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_init_mask(self):
"""
test of initialization of mask
"""
column_name = ['col1', 'col2']
s_ord = pd.DataFrame([[0.1, 0.43], [-0.78, 0.002], [0.62, -0.008]],
columns=column_name)
expected = pd.DataFrame([[True, True], [True, True], [True, True]],
columns=column_name)
output = init_mask(s_ord)
assert output.equals(expected)
def init_mask(self, s_contrib, value=True):
"""
Initialize a True mask for the dataset.
Parameters
----------
s_contrib: pd.DataFrame
Matrix with both positive and negative values
value: bool
Value used for initialize the mask
Returns
-------
pd.Dataframe
mask initialized
"""
return init_mask(s_contrib, value)