Predict for a CSR data.

def __pred_for_csr(self, csr, num_iteration, predict_type):
        """Predict for a CSR data."""
        def inner_predict(csr, num_iteration, predict_type, preds=None):
            nrow = len(csr.indptr) - 1
            n_preds = self.__get_num_preds(num_iteration, nrow, predict_type)
            if preds is None:
                preds = np.zeros(n_preds, dtype=np.float64)
            elif len(preds.shape) != 1 or len(preds) != n_preds:
                raise ValueError("Wrong length of pre-allocated predict array")
            out_num_preds = ctypes.c_int64(0)

            ptr_indptr, type_ptr_indptr, __ = c_int_array(csr.indptr)
            ptr_data, type_ptr_data, _ = c_float_array(csr.data)

            assert csr.shape[1] <= MAX_INT32
            csr.indices = csr.indices.astype(np.int32, copy=False)

            _safe_call(_LIB.LGBM_BoosterPredictForCSR(
                self.handle,
                ptr_indptr,
                ctypes.c_int32(type_ptr_indptr),
                csr.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
                ptr_data,
                ctypes.c_int(type_ptr_data),
                ctypes.c_int64(len(csr.indptr)),
                ctypes.c_int64(len(csr.data)),
                ctypes.c_int64(csr.shape[1]),
                ctypes.c_int(predict_type),
                ctypes.c_int(num_iteration),
                c_str(self.pred_parameter),
                ctypes.byref(out_num_preds),
                preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
            if n_preds != out_num_preds.value:
                raise ValueError("Wrong length for predict results")
            return preds, nrow

        nrow = len(csr.indptr) - 1
        if nrow > MAX_INT32:
            sections = [0] + list(np.arange(start=MAX_INT32, stop=nrow, step=MAX_INT32)) + [nrow]
            # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal
            n_preds = [self.__get_num_preds(num_iteration, i, predict_type) for i in np.diff(sections)]
            n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum()
            preds = np.zeros(sum(n_preds), dtype=np.float64)
            for (start_idx, end_idx), (start_idx_pred, end_idx_pred) in zip_(zip_(sections, sections[1:]),
                                                                             zip_(n_preds_sections, n_preds_sections[1:])):
                # avoid memory consumption by arrays concatenation operations
                inner_predict(csr[start_idx:end_idx], num_iteration, predict_type, preds[start_idx_pred:end_idx_pred])
            return preds, nrow
        else:
            return inner_predict(csr, num_iteration, predict_type)