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XGBClassifier.feature_importances_ compatible with sklearn RFECV

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This commit is contained in:
Paulo Alves 2016-02-26 08:56:07 -03:00
parent 81257dcfb4
commit 592004b38f
1 changed files with 3 additions and 1 deletions
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python-package/xgboost/sklearn.py
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@ -44,6 +44,7 @@ def _objective_decorator(func):
return func(labels, preds)
return inner
class XGBModel(XGBModelBase):
# pylint: disable=too-many-arguments, too-many-instance-attributes, invalid-name
"""Implementation of the Scikit-Learn API for XGBoost.
@ -500,7 +501,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
fs_dict = dict(zip(keys, fs.values()))
all_features_dict = dict.fromkeys(range(0, self._features_count), 0)
all_features_dict.update(fs_dict)
return np.array(all_features_dict.values())
all_features = np.fromiter(all_features_dict.values(), np.float32)
return all_features / all_features.sum()
class XGBRegressor(XGBModel, XGBRegressorBase):