kats/models/nowcasting/dynamic_nowcasting.py [83:154]:
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    def __init__(
        self,
        data: TimeSeriesData,
        params: NowcastingParams,
        model: Any = None,
        poly_model: Any = None,
        feature_names: List[str] = [],
        poly_feature_names: List[str] = [],
        scaler: Any = None,
        label_scaler: Any = None,
        y_train_season_obj: Any = None,
    ) -> None:
        super().__init__(data, params)
        if not isinstance(self.data.value, pd.Series):
            msg = "Only support univariate time series, but get {type}.".format(
                type=type(self.data.value)
            )
            logging.error(msg)
            raise ValueError(msg)
        self.df = data.to_dataframe()
        self.step = params.step
        self.model = model
        self.feature_names = feature_names
        self.poly_model = poly_model
        self.df_poly = data.to_dataframe()
        self.poly_feature_names = poly_feature_names
        self.df_nowcasting = data.to_dataframe()
        self.scaler = scaler
        self.label_scaler = label_scaler
        self.y_train_season_obj = y_train_season_obj

    def feature_extraction(self) -> None:
        """
        Extarct features for training
        """
        # Add the hour, minute, and x column to the data
        self.df_poly["hour"] = self.df_poly["time"].apply(lambda y: y.hour)
        self.df_poly["minute"] = self.df_poly["time"].apply(lambda y: y.minute)
        self.df_poly["x"] = self.df_poly["hour"] * 60 + self.df_poly["minute"]

        # Empty list to hold the feature names
        poly_feature_names = []

        # Add the poly columns to the df_poly
        for degree in [0, 1, 2, 3, 4, 5]:
            self.df_poly = poly(self.df_poly, degree)
            poly_feature_names.append("poly_" + str(degree))

        # filterout + - inf, nan
        self.df_poly = self.df_poly[
            ~self.df_poly.isin([np.nan, np.inf, -np.inf]).any(1)
        ]

        # Save the poly feature name
        self.poly_feature_names = poly_feature_names
        feature_names = []

        #########################################################################################
        train_index_poly = self.df_poly[
            ~self.df_poly.isin([np.nan, np.inf, -np.inf]).any(1)
        ].index
        X_train_poly, y_train_poly = (
            self.df_poly[self.poly_feature_names].loc[train_index_poly],
            self.df_poly["y"].loc[train_index_poly],
        )

        # Build the Polynomial Regression Model
        lin_reg = LinearRegression()
        lin_reg.fit(X_train_poly, y_train_poly)
        self.poly_model = lin_reg
        y_train_season = lin_reg.predict(X_train_poly)
        self.y_train_season_obj = y_train_season
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kats/models/nowcasting/nowcastingplus.py [82:153]:
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    def __init__(
        self,
        data: TimeSeriesData,
        params: NowcastingParams,
        model: Any = None,
        poly_model: Any = None,
        feature_names: List[str] = [],
        poly_feature_names: List[str] = [],
        scaler: Any = None,
        label_scaler: Any = None,
        y_train_season_obj: Any = None,
    ) -> None:
        super().__init__(data, params)
        if not isinstance(self.data.value, pd.Series):
            msg = "Only support univariate time series, but get {type}.".format(
                type=type(self.data.value)
            )
            logging.error(msg)
            raise ValueError(msg)
        self.df = data.to_dataframe()
        self.step = params.step
        self.model = model
        self.feature_names = feature_names
        self.poly_model = poly_model
        self.df_poly = data.to_dataframe()
        self.poly_feature_names = poly_feature_names
        self.df_nowcasting = data.to_dataframe()
        self.scaler = scaler
        self.label_scaler = label_scaler
        self.y_train_season_obj = y_train_season_obj

    def feature_extraction(self) -> None:
        """
        Extracts features for time series data.
        """
        # Add the hour, minute, and x column to the data
        self.df_poly["hour"] = self.df_poly["time"].apply(lambda y: y.hour)
        self.df_poly["minute"] = self.df_poly["time"].apply(lambda y: y.minute)
        self.df_poly["x"] = self.df_poly["hour"] * 60 + self.df_poly["minute"]

        # Empty list to hold the feature names
        poly_feature_names = []

        # Add the poly columns to the df_poly
        for degree in [0, 1, 2, 3, 4, 5]:
            self.df_poly = poly(self.df_poly, degree)
            poly_feature_names.append("poly_" + str(degree))

        # filterout + - inf, nan
        self.df_poly = self.df_poly[
            ~self.df_poly.isin([np.nan, np.inf, -np.inf]).any(1)
        ]

        # Save the poly feature name
        self.poly_feature_names = poly_feature_names
        feature_names = []

        #########################################################################################
        train_index_poly = self.df_poly[
            ~self.df_poly.isin([np.nan, np.inf, -np.inf]).any(1)
        ].index
        X_train_poly, y_train_poly = (
            self.df_poly[self.poly_feature_names].loc[train_index_poly],
            self.df_poly["y"].loc[train_index_poly],
        )

        # Build the Polynomial Regression Model
        lin_reg = LinearRegression()
        lin_reg.fit(X_train_poly, y_train_poly)
        self.poly_model = lin_reg
        y_train_season = lin_reg.predict(X_train_poly)
        self.y_train_season_obj = y_train_season
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