def make_all_seasonality_features()

in python/prophet/forecaster.py [0:0]

• 38 lines of code
• 6 McCabe index (conditional complexity)

    def make_all_seasonality_features(self, df):
        """Dataframe with seasonality features.

        Includes seasonality features, holiday features, and added regressors.

        Parameters
        ----------
        df: pd.DataFrame with dates for computing seasonality features and any
            added regressors.

        Returns
        -------
        pd.DataFrame with regression features.
        list of prior scales for each column of the features dataframe.
        Dataframe with indicators for which regression components correspond to
            which columns.
        Dictionary with keys 'additive' and 'multiplicative' listing the
            component names for each mode of seasonality.
        """
        seasonal_features = []
        prior_scales = []
        modes = {'additive': [], 'multiplicative': []}

        # Seasonality features
        for name, props in self.seasonalities.items():
            features = self.make_seasonality_features(
                df['ds'],
                props['period'],
                props['fourier_order'],
                name,
            )
            if props['condition_name'] is not None:
                features[~df[props['condition_name']]] = 0
            seasonal_features.append(features)
            prior_scales.extend(
                [props['prior_scale']] * features.shape[1])
            modes[props['mode']].append(name)

        # Holiday features
        holidays = self.construct_holiday_dataframe(df['ds'])
        if len(holidays) > 0:
            features, holiday_priors, holiday_names = (
                self.make_holiday_features(df['ds'], holidays)
            )
            seasonal_features.append(features)
            prior_scales.extend(holiday_priors)
            modes[self.seasonality_mode].extend(holiday_names)

        # Additional regressors
        for name, props in self.extra_regressors.items():
            seasonal_features.append(pd.DataFrame(df[name]))
            prior_scales.append(props['prior_scale'])
            modes[props['mode']].append(name)

        # Dummy to prevent empty X
        if len(seasonal_features) == 0:
            seasonal_features.append(
                pd.DataFrame({'zeros': np.zeros(df.shape[0])}))
            prior_scales.append(1.)

        seasonal_features = pd.concat(seasonal_features, axis=1)
        component_cols, modes = self.regressor_column_matrix(
            seasonal_features, modes
        )
        return seasonal_features, prior_scales, component_cols, modes