Source code for mars.learn.contrib.xgboost.regressor

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# Licensed under the Apache License, Version 2.0 (the "License");
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from ..utils import make_import_error_func
from .core import xgboost, XGBScikitLearnBase


if not xgboost:
    XGBRegressor = make_import_error_func("xgboost")
else:
    from .core import wrap_evaluation_matrices
    from .train import train
    from .predict import predict

[docs] class XGBRegressor(XGBScikitLearnBase): """ Implementation of the scikit-learn API for XGBoost regressor. """ def fit( self, X, y, sample_weight=None, base_margin=None, eval_set=None, sample_weight_eval_set=None, base_margin_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, evals = wrap_evaluation_matrices( None, X, y, sample_weight, base_margin, eval_set, sample_weight_eval_set, base_margin_eval_set, ) params = self.get_xgb_params() 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", None) return predict( self.get_booster(), data, session=session, run_kwargs=run_kwargs, **kw )