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 unified_scores.py."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  # Dependency imports
  
  from absl.testing import absltest
  from disentanglement_lib.data.ground_truth import dummy_data
  from disentanglement_lib.evaluation.metrics import unified_scores
  import numpy as np
  
  import gin
  
  
  def _identity_discretizer(target, num_bins):
      del num_bins
      return target
  
  
  class UnifiedScoreTest(absltest.TestCase):
  
      def test_metric_mig(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 = unified_scores.compute_unified_scores(
              ground_truth_data, representation_function, random_state, None, 10000,
              100, matrix_fns=[unified_scores.mutual_information_matrix])
          self.assertBetween(
              scores["mutual_information_matrix.mig"], 0.9, 1.0)
          self.assertBetween(
              scores["mutual_information_matrix.modularity"], 0.9, 1.0)
  
      def test_metric_dci(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 = unified_scores.compute_unified_scores(
              ground_truth_data, representation_function, random_state, None, 10000,
              100, matrix_fns=[unified_scores.importance_gbt_matrix])
          self.assertBetween(
              scores["importance_gbt_matrix.dci_disentanglement"], 0.9, 1.0)
  
      def test_duplicated_latent_space_mig(self):
          gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
          gin.bind_parameter("discretizer.num_bins", 10)
          ground_truth_data = dummy_data.IdentityObservationsData()
  
          def representation_function(x):
              x = np.array(x, dtype=np.float64)
              return np.hstack([x, x])
  
          random_state = np.random.RandomState(0)
          scores = unified_scores.compute_unified_scores(
              ground_truth_data, representation_function, random_state, None, 1000,
              1000, matrix_fns=[unified_scores.mutual_information_matrix])
          self.assertBetween(
              scores["mutual_information_matrix.mig"], 0.0, 0.2)
          self.assertBetween(
              scores["mutual_information_matrix.modularity"], 0.9, 1.0)
  
      def test_duplicated_latent_space_dci(self):
          gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
          gin.bind_parameter("discretizer.num_bins", 10)
          ground_truth_data = dummy_data.IdentityObservationsData()
  
          def representation_function(x):
              x = np.array(x, dtype=np.float64)
              return np.hstack([x, x])
  
          random_state = np.random.RandomState(0)
          scores = unified_scores.compute_unified_scores(
              ground_truth_data, representation_function, random_state, None, 1000,
              1000, matrix_fns=[unified_scores.importance_gbt_matrix])
          self.assertBetween(
              scores["importance_gbt_matrix.dci_disentanglement"], 0.9, 1.0)
  
      def test_duplicated_latent_space_sap(self):
          gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
          gin.bind_parameter("discretizer.num_bins", 10)
          ground_truth_data = dummy_data.IdentityObservationsData()
  
          def representation_function(x):
              x = np.array(x, dtype=np.float64)
              return np.hstack([x, x])
  
          random_state = np.random.RandomState(0)
          scores = unified_scores.compute_unified_scores(
              ground_truth_data, representation_function, random_state, None, 1000,
              1000, matrix_fns=[unified_scores.accuracy_svm_matrix])
          self.assertBetween(scores["accuracy_svm_matrix.sap"], 0.0, 0.2)
  
  
  if __name__ == "__main__":
      absltest.main()
  