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| result = encoder.transform(test_X) | ||
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| # D and E should get default value 0.0 for all bits | ||
| d_row = ( |
| """Test that invalid operator raises ValidationError.""" | ||
| from pydantic import ValidationError | ||
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| X = pl.DataFrame({"A": [10, 20, 30], "B": [15, 10, 30]}) |
| """Test that invalid reference_date types raise ValidationError from Pydantic.""" | ||
| from pydantic import ValidationError | ||
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| X = pl.DataFrame({"offset": [1, 2, 3]}) |
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| def test_validation_mismatched_new_column_names_length(): | ||
| """Test validation error when new_column_names length doesn't match.""" | ||
| X = pl.DataFrame({"value": [10, 20], "group": ["A", "B"]}) |
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| def test_validation_mismatched_new_column_names_length(): | ||
| """Test validation error when new_column_names length doesn't match.""" | ||
| X = pl.DataFrame({"value": [10, 20], "group": ["A", "B"]}) |
| # With low smoothing | ||
| encoder_low = LeaveOneOutEncoder(subset=["category"], smoothing=0.1, inplace=False) | ||
| encoder_low.fit(X, y=target) | ||
| result_low = encoder_low.transform(X) |
| # Strings 'a', 'b', 'c' are too short for trigrams | ||
| # Only 'ab' is still too short | ||
| # Should not have any n-grams or very few | ||
| new_features = [col for col in result.columns if col.startswith("text__ng_")] |
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| def test_transform_drop_columns_multiple_groups(): | ||
| """Test drop_columns with multiple groups - validation should catch invalid group.""" | ||
| X = pl.DataFrame({"A": [10, 20], "B": [5, 10], "C": [100, 200], "D": [50, 100]}) |
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| def test_empty_new_column_names_raises_error(): | ||
| """Test that empty new_column_names list raises ValueError.""" | ||
| X = pl.DataFrame({"A": [10, 20, 30]}) |
| } | ||
| ) | ||
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| y = pl.Series("target", [0, 0, 0, 1, 1]) |
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