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adding some docs based on core.Boost.predict (#1865)

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Felipe Arruda Pontes 2018-02-09 12:38:38 -02:00 committed by Philip Cho
parent b99f56e386
commit 81d1b17f9c
1 changed files with 37 additions and 0 deletions
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python-package/xgboost/sklearn.py
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@ -520,6 +520,24 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
return self return self
def predict(self, data, output_margin=False, ntree_limit=0): def predict(self, data, output_margin=False, ntree_limit=0):
"""
Predict with `data`.
NOTE: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call xgb.copy() to make copies
of model object and then call predict
Parameters
----------
data : DMatrix
The dmatrix storing the input.
output_margin : bool
Whether to output the raw untransformed margin value.
ntree_limit : int
Limit number of trees in the prediction; defaults to 0 (use all trees).
Returns
-------
prediction : numpy array
"""
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs) test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
class_probs = self.get_booster().predict(test_dmatrix, class_probs = self.get_booster().predict(test_dmatrix,
output_margin=output_margin, output_margin=output_margin,
@ -532,6 +550,25 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
return self._le.inverse_transform(column_indexes) return self._le.inverse_transform(column_indexes)
def predict_proba(self, data, output_margin=False, ntree_limit=0): def predict_proba(self, data, output_margin=False, ntree_limit=0):
"""
Predict the probability of each `data` example being of a given class.
NOTE: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call xgb.copy() to make copies
of model object and then call predict
Parameters
----------
data : DMatrix
The dmatrix storing the input.
output_margin : bool
Whether to output the raw untransformed margin value.
ntree_limit : int
Limit number of trees in the prediction; defaults to 0 (use all trees).
Returns
-------
prediction : numpy array
a numpy array with the probability of each data example being of a given class.
"""
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs) test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
class_probs = self.get_booster().predict(test_dmatrix, class_probs = self.get_booster().predict(test_dmatrix,
output_margin=output_margin, output_margin=output_margin,