def get_feature_importances_from_booster(cls,
booster: Booster) -> np.ndarray:
"""Gets feauture importances from a XGB booster.
This is based on the feature_importance_ property defined in:
https://github.com/dmlc/xgboost/blob/master/python-package/xgboost/sklearn.py
Args:
booster(Booster): Booster object,
most of the times the median model (quantile=0.5) is preferred
Returns:
(np.ndarray) with normalized feature importances
"""
# Get score
score = booster.get_score(importance_type="gain")
# Get feature names from booster
feature_names = booster.feature_names
# Get importance
feature_importance = [score.get(f, 0.0) for f in feature_names]
# Convert to array
features_importance_array = np.array(feature_importance,
dtype=np.float32)
total = features_importance_array.sum() # For normalizing
if total == 0:
return features_importance_array
return features_importance_array / total # Normalize
def merge_labeled_weight_importance(model: Booster, label_encoder: OneHotLabelEncoder) -> Dict:
f_imp = model.get_score(importance_type='weight')
merged: Dict[str, int] = {}
for f in f_imp:
src_feature = label_encoder.source_column(f)
merged[src_feature] = merged.get(src_feature, 0) + f_imp[f]
return merged
def merge_labeled_weight_importance(
model: Booster,
dummy_col_sep=categorical_util.DUMMY_COL_SEP) -> Dict[str, int]:
f_imp = model.get_score(importance_type='weight')
merged: Dict[str, int] = {}
for f in f_imp:
src_feature = categorical_util.get_source_name_from_dummy(
f, dummy_col_sep)
merged[src_feature] = merged.get(src_feature, 0) + f_imp[f]
return merged
