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adding sample weights for XGBRegressor (was this forgotten?) (#1874)

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Srivatsan Ramanujam 2017-01-21 11:58:03 -08:00 committed by Tianqi Chen
parent 2b5b96d760
commit 036ee55fe0
1 changed files with 7 additions and 2 deletions
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9
python-package/xgboost/sklearn.py
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@ -182,7 +182,7 @@ class XGBModel(XGBModelBase):
xgb_params.pop('nthread', None)
return xgb_params
def fit(self, X, y, eval_set=None, eval_metric=None,
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True):
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
"""
@ -194,6 +194,8 @@ class XGBModel(XGBModelBase):
Feature matrix
y : array_like
Labels
sample_weight : array_like
instance weights
eval_set : list, optional
A list of (X, y) tuple pairs to use as a validation set for
early-stopping
@ -219,7 +221,10 @@ class XGBModel(XGBModelBase):
If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr.
"""
trainDmatrix = DMatrix(X, label=y, missing=self.missing)
if sample_weight is not None:
trainDmatrix = DMatrix(X, label=y, weight=sample_weight, missing=self.missing)
else:
trainDmatrix = DMatrix(X, label=y, missing=self.missing)
evals_result = {}
if eval_set is not None: