Add distribution drift detection#290
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stats had A/B experiment tests but no Population Stability Index and no Kolmogorov-Smirnov two-sample test for the reference-vs-current "is today's data shaped like the baseline" check. Add psi, ks_two_sample, categorical_drift, and a detect_drift wrapper that pair with data_profile. Wired through facade, executor (AC_detect_drift / AC_categorical_drift), MCP, and the Script Builder with a headless test batch and EN/Zh docs.
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 55 |
| Duplication | 0 |
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What
Adds distribution-drift checks the
statsmodule lacked — it had A/B experiment tests but no PSI and no KS two-sample test for "is today's data shaped like the baseline".psi(reference, current, bins=10)— Population Stability Index (reference quantile bins, log-ratio per bin).ks_two_sample(reference, current)→{statistic, p_value}(Kolmogorov distribution p-value).categorical_drift(reference, current)→{chi_square, total_variation, categories}.detect_drift(reference, current, *, threshold=0.25, bins=10)→{psi, drifted, ks}.Pairs with
data_profile; reusesstats.percentile.Layers
utils/data_drift/(pure stdlibmath/bisect/collections+stats, zero PySide6).__all__.AC_detect_drift,AC_categorical_drift.ac_detect_drift,ac_categorical_drift(read-only).test/unit_test/headless/test_data_drift_batch.py(10 tests,pytest.approxfor floats).v82_features_doc.rst(EN + Zh) + toctrees + 3 README What's-new sections.Verification
pytest test/unit_test/headless/test_data_drift_batch.py→ 10 passed.ruff check je_auto_control/clean; pylint 10.00/10; bandit clean; radon CC clean.