Extend the CCA class of scikit-learn to return reconstructions of the Y as well.

CCA_extended.py

from sklearn.cross_decomposition import CCA
from sklearn.utils import check_array
from sklearn.utils.validation import check_is_fitted, FLOAT_DTYPES

class CCA_extended(CCA):
  
    def __init__(self,*args,**kwargs):
        super().__init__(*args,**kwargs)
        return
      
    def inverse_transform_xy(self,X,Y):
        """
        This module calculates the inverse transform for both X and Y
        CCA.inverse_transform module only calculates reconstructions for X
        """
        check_is_fitted(self)
        X = check_array(X, dtype=FLOAT_DTYPES)
        Y = check_array(Y, dtype=FLOAT_DTYPES)
        
        x = np.matmul(X, cca.x_loadings_.T)
        x *= cca.x_std_
        x += cca.x_mean_
        
        y = np.matmul(Y, cca.y_loadings_.T)
        y *= cca.y_std_
        y += cca.y_mean_
        return x,y