def test_eval_strategy_logreg(data_setup):
data_set = data_setup
train, test = train_test_data_setup(data=data_set)
test_skl_model = LogisticRegression(C=10., random_state=1,
solver='liblinear',
max_iter=10000, verbose=0)
test_skl_model.fit(train.features, train.target)
expected_result = test_skl_model.predict(test.features)
test_model_node = PrimaryNode(model_type='logit')
test_model_node.fit(input_data=train)
actual_result = test_model_node.predict(input_data=test)
assert len(actual_result.predict) == len(expected_result)
def test_node_with_manual_preprocessing_has_correct_behaviour_and_attributes(
data_setup):
model_type = 'logit'
node_default = PrimaryNode(model_type=model_type)
node_manual = PrimaryNode(model_type=model_type,
manual_preprocessing_func=Normalization)
default_node_prediction = node_default.fit(data_setup)
manual_node_prediction = node_manual.fit(data_setup)
assert node_manual.manual_preprocessing_func is not None
assert node_default.manual_preprocessing_func != node_manual.manual_preprocessing_func
assert not np.array_equal(default_node_prediction.predict,
manual_node_prediction.predict)
assert node_manual.descriptive_id == '/n_logit_default_params_custom_preprocessing=Normalization'
