wehub-resource-sync
214 lines
6.7 KiB
Python
214 lines
6.7 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.binomial import Binomial
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'total_count', 'probs'),
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[
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(
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'zero-dim',
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np.array(1000),
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np.array(0.6),
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),
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(
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'one-dim',
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1000,
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np.array([0.4]).astype('float32'),
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),
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(
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'multi-dim-total_count-probability',
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parameterize.xrand((2, 1), min=1, max=100).astype('int32'),
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parameterize.xrand((2, 3), dtype='float64', min=0.3, max=1),
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),
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],
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)
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class TestBinomial(unittest.TestCase):
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def setUp(self):
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self._dist = Binomial(
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total_count=paddle.to_tensor(self.total_count),
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probs=paddle.to_tensor(self.probs),
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)
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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.probs.dtype)
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np.testing.assert_allclose(
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mean,
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self._np_mean(),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.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.probs.dtype)
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np.testing.assert_allclose(
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var,
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self._np_variance(),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.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.probs.dtype)
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np.testing.assert_allclose(
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entropy,
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self._np_entropy(),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.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(
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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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def _np_variance(self):
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return scipy.stats.binom.var(self.total_count, self.probs)
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def _np_mean(self):
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return scipy.stats.binom.mean(self.total_count, self.probs)
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def _np_entropy(self):
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return scipy.stats.binom.entropy(self.total_count, self.probs)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'total_count', 'probs', 'value'),
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[
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(
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'value-same-shape',
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1000,
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np.array([0.12, 0.3, 0.85]).astype('float64'),
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np.array([2.0, 55.0, 999.0]).astype('float64'),
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),
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(
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'value-broadcast-shape',
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10,
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np.array([[0.3, 0.7], [0.5, 0.5]]),
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np.array([[[4.0, 6], [8, 2]], [[2.0, 4], [9, 7]]]),
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),
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],
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)
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class TestBinomialProbs(unittest.TestCase):
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def setUp(self):
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self._dist = Binomial(
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total_count=paddle.to_tensor(self.total_count),
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probs=paddle.to_tensor(self.probs),
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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._dist.prob(paddle.to_tensor(self.value)),
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scipy.stats.binom.pmf(self.value, self.total_count, self.probs),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.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.binom.logpmf(self.value, self.total_count, self.probs),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.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, 'n_1', 'p_1', 'n_2', 'p_2'),
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[
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(
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'one-dim-probability',
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np.array([3333]),
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parameterize.xrand((1,), dtype='float32', min=0, max=1),
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np.array([3333]),
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parameterize.xrand((1,), dtype='float32', min=0, max=1),
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),
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(
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'multi-dim-probability',
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np.array([25, 25, 25]),
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parameterize.xrand((2, 3), dtype='float64', min=0, max=1),
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np.array([25, 25, 25]),
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parameterize.xrand((2, 3), dtype='float64', min=0, max=1),
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),
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],
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)
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class TestBinomialKL(unittest.TestCase):
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def setUp(self):
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self._dist1 = Binomial(
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total_count=paddle.to_tensor(self.n_1),
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probs=paddle.to_tensor(self.p_1),
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)
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self._dist2 = Binomial(
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total_count=paddle.to_tensor(self.n_2),
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probs=paddle.to_tensor(self.p_2),
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)
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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(self._dist1, self._dist2)
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self.assertEqual(tuple(kl0.shape), self.p_1.shape)
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self.assertEqual(tuple(kl1.shape), self.p_1.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.p_1.dtype)),
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atol=config.ATOL.get(str(self.p_1.dtype)),
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)
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def kl_divergence(self, dist1, dist2):
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support = np.arange(1 + self.n_1.max(), dtype=self.p_1.dtype)
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support = support.reshape((-1,) + (1,) * len(self.p_1.shape))
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log_prob_1 = scipy.stats.binom.logpmf(
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support, dist1.total_count, dist1.probs
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)
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log_prob_2 = scipy.stats.binom.logpmf(
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support, dist2.total_count, dist2.probs
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)
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return (np.exp(log_prob_1) * (log_prob_1 - log_prob_2)).sum(0)
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if __name__ == '__main__':
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unittest.main()
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