def validate_contributions(self, contributions):
"""
Check len of list if _case is "classification"
Check contributions object type if _case is "regression"
Check type of contributions and transform into (list of) pd.Dataframe if necessary
Parameters
----------
contributions : pandas.DataFrame, np.ndarray or list
Returns
-------
pandas.DataFrame or list
"""
check_contribution_object(self._case, self._classes, contributions)
return self.state.validate_contributions(contributions, self.data["x_preprocessed"])
def test_check_contribution_object_1(self):
"""
Unit test check_contribution_object 1
"""
contributions_1 = [
np.array([[2, 1], [8, 4]]),
np.array([[5, 5], [0, 0]])
]
contributions_2 = np.array([[2, 1], [8, 4]])
model = lambda: None
model._classes = np.array([1, 3])
model.predict = types.MethodType(self.predict, model)
model.predict_proba = types.MethodType(self.predict_proba, model)
_case = "classification"
_classes = list(model._classes)
check_contribution_object(_case, _classes, contributions_1)
assert len(contributions_1) == len(_classes)
assert isinstance(contributions_1, list)
check_contribution_object("regression", None, contributions_2)
assert isinstance(contributions_2, np.ndarray)
with self.assertRaises(ValueError):
check_contribution_object(_case, _classes, contributions_2)
check_mask_params("regression", None, contributions_1)