Python PLSRegression : obtaining the latent variables scores using loadings
Actually, I just had to better understand the fit() and transform() methods of Sklearn. I need to use transform(W) to obtain the latent variables scores of the W array:
1.Fit(): generates learning model parameters from training data
2.Transform(): uses the parameters generated from fit() method to transform a particular dataset
In sklearn.cross_decomposition.PLSRegression, we can obtain the latent variables scores from the X array using x_scores_.
I would like to extract the loadings to calculate the latent variables scores for a new array W. Intuitively, what I whould do is: scores = W*loadings  (matrix multiplication). I tried this using either x_loadings_, x_weights_, and x_rotations_ as loadings as I could not figure out which array was the good one (there is little info on the sklearn website). I also tried to standardize W (subtracting the mean and dividing by the standard deviation of X) before multiplying by the loadings. But none of these works (I tried using the X array and I cannot obtain the same scores as in the  x_scores_ array).
Any help with this?