Replication code for the leakage-premium study
v1.0.0 — Initial public release First release of leakcheck: a Python package that screens a reported classification result for the signature of synthetic over-sampling applied before the train/test split. This is the version intended for archival (Zenodo DOI) and for software-paper review. Install pip install -e ".[dev]" python -m pytest -q Runtime dependency: NumPy only. The trained model ships as src/leakcheck/model.json and is evaluated by a pure-NumPy tree walker, so scores do not change when scikit-learn does. Use import leakcheck r = leakcheck.check( f1=0.92, imbalance_ratio=5.2, n_minority=237, n_features=44, clf="RandomForest", ) print(r.probability, r.verdict) print(r.explain()) leakcheck --f1 0.92 --ir 5.2 --n-minority 237 --n-features 44 --clf RandomForest What's in this version Screening model fitted on 22,400 labelled leaked/honest pairs from 41 datasets, validated leave-one-dataset-out (AUC 0.795). CLI (leakcheck), batch screening (check_many), and selftest() fixtures that match scikit-learn to 1e-9. paper.md / paper.bib for the software paper. Code/04_export_model.py to refit and rewrite src/leakcheck/model.json from results/raw_results.csv. Code/analysis.ipynb and figures/fig05_diagnostic.png. Verdicts | Probability | Label | |---|---| | < 0.35 | unremarkable | | 0.35–0.65 | worth asking about | | ≥ 0.65 | likely leaked | A high probability is a prompt to ask where the resampler sits relative to the split. It is not a finding of error or misconduct. Requirements Python ≥ 3.9; numpy>=1.21. Optional: pandas (pip install 'leakcheck[table]'). License BSD 3-Clause.