Upcoming breaking changes

[ziatdinovmax]

I'm in the process of streamlining GPax to broaden its applicability and make it easier to build applications on top of it. Unfortunately, this will come with a few breaking changes.

Breaking change 1:
There will be no need to provide a random number generator key in training and prediction since it will be handled internally. So instead of

rng_key, rng_key_predict = gpax.utils.get_keys()
gp_model = gpax.ExactGP(1, kernel='RBF')
gp_model.fit(rng_key, X, y)

you will be using

gp_model = gpax.ExactGP(1, kernel='RBF')
gp_model.fit(X, y)

The same is true for prediction.

Breaking change 2:
The prediction will return directly posterior variance instead of samples. To get samples, you can use a sample_from_posterior method.

So instead of

posterior_mean, f_samples = gp_model.predict(rng_key_predict, X_test)

you will be using:

posterior_mean, posterior_var = gp_model.predict(X_test)

To get posterior samples, use

f_samples = gp_model.sample_from_posterior(X_test)

I do not expect any other major changes to API.

Please feel free to comment and let me know your thoughts.