def explain_weights_pipeline(estimator, feature_names=None, **kwargs):
last_estimator = estimator.steps[-1][1]
transform_pipeline = Pipeline(estimator.steps[:-1])
if 'vec' in kwargs:
feature_names = get_feature_names(feature_names, vec=kwargs.pop('vec'))
feature_names = transform_feature_names(transform_pipeline, feature_names)
out = explain_weights(last_estimator,
feature_names=feature_names,
**kwargs)
out.estimator = repr(estimator)
return out
def _union_names(est, in_names=None):
return [
'{}:{}'.format(trans_name, feat_name)
for trans_name, trans, _ in est._iter()
for feat_name in transform_feature_names(trans, in_names)
]
def _pipeline_names(est, in_names=None):
names = in_names
for name, trans in est.steps:
if trans is not None:
names = transform_feature_names(trans, names)
return names
