def get_score_feature_weights(_label_id):
_score, _feature_weights = scores_weights[_label_id]
_x = x
if flt_indices is not None:
_x = mask(_x, flt_indices)
_feature_weights = mask(_feature_weights, flt_indices)
return _score, get_top_features(feature_names, _feature_weights, top,
_x)
def get_score_feature_weights(_label_id):
_weights = _target_feature_weights(
weight_dicts[_label_id],
num_features=len(feature_names),
bias_idx=feature_names.bias_idx,
)
_score = _get_score(weight_dicts[_label_id])
_x = x
if flt_indices is not None:
_x = mask(_x, flt_indices)
_weights = mask(_weights, flt_indices)
return _score, get_top_features(flt_feature_names, _weights, top, _x)
def _weights(label_id):
scores = feature_weights[:, label_id]
_x = x
if flt_indices is not None:
scores = scores[flt_indices]
_x = mask(_x, flt_indices)
return get_top_features(feature_names, scores, top, _x)
def _weights(label_id):
coef = get_coef(clf, label_id)
_x = x
scores = _multiply(_x, coef)
if flt_indices is not None:
scores = scores[flt_indices]
_x = mask(_x, flt_indices)
return get_top_features(feature_names, scores, top, _x)
