xgradient_boosting.py 文件源码

def fit(self, X, y):
        import xgboost as xgb

        self.learning_rate = float(self.learning_rate)
        self.n_estimators = int(self.n_estimators)
        self.subsample = float(self.subsample)
        self.max_depth = int(self.max_depth)

        # (TODO) Gb used at most half of the features, here we use all
        self.colsample_bylevel = float(self.colsample_bylevel)

        self.colsample_bytree = float(self.colsample_bytree)
        self.gamma = float(self.gamma)
        self.min_child_weight = int(self.min_child_weight)
        self.max_delta_step = int(self.max_delta_step)
        self.reg_alpha = float(self.reg_alpha)
        self.reg_lambda = float(self.reg_lambda)
        self.nthread = int(self.nthread)
        self.base_score = float(self.base_score)
        self.scale_pos_weight = float(self.scale_pos_weight)

        # We don't support multilabel, so we only need 1 objective function
        if len(numpy.unique(y)) == 2:
            # We probably have binary classification
            self.objective = 'binary:logistic'
        else:
            self.objective = 'multi:softprob'

        self.estimator = xgb.XGBClassifier(
                max_depth=self.max_depth,
                learning_rate=self.learning_rate,
                n_estimators=self.n_estimators,
                silent=self.silent,
                objective=self.objective,
                nthread=self.nthread,
                gamma=self.gamma,
                scale_pos_weight=self.scale_pos_weight,
                min_child_weight=self.min_child_weight,
                max_delta_step=self.max_delta_step,
                subsample=self.subsample,
                colsample_bytree=self.colsample_bytree,
                colsample_bylevel=self.colsample_bylevel,
                reg_alpha=self.reg_alpha,
                reg_lambda=self.reg_lambda,
                base_score=self.base_score,
                seed=self.seed
                )
        self.estimator.fit(X, y, eval_metric='auc')

        return self