import numpy as np
from flaml import AutoML


def test_forecast_automl(budget=5):
    # using dataframe
    import statsmodels.api as sm
    data = sm.datasets.co2.load_pandas().data['co2'].resample('MS').mean()
    data = data.fillna(data.bfill()).to_frame().reset_index().rename(
        columns={'index': 'ds', 'co2': 'y'})
    num_samples = data.shape[0]
    time_horizon = 12
    split_idx = num_samples - time_horizon
    df = data[:split_idx]
    X_test = data[split_idx:]['ds']
    y_test = data[split_idx:]['y']
    automl = AutoML()
    settings = {
        "time_budget": budget,  # total running time in seconds
        "metric": 'mape',  # primary metric
        "task": 'forecast',  # task type
        "log_file_name": 'CO2_forecast.log',  # flaml log file
        "eval_method": "holdout",
        "label": ('ds', 'y'),
    }
    '''The main flaml automl API'''
    try:
        automl.fit(dataframe=df, **settings, period=time_horizon)
    except ImportError:
        print("not using FBProphet due to ImportError")
        automl.fit(dataframe=df, **settings, estimator_list=[
            'arima', 'sarimax'], period=time_horizon)
    ''' retrieve best config and best learner'''
    print('Best ML leaner:', automl.best_estimator)
    print('Best hyperparmeter config:', automl.best_config)
    print(f'Best mape on validation data: {automl.best_loss}')
    print(f'Training duration of best run: {automl.best_config_train_time}s')
    print(automl.model.estimator)
    ''' pickle and save the automl object '''
    import pickle
    with open('automl.pkl', 'wb') as f:
        pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)
    ''' compute predictions of testing dataset '''
    y_pred = automl.predict(X_test)
    print('Predicted labels', y_pred)
    print('True labels', y_test)
    ''' compute different metric values on testing dataset'''
    from flaml.ml import sklearn_metric_loss_score
    print('mape', '=', sklearn_metric_loss_score('mape', y_pred, y_test))
    from flaml.data import get_output_from_log
    time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \
        get_output_from_log(filename=settings['log_file_name'], time_budget=budget)
    for config in config_history:
        print(config)
    print(automl.prune_attr)
    print(automl.max_resource)
    print(automl.min_resource)

    X_train = df['ds']
    y_train = df['y']
    automl = AutoML()
    try:
        automl.fit(X_train=X_train, y_train=y_train, **settings, period=time_horizon)
    except ImportError:
        print("not using FBProphet due to ImportError")
        automl.fit(X_train=X_train, y_train=y_train, **settings, estimator_list=[
            'arima', 'sarimax'], period=time_horizon)


def test_numpy():
    X_train = np.arange('2014-01', '2021-01', dtype='datetime64[M]')
    y_train = np.random.random(size=72)
    automl = AutoML()
    try:
        automl.fit(
            X_train=X_train[:60],  # a single column of timestamp
            y_train=y_train,  # value for each timestamp
            period=12,  # time horizon to forecast, e.g., 12 months
            task='forecast', time_budget=3,  # time budget in seconds
            log_file_name="test/forecast.log")
        print(automl.predict(X_train[60:]))
        print(automl.predict(12))
    except ValueError:
        print("ValueError for FBProphet is raised as expected.")
    except ImportError:
        print("not using FBProphet due to ImportError")
        automl = AutoML()
        automl.fit(
            X_train=X_train[:72],  # a single column of timestamp
            y_train=y_train,  # value for each timestamp
            period=12,  # time horizon to forecast, e.g., 12 months
            task='forecast', time_budget=1,  # time budget in seconds
            estimator_list=['arima', 'sarimax'],
            log_file_name="test/forecast.log")
        print(automl.predict(X_train[72:]))
        # an alternative way to specify predict steps for arima/sarimax
        print(automl.predict(12))


if __name__ == "__main__":
    test_forecast_automl(60)