wehub-resource-sync
466 lines
16 KiB
Python
466 lines
16 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import parameterize
|
|
import scipy
|
|
from distribution import config
|
|
from parameterize import (
|
|
TEST_CASE_NAME,
|
|
parameterize_cls,
|
|
)
|
|
|
|
import paddle
|
|
from paddle.distribution import constraint
|
|
from paddle.distribution.multivariate_normal import MultivariateNormal
|
|
|
|
|
|
@parameterize.place(config.DEVICES)
|
|
@parameterize.parameterize_cls(
|
|
(parameterize.TEST_CASE_NAME, 'loc', 'covariance_matrix'),
|
|
[
|
|
(
|
|
'one-batch',
|
|
parameterize.xrand((2,), dtype='float32', min=1, max=2),
|
|
np.array([[2.0, 1.0], [1.0, 2.0]]),
|
|
),
|
|
(
|
|
'multi-batch',
|
|
parameterize.xrand((2, 3), dtype='float64', min=-2, max=-1),
|
|
np.array([[4.0, 2.5, 2.0], [2.5, 3.0, 1.2], [2.0, 1.2, 4.0]]),
|
|
),
|
|
],
|
|
)
|
|
class TestMVN(unittest.TestCase):
|
|
def setUp(self):
|
|
self._dist = MultivariateNormal(
|
|
loc=paddle.to_tensor(self.loc),
|
|
covariance_matrix=paddle.to_tensor(self.covariance_matrix),
|
|
)
|
|
|
|
def test_mean(self):
|
|
mean = self._dist.mean
|
|
self.assertEqual(mean.numpy().dtype, self.loc.dtype)
|
|
np.testing.assert_allclose(
|
|
mean,
|
|
self._np_mean(),
|
|
rtol=config.RTOL.get(str(self.loc.dtype)),
|
|
atol=config.ATOL.get(str(self.loc.dtype)),
|
|
)
|
|
|
|
def test_variance(self):
|
|
var = self._dist.variance
|
|
self.assertEqual(var.numpy().dtype, self.loc.dtype)
|
|
np.testing.assert_allclose(
|
|
var,
|
|
self._np_variance(),
|
|
rtol=config.RTOL.get(str(self.loc.dtype)),
|
|
atol=config.ATOL.get(str(self.loc.dtype)),
|
|
)
|
|
|
|
def test_entropy(self):
|
|
entropy = self._dist.entropy()
|
|
self.assertEqual(entropy.numpy().dtype, self.loc.dtype)
|
|
np.testing.assert_allclose(
|
|
entropy,
|
|
self._np_entropy(),
|
|
rtol=config.RTOL.get(str(self.loc.dtype)),
|
|
atol=config.ATOL.get(str(self.loc.dtype)),
|
|
)
|
|
|
|
def test_sample(self):
|
|
sample_shape = ()
|
|
samples = self._dist.sample(sample_shape)
|
|
self.assertEqual(samples.numpy().dtype, self.loc.dtype)
|
|
self.assertEqual(
|
|
tuple(samples.shape),
|
|
sample_shape + self._dist.batch_shape + self._dist.event_shape,
|
|
)
|
|
|
|
sample_shape = (50000,)
|
|
samples = self._dist.sample(sample_shape)
|
|
sample_mean = samples.mean(axis=0)
|
|
sample_variance = samples.var(axis=0)
|
|
|
|
# `atol` and `rtol` refer to ``test_distribution_normal`` and ``test_distribution_lognormal``
|
|
np.testing.assert_allclose(
|
|
sample_mean, self._dist.mean, atol=0.0, rtol=0.1
|
|
)
|
|
np.testing.assert_allclose(
|
|
sample_variance, self._dist.variance, atol=0.0, rtol=0.1
|
|
)
|
|
|
|
def _np_variance(self):
|
|
batch_shape = np.broadcast_shapes(
|
|
self.covariance_matrix.shape[:-2], self.loc.shape[:-1]
|
|
)
|
|
event_shape = self.loc.shape[-1:]
|
|
return np.broadcast_to(
|
|
np.diag(self.covariance_matrix), batch_shape + event_shape
|
|
)
|
|
|
|
def _np_mean(self):
|
|
return self.loc
|
|
|
|
def _np_entropy(self):
|
|
if len(self.loc.shape) <= 1:
|
|
return scipy.stats.multivariate_normal.entropy(
|
|
self.loc, self.covariance_matrix
|
|
)
|
|
else:
|
|
return np.apply_along_axis(
|
|
lambda i: scipy.stats.multivariate_normal.entropy(
|
|
i, self.covariance_matrix
|
|
),
|
|
axis=1,
|
|
arr=self.loc,
|
|
)
|
|
|
|
|
|
@parameterize.place(config.DEVICES)
|
|
@parameterize.parameterize_cls(
|
|
(parameterize.TEST_CASE_NAME, 'loc', 'precision_matrix', 'value'),
|
|
[
|
|
(
|
|
'value-same-shape',
|
|
parameterize.xrand((2,), dtype='float32', min=-2, max=2),
|
|
np.array([[2.0, 1.0], [1.0, 2.0]]),
|
|
parameterize.xrand((2,), dtype='float32', min=-5, max=5),
|
|
),
|
|
(
|
|
'value-broadcast-shape',
|
|
parameterize.xrand((2,), dtype='float64', min=-2, max=2),
|
|
np.array([[2.0, 1.0], [1.0, 2.0]]),
|
|
parameterize.xrand((3, 2), dtype='float64', min=-5, max=5),
|
|
),
|
|
],
|
|
)
|
|
class TestMVNProbs(unittest.TestCase):
|
|
def setUp(self):
|
|
self._dist = MultivariateNormal(
|
|
loc=paddle.to_tensor(self.loc),
|
|
precision_matrix=paddle.to_tensor(self.precision_matrix),
|
|
)
|
|
self.cov = np.linalg.inv(self.precision_matrix)
|
|
|
|
def test_prob(self):
|
|
if len(self.value.shape) <= 1:
|
|
scipy_pdf = scipy.stats.multivariate_normal.pdf(
|
|
self.value, self.loc, self.cov
|
|
)
|
|
else:
|
|
scipy_pdf = np.apply_along_axis(
|
|
lambda i: scipy.stats.multivariate_normal.pdf(
|
|
i, self.loc, self.cov
|
|
),
|
|
axis=1,
|
|
arr=self.value,
|
|
)
|
|
np.testing.assert_allclose(
|
|
self._dist.prob(paddle.to_tensor(self.value)),
|
|
scipy_pdf,
|
|
rtol=config.RTOL.get(str(self.loc.dtype)),
|
|
atol=config.ATOL.get(str(self.loc.dtype)),
|
|
)
|
|
|
|
def test_log_prob(self):
|
|
if len(self.value.shape) <= 1:
|
|
scipy_logpdf = scipy.stats.multivariate_normal.logpdf(
|
|
self.value, self.loc, self.cov
|
|
)
|
|
else:
|
|
scipy_logpdf = np.apply_along_axis(
|
|
lambda i: scipy.stats.multivariate_normal.logpdf(
|
|
i, self.loc, self.cov
|
|
),
|
|
axis=1,
|
|
arr=self.value,
|
|
)
|
|
np.testing.assert_allclose(
|
|
self._dist.log_prob(paddle.to_tensor(self.value)),
|
|
scipy_logpdf,
|
|
rtol=config.RTOL.get(str(self.loc.dtype)),
|
|
atol=config.ATOL.get(str(self.loc.dtype)),
|
|
)
|
|
|
|
|
|
@parameterize.place(config.DEVICES)
|
|
@parameterize.parameterize_cls(
|
|
(parameterize.TEST_CASE_NAME, 'mu_1', 'tril_1', 'mu_2', 'tril_2'),
|
|
[
|
|
(
|
|
'one-batch',
|
|
parameterize.xrand((2,), dtype='float32', min=-2, max=2),
|
|
np.array([[2.0, 0.0], [1.0, 2.0]]),
|
|
parameterize.xrand((2,), dtype='float32', min=-2, max=2),
|
|
np.array([[3.0, 0.0], [2.0, 3.0]]),
|
|
)
|
|
],
|
|
)
|
|
class TestMVNKL(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
self._dist1 = MultivariateNormal(
|
|
loc=paddle.to_tensor(self.mu_1),
|
|
scale_tril=paddle.to_tensor(self.tril_1),
|
|
)
|
|
self._dist2 = MultivariateNormal(
|
|
loc=paddle.to_tensor(self.mu_2),
|
|
scale_tril=paddle.to_tensor(self.tril_2),
|
|
)
|
|
|
|
def test_kl_divergence(self):
|
|
kl0 = self._dist1.kl_divergence(self._dist2)
|
|
kl1 = self.kl_divergence(self._dist1, self._dist2)
|
|
|
|
self.assertEqual(tuple(kl0.shape), self._dist1.batch_shape)
|
|
self.assertEqual(tuple(kl1.shape), self._dist1.batch_shape)
|
|
np.testing.assert_allclose(
|
|
kl0,
|
|
kl1,
|
|
rtol=config.RTOL.get(str(self.mu_1.dtype)),
|
|
atol=config.ATOL.get(str(self.mu_1.dtype)),
|
|
)
|
|
|
|
def kl_divergence(self, dist1, dist2):
|
|
t1 = np.array(dist1._unbroadcasted_scale_tril)
|
|
t2 = np.array(dist2._unbroadcasted_scale_tril)
|
|
half_log_det_1 = np.log(t1.diagonal(axis1=-2, axis2=-1)).sum(-1)
|
|
half_log_det_2 = np.log(t2.diagonal(axis1=-2, axis2=-1)).sum(-1)
|
|
new_perm = list(range(len(t1.shape)))
|
|
new_perm[-1], new_perm[-2] = new_perm[-2], new_perm[-1]
|
|
cov_mat_1 = np.matmul(t1, t1.transpose(new_perm))
|
|
cov_mat_2 = np.matmul(t2, t2.transpose(new_perm))
|
|
expectation = (
|
|
np.linalg.solve(cov_mat_2, cov_mat_1)
|
|
.diagonal(axis1=-2, axis2=-1)
|
|
.sum(-1)
|
|
)
|
|
tmp = np.linalg.solve(t2, self.mu_1 - self.mu_2)
|
|
expectation += np.matmul(tmp.T, tmp)
|
|
return half_log_det_2 - half_log_det_1 + 0.5 * (expectation - 2.0)
|
|
|
|
|
|
@parameterize.place(config.DEVICES)
|
|
@parameterize_cls([TEST_CASE_NAME], ['MVNTestError'])
|
|
class MVNTestError(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static(self.place)
|
|
|
|
|
|
class TestMVNValidateArgsAndExpand(unittest.TestCase):
|
|
def test_mode_and_expand(self):
|
|
paddle.disable_static()
|
|
loc = paddle.to_tensor([1.0, -2.0], dtype='float32')
|
|
cov = paddle.to_tensor([[2.0, 0.5], [0.5, 1.5]], dtype='float32')
|
|
dist = MultivariateNormal(
|
|
loc=loc, covariance_matrix=cov, validate_args=True
|
|
)
|
|
self.assertTrue(dist._validate_args_enabled)
|
|
np.testing.assert_allclose(dist.mode.numpy(), loc.numpy())
|
|
|
|
expanded = dist.expand((3,))
|
|
self.assertTrue(expanded._validate_args_enabled)
|
|
self.assertEqual(expanded.batch_shape, (3,))
|
|
self.assertEqual(expanded.event_shape, (2,))
|
|
np.testing.assert_allclose(
|
|
expanded.mode.numpy(), np.broadcast_to(loc.numpy(), (3, 2))
|
|
)
|
|
np.testing.assert_allclose(
|
|
expanded.mean.numpy(), np.broadcast_to(loc.numpy(), (3, 2))
|
|
)
|
|
np.testing.assert_allclose(
|
|
expanded.variance.numpy(),
|
|
np.broadcast_to(np.diag(cov.numpy()), (3, 2)),
|
|
)
|
|
|
|
def test_validate_args_errors(self):
|
|
paddle.disable_static()
|
|
loc = paddle.to_tensor([0.0, 0.0], dtype='float32')
|
|
bad_cov = paddle.to_tensor([[1.0, 2.0], [2.0, 1.0]], dtype='float32')
|
|
bad_scale = paddle.to_tensor([[1.0, 0.0], [0.1, -1.0]], dtype='float32')
|
|
good_cov = paddle.to_tensor([[2.0, 0.5], [0.5, 1.5]], dtype='float32')
|
|
|
|
with self.assertRaises(ValueError):
|
|
MultivariateNormal(
|
|
loc=loc, covariance_matrix=bad_cov, validate_args=True
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
MultivariateNormal(
|
|
loc=loc, scale_tril=bad_scale, validate_args=True
|
|
)
|
|
|
|
dist = MultivariateNormal(
|
|
loc=loc, covariance_matrix=good_cov, validate_args=True
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
dist.log_prob(paddle.to_tensor([np.nan, 0.0], dtype='float32'))
|
|
|
|
def test_validate_args_additional_errors(self):
|
|
paddle.disable_static()
|
|
loc = paddle.to_tensor([0.0, 0.0], dtype='float32')
|
|
cov = paddle.to_tensor([[2.0, 0.5], [0.5, 1.5]], dtype='float32')
|
|
|
|
with self.assertRaises(ValueError):
|
|
MultivariateNormal(
|
|
loc=paddle.to_tensor(0.0),
|
|
covariance_matrix=paddle.to_tensor([[1.0]], dtype='float32'),
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
MultivariateNormal(loc=loc, covariance_matrix=paddle.ones([2]))
|
|
|
|
with self.assertRaises(ValueError):
|
|
MultivariateNormal(loc=loc, scale_tril=paddle.ones([2]))
|
|
|
|
with self.assertRaises(ValueError):
|
|
MultivariateNormal(loc=loc, precision_matrix=paddle.ones([2]))
|
|
with self.assertRaises(ValueError):
|
|
MultivariateNormal(
|
|
loc=loc,
|
|
precision_matrix=paddle.to_tensor(
|
|
[[1.0, 2.0], [2.0, 1.0]], dtype='float32'
|
|
),
|
|
validate_args=True,
|
|
)
|
|
|
|
dist = MultivariateNormal(
|
|
loc=loc, covariance_matrix=cov, validate_args=True
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
dist.log_prob(paddle.zeros([3], dtype='float32'))
|
|
batch_dist = MultivariateNormal(
|
|
loc=paddle.zeros([2, 2], dtype='float32'),
|
|
covariance_matrix=cov,
|
|
validate_args=True,
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
batch_dist.log_prob(paddle.zeros([3, 2], dtype='float32'))
|
|
|
|
def test_validate_args_false_and_lazy_properties(self):
|
|
paddle.disable_static()
|
|
loc = paddle.to_tensor([0.0, 0.0], dtype='float32')
|
|
bad_scale = paddle.to_tensor([[1.0, 2.0], [0.0, 1.0]], dtype='float32')
|
|
dist = MultivariateNormal(
|
|
loc=loc, scale_tril=bad_scale, validate_args=False
|
|
)
|
|
self.assertFalse(dist._validate_args_enabled)
|
|
|
|
cov = paddle.to_tensor([[2.0, 0.5], [0.5, 1.5]], dtype='float32')
|
|
precision = paddle.linalg.inv(cov)
|
|
scale = paddle.linalg.cholesky(cov)
|
|
|
|
cov_dist = MultivariateNormal(loc=loc, covariance_matrix=cov)
|
|
np.testing.assert_allclose(cov_dist.scale_tril.numpy(), scale.numpy())
|
|
np.testing.assert_allclose(
|
|
cov_dist.precision_matrix.numpy(), precision.numpy(), rtol=1e-5
|
|
)
|
|
|
|
scale_dist = MultivariateNormal(loc=loc, scale_tril=scale)
|
|
scale_expanded = scale_dist.expand((3,))
|
|
np.testing.assert_allclose(
|
|
scale_expanded.scale_tril.numpy(),
|
|
np.broadcast_to(scale.numpy(), (3, 2, 2)),
|
|
)
|
|
|
|
precision_dist = MultivariateNormal(loc=loc, precision_matrix=precision)
|
|
precision_expanded = precision_dist.expand((3,))
|
|
np.testing.assert_allclose(
|
|
precision_dist.covariance_matrix.numpy(), cov.numpy(), rtol=1e-5
|
|
)
|
|
np.testing.assert_allclose(
|
|
precision_expanded.precision_matrix.numpy(),
|
|
np.broadcast_to(precision.numpy(), (3, 2, 2)),
|
|
rtol=1e-5,
|
|
)
|
|
|
|
|
|
class TestMVNConstraints(unittest.TestCase):
|
|
def test_constraints_check(self):
|
|
paddle.disable_static()
|
|
with self.assertRaises(NotImplementedError):
|
|
constraint.Constraint()(paddle.ones([1], dtype='float32'))
|
|
|
|
np.testing.assert_array_equal(
|
|
constraint.real_vector.check(
|
|
paddle.to_tensor([1.0, np.nan], dtype='float32')
|
|
).numpy(),
|
|
np.array(False),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
constraint.real_vector.check(
|
|
paddle.to_tensor(1.0, dtype='float32')
|
|
).numpy(),
|
|
np.array(False),
|
|
)
|
|
|
|
lower = paddle.to_tensor([[1.0, 0.0], [2.0, 3.0]], dtype='float32')
|
|
not_lower = paddle.to_tensor([[1.0, 2.0], [0.0, 3.0]], dtype='float32')
|
|
np.testing.assert_array_equal(
|
|
constraint.lower_triangular.check(lower).numpy(), np.array(True)
|
|
)
|
|
np.testing.assert_array_equal(
|
|
constraint.lower_triangular.check(not_lower).numpy(),
|
|
np.array(False),
|
|
)
|
|
np.testing.assert_array_equal(
|
|
constraint.lower_triangular.check(
|
|
paddle.to_tensor([1.0, 2.0], dtype='float32')
|
|
).numpy(),
|
|
np.array(False),
|
|
)
|
|
|
|
bad_cholesky = paddle.to_tensor(
|
|
[[1.0, 0.0], [2.0, -3.0]], dtype='float32'
|
|
)
|
|
np.testing.assert_array_equal(
|
|
constraint.lower_cholesky.check(lower).numpy(), np.array(True)
|
|
)
|
|
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)
|