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Add validate_features parameter to sklearn API (#3653)

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Philip Hyunsu Cho 2018-08-29 23:21:46 -07:00 committed by GitHub
parent 72cd1517d6
commit 7b1427f926
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1 changed files with 21 additions and 8 deletions
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python-package/xgboost/sklearn.py
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@ -339,7 +339,7 @@ class XGBModel(XGBModelBase):
self.best_ntree_limit = self._Booster.best_ntree_limit self.best_ntree_limit = self._Booster.best_ntree_limit
return self return self
def predict(self, data, output_margin=False, ntree_limit=None): def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
""" """
Predict with `data`. Predict with `data`.
@ -369,6 +369,9 @@ class XGBModel(XGBModelBase):
ntree_limit : int ntree_limit : int
Limit number of trees in the prediction; defaults to best_ntree_limit if defined Limit number of trees in the prediction; defaults to best_ntree_limit if defined
(i.e. it has been trained with early stopping), otherwise 0 (use all trees). (i.e. it has been trained with early stopping), otherwise 0 (use all trees).
validate_features : bool
When this is True, validate that the Booster's and data's feature_names are identical.
Otherwise, it is assumed that the feature_names are the same.
Returns Returns
------- -------
prediction : numpy array prediction : numpy array
@ -381,7 +384,8 @@ class XGBModel(XGBModelBase):
ntree_limit = getattr(self, "best_ntree_limit", 0) ntree_limit = getattr(self, "best_ntree_limit", 0)
return self.get_booster().predict(test_dmatrix, return self.get_booster().predict(test_dmatrix,
output_margin=output_margin, output_margin=output_margin,
ntree_limit=ntree_limit) ntree_limit=ntree_limit,
validate_features=validate_features)
def apply(self, X, ntree_limit=0): def apply(self, X, ntree_limit=0):
"""Return the predicted leaf every tree for each sample. """Return the predicted leaf every tree for each sample.
@ -604,7 +608,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
return self return self
def predict(self, data, output_margin=False, ntree_limit=None): def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
""" """
Predict with `data`. Predict with `data`.
@ -634,6 +638,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
ntree_limit : int ntree_limit : int
Limit number of trees in the prediction; defaults to best_ntree_limit if defined Limit number of trees in the prediction; defaults to best_ntree_limit if defined
(i.e. it has been trained with early stopping), otherwise 0 (use all trees). (i.e. it has been trained with early stopping), otherwise 0 (use all trees).
validate_features : bool
When this is True, validate that the Booster's and data's feature_names are identical.
Otherwise, it is assumed that the feature_names are the same.
Returns Returns
------- -------
prediction : numpy array prediction : numpy array
@ -643,7 +650,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
ntree_limit = getattr(self, "best_ntree_limit", 0) ntree_limit = getattr(self, "best_ntree_limit", 0)
class_probs = self.get_booster().predict(test_dmatrix, class_probs = self.get_booster().predict(test_dmatrix,
output_margin=output_margin, output_margin=output_margin,
ntree_limit=ntree_limit) ntree_limit=ntree_limit,
validate_features=validate_features)
if len(class_probs.shape) > 1: if len(class_probs.shape) > 1:
column_indexes = np.argmax(class_probs, axis=1) column_indexes = np.argmax(class_probs, axis=1)
else: else:
@ -651,7 +659,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
column_indexes[class_probs > 0.5] = 1 column_indexes[class_probs > 0.5] = 1
return self._le.inverse_transform(column_indexes) return self._le.inverse_transform(column_indexes)
def predict_proba(self, data, ntree_limit=None): def predict_proba(self, data, ntree_limit=None, validate_features=True):
""" """
Predict the probability of each `data` example being of a given class. Predict the probability of each `data` example being of a given class.
@ -668,6 +676,9 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
ntree_limit : int ntree_limit : int
Limit number of trees in the prediction; defaults to best_ntree_limit if defined Limit number of trees in the prediction; defaults to best_ntree_limit if defined
(i.e. it has been trained with early stopping), otherwise 0 (use all trees). (i.e. it has been trained with early stopping), otherwise 0 (use all trees).
validate_features : bool
When this is True, validate that the Booster's and data's feature_names are identical.
Otherwise, it is assumed that the feature_names are the same.
Returns Returns
------- -------
@ -678,7 +689,8 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
if ntree_limit is None: if ntree_limit is None:
ntree_limit = getattr(self, "best_ntree_limit", 0) ntree_limit = getattr(self, "best_ntree_limit", 0)
class_probs = self.get_booster().predict(test_dmatrix, class_probs = self.get_booster().predict(test_dmatrix,
ntree_limit=ntree_limit) ntree_limit=ntree_limit,
validate_features=validate_features)
if self.objective == "multi:softprob": if self.objective == "multi:softprob":
return class_probs return class_probs
else: else:
@ -964,7 +976,7 @@ class XGBRanker(XGBModel):
return self return self
def predict(self, data, output_margin=False, ntree_limit=0): def predict(self, data, output_margin=False, ntree_limit=0, validate_features=True):
test_dmatrix = DMatrix(data, missing=self.missing) test_dmatrix = DMatrix(data, missing=self.missing)
if ntree_limit is None: if ntree_limit is None:
@ -972,6 +984,7 @@ class XGBRanker(XGBModel):
return self.get_booster().predict(test_dmatrix, return self.get_booster().predict(test_dmatrix,
output_margin=output_margin, output_margin=output_margin,
ntree_limit=ntree_limit) ntree_limit=ntree_limit,
validate_features=validate_features)
predict.__doc__ = XGBModel.predict.__doc__ predict.__doc__ = XGBModel.predict.__doc__