def sample_posterior_predictive()

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

• 23 lines of code
• 5 McCabe index (conditional complexity)

    def sample_posterior_predictive(self, df):
        """Prophet posterior predictive samples.

        Parameters
        ----------
        df: Prediction dataframe.

        Returns
        -------
        Dictionary with posterior predictive samples for the forecast yhat and
        for the trend component.
        """
        n_iterations = self.params['k'].shape[0]
        samp_per_iter = max(1, int(np.ceil(
            self.uncertainty_samples / float(n_iterations)
        )))

        # Generate seasonality features once so we can re-use them.
        seasonal_features, _, component_cols, _ = (
            self.make_all_seasonality_features(df)
        )

        sim_values = {'yhat': [], 'trend': []}
        for i in range(n_iterations):
            for _j in range(samp_per_iter):
                sim = self.sample_model(
                    df=df,
                    seasonal_features=seasonal_features,
                    iteration=i,
                    s_a=component_cols['additive_terms'],
                    s_m=component_cols['multiplicative_terms'],
                )
                for key in sim_values:
                    sim_values[key].append(sim[key])
        for k, v in sim_values.items():
            sim_values[k] = np.column_stack(v)
        return sim_values