# 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/mig.py
# def compute_mig(ground_truth_data,
#                 representation_function,
#                 random_state,
#                 artifact_dir=None,
#                 num_train=gin.REQUIRED,
#                 batch_size=16):
# def _compute_mig(mus_train, ys_train):
# def compute_mig_on_fixed_data(observations, labels, representation_function,
#                               batch_size=100):


from absl.testing import absltest
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.evaluation.metrics import mig
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 mig.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


def _identity_discretizer(target, num_bins):
  del num_bins
  return target


class MIGTest(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: x
    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/mig.py
# def compute_mig(ground_truth_data,
#                 representation_function,
#                 random_state,
#                 artifact_dir=None,
#                 num_train=gin.REQUIRED,
#                 batch_size=16):
# def _compute_mig(mus_train, ys_train):
# def compute_mig_on_fixed_data(observations, labels, representation_function,
#                               batch_size=100):


# Path: disentanglement_lib/evaluation/metrics/mig_test.py
from absl.testing import absltest
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.evaluation.metrics import mig
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 mig.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


def _identity_discretizer(target, num_bins):
  del num_bins
  return target


class MIGTest(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: x
    random_state = np.random.RandomState(0)

    scores = mig.compute_mig(