models.py 文件源码

def fit(self, X, y):
        le = preprocessing.LabelEncoder()
        y = le.fit_transform(y)
        self.num_classes = np.unique(y).shape[0]
        sf = xgb.DMatrix(X, y)
        params = {"objective": 'multi:softprob',
          "eta": self.eta,
          "gamma": self.gamma,
          "max_depth": self.max_depth,
          "min_child_weight": self.min_child_weight,
          "max_delta_step": self.max_delta_step,
          "subsample": self.subsample,
          "silent": self.silent,
          "colsample_bytree": self.colsample_bytree,
          "seed": self.seed,
          "lambda": self.l2_reg,
          "alpha": self.l1_reg,
          "num_class": self.num_classes}
        self.model = xgb.train(params, sf, self.num_round)

        return self