def test_rai_insights_binary(self, manager_type):
X_train, y_train, X_test, y_test, classes = \
create_binary_classification_dataset()
models = create_models_classification(X_train, y_train)
X_train[LABELS] = y_train
X_test[LABELS] = y_test
manager_args = {ManagerParams.TREATMENT_FEATURES: ['col0']}
for model in models:
run_rai_insights(model,
X_train,
X_test,
LABELS,
None,
manager_type,
manager_args,
classes=classes)
def test_treatment_features_list_not_having_train_features(self):
X_train, y_train, X_test, y_test, _ = \
create_binary_classification_dataset()
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')
message = ("Feature names in treatment_features "
"do not exist in train data: \\['not_a_feature'\\]")
with pytest.raises(UserConfigValidationException, match=message):
rai_insights.causal.add(treatment_features=['not_a_feature'])
def test_desired_class_not_set(self):
X_train, y_train, X_test, y_test, _ = \
create_binary_classification_dataset()
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 be '
'either \'opposite\' for binary classification or '
'the class value for multi-classification scenarios.'):
rai_insights.counterfactual.add(total_CFs=10, method='random')
def test_permitted_range_not_having_train_features(self):
X_train, y_train, X_test, y_test, _ = \
create_binary_classification_dataset()
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')
message = ("Feature names in permitted_range do "
"not exist in train data: \\['not_a_feature'\\]")
with pytest.raises(UserConfigValidationException, match=message):
rai_insights.counterfactual.add(
total_CFs=10, permitted_range={'not_a_feature': [20, 40]})