Deprecate LabelEncoder in XGBClassifier; Enable cuDF/cuPy inputs in XGBClassifier (#6269)
* Deprecate LabelEncoder in XGBClassifier; skip LabelEncoder for cuDF/cuPy inputs * Add unit tests for cuDF and cuPy inputs with XGBClassifier * Fix lint * Clarify warning * Move use_label_encoder option to XGBClassifier constructor * Add a test for cudf.Series * Add use_label_encoder to XGBRFClassifier doc * Address reviewer feedback
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Philip Hyunsu Cho
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@@ -7,6 +7,7 @@ import json
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import numpy as np
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from .core import Booster, DMatrix, XGBoostError
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from .training import train
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from .data import _is_cudf_df, _is_cudf_ser, _is_cupy_array
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# Do not use class names on scikit-learn directly. Re-define the classes on
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# .compat to guarantee the behavior without scikit-learn
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@@ -345,7 +346,7 @@ class XGBModel(XGBModelBase):
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params = self.get_params()
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# Parameters that should not go into native learner.
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wrapper_specific = {
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'importance_type', 'kwargs', 'missing', 'n_estimators'}
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'importance_type', 'kwargs', 'missing', 'n_estimators', 'use_label_encoder'}
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filtered = dict()
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for k, v in params.items():
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if k not in wrapper_specific:
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@@ -430,6 +431,9 @@ class XGBModel(XGBModelBase):
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if k == 'classes_':
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self.classes_ = np.array(v)
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continue
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if k == 'use_label_encoder':
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self.use_label_encoder = bool(v)
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continue
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if k == 'type' and type(self).__name__ != v:
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msg = 'Current model type: {}, '.format(type(self).__name__) + \
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'type of model in file: {}'.format(v)
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@@ -763,21 +767,53 @@ class XGBModel(XGBModelBase):
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['model', 'objective'], extra_parameters='''
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n_estimators : int
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Number of boosting rounds.
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use_label_encoder : bool
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(Deprecated) Use the label encoder from scikit-learn to encode the labels. For new code,
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we recommend that you set this parameter to False.
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''')
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class XGBClassifier(XGBModel, XGBClassifierBase):
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# pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
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def __init__(self, objective="binary:logistic", **kwargs):
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def __init__(self, objective="binary:logistic", use_label_encoder=True, **kwargs):
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self.use_label_encoder = use_label_encoder
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super().__init__(objective=objective, **kwargs)
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def fit(self, X, y, sample_weight=None, base_margin=None,
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eval_set=None, eval_metric=None,
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early_stopping_rounds=None, verbose=True, xgb_model=None,
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sample_weight_eval_set=None, feature_weights=None, callbacks=None):
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# pylint: disable = attribute-defined-outside-init,arguments-differ
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# pylint: disable = attribute-defined-outside-init,arguments-differ,too-many-statements
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can_use_label_encoder = True
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label_encoding_check_error = (
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'The label must consist of integer labels of form 0, 1, 2, ..., [num_class - 1].')
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label_encoder_deprecation_msg = (
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'The use of label encoder in XGBClassifier is deprecated and will be ' +
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'removed in a future release. To remove this warning, do the ' +
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'following: 1) Pass option use_label_encoder=False when constructing ' +
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'XGBClassifier object; and 2) Encode your labels (y) as integers ' +
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'starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].')
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evals_result = {}
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self.classes_ = np.unique(y)
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self.n_classes_ = len(self.classes_)
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if _is_cudf_df(y) or _is_cudf_ser(y):
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import cupy as cp # pylint: disable=E0401
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self.classes_ = cp.unique(y.values)
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self.n_classes_ = len(self.classes_)
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can_use_label_encoder = False
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if not cp.array_equal(self.classes_, cp.arange(self.n_classes_)):
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raise ValueError(label_encoding_check_error)
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elif _is_cupy_array(y):
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import cupy as cp # pylint: disable=E0401
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self.classes_ = cp.unique(y)
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self.n_classes_ = len(self.classes_)
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can_use_label_encoder = False
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if not cp.array_equal(self.classes_, cp.arange(self.n_classes_)):
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raise ValueError(label_encoding_check_error)
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else:
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self.classes_ = np.unique(y)
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self.n_classes_ = len(self.classes_)
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if not self.use_label_encoder and (
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not np.array_equal(self.classes_, np.arange(self.n_classes_))):
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raise ValueError(label_encoding_check_error)
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xgb_options = self.get_xgb_params()
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@@ -801,8 +837,18 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
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else:
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xgb_options.update({"eval_metric": eval_metric})
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self._le = XGBoostLabelEncoder().fit(y)
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training_labels = self._le.transform(y)
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if self.use_label_encoder:
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if not can_use_label_encoder:
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raise ValueError('The option use_label_encoder=True is incompatible with inputs ' +
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'of type cuDF or cuPy. Please set use_label_encoder=False when ' +
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'constructing XGBClassifier object. NOTE: ' +
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label_encoder_deprecation_msg)
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warnings.warn(label_encoder_deprecation_msg, UserWarning)
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self._le = XGBoostLabelEncoder().fit(y)
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label_transform = self._le.transform
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else:
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label_transform = (lambda x: x)
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training_labels = label_transform(y)
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if eval_set is not None:
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if sample_weight_eval_set is None:
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@@ -811,7 +857,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
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assert len(sample_weight_eval_set) == len(eval_set)
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evals = list(
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DMatrix(eval_set[i][0],
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label=self._le.transform(eval_set[i][1]),
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label=label_transform(eval_set[i][1]),
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missing=self.missing, weight=sample_weight_eval_set[i],
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nthread=self.n_jobs)
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for i in range(len(eval_set))
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@@ -919,9 +965,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
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if hasattr(self, '_le'):
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return self._le.inverse_transform(column_indexes)
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warnings.warn(
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'Label encoder is not defined. Returning class probability.')
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return class_probs
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return column_indexes
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def predict_proba(self, data, ntree_limit=None, validate_features=False,
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base_margin=None):
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@@ -1012,6 +1056,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
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extra_parameters='''
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n_estimators : int
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Number of trees in random forest to fit.
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use_label_encoder : bool
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(Deprecated) Use the label encoder from scikit-learn to encode the labels. For new code,
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we recommend that you set this parameter to False.
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''')
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class XGBRFClassifier(XGBClassifier):
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# pylint: disable=missing-docstring
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@@ -1020,11 +1067,13 @@ class XGBRFClassifier(XGBClassifier):
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subsample=0.8,
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colsample_bynode=0.8,
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reg_lambda=1e-5,
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use_label_encoder=True,
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**kwargs):
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super().__init__(learning_rate=learning_rate,
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subsample=subsample,
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colsample_bynode=colsample_bynode,
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reg_lambda=reg_lambda,
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use_label_encoder=use_label_encoder,
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**kwargs)
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def get_xgb_params(self):
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