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)
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.