code: # coding=utf-8
  # Copyright 2018 The DisentanglementLib Authors.  All rights reserved.
  #
  # 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,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Tests for beta_vae.py."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl.testing import absltest
  from disentanglement_lib.data.ground_truth import dummy_data
  from disentanglement_lib.evaluation.metrics import beta_vae
  import numpy as np
  
  
  class BetaVaeTest(absltest.TestCase):
  
    def test_metric(self):
      ground_truth_data = dummy_data.IdentityObservationsData()
      representation_function = lambda x: x
      random_state = np.random.RandomState(0)
      scores = beta_vae.compute_beta_vae_sklearn(
          ground_truth_data, representation_function, random_state, None, 5,
          2000, 2000)
      self.assertBetween(scores["train_accuracy"], 0.9, 1.0)
      self.assertBetween(scores["eval_accuracy"], 0.9, 1.0)
  
  
  if __name__ == "__main__":
    absltest.main()
  
apis: numpy.random.RandomState