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Extended Model class to support any sklearn estimator type while maintaining full backward compatibility with existing frozen models. Changes: - Added 'estimator' parameter alongside 'clf' for clearer API - Internal storage remains '_clf' to ensure old pickles load correctly - Implemented automatic type detection (classifier/regressor/transformer) - Added transform() method for transformers - Updated fit(), predict(), predict_proba(), evaluate() with type checking - Modified Data.classes property to handle non-classification cases - Updated mixins with type validation and informative error messages - Added summary() field for estimator type - Comprehensive test coverage for all estimator types - Updated documentation with usage examples Backward compatibility: - Zero migration code needed for existing pickles - Both 'clf' and 'estimator' parameters work - All existing code continues to function unchanged - 61 tests pass with 97% coverage
Added a new section demonstrating how to use sklearn transformers (StandardScaler) with ColdSnap. The example uses the iris dataset from sklearn for easy testing without additional dependencies. Example shows: - Creating a Model with a transformer using the estimator parameter - Fitting and transforming data - Saving and loading fitted transformers - Viewing before/after transformation results
Add comprehensive tests to cover all remaining uncovered code paths: - Data.classes returning None after purge - Model.estimator setter property - Error handling for predict/transform/predict_proba without estimator - Edge case for transform on estimator without transform method - TypeError enforcement for mixin methods on non-classifiers All 68 tests now pass with 100% coverage across all source files.
Version 0.1.0 introduces significant new functionality: - Support for sklearn transformers and regressors - New estimator parameter with backward-compatible clf parameter - 100% test coverage - Enhanced type detection and error handling Add CHANGELOG.md to document all changes between versions.
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