def fit()

in orbit/forecaster/forecaster.py [0:0]

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

    def fit(self, df, **kwargs):
        """Core process for fitting a model within a forecaster"""
        estimator = self.estimator
        model_name = self._model.get_model_name()
        df = df.copy()

        # default set and validation of input data frame
        self._validate_training_df(df)
        # extract standard training metadata
        self._set_training_meta(df)
        # customize module
        self._model.set_dynamic_attributes(
            df=df, training_meta=self.get_training_meta()
        )
        # based on the model and df, set training input
        self.set_training_data_input()
        # if model provide initial values, set it
        self._model.set_init_values()

        # estimator inputs
        data_input = self.get_training_data_input()
        init_values = self._model.get_init_values()
        model_param_names = self._model.get_model_param_names()

        # note that estimator will search for the .stan, .pyro model file based on the
        # estimator type and model_name provided
        _posterior_samples, training_metrics = estimator.fit(
            model_name=model_name,
            model_param_names=model_param_names,
            data_input=data_input,
            fitter=self._model.get_fitter(),
            init_values=init_values,
            **kwargs,
        )
        self._posterior_samples = _posterior_samples
        self._training_metrics = training_metrics