解决lightgbm 没有与xgboost.apply()对应的apply方法

在GBDT+LR、XGBoost Blending Stacking 等方法中，使用了apply()方法：

生成训练数据X_train在训练好的模型里每棵树中所处的叶子节点的位置（索引）

即：Return the predicted leaf every tree for each sample.

如：

https://www.cnblogs.com/wkang/p/9657032.html

https://github.com/lytforgood/MachineLearningTrick

xgboost中的apply方法：

    def apply(self, X, ntree_limit=0):
        """Return the predicted leaf every tree for each sample.

        Parameters
        ----------
        X : array_like, shape=[n_samples, n_features]
            Input features matrix.

        ntree_limit : int
            Limit number of trees in the prediction; defaults to 0 (use all trees).

        Returns
        -------
        X_leaves : array_like, shape=[n_samples, n_trees]
            For each datapoint x in X and for each tree, return the index of the
            leaf x ends up in. Leaves are numbered within
            ``[0; 2**(self.max_depth+1))``, possibly with gaps in the numbering.
        """
        test_dmatrix = DMatrix(X, missing=self.missing, nthread=self.n_jobs)
        return self.get_booster().predict(test_dmatrix,
                                          pred_leaf=True,
                                          ntree_limit=ntree_limit)

从return 的内容可以看出，调用的方法是predict()，并指定参数pred_leaf=True。由此，可以确定通过使用lightGBM的predict()方法即可实现同样的结果