def test_to_pandas_1(self):
"""
Unit test to pandas 1
"""
xpl = SmartExplainer()
xpl.state = SmartState()
data = {}
data['contrib_sorted'] = pd.DataFrame(
[[0.32230754, 0.1550689, 0.10183475, 0.05471339],
[-0.58547512, -0.37050409, -0.07249285, 0.00171975],
[-0.48666675, 0.25507156, -0.16968889, 0.0757443]],
columns=[
'contribution_0', 'contribution_1', 'contribution_2',
'contribution_3'
],
index=[0, 1, 2])
data['var_dict'] = pd.DataFrame(
[[1, 0, 2, 3], [1, 0, 3, 2], [1, 0, 2, 3]],
columns=['feature_0', 'feature_1', 'feature_2', 'feature_3'],
index=[0, 1, 2])
data['x_sorted'] = pd.DataFrame(
[[1., 3., 22., 1.], [2., 1., 2., 38.], [2., 3., 26., 1.]],
columns=['feature_0', 'feature_1', 'feature_2', 'feature_3'],
index=[0, 1, 2])
xpl.data = data
xpl.columns_dict = {0: 'Pclass', 1: 'Sex', 2: 'Age', 3: 'Embarked'}
xpl.features_dict = {
'Pclass': 'Pclass',
'Sex': 'Sex',
'Age': 'Age',
'Embarked': 'Embarked'
}
xpl.x = pd.DataFrame(
[[3., 1., 22., 1.], [1., 2., 38., 2.], [3., 2., 26., 1.]],
columns=['Pclass', 'Sex', 'Age', 'Embarked'],
index=[0, 1, 2])
xpl.x_pred = xpl.x
xpl.contributions = data['contrib_sorted']
xpl.y_pred = pd.DataFrame([1, 2, 3], columns=['pred'], index=[0, 1, 2])
model = lambda: None
model.predict = types.MethodType(self.predict, model)
xpl.model = model
xpl._case, xpl._classes = xpl.check_model()
xpl.state = xpl.choose_state(xpl.contributions)
output = xpl.to_pandas(max_contrib=2)
expected = pd.DataFrame(
[[1, 'Sex', 1.0, 0.32230754, 'Pclass', 3.0, 0.1550689],
[2, 'Sex', 2.0, -0.58547512, 'Pclass', 1.0, -0.37050409],
[3, 'Sex', 2.0, -0.48666675, 'Pclass', 3.0, 0.25507156]],
columns=[
'pred', 'feature_1', 'value_1', 'contribution_1', 'feature_2',
'value_2', 'contribution_2'
],
index=[0, 1, 2],
dtype=object)
expected['pred'] = expected['pred'].astype(int)
assert not pd.testing.assert_frame_equal(expected, output)
def test_filter_7(self):
"""
Unit test filter 7
"""
xpl = SmartExplainer()
contributions = [
pd.DataFrame(
data=[[0.5, 0.4, 0.3], [0.9, 0.8, 0.7]],
columns=['Col1', 'Col2', 'Col3']
),
pd.DataFrame(
data=[[0.3, 0.2, 0.1], [0.6, 0.5, 0.4]],
columns=['Col1', 'Col2', 'Col3']
)
]
xpl.data = {'var_dict': 1, 'contrib_sorted': contributions, 'x_sorted': 3}
xpl.state = MultiDecorator(SmartState())
xpl.filter(threshold=0.5, max_contrib=2)
expected_mask = [
pd.DataFrame(
data=[[True, False, False], [True, True, False]],
columns=['contrib_1', 'contrib_2', 'contrib_3']
),
pd.DataFrame(
data=[[False, False, False], [True, True, False]],
columns=['contrib_1', 'contrib_2', 'contrib_3']
)
]
assert len(expected_mask) == len(xpl.mask)
test_list = [pd.testing.assert_frame_equal(e, m) for e, m in zip(expected_mask, xpl.mask)]
assert all(x is None for x in test_list)
expected_masked_contributions = [
pd.DataFrame(
data=[[0.0, 0.7], [0.0, 0.7]],
columns=['masked_neg', 'masked_pos']
),
pd.DataFrame(
data=[[0.0, 0.6], [0.0, 0.4]],
columns=['masked_neg', 'masked_pos']
)
]
assert len(expected_masked_contributions) == len(xpl.masked_contributions)
test_list = [pd.testing.assert_frame_equal(e, m) for e, m in
zip(expected_masked_contributions, xpl.masked_contributions)]
assert all(x is None for x in test_list)
expected_param_dict = {
'features_to_hide': None,
'threshold': 0.5,
'positive': None,
'max_contrib': 2
}
self.assertDictEqual(expected_param_dict, xpl.mask_params)
def test_filter_6(self):
"""
Unit test filter 6
"""
xpl = SmartExplainer()
mock_data = {'var_dict': 1, 'contrib_sorted': 2, 'x_sorted': 3}
xpl.data = mock_data
mock_state = Mock()
xpl.state = mock_state
xpl.filter()
mock_state.init_mask.assert_called()
mock_state.hide_contributions.assert_not_called()
mock_state.cap_contributions.assert_not_called()
mock_state.sign_contributions.assert_not_called()
mock_state.combine_masks.assert_called()
mock_state.cutoff_contributions.assert_not_called()
assert hasattr(xpl, 'mask')
mock_state.compute_masked_contributions.assert_called()
assert hasattr(xpl, 'masked_contributions')
def test_filter_1(self, mock_check_features_name):
"""
Unit test filter 1
Parameters
----------
mock_check_features_name : [type]
[description]
"""
xpl = SmartExplainer()
mock_check_features_name.return_value = [1, 2]
mock_data = {'var_dict': 1, 'contrib_sorted': 2, 'x_sorted': 3}
xpl.data = mock_data
mock_state = Mock()
xpl.state = mock_state
xpl.filter(features_to_hide=['X1', 'X2'])
mock_state.init_mask.assert_called()
mock_state.hide_contributions.assert_called()
mock_state.cap_contributions.assert_not_called()
mock_state.sign_contributions.assert_not_called()
mock_state.combine_masks.assert_called()
mock_state.cutoff_contributions.assert_not_called()
assert hasattr(xpl, 'mask')
mock_state.compute_masked_contributions.assert_called()
assert hasattr(xpl, 'masked_contributions')
