# Path: disentanglement_lib/data/ground_truth/dummy_data.py
# class IdentityObservationsData(ground_truth_data.GroundTruthData):
# class DummyData(ground_truth_data.GroundTruthData):
#   def num_factors(self):
#   def observation_shape(self):
#   def factors_num_values(self):
#   def sample_factors(self, num, random_state):
#   def sample_observations_from_factors(self, factors, random_state):
#   def factor_names(self):
#   def num_factors(self):
#   def factors_num_values(self):
#   def observation_shape(self):
#   def sample_factors(self, num, random_state):
#   def sample_observations_from_factors(self, factors, random_state):
# 
# Path: disentanglement_lib/evaluation/metrics/fairness.py
# def compute_fairness(ground_truth_data,
#                      representation_function,
#                      random_state,
#                      artifact_dir=None,
#                      num_train=gin.REQUIRED,
#                      num_test_points_per_class=gin.REQUIRED,
#                      batch_size=16):
# def compute_scores_dict(metric, prefix):
# def inter_group_fairness(counts):
# 
# Path: disentanglement_lib/evaluation/metrics/utils.py
# def generate_batch_factor_code(ground_truth_data, representation_function,
#                                num_points, random_state, batch_size):
# def split_train_test(observations, train_percentage):
# def obtain_representation(observations, representation_function, batch_size):
# def discrete_mutual_info(mus, ys):
# def discrete_entropy(ys):
# def make_discretizer(target, num_bins=gin.REQUIRED,
#                      discretizer_fn=gin.REQUIRED):
# def _histogram_discretize(target, num_bins=gin.REQUIRED):
# def normalize_data(data, mean=None, stddev=None):
# def make_predictor_fn(predictor_fn=gin.REQUIRED):
# def logistic_regression_cv():
# def gradient_boosting_classifier():


from absl.testing import absltest
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.evaluation.metrics import fairness
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import gin.tf
# 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 fairness.py."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# Dependency imports



class FairnessTest(absltest.TestCase):

  def test_metric(self):
    gin.bind_parameter("predictor.predictor_fn",
                       utils.gradient_boosting_classifier)
    ground_truth_data = dummy_data.DummyData()
    def representation_function(x):
      return np.array(x, dtype=np.float64)[:, :, 0, 0]
    random_state = np.random.RandomState(0)

# Path: disentanglement_lib/data/ground_truth/dummy_data.py
# class IdentityObservationsData(ground_truth_data.GroundTruthData):
# class DummyData(ground_truth_data.GroundTruthData):
#   def num_factors(self):
#   def observation_shape(self):
#   def factors_num_values(self):
#   def sample_factors(self, num, random_state):
#   def sample_observations_from_factors(self, factors, random_state):
#   def factor_names(self):
#   def num_factors(self):
#   def factors_num_values(self):
#   def observation_shape(self):
#   def sample_factors(self, num, random_state):
#   def sample_observations_from_factors(self, factors, random_state):
# 
# Path: disentanglement_lib/evaluation/metrics/fairness.py
# def compute_fairness(ground_truth_data,
#                      representation_function,
#                      random_state,
#                      artifact_dir=None,
#                      num_train=gin.REQUIRED,
#                      num_test_points_per_class=gin.REQUIRED,
#                      batch_size=16):
# def compute_scores_dict(metric, prefix):
# def inter_group_fairness(counts):
# 
# Path: disentanglement_lib/evaluation/metrics/utils.py
# def generate_batch_factor_code(ground_truth_data, representation_function,
#                                num_points, random_state, batch_size):
# def split_train_test(observations, train_percentage):
# def obtain_representation(observations, representation_function, batch_size):
# def discrete_mutual_info(mus, ys):
# def discrete_entropy(ys):
# def make_discretizer(target, num_bins=gin.REQUIRED,
#                      discretizer_fn=gin.REQUIRED):
# def _histogram_discretize(target, num_bins=gin.REQUIRED):
# def normalize_data(data, mean=None, stddev=None):
# def make_predictor_fn(predictor_fn=gin.REQUIRED):
# def logistic_regression_cv():
# def gradient_boosting_classifier():


# Path: disentanglement_lib/evaluation/metrics/fairness_test.py
from absl.testing import absltest
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.evaluation.metrics import fairness
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import gin.tf

# 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 fairness.py."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# Dependency imports



class FairnessTest(absltest.TestCase):

  def test_metric(self):
    gin.bind_parameter("predictor.predictor_fn",
                       utils.gradient_boosting_classifier)
    ground_truth_data = dummy_data.DummyData()
    def representation_function(x):
      return np.array(x, dtype=np.float64)[:, :, 0, 0]
    random_state = np.random.RandomState(0)

    _ = fairness.compute_fairness(ground_truth_data, representation_function,