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_sign_contributions_2(self):
"""
Unit test sign contributions 2
"""
dataframe = pd.DataFrame({
'val1': [1, -1],
'val2': [-2, -2],
'val3': [0.5, -2]
})
output = sign_contributions(dataframe, positive=False)
expected = pd.DataFrame({
'val1': [False, True],
'val2': [True, True],
'val3': [False, True]
})
pd.testing.assert_frame_equal(output, expected)
def sign_contributions(self, dataframe, positive=True):
"""
Returns Boolean values depending on
the signs of local contributions
stored in dataframe and on the positive parameter.
Parameters
----------
dataframe : pandas.DataFrame
Local contributions of the model.
positive : boolean (default=True)
True to evaluate positive value.
False to evaluate negative value.
Returns
-------
pandas.DataFrame
Dataframe with boolean value.
"""
return sign_contributions(dataframe, positive=positive)