Predict with data.

        NOTE: This function is not thread safe.
              For each booster object, predict can only be called from one thread.
              If you want to run prediction using multiple thread, call bst.copy() to make copies
              of model object and then call predict

        Parameters
        ----------
        data : DMatrix
            The dmatrix storing the input.

        output_margin : bool
            Whether to output the raw untransformed margin value.

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

        pred_leaf : bool
            When this option is on, the output will be a matrix of (nsample, ntrees)
            with each record indicating the predicted leaf index of each sample in each tree.
            Note that the leaf index of a tree is unique per tree, so you may find leaf 1
            in both tree 1 and tree 0.

        Returns
        -------
        prediction : numpy array

def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False):
        """
        Predict with data.

        NOTE: This function is not thread safe.
              For each booster object, predict can only be called from one thread.
              If you want to run prediction using multiple thread, call bst.copy() to make copies
              of model object and then call predict

        Parameters
        ----------
        data : DMatrix
            The dmatrix storing the input.

        output_margin : bool
            Whether to output the raw untransformed margin value.

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

        pred_leaf : bool
            When this option is on, the output will be a matrix of (nsample, ntrees)
            with each record indicating the predicted leaf index of each sample in each tree.
            Note that the leaf index of a tree is unique per tree, so you may find leaf 1
            in both tree 1 and tree 0.

        Returns
        -------
        prediction : numpy array
        """
        option_mask = 0x00
        if output_margin:
            option_mask |= 0x01
        if pred_leaf:
            option_mask |= 0x02

        self._validate_features(data)

        length = ctypes.c_ulong()
        preds = ctypes.POINTER(ctypes.c_float)()
        _check_call(_LIB.XGBoosterPredict(self.handle, data.handle,
                                          option_mask, ntree_limit,
                                          ctypes.byref(length),
                                          ctypes.byref(preds)))
        preds = ctypes2numpy(preds, length.value, np.float32)
        if pred_leaf:
            preds = preds.astype(np.int32)
        nrow = data.num_row()
        if preds.size != nrow and preds.size % nrow == 0:
            preds = preds.reshape(nrow, preds.size / nrow)
        return preds