wehub-resource-sync
149 lines
5.3 KiB
Python
149 lines
5.3 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 import config
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from distribution.config import ATOL, RTOL
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from parameterize import xrand
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import paddle
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np.random.seed(2022)
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paddle.enable_static()
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@param.place(config.DEVICES)
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@param.parameterize_cls(
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(param.TEST_CASE_NAME, 'alpha', 'beta'),
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[
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('test-tensor', xrand((10, 10)), xrand((10, 10))),
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('test-broadcast', xrand((2, 1)), xrand((2, 5))),
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('test-larger-data', xrand((10, 20)), xrand((10, 20))),
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],
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)
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class TestBeta(unittest.TestCase):
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def setUp(self):
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self.program = paddle.static.Program()
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self.executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(self.program):
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# scale no need convert to tensor for scale input unittest
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alpha = paddle.static.data(
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'alpha', self.alpha.shape, self.alpha.dtype
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)
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beta = paddle.static.data('beta', self.beta.shape, self.beta.dtype)
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self._paddle_beta = paddle.distribution.Beta(alpha, beta)
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self.feeds = {'alpha': self.alpha, 'beta': self.beta}
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def test_mean(self):
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with paddle.static.program_guard(self.program):
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[mean] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_beta.mean],
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)
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np.testing.assert_allclose(
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mean,
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scipy.stats.beta.mean(self.alpha, self.beta),
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rtol=RTOL.get(str(self.alpha.dtype)),
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atol=ATOL.get(str(self.alpha.dtype)),
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)
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def test_variance(self):
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with paddle.static.program_guard(self.program):
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[variance] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_beta.variance],
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)
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np.testing.assert_allclose(
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variance,
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scipy.stats.beta.var(self.alpha, self.beta),
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rtol=RTOL.get(str(self.alpha.dtype)),
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atol=ATOL.get(str(self.alpha.dtype)),
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)
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def test_prob(self):
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with paddle.static.program_guard(self.program):
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value = paddle.static.data(
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'value',
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self._paddle_beta.alpha.shape,
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self._paddle_beta.alpha.dtype,
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)
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prob = self._paddle_beta.prob(value)
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random_number = np.random.rand(*self._paddle_beta.alpha.shape)
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feeds = dict(self.feeds, value=random_number)
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[prob] = self.executor.run(
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self.program, feed=feeds, fetch_list=[prob]
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)
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np.testing.assert_allclose(
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prob,
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scipy.stats.beta.pdf(random_number, self.alpha, self.beta),
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rtol=RTOL.get(str(self.alpha.dtype)),
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atol=ATOL.get(str(self.alpha.dtype)),
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)
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def test_log_prob(self):
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with paddle.static.program_guard(self.program):
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value = paddle.static.data(
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'value',
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self._paddle_beta.alpha.shape,
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self._paddle_beta.alpha.dtype,
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)
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prob = self._paddle_beta.log_prob(value)
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random_number = np.random.rand(*self._paddle_beta.alpha.shape)
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feeds = dict(self.feeds, value=random_number)
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[prob] = self.executor.run(
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self.program, feed=feeds, fetch_list=[prob]
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)
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np.testing.assert_allclose(
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prob,
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scipy.stats.beta.logpdf(random_number, self.alpha, self.beta),
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rtol=RTOL.get(str(self.alpha.dtype)),
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atol=ATOL.get(str(self.alpha.dtype)),
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)
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def test_entropy(self):
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with paddle.static.program_guard(self.program):
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[entropy] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_beta.entropy()],
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)
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np.testing.assert_allclose(
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entropy,
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scipy.stats.beta.entropy(self.alpha, self.beta),
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rtol=RTOL.get(str(self.alpha.dtype)),
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atol=ATOL.get(str(self.alpha.dtype)),
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)
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def test_sample(self):
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with paddle.static.program_guard(self.program):
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[data] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_beta.sample(),
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)
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self.assertTrue(
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data.shape
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== np.broadcast_arrays(self.alpha, self.beta)[0].shape
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)
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if __name__ == '__main__':
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unittest.main()
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