def get_feature_names_out(self, input_features: List[str] = None):
from sklearn.utils.validation import _check_feature_names_in
names = _check_feature_names_in(self, input_features)
if self.add_indicator:
columns = [
f'missing_{input_features[idx]}'
for idx in self.estimator_.indicator_.features_
]
names = np.append(names, columns)
return names
def monkey_patch_get_feature_names_out():
# Some transformers do not implement get_feature_names_out, monkey-patch it in
if 'get_feature_names_out_patched' not in globals():
func = lambda est, input_features=None: _check_feature_names_in(
est, input_features)
SimpleImputer.get_feature_names_out = func
OrdinalEncoder.get_feature_names_out = func
# add marker to globals to prevent second execution
# noinspection PyGlobalUndefined
global get_feature_names_out_patched
get_feature_names_out_patched = True
def get_feature_names_out(self, input_features: List[str] = None):
from sklearn.utils.validation import _check_feature_names_in
names = _check_feature_names_in(self, input_features)
if self.add_indicator:
imp = self.estimator_.transformer_list[0][1]
mis = self.estimator_.transformer_list[1][1]
names = np.append(names[imp._columns[0]], names[imp._columns[1]])
names = np.append(names, mis.get_feature_names_out())
else:
names = np.append(names[self.estimator_._columns[0]], names[self.estimator_._columns[1]])
return names
def get_feature_names_out(self, input_features: List[str] = None):
return _check_feature_names_in(self, input_features)
def get_feature_names_out(self, input_features: List[str] = None):
from sklearn.utils.validation import _check_feature_names_in
return _check_feature_names_in(self, input_features)