[python-package] scikit-learn fit() methods: add eval_X, eval_y, deprecate eval_set #6857
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| Original file line number | Diff line number | Diff line change |
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@@ -2,6 +2,7 @@ | |
| """Scikit-learn wrapper interface for LightGBM.""" | ||
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| import copy | ||
| import warnings | ||
| from inspect import signature | ||
| from pathlib import Path | ||
| from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union | ||
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@@ -13,6 +14,7 @@ | |
| _MULTICLASS_OBJECTIVES, | ||
| Booster, | ||
| Dataset, | ||
| LGBMDeprecationWarning, | ||
| LightGBMError, | ||
| _choose_param_value, | ||
| _ConfigAliases, | ||
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@@ -341,7 +343,9 @@ def __call__( | |
| For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, | ||
| where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. | ||
| eval_set : list or None, optional (default=None) | ||
| .. deprecated:: 4.7.0 | ||
| A list of (X, y) tuple pairs to use as validation sets. | ||
| Use ``eval_X`` and ``eval_y`` instead. | ||
| eval_names : list of str, or None, optional (default=None) | ||
| Names of eval_set. | ||
| eval_sample_weight : {eval_sample_weight_shape} | ||
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@@ -376,6 +380,10 @@ def __call__( | |
| See Callbacks in Python API for more information. | ||
| init_model : str, pathlib.Path, Booster, LGBMModel or None, optional (default=None) | ||
| Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training. | ||
| eval_X : {X_shape}, or tuple of such inputs, or None, optional (default=None) | ||
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| Feature matrix or tuple thereof, e.g. ``(X_val0, X_val1)``, to use as validation sets. | ||
| eval_y : {y_shape}, or tuple of such inputs, or None, optional (default=None) | ||
| Target values or tuple thereof, e.g. ``(y_val0, y_val1)``, to use as validation sets. | ||
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| Returns | ||
| ------- | ||
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@@ -485,6 +493,42 @@ def _extract_evaluation_meta_data( | |
| raise TypeError(f"{name} should be dict or list") | ||
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| def _validate_eval_set_Xy( | ||
| *, | ||
| eval_set: Optional[List[_LGBM_ScikitValidSet]], | ||
| eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]], | ||
| eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]], | ||
| ) -> Optional[List[_LGBM_ScikitValidSet]]: | ||
| """Validate eval args. | ||
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| Returns | ||
| ------- | ||
| eval_set | ||
| """ | ||
| if eval_set is not None: | ||
| msg = "The argument 'eval_set' is deprecated, use 'eval_X' and 'eval_y' instead." | ||
| warnings.warn(msg, category=LGBMDeprecationWarning, stacklevel=2) | ||
| if eval_X is not None or eval_y is not None: | ||
| raise ValueError("Specify either 'eval_set' or 'eval_X' and 'eval_y', but not both.") | ||
| if isinstance(eval_set, tuple): | ||
| return [eval_set] | ||
| else: | ||
| return eval_set | ||
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Collaborator
There was a problem hiding this comment. (changed by me in d7e0fff) Although providing something like LightGBM/python-package/lightgbm/sklearn.py Line 914 in 544d439 ... it has been supported in LightGBM/python-package/lightgbm/sklearn.py Lines 990 to 992 in 544d439 Adding this line preserves that behavior. Changing the existing behavior when |
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| if (eval_X is None) != (eval_y is None): | ||
| raise ValueError("You must specify eval_X and eval_y, not just one of them.") | ||
| if eval_set is None and eval_X is not None: | ||
| if isinstance(eval_X, tuple) != isinstance(eval_y, tuple): | ||
| raise ValueError("If eval_X is a tuple, y_val must be a tuple of same length, and vice versa.") | ||
| if isinstance(eval_X, tuple) and isinstance(eval_y, tuple): | ||
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Collaborator
There was a problem hiding this comment. (changed by me in d7e0fff) This |
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| if len(eval_X) != len(eval_y): | ||
| raise ValueError("If eval_X is a tuple, y_val must be a tuple of same length, and vice versa.") | ||
| if isinstance(eval_X, tuple) and isinstance(eval_y, tuple): | ||
| eval_set = list(zip(eval_X, eval_y)) | ||
| else: | ||
| eval_set = [(eval_X, eval_y)] | ||
| return eval_set | ||
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| class LGBMModel(_LGBMModelBase): | ||
| """Implementation of the scikit-learn API for LightGBM.""" | ||
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@@ -932,6 +976,9 @@ def fit( | |
| categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", | ||
| callbacks: Optional[List[Callable]] = None, | ||
| init_model: Optional[Union[str, Path, Booster, "LGBMModel"]] = None, | ||
| *, | ||
| eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, | ||
| eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, | ||
| ) -> "LGBMModel": | ||
| """Docstring is set after definition, using a template.""" | ||
| params = self._process_params(stage="fit") | ||
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@@ -1000,9 +1047,15 @@ def fit( | |
| ) | ||
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| valid_sets: List[Dataset] = [] | ||
| eval_set = _validate_eval_set_Xy(eval_set=eval_set, eval_X=eval_X, eval_y=eval_y) | ||
| if eval_set is not None: | ||
| # check eval_group (only relevant for ranking tasks) | ||
| if eval_group is not None: | ||
| if len(eval_group) != len(eval_set): | ||
| raise ValueError( | ||
| f"Length of eval_group ({len(eval_group)}) not equal to length of eval_set ({len(eval_set)})" | ||
| ) | ||
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| for i, valid_data in enumerate(eval_set): | ||
| # reduce cost for prediction training data | ||
| if valid_data[0] is X and valid_data[1] is y: | ||
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@@ -1406,6 +1459,9 @@ def fit( # type: ignore[override] | |
| categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", | ||
| callbacks: Optional[List[Callable]] = None, | ||
| init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, | ||
| *, | ||
| eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, | ||
| eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, | ||
| ) -> "LGBMRegressor": | ||
| """Docstring is inherited from the LGBMModel.""" | ||
| super().fit( | ||
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@@ -1414,6 +1470,8 @@ def fit( # type: ignore[override] | |
| sample_weight=sample_weight, | ||
| init_score=init_score, | ||
| eval_set=eval_set, | ||
| eval_X=eval_X, | ||
| eval_y=eval_y, | ||
| eval_names=eval_names, | ||
| eval_sample_weight=eval_sample_weight, | ||
| eval_init_score=eval_init_score, | ||
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@@ -1521,6 +1579,9 @@ def fit( # type: ignore[override] | |
| categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", | ||
| callbacks: Optional[List[Callable]] = None, | ||
| init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, | ||
| *, | ||
| eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, | ||
| eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, | ||
| ) -> "LGBMClassifier": | ||
| """Docstring is inherited from the LGBMModel.""" | ||
| _LGBMAssertAllFinite(y) | ||
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@@ -1578,6 +1639,8 @@ def fit( # type: ignore[override] | |
| init_score=init_score, | ||
| eval_set=valid_sets, | ||
| eval_names=eval_names, | ||
| eval_X=eval_X, | ||
| eval_y=eval_y, | ||
| eval_sample_weight=eval_sample_weight, | ||
| eval_class_weight=eval_class_weight, | ||
| eval_init_score=eval_init_score, | ||
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@@ -1773,27 +1836,17 @@ def fit( # type: ignore[override] | |
| categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", | ||
| callbacks: Optional[List[Callable]] = None, | ||
| init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, | ||
| *, | ||
| eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, | ||
| eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, | ||
| ) -> "LGBMRanker": | ||
| """Docstring is inherited from the LGBMModel.""" | ||
| # check group data | ||
| if group is None: | ||
| raise ValueError("Should set group for ranking task") | ||
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Comment on lines
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Collaborator
There was a problem hiding this comment. (changed by me in d7e0fff)
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| if eval_group is None and (eval_set is not None or eval_X is not None or eval_y is not None): | ||
| raise ValueError("eval_group cannot be None if any of eval_set, eval_X, or eval_y are provided") | ||
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| self._eval_at = eval_at | ||
| super().fit( | ||
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@@ -1804,6 +1857,8 @@ def fit( # type: ignore[override] | |
| group=group, | ||
| eval_set=eval_set, | ||
| eval_names=eval_names, | ||
| eval_X=eval_X, | ||
| eval_y=eval_y, | ||
| eval_sample_weight=eval_sample_weight, | ||
| eval_init_score=eval_init_score, | ||
| eval_group=eval_group, | ||
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(changed by me in d7e0fff)
If we're going to consider
eval_setdeprecated andeval_{X,y}the new recommended pattern, l think we should nudge users towards that by updating all of the documentation. I've done that here (examples/is the only place with such code).There was a problem hiding this comment.
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I think
eval_X=X_test, eval_y=y_testwithout wrapping into a tuple is fine, too.There was a problem hiding this comment.
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For sure, either will work. I have a very weak preference for the tuple form in these docs, to make it a little clearer that providing multiple validation sets is supported.