38 lines
2.0 KiB
Markdown
38 lines
2.0 KiB
Markdown
# Documentation Index
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This folder tracks project decisions, experiments, and evolving ideas.
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- `decisions.md`: design/architecture changes and rationales
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- `experiments.md`: experiment runs and results
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- `ideas.md`: future ideas and hypotheses
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- `architecture.md`: system overview and module boundaries
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- `evaluation.md`: evaluation protocol and metric usage
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Conventions:
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- Append new entries instead of overwriting old ones.
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- Record exact config file and key overrides when possible.
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- Keep metrics in the order: avg_ks / avg_jsd / avg_lag1_diff.
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Tools:
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- `example/diagnose_ks.py` for per-feature KS + CDF plots.
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- `example/run_all.py` for one-command full pipeline (train/export/eval/postprocess/diagnostics).
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- `example/run_all_full.py` legacy full pipeline runner.
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- `example/filtered_metrics.py` for filtered KS after removing collapsed/outlier features.
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- `example/ranked_ks.py` for ranked KS table + cumulative avg_ks if removing top features.
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- `example/evaluate_generated.py` for full-reference metrics (now supports glob over all train*.csv.gz).
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- `example/program_stats.py` for dwell/change/step stats on program-like features.
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- `example/controller_stats.py` for controller saturation/change stats.
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- `example/actuator_stats.py` for spike/dwell stats on actuators.
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- `example/pv_stats.py` for PV quantile/tail stats.
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- `example/aux_stats.py` for aux signal mean/std/lag1 stats.
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- `example/postprocess_types.py` for type-based postprocessing (Type1/2/3/5/6).
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Notes:
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- If `use_quantile_transform` is enabled, run `prepare_data.py` with `full_stats: true` to build quantile tables.
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Current status (high level):
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- Two-stage pipeline (GRU trend + diffusion residuals).
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- Quantile transform + post-hoc calibration enabled for continuous features.
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- KS evaluation uses full reference glob and tie-aware KS implementation.
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- Type-based postprocess (empirical resample for Type1/2/3/5/6) used as a KS-lowering baseline.
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- Latest model run (2026-01-27 21:22): avg_ks ~0.405 / avg_jsd ~0.038 / avg_lag1_diff ~0.145.
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