Batch phase 4: BatchPLS to final quality - #462
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BatchPLS mirrors BatchPCA's construction (unfold the batches batchwise, join an optional initial-conditions Z block onto the one-row-per-batch matrix) but fits the existing multivariate.PLS against a batch-indexed final-quality block Y. It relates the initial conditions and time-varying trajectory deviations to the final product quality, and predicts the quality of a completed batch. The X-weights keep the (tag, sequence) index so they reshape to a variable-by-time grid and plot with time_varying_loading_plot; predict and transform return results in the input batch order. Bumps to 1.58.0 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
BatchPLS: batchwise-unfolded (multiway) PLS relating the unfolded[Z | X]batch matrix to a final-quality blockY(one row per batch), built by composition overmultivariate.PLS. It mirrorsBatchPCA's construction (batchwise unfold, optional initial-conditions Z join) but is supervised: it relates the initial conditions and time-varying trajectory deviations to the final product quality, and predicts the quality of a completed batch.(tag, sequence)index so they reshape to a variable-by-time grid and plot with the existingtime_varying_loading_plot.predictandtransformreturn results in the input batch order.This is Phase 4 (the final phase) of the batch modernization plan: the batch regression / prediction counterpart to
BatchPCA.Note on base branch: stacked on
claude/batch-phase3-transformers(#461) → #460 → #459. GitHub retargets the base automatically as each parent merges. Review order: #459, #460, #461, then this.Test plan
tests/batch/test_batch_pls.py: fit + predict on the real dryer dataset (scores shape, MultiIndex weights, R2, prediction correlates with quality); weights reshape to a (tag, time) grid; Z-block join; a synthetic batch whose quality is a known function of its trajectories is recovered; Y type/index guards; wrong-length rejection; transform returns scoresruff check .andmypy src/process_improveclean (144 files)Note on the OWU phase
The originally-planned Phase 4 was observationwise unfolding (OWU / OWU-TBWU) plus maturity-variable alignment. Per your steer, OWU was dropped (batchwise unfolding only, one row per batch, Z included), so this phase delivers
BatchPLSon that same[Z | X]matrix instead.Checklist
pyproject.toml(MINOR: 1.57.0 -> 1.58.0) andCITATION.cffsyncedruff check .passesCHANGELOG.mdupdated🤖 Generated with Claude Code
https://claude.ai/code/session_01SyEpexCyqHn1rwTMYQSiP6
Generated by Claude Code