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_compute_masked_contributions_3(self):
"""
test of compute masked contributions 3
"""
column_name = ['col1', 'col2', 'col3']
xmatr = pd.DataFrame(
[[0.1, 0.43, -0.02], [-0.78, 0.002, -0.3], [0.62, -0.008, 0.4]],
columns=column_name)
masktest = pd.DataFrame(
[[True, True, True], [True, True, True], [True, True, True]],
columns=column_name)
output = compute_masked_contributions(xmatr, masktest)
expected = pd.DataFrame([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]],
columns=['masked_neg', 'masked_pos'])
assert (xmatr.shape[0], 2) == output.shape
assert output.equals(expected)
def compute_masked_contributions(self, s_contrib, masks):
"""
Compute the summed contributions of hidden features.
Parameters
----------
s_contrib: pd.DataFrame
Matrix with both positive and negative values
mask: pd.DataFrame
Matrix with only True or False elements. False elements are the hidden elements.
Returns
-------
pd.series
Sum of contributions of hidden features.
"""
return compute_masked_contributions(s_contrib, masks)