wehub-resource-sync
120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import parameterize as param
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from distribution import config
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import paddle
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np.random.seed(2022)
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@param.place(config.DEVICES)
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@param.param_cls(
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(param.TEST_CASE_NAME, 'base', 'reinterpreted_batch_rank'),
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[
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(
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'base_beta',
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paddle.distribution.Beta(paddle.rand([1, 2]), paddle.rand([1, 2])),
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1,
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)
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],
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)
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class TestIndependent(unittest.TestCase):
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def setUp(self):
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self._t = paddle.distribution.Independent(
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self.base, self.reinterpreted_batch_rank
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)
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def test_mean(self):
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np.testing.assert_allclose(
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self.base.mean,
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self._t.mean,
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rtol=config.RTOL.get(str(self.base.alpha.numpy().dtype)),
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atol=config.ATOL.get(str(self.base.alpha.numpy().dtype)),
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)
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def test_variance(self):
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np.testing.assert_allclose(
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self.base.variance,
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self._t.variance,
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rtol=config.RTOL.get(str(self.base.alpha.numpy().dtype)),
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atol=config.ATOL.get(str(self.base.alpha.numpy().dtype)),
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)
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def test_entropy(self):
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np.testing.assert_allclose(
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self._np_sum_rightmost(
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self.base.entropy().numpy(), self.reinterpreted_batch_rank
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),
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self._t.entropy(),
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rtol=config.RTOL.get(str(self.base.alpha.numpy().dtype)),
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atol=config.ATOL.get(str(self.base.alpha.numpy().dtype)),
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)
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def _np_sum_rightmost(self, value, n):
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return np.sum(value, tuple(range(-n, 0))) if n > 0 else value
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def test_log_prob(self):
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value = np.random.rand(1)
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np.testing.assert_allclose(
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self._np_sum_rightmost(
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self.base.log_prob(paddle.to_tensor(value)).numpy(),
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self.reinterpreted_batch_rank,
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),
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self._t.log_prob(paddle.to_tensor(value)).numpy(),
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rtol=config.RTOL.get(str(self.base.alpha.numpy().dtype)),
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atol=config.ATOL.get(str(self.base.alpha.numpy().dtype)),
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)
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# TODO(cxxly): Add Kolmogorov-Smirnov test for sample result.
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def test_sample(self):
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shape = (5, 10, 8)
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expected_shape = (5, 10, 8, 1, 2)
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data = self._t.sample(shape)
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self.assertEqual(tuple(data.shape), expected_shape)
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self.assertEqual(data.dtype, self.base.alpha.dtype)
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@param.place(config.DEVICES)
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@param.param_cls(
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(
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param.TEST_CASE_NAME,
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'base',
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'reinterpreted_batch_rank',
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'expected_exception',
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),
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[
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('base_not_transform', '', 1, TypeError),
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(
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'rank_less_than_zero',
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paddle.distribution.Transform(),
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-1,
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ValueError,
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),
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],
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)
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class TestIndependentException(unittest.TestCase):
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def test_init(self):
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with self.assertRaises(self.expected_exception):
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paddle.distribution.IndependentTransform(
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self.base, self.reinterpreted_batch_rank
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)
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if __name__ == '__main__':
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unittest.main()
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