chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# 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
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from distribution import config
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from parameterize import (
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TEST_CASE_NAME,
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parameterize_cls,
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)
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import paddle
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from paddle.distribution import constraint
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from paddle.distribution.multivariate_normal import MultivariateNormal
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'loc', 'covariance_matrix'),
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[
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(
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'one-batch',
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parameterize.xrand((2,), dtype='float32', min=1, max=2),
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np.array([[2.0, 1.0], [1.0, 2.0]]),
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),
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(
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'multi-batch',
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parameterize.xrand((2, 3), dtype='float64', min=-2, max=-1),
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np.array([[4.0, 2.5, 2.0], [2.5, 3.0, 1.2], [2.0, 1.2, 4.0]]),
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),
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],
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)
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class TestMVN(unittest.TestCase):
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def setUp(self):
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self._dist = MultivariateNormal(
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loc=paddle.to_tensor(self.loc),
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covariance_matrix=paddle.to_tensor(self.covariance_matrix),
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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.loc.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.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.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.loc.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.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.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.loc.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.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.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.loc.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 = (50000,)
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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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# `atol` and `rtol` refer to ``test_distribution_normal`` and ``test_distribution_lognormal``
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np.testing.assert_allclose(
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sample_mean, self._dist.mean, atol=0.0, rtol=0.1
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)
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np.testing.assert_allclose(
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sample_variance, self._dist.variance, atol=0.0, rtol=0.1
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)
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def _np_variance(self):
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batch_shape = np.broadcast_shapes(
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self.covariance_matrix.shape[:-2], self.loc.shape[:-1]
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)
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event_shape = self.loc.shape[-1:]
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return np.broadcast_to(
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np.diag(self.covariance_matrix), batch_shape + event_shape
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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_entropy(self):
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if len(self.loc.shape) <= 1:
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return scipy.stats.multivariate_normal.entropy(
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self.loc, self.covariance_matrix
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)
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else:
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return np.apply_along_axis(
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lambda i: scipy.stats.multivariate_normal.entropy(
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i, self.covariance_matrix
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),
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axis=1,
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arr=self.loc,
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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, 'loc', 'precision_matrix', 'value'),
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[
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(
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'value-same-shape',
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parameterize.xrand((2,), dtype='float32', min=-2, max=2),
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np.array([[2.0, 1.0], [1.0, 2.0]]),
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parameterize.xrand((2,), dtype='float32', min=-5, max=5),
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),
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(
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'value-broadcast-shape',
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parameterize.xrand((2,), dtype='float64', min=-2, max=2),
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np.array([[2.0, 1.0], [1.0, 2.0]]),
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parameterize.xrand((3, 2), dtype='float64', min=-5, max=5),
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),
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],
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)
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class TestMVNProbs(unittest.TestCase):
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def setUp(self):
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self._dist = MultivariateNormal(
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loc=paddle.to_tensor(self.loc),
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precision_matrix=paddle.to_tensor(self.precision_matrix),
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)
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self.cov = np.linalg.inv(self.precision_matrix)
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def test_prob(self):
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if len(self.value.shape) <= 1:
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scipy_pdf = scipy.stats.multivariate_normal.pdf(
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self.value, self.loc, self.cov
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)
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else:
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scipy_pdf = np.apply_along_axis(
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lambda i: scipy.stats.multivariate_normal.pdf(
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i, self.loc, self.cov
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),
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axis=1,
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arr=self.value,
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)
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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_pdf,
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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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if len(self.value.shape) <= 1:
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scipy_logpdf = scipy.stats.multivariate_normal.logpdf(
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self.value, self.loc, self.cov
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)
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else:
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scipy_logpdf = np.apply_along_axis(
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lambda i: scipy.stats.multivariate_normal.logpdf(
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i, self.loc, self.cov
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),
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axis=1,
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arr=self.value,
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)
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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_logpdf,
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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, 'mu_1', 'tril_1', 'mu_2', 'tril_2'),
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[
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(
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'one-batch',
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parameterize.xrand((2,), dtype='float32', min=-2, max=2),
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np.array([[2.0, 0.0], [1.0, 2.0]]),
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parameterize.xrand((2,), dtype='float32', min=-2, max=2),
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np.array([[3.0, 0.0], [2.0, 3.0]]),
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)
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],
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)
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class TestMVNKL(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self._dist1 = MultivariateNormal(
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loc=paddle.to_tensor(self.mu_1),
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scale_tril=paddle.to_tensor(self.tril_1),
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)
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self._dist2 = MultivariateNormal(
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loc=paddle.to_tensor(self.mu_2),
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scale_tril=paddle.to_tensor(self.tril_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._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.mu_1.dtype)),
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atol=config.ATOL.get(str(self.mu_1.dtype)),
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)
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def kl_divergence(self, dist1, dist2):
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t1 = np.array(dist1._unbroadcasted_scale_tril)
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t2 = np.array(dist2._unbroadcasted_scale_tril)
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half_log_det_1 = np.log(t1.diagonal(axis1=-2, axis2=-1)).sum(-1)
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half_log_det_2 = np.log(t2.diagonal(axis1=-2, axis2=-1)).sum(-1)
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new_perm = list(range(len(t1.shape)))
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new_perm[-1], new_perm[-2] = new_perm[-2], new_perm[-1]
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cov_mat_1 = np.matmul(t1, t1.transpose(new_perm))
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cov_mat_2 = np.matmul(t2, t2.transpose(new_perm))
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expectation = (
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np.linalg.solve(cov_mat_2, cov_mat_1)
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.diagonal(axis1=-2, axis2=-1)
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.sum(-1)
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)
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tmp = np.linalg.solve(t2, self.mu_1 - self.mu_2)
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expectation += np.matmul(tmp.T, tmp)
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return half_log_det_2 - half_log_det_1 + 0.5 * (expectation - 2.0)
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@parameterize.place(config.DEVICES)
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@parameterize_cls([TEST_CASE_NAME], ['MVNTestError'])
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class MVNTestError(unittest.TestCase):
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def setUp(self):
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paddle.disable_static(self.place)
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class TestMVNValidateArgsAndExpand(unittest.TestCase):
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def test_mode_and_expand(self):
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paddle.disable_static()
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loc = paddle.to_tensor([1.0, -2.0], dtype='float32')
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cov = paddle.to_tensor([[2.0, 0.5], [0.5, 1.5]], dtype='float32')
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dist = MultivariateNormal(
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loc=loc, covariance_matrix=cov, validate_args=True
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)
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self.assertTrue(dist._validate_args_enabled)
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np.testing.assert_allclose(dist.mode.numpy(), loc.numpy())
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expanded = dist.expand((3,))
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self.assertTrue(expanded._validate_args_enabled)
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self.assertEqual(expanded.batch_shape, (3,))
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self.assertEqual(expanded.event_shape, (2,))
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np.testing.assert_allclose(
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expanded.mode.numpy(), np.broadcast_to(loc.numpy(), (3, 2))
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)
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np.testing.assert_allclose(
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expanded.mean.numpy(), np.broadcast_to(loc.numpy(), (3, 2))
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)
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np.testing.assert_allclose(
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expanded.variance.numpy(),
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np.broadcast_to(np.diag(cov.numpy()), (3, 2)),
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)
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def test_validate_args_errors(self):
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paddle.disable_static()
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loc = paddle.to_tensor([0.0, 0.0], dtype='float32')
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bad_cov = paddle.to_tensor([[1.0, 2.0], [2.0, 1.0]], dtype='float32')
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bad_scale = paddle.to_tensor([[1.0, 0.0], [0.1, -1.0]], dtype='float32')
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good_cov = paddle.to_tensor([[2.0, 0.5], [0.5, 1.5]], dtype='float32')
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with self.assertRaises(ValueError):
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MultivariateNormal(
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loc=loc, covariance_matrix=bad_cov, validate_args=True
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)
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with self.assertRaises(ValueError):
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MultivariateNormal(
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loc=loc, scale_tril=bad_scale, validate_args=True
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)
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dist = MultivariateNormal(
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loc=loc, covariance_matrix=good_cov, validate_args=True
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)
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with self.assertRaises(ValueError):
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dist.log_prob(paddle.to_tensor([np.nan, 0.0], dtype='float32'))
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def test_validate_args_additional_errors(self):
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paddle.disable_static()
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loc = paddle.to_tensor([0.0, 0.0], dtype='float32')
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cov = paddle.to_tensor([[2.0, 0.5], [0.5, 1.5]], dtype='float32')
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with self.assertRaises(ValueError):
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MultivariateNormal(
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loc=paddle.to_tensor(0.0),
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covariance_matrix=paddle.to_tensor([[1.0]], dtype='float32'),
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)
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with self.assertRaises(ValueError):
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MultivariateNormal(loc=loc, covariance_matrix=paddle.ones([2]))
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with self.assertRaises(ValueError):
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MultivariateNormal(loc=loc, scale_tril=paddle.ones([2]))
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with self.assertRaises(ValueError):
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MultivariateNormal(loc=loc, precision_matrix=paddle.ones([2]))
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with self.assertRaises(ValueError):
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MultivariateNormal(
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loc=loc,
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precision_matrix=paddle.to_tensor(
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[[1.0, 2.0], [2.0, 1.0]], dtype='float32'
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),
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validate_args=True,
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)
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dist = MultivariateNormal(
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loc=loc, covariance_matrix=cov, validate_args=True
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)
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with self.assertRaises(ValueError):
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dist.log_prob(paddle.zeros([3], dtype='float32'))
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batch_dist = MultivariateNormal(
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loc=paddle.zeros([2, 2], dtype='float32'),
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covariance_matrix=cov,
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validate_args=True,
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)
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with self.assertRaises(ValueError):
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batch_dist.log_prob(paddle.zeros([3, 2], dtype='float32'))
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def test_validate_args_false_and_lazy_properties(self):
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paddle.disable_static()
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loc = paddle.to_tensor([0.0, 0.0], dtype='float32')
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bad_scale = paddle.to_tensor([[1.0, 2.0], [0.0, 1.0]], dtype='float32')
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dist = MultivariateNormal(
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loc=loc, scale_tril=bad_scale, validate_args=False
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)
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self.assertFalse(dist._validate_args_enabled)
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cov = paddle.to_tensor([[2.0, 0.5], [0.5, 1.5]], dtype='float32')
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precision = paddle.linalg.inv(cov)
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scale = paddle.linalg.cholesky(cov)
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cov_dist = MultivariateNormal(loc=loc, covariance_matrix=cov)
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np.testing.assert_allclose(cov_dist.scale_tril.numpy(), scale.numpy())
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np.testing.assert_allclose(
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cov_dist.precision_matrix.numpy(), precision.numpy(), rtol=1e-5
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)
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scale_dist = MultivariateNormal(loc=loc, scale_tril=scale)
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scale_expanded = scale_dist.expand((3,))
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np.testing.assert_allclose(
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scale_expanded.scale_tril.numpy(),
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np.broadcast_to(scale.numpy(), (3, 2, 2)),
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)
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precision_dist = MultivariateNormal(loc=loc, precision_matrix=precision)
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precision_expanded = precision_dist.expand((3,))
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np.testing.assert_allclose(
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precision_dist.covariance_matrix.numpy(), cov.numpy(), rtol=1e-5
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)
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np.testing.assert_allclose(
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precision_expanded.precision_matrix.numpy(),
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np.broadcast_to(precision.numpy(), (3, 2, 2)),
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rtol=1e-5,
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)
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class TestMVNConstraints(unittest.TestCase):
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def test_constraints_check(self):
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paddle.disable_static()
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with self.assertRaises(NotImplementedError):
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constraint.Constraint()(paddle.ones([1], dtype='float32'))
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np.testing.assert_array_equal(
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constraint.real_vector.check(
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paddle.to_tensor([1.0, np.nan], dtype='float32')
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).numpy(),
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np.array(False),
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)
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np.testing.assert_array_equal(
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constraint.real_vector.check(
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paddle.to_tensor(1.0, dtype='float32')
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).numpy(),
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np.array(False),
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)
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lower = paddle.to_tensor([[1.0, 0.0], [2.0, 3.0]], dtype='float32')
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not_lower = paddle.to_tensor([[1.0, 2.0], [0.0, 3.0]], dtype='float32')
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np.testing.assert_array_equal(
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constraint.lower_triangular.check(lower).numpy(), np.array(True)
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)
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np.testing.assert_array_equal(
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constraint.lower_triangular.check(not_lower).numpy(),
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np.array(False),
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)
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np.testing.assert_array_equal(
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constraint.lower_triangular.check(
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paddle.to_tensor([1.0, 2.0], dtype='float32')
|
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).numpy(),
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np.array(False),
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)
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bad_cholesky = paddle.to_tensor(
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[[1.0, 0.0], [2.0, -3.0]], dtype='float32'
|
||||
)
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np.testing.assert_array_equal(
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constraint.lower_cholesky.check(lower).numpy(), np.array(True)
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)
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np.testing.assert_array_equal(
|
||||
constraint.lower_cholesky.check(bad_cholesky).numpy(),
|
||||
np.array(False),
|
||||
)
|
||||
|
||||
square = paddle.eye(2, dtype='float32')
|
||||
not_square = paddle.ones([2, 3], dtype='float32')
|
||||
not_symmetric = paddle.to_tensor(
|
||||
[[1.0, 2.0], [0.0, 1.0]], dtype='float32'
|
||||
)
|
||||
not_positive_definite = paddle.to_tensor(
|
||||
[[1.0, 2.0], [2.0, 1.0]], dtype='float32'
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
constraint.square.check(square).numpy(), np.array(True)
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
constraint.square.check(not_square).numpy(), np.array(False)
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
constraint.symmetric.check(not_symmetric).numpy(), np.array(False)
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
constraint.positive_definite.check(square).numpy(), np.array(True)
|
||||
)
|
||||
np.testing.assert_array_equal(
|
||||
constraint.positive_definite.check(not_positive_definite).numpy(),
|
||||
np.array(False),
|
||||
)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main(argv=[''], verbosity=3, exit=False)
|
||||
Reference in New Issue
Block a user