def fit()

in kats/utils/cupik.py [0:0]

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

    def fit(self, data: Any, params: Optional[Dict[str, Any]] = None, **kwargs) -> Any:
        """
        This function is the external function for user to fit the pipeline

        inputs:
        data: a single univariate time series data or a list of multiple univariate
              time series data

        params: a dictionary with the extra parameters for each step. The dictionary
                holds the format of {"user_defined_method_name": {"parameter": value}}

        extra key word arguments:
        remove: a boolean for telling the pipeline to remove outlier or not

        useFeatures: a boolean for telling the pipeline whether to use TsFeatures to process
                     the data for sklearn models, or merely getting the features as metadata
                     for other usage

        y: label data for fitting sklearn model, an array or a list

        outputs:
        data: output a single result for univariate data, or a list of results for multiple
              univariate time series data fed originally in the format of a list. Determined by
              the last step, the output could be processed time series data, or fitted kats/sklearn
              model, etc.
        """
        # Initialize a place holder for params
        if params is None:
            params = {}

        # Judging if extra functions needed
        ####
        self.remove = kwargs.get("remove", False)  # remove outliers or not
        self.useFeatures = kwargs.get(
            "useFeatures", False
        )  # do you want to use tsfeatures as transformation or analyzer
        self.y = kwargs.get("y", None)
        ####
        # Extra parameters for specific method of each step
        self.extra_fitting_params = params

        if type(data) != list:  # Since we support multiple timeseries in a list,
            # when data is univariate, we put them in a list
            self.univariate = True
            data = [data]

        for (
            n,
            s,
        ) in self.steps:  # Iterate through each step and perform the internal fitting
            # function
            data = self.__fit__(n, s, data)

        if (
            self.univariate
        ):  # When input data is one univariate time series, we directly
            # present the output (not in a list)
            return data[0]
        else:
            return data