# 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/udr/metrics/udr.py
# def relative_strength_disentanglement(corr_matrix):
# def spearman_correlation_conv(vec1, vec2):
# def lasso_correlation_matrix(vec1, vec2, random_state=None):
# def _generate_representation_batch(ground_truth_data, representation_functions,
#                                    batch_size, random_state):
# def _generate_representation_dataset(ground_truth_data,
#                                      representation_functions, batch_size,
#                                      num_data_points, random_state):
# def compute_udr_sklearn(ground_truth_data,
#                         representation_functions,
#                         random_state,
#                         batch_size,
#                         num_data_points,
#                         correlation_matrix="lasso",
#                         filter_low_kl=True,
#                         include_raw_correlations=True,
#                         kl_filter_threshold=0.01):


from absl.testing import absltest
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.evaluation.udr.metrics import udr
import numpy as np
# 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 udr.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


class UdrTest(absltest.TestCase):

  def test_metric_spearman(self):

# 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/udr/metrics/udr.py
# def relative_strength_disentanglement(corr_matrix):
# def spearman_correlation_conv(vec1, vec2):
# def lasso_correlation_matrix(vec1, vec2, random_state=None):
# def _generate_representation_batch(ground_truth_data, representation_functions,
#                                    batch_size, random_state):
# def _generate_representation_dataset(ground_truth_data,
#                                      representation_functions, batch_size,
#                                      num_data_points, random_state):
# def compute_udr_sklearn(ground_truth_data,
#                         representation_functions,
#                         random_state,
#                         batch_size,
#                         num_data_points,
#                         correlation_matrix="lasso",
#                         filter_low_kl=True,
#                         include_raw_correlations=True,
#                         kl_filter_threshold=0.01):


# Path: disentanglement_lib/evaluation/udr/metrics/udr_test.py
from absl.testing import absltest
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.evaluation.udr.metrics import udr
import numpy as np

# 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 udr.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


class UdrTest(absltest.TestCase):

  def test_metric_spearman(self):

    ground_truth_data = dummy_data.DummyData()