import unittest

from sklearn.datasets import fetch_openml
from flaml.automl import AutoML
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score


dataset = "credit-g"


def _test(split_type):
    from sklearn.externals._arff import ArffException

    automl = AutoML()

    automl_settings = {
        "time_budget": 2,
        # "metric": 'accuracy',
        "task": 'classification',
        "log_file_name": "test/{}.log".format(dataset),
        "model_history": True,
        "log_training_metric": True,
        "split_type": split_type,
    }

    try:
        X, y = fetch_openml(name=dataset, return_X_y=True)
    except (ArffException, ValueError):
        from sklearn.datasets import load_wine
        X, y = load_wine(return_X_y=True)
    if split_type != 'time':
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,
                                                            random_state=42)
    else:
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33,
                                                            shuffle=False)
    automl.fit(X_train=X_train, y_train=y_train, **automl_settings)

    pred = automl.predict(X_test)
    acc = accuracy_score(y_test, pred)

    print(acc)


def _test_uniform():
    _test(split_type="uniform")


def test_time():
    _test(split_type="time")


def test_groups():
    from sklearn.externals._arff import ArffException
    try:
        X, y = fetch_openml(name=dataset, return_X_y=True)
    except (ArffException, ValueError):
        from sklearn.datasets import load_wine
        X, y = load_wine(return_X_y=True)

    import numpy as np
    automl = AutoML()
    automl_settings = {
        "time_budget": 2,
        "task": 'classification',
        "log_file_name": "test/{}.log".format(dataset),
        "model_history": True,
        "eval_method": "cv",
        "groups": np.random.randint(low=0, high=10, size=len(y)),
        "estimator_list": ['lgbm', 'rf', 'xgboost', 'kneighbor'],  # list of ML learners
        "learner_selector": "roundrobin",
    }
    automl.fit(X, y, **automl_settings)


if __name__ == "__main__":
    unittest.main()