def filter(self):
"""
The filter method is an important method which allows to summarize the local explainability
by using the user defined mask_params parameters which correspond to its use case.
"""
mask = [init_mask(self.summary['contrib_sorted'], True)]
if self.mask_params["features_to_hide"] is not None:
mask.append(
hide_contributions(self.summary['var_dict'],
features_list=self.check_features_name(
self.mask_params["features_to_hide"])))
if self.mask_params["threshold"] is not None:
mask.append(
cap_contributions(self.summary['contrib_sorted'],
threshold=self.mask_params["threshold"]))
if self.mask_params["positive"] is not None:
mask.append(
sign_contributions(self.summary['contrib_sorted'],
positive=self.mask_params["positive"]))
self.mask = combine_masks(mask)
if self.mask_params["max_contrib"] is not None:
self.mask = cutoff_contributions(mask=self.mask,
k=self.mask_params["max_contrib"])
self.masked_contributions = compute_masked_contributions(
self.summary['contrib_sorted'], self.mask)
def test_hide_contributions_3(self):
"""
Unit test hide contributions 3
"""
dataframe = pd.DataFrame([[2, 0, 1], [0, 1, 2], [2, 3, 1]],
columns=['col1', 'col2', 'col3'])
output = hide_contributions(dataframe, [])
expected = pd.DataFrame(
[[True, True, True], [True, True, True], [True, True, True]],
columns=['col1', 'col2', 'col3'])
pd.testing.assert_frame_equal(output, expected)
def hide_contributions(self, var_dict, features_list):
"""
Returns Boolean dataframe with True/False depending if the
feature is present or not in the list of
feature to hide.
Parameters
----------
var_dict: pd.DataFrame
Dataframe with features indexes ordered
by contribution.
feature_list: List
List of index, feature to hide.
Returns
-------
pd.DataFrame
Boolean dataframe depend on hidden features.
"""
return hide_contributions(var_dict, features_list)