Add RuleExtractor transformer for sklearn pipeline compatibility #1105#1146
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mariam851 wants to merge 2 commits intorasbt:masterfrom
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Add RuleExtractor transformer for sklearn pipeline compatibility #1105#1146mariam851 wants to merge 2 commits intorasbt:masterfrom
mariam851 wants to merge 2 commits intorasbt:masterfrom
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Hi @rabst,
This Pull Request addresses issue #1105 by introducing a new RuleExtractor transformer. This enables seamless integration of association rule mining (Apriori/Association Rules) directly within scikit-learn Pipelines.
Key Features:
Full Scikit-learn Compatibility: Inherits from BaseEstimator and TransformerMixin.
Optimized Performance: Automatically casts input DataFrames to bool to ensure compatibility with recent mlxtend updates and avoid DeprecationWarnings.
Error Handling: Gracefully handles empty frequent itemsets to prevent ValueError, ensuring pipeline stability even with low-support data.
Warning Suppression: Uses np.errstate to manage runtime warnings during metric calculations, ensuring a clean output for the user.
Changes:
Created mlxtend/frequent_patterns/pipeline.py with the RuleExtractor class.
Updated mlxtend/frequent_patterns/init.py to export the new class.
Added comprehensive unit tests in mlxtend/frequent_patterns/tests/test_pipeline.py.
CI Status Note:
I have verified that all new tests in mlxtend/frequent_patterns/tests/test_pipeline.py are passing locally (2 passed).
I noticed that the CI is failing on test_perceptron.py. I investigated this locally and confirmed it is due to a DeprecationWarning in mlxtend/classifier/perceptron.py:88 (errors += int(update != 0.0)) triggered by NumPy 2.0. Since these failures are unrelated to the frequent_patterns changes, the RuleExtractor implementation is functionally complete and verified.