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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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from ..utils import make_import_error_func
from .core import xgboost, XGBScikitLearnBase
XGBClassifier = make_import_error_func('xgboost')
if xgboost:
from xgboost.sklearn import XGBClassifierBase
from .... import tensor as mt
from .dmatrix import MarsDMatrix
from .core import evaluation_matrices
from .train import train
from .predict import predict
[docs] class XGBClassifier(XGBScikitLearnBase, XGBClassifierBase):
"""
Implementation of the scikit-learn API for XGBoost classification.
"""
def fit(self, X, y, sample_weights=None, eval_set=None, sample_weight_eval_set=None, **kw):
session = kw.pop('session', None)
run_kwargs = kw.pop('run_kwargs', dict())
if kw:
raise TypeError(f"fit got an unexpected keyword argument '{next(iter(kw))}'")
dtrain = MarsDMatrix(X, label=y, weight=sample_weights,
session=session, run_kwargs=run_kwargs)
params = self.get_xgb_params()
self.classes_ = mt.unique(y, aggregate_size=1).to_numpy(session=session, **run_kwargs)
self.n_classes_ = len(self.classes_)
if self.n_classes_ > 2:
params['objective'] = 'multi:softprob'
params['num_class'] = self.n_classes_
else:
params['objective'] = 'binary:logistic'
evals = evaluation_matrices(eval_set, sample_weight_eval_set,
session=session, run_kwargs=run_kwargs)
self.evals_result_ = dict()
result = train(params, dtrain, num_boost_round=self.get_num_boosting_rounds(),
evals=evals, evals_result=self.evals_result_,
session=session, run_kwargs=run_kwargs)
self._Booster = result
return self
def predict(self, data, **kw):
session = kw.pop('session', None)
run_kwargs = kw.pop('run_kwargs', dict())
run = kw.pop('run', True)
prob = predict(self.get_booster(), data, run=False, **kw)
if prob.ndim > 1:
prediction = mt.argmax(prob, axis=1)
else:
prediction = (prob > 0.5).astype(mt.int64)
if run:
prediction.execute(session=session, **run_kwargs)
return prediction
def predict_proba(self, data, ntree_limit=None, **kw):
if ntree_limit is not None:
raise NotImplementedError('ntree_limit is not currently supported')
return predict(self.get_booster(), data, **kw)