empirical_kl_divergence_test.py

# coding=utf-8
# Copyright 2024 GPax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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"""Test for the empirical KL divergence."""

import logging

from absl.testing import absltest
from gpax import utils
from gpax.models import gp
from gpax.objectives import empirical_kl_divergence as ekl
from jax import numpy as jnp
from jax import random


class EmpiricalKLTest(absltest.TestCase):

  def test_objective(self):
    key1 = random.PRNGKey(0)
    dataset = [
        utils.SubDataset(
            random.uniform(key1, (8, 5)), random.uniform(key1, (8,))),
        utils.SubDataset(
            random.uniform(key1, (8, 5)), random.uniform(key1, (8,)))
    ]

    model = gp.GaussianProcess()
    params = model.init(key1, dataset[0].x)
    partial_objective = ekl.objective(model, params, dataset, partial=True)
    objective = ekl.objective(model, params, dataset, partial=False)

    self.assertNotEqual(partial_objective, jnp.nan)
    self.assertEmpty(partial_objective.shape)
    self.assertNotEqual(objective, jnp.nan)
    self.assertEmpty(objective.shape)
    logging.info(msg=f'partial kl = {partial_objective}; kl = {objective}')


if __name__ == '__main__':
  absltest.main()