Get feature importances.

        Parameters
        ----------
        importance_type : string, optional (default="split")
            How the importance is calculated.
            If "split", result contains numbers of times the feature is used in a model.
            If "gain", result contains total gains of splits which use the feature.
        iteration : int or None, optional (default=None)
            Limit number of iterations in the feature importance calculation.
            If None, if the best iteration exists, it is used; otherwise, all trees are used.
            If <= 0, all trees are used (no limits).

        Returns
        -------
        result : numpy array
            Array with feature importances.

def feature_importance(self, importance_type='split', iteration=None):
        """Get feature importances.

        Parameters
        ----------
        importance_type : string, optional (default="split")
            How the importance is calculated.
            If "split", result contains numbers of times the feature is used in a model.
            If "gain", result contains total gains of splits which use the feature.
        iteration : int or None, optional (default=None)
            Limit number of iterations in the feature importance calculation.
            If None, if the best iteration exists, it is used; otherwise, all trees are used.
            If <= 0, all trees are used (no limits).

        Returns
        -------
        result : numpy array
            Array with feature importances.
        """
        if iteration is None:
            iteration = self.best_iteration
        if importance_type == "split":
            importance_type_int = 0
        elif importance_type == "gain":
            importance_type_int = 1
        else:
            importance_type_int = -1
        result = np.zeros(self.num_feature(), dtype=np.float64)
        _safe_call(_LIB.LGBM_BoosterFeatureImportance(
            self.handle,
            ctypes.c_int(iteration),
            ctypes.c_int(importance_type_int),
            result.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
        if importance_type_int == 0:
            return result.astype(int)
        else:
            return result