wehub-resource-sync
202 lines
6.6 KiB
Python
202 lines
6.6 KiB
Python
# Copyright (c) 2022 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
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import scipy.stats
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from distribution import config
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import paddle
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from paddle.distribution.gumbel import Gumbel
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paddle.enable_static()
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'loc', 'scale'),
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[
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('one-dim', parameterize.xrand((4,)), parameterize.xrand((4,))),
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('multi-dim', parameterize.xrand((5, 3)), parameterize.xrand((5, 3))),
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],
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)
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class TestGumbel(unittest.TestCase):
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def setUp(self):
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(main_program, startup_program):
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loc = paddle.static.data('loc', self.loc.shape, self.loc.dtype)
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scale = paddle.static.data(
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'scale', self.scale.shape, self.scale.dtype
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)
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self._dist = Gumbel(loc=loc, scale=scale)
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self.sample_shape = [50000]
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mean = self._dist.mean
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var = self._dist.variance
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stddev = self._dist.stddev
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entropy = self._dist.entropy()
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samples = self._dist.sample(self.sample_shape)
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fetch_list = [mean, var, stddev, entropy, samples]
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self.feeds = {'loc': self.loc, 'scale': self.scale}
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executor.run(startup_program)
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[
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self.mean,
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self.var,
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self.stddev,
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self.entropy,
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self.samples,
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] = executor.run(main_program, feed=self.feeds, fetch_list=fetch_list)
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def test_mean(self):
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self.assertEqual(str(self.mean.dtype).split('.')[-1], self.scale.dtype)
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np.testing.assert_allclose(
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self.mean,
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self._np_mean(),
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rtol=config.RTOL.get(str(self.scale.dtype)),
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atol=config.ATOL.get(str(self.scale.dtype)),
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)
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def test_variance(self):
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self.assertEqual(str(self.var.dtype).split('.')[-1], self.scale.dtype)
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np.testing.assert_allclose(
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self.var,
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self._np_variance(),
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rtol=config.RTOL.get(str(self.scale.dtype)),
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atol=config.ATOL.get(str(self.scale.dtype)),
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)
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def test_stddev(self):
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self.assertEqual(
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str(self.stddev.dtype).split('.')[-1], self.scale.dtype
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)
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np.testing.assert_allclose(
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self.stddev,
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self._np_stddev(),
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rtol=config.RTOL.get(str(self.scale.dtype)),
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atol=config.ATOL.get(str(self.scale.dtype)),
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)
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def test_entropy(self):
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self.assertEqual(
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str(self.entropy.dtype).split('.')[-1], self.scale.dtype
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)
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def test_sample(self):
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self.assertEqual(self.samples.dtype, self.scale.dtype)
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np.testing.assert_allclose(
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self.samples.mean(axis=0),
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scipy.stats.gumbel_r.mean(self.loc, scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self.scale.dtype)),
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)
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np.testing.assert_allclose(
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self.samples.var(axis=0),
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scipy.stats.gumbel_r.var(self.loc, scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self.scale.dtype)),
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)
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def _np_mean(self):
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return self.loc + self.scale * np.euler_gamma
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def _np_stddev(self):
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return np.sqrt(self._np_variance())
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def _np_variance(self):
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return np.divide(
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np.multiply(np.power(self.scale, 2), np.power(np.pi, 2)), 6
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)
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def _np_entropy(self):
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return np.log(self.scale) + 1 + np.euler_gamma
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'loc', 'scale', 'value'),
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[
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(
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'value-float',
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np.array([0.1, 0.4]),
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np.array([1.0, 4.0]),
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np.array([3.0, 7.0]),
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),
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('value-int', np.array([0.1, 0.4]), np.array([1, 4]), np.array([3, 7])),
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(
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'value-multi-dim',
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np.array([0.1, 0.4]),
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np.array([1, 4]),
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np.array([[5.0, 4], [6, 2]]),
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),
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],
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)
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class TestGumbelPDF(unittest.TestCase):
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def setUp(self):
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(main_program, startup_program):
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loc = paddle.static.data('loc', self.loc.shape, self.loc.dtype)
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scale = paddle.static.data(
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'scale', self.scale.shape, self.scale.dtype
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)
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value = paddle.static.data(
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'value', self.value.shape, self.value.dtype
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)
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self._dist = Gumbel(loc=loc, scale=scale)
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prob = self._dist.prob(value)
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log_prob = self._dist.log_prob(value)
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cdf = self._dist.cdf(value)
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fetch_list = [prob, log_prob, cdf]
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self.feeds = {'loc': self.loc, 'scale': self.scale, 'value': self.value}
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executor.run(startup_program)
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[self.prob, self.log_prob, self.cdf] = executor.run(
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main_program, feed=self.feeds, fetch_list=fetch_list
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)
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def test_prob(self):
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np.testing.assert_allclose(
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self.prob,
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scipy.stats.gumbel_r.pdf(self.value, self.loc, self.scale),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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def test_log_prob(self):
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np.testing.assert_allclose(
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self.log_prob,
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scipy.stats.gumbel_r.logpdf(self.value, self.loc, self.scale),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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def test_cdf(self):
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np.testing.assert_allclose(
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self.cdf,
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scipy.stats.gumbel_r.cdf(self.value, self.loc, self.scale),
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rtol=0.3,
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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if __name__ == '__main__':
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unittest.main()
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