The "predict" method in the Deep GP tutorial does not work correctly.

[gpleiss]

I have found the issue here:

def predict(self, test_x):
        with torch.no_grad():

            # The output of the model is a multitask MVN, where both the data points
            # and the tasks are jointly distributed
            # To compute the marginal predictive NLL of each data point,
            # we will call `to_data_independent_dist`,
            # which removes the data cross-covariance terms from the distribution.
            preds = model.likelihood(model(test_x)).to_data_independent_dist()

        return preds.mean.mean(0), preds.variance.mean(0)

The predict method in the Deep GP model of the GPyTorch tutorial on Deep GPs does not work correctly. Especially, .to_data_independent_dist() seems to do something wrong. I suspect it is about some reshaping.

Using this method the uncertainties become correct:

def predict(self, test_x):
    with torch.no_grad():

      preds = model.likelihood(model(test_x))

      preds_mean = preds.mean.mean(axis=0)
      preds_var = preds.covariance_matrix.mean(axis=0).diag().reshape(num_tasks, num_tasks * self.train_x_shape[0]).T

    return preds_mean, preds_var

Originally posted by @fweberling in #1862 (reply in thread)

[gpleiss]

This should be closed by #2123