orbit/template/dlt.py [485:518]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            ]

    def _validate_training_df_with_regression(self, df):
        df_columns = df.columns
        # validate regression columns
        if self.regressor_col is not None and not set(self.regressor_col).issubset(
            df_columns
        ):
            raise ModelException(
                "DataFrame does not contain specified regressor column(s)."
            )

    def _set_regressor_matrix(self, df, num_of_observations):
        """Set regressor matrix based on the input data-frame.
        Notes
        -----
        In case of absence of regression, they will be set to np.array with dim (num_of_obs, 0) to fit Stan requirement
        """
        # init of regression matrix depends on length of response vector
        self.positive_regressor_matrix = np.zeros(
            (num_of_observations, 0), dtype=np.double
        )
        self.negative_regressor_matrix = np.zeros(
            (num_of_observations, 0), dtype=np.double
        )
        self.regular_regressor_matrix = np.zeros(
            (num_of_observations, 0), dtype=np.double
        )

        # update regression matrices
        if self.num_of_positive_regressors > 0:
            self.positive_regressor_matrix = df.filter(
                items=self.positive_regressor_col,
            ).values
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



orbit/template/lgt.py [402:435]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            ]

    def _validate_training_df_with_regression(self, df):
        df_columns = df.columns
        # validate regression columns
        if self.regressor_col is not None and not set(self.regressor_col).issubset(
            df_columns
        ):
            raise ModelException(
                "DataFrame does not contain specified regressor column(s)."
            )

    def _set_regressor_matrix(self, df, num_of_observations):
        """Set regressor matrix based on the input data-frame.
        Notes
        -----
        In case of absence of regression, they will be set to np.array with dim (num_of_obs, 0) to fit Stan requirement
        """
        # init of regression matrix depends on length of response vector
        self.positive_regressor_matrix = np.zeros(
            (num_of_observations, 0), dtype=np.double
        )
        self.negative_regressor_matrix = np.zeros(
            (num_of_observations, 0), dtype=np.double
        )
        self.regular_regressor_matrix = np.zeros(
            (num_of_observations, 0), dtype=np.double
        )

        # update regression matrices
        if self.num_of_positive_regressors > 0:
            self.positive_regressor_matrix = df.filter(
                items=self.positive_regressor_col,
            ).values
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -