def compute_importances(importances: pd.DataFrame, columns: List[str], model: lgb.Booster, fold: int) -> pd.DataFrame: imp_df = pd.DataFrame() imp_df['feature'] = columns imp_df['gain'] = model.feature_importance('gain') imp_df['fold'] = fold + 1 importances = pd.concat([importances, imp_df], axis=0, sort=False) return importances
def _get_importance(model: lgb.Booster, features: List[str],) -> pd.DataFrame: df = pd.DataFrame() df["feature"] = features df["importance"] = model.feature_importance( importance_type="gain", iteration=model.best_iteration ) return df