wehub-resource-sync
243 lines
7.5 KiB
Python
243 lines
7.5 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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@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((2,)), parameterize.xrand((2,))),
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('multi-dim', parameterize.xrand((5, 5)), parameterize.xrand((5, 5))),
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],
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)
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class TestLaplace(unittest.TestCase):
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def setUp(self):
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self._dist = paddle.distribution.Laplace(
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loc=paddle.to_tensor(self.loc), scale=paddle.to_tensor(self.scale)
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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.scale.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.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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var = self._dist.variance
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self.assertEqual(var.numpy().dtype, self.scale.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.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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stddev = self._dist.stddev
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self.assertEqual(stddev.numpy().dtype, self.scale.dtype)
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np.testing.assert_allclose(
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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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entropy = self._dist.entropy()
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self.assertEqual(entropy.numpy().dtype, self.scale.dtype)
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def test_sample(self):
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sample_shape = (50000,)
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samples = self._dist.sample(sample_shape)
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sample_values = samples.numpy()
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self.assertEqual(samples.numpy().dtype, self.scale.dtype)
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self.assertEqual(
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tuple(samples.shape), tuple(self._dist._extend_shape(sample_shape))
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)
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self.assertEqual(samples.shape, list(sample_shape + self.loc.shape))
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self.assertEqual(sample_values.shape, sample_shape + self.loc.shape)
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np.testing.assert_allclose(
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sample_values.mean(axis=0),
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scipy.stats.laplace.mean(self.loc, scale=self.scale),
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rtol=0.2,
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atol=0.0,
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)
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np.testing.assert_allclose(
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sample_values.var(axis=0),
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scipy.stats.laplace.var(self.loc, scale=self.scale),
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rtol=0.1,
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atol=0.0,
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)
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def _np_mean(self):
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return self.loc
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def _np_stddev(self):
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return (2**0.5) * self.scale
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def _np_variance(self):
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stddev = (2**0.5) * self.scale
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return np.power(stddev, 2)
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def _np_entropy(self):
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return scipy.stats.laplace.entropy(loc=self.loc, scale=self.scale)
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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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('float', 1.0, 2.0),
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('int', 3, 4),
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],
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)
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class TestLaplaceKS(unittest.TestCase):
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def setUp(self):
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self._dist = paddle.distribution.Laplace(loc=self.loc, scale=self.scale)
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def test_sample(self):
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sample_shape = (20000,)
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samples = self._dist.sample(sample_shape)
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sample_values = samples.numpy()
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self.assertTrue(self._kstest(self.loc, self.scale, sample_values))
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def _kstest(self, loc, scale, samples):
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# Uses the Kolmogorov-Smirnov test for goodness of fit.
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ks, p_value = scipy.stats.kstest(
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samples, scipy.stats.laplace(loc, scale=scale).cdf
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)
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return ks < 0.02
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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([0.2, 0.3]),
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np.array([2.0, 3.0]),
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np.array([2.0, 5.0]),
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),
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(
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'value-int',
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np.array([0.2, 0.3]),
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np.array([2.0, 3.0]),
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np.array([2, 5]),
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),
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(
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'value-multi-dim',
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np.array([0.2, 0.3]),
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np.array([2.0, 3.0]),
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np.array([[4.0, 6], [8, 2]]),
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),
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],
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)
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class TestLaplacePDF(unittest.TestCase):
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def setUp(self):
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self._dist = paddle.distribution.Laplace(
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loc=paddle.to_tensor(self.loc), scale=paddle.to_tensor(self.scale)
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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.laplace.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._dist.log_prob(paddle.to_tensor(self.value)),
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scipy.stats.laplace.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._dist.cdf(paddle.to_tensor(self.value)),
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scipy.stats.laplace.cdf(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_icdf(self):
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np.testing.assert_allclose(
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self._dist.icdf(paddle.to_tensor(self.value)),
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scipy.stats.laplace.ppf(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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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'loc1', 'scale1', 'loc2', 'scale2'),
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[
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(
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'kl',
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np.array([0.0]),
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np.array([1.0]),
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np.array([1.0]),
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np.array([0.5]),
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)
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],
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)
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class TestLaplaceAndLaplaceKL(unittest.TestCase):
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def setUp(self):
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self._dist_1 = paddle.distribution.Laplace(
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loc=paddle.to_tensor(self.loc1), scale=paddle.to_tensor(self.scale1)
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)
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self._dist_2 = paddle.distribution.Laplace(
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loc=paddle.to_tensor(self.loc2), scale=paddle.to_tensor(self.scale2)
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)
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def test_kl_divergence(self):
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np.testing.assert_allclose(
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paddle.distribution.kl_divergence(self._dist_1, self._dist_2),
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self._np_kl(),
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atol=0,
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rtol=0.50,
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)
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def _np_kl(self):
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x = np.linspace(
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scipy.stats.laplace.ppf(0.01), scipy.stats.laplace.ppf(0.99), 1000
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)
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d1 = scipy.stats.laplace.pdf(x, loc=0.0, scale=1.0)
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d2 = scipy.stats.laplace.pdf(x, loc=1.0, scale=0.5)
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return scipy.stats.entropy(d1, d2)
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if __name__ == '__main__':
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unittest.main()
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