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# 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 mock_data as mock
import numpy as np
import parameterize as param
import scipy.special
import scipy.stats
from distribution import config
import paddle
from paddle.distribution import kl
np.random.seed(2022)
paddle.seed(2022)
paddle.set_default_dtype('float64')
@param.place(config.DEVICES)
@param.parameterize_cls(
(param.TEST_CASE_NAME, 'a1', 'b1', 'a2', 'b2'),
[
(
'test_regular_input',
6.0 * np.random.random((4, 5)) + 1e-4,
6.0 * np.random.random((4, 5)) + 1e-4,
6.0 * np.random.random((4, 5)) + 1e-4,
6.0 * np.random.random((4, 5)) + 1e-4,
),
],
)
class TestKLBetaBeta(unittest.TestCase):
def setUp(self):
self.p = paddle.distribution.Beta(
paddle.to_tensor(self.a1), paddle.to_tensor(self.b1)
)
self.q = paddle.distribution.Beta(
paddle.to_tensor(self.a2), paddle.to_tensor(self.b2)
)
def test_kl_divergence(self):
with paddle.base.dygraph.guard(self.place):
np.testing.assert_allclose(
paddle.distribution.kl_divergence(self.p, self.q),
self.scipy_kl_beta_beta(self.a1, self.b1, self.a2, self.b2),
rtol=config.RTOL.get(str(self.a1.dtype)),
atol=config.ATOL.get(str(self.a1.dtype)),
)
def scipy_kl_beta_beta(self, a1, b1, a2, b2):
return (
scipy.special.betaln(a2, b2)
- scipy.special.betaln(a1, b1)
+ (a1 - a2) * scipy.special.digamma(a1)
+ (b1 - b2) * scipy.special.digamma(b1)
+ (a2 - a1 + b2 - b1) * scipy.special.digamma(a1 + b1)
)
@param.place(config.DEVICES)
@param.param_cls(
(param.TEST_CASE_NAME, 'conc1', 'conc2'),
[
(
'test-regular-input',
np.random.random((5, 7, 8, 10)),
np.random.random((5, 7, 8, 10)),
),
],
)
class TestKLDirichletDirichlet(unittest.TestCase):
def setUp(self):
self.p = paddle.distribution.Dirichlet(paddle.to_tensor(self.conc1))
self.q = paddle.distribution.Dirichlet(paddle.to_tensor(self.conc2))
def test_kl_divergence(self):
with paddle.base.dygraph.guard(self.place):
np.testing.assert_allclose(
paddle.distribution.kl_divergence(self.p, self.q),
self.scipy_kl_diric_diric(self.conc1, self.conc2),
rtol=config.RTOL.get(str(self.conc1.dtype)),
atol=config.ATOL.get(str(self.conc1.dtype)),
)
def scipy_kl_diric_diric(self, conc1, conc2):
return (
scipy.special.gammaln(np.sum(conc1, -1))
- scipy.special.gammaln(np.sum(conc2, -1))
- np.sum(
scipy.special.gammaln(conc1) - scipy.special.gammaln(conc2), -1
)
+ np.sum(
(conc1 - conc2)
* (
scipy.special.digamma(conc1)
- scipy.special.digamma(np.sum(conc1, -1, keepdims=True))
),
-1,
)
)
class DummyDistribution(paddle.distribution.Distribution):
pass
@param.place(config.DEVICES)
@param.param_cls(
(param.TEST_CASE_NAME, 'p', 'q'),
[('test-unregister', DummyDistribution(), DummyDistribution)],
)
class TestDispatch(unittest.TestCase):
def test_dispatch_with_unregister(self):
with self.assertRaises(NotImplementedError):
paddle.distribution.kl_divergence(self.p, self.q)
@param.place(config.DEVICES)
@param.param_cls(
(param.TEST_CASE_NAME, 'p', 'q'),
[
(
'test-diff-dist',
mock.Exponential(paddle.rand((100, 200, 100)) + 1.0),
mock.Exponential(paddle.rand((100, 200, 100)) + 2.0),
),
(
'test-same-dist',
mock.Exponential(paddle.to_tensor([1.0])),
mock.Exponential(paddle.to_tensor([1.0])),
),
],
)
class TestKLExpfamilyExpFamily(unittest.TestCase):
def test_kl_expfamily_expfamily(self):
np.testing.assert_allclose(
paddle.distribution.kl_divergence(self.p, self.q),
kl._kl_expfamily_expfamily(self.p, self.q),
rtol=config.RTOL.get(config.DEFAULT_DTYPE),
atol=config.ATOL.get(config.DEFAULT_DTYPE),
)
if __name__ == '__main__':
unittest.main()