wehub-resource-sync
124 lines
4.1 KiB
Python
124 lines
4.1 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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import scipy.stats
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from distribution.config import ATOL, DEVICES, RTOL
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import paddle
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np.random.seed(2022)
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@param.place(DEVICES)
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@param.param_cls(
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(param.TEST_CASE_NAME, 'concentration'),
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[
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('test-one-dim', param.xrand((89,))),
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# ('test-multi-dim', config.xrand((10, 20, 30)))
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],
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)
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class TestDirichlet(unittest.TestCase):
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def setUp(self):
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self._paddle_diric = paddle.distribution.Dirichlet(
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paddle.to_tensor(self.concentration)
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)
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def test_mean(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_diric.mean,
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scipy.stats.dirichlet.mean(self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_variance(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_diric.variance,
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scipy.stats.dirichlet.var(self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_prob(self):
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value = [np.random.rand(*self.concentration.shape)]
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value = [v / v.sum() for v in value]
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for v in value:
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_diric.prob(paddle.to_tensor(v)),
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scipy.stats.dirichlet.pdf(v, self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_log_prob(self):
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value = [np.random.rand(*self.concentration.shape)]
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value = [v / v.sum() for v in value]
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for v in value:
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_diric.log_prob(paddle.to_tensor(v)),
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scipy.stats.dirichlet.logpdf(v, self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_entropy(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_diric.entropy(),
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scipy.stats.dirichlet.entropy(self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_natural_parameters(self):
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self.assertTrue(
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isinstance(self._paddle_diric._natural_parameters, tuple)
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)
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def test_log_normalizer(self):
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self.assertTrue(
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np.all(
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self._paddle_diric._log_normalizer(
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paddle.to_tensor(param.xrand((100, 100, 100)))
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).numpy()
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< 0.0
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)
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)
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@param.place(DEVICES)
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@param.param_cls(
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(param.TEST_CASE_NAME, 'concentration'),
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[('test-zero-dim', np.array(1.0))],
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)
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class TestDirichletException(unittest.TestCase):
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def TestInit(self):
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with self.assertRaises(ValueError):
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paddle.distribution.Dirichlet(
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paddle.squeeze(self.concentration)
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)
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if __name__ == '__main__':
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unittest.main()
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