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rewards_test.py
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rewards_test.py
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# coding=utf-8
# Copyright 2022 The ML Fairness Gym Authors.
#
# 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.
# Lint as: python2, python3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import absltest
import rewards
import numpy as np
class RewardsTest(absltest.TestCase):
def test_scalar_delta_reward(self):
reward = rewards.ScalarDeltaReward('x', baseline=0)
# Variable goes up from baseline.
self.assertEqual(reward({'x': 1}), 1)
# Variable goes up again.
self.assertEqual(reward({'x': 5}), 4)
def test_invalid_vector_in_scalar_delta_reward(self):
observation = {'x': np.array([1, 1])}
reward = rewards.ScalarDeltaReward('x', baseline=0)
with self.assertRaises(TypeError):
reward(observation)
def test_binarized_scalar_delta_reward(self):
observation = {'x': 1}
reward = rewards.BinarizedScalarDeltaReward('x', baseline=0)
# Variable goes up from baseline.
self.assertEqual(reward(observation), 1)
# Variable goes up.
observation['x'] = 5
self.assertEqual(reward(observation), 1)
# Variable stays constant.
self.assertIsNone(reward(observation))
# Variable goes down.
observation['x'] = 4
self.assertEqual(reward(observation), 0)
def test_vector_sum_reward(self):
reward = rewards.VectorSumReward('x')
self.assertEqual(reward({'x': [1, 5, 2, 4, 7]}), 19)
if __name__ == '__main__':
absltest.main()