Fix missing argument in StratifiedKFold.split() #1304
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In the `evaluate_model_CV` method of the `generic_task.py` module, the `StratifiedKFold.split()` method was missing a required positional argument 'y'. This caused a TypeError when attempting to split the dataset for cross-validation. To address this issue, I added the missing 'y' argument to the `StratifiedKFold.split()` method call. The corrected line of code now reads: ```python kf = kf.split(X_train_split, y_train_split) ``` This modification ensures that the StratifiedKFold cross-validation splits the dataset correctly, allowing the AutoML fit method to run without encountering errors. This fix resolves the issue reported in the FLAML library when using custom StratifiedKFold cross-validation.
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@microsoft-github-policy-service agree |
Borda
reviewed
Aug 12, 2024
| state.fit_kwargs[ | ||
| "sample_weight" | ||
| ] = ( | ||
| state.fit_kwargs["sample_weight"] = ( |
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lets reduce the change, keep it focus
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In the
evaluate_model_CVmethod of thegeneric_task.pymodule, theStratifiedKFold.split()method was missing a required positional argument 'y'. This caused a TypeError when attempting to split the dataset for cross-validation.To address this issue #1303, I added the missing 'y' argument to the
StratifiedKFold.split()method call. The corrected line of code now reads:This modification ensures that the StratifiedKFold cross-validation splits the dataset correctly, allowing the AutoML fit method to run without encountering errors.
This fix resolves the issue reported in the FLAML library when using custom StratifiedKFold cross-validation.
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