datalets v0.5.0 — pip install datalets

datalets — partition-then-fit machine learning. Cluster your data into datalets, run a model tournament per datalet, route every prediction to the right specialist. Many tiles, one picture.

pip install datalets            # import datalets

First release on PyPI, and a complete rebrand: one name everywhere (formerly developed as MOSAIC — renamed after a PyPI normalized-name collision with the unrelated mosaicml/Composer package).

• PartitionedClassifier / PartitionedRegressor: X-only clustering (12-clusterer zoo + inductive KNN-gate router), stratified-CV model tournaments per cluster (scikit-learn + XGBoost/LightGBM/CatBoost candidates), cross-fitted per-cluster thresholds, soft/hard routing, tournament-selected global fallback, and an honesty report that says when partitioning is NOT worth it.
• TournamentClassifier / TournamentRegressor (model selection standalone), DataletEnsembleClassifier / DataletEnsembleRegressor (ensemble across partition scales).
• clusterer="auto" with silhouette/BIC diagnostics and a fewest-clusters tie-break, sample_weight everywhere, calibrated routing gate, narrated fits with a live progress bar and the tree() finish tree, honest_cv leakage-free evaluation, DATALETS_QUIET=1 to silence it all.
• 478 tests, scikit-learn check_estimator compliant (6 estimators), hardened by two adversarial reviews. Fully offline.

Docs: DOCUMENTATION.md · CHANGELOG.md