Batch phase 3: sklearn transformer facades and f_rupture - #461
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Adds BaseEstimator/TransformerMixin facades over the existing batch free functions so a batch workflow can be an sklearn pipeline, with the learned state carried on the fitted estimator: BatchScaler (range scaling with inverse_transform), ResampleAligner (linear resampling to a common length), DTWAligner (iterative weighted DTW, learning a reference and weights and aligning new batches to them), and BatchFeatureExtractor (batch dict to a batch-by-feature matrix for PLS-to-quality). The free functions are unchanged and remain the implementation layer. Implements the previously-stubbed f_rupture via the optional ruptures library (PELT changepoint detection), and re-points the batch extra to ruptures - it previously declared openpyxl and scikit-image, which were imported nowhere. Bumps to 1.57.0 with CITATION, CHANGELOG, and docs updates. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01SyEpexCyqHn1rwTMYQSiP6
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…plots, loaders Consolidates the stale #459 / #460 / #462 stack onto current main, as one commit series inside the mid-course-correction PR: - BatchPCA: batchwise-unfolded (Nomikos-MacGregor) PCA with an optional initial-conditions (Z) join, composed over multivariate.PCA and MCUVScaler; batch-level scores, SPE, T2, contributions and the plot and limit conveniences. - BatchPCA.predict_online: projection to the model plane for a partially-observed batch (upgraded to the shared missing-data projection API in a later commit series). - BatchMonitor: time-varying online SPE and T2 limits learned from good batches, with per-sample alarms, plus online_monitoring_plot. - BatchPLS: batchwise-unfolded [Z | X] -> Y regression to final quality. Refreshed beyond the original: the class now owns both scalers publicly (center_/scale_ for the unfolded block, y_center_/y_scale_ for the quality block; the inner PLS runs with scale=False), reports beta and RMSE in engineering units, and adds a prediction_interval wrapper. The mid-course corrector reads these public attributes instead of any PLS internals. - time_varying_loading_plot and contribution_at_time_plot for the unfolded structure; dataset loaders load_nylon, load_dryer and load_batch_fake_data for the bundled batch CSVs. - Integration test: BatchPLS on a simulator historical campaign (80 batches, 4 components) reaches fit R2 0.83 and out-of-sample correlation 0.80 against fresh batches; thresholds pinned below with margin. The #461 transformer facades are deliberately not re-landed; they can be revived separately if wanted. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NZAyiPT8xURSdi24yFKCra
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Summary
BaseEstimator/TransformerMixinfacades over the existing batch free functions, so a batch workflow can be expressed as a pipeline and the learned state is carried on the fitted estimator:BatchScaler(range scaling withinverse_transform),ResampleAligner(linear resampling to a common length),DTWAligner(iterative weighted DTW that learns a reference and weights and aligns new batches to them), andBatchFeatureExtractor(a batch dict to a batch-by-feature matrix suitable as the X block of a PLS-to-quality model). The free functions are unchanged and remain the implementation layer.f_rupturevia the optionalruptureslibrary (PELT changepoint detection), and re-points thebatchextra toruptures- it previously declaredopenpyxlandscikit-image, which were imported nowhere.This is Phase 3 of the batch modernization plan (pipeline ergonomics + feature completion).
Note on base branch: stacked on
claude/batch-phase2-online-monitoring(#460), which is stacked on Phase 1 (#459). GitHub will retarget the base automatically as each parent merges. Review #459, then #460, then this.Test plan
tests/batch/test_transformers.py: scaler round-trip; resampler equal-length + auto-reference; DTW aligns the training set and a held-out batch to the reference length; feature matrix shape/index and feeding a PLS model; unknown-feature guard;f_rupturedetects an injected step (guarded byimportorskip("ruptures")) and rejects multi-column inputruff check .andmypy src/process_improvecleanChecklist
pyproject.toml(MINOR: 1.56.0 -> 1.57.0) andCITATION.cffsyncedruff check .passesCHANGELOG.mdupdated🤖 Generated with Claude Code
https://claude.ai/code/session_01SyEpexCyqHn1rwTMYQSiP6
Generated by Claude Code