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 combine_masks(self, masks):
"""
Combine a list of masks with the AND operator.
Parameters
----------
masks : list
List of boolean pandas.DataFrames.
Returns
-------
pd.Dataframe
Combination of all masks.
"""
return combine_masks(masks)
def test_combine_masks_2(self):
"""
Unit test combine masks 2
"""
df1 = pd.DataFrame(
[[True, False, True], [True, True, True], [False, False, False]],
columns=['col1', 'col2', 'col3'])
df2 = pd.DataFrame(
[[False, False, True], [True, False, True], [True, False, False]],
columns=['contrib_1', 'contrib_2', 'contrib_3'])
output = combine_masks([df1, df2])
expected_output = pd.DataFrame(
[[False, False, True], [True, False, True], [False, False, False]],
columns=['contrib_1', 'contrib_2', 'contrib_3'])
pd.testing.assert_frame_equal(output, expected_output)