text: # 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 irs.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 irs
  import numpy as np
  import gin.tf
  
  
  def _identity_discretizer(target, num_bins):
    del num_bins
    return target
  
  
  class IrsTest(absltest.TestCase):
  
    def test_metric(self):
      gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
      gin.bind_parameter("discretizer.num_bins", 10)
  
      ground_truth_data = dummy_data.IdentityObservationsData()
      representation_function = lambda x: np.array(x, dtype=np.float64)
      random_state = np.random.RandomState(0)
      scores = irs.compute_irs(ground_truth_data, representation_function,
                               random_state, None, 0.99, 3000, 3000)
      self.assertBetween(scores["IRS"], 0.9, 1.0)
  
    def test_bad_metric(self):
      gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
      gin.bind_parameter("discretizer.num_bins", 10)
  
      ground_truth_data = dummy_data.IdentityObservationsData()
      representation_function = lambda x: np.zeros_like(x, dtype=np.float64)
      random_state = np.random.RandomState(0)
      scores = irs.compute_irs(ground_truth_data, representation_function,
                               random_state, None, 0.99, 3000, 3000)
      self.assertBetween(scores["IRS"], 0.0, 0.1)
  
    def test_drop_constant_dims(self):
      random_state = np.random.RandomState(0)
      ys = random_state.normal(0.0, 1.0, (100, 100))
      ys[0, :] = 1.
      ys[-1, :] = 0.
      active_ys = irs._drop_constant_dims(ys)
      np.testing.assert_array_equal(active_ys, ys[1:-1])
  
  
  if __name__ == "__main__":
    absltest.main()
  