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