def test_apply_postprocessing_2(self):
"""
Unit test apply_postprocessing 2
"""
xpl = SmartExplainer()
xpl.x_pred = pd.DataFrame([[1, 2], [3, 4]],
columns=['Col1', 'Col2'],
index=['Id1', 'Id2'])
xpl.features_dict = {'Col1': 'Column1', 'Col2': 'Column2'}
xpl.columns_dict = {0: 'Col1', 1: 'Col2'}
xpl.inv_features_dict = {'Column1': 'Col1', 'Column2': 'Col2'}
postprocessing = {
'Col1': {
'type': 'suffix',
'rule': ' t'
},
'Col2': {
'type': 'prefix',
'rule': 'test'
}
}
expected_output = pd.DataFrame(data=[['1 t', 'test2'],
['3 t', 'test4']],
columns=['Col1', 'Col2'],
index=['Id1', 'Id2'])
output = xpl.apply_postprocessing(postprocessing)
assert np.array_equal(output, expected_output)
def test_check_postprocessing_1(self):
"""
Unit test check_postprocessing
"""
xpl = SmartExplainer()
xpl.x_pred = pd.DataFrame(
[[1, 2],
[3, 4]],
columns=['Col1', 'Col2'],
index=['Id1', 'Id2']
)
xpl.features_dict = {'Col1': 'Column1', 'Col2': 'Column2'}
xpl.columns_dict = {0: 'Col1', 1: 'Col2'}
xpl.inv_features_dict = {'Column1': 'Col1', 'Column2': 'Col2'}
postprocessing1 = {0: {'Error': 'suffix', 'rule': ' t'}}
postprocessing2 = {0: {'type': 'Error', 'rule': ' t'}}
postprocessing3 = {0: {'type': 'suffix', 'Error': ' t'}}
postprocessing4 = {0: {'type': 'suffix', 'rule': ' '}}
postprocessing5 = {0: {'type': 'case', 'rule': 'lower'}}
postprocessing6 = {0: {'type': 'case', 'rule': 'Error'}}
with self.assertRaises(ValueError):
xpl.check_postprocessing(postprocessing1)
xpl.check_postprocessing(postprocessing2)
xpl.check_postprocessing(postprocessing3)
xpl.check_postprocessing(postprocessing4)
xpl.check_postprocessing(postprocessing5)
xpl.check_postprocessing(postprocessing6)
def test_modify_postprocessing_1(self):
"""
Unit test modify postprocessing 1
"""
xpl = SmartExplainer()
xpl.x_pred = pd.DataFrame([[1, 2], [3, 4]],
columns=['Col1', 'Col2'],
index=['Id1', 'Id2'])
xpl.features_dict = {'Col1': 'Column1', 'Col2': 'Column2'}
xpl.columns_dict = {0: 'Col1', 1: 'Col2'}
xpl.inv_features_dict = {'Column1': 'Col1', 'Column2': 'Col2'}
postprocessing = {
0: {
'type': 'suffix',
'rule': ' t'
},
'Column2': {
'type': 'prefix',
'rule': 'test'
}
}
expected_output = {
'Col1': {
'type': 'suffix',
'rule': ' t'
},
'Col2': {
'type': 'prefix',
'rule': 'test'
}
}
output = xpl.modify_postprocessing(postprocessing)
assert output == expected_output
def test_add_3(self):
"""
Unit test add 3
"""
xpl = SmartExplainer()
xpl.columns_dict = {0: 'Age', 1: 'Education', 2: 'Sex'}
xpl.add(features_dict={'Age': 'Age (Years Old)'})
assert xpl.features_dict['Age'] == 'Age (Years Old)'
assert xpl.features_dict['Education'] == 'Education'
def test_check_features_dict_1(self):
"""
Unit test check features dict 1
"""
xpl = SmartExplainer(features_dict={'Age': 'Age (Years Old)'})
xpl.columns_dict = {0: 'Age', 1: 'Education', 2: 'Sex'}
xpl.check_features_dict()
assert xpl.features_dict['Age'] == 'Age (Years Old)'
assert xpl.features_dict['Education'] == 'Education'
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_check_features_name_4(self):
"""
Unit test check features name 4
"""
xpl = SmartExplainer()
xpl.columns_dict = None
xpl.features_dict = None
feature_list = [1, 2, 4]
output = xpl.check_features_name(feature_list)
expected_output = feature_list
np.testing.assert_array_equal(output, expected_output)
def test_check_features_name_3(self):
"""
Unit test check features name 3
"""
xpl = SmartExplainer()
xpl.columns_dict = {0: 'tech_0', 1: 'tech_1', 2: 'tech_2'}
xpl.inv_columns_dict = {v: k for k, v in xpl.columns_dict.items()}
feature_list = ['tech_2']
output = xpl.check_features_name(feature_list)
expected_output = [2]
np.testing.assert_array_equal(output, expected_output)
def test_apply_postprocessing_1(self):
"""
Unit test apply_postprocessing 1
"""
xpl = SmartExplainer()
xpl.x_pred = pd.DataFrame([[1, 2], [3, 4]],
columns=['Col1', 'Col2'],
index=['Id1', 'Id2'])
xpl.features_dict = {'Col1': 'Column1', 'Col2': 'Column2'}
xpl.columns_dict = {0: 'Col1', 1: 'Col2'}
xpl.inv_features_dict = {'Column1': 'Col1', 'Column2': 'Col2'}
assert np.array_equal(xpl.x_pred, xpl.apply_postprocessing())
def test_to_pandas_2(self):
"""
Unit test to_pandas :
test to_pandas method in classification case with
predict_proba output and column_dict attribute
"""
xpl = SmartExplainer()
contrib = 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]],
index=[0, 1, 2])
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)
x = pd.DataFrame(
[[3., 1., 22., 1.], [1., 2., 38., 2.], [3., 2., 26., 1.]],
index=[0, 1, 2])
pred = pd.DataFrame([3, 1, 1], columns=['pred'], index=[0, 1, 2])
xpl.compile(contributions=contrib, x=x, model=model, y_pred=pred)
xpl.columns_dict = {0: 'Pclass', 1: 'Sex', 2: 'Age', 3: 'Embarked'}
xpl.features_dict = {
'Pclass': 'Pclass',
'Sex': 'Sex',
'Age': 'Age',
'Embarked': 'Embarked'
}
output = xpl.to_pandas(max_contrib=3, positive=True, proba=True)
expected = pd.DataFrame([[
3, 0.8, 'Pclass', 3.0, 0.32230754, 'Sex', 1.0, 0.1550689, 'Age',
22.0, 0.10183475
],
[
1, 0.3, 'Pclass', 1.0, 0.58547512, 'Sex',
2.0, 0.37050409, 'Age', 38.0, 0.07249285
],
[
1, 0.4, 'Pclass', 3.0, 0.48666675, 'Age',
26.0, 0.16968889, np.nan, np.nan, np.nan
]],
columns=[
'pred', 'proba', 'feature_1', 'value_1',
'contribution_1', 'feature_2', 'value_2',
'contribution_2', 'feature_3', 'value_3',
'contribution_3'
],
index=[0, 1, 2],
dtype=object)
expected['pred'] = expected['pred'].astype(int)
expected['proba'] = expected['proba'].astype(float)
assert not pd.testing.assert_frame_equal(expected, output)
def test_check_features_name_1(self):
"""
Unit test check features name 1
"""
xpl = SmartExplainer()
xpl.features_dict = {'tech_0': 'domain_0', 'tech_1': 'domain_1', 'tech_2': 'domain_2'}
xpl.inv_features_dict = {v: k for k, v in xpl.features_dict.items()}
xpl.columns_dict = {0: 'tech_0', 1: 'tech_1', 2: 'tech_2'}
xpl.inv_columns_dict = {v: k for k, v in xpl.columns_dict.items()}
feature_list_1 = ['domain_0', 'tech_1']
feature_list_2 = ['domain_0', 0]
self.assertRaises(ValueError, xpl.check_features_name, feature_list_1)
self.assertRaises(ValueError, xpl.check_features_name, feature_list_2)
def test_modify_postprocessing_2(self):
"""
Unit test modify postprocessing 2
"""
xpl = SmartExplainer()
xpl.x_pred = pd.DataFrame([[1, 2], [3, 4]],
columns=['Col1', 'Col2'],
index=['Id1', 'Id2'])
xpl.features_dict = {'Col1': 'Column1', 'Col2': 'Column2'}
xpl.columns_dict = {0: 'Col1', 1: 'Col2'}
xpl.inv_features_dict = {'Column1': 'Col1', 'Column2': 'Col2'}
postprocessing = {'Error': {'type': 'suffix', 'rule': ' t'}}
with self.assertRaises(ValueError):
xpl.modify_postprocessing(postprocessing)
