Example #1
0
    def predict(self, rows_new):
        """

        :param rows_new: 待预测数据集
        :return: 判别: 投票最高标签; 回归: 预测结果均值
        """
        results = []
        for r in rows_new:
            sub_results = []
            for t in self.trees:
                predicted = dts.predict_single(t, r, out="value")
                sub_results.append(predicted)
            if self.target == "classification":  # 判别: 投票
                tmp_dict = {}
                for i in sub_results:
                    tmp_dict[i] = tmp_dict.get(i, 0) + 1
                k_tracker = None
                v_tracker = 0.0
                for k, v in tmp_dict.items():
                    if v > v_tracker:
                        k_tracker = k
                        v_tracker = v
                results.append(k_tracker)
            else:  # 回归: 求均值
                results.append(np.mean(sub_results))
        return results
Example #2
0
 def predict(self, rows_new):
     """
     将self.trees的每个预测结果累加, 得到最终预测值
     :param rows_new: 待预测数据, 嵌套的list或tuple
     :return: gbdt预测的y值
     """
     result = []
     for r in rows_new:
         y_new = 0.0
         # 预测结果为所有树的结果累加
         for t in self.trees:
             y_new += dts.predict_single(t, r)
         result.append(y_new)
     return result