conditional log score using only `reference_train` rows; site-5 adaptation and transport-test rows do not participate in that selection. +HBR and site evidence retain the simulated rows, posterior predictions, +per-draw divergence indicators, convergence summaries, Referent predictions, +prior-selection tables, and site comparison. Both receipts bind every table by +SHA-256, row count, and exact column schema. The standalone validator recomputes +row identities, convergence summaries, HBR/site metrics, prior selection, and +all registered gates. Undefined R-hat is accepted only for retained posterior +rows whose standard deviation is exactly zero; those rows are counted in the +stage receipt rather than silently dropped. + Machine-readable results are retained alongside a human-readable evidence table. Documentation may say “matches PCNtoolkit” only for a named evidence class and scenario, and may say “outperforms” only for a locked held-out diff --git a/docs/evidence/nhanes/README.md b/docs/evidence/nhanes/README.md index c8262c6..02f0587 100644 --- a/docs/evidence/nhanes/README.md +++ b/docs/evidence/nhanes/README.md @@ -28,14 +28,27 @@ model addresses the observed diagnostic failure on the development cohort; it does not support an independent conditional-transport claim. Before any NHANES 2013-2014 outcomes were inspected, Mote issue -`referent-32n` froze a genuinely untouched confirmation: the already-fixed -2015-2016 model, 2013-2014 evaluation cycle, eligibility rules, outcomes, -sample size and seed, uncertainty mode, and unchanged calibration gates. Any -result from that confirmation will be retained whether it passes or fails. +`referent-32n` and published commit `b1301a4` froze a genuinely untouched +confirmation: the already-fixed 2015-2016 model, 2013-2014 evaluation cycle, +eligibility rules, outcomes, sample size and seed, uncertainty mode, and +unchanged calibration gates. + +That confirmation was then executed once. It used all 5,392 eligible training +rows and a fixed-seed sample of 2,000 of 5,592 eligible 2013-2014 participants; +the minimum outcome count was 1,892. All fits were valid and the marginal, +tail, shape, and conditional gates passed. Across outcomes, MACE ranged from +0.0049 to 0.0142 and 95% coverage from 0.944 to 0.955. The retained artifacts +are in `confirmation-2013-2014/`, and their receipt points back to the prior +Mote/Git registration. This supports the named, unweighted cross-cycle +conditional-transport claim; it is not a national prevalence claim or evidence +for arbitrary cohorts. Run with: ```sh Rscript tools/validation/run_nhanes_external_validation.R \ - evidence/nhanes-external /tmp/nhanes-cache + evidence/nhanes-development /tmp/nhanes-cache development + +Rscript tools/validation/run_nhanes_external_validation.R \ + evidence/nhanes-confirmation /tmp/nhanes-cache confirmation ``` diff --git a/docs/evidence/nhanes/confirmation-2013-2014/conditional.csv b/docs/evidence/nhanes/confirmation-2013-2014/conditional.csv new file mode 100644 index 0000000..32c04f6 --- /dev/null +++ b/docs/evidence/nhanes/confirmation-2013-2014/conditional.csv @@ -0,0 +1,37 @@ +".outcome","covariate","level","n","location_drift","location_se","location_p","scale_drift","scale_se","scale_p" +"height_cm","age",NA,1976,0.109701182041215,0.0333191350546815,0.125245397305344,0.0998078282563931,0.0756311887825771,0.509263323074516 +"height_cm","sex","female",1056,0.0305312360962516,0.0310584170915498,0.325594569942396,0.0186118185830323,0.0471017708835068,0.692739180925511 +"height_cm","sex","male",920,0.0947351509315945,0.0344174689570338,0.00591365437047144,0.0975873825083421,0.0506655828757005,0.0540908948363293 +"height_cm","race_ethnicity","1",284,0.201197979718689,0.0590306323817671,0.000653532579075207,0.0266268303506284,0.077901090903546,0.732499115264356 +"height_cm","race_ethnicity","2",173,0.0392021651237229,0.082391180474076,0.634213052311761,0.169125548375211,0.121031113316446,0.162301586849355 +"height_cm","race_ethnicity","3",796,-0.0159477496424446,0.0366681503799045,0.663619751019554,0.0691741662838768,0.0583054164313474,0.235460172813011 +"height_cm","race_ethnicity","4",426,0.159300916850716,0.050105630922508,0.00147630350955134,0.092370838420079,0.0753440582229245,0.220203554617724 +"height_cm","race_ethnicity","6",233,-0.0235444795690673,0.0636932021864126,0.711639928828751,-0.0582624883776786,0.0858150631460234,0.497181717314323 +"height_cm","race_ethnicity","7",64,0.0905188319495387,0.117061284924017,0.439368408292221,-0.128495642094586,0.183619660018293,0.484056963320892 +"weight_kg","age",NA,1977,0.105818706923606,0.0569161646337706,0.0612078596080557,0.243520426686286,0.137645827920523,0.263732426763426 +"weight_kg","sex","female",1057,-0.0231901279309448,0.0316503769491663,0.463743414273295,0.0583819392751347,0.0458816618685583,0.203214682227283 +"weight_kg","sex","male",920,0.00670075361348879,0.0311832013770245,0.829858195492314,-0.106326807678671,0.046891823210705,0.0233602213051556 +"weight_kg","race_ethnicity","1",286,-0.0650608154612999,0.0567113383809597,0.251287546789734,-0.0791569585179912,0.0786899894861649,0.314447181239839 +"weight_kg","race_ethnicity","2",174,-0.109628337663159,0.0785298837332851,0.162712117792537,0.0788994489940344,0.115554115493165,0.494738157727457 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--- /dev/null +++ b/docs/evidence/nhanes/confirmation-2013-2014/marginal.csv @@ -0,0 +1,5 @@ +".outcome","n","mean_z","var_z","skew_z","excess_kurtosis_z","shapiro_w","mace","cover_50","cover_80","cover_90","cover_95","cover_99" +"bmi",1972,-0.0327300672726428,0.983674690801996,0.103332057870159,0.225855926820696,0.998354237482559,0.0107505070993915,0.512170385395537,0.8052738336714,0.900608519269777,0.955375253549696,0.987829614604463 +"height_cm",1976,0.060423747051978,1.05226331069202,0.061405628694999,0.0936875453539048,0.999153152996188,0.01417004048583,0.488866396761134,0.791497975708502,0.895242914979757,0.944331983805668,0.98836032388664 +"waist_cm",1892,-0.0289535852080061,0.971965536391736,0.0354132246529941,-0.0296929581247838,0.999046561947571,0.00847780126849896,0.504756871035941,0.809196617336152,0.909090909090909,0.951902748414376,0.993657505285412 +"weight_kg",1977,-0.00928036009033842,0.982145130293293,0.0598765791470141,0.214869671474232,0.998148242311904,0.0049266565503288,0.514415781487102,0.809812847749115,0.906929691451694,0.951947395042994,0.989883662114315 diff --git a/docs/evidence/nhanes/confirmation-2013-2014/overall.csv b/docs/evidence/nhanes/confirmation-2013-2014/overall.csv new file mode 100644 index 0000000..35e3966 --- /dev/null +++ b/docs/evidence/nhanes/confirmation-2013-2014/overall.csv @@ -0,0 +1,5 @@ +".outcome","mean_log_score","mae","rmse" +"bmi",-3.21610316468512,5.09323833879873,7.0317291934037 +"height_cm",-3.32678857571662,5.33013446161589,6.77165943465241 +"waist_cm",-4.10563605335855,12.1055741081313,15.7850691133404 +"weight_kg",-4.31171815101122,15.0553311384577,20.7004395689098 diff --git a/docs/evidence/nhanes/confirmation-2013-2014/receipt.json b/docs/evidence/nhanes/confirmation-2013-2014/receipt.json new file mode 100644 index 0000000..2bd48e3 --- /dev/null +++ b/docs/evidence/nhanes/confirmation-2013-2014/receipt.json @@ -0,0 +1,83 @@ +{ + "schema_version": "1.1.0", + "evidence_role": "untouched_confirmation", + "design_registration": { + "tracker": "mote", + "issue": "referent-32n", + "commit": "b1301a4e0426eb5b5a83935059053131331670d9", + "status": "frozen and published before confirmation execution" + }, + "estimand": "unweighted adult cohort transport, conditional predictive distribution", + "train_cohort": "NHANES 2015-2016", + "evaluation_cohort": "NHANES 2013-2014", + "age_range": [18, 79], + "outcomes": ["height_cm", "weight_kg", "bmi", "waist_cm"], + "train_rows": 5392, + "evaluation_population_rows": 5592, + "evaluation_rows": 2000, + "evaluation_sampling": "fixed-seed simple random sample without replacement (seed 20260824)", + "uncertainty": "total", + "n_draw": 1000, + "model_specification": { + "family": "SHASH", + "location": "~ s(age, k = 8) + sex + race_ethnicity", + "scale": "~ s(age, k = 5) + sex + race_ethnicity", + "skew": "~ 1", + "tail": "~ 1", + "seed": 20260824 + }, + "package_version": "0.1.0", + "r_version": "R version 4.5.1 (2025-06-13)", + "input_files": { + "demo_train": { + "url": "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/DEMO_I.XPT", + "sha256": "c9297c6c37ae8f78f29be9568fa2a03cf3b112616a39afee04030fc775a66a0d", + "bytes": 3756480 + }, + "body_train": { + "url": "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/BMX_I.XPT", + "sha256": "d31da84e14212b4e58e8340598a5b8e2144fac83333563e966eb1b332e4141d6", + "bytes": 1989600 + }, + "demo_test": { + "url": "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DEMO_H.XPT", + "sha256": "f8f0cbb3085a323d4cde22349b164878fea1e64dbc404e65b5815c7816b547d7", + "bytes": 3833200 + }, + "body_test": { + "url": "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BMX_H.XPT", + "sha256": "fd5e9fc6e6aab0a4aee6e699f51497bbc9b62101f7f43aee924c473e38fd9442", + "bytes": 2045520 + } + }, + "output_files": { + "overall": { + "sha256": "f312f5a08e90222cf14c42fc2fb81a9d4fa87d8ea3d90f7efd4abd140441f0d7", + "rows": 4, + "columns": [".outcome", "mean_log_score", "mae", "rmse"] + }, + "marginal": { + "sha256": "5a18b70f1b319d90f203fd454d88cf47722cc7f3e7c06801f0664329c619423a", + "rows": 4, + "columns": [".outcome", "n", "mean_z", "var_z", "skew_z", "excess_kurtosis_z", "shapiro_w", "mace", "cover_50", "cover_80", "cover_90", "cover_95", "cover_99"] + }, + "conditional": { + "sha256": "3a047360d75cad5c6cc0639ab4db5c9e1dbfbca416ba9ca9a7c4506b97696082", + "rows": 36, + "columns": [".outcome", "covariate", "level", "n", "location_drift", "location_se", "location_p", "scale_drift", "scale_se", "scale_p"] + }, + "tail": { + "sha256": "878f58d5e5139871b746a0846495e683493025fe1e6cf2dc0a9ab4fcf195ad69", + "rows": 16, + "columns": [".outcome", "tail_level", "expected", "observed", "n", "se"] + }, + "gate": { + "sha256": "1fb1263b44a490409093cf4e835818a13a2cef2fe7de655ca9730880fb7ad0a7", + "rows": 1, + "columns": ["all_fits_valid", "held_out", "minimum_outcome_n", "complete_evidence", "calibration_pass", "pass"] + } + }, + "all_fits_valid": true, + "calibration_pass": true, + "pass": true +} diff --git a/docs/evidence/nhanes/confirmation-2013-2014/tail.csv b/docs/evidence/nhanes/confirmation-2013-2014/tail.csv new file mode 100644 index 0000000..89acc80 --- /dev/null +++ b/docs/evidence/nhanes/confirmation-2013-2014/tail.csv @@ -0,0 +1,17 @@ +".outcome","tail_level","expected","observed","n","se" +"bmi",0.005,0.005,0.00659229208924949,1972,0.00158833858878283 +"bmi",0.01,0.01,0.0121703853955375,1972,0.00224059902161436 +"bmi",0.025,0.025,0.0238336713995943,1972,0.00351575705465307 +"bmi",0.05,0.05,0.0446247464503043,1972,0.00490787337808696 +"height_cm",0.005,0.005,0.00759109311740891,1976,0.00158673014422973 +"height_cm",0.01,0.01,0.0116396761133603,1976,0.00223833005999783 +"height_cm",0.025,0.025,0.0298582995951417,1976,0.00351219679343135 +"height_cm",0.05,0.05,0.055668016194332,1976,0.0049029033784546 +"waist_cm",0.005,0.005,0.00317124735729387,1892,0.00162157102848529 +"waist_cm",0.01,0.01,0.00634249471458774,1892,0.00228747855498907 +"waist_cm",0.025,0.025,0.0227272727272727,1892,0.00358931642363923 +"waist_cm",0.05,0.05,0.0480972515856237,1892,0.00501055967385312 +"weight_kg",0.005,0.005,0.00758725341426404,1977,0.00158632879601432 +"weight_kg",0.01,0.01,0.0101163378856854,1977,0.00223776389581525 +"weight_kg",0.025,0.025,0.0278199291856348,1977,0.00351130841684109 +"weight_kg",0.05,0.05,0.0480526049570056,1977,0.00490166323593357 diff --git a/docs/evidence/nhanes/receipt.json b/docs/evidence/nhanes/receipt.json index f2f13bd..148c40d 100644 --- a/docs/evidence/nhanes/receipt.json +++ b/docs/evidence/nhanes/receipt.json @@ -1,5 +1,10 @@ { - "schema_version": "1.0.0", + "schema_version": "1.1.0", + "evidence_role": "post_hoc_model_development", + "design_registration": { + "status": "post-hoc model development", + "reason": "the earlier 2017-2018 conditional-scale failure informed the demographic scale terms before this rerun" + }, "estimand": "unweighted adult cohort transport, conditional predictive distribution", "train_cohort": "NHANES 2015-2016", "evaluation_cohort": "NHANES 2017-2018", @@ -11,6 +16,14 @@ "evaluation_sampling": "fixed-seed simple random sample without replacement (seed 20260824)", "uncertainty": "total", "n_draw": 1000, + "model_specification": { + "family": "SHASH", + "location": "~ s(age, k = 8) + sex + race_ethnicity", + "scale": "~ s(age, k = 5) + sex + race_ethnicity", + "skew": "~ 1", + "tail": "~ 1", + "seed": 20260824 + }, "package_version": "0.1.0", "r_version": "R version 4.5.1 (2025-06-13)", "input_files": { @@ -38,23 +51,28 @@ "output_files": { "overall": { "sha256": "23b4f9f8e6ed7321e44e738c7f295c2434365e5d7a2f9fa59fa3bcd84cf70d6a", - "rows": 4 + "rows": 4, + "columns": [".outcome", "mean_log_score", "mae", "rmse"] }, "marginal": { "sha256": "216132669837ce683e53a2098f938f0f78134acdff89bbd3be4d24c115ab9c09", - "rows": 4 + "rows": 4, + "columns": [".outcome", "n", "mean_z", "var_z", "skew_z", "excess_kurtosis_z", "shapiro_w", "mace", "cover_50", "cover_80", "cover_90", "cover_95", "cover_99"] }, "conditional": { "sha256": "d3491d7ff4e333b54e729e37515831574b146d7ffc76c5c75f516393be43ffdd", - "rows": 36 + "rows": 36, + "columns": [".outcome", "covariate", "level", "n", "location_drift", "location_se", "location_p", "scale_drift", "scale_se", "scale_p"] }, "tail": { "sha256": "e1cda11c53661300e767d59202247fe64243a24969c36d11a8f132df1e770d77", - "rows": 16 + "rows": 16, + "columns": [".outcome", "tail_level", "expected", "observed", "n", "se"] }, "gate": { "sha256": "e4bd4592f88ba9841eedc5f8ed956ebdcf80f2de4fba4ccc6f4064ca5acc07a7", - "rows": 1 + "rows": 1, + "columns": ["all_fits_valid", "held_out", "minimum_outcome_n", "complete_evidence", "calibration_pass", "pass"] } }, "all_fits_valid": true, diff --git a/docs/evidence/pcntoolkit/v1.3.0/README.md b/docs/evidence/pcntoolkit/v1.3.0/README.md index 8b12064..1706ee1 100644 --- a/docs/evidence/pcntoolkit/v1.3.0/README.md +++ b/docs/evidence/pcntoolkit/v1.3.0/README.md @@ -13,10 +13,13 @@ equivalence. - `release_generation_receipt.json` and `release_matrix_receipt.json` bind the comparator warnings, dependencies, seeds, row/file identities, package version, and release verdict. -- `hbr_receipt.json`, `hbr_convergence.csv`, `site_comparison.csv`, - `adaptation_selection.csv`, `adaptation_selection_summary.csv`, and - `site_referent_receipt.json` retain the HBR/transport result and the - observed-site-only adaptation-prior selection. +- `site_data.csv`, `pcntoolkit_predictions.csv`, + `pcntoolkit_site_summary.csv`, `hbr_convergence.csv`, and + `hbr_divergences.csv` retain the raw HBR evidence bound by + `hbr_receipt.json`. +- `referent_predictions.csv`, `site_comparison.csv`, + `adaptation_selection.csv`, and `adaptation_selection_summary.csv` retain the + site evidence bound by `site_referent_receipt.json`. The exact and converted distribution lanes pass their strict semantic gates. Five deliberately matched fitted scenarios pass all prediction, scale, Z, and @@ -43,6 +46,14 @@ were chosen by leave-one-observed-site-out conditional log score using only MACE 0.0267, mean Z 0.0563, and variance Z 1.051. A pooled average is never used to override a site result. +The receipts do not establish their own verdicts. Every retained CSV is bound +by SHA-256, row count, and exact column schema; the validator independently +recomputes row identities, per-draw divergence counts, R-hat/ESS summaries, +prediction summaries, adaptation-prior selection, and all ten per-site gates. +The two transfer-stage parameters with undefined R-hat are exactly constant +posterior rows (`sd = 0`), are retained and counted explicitly, and do not enter +the maximum finite R-hat calculation. + Regenerate the fixture-derived tables with: ```sh @@ -52,3 +63,12 @@ Rscript tools/pcntoolkit/render_evidence.R The release matrix, HBR convergence, and site-transfer receipts are generated by their dedicated pinned workflows in `tools/pcntoolkit`; their JSON receipts record the exact commands, environments, seeds, and file hashes. + +Validate the retained HBR/site bundle with: + +```sh +Rscript tools/pcntoolkit/validate_hbr_site_evidence.R \ + docs/evidence/pcntoolkit/v1.3.0 \ + docs/evidence/pcntoolkit/v1.3.0 \ + hbr_receipt.json site_referent_receipt.json +``` diff --git a/docs/evidence/pcntoolkit/v1.3.0/hbr_divergences.csv b/docs/evidence/pcntoolkit/v1.3.0/hbr_divergences.csv new file mode 100644 index 0000000..7ea8420 --- /dev/null +++ b/docs/evidence/pcntoolkit/v1.3.0/hbr_divergences.csv @@ -0,0 +1,12001 @@ +stage,chain,draw,diverging +base,0,0,False +base,0,1,False +base,0,2,False +base,0,3,False +base,0,4,False +base,0,5,False +base,0,6,False +base,0,7,False +base,0,8,False +base,0,9,False +base,0,10,False +base,0,11,False +base,0,12,False +base,0,13,False +base,0,14,False +base,0,15,False +base,0,16,False +base,0,17,False +base,0,18,False +base,0,19,False +base,0,20,False +base,0,21,False +base,0,22,False +base,0,23,False +base,0,24,False +base,0,25,False +base,0,26,False +base,0,27,False +base,0,28,False +base,0,29,False +base,0,30,False +base,0,31,False +base,0,32,False +base,0,33,False +base,0,34,False +base,0,35,False +base,0,36,False +base,0,37,False +base,0,38,False +base,0,39,False 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a/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json +++ b/docs/evidence/pcntoolkit/v1.3.0/site_referent_receipt.json @@ -1,7 +1,66 @@ { + "schema_version": "1.1.0", "estimand": "separate adaptation and transfer policies; no rowwise parity claim", "referent_version": "0.1.0", "r_version": "R version 4.5.1 (2025-06-13)", + "gate_thresholds": { + "coverage90_floor": 0.8, + "mace_ceiling": 0.08, + "mean_z_absolute_ceiling": 0.35 + }, + "input_files": { + "site_data.csv": { + "sha256": "261346b98fea033d9e22bc17f842017c1c1240f98308be2bef2fd46b9ee9b951", + "rows": 420, + "columns": ["row_id", "site", "split", "x", "y"] + }, + "pcntoolkit_predictions.csv": { + "sha256": "336ca1d95922303e7ebf3814e0ec4327aafe6ad045a9f1973bb1d33ddf577cad", + "rows": 195, + "columns": ["lane", "row_id", "site", "observed", "reported_mean_draw_z", "reported_mean_draw_log_density", "reported_mean_draw_q05", "reported_mean_draw_q50", "reported_mean_draw_q95", "mixture_centile", "mixture_z", "mixture_log_density", "mixture_q05", "mixture_q50", "mixture_q95"] + }, + "pcntoolkit_site_summary.csv": { + "sha256": "9cc6537a1fbb69273832ef333e29c4a4e92467fdb2ac3735765e16d5a459ca1f", + "rows": 5, + "columns": ["method", "lane", "site", "n", "mean_log_score", "median_rmse", "coverage90", "mace", "mean_z", "var_z", "coverage90_se"] + }, + "hbr_convergence.csv": { + "sha256": "2814e4c260ef7898e7edf44888ac0a61ae84197c1a7208465ac4c69926821190", + "rows": 53, + "columns": ["stage", "parameter", "mean", "sd", "hdi_3%", "hdi_97%", "mcse_mean", "mcse_sd", "ess_bulk", "ess_tail", "r_hat"] + }, + "hbr_divergences.csv": { + "sha256": "72f5f620d51ec7a2bc60bad28f982396e41eedda0a9c1ef6913275f7d8c61124", + "rows": 12000, + "columns": ["stage", "chain", "draw", "diverging"] + }, + "receipt.json": { + "sha256": "4cf5c7355d4436972106543f9253c22743879bdbee585abd9e8d86800c7b5589", + "bytes": 3272 + } + }, + "output_files": { + "referent_predictions.csv": { + "sha256": "cbec25bf24e542aabc430a296bb593fd9510e0d75cc6ec40af739c718220b34c", + "rows": 195, + "columns": ["method", "lane", "row_id", "site", "observed", "centile", "log_density", "q05", "q50", "q95"] + }, + "site_comparison.csv": { + "sha256": "868d5ca8923ac4d3980a69c9898f5c86ce58ae9fac32d4a85b4bc1a9ef9ae2ea", + "rows": 10, + "columns": ["method", "lane", "site", "n", "mean_log_score", "median_rmse", "coverage90", "mace", "mean_z", "var_z", "coverage90_se", "pass"] + }, + "adaptation_selection.csv": { + "sha256": "a5d80752bd930e043850d799904ae0007eac71551dd6dc2a1e6b71c41a170875", + "rows": 64, + "columns": ["site", "adaptation_n", "validation_n", "location_prior_n", "scale_prior_n", "mean_log_score"] + }, + "adaptation_selection_summary.csv": { + "sha256": "adce786fbd34840086af9cac69c22db50df8576affa8c697198c857109da2ac7", + "rows": 16, + "columns": ["location_prior_n", "scale_prior_n", "mean_log_score", "validated_sites", "selected"] + } + }, "all_sites_pass": true, "failed_sites": {}, "adaptation_prior_selection": { @@ -29,10 +88,6 @@ "selected": { "location_prior_n": 0, "scale_prior_n": 25 - }, - "files": { - "adaptation_selection.csv": "4e60f3776b01772e76ffccc098e9adf4", - "adaptation_selection_summary.csv": "c49a6d0326a0e1f715605c634e4aad9a" } }, "referent_adaptation": { diff --git a/inst/pcntoolkit/run_hbr_site_evidence.py b/inst/pcntoolkit/run_hbr_site_evidence.py index 52a0414..97a92cb 100644 --- a/inst/pcntoolkit/run_hbr_site_evidence.py +++ b/inst/pcntoolkit/run_hbr_site_evidence.py @@ -21,6 +21,7 @@ from __future__ import annotations import argparse +import hashlib import importlib.metadata import json from pathlib import Path @@ -101,26 +102,47 @@ def norm_data(name: str, frame: pd.DataFrame) -> NormData: ) -def convergence(model: NormativeModel, stage: str) -> tuple[pd.DataFrame, dict[str, object]]: +def convergence( + model: NormativeModel, stage: str +) -> tuple[pd.DataFrame, pd.DataFrame, dict[str, object]]: idata = model["y"].idata # The fitted idata does not contain the temporary per-subject variables # created during prediction, so the full posterior is the convergence set. summary = az.summary(idata, fmt="wide") summary.insert(0, "parameter", summary.index.astype(str)) summary.insert(0, "stage", stage) - divergences = int(idata.sample_stats["diverging"].sum().item()) + divergence_table = ( + idata.sample_stats["diverging"] + .to_dataframe(name="diverging") + .reset_index()[["chain", "draw", "diverging"]] + ) + divergence_table.insert(0, "stage", stage) + divergences = int(divergence_table["diverging"].sum()) max_rhat = float(np.nanmax(summary["r_hat"].to_numpy())) min_bulk = float(np.nanmin(summary["ess_bulk"].to_numpy())) min_tail = float(np.nanmin(summary["ess_tail"].to_numpy())) + undefined_rhat_constant_rows = int( + (summary["r_hat"].isna() & summary["sd"].eq(0)).sum() + ) receipt = { "stage": stage, "divergences": divergences, "max_rhat": max_rhat, "min_ess_bulk": min_bulk, "min_ess_tail": min_tail, + "undefined_rhat_constant_rows": undefined_rhat_constant_rows, "pass": bool(divergences == 0 and max_rhat <= 1.01 and min_bulk >= 400 and min_tail >= 400), } - return summary.reset_index(drop=True), receipt + return summary.reset_index(drop=True), divergence_table, receipt + + +def csv_receipt(path: Path) -> dict[str, object]: + frame = pd.read_csv(path) + return { + "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + "rows": len(frame), + "columns": list(frame.columns), + } def posterior_normal_draws(model: NormativeModel, frame: pd.DataFrame) -> tuple[np.ndarray, np.ndarray]: @@ -220,6 +242,7 @@ def site_summary(predictions: pd.DataFrame) -> pd.DataFrame: "mace": np.mean([abs(q - np.mean(u <= q)) for q in grid]), "mean_z": group["mixture_z"].mean(), "var_z": group["mixture_z"].var(ddof=1), + "coverage90_se": np.sqrt(0.9 * 0.1 / len(group)), } ) return pd.DataFrame(rows) @@ -266,7 +289,7 @@ def main() -> None: target_accept=args.target_accept, random_seed=args.seed, ) - base_convergence, base_receipt = convergence(base, "base") + base_convergence, base_divergences, base_receipt = convergence(base, "base") observed_predictions = predict_with_mixture( base, observed_test, "observed_site", outcome_mean, outcome_sd ) @@ -285,18 +308,40 @@ def main() -> None: target_accept=args.target_accept, random_seed=args.seed + 1, ) - transfer_convergence, transfer_receipt = convergence(transferred, "transfer") + transfer_convergence, transfer_divergences, transfer_receipt = convergence( + transferred, "transfer" + ) transport_predictions = predict_with_mixture( transferred, transport_test, "transferred_site", outcome_mean, outcome_sd ) predictions = pd.concat([observed_predictions, transport_predictions], ignore_index=True) convergence_table = pd.concat([base_convergence, transfer_convergence], ignore_index=True) + divergence_table = pd.concat( + [base_divergences, transfer_divergences], ignore_index=True + ) - data.to_csv(args.output / "site_data.csv", index=False, float_format="%.17g") - predictions.to_csv(args.output / "pcntoolkit_predictions.csv", index=False, float_format="%.17g") - site_summary(predictions).to_csv(args.output / "pcntoolkit_site_summary.csv", index=False, float_format="%.17g") - convergence_table.to_csv(args.output / "hbr_convergence.csv", index=False, float_format="%.17g") + output_paths = { + "site_data.csv": args.output / "site_data.csv", + "pcntoolkit_predictions.csv": args.output / "pcntoolkit_predictions.csv", + "pcntoolkit_site_summary.csv": args.output / "pcntoolkit_site_summary.csv", + "hbr_convergence.csv": args.output / "hbr_convergence.csv", + "hbr_divergences.csv": args.output / "hbr_divergences.csv", + } + data.to_csv(output_paths["site_data.csv"], index=False, float_format="%.17g") + predictions.to_csv( + output_paths["pcntoolkit_predictions.csv"], index=False, float_format="%.17g" + ) + site_summary(predictions).to_csv( + output_paths["pcntoolkit_site_summary.csv"], + index=False, + float_format="%.17g", + ) + convergence_table.to_csv( + output_paths["hbr_convergence.csv"], index=False, float_format="%.17g" + ) + divergence_table.to_csv(output_paths["hbr_divergences.csv"], index=False) receipt = { + "schema_version": "1.1.0", "comparator": "pcntoolkit", "version": importlib.metadata.version("pcntoolkit"), "draws": args.draws, @@ -324,8 +369,14 @@ def main() -> None: "min_ess_tail": 400, "divergences": 0, }, + "convergence_policy": { + "undefined_rhat": ( + "allowed only for retained posterior-summary rows with sd exactly zero" + ) + }, "stages": [base_receipt, transfer_receipt], "all_stages_pass": bool(base_receipt["pass"] and transfer_receipt["pass"]), + "files": {name: csv_receipt(path) for name, path in output_paths.items()}, } (args.output / "receipt.json").write_text( json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8" diff --git a/inst/pcntoolkit/validate_hbr_site_evidence.R b/inst/pcntoolkit/validate_hbr_site_evidence.R new file mode 100644 index 0000000..ae2226d --- /dev/null +++ b/inst/pcntoolkit/validate_hbr_site_evidence.R @@ -0,0 +1,675 @@ +# Independently validate retained HBR and site-transfer evidence. Receipt +# booleans are never accepted as verdicts: hashes, schemas, row identities, +# convergence summaries, site metrics, prior selection, and gates are all +# recomputed from the retained tables. + +pcn_evidence_sha256 <- function(path) { + if (!requireNamespace("digest", quietly = TRUE)) { + stop("digest is required to validate evidence", call. = FALSE) + } + digest::digest(file = path, algo = "sha256") +} + +pcn_evidence_read_csv <- function(path) { + if (!file.exists(path)) { + stop("missing evidence file: ", path, call. = FALSE) + } + utils::read.csv( + path, check.names = FALSE, stringsAsFactors = FALSE, + na.strings = c("NA", "") + ) +} + +pcn_evidence_file_receipt <- function(path) { + dat <- pcn_evidence_read_csv(path) + list( + sha256 = pcn_evidence_sha256(path), + rows = nrow(dat), + columns = names(dat) + ) +} + +pcn_evidence_blob_receipt <- function(path) { + if (!file.exists(path)) { + stop("missing evidence file: ", path, call. = FALSE) + } + list( + sha256 = pcn_evidence_sha256(path), + bytes = unname(file.info(path)$size) + ) +} + +pcn_evidence_validate_files <- function(root, records, expected, + aliases = character()) { + if (!setequal(names(records), expected)) { + stop("evidence receipt has an unexpected file set", call. = FALSE) + } + for (name in expected) { + filename <- if (name %in% names(aliases)) aliases[[name]] else name + path <- file.path(root, filename) + recorded <- records[[name]] + if (!is.null(recorded$bytes)) { + actual <- pcn_evidence_blob_receipt(path) + valid <- identical(actual$sha256, recorded$sha256) && + identical(actual$bytes, as.numeric(recorded$bytes)) + } else { + actual <- pcn_evidence_file_receipt(path) + valid <- identical(actual$sha256, recorded$sha256) && + identical(actual$rows, as.integer(recorded$rows)) && + identical(actual$columns, unlist(recorded$columns, use.names = FALSE)) + } + if (!valid) { + stop("evidence file receipt mismatch: ", name, call. = FALSE) + } + } + invisible(TRUE) +} + +pcn_evidence_assert_columns <- function(dat, required, label) { + if (!all(required %in% names(dat))) { + stop(label, " is missing required columns", call. = FALSE) + } +} + +pcn_evidence_assert_exact_columns <- function(dat, expected, label) { + if (!identical(names(dat), expected)) { + stop(label, " does not match the registered schema", call. = FALSE) + } +} + +pcn_evidence_assert_finite <- function(dat, columns, label) { + if (any(!vapply(dat[columns], is.numeric, logical(1))) || + any(!is.finite(as.matrix(dat[columns])))) { + stop(label, " contains non-finite numeric evidence", call. = FALSE) + } +} + +pcn_evidence_assert_frame <- function(actual, expected, keys, label, + tolerance = 1e-11) { + if (!setequal(names(actual), names(expected))) { + stop(label, " schema does not match recomputed evidence", call. = FALSE) + } + actual <- actual[, names(expected), drop = FALSE] + order_rows <- function(dat) { + values <- unname(lapply(dat[keys], as.character)) + do.call(order, c(values, list(na.last = TRUE))) + } + actual <- actual[order_rows(actual), , drop = FALSE] + expected <- expected[order_rows(expected), , drop = FALSE] + rownames(actual) <- rownames(expected) <- NULL + if (nrow(actual) != nrow(expected)) { + stop(label, " row count does not match recomputed evidence", call. = FALSE) + } + for (name in names(expected)) { + a <- actual[[name]] + b <- expected[[name]] + if (!identical(is.na(a), is.na(b))) { + stop(label, " NA pattern differs in ", name, call. = FALSE) + } + keep <- !is.na(a) + if (is.numeric(a) && is.numeric(b)) { + scale <- pmax(1, abs(a[keep]), abs(b[keep])) + if (any(abs(a[keep] - b[keep]) > tolerance * scale)) { + stop(label, " differs from recomputed values in ", name, call. = FALSE) + } + } else if (!identical(as.character(a[keep]), as.character(b[keep]))) { + stop(label, " differs from recomputed values in ", name, call. = FALSE) + } + } + invisible(TRUE) +} + +pcn_evidence_mace <- function(centile) { + grid <- c(0.05, 0.25, 0.5, 0.75, 0.95) + mean(vapply(grid, function(p) abs(p - mean(centile <= p)), numeric(1))) +} + +pcn_evidence_site_summary <- function(prediction, method) { + required <- c( + "lane", "site", "observed", "centile", "log_density", "q05", "q50", "q95" + ) + pcn_evidence_assert_columns(prediction, required, "prediction table") + numeric <- c("observed", "centile", "log_density", "q05", "q50", "q95") + pcn_evidence_assert_finite(prediction, numeric, "prediction table") + if (any(prediction$centile <= 0 | prediction$centile >= 1)) { + stop("prediction centiles must lie strictly inside (0, 1)", call. = FALSE) + } + groups <- split( + seq_len(nrow(prediction)), + interaction(prediction$lane, prediction$site, drop = TRUE, lex.order = TRUE) + ) + rows <- lapply(groups, function(index) { + d <- prediction[index, , drop = FALSE] + z <- stats::qnorm(d$centile) + data.frame( + method = method, + lane = unique(d$lane), + site = unique(d$site), + n = nrow(d), + mean_log_score = mean(d$log_density), + median_rmse = sqrt(mean((d$observed - d$q50)^2)), + coverage90 = mean(d$observed >= d$q05 & d$observed <= d$q95), + mace = pcn_evidence_mace(d$centile), + mean_z = mean(z), + var_z = stats::var(z), + coverage90_se = sqrt(0.9 * 0.1 / nrow(d)), + stringsAsFactors = FALSE + ) + }) + out <- do.call(rbind, rows) + rownames(out) <- NULL + out +} + +pcn_evidence_site_gate <- function(summary) { + summary$pass <- summary$coverage90 >= 0.80 & summary$mace <= 0.08 & + abs(summary$mean_z) <= 0.35 + summary +} + +pcn_evidence_as_logical <- function(x, label) { + if (is.logical(x)) return(x) + if (is.numeric(x) && all(x %in% c(0, 1))) return(as.logical(x)) + value <- tolower(as.character(x)) + if (!all(value %in% c("true", "false"))) { + stop(label, " is not a valid logical column", call. = FALSE) + } + value == "true" +} + +pcn_validate_hbr_evidence <- function(root, receipt_file = "receipt.json") { + if (!requireNamespace("jsonlite", quietly = TRUE)) { + stop("jsonlite is required to validate evidence", call. = FALSE) + } + receipt_path <- file.path(root, receipt_file) + if (!file.exists(receipt_path)) { + stop("missing HBR receipt: ", receipt_path, call. = FALSE) + } + receipt <- jsonlite::read_json(receipt_path, simplifyVector = FALSE) + thresholds <- receipt$convergence_thresholds + undefined_rhat_policy <- paste( + "allowed only for retained posterior-summary rows with sd exactly zero" + ) + aggregation <- receipt$aggregation + if (!identical(receipt$schema_version, "1.1.0") || + !identical(receipt$comparator, "pcntoolkit") || + !identical(receipt$version, "1.3.0") || + !identical(as.integer(receipt$draws), 1500L) || + !identical(as.integer(receipt$tune), 1000L) || + !identical(as.integer(receipt$chains), 4L) || + !identical(as.integer(receipt$cores), 4L) || + !identical(receipt$nuts_sampler, "nutpie") || + !isTRUE(all.equal(receipt$target_accept, 0.99)) || + !identical(as.integer(receipt$requested_seed), 20260823L) || + !identical(as.integer(receipt$sampling_seeds$base), 20260823L) || + !identical(as.integer(receipt$sampling_seeds$transfer), 20260824L) || + !identical(receipt$hbr_public_random_seed_argument, FALSE) || + !isTRUE(all.equal(thresholds$max_rhat, 1.01)) || + !identical(as.integer(thresholds$min_ess_bulk), 400L) || + !identical(as.integer(thresholds$min_ess_tail), 400L) || + !identical(as.integer(thresholds$divergences), 0L) || + !identical( + receipt$convergence_policy$undefined_rhat, undefined_rhat_policy + ) || + !setequal( + names(aggregation), + c("reported_z", "reported_quantile", "reported_logp", "mixture_outputs") + ) || + !identical(aggregation$reported_z, "mean of draw-specific z") || + !identical( + aggregation$reported_quantile, "mean of draw-specific quantiles" + ) || + !identical( + aggregation$reported_logp, "mean of draw-specific log densities" + ) || + !identical( + aggregation$mixture_outputs, + "CDF and density averaged over posterior draws, then transformed" + )) { + stop("HBR receipt does not match the registered contract", call. = FALSE) + } + expected_files <- c( + "site_data.csv", "pcntoolkit_predictions.csv", + "pcntoolkit_site_summary.csv", "hbr_convergence.csv", + "hbr_divergences.csv" + ) + pcn_evidence_validate_files(root, receipt$files, expected_files) + + convergence <- pcn_evidence_read_csv(file.path(root, "hbr_convergence.csv")) + divergences <- pcn_evidence_read_csv(file.path(root, "hbr_divergences.csv")) + pcn_evidence_assert_exact_columns( + convergence, + c( + "stage", "parameter", "mean", "sd", "hdi_3%", "hdi_97%", + "mcse_mean", "mcse_sd", "ess_bulk", "ess_tail", "r_hat" + ), + "HBR convergence table" + ) + pcn_evidence_assert_exact_columns( + divergences, c("stage", "chain", "draw", "diverging"), + "HBR divergence table" + ) + pcn_evidence_assert_finite( + convergence, c("sd", "ess_bulk", "ess_tail"), "HBR convergence table" + ) + pcn_evidence_assert_finite( + divergences, c("chain", "draw"), "HBR divergence table" + ) + if (anyDuplicated(convergence[c("stage", "parameter")]) || + anyDuplicated(divergences[c("stage", "chain", "draw")])) { + stop("HBR diagnostic row identities are duplicated", call. = FALSE) + } + undefined_rhat <- is.na(convergence$r_hat) + if (any(is.infinite(convergence$r_hat)) || + any(undefined_rhat & convergence$sd != 0)) { + stop("HBR R-hat is undefined outside an exactly constant row", + call. = FALSE) + } + divergences$diverging <- pcn_evidence_as_logical( + divergences$diverging, "HBR diverging" + ) + stages <- c("base", "transfer") + if (!setequal(unique(convergence$stage), stages) || + !setequal(unique(divergences$stage), stages)) { + stop("HBR diagnostic stages are incomplete", call. = FALSE) + } + expected_draws <- expand.grid( + stage = stages, + chain = 0:3, + draw = 0:1499, + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = FALSE + ) + pcn_evidence_assert_frame( + divergences[c("stage", "chain", "draw")], expected_draws, + c("stage", "chain", "draw"), "HBR divergence draw identities" + ) + recomputed_stages <- do.call(rbind, lapply(stages, function(stage) { + conv <- convergence[convergence$stage == stage, , drop = FALSE] + div <- divergences[divergences$stage == stage, , drop = FALSE] + if (!any(is.finite(conv$r_hat))) { + stop("HBR stage has no finite R-hat values: ", stage, call. = FALSE) + } + values <- data.frame( + stage = stage, + divergences = sum(div$diverging), + max_rhat = max(conv$r_hat, na.rm = TRUE), + min_ess_bulk = min(conv$ess_bulk), + min_ess_tail = min(conv$ess_tail), + undefined_rhat_constant_rows = sum(is.na(conv$r_hat) & conv$sd == 0), + stringsAsFactors = FALSE + ) + values$pass <- values$divergences == 0L && values$max_rhat <= 1.01 && + values$min_ess_bulk >= 400 && values$min_ess_tail >= 400 + values + })) + receipt_stages <- do.call(rbind, lapply(receipt$stages, function(stage) { + data.frame( + stage = stage$stage, + divergences = as.integer(stage$divergences), + max_rhat = stage$max_rhat, + min_ess_bulk = stage$min_ess_bulk, + min_ess_tail = stage$min_ess_tail, + undefined_rhat_constant_rows = as.integer( + stage$undefined_rhat_constant_rows + ), + pass = isTRUE(stage$pass), + stringsAsFactors = FALSE + ) + })) + pcn_evidence_assert_frame( + receipt_stages, recomputed_stages, "stage", "HBR stage receipt" + ) + if (!identical(isTRUE(receipt$all_stages_pass), all(recomputed_stages$pass))) { + stop("HBR receipt verdict does not match recomputed diagnostics", call. = FALSE) + } + + data <- pcn_evidence_read_csv(file.path(root, "site_data.csv")) + prediction <- pcn_evidence_read_csv(file.path(root, "pcntoolkit_predictions.csv")) + pcn_evidence_assert_exact_columns( + data, c("row_id", "site", "split", "x", "y"), "site data" + ) + pcn_evidence_assert_exact_columns( + prediction, + c( + "lane", "row_id", "site", "observed", + "reported_mean_draw_z", "reported_mean_draw_log_density", + "reported_mean_draw_q05", "reported_mean_draw_q50", + "reported_mean_draw_q95", "mixture_centile", "mixture_z", + "mixture_log_density", "mixture_q05", "mixture_q50", "mixture_q95" + ), + "PCNtoolkit prediction table" + ) + pcn_evidence_assert_finite(data, c("x", "y"), "site data") + pcn_evidence_assert_finite( + prediction, + c( + "observed", "reported_mean_draw_z", "reported_mean_draw_log_density", + "reported_mean_draw_q05", "reported_mean_draw_q50", + "reported_mean_draw_q95", "mixture_centile", "mixture_z", + "mixture_log_density", "mixture_q05", "mixture_q50", "mixture_q95" + ), + "PCNtoolkit prediction table" + ) + if (anyDuplicated(data$row_id) || anyDuplicated(prediction$row_id)) { + stop("HBR evidence has duplicate row identities", call. = FALSE) + } + expected_structure <- data.frame( + site = c("site-1", "site-1", "site-2", "site-2", "site-3", "site-3", + "site-4", "site-4", "site-5", "site-5"), + split = rep( + c("reference_train", "observed_site_test"), 4L + ) |> c("adapt", "transport_test"), + n = c(rep(c(50L, 30L), 4L), 25L, 75L), + stringsAsFactors = FALSE + ) + actual_structure <- stats::aggregate( + list(n = data$row_id), list(site = data$site, split = data$split), length + ) + pcn_evidence_assert_frame( + actual_structure, expected_structure, c("site", "split"), + "site data split structure" + ) + if (any(!grepl( + "^site-[1-5]-(reference_train|observed_site_test|adapt|transport_test)-[0-9]{4}$", + data$row_id + ))) { + stop("site data row identities do not match the registered design", + call. = FALSE) + } + evaluation <- data[data$split %in% c("observed_site_test", "transport_test"), ] + evaluation$lane <- ifelse( + evaluation$split == "observed_site_test", "observed_site", "transferred_site" + ) + if (nrow(prediction) != nrow(evaluation) || + !setequal(prediction$row_id, evaluation$row_id)) { + stop("HBR predictions do not cover every retained evaluation row", + call. = FALSE) + } + evaluation <- evaluation[match(prediction$row_id, evaluation$row_id), ] + if (anyNA(evaluation$row_id) || + !identical(prediction$row_id, evaluation$row_id) || + !identical(prediction$site, evaluation$site) || + !identical(prediction$lane, evaluation$lane) || + any(abs(prediction$observed - evaluation$y) > 1e-12)) { + stop("HBR predictions are not bound to the retained site rows", call. = FALSE) + } + if (any(prediction$mixture_centile <= 0 | prediction$mixture_centile >= 1) || + any(prediction$mixture_q05 > prediction$mixture_q50) || + any(prediction$mixture_q50 > prediction$mixture_q95) || + any(abs( + prediction$mixture_z - stats::qnorm(prediction$mixture_centile) + ) > 1e-11 * pmax(1, abs(prediction$mixture_z)))) { + stop("PCNtoolkit mixture predictions violate distribution invariants", + call. = FALSE) + } + standardized <- data.frame( + lane = prediction$lane, + site = prediction$site, + observed = prediction$observed, + centile = prediction$mixture_centile, + log_density = prediction$mixture_log_density, + q05 = prediction$mixture_q05, + q50 = prediction$mixture_q50, + q95 = prediction$mixture_q95, + stringsAsFactors = FALSE + ) + recomputed_summary <- pcn_evidence_site_summary( + standardized, "pcntoolkit_hbr_mixture" + ) + recorded_summary <- pcn_evidence_read_csv( + file.path(root, "pcntoolkit_site_summary.csv") + ) + pcn_evidence_assert_frame( + recorded_summary, recomputed_summary, c("method", "lane", "site"), + "PCNtoolkit site summary" + ) + invisible(list( + receipt = receipt, + stages = recomputed_stages, + site_summary = recomputed_summary, + data = data + )) +} + +pcn_evidence_selection_summary <- function(validation) { + required <- c("site", "location_prior_n", "scale_prior_n", "mean_log_score") + pcn_evidence_assert_columns(validation, required, "adaptation selection table") + pcn_evidence_assert_finite( + validation, c("location_prior_n", "scale_prior_n", "mean_log_score"), + "adaptation selection table" + ) + if (anyDuplicated(validation[c("site", "location_prior_n", "scale_prior_n")])) { + stop("adaptation selection has duplicate site-candidate rows", call. = FALSE) + } + counts <- stats::aggregate( + site ~ location_prior_n + scale_prior_n, validation, + function(x) length(unique(x)) + ) + names(counts)[names(counts) == "site"] <- "validated_sites" + expected_sites <- length(unique(validation$site)) + if (any(counts$validated_sites != expected_sites)) { + stop("adaptation selection omits an observed site", call. = FALSE) + } + scores <- stats::aggregate( + mean_log_score ~ location_prior_n + scale_prior_n, validation, mean + ) + out <- merge( + scores, counts, by = c("location_prior_n", "scale_prior_n"), sort = FALSE + ) + out <- out[order(-out$mean_log_score, out$location_prior_n, out$scale_prior_n), ] + rownames(out) <- NULL + out$selected <- seq_len(nrow(out)) == 1L + out +} + +pcn_validate_site_evidence <- function(input_dir, output_dir, + hbr_receipt_file = "receipt.json", + site_receipt_file = "referent_receipt.json") { + hbr <- pcn_validate_hbr_evidence(input_dir, hbr_receipt_file) + receipt_path <- file.path(output_dir, site_receipt_file) + if (!file.exists(receipt_path)) { + stop("missing site receipt: ", receipt_path, call. = FALSE) + } + receipt <- jsonlite::read_json(receipt_path, simplifyVector = FALSE) + thresholds <- receipt$gate_thresholds + if (!identical(receipt$schema_version, "1.1.0") || + !identical( + receipt$estimand, + "separate adaptation and transfer policies; no rowwise parity claim" + ) || + !identical(receipt$referent_version, "0.1.0") || + !isTRUE(all.equal(thresholds$coverage90_floor, 0.80)) || + !isTRUE(all.equal(thresholds$mace_ceiling, 0.08)) || + !isTRUE(all.equal(thresholds$mean_z_absolute_ceiling, 0.35))) { + stop("site receipt does not match the registered contract", call. = FALSE) + } + hbr_files <- c( + "site_data.csv", "pcntoolkit_predictions.csv", + "pcntoolkit_site_summary.csv", "hbr_convergence.csv", + "hbr_divergences.csv", "receipt.json" + ) + pcn_evidence_validate_files( + input_dir, receipt$input_files, hbr_files, + aliases = stats::setNames(hbr_receipt_file, "receipt.json") + ) + output_files <- c( + "referent_predictions.csv", "site_comparison.csv", + "adaptation_selection.csv", "adaptation_selection_summary.csv" + ) + pcn_evidence_validate_files(output_dir, receipt$output_files, output_files) + + prediction <- pcn_evidence_read_csv( + file.path(output_dir, "referent_predictions.csv") + ) + pcn_evidence_assert_exact_columns( + prediction, + c("method", "lane", "row_id", "site", "observed", "centile", + "log_density", "q05", "q50", "q95"), + "Referent prediction table" + ) + pcn_evidence_assert_finite( + prediction, + c("observed", "centile", "log_density", "q05", "q50", "q95"), + "Referent prediction table" + ) + if (anyDuplicated(prediction$row_id) || + any(prediction$method != "referent_adaptation")) { + stop("Referent prediction identities are invalid", call. = FALSE) + } + evaluation <- hbr$data[ + hbr$data$split %in% c("observed_site_test", "transport_test"), + ] + evaluation$lane <- ifelse( + evaluation$split == "observed_site_test", "observed_site", "transferred_site" + ) + if (nrow(prediction) != nrow(evaluation) || + !setequal(prediction$row_id, evaluation$row_id)) { + stop("Referent predictions do not cover every retained evaluation row", + call. = FALSE) + } + evaluation <- evaluation[match(prediction$row_id, evaluation$row_id), ] + if (anyNA(evaluation$row_id) || + !identical(prediction$row_id, evaluation$row_id) || + !identical(prediction$site, evaluation$site) || + !identical(prediction$lane, evaluation$lane) || + any(abs(prediction$observed - evaluation$y) > 1e-12)) { + stop("Referent predictions are not bound to the retained site rows", call. = FALSE) + } + if (any(prediction$centile <= 0 | prediction$centile >= 1) || + any(prediction$q05 > prediction$q50) || + any(prediction$q50 > prediction$q95)) { + stop("Referent predictions violate distribution invariants", call. = FALSE) + } + ref_summary <- pcn_evidence_site_summary( + prediction, "referent_adaptation" + ) + recomputed_comparison <- pcn_evidence_site_gate(rbind( + ref_summary[, names(hbr$site_summary), drop = FALSE], + hbr$site_summary + )) + recorded_comparison <- pcn_evidence_read_csv( + file.path(output_dir, "site_comparison.csv") + ) + pcn_evidence_assert_frame( + recorded_comparison, recomputed_comparison, + c("method", "lane", "site"), "site comparison" + ) + + validation <- pcn_evidence_read_csv( + file.path(output_dir, "adaptation_selection.csv") + ) + pcn_evidence_assert_exact_columns( + validation, + c( + "site", "adaptation_n", "validation_n", "location_prior_n", + "scale_prior_n", "mean_log_score" + ), + "adaptation selection table" + ) + selection <- pcn_evidence_selection_summary(validation) + recorded_selection <- pcn_evidence_read_csv( + file.path(output_dir, "adaptation_selection_summary.csv") + ) + pcn_evidence_assert_frame( + recorded_selection, selection, c("location_prior_n", "scale_prior_n"), + "adaptation selection summary" + ) + reference_sites <- sort(unique(hbr$data$site[hbr$data$split == "reference_train"])) + expected_candidates <- expand.grid( + site = reference_sites, + location_prior_n = c(0, 2, 5, 10), + scale_prior_n = c(0, 5, 10, 25), + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = FALSE + ) + pcn_evidence_assert_frame( + validation[c("site", "location_prior_n", "scale_prior_n")], + expected_candidates, c("site", "location_prior_n", "scale_prior_n"), + "adaptation candidate grid" + ) + if (any(validation$adaptation_n != 25L) || + any(validation$validation_n != 25L) || + !identical( + receipt$adaptation_prior_selection$rule, + "leave-one-observed-site-out mean conditional log score" + )) { + stop("adaptation selection does not match the registered folds", + call. = FALSE) + } + if (!identical(sort(unique(validation$site)), reference_sites) || + !identical( + sort(unlist(receipt$adaptation_prior_selection$source_sites, use.names = FALSE)), + reference_sites + ) || + !identical(receipt$adaptation_prior_selection$source_split, "reference_train")) { + stop("adaptation selection is not restricted to observed reference sites", + call. = FALSE) + } + for (site in reference_sites) { + fold <- receipt$adaptation_prior_selection$fold_sizes[[site]] + if (!identical(as.integer(fold$adaptation), 25L) || + !identical(as.integer(fold$validation), 25L)) { + stop("adaptation fold-size receipt does not match retained evidence", + call. = FALSE) + } + } + selected <- selection[selection$selected, c("location_prior_n", "scale_prior_n")] + recorded_selected <- receipt$adaptation_prior_selection$selected + if (nrow(selected) != 1L || + !isTRUE(all.equal( + selected$location_prior_n[[1L]], recorded_selected$location_prior_n + )) || + !isTRUE(all.equal( + selected$scale_prior_n[[1L]], recorded_selected$scale_prior_n + ))) { + stop("adaptation-prior receipt does not match recomputed selection", + call. = FALSE) + } + if (!identical( + isTRUE(receipt$all_sites_pass), all(recomputed_comparison$pass) + )) { + stop("site receipt verdict does not match recomputed gates", call. = FALSE) + } + expected_failed <- split( + as.character(recomputed_comparison$site[!recomputed_comparison$pass]), + as.character(recomputed_comparison$method[!recomputed_comparison$pass]) + ) + recorded_failed <- lapply( + receipt$failed_sites, + function(x) sort(unlist(x, use.names = FALSE)) + ) + expected_failed <- lapply(expected_failed, sort) + if (!setequal(names(recorded_failed), names(expected_failed)) || + any(vapply(names(expected_failed), function(name) { + !identical(recorded_failed[[name]], expected_failed[[name]]) + }, logical(1)))) { + stop("site receipt failed-site list does not match recomputed gates", + call. = FALSE) + } + invisible(list( + hbr = hbr, + receipt = receipt, + comparison = recomputed_comparison, + selection = selection + )) +} + +if (sys.nframe() == 0L) { + args <- commandArgs(trailingOnly = TRUE) + if (length(args) < 2L || length(args) > 4L) { + stop(paste( + "usage: validate_hbr_site_evidence.R INPUT_DIR OUTPUT_DIR", + "[HBR_RECEIPT_FILE] [SITE_RECEIPT_FILE]" + ), call. = FALSE) + } + hbr_receipt <- if (length(args) >= 3L) args[[3L]] else "receipt.json" + site_receipt <- if (length(args) >= 4L) args[[4L]] else "referent_receipt.json" + validated <- pcn_validate_site_evidence( + args[[1L]], args[[2L]], hbr_receipt, site_receipt + ) + cat( + "validated HBR stages:", nrow(validated$hbr$stages), + "; validated site gates:", nrow(validated$comparison), "\n" + ) +} diff --git a/tests/testthat/test-pcntoolkit-hbr-site.R b/tests/testthat/test-pcntoolkit-hbr-site.R index fc6b287..f13b616 100644 --- a/tests/testthat/test-pcntoolkit-hbr-site.R +++ b/tests/testthat/test-pcntoolkit-hbr-site.R @@ -73,23 +73,155 @@ test_that("HBR release runner records mandatory convergence and aggregation fiel expect_match(text, '--target-accept", type=float, default=0.99', fixed = TRUE) expect_match(text, '"sampling_seeds"', fixed = TRUE) expect_match(text, "with_sampling_controls", fixed = TRUE) + expect_match(text, '"hbr_divergences.csv"', fixed = TRUE) + expect_match(text, '"sha256"', fixed = TRUE) expect_match(text, '"reported_z": "mean of draw-specific z"', fixed = TRUE) expect_match(text, '"mixture_outputs"', fixed = TRUE) expect_match(text, '"hbr_public_random_seed_argument": False', fixed = TRUE) }) -test_that("retained HBR and site receipts satisfy every registered gate", { +test_that("retained HBR and site artifacts recompute every registered gate", { root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) evidence <- file.path(root, "docs", "evidence", "pcntoolkit", "v1.3.0") hbr_path <- file.path(evidence, "hbr_receipt.json") site_path <- file.path(evidence, "site_referent_receipt.json") skip_if_not(all(file.exists(hbr_path, site_path)), "release receipts are not bundled") - hbr <- jsonlite::read_json(hbr_path, simplifyVector = TRUE) - site <- jsonlite::read_json(site_path, simplifyVector = TRUE) - expect_true(hbr$all_stages_pass) - expect_true(all(hbr$stages$divergences == 0L)) - expect_equal(hbr$target_accept, 0.99) - expect_true(site$all_sites_pass) - expect_identical(site$adaptation_prior_selection$source_split, "reference_train") - expect_equal(site$adaptation_prior_selection$selected$location_prior_n, 0) + validator_path <- system.file( + "pcntoolkit", "validate_hbr_site_evidence.R", package = "referent" + ) + validator <- new.env(parent = globalenv()) + sys.source(validator_path, envir = validator) + validated <- validator$pcn_validate_site_evidence( + evidence, evidence, "hbr_receipt.json", "site_referent_receipt.json" + ) + expect_true(all(validated$hbr$stages$pass)) + expect_true(all(validated$hbr$stages$divergences == 0L)) + expect_true(all(validated$comparison$pass)) + expect_equal( + validated$receipt$adaptation_prior_selection$selected$location_prior_n, 0 + ) +}) + +test_that("HBR and site validators reject drift, false verdicts, and omissions", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + evidence <- file.path(root, "docs", "evidence", "pcntoolkit", "v1.3.0") + validator_path <- system.file( + "pcntoolkit", "validate_hbr_site_evidence.R", package = "referent" + ) + validator <- new.env(parent = globalenv()) + sys.source(validator_path, envir = validator) + files <- c( + "site_data.csv", "pcntoolkit_predictions.csv", "pcntoolkit_site_summary.csv", + "hbr_convergence.csv", "hbr_divergences.csv", "hbr_receipt.json", + "referent_predictions.csv", "site_comparison.csv", "adaptation_selection.csv", + "adaptation_selection_summary.csv", "site_referent_receipt.json" + ) + skip_if_not(all(file.exists(file.path(evidence, files))), "raw receipts are not bundled") + + drift <- file.path(tempdir(), "referent-hbr-hash-drift") + dir.create(drift, recursive = TRUE, showWarnings = FALSE) + expect_true(all(file.copy(file.path(evidence, files), drift, overwrite = TRUE))) + convergence <- utils::read.csv(file.path(drift, "hbr_convergence.csv")) + convergence$r_hat[[1L]] <- convergence$r_hat[[1L]] + 0.001 + utils::write.csv(convergence, file.path(drift, "hbr_convergence.csv"), row.names = FALSE) + expect_error( + validator$pcn_validate_hbr_evidence(drift, "hbr_receipt.json"), + "receipt mismatch" + ) + + hbr_verdict <- file.path(tempdir(), "referent-hbr-verdict-drift") + dir.create(hbr_verdict, recursive = TRUE, showWarnings = FALSE) + expect_true(all(file.copy( + file.path(evidence, files), hbr_verdict, overwrite = TRUE + ))) + hbr <- jsonlite::read_json( + file.path(hbr_verdict, "hbr_receipt.json"), simplifyVector = FALSE + ) + hbr$all_stages_pass <- FALSE + jsonlite::write_json( + hbr, file.path(hbr_verdict, "hbr_receipt.json"), + auto_unbox = TRUE, pretty = TRUE, null = "null" + ) + expect_error( + validator$pcn_validate_hbr_evidence(hbr_verdict, "hbr_receipt.json"), + "verdict does not match" + ) + + verdict <- file.path(tempdir(), "referent-site-verdict-drift") + dir.create(verdict, recursive = TRUE, showWarnings = FALSE) + expect_true(all(file.copy(file.path(evidence, files), verdict, overwrite = TRUE))) + site <- jsonlite::read_json( + file.path(verdict, "site_referent_receipt.json"), simplifyVector = FALSE + ) + site$all_sites_pass <- FALSE + jsonlite::write_json( + site, file.path(verdict, "site_referent_receipt.json"), + auto_unbox = TRUE, pretty = TRUE, null = "null" + ) + expect_error( + validator$pcn_validate_site_evidence( + verdict, verdict, "hbr_receipt.json", "site_referent_receipt.json" + ), + "verdict does not match" + ) + + missing_draw <- file.path(tempdir(), "referent-hbr-resigned-missing-draw") + dir.create(missing_draw, recursive = TRUE, showWarnings = FALSE) + expect_true(all(file.copy( + file.path(evidence, files), missing_draw, overwrite = TRUE + ))) + divergence <- utils::read.csv(file.path(missing_draw, "hbr_divergences.csv")) + divergence <- divergence[-nrow(divergence), , drop = FALSE] + utils::write.csv( + divergence, file.path(missing_draw, "hbr_divergences.csv"), row.names = FALSE + ) + hbr <- jsonlite::read_json( + file.path(missing_draw, "hbr_receipt.json"), simplifyVector = FALSE + ) + hbr$files[["hbr_divergences.csv"]] <- validator$pcn_evidence_file_receipt( + file.path(missing_draw, "hbr_divergences.csv") + ) + jsonlite::write_json( + hbr, file.path(missing_draw, "hbr_receipt.json"), + auto_unbox = TRUE, pretty = TRUE, null = "null" + ) + expect_error( + validator$pcn_validate_hbr_evidence(missing_draw, "hbr_receipt.json"), + "draw identities" + ) + + missing_prediction <- file.path( + tempdir(), "referent-site-resigned-missing-prediction" + ) + dir.create(missing_prediction, recursive = TRUE, showWarnings = FALSE) + expect_true(all(file.copy( + file.path(evidence, files), missing_prediction, overwrite = TRUE + ))) + prediction <- utils::read.csv( + file.path(missing_prediction, "referent_predictions.csv") + ) + prediction <- prediction[-nrow(prediction), , drop = FALSE] + utils::write.csv( + prediction, file.path(missing_prediction, "referent_predictions.csv"), + row.names = FALSE + ) + site <- jsonlite::read_json( + file.path(missing_prediction, "site_referent_receipt.json"), + simplifyVector = FALSE + ) + site$output_files[["referent_predictions.csv"]] <- + validator$pcn_evidence_file_receipt( + file.path(missing_prediction, "referent_predictions.csv") + ) + jsonlite::write_json( + site, file.path(missing_prediction, "site_referent_receipt.json"), + auto_unbox = TRUE, pretty = TRUE, null = "null" + ) + expect_error( + validator$pcn_validate_site_evidence( + missing_prediction, missing_prediction, + "hbr_receipt.json", "site_referent_receipt.json" + ), + "cover every retained evaluation row" + ) }) diff --git a/tests/testthat/test-pcntoolkit-workflow.R b/tests/testthat/test-pcntoolkit-workflow.R index 84a7243..8d78980 100644 --- a/tests/testthat/test-pcntoolkit-workflow.R +++ b/tests/testthat/test-pcntoolkit-workflow.R @@ -28,6 +28,7 @@ test_that("release workflow runs every declared evidence lane", { expect_match(workflow, "--nuts-sampler nutpie", fixed = TRUE) expect_match(workflow, "--tune 1000", fixed = TRUE) expect_match(workflow, "--target-accept 0.99", fixed = TRUE) + expect_match(workflow, "validate_hbr_site_evidence.R", fixed = TRUE) expect_match(workflow, "generate_release_superiority.py", fixed = TRUE) expect_match(workflow, "--replicates 20", fixed = TRUE) expect_match(workflow, "--seed-start 20260924", fixed = TRUE) diff --git a/tests/testthat/test-real-cohort-workflow.R b/tests/testthat/test-real-cohort-workflow.R index f87110b..532c844 100644 --- a/tests/testthat/test-real-cohort-workflow.R +++ b/tests/testthat/test-real-cohort-workflow.R @@ -1,4 +1,22 @@ -test_that("real-cohort lane declares independent NHANES cohorts and durable evidence", { +nhanes_expect_bound_receipt <- function(evidence) { + receipt <- jsonlite::read_json( + file.path(evidence, "receipt.json"), simplifyVector = FALSE + ) + for (name in names(receipt$output_files)) { + path <- file.path(evidence, paste0(name, ".csv")) + table <- utils::read.csv(path, check.names = FALSE) + record <- receipt$output_files[[name]] + expect_identical(digest::digest(file = path, algo = "sha256"), record$sha256) + expect_identical(nrow(table), as.integer(record$rows)) + expect_identical(names(table), unlist(record$columns, use.names = FALSE)) + } + gate <- utils::read.csv(file.path(evidence, "gate.csv")) + expect_identical(isTRUE(receipt$calibration_pass), isTRUE(gate$calibration_pass)) + expect_identical(isTRUE(receipt$pass), isTRUE(gate$pass)) + receipt +} + +test_that("real-cohort lane separates development from untouched confirmation", { root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) runner_path <- file.path(root, "tools", "validation", "run_nhanes_external_validation.R") @@ -9,7 +27,11 @@ test_that("real-cohort lane declares independent NHANES cohorts and durable evid runner <- paste(readLines(runner_path, warn = FALSE), collapse = "\n") workflow <- paste(readLines(workflow_path, warn = FALSE), collapse = "\n") expect_match(runner, "NHANES 2015-2016", fixed = TRUE) - expect_match(runner, "NHANES 2017-2018", fixed = TRUE) + expect_match(runner, 'cohort = "2017-2018"', fixed = TRUE) + expect_match(runner, 'cohort = "2013-2014"', fixed = TRUE) + expect_match(runner, "post_hoc_model_development", fixed = TRUE) + expect_match(runner, "untouched_confirmation", fixed = TRUE) + expect_match(runner, "b1301a4e0426eb5b5a83935059053131331670d9", fixed = TRUE) expect_match(runner, "acceptable_calibration", fixed = TRUE) expect_match( runner, @@ -18,19 +40,39 @@ test_that("real-cohort lane declares independent NHANES cohorts and durable evid ) expect_match(runner, "uncertainty = \"total\"", fixed = TRUE) expect_match(workflow, "if: always()", fixed = TRUE) + expect_match(workflow, "confirmation", fixed = TRUE) expect_match(workflow, "nhanes-external-validation-evidence", fixed = TRUE) }) -test_that("retained NHANES receipt passes the full conditional contract", { +test_that("retained 2017-2018 result is labeled post-hoc development evidence", { root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) evidence <- file.path(root, "docs", "evidence", "nhanes") - receipt_path <- file.path(evidence, "receipt.json") - gate_path <- file.path(evidence, "gate.csv") - skip_if_not(all(file.exists(receipt_path, gate_path)), "cohort receipts are not bundled") - receipt <- jsonlite::read_json(receipt_path, simplifyVector = TRUE) - gate <- utils::read.csv(gate_path, stringsAsFactors = FALSE) - expect_true(receipt$calibration_pass) - expect_true(receipt$pass) - expect_true(gate$calibration_pass) - expect_true(gate$pass) + skip_if_not(file.exists(file.path(evidence, "receipt.json")), "receipt is not bundled") + receipt <- nhanes_expect_bound_receipt(evidence) + expect_identical(receipt$evidence_role, "post_hoc_model_development") + expect_identical(receipt$evaluation_cohort, "NHANES 2017-2018") + expect_identical(receipt$design_registration$status, "post-hoc model development") +}) + +test_that("untouched NHANES confirmation is bound to the prior Mote contract", { + root <- normalizePath(testthat::test_path("..", ".."), mustWork = TRUE) + development <- file.path(root, "docs", "evidence", "nhanes") + evidence <- file.path(development, "confirmation-2013-2014") + skip_if_not(file.exists(file.path(evidence, "receipt.json")), "confirmation is not bundled") + receipt <- nhanes_expect_bound_receipt(evidence) + development_receipt <- jsonlite::read_json( + file.path(development, "receipt.json"), simplifyVector = FALSE + ) + expect_identical(receipt$evidence_role, "untouched_confirmation") + expect_identical(receipt$evaluation_cohort, "NHANES 2013-2014") + expect_identical(receipt$design_registration$tracker, "mote") + expect_identical(receipt$design_registration$issue, "referent-32n") + expect_identical( + receipt$design_registration$commit, + "b1301a4e0426eb5b5a83935059053131331670d9" + ) + expect_false(identical( + receipt$input_files$demo_test$sha256, + development_receipt$input_files$demo_test$sha256 + )) }) diff --git a/tools/pcntoolkit/run_hbr_site_evidence.py b/tools/pcntoolkit/run_hbr_site_evidence.py index 52a0414..97a92cb 100644 --- a/tools/pcntoolkit/run_hbr_site_evidence.py +++ b/tools/pcntoolkit/run_hbr_site_evidence.py @@ -21,6 +21,7 @@ from __future__ import annotations import argparse +import hashlib import importlib.metadata import json from pathlib import Path @@ -101,26 +102,47 @@ def norm_data(name: str, frame: pd.DataFrame) -> NormData: ) -def convergence(model: NormativeModel, stage: str) -> tuple[pd.DataFrame, dict[str, object]]: +def convergence( + model: NormativeModel, stage: str +) -> tuple[pd.DataFrame, pd.DataFrame, dict[str, object]]: idata = model["y"].idata # The fitted idata does not contain the temporary per-subject variables # created during prediction, so the full posterior is the convergence set. summary = az.summary(idata, fmt="wide") summary.insert(0, "parameter", summary.index.astype(str)) summary.insert(0, "stage", stage) - divergences = int(idata.sample_stats["diverging"].sum().item()) + divergence_table = ( + idata.sample_stats["diverging"] + .to_dataframe(name="diverging") + .reset_index()[["chain", "draw", "diverging"]] + ) + divergence_table.insert(0, "stage", stage) + divergences = int(divergence_table["diverging"].sum()) max_rhat = float(np.nanmax(summary["r_hat"].to_numpy())) min_bulk = float(np.nanmin(summary["ess_bulk"].to_numpy())) min_tail = float(np.nanmin(summary["ess_tail"].to_numpy())) + undefined_rhat_constant_rows = int( + (summary["r_hat"].isna() & summary["sd"].eq(0)).sum() + ) receipt = { "stage": stage, "divergences": divergences, "max_rhat": max_rhat, "min_ess_bulk": min_bulk, "min_ess_tail": min_tail, + "undefined_rhat_constant_rows": undefined_rhat_constant_rows, "pass": bool(divergences == 0 and max_rhat <= 1.01 and min_bulk >= 400 and min_tail >= 400), } - return summary.reset_index(drop=True), receipt + return summary.reset_index(drop=True), divergence_table, receipt + + +def csv_receipt(path: Path) -> dict[str, object]: + frame = pd.read_csv(path) + return { + "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + "rows": len(frame), + "columns": list(frame.columns), + } def posterior_normal_draws(model: NormativeModel, frame: pd.DataFrame) -> tuple[np.ndarray, np.ndarray]: @@ -220,6 +242,7 @@ def site_summary(predictions: pd.DataFrame) -> pd.DataFrame: "mace": np.mean([abs(q - np.mean(u <= q)) for q in grid]), "mean_z": group["mixture_z"].mean(), "var_z": group["mixture_z"].var(ddof=1), + "coverage90_se": np.sqrt(0.9 * 0.1 / len(group)), } ) return pd.DataFrame(rows) @@ -266,7 +289,7 @@ def main() -> None: target_accept=args.target_accept, random_seed=args.seed, ) - base_convergence, base_receipt = convergence(base, "base") + base_convergence, base_divergences, base_receipt = convergence(base, "base") observed_predictions = predict_with_mixture( base, observed_test, "observed_site", outcome_mean, outcome_sd ) @@ -285,18 +308,40 @@ def main() -> None: target_accept=args.target_accept, random_seed=args.seed + 1, ) - transfer_convergence, transfer_receipt = convergence(transferred, "transfer") + transfer_convergence, transfer_divergences, transfer_receipt = convergence( + transferred, "transfer" + ) transport_predictions = predict_with_mixture( transferred, transport_test, "transferred_site", outcome_mean, outcome_sd ) predictions = pd.concat([observed_predictions, transport_predictions], ignore_index=True) convergence_table = pd.concat([base_convergence, transfer_convergence], ignore_index=True) + divergence_table = pd.concat( + [base_divergences, transfer_divergences], ignore_index=True + ) - data.to_csv(args.output / "site_data.csv", index=False, float_format="%.17g") - predictions.to_csv(args.output / "pcntoolkit_predictions.csv", index=False, float_format="%.17g") - site_summary(predictions).to_csv(args.output / "pcntoolkit_site_summary.csv", index=False, float_format="%.17g") - convergence_table.to_csv(args.output / "hbr_convergence.csv", index=False, float_format="%.17g") + output_paths = { + "site_data.csv": args.output / "site_data.csv", + "pcntoolkit_predictions.csv": args.output / "pcntoolkit_predictions.csv", + "pcntoolkit_site_summary.csv": args.output / "pcntoolkit_site_summary.csv", + "hbr_convergence.csv": args.output / "hbr_convergence.csv", + "hbr_divergences.csv": args.output / "hbr_divergences.csv", + } + data.to_csv(output_paths["site_data.csv"], index=False, float_format="%.17g") + predictions.to_csv( + output_paths["pcntoolkit_predictions.csv"], index=False, float_format="%.17g" + ) + site_summary(predictions).to_csv( + output_paths["pcntoolkit_site_summary.csv"], + index=False, + float_format="%.17g", + ) + convergence_table.to_csv( + output_paths["hbr_convergence.csv"], index=False, float_format="%.17g" + ) + divergence_table.to_csv(output_paths["hbr_divergences.csv"], index=False) receipt = { + "schema_version": "1.1.0", "comparator": "pcntoolkit", "version": importlib.metadata.version("pcntoolkit"), "draws": args.draws, @@ -324,8 +369,14 @@ def main() -> None: "min_ess_tail": 400, "divergences": 0, }, + "convergence_policy": { + "undefined_rhat": ( + "allowed only for retained posterior-summary rows with sd exactly zero" + ) + }, "stages": [base_receipt, transfer_receipt], "all_stages_pass": bool(base_receipt["pass"] and transfer_receipt["pass"]), + "files": {name: csv_receipt(path) for name, path in output_paths.items()}, } (args.output / "receipt.json").write_text( json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8" diff --git a/tools/pcntoolkit/run_site_transport_evidence.R b/tools/pcntoolkit/run_site_transport_evidence.R index 17b5881..a2d8086 100644 --- a/tools/pcntoolkit/run_site_transport_evidence.R +++ b/tools/pcntoolkit/run_site_transport_evidence.R @@ -4,6 +4,7 @@ source("tools/pcntoolkit/benchmark_helpers.R") source("tools/pcntoolkit/site_evidence.R") +source("tools/pcntoolkit/validate_hbr_site_evidence.R") pcn_fit_site_model <- function(data) { data$site <- factor(data$site) @@ -75,6 +76,7 @@ pcn_validate_adaptation_priors <- function(reference, grid = pcn_adaptation_grid } run_referent_site_evidence <- function(input_dir, output_dir) { + pcn_validate_hbr_evidence(input_dir) data <- utils::read.csv(file.path(input_dir, "site_data.csv"), stringsAsFactors = FALSE) train <- data[data$split == "reference_train", , drop = FALSE] observed <- data[data$split == "observed_site_test", , drop = FALSE] @@ -130,16 +132,52 @@ run_referent_site_evidence <- function(input_dir, output_dir) { ) gated <- pcn_site_gate(summary) dir.create(output_dir, recursive = TRUE, showWarnings = FALSE) - utils::write.csv(predictions, file.path(output_dir, "referent_predictions.csv"), row.names = FALSE) - utils::write.csv(gated, file.path(output_dir, "site_comparison.csv"), row.names = FALSE) - selection_validation_path <- file.path(output_dir, "adaptation_selection.csv") - selection_summary_path <- file.path(output_dir, "adaptation_selection_summary.csv") - utils::write.csv(selection$validation, selection_validation_path, row.names = FALSE) - utils::write.csv(selection$summary, selection_summary_path, row.names = FALSE) + output_paths <- c( + referent_predictions.csv = file.path(output_dir, "referent_predictions.csv"), + site_comparison.csv = file.path(output_dir, "site_comparison.csv"), + adaptation_selection.csv = file.path(output_dir, "adaptation_selection.csv"), + adaptation_selection_summary.csv = file.path( + output_dir, "adaptation_selection_summary.csv" + ) + ) + utils::write.csv(predictions, output_paths[["referent_predictions.csv"]], row.names = FALSE) + utils::write.csv(gated, output_paths[["site_comparison.csv"]], row.names = FALSE) + utils::write.csv( + selection$validation, output_paths[["adaptation_selection.csv"]], row.names = FALSE + ) + utils::write.csv( + selection$summary, output_paths[["adaptation_selection_summary.csv"]], + row.names = FALSE + ) + input_paths <- c( + site_data.csv = file.path(input_dir, "site_data.csv"), + pcntoolkit_predictions.csv = file.path(input_dir, "pcntoolkit_predictions.csv"), + pcntoolkit_site_summary.csv = file.path(input_dir, "pcntoolkit_site_summary.csv"), + hbr_convergence.csv = file.path(input_dir, "hbr_convergence.csv"), + hbr_divergences.csv = file.path(input_dir, "hbr_divergences.csv"), + receipt.json = file.path(input_dir, "receipt.json") + ) + input_receipts <- lapply(names(input_paths), function(name) { + if (identical(name, "receipt.json")) { + pcn_evidence_blob_receipt(input_paths[[name]]) + } else { + pcn_evidence_file_receipt(input_paths[[name]]) + } + }) + names(input_receipts) <- names(input_paths) + output_receipts <- lapply(output_paths, pcn_evidence_file_receipt) receipt <- list( + schema_version = "1.1.0", estimand = "separate adaptation and transfer policies; no rowwise parity claim", referent_version = as.character(utils::packageVersion("referent")), r_version = R.version.string, + gate_thresholds = list( + coverage90_floor = 0.80, + mace_ceiling = 0.08, + mean_z_absolute_ceiling = 0.35 + ), + input_files = input_receipts, + output_files = output_receipts, all_sites_pass = all(gated$pass), failed_sites = split(gated$site[!gated$pass], gated$method[!gated$pass]), adaptation_prior_selection = list( @@ -147,11 +185,7 @@ run_referent_site_evidence <- function(input_dir, output_dir) { source_split = "reference_train", source_sites = selection$source_sites, fold_sizes = selection$fold_sizes, - selected = unclass(selection$selected), - files = list( - adaptation_selection.csv = unname(tools::md5sum(selection_validation_path)), - adaptation_selection_summary.csv = unname(tools::md5sum(selection_summary_path)) - ) + selected = unclass(selection$selected) ), referent_adaptation = unclass(adapted$adaptation) ) @@ -159,6 +193,7 @@ run_referent_site_evidence <- function(input_dir, output_dir) { receipt, file.path(output_dir, "referent_receipt.json"), auto_unbox = TRUE, pretty = TRUE, null = "null" ) + pcn_validate_site_evidence(input_dir, output_dir) invisible(gated) } diff --git a/tools/pcntoolkit/validate_hbr_site_evidence.R b/tools/pcntoolkit/validate_hbr_site_evidence.R new file mode 100644 index 0000000..ae2226d --- /dev/null +++ b/tools/pcntoolkit/validate_hbr_site_evidence.R @@ -0,0 +1,675 @@ +# Independently validate retained HBR and site-transfer evidence. Receipt +# booleans are never accepted as verdicts: hashes, schemas, row identities, +# convergence summaries, site metrics, prior selection, and gates are all +# recomputed from the retained tables. + +pcn_evidence_sha256 <- function(path) { + if (!requireNamespace("digest", quietly = TRUE)) { + stop("digest is required to validate evidence", call. = FALSE) + } + digest::digest(file = path, algo = "sha256") +} + +pcn_evidence_read_csv <- function(path) { + if (!file.exists(path)) { + stop("missing evidence file: ", path, call. = FALSE) + } + utils::read.csv( + path, check.names = FALSE, stringsAsFactors = FALSE, + na.strings = c("NA", "") + ) +} + +pcn_evidence_file_receipt <- function(path) { + dat <- pcn_evidence_read_csv(path) + list( + sha256 = pcn_evidence_sha256(path), + rows = nrow(dat), + columns = names(dat) + ) +} + +pcn_evidence_blob_receipt <- function(path) { + if (!file.exists(path)) { + stop("missing evidence file: ", path, call. = FALSE) + } + list( + sha256 = pcn_evidence_sha256(path), + bytes = unname(file.info(path)$size) + ) +} + +pcn_evidence_validate_files <- function(root, records, expected, + aliases = character()) { + if (!setequal(names(records), expected)) { + stop("evidence receipt has an unexpected file set", call. = FALSE) + } + for (name in expected) { + filename <- if (name %in% names(aliases)) aliases[[name]] else name + path <- file.path(root, filename) + recorded <- records[[name]] + if (!is.null(recorded$bytes)) { + actual <- pcn_evidence_blob_receipt(path) + valid <- identical(actual$sha256, recorded$sha256) && + identical(actual$bytes, as.numeric(recorded$bytes)) + } else { + actual <- pcn_evidence_file_receipt(path) + valid <- identical(actual$sha256, recorded$sha256) && + identical(actual$rows, as.integer(recorded$rows)) && + identical(actual$columns, unlist(recorded$columns, use.names = FALSE)) + } + if (!valid) { + stop("evidence file receipt mismatch: ", name, call. = FALSE) + } + } + invisible(TRUE) +} + +pcn_evidence_assert_columns <- function(dat, required, label) { + if (!all(required %in% names(dat))) { + stop(label, " is missing required columns", call. = FALSE) + } +} + +pcn_evidence_assert_exact_columns <- function(dat, expected, label) { + if (!identical(names(dat), expected)) { + stop(label, " does not match the registered schema", call. = FALSE) + } +} + +pcn_evidence_assert_finite <- function(dat, columns, label) { + if (any(!vapply(dat[columns], is.numeric, logical(1))) || + any(!is.finite(as.matrix(dat[columns])))) { + stop(label, " contains non-finite numeric evidence", call. = FALSE) + } +} + +pcn_evidence_assert_frame <- function(actual, expected, keys, label, + tolerance = 1e-11) { + if (!setequal(names(actual), names(expected))) { + stop(label, " schema does not match recomputed evidence", call. = FALSE) + } + actual <- actual[, names(expected), drop = FALSE] + order_rows <- function(dat) { + values <- unname(lapply(dat[keys], as.character)) + do.call(order, c(values, list(na.last = TRUE))) + } + actual <- actual[order_rows(actual), , drop = FALSE] + expected <- expected[order_rows(expected), , drop = FALSE] + rownames(actual) <- rownames(expected) <- NULL + if (nrow(actual) != nrow(expected)) { + stop(label, " row count does not match recomputed evidence", call. = FALSE) + } + for (name in names(expected)) { + a <- actual[[name]] + b <- expected[[name]] + if (!identical(is.na(a), is.na(b))) { + stop(label, " NA pattern differs in ", name, call. = FALSE) + } + keep <- !is.na(a) + if (is.numeric(a) && is.numeric(b)) { + scale <- pmax(1, abs(a[keep]), abs(b[keep])) + if (any(abs(a[keep] - b[keep]) > tolerance * scale)) { + stop(label, " differs from recomputed values in ", name, call. = FALSE) + } + } else if (!identical(as.character(a[keep]), as.character(b[keep]))) { + stop(label, " differs from recomputed values in ", name, call. = FALSE) + } + } + invisible(TRUE) +} + +pcn_evidence_mace <- function(centile) { + grid <- c(0.05, 0.25, 0.5, 0.75, 0.95) + mean(vapply(grid, function(p) abs(p - mean(centile <= p)), numeric(1))) +} + +pcn_evidence_site_summary <- function(prediction, method) { + required <- c( + "lane", "site", "observed", "centile", "log_density", "q05", "q50", "q95" + ) + pcn_evidence_assert_columns(prediction, required, "prediction table") + numeric <- c("observed", "centile", "log_density", "q05", "q50", "q95") + pcn_evidence_assert_finite(prediction, numeric, "prediction table") + if (any(prediction$centile <= 0 | prediction$centile >= 1)) { + stop("prediction centiles must lie strictly inside (0, 1)", call. = FALSE) + } + groups <- split( + seq_len(nrow(prediction)), + interaction(prediction$lane, prediction$site, drop = TRUE, lex.order = TRUE) + ) + rows <- lapply(groups, function(index) { + d <- prediction[index, , drop = FALSE] + z <- stats::qnorm(d$centile) + data.frame( + method = method, + lane = unique(d$lane), + site = unique(d$site), + n = nrow(d), + mean_log_score = mean(d$log_density), + median_rmse = sqrt(mean((d$observed - d$q50)^2)), + coverage90 = mean(d$observed >= d$q05 & d$observed <= d$q95), + mace = pcn_evidence_mace(d$centile), + mean_z = mean(z), + var_z = stats::var(z), + coverage90_se = sqrt(0.9 * 0.1 / nrow(d)), + stringsAsFactors = FALSE + ) + }) + out <- do.call(rbind, rows) + rownames(out) <- NULL + out +} + +pcn_evidence_site_gate <- function(summary) { + summary$pass <- summary$coverage90 >= 0.80 & summary$mace <= 0.08 & + abs(summary$mean_z) <= 0.35 + summary +} + +pcn_evidence_as_logical <- function(x, label) { + if (is.logical(x)) return(x) + if (is.numeric(x) && all(x %in% c(0, 1))) return(as.logical(x)) + value <- tolower(as.character(x)) + if (!all(value %in% c("true", "false"))) { + stop(label, " is not a valid logical column", call. = FALSE) + } + value == "true" +} + +pcn_validate_hbr_evidence <- function(root, receipt_file = "receipt.json") { + if (!requireNamespace("jsonlite", quietly = TRUE)) { + stop("jsonlite is required to validate evidence", call. = FALSE) + } + receipt_path <- file.path(root, receipt_file) + if (!file.exists(receipt_path)) { + stop("missing HBR receipt: ", receipt_path, call. = FALSE) + } + receipt <- jsonlite::read_json(receipt_path, simplifyVector = FALSE) + thresholds <- receipt$convergence_thresholds + undefined_rhat_policy <- paste( + "allowed only for retained posterior-summary rows with sd exactly zero" + ) + aggregation <- receipt$aggregation + if (!identical(receipt$schema_version, "1.1.0") || + !identical(receipt$comparator, "pcntoolkit") || + !identical(receipt$version, "1.3.0") || + !identical(as.integer(receipt$draws), 1500L) || + !identical(as.integer(receipt$tune), 1000L) || + !identical(as.integer(receipt$chains), 4L) || + !identical(as.integer(receipt$cores), 4L) || + !identical(receipt$nuts_sampler, "nutpie") || + !isTRUE(all.equal(receipt$target_accept, 0.99)) || + !identical(as.integer(receipt$requested_seed), 20260823L) || + !identical(as.integer(receipt$sampling_seeds$base), 20260823L) || + !identical(as.integer(receipt$sampling_seeds$transfer), 20260824L) || + !identical(receipt$hbr_public_random_seed_argument, FALSE) || + !isTRUE(all.equal(thresholds$max_rhat, 1.01)) || + !identical(as.integer(thresholds$min_ess_bulk), 400L) || + !identical(as.integer(thresholds$min_ess_tail), 400L) || + !identical(as.integer(thresholds$divergences), 0L) || + !identical( + receipt$convergence_policy$undefined_rhat, undefined_rhat_policy + ) || + !setequal( + names(aggregation), + c("reported_z", "reported_quantile", "reported_logp", "mixture_outputs") + ) || + !identical(aggregation$reported_z, "mean of draw-specific z") || + !identical( + aggregation$reported_quantile, "mean of draw-specific quantiles" + ) || + !identical( + aggregation$reported_logp, "mean of draw-specific log densities" + ) || + !identical( + aggregation$mixture_outputs, + "CDF and density averaged over posterior draws, then transformed" + )) { + stop("HBR receipt does not match the registered contract", call. = FALSE) + } + expected_files <- c( + "site_data.csv", "pcntoolkit_predictions.csv", + "pcntoolkit_site_summary.csv", "hbr_convergence.csv", + "hbr_divergences.csv" + ) + pcn_evidence_validate_files(root, receipt$files, expected_files) + + convergence <- pcn_evidence_read_csv(file.path(root, "hbr_convergence.csv")) + divergences <- pcn_evidence_read_csv(file.path(root, "hbr_divergences.csv")) + pcn_evidence_assert_exact_columns( + convergence, + c( + "stage", "parameter", "mean", "sd", "hdi_3%", "hdi_97%", + "mcse_mean", "mcse_sd", "ess_bulk", "ess_tail", "r_hat" + ), + "HBR convergence table" + ) + pcn_evidence_assert_exact_columns( + divergences, c("stage", "chain", "draw", "diverging"), + "HBR divergence table" + ) + pcn_evidence_assert_finite( + convergence, c("sd", "ess_bulk", "ess_tail"), "HBR convergence table" + ) + pcn_evidence_assert_finite( + divergences, c("chain", "draw"), "HBR divergence table" + ) + if (anyDuplicated(convergence[c("stage", "parameter")]) || + anyDuplicated(divergences[c("stage", "chain", "draw")])) { + stop("HBR diagnostic row identities are duplicated", call. = FALSE) + } + undefined_rhat <- is.na(convergence$r_hat) + if (any(is.infinite(convergence$r_hat)) || + any(undefined_rhat & convergence$sd != 0)) { + stop("HBR R-hat is undefined outside an exactly constant row", + call. = FALSE) + } + divergences$diverging <- pcn_evidence_as_logical( + divergences$diverging, "HBR diverging" + ) + stages <- c("base", "transfer") + if (!setequal(unique(convergence$stage), stages) || + !setequal(unique(divergences$stage), stages)) { + stop("HBR diagnostic stages are incomplete", call. = FALSE) + } + expected_draws <- expand.grid( + stage = stages, + chain = 0:3, + draw = 0:1499, + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = FALSE + ) + pcn_evidence_assert_frame( + divergences[c("stage", "chain", "draw")], expected_draws, + c("stage", "chain", "draw"), "HBR divergence draw identities" + ) + recomputed_stages <- do.call(rbind, lapply(stages, function(stage) { + conv <- convergence[convergence$stage == stage, , drop = FALSE] + div <- divergences[divergences$stage == stage, , drop = FALSE] + if (!any(is.finite(conv$r_hat))) { + stop("HBR stage has no finite R-hat values: ", stage, call. = FALSE) + } + values <- data.frame( + stage = stage, + divergences = sum(div$diverging), + max_rhat = max(conv$r_hat, na.rm = TRUE), + min_ess_bulk = min(conv$ess_bulk), + min_ess_tail = min(conv$ess_tail), + undefined_rhat_constant_rows = sum(is.na(conv$r_hat) & conv$sd == 0), + stringsAsFactors = FALSE + ) + values$pass <- values$divergences == 0L && values$max_rhat <= 1.01 && + values$min_ess_bulk >= 400 && values$min_ess_tail >= 400 + values + })) + receipt_stages <- do.call(rbind, lapply(receipt$stages, function(stage) { + data.frame( + stage = stage$stage, + divergences = as.integer(stage$divergences), + max_rhat = stage$max_rhat, + min_ess_bulk = stage$min_ess_bulk, + min_ess_tail = stage$min_ess_tail, + undefined_rhat_constant_rows = as.integer( + stage$undefined_rhat_constant_rows + ), + pass = isTRUE(stage$pass), + stringsAsFactors = FALSE + ) + })) + pcn_evidence_assert_frame( + receipt_stages, recomputed_stages, "stage", "HBR stage receipt" + ) + if (!identical(isTRUE(receipt$all_stages_pass), all(recomputed_stages$pass))) { + stop("HBR receipt verdict does not match recomputed diagnostics", call. = FALSE) + } + + data <- pcn_evidence_read_csv(file.path(root, "site_data.csv")) + prediction <- pcn_evidence_read_csv(file.path(root, "pcntoolkit_predictions.csv")) + pcn_evidence_assert_exact_columns( + data, c("row_id", "site", "split", "x", "y"), "site data" + ) + pcn_evidence_assert_exact_columns( + prediction, + c( + "lane", "row_id", "site", "observed", + "reported_mean_draw_z", "reported_mean_draw_log_density", + "reported_mean_draw_q05", "reported_mean_draw_q50", + "reported_mean_draw_q95", "mixture_centile", "mixture_z", + "mixture_log_density", "mixture_q05", "mixture_q50", "mixture_q95" + ), + "PCNtoolkit prediction table" + ) + pcn_evidence_assert_finite(data, c("x", "y"), "site data") + pcn_evidence_assert_finite( + prediction, + c( + "observed", "reported_mean_draw_z", "reported_mean_draw_log_density", + "reported_mean_draw_q05", "reported_mean_draw_q50", + "reported_mean_draw_q95", "mixture_centile", "mixture_z", + "mixture_log_density", "mixture_q05", "mixture_q50", "mixture_q95" + ), + "PCNtoolkit prediction table" + ) + if (anyDuplicated(data$row_id) || anyDuplicated(prediction$row_id)) { + stop("HBR evidence has duplicate row identities", call. = FALSE) + } + expected_structure <- data.frame( + site = c("site-1", "site-1", "site-2", "site-2", "site-3", "site-3", + "site-4", "site-4", "site-5", "site-5"), + split = rep( + c("reference_train", "observed_site_test"), 4L + ) |> c("adapt", "transport_test"), + n = c(rep(c(50L, 30L), 4L), 25L, 75L), + stringsAsFactors = FALSE + ) + actual_structure <- stats::aggregate( + list(n = data$row_id), list(site = data$site, split = data$split), length + ) + pcn_evidence_assert_frame( + actual_structure, expected_structure, c("site", "split"), + "site data split structure" + ) + if (any(!grepl( + "^site-[1-5]-(reference_train|observed_site_test|adapt|transport_test)-[0-9]{4}$", + data$row_id + ))) { + stop("site data row identities do not match the registered design", + call. = FALSE) + } + evaluation <- data[data$split %in% c("observed_site_test", "transport_test"), ] + evaluation$lane <- ifelse( + evaluation$split == "observed_site_test", "observed_site", "transferred_site" + ) + if (nrow(prediction) != nrow(evaluation) || + !setequal(prediction$row_id, evaluation$row_id)) { + stop("HBR predictions do not cover every retained evaluation row", + call. = FALSE) + } + evaluation <- evaluation[match(prediction$row_id, evaluation$row_id), ] + if (anyNA(evaluation$row_id) || + !identical(prediction$row_id, evaluation$row_id) || + !identical(prediction$site, evaluation$site) || + !identical(prediction$lane, evaluation$lane) || + any(abs(prediction$observed - evaluation$y) > 1e-12)) { + stop("HBR predictions are not bound to the retained site rows", call. = FALSE) + } + if (any(prediction$mixture_centile <= 0 | prediction$mixture_centile >= 1) || + any(prediction$mixture_q05 > prediction$mixture_q50) || + any(prediction$mixture_q50 > prediction$mixture_q95) || + any(abs( + prediction$mixture_z - stats::qnorm(prediction$mixture_centile) + ) > 1e-11 * pmax(1, abs(prediction$mixture_z)))) { + stop("PCNtoolkit mixture predictions violate distribution invariants", + call. = FALSE) + } + standardized <- data.frame( + lane = prediction$lane, + site = prediction$site, + observed = prediction$observed, + centile = prediction$mixture_centile, + log_density = prediction$mixture_log_density, + q05 = prediction$mixture_q05, + q50 = prediction$mixture_q50, + q95 = prediction$mixture_q95, + stringsAsFactors = FALSE + ) + recomputed_summary <- pcn_evidence_site_summary( + standardized, "pcntoolkit_hbr_mixture" + ) + recorded_summary <- pcn_evidence_read_csv( + file.path(root, "pcntoolkit_site_summary.csv") + ) + pcn_evidence_assert_frame( + recorded_summary, recomputed_summary, c("method", "lane", "site"), + "PCNtoolkit site summary" + ) + invisible(list( + receipt = receipt, + stages = recomputed_stages, + site_summary = recomputed_summary, + data = data + )) +} + +pcn_evidence_selection_summary <- function(validation) { + required <- c("site", "location_prior_n", "scale_prior_n", "mean_log_score") + pcn_evidence_assert_columns(validation, required, "adaptation selection table") + pcn_evidence_assert_finite( + validation, c("location_prior_n", "scale_prior_n", "mean_log_score"), + "adaptation selection table" + ) + if (anyDuplicated(validation[c("site", "location_prior_n", "scale_prior_n")])) { + stop("adaptation selection has duplicate site-candidate rows", call. = FALSE) + } + counts <- stats::aggregate( + site ~ location_prior_n + scale_prior_n, validation, + function(x) length(unique(x)) + ) + names(counts)[names(counts) == "site"] <- "validated_sites" + expected_sites <- length(unique(validation$site)) + if (any(counts$validated_sites != expected_sites)) { + stop("adaptation selection omits an observed site", call. = FALSE) + } + scores <- stats::aggregate( + mean_log_score ~ location_prior_n + scale_prior_n, validation, mean + ) + out <- merge( + scores, counts, by = c("location_prior_n", "scale_prior_n"), sort = FALSE + ) + out <- out[order(-out$mean_log_score, out$location_prior_n, out$scale_prior_n), ] + rownames(out) <- NULL + out$selected <- seq_len(nrow(out)) == 1L + out +} + +pcn_validate_site_evidence <- function(input_dir, output_dir, + hbr_receipt_file = "receipt.json", + site_receipt_file = "referent_receipt.json") { + hbr <- pcn_validate_hbr_evidence(input_dir, hbr_receipt_file) + receipt_path <- file.path(output_dir, site_receipt_file) + if (!file.exists(receipt_path)) { + stop("missing site receipt: ", receipt_path, call. = FALSE) + } + receipt <- jsonlite::read_json(receipt_path, simplifyVector = FALSE) + thresholds <- receipt$gate_thresholds + if (!identical(receipt$schema_version, "1.1.0") || + !identical( + receipt$estimand, + "separate adaptation and transfer policies; no rowwise parity claim" + ) || + !identical(receipt$referent_version, "0.1.0") || + !isTRUE(all.equal(thresholds$coverage90_floor, 0.80)) || + !isTRUE(all.equal(thresholds$mace_ceiling, 0.08)) || + !isTRUE(all.equal(thresholds$mean_z_absolute_ceiling, 0.35))) { + stop("site receipt does not match the registered contract", call. = FALSE) + } + hbr_files <- c( + "site_data.csv", "pcntoolkit_predictions.csv", + "pcntoolkit_site_summary.csv", "hbr_convergence.csv", + "hbr_divergences.csv", "receipt.json" + ) + pcn_evidence_validate_files( + input_dir, receipt$input_files, hbr_files, + aliases = stats::setNames(hbr_receipt_file, "receipt.json") + ) + output_files <- c( + "referent_predictions.csv", "site_comparison.csv", + "adaptation_selection.csv", "adaptation_selection_summary.csv" + ) + pcn_evidence_validate_files(output_dir, receipt$output_files, output_files) + + prediction <- pcn_evidence_read_csv( + file.path(output_dir, "referent_predictions.csv") + ) + pcn_evidence_assert_exact_columns( + prediction, + c("method", "lane", "row_id", "site", "observed", "centile", + "log_density", "q05", "q50", "q95"), + "Referent prediction table" + ) + pcn_evidence_assert_finite( + prediction, + c("observed", "centile", "log_density", "q05", "q50", "q95"), + "Referent prediction table" + ) + if (anyDuplicated(prediction$row_id) || + any(prediction$method != "referent_adaptation")) { + stop("Referent prediction identities are invalid", call. = FALSE) + } + evaluation <- hbr$data[ + hbr$data$split %in% c("observed_site_test", "transport_test"), + ] + evaluation$lane <- ifelse( + evaluation$split == "observed_site_test", "observed_site", "transferred_site" + ) + if (nrow(prediction) != nrow(evaluation) || + !setequal(prediction$row_id, evaluation$row_id)) { + stop("Referent predictions do not cover every retained evaluation row", + call. = FALSE) + } + evaluation <- evaluation[match(prediction$row_id, evaluation$row_id), ] + if (anyNA(evaluation$row_id) || + !identical(prediction$row_id, evaluation$row_id) || + !identical(prediction$site, evaluation$site) || + !identical(prediction$lane, evaluation$lane) || + any(abs(prediction$observed - evaluation$y) > 1e-12)) { + stop("Referent predictions are not bound to the retained site rows", call. = FALSE) + } + if (any(prediction$centile <= 0 | prediction$centile >= 1) || + any(prediction$q05 > prediction$q50) || + any(prediction$q50 > prediction$q95)) { + stop("Referent predictions violate distribution invariants", call. = FALSE) + } + ref_summary <- pcn_evidence_site_summary( + prediction, "referent_adaptation" + ) + recomputed_comparison <- pcn_evidence_site_gate(rbind( + ref_summary[, names(hbr$site_summary), drop = FALSE], + hbr$site_summary + )) + recorded_comparison <- pcn_evidence_read_csv( + file.path(output_dir, "site_comparison.csv") + ) + pcn_evidence_assert_frame( + recorded_comparison, recomputed_comparison, + c("method", "lane", "site"), "site comparison" + ) + + validation <- pcn_evidence_read_csv( + file.path(output_dir, "adaptation_selection.csv") + ) + pcn_evidence_assert_exact_columns( + validation, + c( + "site", "adaptation_n", "validation_n", "location_prior_n", + "scale_prior_n", "mean_log_score" + ), + "adaptation selection table" + ) + selection <- pcn_evidence_selection_summary(validation) + recorded_selection <- pcn_evidence_read_csv( + file.path(output_dir, "adaptation_selection_summary.csv") + ) + pcn_evidence_assert_frame( + recorded_selection, selection, c("location_prior_n", "scale_prior_n"), + "adaptation selection summary" + ) + reference_sites <- sort(unique(hbr$data$site[hbr$data$split == "reference_train"])) + expected_candidates <- expand.grid( + site = reference_sites, + location_prior_n = c(0, 2, 5, 10), + scale_prior_n = c(0, 5, 10, 25), + KEEP.OUT.ATTRS = FALSE, + stringsAsFactors = FALSE + ) + pcn_evidence_assert_frame( + validation[c("site", "location_prior_n", "scale_prior_n")], + expected_candidates, c("site", "location_prior_n", "scale_prior_n"), + "adaptation candidate grid" + ) + if (any(validation$adaptation_n != 25L) || + any(validation$validation_n != 25L) || + !identical( + receipt$adaptation_prior_selection$rule, + "leave-one-observed-site-out mean conditional log score" + )) { + stop("adaptation selection does not match the registered folds", + call. = FALSE) + } + if (!identical(sort(unique(validation$site)), reference_sites) || + !identical( + sort(unlist(receipt$adaptation_prior_selection$source_sites, use.names = FALSE)), + reference_sites + ) || + !identical(receipt$adaptation_prior_selection$source_split, "reference_train")) { + stop("adaptation selection is not restricted to observed reference sites", + call. = FALSE) + } + for (site in reference_sites) { + fold <- receipt$adaptation_prior_selection$fold_sizes[[site]] + if (!identical(as.integer(fold$adaptation), 25L) || + !identical(as.integer(fold$validation), 25L)) { + stop("adaptation fold-size receipt does not match retained evidence", + call. = FALSE) + } + } + selected <- selection[selection$selected, c("location_prior_n", "scale_prior_n")] + recorded_selected <- receipt$adaptation_prior_selection$selected + if (nrow(selected) != 1L || + !isTRUE(all.equal( + selected$location_prior_n[[1L]], recorded_selected$location_prior_n + )) || + !isTRUE(all.equal( + selected$scale_prior_n[[1L]], recorded_selected$scale_prior_n + ))) { + stop("adaptation-prior receipt does not match recomputed selection", + call. = FALSE) + } + if (!identical( + isTRUE(receipt$all_sites_pass), all(recomputed_comparison$pass) + )) { + stop("site receipt verdict does not match recomputed gates", call. = FALSE) + } + expected_failed <- split( + as.character(recomputed_comparison$site[!recomputed_comparison$pass]), + as.character(recomputed_comparison$method[!recomputed_comparison$pass]) + ) + recorded_failed <- lapply( + receipt$failed_sites, + function(x) sort(unlist(x, use.names = FALSE)) + ) + expected_failed <- lapply(expected_failed, sort) + if (!setequal(names(recorded_failed), names(expected_failed)) || + any(vapply(names(expected_failed), function(name) { + !identical(recorded_failed[[name]], expected_failed[[name]]) + }, logical(1)))) { + stop("site receipt failed-site list does not match recomputed gates", + call. = FALSE) + } + invisible(list( + hbr = hbr, + receipt = receipt, + comparison = recomputed_comparison, + selection = selection + )) +} + +if (sys.nframe() == 0L) { + args <- commandArgs(trailingOnly = TRUE) + if (length(args) < 2L || length(args) > 4L) { + stop(paste( + "usage: validate_hbr_site_evidence.R INPUT_DIR OUTPUT_DIR", + "[HBR_RECEIPT_FILE] [SITE_RECEIPT_FILE]" + ), call. = FALSE) + } + hbr_receipt <- if (length(args) >= 3L) args[[3L]] else "receipt.json" + site_receipt <- if (length(args) >= 4L) args[[4L]] else "referent_receipt.json" + validated <- pcn_validate_site_evidence( + args[[1L]], args[[2L]], hbr_receipt, site_receipt + ) + cat( + "validated HBR stages:", nrow(validated$hbr$stages), + "; validated site gates:", nrow(validated$comparison), "\n" + ) +} diff --git a/tools/validation/run_nhanes_external_validation.R b/tools/validation/run_nhanes_external_validation.R index d3461a9..452999f 100644 --- a/tools/validation/run_nhanes_external_validation.R +++ b/tools/validation/run_nhanes_external_validation.R @@ -1,21 +1,44 @@ -# Independent real-cohort validation using public CDC NHANES files. -# -# Train: 2015-2016 adults. External evaluation: 2017-2018 adults. -# This lane is deliberately unweighted: it validates transport between two -# independently sampled cohorts, not survey-weighted U.S. prevalence. +# Real-cohort validation using public CDC NHANES files. The 2017-2018 lane is +# post-hoc model development; the separately registered 2013-2014 lane is the +# untouched confirmation. Both are deliberately unweighted and therefore do +# not estimate survey-weighted U.S. prevalence. -nhanes_files <- function() { +nhanes_evaluation_registry <- function() { + list( + development = list( + cohort = "2017-2018", + evidence_role = "post_hoc_model_development", + demo = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/DEMO_J.XPT", + body = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/BMX_J.XPT" + ), + confirmation = list( + cohort = "2013-2014", + evidence_role = "untouched_confirmation", + demo = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/DEMO_H.XPT", + body = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2013/DataFiles/BMX_H.XPT" + ) + ) +} + +nhanes_evaluation <- function(evaluation) { + registry <- nhanes_evaluation_registry() + evaluation <- match.arg(evaluation, names(registry)) + c(list(key = evaluation), registry[[evaluation]]) +} + +nhanes_files <- function(evaluation = "confirmation") { + selected <- nhanes_evaluation(evaluation) c( demo_train = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/DEMO_I.XPT", body_train = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2015/DataFiles/BMX_I.XPT", - demo_test = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/DEMO_J.XPT", - body_test = "https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/BMX_J.XPT" + demo_test = selected$demo, + body_test = selected$body ) } -nhanes_download <- function(cache_dir) { +nhanes_download <- function(cache_dir, evaluation = "confirmation") { dir.create(cache_dir, recursive = TRUE, showWarnings = FALSE) - urls <- nhanes_files() + urls <- nhanes_files(evaluation) paths <- file.path(cache_dir, basename(urls)) for (i in seq_along(urls)) { if (!file.exists(paths[[i]]) || file.info(paths[[i]])$size == 0) { @@ -100,17 +123,19 @@ nhanes_external_assess <- function(fit, data, n_draw) { ) } -run_nhanes_external_validation <- function(output_dir, cache_dir) { +run_nhanes_external_validation <- function(output_dir, cache_dir, + evaluation = "confirmation") { if (!requireNamespace("referent", quietly = TRUE) || !requireNamespace("foreign", quietly = TRUE) || !requireNamespace("jsonlite", quietly = TRUE) || !requireNamespace("digest", quietly = TRUE)) { stop("referent, foreign, jsonlite, and digest are required", call. = FALSE) } - paths <- nhanes_download(cache_dir) + selected <- nhanes_evaluation(evaluation) + paths <- nhanes_download(cache_dir, selected$key) train <- nhanes_cohort(paths[["demo_train"]], paths[["body_train"]], "2015-2016") test_population <- nhanes_cohort( - paths[["demo_test"]], paths[["body_test"]], "2017-2018" + paths[["demo_test"]], paths[["body_test"]], selected$cohort ) set.seed(20260824L) evaluation_n <- min(2000L, nrow(test_population)) @@ -143,23 +168,43 @@ run_nhanes_external_validation <- function(output_dir, cache_dir) { }, character(1)) input_receipts <- lapply(seq_along(paths), function(i) { list( - url = unname(nhanes_files()[[i]]), + url = unname(nhanes_files(selected$key)[[i]]), sha256 = digest::digest(file = paths[[i]], algo = "sha256"), bytes = unname(file.info(paths[[i]])$size) ) }) names(input_receipts) <- names(paths) output_receipts <- lapply(output_paths, function(path) { + table <- utils::read.csv(path, check.names = FALSE) list( sha256 = digest::digest(file = path, algo = "sha256"), - rows = nrow(utils::read.csv(path, check.names = FALSE)) + rows = nrow(table), + columns = names(table) ) }) + design_registration <- if (identical(selected$key, "confirmation")) { + list( + tracker = "mote", + issue = "referent-32n", + commit = "b1301a4e0426eb5b5a83935059053131331670d9", + status = "frozen and published before confirmation execution" + ) + } else { + list( + status = "post-hoc model development", + reason = paste( + "the earlier 2017-2018 conditional-scale failure informed", + "the demographic scale terms before this rerun" + ) + ) + } receipt <- list( - schema_version = "1.0.0", + schema_version = "1.1.0", + evidence_role = selected$evidence_role, + design_registration = design_registration, estimand = "unweighted adult cohort transport, conditional predictive distribution", train_cohort = "NHANES 2015-2016", - evaluation_cohort = "NHANES 2017-2018", + evaluation_cohort = paste("NHANES", selected$cohort), age_range = c(18L, 79L), outcomes = outcomes, train_rows = nrow(train), @@ -168,6 +213,14 @@ run_nhanes_external_validation <- function(output_dir, cache_dir) { evaluation_sampling = "fixed-seed simple random sample without replacement (seed 20260824)", uncertainty = "total", n_draw = 1000L, + model_specification = list( + family = "SHASH", + location = "~ s(age, k = 8) + sex + race_ethnicity", + scale = "~ s(age, k = 5) + sex + race_ethnicity", + skew = "~ 1", + tail = "~ 1", + seed = 20260824L + ), package_version = as.character(utils::packageVersion("referent")), r_version = R.version.string, input_files = input_receipts, @@ -185,11 +238,16 @@ run_nhanes_external_validation <- function(output_dir, cache_dir) { if (sys.nframe() == 0L) { args <- commandArgs(trailingOnly = TRUE) - if (length(args) < 1L || length(args) > 2L) { - stop("usage: run_nhanes_external_validation.R OUTPUT_DIR [CACHE_DIR]", call. = FALSE) + if (length(args) < 1L || length(args) > 3L) { + stop(paste( + "usage: run_nhanes_external_validation.R OUTPUT_DIR", + "[CACHE_DIR] [confirmation|development]" + ), call. = FALSE) } cache <- if (length(args) == 2L) args[[2L]] else file.path(tempdir(), "nhanes-cache") - result <- run_nhanes_external_validation(args[[1L]], cache) + if (length(args) == 3L) cache <- args[[2L]] + evaluation <- if (length(args) == 3L) args[[3L]] else "confirmation" + result <- run_nhanes_external_validation(args[[1L]], cache, evaluation) print(result$gate, row.names = FALSE) if (!isTRUE(result$gate$pass)) quit(status = 1L) } From b1080373fe22fec3f424d0eb025a3c8405f8a1cd Mon Sep 17 00:00:00 2001 From: bbuchsbaum Date: Mon, 24 Aug 2026 20:34:44 -0400 Subject: [PATCH 13/13] Correct release documentation and diagnostic plots --- .beads/issues.jsonl | 10 +++++++++ R/plot.R | 23 +++++++++++++------- README.md | 25 ++++++++++++++++------ tests/testthat/test-plot-card.R | 5 ++++- vignettes/brain-charts-across-sites.Rmd | 28 +++++++++++++++---------- vignettes/validate-reference.Rmd | 10 +++++++++ 6 files changed, 76 insertions(+), 25 deletions(-) diff --git a/.beads/issues.jsonl b/.beads/issues.jsonl index dc7f6ef..32f5e8a 100644 --- a/.beads/issues.jsonl +++ b/.beads/issues.jsonl @@ -1,6 +1,16 @@ {"id":"referent-c48","title":"Dynamics kernel estimator: scale-unidentified, silent fallback to start values","description":"R/dynamics.R:104-151. process_correlation normalises so (tau_b,tau_g,sigma_e) are unidentified; BFGS walks ridge, eigen fails, tryCatch returns START values with identified=TRUE. norm_stable (ell=Inf) never fits. Fix: simplex parameterization + bounded log(ell), L-BFGS-B, check convergence, never silent fallback, stable+nugget when lags fixed, report normalised variance fractions. Also A4: NA velocity/forecast quantities when unidentified. Add OOS test on irregular lags: var(innovation) in 1±0.1, FPR in 0.05±0.015. Review A3/A4.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:18Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:20Z","started_at":"2026-08-22T13:02:50Z","closed_at":"2026-08-22T13:28:20Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-0rh","title":"Fix site scale adaptation bias (E[log|Z|] = -0.635)","description":"R/adapt.R:47-50 uses mean(log|r/s|); identical-site adaptation shrinks sigma to 0.55x, held-out var(z)=3.2-3.9. Fix: 0.5*log(mean((r/s)^2)) with z=qnorm(centile); add test that identical-site adaptation leaves var(z)~1. Review A2.","status":"closed","priority":0,"issue_type":"bug","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:17Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:28:19Z","started_at":"2026-08-22T13:02:49Z","closed_at":"2026-08-22T13:28:19Z","close_reason":"Phases 2-3","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-4du","title":"Derive covariates from spec formulas, not all non-outcome columns","description":"R/fit.R:30,45,260. Covariates are every non-outcome column: NA in unused columns silently drops rows; Mahalanobis support is computed over other outcomes; predict errors if unused column absent. Fix: all.vars() over spec formulas; complete-case on outcome+those; message dropped rows. See docs/review-2026-08-22.md A1.","status":"closed","priority":0,"issue_type":"bug","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T12:45:15Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T13:02:48Z","started_at":"2026-08-22T12:51:42Z","closed_at":"2026-08-22T13:02:48Z","close_reason":"Phase 1 fdb58e2","dependency_count":0,"dependent_count":0,"comment_count":0} +{"id":"referent-03k","title":"Correct README and vignette release documentation","description":"Resolve the five documentation audit findings: remove the false frozen-bundle privacy guarantee, align install/site wording with the open 0.1.0 candidate, describe the actual SHA-256 data hash, repair clipped diagnostic figures, and expose support filtering plus four-strategy wording. Regenerate and verify affected vignette/site outputs.","acceptance_criteria":"All five findings are corrected in source; affected vignettes render; local Starlight build and route checks pass; README quickstart reports evaluated support/count; git diff check and relevant tests pass.","notes":"Corrected candidate install/status wording and support denominator in README; corrected site strategy count, bundle privacy scope, and SHA-256 provenance semantics; wrapped diagnostic subtitles and shortened envelope annotations with regression tests. Verified 1,233 tests, six installed-package articles, 37 static routes, and R CMD check with 0 errors/0 warnings/1 expected New submission note.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-25T00:05:53Z","created_by":"bbuchsbaum","updated_at":"2026-08-25T00:32:11Z","started_at":"2026-08-25T00:05:56Z","closed_at":"2026-08-25T00:32:11Z","close_reason":"All five documentation findings corrected and verified.","dependency_count":0,"dependent_count":0,"comment_count":0} +{"id":"referent-32n","title":"Repair release evidence gates without relaxing contracts","description":"The retained release evidence has four honest failures: PCNtoolkit HBR divergences, Referent site-5 adaptation calibration, NHANES race-conditional scale drift, and skew-heavy comparator warnings. Implement deterministic high-target-accept HBR sampling controls, choose adaptation priors by leave-one-observed-site-out log score without using site-5 test rows, include declared demographic covariates in the NHANES scale model, and stabilize the pinned PCNtoolkit optimizer finite-difference step. Preserve all registered thresholds and warning classification rules.","acceptance_criteria":"HBR base and transfer receipts satisfy zero-divergence/R-hat/ESS gates under recorded seeds; site-5 passes using priors selected only from observed-site reference rows; NHANES locked cohort gate passes with no conditional offenders; all 20 skew-heavy comparator fits have zero critical warnings and the scenario is classified only from regenerated evidence; focused and full package checks pass; evidence and provenance are committed and pushed.","notes":"Registered thresholds unchanged. Final receipts: HBR base/transfer zero divergences with R-hat 1.00 and ESS above 1700; observed-site-only prior selection chose location_prior_n=0 and scale_prior_n=25 and site-5 passed; NHANES full gate passed; 20/20 skew-heavy comparator fits valid with zero critical warnings and independent confirmation classified superior. Validation: fixture drift exact; focused tests 158/158 plus strengthened workflow 22/22; full testthat 1170 pass with two environment binary-version warnings; final R CMD check --no-manual --as-cran completed with only expected New submission NOTE.","status":"closed","priority":1,"issue_type":"bug","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-24T16:32:55Z","created_by":"bbuchsbaum","updated_at":"2026-08-24T16:58:32Z","started_at":"2026-08-24T16:32:59Z","closed_at":"2026-08-24T16:58:32Z","close_reason":"All four registered release-evidence failures repaired, independently regenerated, fully checked, and ready for publication","dependency_count":0,"dependent_count":0,"comment_count":0} +{"id":"referent-eo6.7","title":"Publish tiered CI and documentation evidence","description":"Add fast offline PR checks, scheduled comparator regeneration and scenario runs, release-level HBR and multisite gates, machine-readable reports, and reader-facing evidence tables.","design":"Pinned fixtures remain stable until an explicit comparator upgrade. CI reports exact conformance separately from scientific superiority and unsupported lanes.","acceptance_criteria":"CI has documented PR, scheduled, and release tiers; the vignette links evidence and limitations; an upgrade produces reviewed drift rather than silently replacing baselines.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:59Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:52Z","started_at":"2026-08-23T14:01:27Z","closed_at":"2026-08-23T14:20:52Z","close_reason":"Added offline PR oracles, weekly pinned regeneration/drift rejection, release HBR/site gates, reproducible machine-readable evidence, and linked reader-facing documentation","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:59Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:14Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:15Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:16Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.7","depends_on_id":"referent-eo6.6","type":"blocks","created_at":"2026-08-23T09:30:17Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":4,"dependent_count":0,"comment_count":0} +{"id":"referent-eo6.6","title":"Evaluate HBR SHASH and site transport separately","description":"Add scheduled evidence for HBR posterior aggregation, SHASH fitted performance, site effects, unseen-site transport, and Referent adaptation without claiming false rowwise equivalence.","design":"Require convergence and Monte Carlo receipts. Use leave-one-site-out calibration and performance; keep PCNtoolkit transfer and Referent adaptation labeled as different estimands.","acceptance_criteria":"HBR and transport reports expose convergence, uncertainty, score and calibration evidence, and no small-site failure is averaged away.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:58Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:01:25Z","started_at":"2026-08-23T13:53:31Z","closed_at":"2026-08-23T14:01:25Z","close_reason":"HBR/transfer runner emits proper mixture and draw-averaged outputs, convergence receipts, per-site evidence, and a smoke run proved both convergence and small-site gates fail closed; release-grade evidence remains CI-gated","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:57Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.6","depends_on_id":"referent-eo6.5","type":"blocks","created_at":"2026-08-23T09:30:13Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6.5","title":"Establish held-out superiority and non-inferiority gates","description":"Implement preregistered synthetic scenario families, untouched test splits, paired proper-score comparisons, cluster-aware uncertainty, calibration non-inferiority gates, and negative controls.","design":"A differing Referent method is superior only when its paired held-out proper-score interval clears a practical margin and no marginal, conditional, site, or tail calibration gate materially regresses.","acceptance_criteria":"The benchmark classifies conformant, equivalent, superior, trade-off, and inferior outcomes; null and deliberately overconfident negative controls behave correctly.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:56Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:53:27Z","started_at":"2026-08-23T13:47:07Z","closed_at":"2026-08-23T13:53:27Z","close_reason":"Locked design registry, cluster bootstrap, calibration non-inferiority, full classification rules, and negative controls pass; skew-heavy held-out case is superior with positive log-score CI and better calibration","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:56Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.5","depends_on_id":"referent-eo6.4","type":"blocks","created_at":"2026-08-23T09:30:12Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.4","title":"Certify fitted-model concordance in overlap cases","description":"Compare identical train and test rows for intercept-only, linear, categorical, heteroskedastic, smooth, transformed, and balanced-site model configurations using per-row predictive outputs.","design":"Use normalized prediction error, predictive-scale error, Z RMSE, central quantile errors, log tails, and coverage. Avoid a sole maximum-error gate.","acceptance_criteria":"All registered overlap cases meet locked scenario-level concordance margins or have a documented non-equivalent classification.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:55Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:47:05Z","started_at":"2026-08-23T13:43:50Z","closed_at":"2026-08-23T13:47:05Z","close_reason":"Four matched linear/log Gaussian scenarios meet all locked prediction, scale, Z, tail-quantile, and coverage margins; divergent smooth/site cases are explicitly diagnostic","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:54Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.4","depends_on_id":"referent-eo6.3","type":"blocks","created_at":"2026-08-23T09:30:11Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.3","title":"Prove exact shared distribution and metric semantics","description":"Add analytic and differential tests for Gaussian, SHASHb to mgcv SHASH conversion, transforms, log tails, log densities, and explicitly normalized metric definitions.","design":"Use independent analytic formulas alongside external fixtures. Expected definition differences must be tested as differences, not hidden by broad tolerances.","acceptance_criteria":"Gaussian and SHASH semantic fixtures pass scale-aware declared tolerances, adversarial tails remain finite, and metric relationships are independently verified.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:53Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:43:48Z","started_at":"2026-08-23T13:41:06Z","closed_at":"2026-08-23T13:43:48Z","close_reason":"Independent R analytic, Referent, and pinned PCNtoolkit paths agree for Gaussian/converted SHASH/transforms; tail and definition-difference tests pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:53Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.3","depends_on_id":"referent-eo6.2","type":"blocks","created_at":"2026-08-23T09:30:10Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":2,"comment_count":0} +{"id":"referent-eo6.2","title":"Build reproducible offline PCNtoolkit fixture harness","description":"Add a pinned Python generator, neutral inputs, checked-in per-row PCNtoolkit outputs, provenance receipts, and an R comparison reader without adding Python to normal package tests.","design":"PCNtoolkit runs only during explicit regeneration or scheduled CI. Checked-in fixtures are versioned by comparator release and carry hashes and resolved environment metadata.","acceptance_criteria":"Fixture generation is deterministic; schema and hashes validate in R; normal testthat runs need no Python or network.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:52Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:41:04Z","started_at":"2026-08-23T13:34:09Z","closed_at":"2026-08-23T13:41:04Z","close_reason":"Pinned Python generator is byte-stable across two runs; checked-in fixture manifest, schema, hashes, and offline R validation pass","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:51Z","created_by":"bbuchsbaum","metadata":"{}"},{"issue_id":"referent-eo6.2","depends_on_id":"referent-eo6.1","type":"blocks","created_at":"2026-08-23T09:30:09Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":1,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6.1","title":"Define pinned PCNtoolkit validation contract","description":"Write the normative comparison contract for PCNtoolkit 1.3.0, classify outputs as exact, parameter-converted, matched-estimator, same-estimand, or non-equivalent, and reconcile the public comparison vignette with executed definitions.","design":"Separate semantic conformance from scientific superiority. Record conditional versus total uncertainty and executed versus documented comparator behavior.","acceptance_criteria":"A reviewed design document and corrected vignette define every comparison lane, estimand, output, uncertainty mode, and mismatch disposition.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:50Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T13:34:07Z","started_at":"2026-08-23T13:30:19Z","closed_at":"2026-08-23T13:34:07Z","close_reason":"Pinned comparison contract and corrected current PCNtoolkit 1.3.0 semantics in the public vignette","labels":["pcntoolkit","scientific-evidence","validation"],"dependencies":[{"issue_id":"referent-eo6.1","depends_on_id":"referent-eo6","type":"parent-child","created_at":"2026-08-23T09:29:50Z","created_by":"bbuchsbaum","metadata":"{}"}],"dependency_count":0,"dependent_count":1,"comment_count":0} +{"id":"referent-eo6","title":"Establish PCNtoolkit conformance and superiority evidence","description":"Implement the seven-part validation program: pin and document comparison contracts, generate reproducible PCNtoolkit fixtures, prove exact shared semantics, measure fitted-model concordance, establish held-out superiority without sacrificing calibration, evaluate HBR and site transport separately, and publish tiered CI evidence.","design":"PCNtoolkit is an external differential oracle only for shared contracts. Alternative Referent methods may diverge when paired held-out proper-score superiority and calibration non-inferiority are demonstrated. Normal R tests consume offline fixtures; Python regeneration is scheduled or manual.","acceptance_criteria":"All seven child work packages are complete; exact relationships pass declared tolerances; every mismatch is classified; superiority claims use untouched test data and calibration gates; normal R tests remain Python-free; documentation and CI expose evidence tiers.","status":"closed","priority":1,"issue_type":"epic","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-23T13:29:20Z","created_by":"bbuchsbaum","updated_at":"2026-08-23T14:20:54Z","started_at":"2026-08-23T13:30:18Z","closed_at":"2026-08-23T14:20:54Z","close_reason":"All seven PCNtoolkit conformance and superiority evidence lanes implemented and validated","labels":["pcntoolkit","scientific-evidence","validation"],"dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-xzc","title":"Make referent vignettes surpass PCNtoolkit docs","description":"Audit and improve referent's starlightdown vignette site against the live PCNtoolkit documentation. Establish a direct visual and reader-journey baseline, revise source vignettes and site navigation, verify executable examples and rendered desktop/mobile output, and publish the completed branch without touching unrelated work.","acceptance_criteria":"All public vignette pages use current exported APIs and render successfully; newcomer journey, task navigation, interpretation, and recovery are materially stronger than the live PCNtoolkit stable site; representative pages are compared at desktop and mobile viewports; repository tests and documentation build pass; intended changes are committed and pushed with tracking/live SHA verification.","notes":"Gauntlet complete. Independent critic final PASS and blind choice referent in all six categories. Fixed discovered S3 route blocker via docsLoader generateId preserving dotted method paths; added fail-closed generated plot alt postprocessor and generic 35-route identity/canonical/redirect/alt checker. Final gates: cold starlightdown build 35 routes, Astro 36 pages plus Pagefind, browser desktop/mobile axe clean and search working, R CMD build passed, LC_ALL=en_US.UTF-8 R CMD check --no-manual Status OK.","status":"closed","priority":1,"issue_type":"task","assignee":"bbuchsbaum","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T18:05:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T18:49:55Z","started_at":"2026-08-22T18:05:43Z","closed_at":"2026-08-22T18:49:55Z","close_reason":"Closed","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e4m","title":"Gauntlet 2: thrive lines and velocity","description":"thrive type was removed from autoplot.norm_forecast in Phase 5; referent now has no thrive capability. Bar for thrive: PCNtoolkit nb14 CorrelationMatrix + ZGainScore.get_thrivelines + plot_centiles_advanced on a shared constructed two-visit dataset (LNM_data.csv is not public). Bar for velocity: ground-truth recovery on simulated data, since PCNtoolkit has no velocity estimand.","status":"closed","priority":1,"issue_type":"feature","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:16:57Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:23Z","closed_at":"2026-08-22T19:13:23Z","close_reason":"Both lanes won round 1 and were verified by independent critics. Patches pending integration (thrive rebased needed; velocity chart_velocity_se drop).","dependency_count":0,"dependent_count":0,"comment_count":0} {"id":"referent-e6o","title":"Close WM-hypointensities MSLL gap vs information-matched PCN opponent","description":"referent -0.470 vs fair opponent -0.491. Test-split var_z 1.17-1.23 indicates 10-20 pct out-of-sample overconfidence. Try norm_calibrate on out-of-fold train scores (train-only) before rescoring.","status":"closed","priority":1,"issue_type":"task","owner":"brad.buchsbaum@gmail.com","created_at":"2026-08-22T16:09:37Z","created_by":"bbuchsbaum","updated_at":"2026-08-22T19:13:22Z","closed_at":"2026-08-22T19:13:22Z","close_reason":"Closed via ref_spec(transform=) Box-Cox response transform (commit 4dd3683). WM -0.49154 vs PCN fair -0.49087. NOTE: margin is +0.00068 nats with paired per-subject SE 0.033 (t=-0.02) - a point-estimate win only, not statistical evidence. Root cause was a missing capability, not tuning: SHASH moves conditional residual shape; PCN's warp is a monotone map on y.","dependency_count":0,"dependent_count":0,"comment_count":0} diff --git a/R/plot.R b/R/plot.R index 0f9c008..76bafa9 100644 --- a/R/plot.R +++ b/R/plot.R @@ -477,7 +477,7 @@ plot_qq <- function(assessment, level = 0.95, detrend = FALSE, expected = xlim[[1L]] + 0.04 * diff(xlim), y = ylim[[2L]] - 0.03 * diff(ylim), label = sprintf( - "outside simultaneous: %d of %d\noutside pointwise: %d (%.1f%%)", + "simultaneous: %d/%d\npointwise: %d (%.1f%%)", k_sim, n, k_pt, 100 * k_pt / n ) ) @@ -527,15 +527,24 @@ plot_qq <- function(assessment, level = 0.95, detrend = FALSE, if (multi) { p <- p + ggplot2::facet_wrap(~.outcome, nrow = 1) } + subtitle <- paste( + c( + if (!is.null(note)) note, + strwrap( + paste0( + pct, "% pointwise and simultaneous envelopes for a calibrated model; ", + format(100 * (1 - level), trim = TRUE), + "% of points are expected outside the pointwise band" + ), + width = 48L + ) + ), + collapse = "\n" + ) finish_plot( p, title = if (multi) NULL else as.character(df$.outcome[[1L]]), - subtitle = paste0( - if (!is.null(note)) paste0(note, "; "), - pct, "% pointwise and simultaneous envelopes for a calibrated model; ", - format(100 * (1 - level), trim = TRUE), - "% of points are expected outside the pointwise band" - ), + subtitle = subtitle, xlab = "Expected Z", ylab = if (detrend) paste(ylab, "minus expected") else ylab ) + diff --git a/README.md b/README.md index 211b89c..a508338 100644 --- a/README.md +++ b/README.md @@ -12,8 +12,10 @@ a threshold exceedance with a known chance rate, not an abnormality. The package is CDF-first: a Z-score is one representation of a centile, not a raw standardized residual. -> **Development status:** `referent` 0.1.0 is an initial, deliberately scoped -> release candidate. The current fitting surface supports numeric outcomes with +> **Development status:** `referent` 0.1.0 is an initial, deliberately scoped, +> source-only release candidate in [PR #4](https://github.com/bbuchsbaum/referent/pull/4). +> It is not yet on the default branch or available as a tagged release. The +> current fitting surface supports numeric outcomes with > Gaussian or sinh-arcsinh (SHASH) predictive families; categorical outcomes > are reported as unsupported instead of being silently coerced. Exact and > matched-estimator PCNtoolkit lanes pass. The replicated release matrix finds @@ -24,8 +26,9 @@ raw standardized residual. > same frozen conditional-calibration contract. These are scoped results, not a > blanket equivalence or superiority claim. -Browse the [documentation site](https://bbuchsbaum.github.io/referent/) -(or `browseVignettes("referent")` after installing), and start with [Getting started](https://bbuchsbaum.github.io/referent/articles/getting-started/). Keep +For the candidate documentation, use `browseVignettes("referent")` after +installing it. The [published documentation site](https://bbuchsbaum.github.io/referent/) +tracks the default branch and may lag an open candidate. Start with [Getting started](https://bbuchsbaum.github.io/referent/articles/getting-started/), and keep [Troubleshooting reference-model workflows](https://bbuchsbaum.github.io/referent/articles/troubleshooting/) nearby for support, transport, calibration, panel-fit, and longitudinal failure modes. The complete article map is below. @@ -34,9 +37,13 @@ failure modes. The complete article map is below. ```r # install.packages("pak") -pak::pak("bbuchsbaum/referent") +# Install the current source-only 0.1.0 candidate from PR #4: +pak::pak("bbuchsbaum/referent#4") ``` +The PR-qualified reference is intentional while 0.1.0 remains source-only; +`bbuchsbaum/referent` without `#4` installs the repository's default branch. + ## A first reference model ```r @@ -54,11 +61,17 @@ fit target <- ref_simulate(100, kind = "gaussian", scale = "age", seed = 2) scores <- predict(fit, newdata = target, type = "scores") scores[1:3, c(".id", "observed", "median", "centile", "z", "tail_prob", "support")] +table(scores$support) -ref_assess(fit, newdata = target)$marginal[, c("mean_z", "var_z", "cover_95")] +assessment <- ref_assess(fit, newdata = target) +assessment$marginal[, c("n", "mean_z", "var_z", "cover_95")] autoplot(fit, type = "centiles", by = sex, newdata = target) ``` +Support is part of the scoring result: the table makes boundary masking +visible, while `n` states the number of rows that actually entered the held-out +assessment. + Predictive distributions are [distributional](https://pkg.mitchelloharawild.com/distributional/) vectors, so the usual generics apply (`cdf()`, `quantile()`, `density()`, diff --git a/tests/testthat/test-plot-card.R b/tests/testthat/test-plot-card.R index 49b2153..6372041 100644 --- a/tests/testthat/test-plot-card.R +++ b/tests/testthat/test-plot-card.R @@ -106,10 +106,13 @@ test_that("assessment plots: calibration, qq, worm, conditional", { # qq and worm share the envelope: the worm is the qq chart minus the expected line pq <- autoplot(a, type = "qq") pw <- autoplot(a, type = "worm") + expect_true(grepl("\n", plot_label(pq, "subtitle"), fixed = TRUE)) + expect_true(grepl("\n", plot_label(pw, "subtitle"), fixed = TRUE)) bq <- expect_builds(pq) bw <- expect_builds(pw) clq <- layer_classes(pq) expect_equal(sum(clq == "GeomRibbon"), 2L) + expect_true(all(grepl("^simultaneous:", bq$data[[which(clq == "GeomText")]]$label))) expect_equal(nrow(bw$data[[which(layer_classes(pw) == "GeomPoint")]]), 2L * 80L) env_q <- pq$data env_w <- pw$data @@ -177,6 +180,7 @@ test_that("dynamics plots: kernel, held-out calibration, transitions, and anchor pc <- autoplot(dyn, type = "calibration", data = held, id = participant_id, time = age) bc <- expect_builds(pc) expect_match(plot_label(pc, "subtitle"), "held-out innovation Z") + expect_true(grepl("\n", plot_label(pc, "subtitle"), fixed = TRUE)) expect_equal(plot_label(pc, "y"), "Innovation Z") tr <- ref_transition(dyn, data = held, id = participant_id, time = age) pts <- bc$data[[which(layer_classes(pc) == "GeomPoint")]] @@ -263,4 +267,3 @@ test_that("thrive lines are ref_forecast() on a grid of one-visit histories", { expect_builds(p) expect_true("GeomRibbon" %in% layer_classes(p)) }) - diff --git a/vignettes/brain-charts-across-sites.Rmd b/vignettes/brain-charts-across-sites.Rmd index d1586b2..ad07eb0 100644 --- a/vignettes/brain-charts-across-sites.Rmd +++ b/vignettes/brain-charts-across-sites.Rmd @@ -24,7 +24,8 @@ each regional measure as a function of age and sex with a site effect, score every reference subject out of sample, and then score new participants, often from a scanner that contributed no controls. This vignette runs that workflow end to end on a simulated panel and -compares the three ways of scoring a new site. +compares four deployment strategies for a new site: the population curve, +adaptation, calibration, and a local refit. ```{r setup} library(referent) @@ -391,11 +392,14 @@ the smooth bases, the terms and factor levels (`xlevels`), plus the support reference (covariate ranges and factor levels), the calibration and adaptation slots if any were estimated, the reference-sample baseline used by `standardized_log_score`, and the model card. It excludes the training rows, -the model frame, fitted values, and residuals. No individual observation can -be recovered from it, which makes it suitable for privacy-preserving sharing. -That privacy property does not make the RDS container safe to open: RDS is -executable R serialization, so accept bundles only from an authenticated, -trusted source and verify their provenance through an independent channel. +the model frame, fitted values, and residuals. That reduces direct disclosure, +but it is not a privacy guarantee: coefficients and retained factor levels can +still reveal information about small or unique groups. Treat a bundle as a +sensitive model artifact, review it under the same disclosure policy as other +fitted models, and do not describe it as anonymised or differentially private. +RDS is also executable R serialization, so accept bundles only from an +authenticated, trusted source and verify their provenance through an +independent channel. A receiving site needs only the file and the package. Everything that applies to a `ref_fit` applies to the bundle: scoring, support checks, adaptation, and @@ -424,11 +428,13 @@ identical( ``` **Versioning.** The printed card records the package, `mgcv`, and R versions -that produced the bundle, and `bundle$data_hash` is a fingerprint of the -training data: its row count, the covariate and outcome names, and a -per-column sum. It distinguishes references fitted to different data without -storing any of it; add `rlang::hash(ref)` to the bundle yourself if you need -a cryptographic digest. Name files by reference version +that produced the bundle. `bundle$data_hash` is a SHA-256 digest of the +R-serialized training frame used by the fit: included columns are sorted and +row names are removed, while values and row order are retained. It binds a +bundle to that exact R-side input without storing the frame, but R serialization +is not a cross-language canonical data format. If interoperable provenance is +required, define and retain a separate canonical file or table digest in the +release receipt. Name files by reference version (`reference-v1.rds`), not by date, and treat a change in the reference cohort, the specification, or the package major version as a new reference: scores from different references are not comparable, and the fingerprint is diff --git a/vignettes/validate-reference.Rmd b/vignettes/validate-reference.Rmd index 3a3677c..e63b7f9 100644 --- a/vignettes/validate-reference.Rmd +++ b/vignettes/validate-reference.Rmd @@ -8,6 +8,16 @@ vignette: > %\VignetteEncoding{UTF-8} --- +```{r vignette-options, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.width = 6.5, + fig.height = 4.2, + dpi = 96 +) +``` + ```{r setup, message = FALSE, warning = FALSE} library(referent) reference <- ref_simulate(400, kind = "gaussian", scale = "age", seed = 1)