def fit()

in kats/models/prophet.py [0:0]

• 77 lines of code
• 12 McCabe index (conditional complexity)

    def fit(self, **kwargs: Any) -> None:
        """fit Prophet model

        Args:
            None.

        Returns:
            The fitted prophet model object
        """
        # prepare dataframe for Prophet.fit()
        df = pd.DataFrame({"ds": self.data.time, "y": self.data.value})
        logging.debug(
            "Call fit() with parameters: "
            "growth:{growth},"
            "changepoints:{changepoints},"
            "n_changepoints:{n_changepoints},"
            "changepoint_range:{changepoint_range},"
            "yearly_seasonality:{yearly_seasonality},"
            "weekly_seasonality:{weekly_seasonality},"
            "daily_seasonality:{daily_seasonality},"
            "holidays:{holidays},"
            "seasonality_mode:{seasonality_mode},"
            "seasonality_prior_scale:{seasonality_prior_scale},"
            "holidays_prior_scale:{holidays_prior_scale},"
            "changepoint_prior_scale:{changepoint_prior_scale},"
            "mcmc_samples:{mcmc_samples},"
            "interval_width:{interval_width},"
            "uncertainty_samples:{uncertainty_samples},"
            "cap:{cap},"
            "floor:{floor},"
            "custom_seasonalities:{custom_seasonalities},"
            "extra_regressors:{extra_regressors}".format(
                growth=self.params.growth,
                changepoints=self.params.changepoints,
                n_changepoints=self.params.n_changepoints,
                changepoint_range=self.params.changepoint_range,
                yearly_seasonality=self.params.yearly_seasonality,
                weekly_seasonality=self.params.weekly_seasonality,
                daily_seasonality=self.params.daily_seasonality,
                holidays=self.params.holidays,
                seasonality_mode=self.params.seasonality_mode,
                seasonality_prior_scale=self.params.seasonality_prior_scale,
                holidays_prior_scale=self.params.holidays_prior_scale,
                changepoint_prior_scale=self.params.changepoint_prior_scale,
                mcmc_samples=self.params.mcmc_samples,
                interval_width=self.params.interval_width,
                uncertainty_samples=self.params.uncertainty_samples,
                cap=self.params.cap,
                floor=self.params.floor,
                custom_seasonalities=self.params.custom_seasonalities,
                extra_regressors=None
                if self.params.extra_regressors is None
                else [x["name"] for x in self.params.extra_regressors],
            ),
        )

        prophet = Prophet(
            growth=self.params.growth,
            changepoints=self.params.changepoints,
            n_changepoints=self.params.n_changepoints,
            changepoint_range=self.params.changepoint_range,
            yearly_seasonality=self.params.yearly_seasonality,
            weekly_seasonality=self.params.weekly_seasonality,
            daily_seasonality=self.params.daily_seasonality,
            holidays=self.params.holidays,
            seasonality_mode=self.params.seasonality_mode,
            seasonality_prior_scale=self.params.seasonality_prior_scale,
            holidays_prior_scale=self.params.holidays_prior_scale,
            changepoint_prior_scale=self.params.changepoint_prior_scale,
            mcmc_samples=self.params.mcmc_samples,
            interval_width=self.params.interval_width,
            uncertainty_samples=self.params.uncertainty_samples,
        )

        if self.params.growth == "logistic":
            # assign cap to a new col as Prophet required
            df["cap"] = self.params.cap

        # Adding floor if available
        if self.params.floor is not None:
            df["floor"] = self.params.floor

        # Add any specified custom seasonalities.
        for custom_seasonality in self.params.custom_seasonalities:
            prophet.add_seasonality(**custom_seasonality)

        # Add any extra regressors
        if self.params.extra_regressors is not None:
            for regressor in self.params.extra_regressors:
                prophet.add_regressor(
                    **{k: v for k, v in regressor.items() if k not in ["value"]}
                )
                df[regressor["name"]] = pd.Series(regressor["value"], index=df.index)

        self.model = prophet.fit(df=df)
        logging.info("Fitted Prophet model. ")