def create_iris_pandas():
X_train, X_test, y_train, y_test, feature_names, _ = create_iris_data()
X_train = pd.DataFrame(X_train, columns=feature_names)
X_test = pd.DataFrame(X_test, columns=feature_names)
return X_train, X_test, y_train, y_test, feature_names
def test_weird_predict_proba_function(self):
X_train, X_test, y_train, y_test, _, _ = create_iris_data()
# A weird model that modifies the input dataset by
# adding back the target column
class WeirdModelPredictProbaWrapper():
def __init__(self, model):
self.model = model
def predict(self, test_data_pandas):
return self.model.predict(test_data_pandas)
def predict_proba(self, test_data_pandas):
if TARGET not in test_data_pandas.columns:
test_data_pandas[TARGET] = 0
return self.model.predict_proba(
test_data_pandas.drop(columns=TARGET))
model = create_lightgbm_classifier(X_train, y_train)
model = WeirdModelPredictProbaWrapper(model)
X_train = X_train.copy()
X_test = X_test.copy()
X_train[TARGET] = y_train
X_test[TARGET] = y_test
message = ('Calling model predict_proba function modifies '
'input dataset features. Please check if '
'predict function is defined correctly.')
with pytest.raises(UserConfigValidationException, match=message):
RAIInsights(model=model,
train=X_train,
test=X_test,
target_column=TARGET,
task_type='classification')
def test_rai_insights_iris(self, manager_type):
X_train, X_test, y_train, y_test, feature_names, classes = \
create_iris_data()
models = create_models_classification(X_train, y_train)
X_train[LABELS] = y_train
X_test[LABELS] = y_test
manager_args = {
ManagerParams.TREATMENT_FEATURES: [feature_names[0]],
ManagerParams.DESIRED_CLASS: 0,
ManagerParams.FEATURE_IMPORTANCE: True
}
for model in models:
run_rai_insights(model, X_train, X_test, LABELS, None,
manager_type, manager_args, classes)
def test_validate_bad_target_name(self):
X_train, X_test, y_train, y_test, _, _ = \
create_iris_data()
model = create_lightgbm_classifier(X_train, y_train)
X_train[TARGET] = y_train
X_test[TARGET] = y_test
with pytest.raises(UserConfigValidationException) as ucve:
RAIInsights(model=model,
train=X_train,
test=X_test,
target_column='bad_target',
task_type='classification')
assert "Target name bad_target not present in train/test data" in \
str(ucve.value)
def test_validate_unsupported_task_type(self):
X_train, X_test, y_train, y_test, _, _ = \
create_iris_data()
model = create_lightgbm_classifier(X_train, y_train)
X_train[TARGET] = y_train
X_test[TARGET] = y_test
message = ("Unsupported task type 'regre'. "
"Should be one of \\['classification', 'regression'\\]")
with pytest.raises(UserConfigValidationException, match=message):
RAIInsights(model=model,
train=X_train,
test=X_test,
target_column=TARGET,
task_type='regre')
def test_validate_categorical_features_not_having_train_features(self):
X_train, X_test, y_train, y_test, _, _ = \
create_iris_data()
model = create_lightgbm_classifier(X_train, y_train)
X_train[TARGET] = y_train
X_test[TARGET] = y_test
message = ("Feature names in categorical_features "
"do not exist in train data: \\['not_a_feature'\\]")
with pytest.raises(UserConfigValidationException, match=message):
RAIInsights(model=model,
train=X_train,
test=X_test,
target_column=TARGET,
task_type='classification',
categorical_features=['not_a_feature'])
def test_validate_categorical_features_having_target(self):
X_train, X_test, y_train, y_test, _, _ = \
create_iris_data()
model = create_lightgbm_classifier(X_train, y_train)
X_train[TARGET] = y_train
X_test[TARGET] = y_test
with pytest.raises(UserConfigValidationException) as ucve:
RAIInsights(model=model,
train=X_train,
test=X_test,
target_column=TARGET,
task_type='classification',
categorical_features=[TARGET])
assert 'Found target name target in categorical feature list' in \
str(ucve.value)
def test_validate_test_data_size(self):
X_train, X_test, y_train, y_test, _, _ = \
create_iris_data()
model = create_lightgbm_classifier(X_train, y_train)
X_train[TARGET] = y_train
X_test[TARGET] = y_test
with pytest.raises(UserConfigValidationException) as ucve:
RAIInsights(model=model,
train=X_train,
test=X_test,
target_column='bad_target',
task_type='classification',
maximum_rows_for_test=len(y_test) - 1)
assert "The test data has 31 rows, but limit is set to 30 rows" in \
str(ucve.value)
assert "Please resample the test data or " +\
"adjust maximum_rows_for_test" in \
str(ucve.value)
def test_desired_class_opposite_multi_classification(self):
X_train, X_test, y_train, y_test, feature_names, classes = \
create_iris_data()
model = create_lightgbm_classifier(X_train, y_train)
X_train[TARGET] = y_train
X_test[TARGET] = y_test
rai_insights = RAIInsights(model=model,
train=X_train,
test=X_test,
target_column=TARGET,
task_type='classification')
with pytest.raises(
UserConfigValidationException,
match='The desired_class attribute should not be \'opposite\''
' It should be the class value for multiclass'
' classification scenario.'):
rai_insights.counterfactual.add(total_CFs=10,
method='random',
desired_class='opposite')
def test_feature_importance_with_less_counterfactuals(self):
X_train, X_test, y_train, y_test, feature_names, classes = \
create_iris_data()
model = create_lightgbm_classifier(X_train, y_train)
X_train[TARGET] = y_train
X_test[TARGET] = y_test
rai_insights = RAIInsights(model=model,
train=X_train,
test=X_test,
target_column=TARGET,
task_type='classification')
with pytest.raises(
UserConfigValidationException,
match="A total_CFs value of at least 10 is required to "
"use counterfactual feature importances. "
"Either increase total_CFs to at least 10 or "
"set feature_importance to False."):
rai_insights.counterfactual.add(total_CFs=5,
method='random',
desired_class=2)
