def transform(self, y, exogenous=None, **_):
        """Transform the new array

        Apply the Box-Cox transformation to the array after learning the
        lambda parameter.

        Parameters
        ----------
        y : array-like or None, shape=(n_samples,)
            The endogenous (time-series) array.

        exogenous : array-like or None, shape=(n_samples, n_features), optional
            The exogenous array of additional covariates. Not used for
            endogenous transformers. Default is None, and non-None values will
            serve as pass-through arrays.

        Returns
        -------
        y_transform : array-like or None
            The Box-Cox transformed y array

        exogenous : array-like or None
            The exog array
        """
        check_is_fitted(self, "lam1_")
        lam1 = self.lam1_
        lam2 = self.lam2_

        y, exog = self._check_y_exog(y, exogenous)
        y += lam2

        neg_mask = y <= 0.
        if neg_mask.any():
            action = self.neg_action
            msg = "Negative or zero values present in y"
            if action == "raise":
                raise ValueError(msg)
            elif action == "warn":
                warnings.warn(msg, UserWarning)
            y[neg_mask] = self.floor

        if lam1 == 0:
            return np.log(y), exog
        return (y ** lam1 - 1) / lam1, exog
def transform ( self , y , exogenous = None , ** _ ) : check_is_fitted ( self , "lam1_" ) lam1 = self . lam1_ lam2 = self . lam2_ y , exog = self . _check_y_exog ( y , exogenous ) y += lam2 neg_mask = y <= 0. if neg_mask . any ( ) : action = self . neg_action msg = "Negative or zero values present in y" if action == "raise" : raise ValueError ( msg ) elif action == "warn" : warnings . warn ( msg , UserWarning ) y [ neg_mask ] = self . floor if lam1 == 0 : return np . log ( y ) , exog return ( y ** lam1 - 1 ) / lam1 , exog
Transform the new array

        Apply the Box-Cox transformation to the array after learning the
        lambda parameter.

        Parameters
        ----------
        y : array-like or None, shape=(n_samples,)
            The endogenous (time-series) array.

        exogenous : array-like or None, shape=(n_samples, n_features), optional
            The exogenous array of additional covariates. Not used for
            endogenous transformers. Default is None, and non-None values will
            serve as pass-through arrays.

        Returns
        -------
        y_transform : array-like or None
            The Box-Cox transformed y array

        exogenous : array-like or None
            The exog array