from .base_test_class import DartsBaseTestClass
from ..models import Prophet
from ..utils import timeseries_generation as tg
from ..timeseries import TimeSeries


class ProphetTestCase(DartsBaseTestClass):

    def test_add_seasonality_calls(self):
        # test if adding seasonality at model creation and with method model.add_seasonality() are equal
        kwargs_mandatory = {'name': 'custom', 'seasonal_periods': 48, 'fourier_order': 4}
        kwargs_mandatory2 = {'name': 'custom2', 'seasonal_periods': 24, 'fourier_order': 1}
        kwargs_all = dict(kwargs_mandatory, **{'prior_scale': 1., 'mode': 'additive'})
        model1 = Prophet(add_seasonalities=kwargs_all)
        model2 = Prophet()
        model2.add_seasonality(**kwargs_all)
        self.assertEqual(model1._add_seasonalities, model2._add_seasonalities)

        # add multiple seasonalities
        model3 = Prophet(add_seasonalities=[kwargs_mandatory, kwargs_mandatory2])
        self.assertEqual(len(model3._add_seasonalities), 2)

        # seasonality already exists
        with self.assertRaises(ValueError):
            model1.add_seasonality(**kwargs_mandatory)

        # missing mandatory arguments
        with self.assertRaises(ValueError):
            for kw, arg in kwargs_mandatory.items():
                Prophet(add_seasonalities={kw: arg})

        # invalid keywords
        with self.assertRaises(ValueError):
            Prophet(add_seasonalities=dict(kwargs_mandatory, **{'some_random_keyword': 'custom'}))

        # invalid value dtypes
        with self.assertRaises(ValueError):
            Prophet(add_seasonalities=dict({kw: None for kw in kwargs_mandatory}))

        with self.assertRaises(ValueError):
            Prophet(add_seasonalities=dict([]))

    def test_prophet_model(self):
        """runs `helper_test_model` with several frequencies and periods
        """
        perform_full_test = False

        test_cases_all = {'A': 12, 'W': 7, 'BM': 12, 'C': 5, 'D': 7, 'MS': 12,
                          'B': 5, 'H': 24, 'BH': 8, 'Q': 4, 'min': 60, 'S': 60}
        test_cases_fast = {key: test_cases_all[key] for key in ['MS', 'D', 'H']}  # monthly, daily, hourly

        test_cases = test_cases_all if perform_full_test else test_cases_fast
        for i, (freq, period) in enumerate(test_cases.items()):
            if not i:
                self.helper_test_prophet_model(period=period, freq=freq, compare_all_models=True)
            else:
                self.helper_test_prophet_model(period=period, freq=freq, compare_all_models=False)

    def helper_test_prophet_model(self, period, freq, compare_all_models=False):
        """Test which includes adding custom seasonalities and future covariates. The tests compare the output of
        univariate and stochastic forecasting with the validation timeseries and Prophet's base model output.

        The underlying curve to forecast is a sine timeseries multiplied with another sine timeseries.
        The curve shape repeats every 2*period timesteps (i.e. for period=24 hours -> seasonal_periods=48).
        We take the second sine wave as a covariate for the model.
        With the added custom seasonality and covariate, the model should have a very accurate forecast.
        """
        repetitions = 8
        ts_sine1 = tg.sine_timeseries(value_frequency=1 / period, length=period * repetitions, freq=freq)
        ts_sine2 = tg.sine_timeseries(value_frequency=1 / (period * 2), length=period * repetitions, freq=freq)
        ts_sine = ts_sine1 * ts_sine2
        covariate = ts_sine2

        split = int(-period * repetitions / 2)
        train, val = ts_sine[:split], ts_sine[split:]
        train_cov, val_cov = covariate[:split], covariate[split:]

        supress_auto_seasonality = {
            'daily_seasonality': False, 'weekly_seasonality': False, 'yearly_seasonality': False
        }
        custom_seasonality = {'name': 'custom', 'seasonal_periods': int(2 * period), 'fourier_order': 4}
        model = Prophet(
            add_seasonalities=custom_seasonality,
            seasonality_mode='additive',
            **supress_auto_seasonality
        )

        model.fit(train, future_covariates=train_cov)

        # univariate, stochastic and Prophet's base model forecast
        pred_darts = model.predict(n=len(val), num_samples=1, future_covariates=val_cov)
        compare_preds = [pred_darts]

        if compare_all_models:
            pred_darts_stochastic = model.predict(n=len(val), num_samples=200, future_covariates=val_cov)
            pred_raw_df = model.predict_raw(n=len(val), future_covariates=val_cov)
            pred_raw = TimeSeries.from_dataframe(pred_raw_df[['ds', 'yhat']], time_col='ds')
            compare_preds += [pred_darts_stochastic.quantile_timeseries(0.5), pred_raw]

        # all predictions should fit the underlying curve very well
        for pred in compare_preds:
            for val_i, pred_i in zip(val.univariate_values(), pred.univariate_values()):
                self.assertAlmostEqual(val_i, pred_i, delta=0.05)
