def test_AutoMLClassifier(get_data):
data = get_data
test_model = AutoMLClassifier(databunch=data, random_state=RANDOM_SEED)
predict_test, predict_train = test_model.opt(timeout=1500, verbose=0,)
assert predict_test is not None
score = sklearn.metrics.roc_auc_score(data.y_test, predict_test)
assert score is not None
assert score >= 0.8
X_train, y_train = X.iloc[train_idx], y.iloc[train_idx]
X_test, y_test = X.iloc[test_idx], y.iloc[test_idx]
#print(DATASET_NAME, X_train.shape, X_test.shape)
# Auto_ml
START_EXPERIMENT = time.time()
model = AutoMLClassifier(X_train, y_train, X_test,
#cat_encoder_names=['OneHotEncoder', 'FrequencyEncoder'],
cat_features=cat_features,
random_state=RANDOM_SEED,
verbose=1)
time.sleep(0.5)
y_test_predict_proba, predict_train = model.opt(timeout=TIME_LIMIT, verbose=2)
#y_test_predict_proba, _ = model.fit_predict()
#y_test_predict = automl.predict(X_test)
print('*'*75)
print('AUC: ', round(roc_auc_score(y_test, y_test_predict_proba),4))
print('AUC mean models: ', round(roc_auc_score(y_test, model.stack_models_predicts['predict_test'].mean()),4))
print('Model_0 FullX: ', round(roc_auc_score(y_test, model.predicts_model_0_full_x['predict_test'].mean()),4))
print('Model_1 FullX: ', round(roc_auc_score(y_test, model.predicts_model_1_full_x['predict_test'].mean()),4))
print('-'*75)
#print('Mean Test AUC: ', round(sklearn.metrics.roc_auc_score(y_test, model._data.X_test_predicts.T.mean()),4))
END_EXPERIMENT = time.time()
#preds = pd.DataFrame(predictions)