Hello! I try this solution but without luck. My problem is that the X input of generate_model(x,y) is not a matrix in my data. In fact, I have several input for x (as numpy array), and in the pm.Model, I have something like log_rr = Deterministic(‘rr’, np.log(nominator/denominator)) and y = pm.Normal(‘y’, mu=log_rr ,sigma = SE, observed = log_rr_array). The ‘nominator’ and ‘denominator’ involve numpy arrays as input. I can run the model on original training data. But when I switch out for the test dataset, the modeling trace keep giving me shape errors such as this:
ValueError: Input dimension mis-match. (input[0].shape[0] = 9003, input[1].shape[0] = 82)