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return best_ntree_limit if early stopped

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catena 2016-02-25 13:42:19 +05:30
parent cdbafafc04
commit 790dc877c3
1 changed files with 10 additions and 2 deletions
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python-package/xgboost/sklearn.py
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@ -206,7 +206,10 @@ class XGBModel(XGBModelBase):
Requires at least one item in evals. If there's more than one, Requires at least one item in evals. If there's more than one,
will use the last. Returns the model from the last iteration will use the last. Returns the model from the last iteration
(not the best one). If early stopping occurs, the model will (not the best one). If early stopping occurs, the model will
have two additional fields: bst.best_score and bst.best_iteration. have three additional fields: bst.best_score, bst.best_iteration
and bst.best_ntree_limit.
(Use bst.best_ntree_limit to get the correct value if num_parallel_tree
and/or num_class appears in the parameters)
verbose : bool verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr. metric measured on the validation set to stderr.
@ -251,6 +254,7 @@ class XGBModel(XGBModelBase):
if early_stopping_rounds is not None: if early_stopping_rounds is not None:
self.best_score = self._Booster.best_score self.best_score = self._Booster.best_score
self.best_iteration = self._Booster.best_iteration self.best_iteration = self._Booster.best_iteration
self.best_ntree_limit = self._Booster.best_ntree_limit
return self return self
def predict(self, data, output_margin=False, ntree_limit=0): def predict(self, data, output_margin=False, ntree_limit=0):
@ -349,7 +353,10 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
Requires at least one item in evals. If there's more than one, Requires at least one item in evals. If there's more than one,
will use the last. Returns the model from the last iteration will use the last. Returns the model from the last iteration
(not the best one). If early stopping occurs, the model will (not the best one). If early stopping occurs, the model will
have two additional fields: bst.best_score and bst.best_iteration. have three additional fields: bst.best_score, bst.best_iteration
and bst.best_ntree_limit.
(Use bst.best_ntree_limit to get the correct value if num_parallel_tree
and/or num_class appears in the parameters)
verbose : bool verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr. metric measured on the validation set to stderr.
@ -416,6 +423,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
if early_stopping_rounds is not None: if early_stopping_rounds is not None:
self.best_score = self._Booster.best_score self.best_score = self._Booster.best_score
self.best_iteration = self._Booster.best_iteration self.best_iteration = self._Booster.best_iteration
self.best_ntree_limit = self._Booster.best_ntree_limit
return self return self