wehub-resource-sync
206 lines
6.8 KiB
Python
206 lines
6.8 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
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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 import Poisson
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'rate'),
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[
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('zero-dim', np.array(100.0).astype('float64')),
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('one-dim', np.array([100.0]).astype('float64')),
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# boundary case and extreme case (`scipy.stats.poisson.entropy` cannot converge for very extreme cases such as rate=10000.0)
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('multi-dim', np.array([0.0, 1000.0]).astype('float32')),
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],
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)
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class TestPoisson(unittest.TestCase):
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def setUp(self):
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self._dist = Poisson(rate=paddle.to_tensor(self.rate))
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def test_mean(self):
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mean = self._dist.mean
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self.assertEqual(mean.numpy().dtype, self.rate.dtype)
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np.testing.assert_allclose(
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mean,
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scipy.stats.poisson.mean(self.rate),
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rtol=config.RTOL.get(str(self.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.dtype)),
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)
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def test_variance(self):
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var = self._dist.variance
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self.assertEqual(var.numpy().dtype, self.rate.dtype)
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np.testing.assert_allclose(
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var,
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scipy.stats.poisson.var(self.rate),
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rtol=config.RTOL.get(str(self.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.dtype)),
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)
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def test_entropy(self):
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entropy = self._dist.entropy()
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self.assertEqual(entropy.numpy().dtype, self.rate.dtype)
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np.testing.assert_allclose(
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entropy,
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scipy.stats.poisson.entropy(self.rate),
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rtol=config.RTOL.get(str(self.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.dtype)),
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)
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def test_sample(self):
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sample_shape = ()
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samples = self._dist.sample(sample_shape)
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self.assertEqual(samples.numpy().dtype, self.rate.dtype)
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self.assertEqual(
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tuple(samples.shape),
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sample_shape + self._dist.batch_shape + self._dist.event_shape,
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)
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sample_shape = (5000,)
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samples = self._dist.sample(sample_shape)
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sample_mean = samples.mean(axis=0)
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sample_variance = samples.var(axis=0)
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np.testing.assert_allclose(
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sample_mean, self._dist.mean, atol=0, rtol=0.20
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)
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np.testing.assert_allclose(
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sample_variance, self._dist.variance, atol=0, rtol=0.20
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)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'rate', 'value'),
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[
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(
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'value-same-shape',
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np.array(1000).astype('float32'),
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np.array(1100).astype('float32'),
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),
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(
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'value-broadcast-shape',
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np.array(10).astype('float64'),
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np.array([2.0, 3.0, 5.0, 10.0, 20.0]).astype('float64'),
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),
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],
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)
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class TestPoissonProbs(unittest.TestCase):
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def setUp(self):
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self._dist = Poisson(rate=paddle.to_tensor(self.rate))
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def test_prob(self):
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np.testing.assert_allclose(
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self._dist.prob(paddle.to_tensor(self.value)),
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scipy.stats.poisson.pmf(self.value, self.rate),
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rtol=config.RTOL.get(str(self.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.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._dist.log_prob(paddle.to_tensor(self.value)),
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scipy.stats.poisson.logpmf(self.value, self.rate),
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rtol=config.RTOL.get(str(self.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.dtype)),
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)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'rate_1', 'rate_2'),
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[
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(
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'zero-dim',
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parameterize.xrand((1,), min=1, max=20)
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.astype('int32')
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.astype('float64')
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.reshape([]),
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parameterize.xrand((1,), min=1, max=20)
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.astype('int32')
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.astype('float64')
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.reshape([]),
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),
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(
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'one-dim',
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parameterize.xrand((1,), min=1, max=20)
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.astype('int32')
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.astype('float64'),
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parameterize.xrand((1,), min=1, max=20)
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.astype('int32')
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.astype('float64'),
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),
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(
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'multi-dim',
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parameterize.xrand((5, 3), min=1, max=20)
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.astype('int32')
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.astype('float32'),
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parameterize.xrand((5, 3), min=1, max=20)
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.astype('int32')
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.astype('float32'),
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),
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],
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)
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class TestPoissonKL(unittest.TestCase):
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def setUp(self):
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self._dist1 = Poisson(rate=paddle.to_tensor(self.rate_1))
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self._dist2 = Poisson(rate=paddle.to_tensor(self.rate_2))
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def test_kl_divergence(self):
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kl0 = self._dist1.kl_divergence(self._dist2)
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kl1 = self.kl_divergence_scipy()
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self.assertEqual(tuple(kl0.shape), self._dist1.batch_shape)
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self.assertEqual(tuple(kl1.shape), self._dist1.batch_shape)
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np.testing.assert_allclose(
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kl0,
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kl1,
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rtol=config.RTOL.get(str(self.rate_1.dtype)),
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atol=config.ATOL.get(str(self.rate_1.dtype)),
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)
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def kl_divergence_scipy(self):
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rate_max = np.max(np.maximum(self.rate_1, self.rate_2))
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rate_min = np.min(np.minimum(self.rate_1, self.rate_2))
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support_max = self.enumerate_bounded_support(rate_max)
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support_min = self.enumerate_bounded_support(rate_min)
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a_min = np.min(support_min)
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a_max = np.max(support_max)
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common_support = np.arange(
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a_min, a_max, dtype=self.rate_1.dtype
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).reshape((-1,) + (1,) * len(self.rate_1.shape))
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log_prob_1 = scipy.stats.poisson.logpmf(common_support, self.rate_1)
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log_prob_2 = scipy.stats.poisson.logpmf(common_support, self.rate_2)
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return (np.exp(log_prob_1) * (log_prob_1 - log_prob_2)).sum(0)
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def enumerate_bounded_support(self, rate):
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s = np.sqrt(rate)
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upper = int(rate + 30 * s)
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lower = int(np.clip(rate - 30 * s, a_min=0, a_max=rate))
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values = np.arange(lower, upper, dtype=self.rate_1.dtype)
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return values
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if __name__ == '__main__':
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unittest.main()
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