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_cap_contributions(self):
"""
Unit test cap contributions
"""
xmatr = pd.DataFrame(
[[0.1, 0.43, -0.02], [-0.78, 0.002, -0.3], [0.62, -0.008, 0.4]],
columns=['c1', 'c2', 'c3'])
thresholdvalue = 0.3
result = pd.DataFrame(
[[False, True, False], [True, False, True], [True, False, True]],
columns=['c1', 'c2', 'c3'])
output = cap_contributions(xmatr, thresholdvalue)
assert xmatr.shape == output.shape
assert output.equals(result)
def cap_contributions(self, s_contrib, threshold=0.1):
"""
Compute a mask indicating where the input matrix
has values above a given threshold in absolute value.
Parameters
----------
s_contrib : pandas.DataFrame
Local contributions, positive and negative values.
threshold: float, optional (default: 0.1)
User defined threshold above which local contributions are hidden.
Returns
-------
pandas.DataFrame
Mask with only True of False elements.
"""
return cap_contributions(s_contrib, threshold=threshold)