# 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

# 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,
                                  random_state, None, 1000, 1000)

  def test_inter_group_fairness(self):
    counts = np.array([[0, 100], [100, 100]])
    mean_fairness, max_fairness = fairness.inter_group_fairness(counts)
    # The overall distribution is 1/3 to 2/3.
    # The first sensitive class has a distribution of 0 to 1.
    # The second sensitive class has a distribution of 1/2 to 1/2.
    # The total variation distances are hence 1/3 and 1/6 respectively.
    # The mean fairness is hence 1/3*1/3 + 2/3*1/6 = 2/9.
    self.assertAlmostEqual(mean_fairness, 2. / 9.)
    self.assertAlmostEqual(max_fairness, 1 / 3.)

  def test_compute_scores_dict(self):
    scores = np.array([[0., 2., 4.], [4., 0., 8.], [2., 10., 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


# 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,
                                  random_state, None, 1000, 1000)

  def test_inter_group_fairness(self):
    counts = np.array([[0, 100], [100, 100]])
    mean_fairness, max_fairness = fairness.inter_group_fairness(counts)
    # The overall distribution is 1/3 to 2/3.
    # The first sensitive class has a distribution of 0 to 1.
    # The second sensitive class has a distribution of 1/2 to 1/2.
    # The total variation distances are hence 1/3 and 1/6 respectively.
    # The mean fairness is hence 1/3*1/3 + 2/3*1/6 = 2/9.
    self.assertAlmostEqual(mean_fairness, 2. / 9.)
    self.assertAlmostEqual(max_fairness, 1 / 3.)

  def test_compute_scores_dict(self):
    scores = np.array([[0., 2., 4.], [4., 0., 8.], [2., 10., 0.]])

    results = fairness.compute_scores_dict(scores, "test")