wehub-resource-sync
510 lines
17 KiB
Python
510 lines
17 KiB
Python
# Copyright (c) 2023 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 import gamma
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np.random.seed(2023)
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paddle.seed(2023)
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paddle.enable_static()
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'concentration', 'rate'),
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[
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(
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'one-dim',
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parameterize.xrand(
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(6,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(6,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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),
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(
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'multi-dim',
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parameterize.xrand(
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(10, 12),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(10, 12),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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),
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(
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'broadcast',
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parameterize.xrand(
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(4, 1),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(4, 6),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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),
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],
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)
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class TestGamma(unittest.TestCase):
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def setUp(self):
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self.program = paddle.static.Program()
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self.executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(self.program):
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self.scale = 1 / self.rate
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concentration = paddle.static.data(
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'concentration',
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self.concentration.shape,
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self.concentration.dtype,
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)
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rate = paddle.static.data('rate', self.rate.shape, self.rate.dtype)
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self._paddle_gamma = gamma.Gamma(concentration, rate)
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self.feeds = {
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'concentration': self.concentration,
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'rate': self.rate,
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}
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def test_mean(self):
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with paddle.static.program_guard(self.program):
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[mean] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_gamma.mean],
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)
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np.testing.assert_allclose(
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mean,
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scipy.stats.gamma.mean(self.concentration, scale=self.scale),
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rtol=config.RTOL.get(str(self.concentration.dtype)),
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atol=config.ATOL.get(str(self.concentration.dtype)),
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)
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def test_variance(self):
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with paddle.static.program_guard(self.program):
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[variance] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_gamma.variance],
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)
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np.testing.assert_allclose(
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variance,
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scipy.stats.gamma.var(self.concentration, scale=self.scale),
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rtol=config.RTOL.get(str(self.concentration.dtype)),
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atol=config.ATOL.get(str(self.concentration.dtype)),
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)
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def test_entropy(self):
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with paddle.static.program_guard(self.program):
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[entropy] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_gamma.entropy()],
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)
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np.testing.assert_allclose(
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entropy,
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scipy.stats.gamma.entropy(self.concentration, scale=self.scale),
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rtol=config.RTOL.get(str(self.concentration.dtype)),
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atol=config.ATOL.get(str(self.concentration.dtype)),
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)
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def test_prob(self):
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with paddle.static.program_guard(self.program):
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value = paddle.static.data(
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'value',
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self._paddle_gamma.concentration.shape,
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self._paddle_gamma.concentration.dtype,
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)
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prob = self._paddle_gamma.prob(value)
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random_number = np.random.rand(
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*self._paddle_gamma.concentration.shape
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).astype(self.concentration.dtype)
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feeds = dict(self.feeds, value=random_number)
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[prob] = self.executor.run(
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self.program, feed=feeds, fetch_list=[prob]
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)
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np.testing.assert_allclose(
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prob,
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scipy.stats.gamma.pdf(
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random_number, self.concentration, scale=self.scale
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),
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rtol=config.RTOL.get(str(self.concentration.dtype)),
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atol=config.ATOL.get(str(self.concentration.dtype)),
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)
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def test_log_prob(self):
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with paddle.static.program_guard(self.program):
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value = paddle.static.data(
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'value',
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self._paddle_gamma.concentration.shape,
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self._paddle_gamma.concentration.dtype,
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)
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log_prob = self._paddle_gamma.log_prob(value)
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random_number = np.random.rand(
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*self._paddle_gamma.concentration.shape
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).astype(self.concentration.dtype)
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feeds = dict(self.feeds, value=random_number)
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[log_prob] = self.executor.run(
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self.program, feed=feeds, fetch_list=[log_prob]
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)
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np.testing.assert_allclose(
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log_prob,
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scipy.stats.gamma.logpdf(
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random_number, self.concentration, scale=self.scale
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),
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rtol=config.RTOL.get(str(self.concentration.dtype)),
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atol=config.ATOL.get(str(self.concentration.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, 'concentration', 'rate'),
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[
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(
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'one-dim',
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parameterize.xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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),
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(
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'multi-dim',
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parameterize.xrand(
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(2, 3),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(2, 3),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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),
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],
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)
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class TestGammaSample(unittest.TestCase):
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def setUp(self):
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self.program = paddle.static.Program()
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self.executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(self.program):
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self.scale = 1 / self.rate
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concentration = paddle.static.data(
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'concentration',
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self.concentration.shape,
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self.concentration.dtype,
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)
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rate = paddle.static.data('rate', self.rate.shape, self.rate.dtype)
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self._paddle_gamma = gamma.Gamma(concentration, rate)
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self.feeds = {
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'concentration': self.concentration,
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'rate': self.rate,
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}
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def test_sample_shape(self):
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cases = [
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{
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'input': (),
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'expect': tuple(np.squeeze(self.rate).shape),
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},
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{
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'input': (4, 2),
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'expect': (4, 2, *np.squeeze(self.rate).shape),
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},
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]
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for case in cases:
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with paddle.static.program_guard(self.program):
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[data] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_gamma.sample(case.get('input')),
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)
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self.assertTrue(data.shape == case.get('expect'))
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def test_rsample_shape(self):
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cases = [
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{
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'input': (),
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'expect': tuple(np.squeeze(self.rate).shape),
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},
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{
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'input': (3, 2),
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'expect': (3, 2, *np.squeeze(self.rate).shape),
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},
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]
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for case in cases:
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with paddle.static.program_guard(self.program):
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[data] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_gamma.rsample(case.get('input')),
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)
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self.assertTrue(data.shape == case.get('expect'))
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def test_sample(self):
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sample_shape = (30000,)
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with paddle.static.program_guard(self.program):
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[data] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_gamma.sample(sample_shape),
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)
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except_shape = sample_shape + np.squeeze(self.rate).shape
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self.assertTrue(data.shape == except_shape)
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np.testing.assert_allclose(
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data.mean(axis=0),
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scipy.stats.gamma.mean(self.concentration, scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self.concentration.dtype)),
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)
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np.testing.assert_allclose(
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data.var(axis=0),
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scipy.stats.gamma.var(self.concentration, scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self.concentration.dtype)),
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)
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def test_rsample(self):
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sample_shape = (30000,)
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with paddle.static.program_guard(self.program):
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[data] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_gamma.rsample(sample_shape),
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)
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except_shape = sample_shape + np.squeeze(self.rate).shape
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self.assertTrue(data.shape == except_shape)
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np.testing.assert_allclose(
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data.mean(axis=0),
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scipy.stats.gamma.mean(self.concentration, scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self.concentration.dtype)),
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)
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np.testing.assert_allclose(
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data.var(axis=0),
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scipy.stats.gamma.var(self.concentration, scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self.concentration.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, 'concentration', 'rate'),
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[
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('0-dim', 0.4, 0.5),
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],
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)
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class TestGammaSampleKS(unittest.TestCase):
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def setUp(self):
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self.program = paddle.static.Program()
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self.executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(self.program):
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self.scale = 1 / self.rate
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concentration = paddle.static.data(
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'concentration',
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(),
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'float',
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)
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rate = paddle.static.data('rate', (), 'float')
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self._paddle_gamma = gamma.Gamma(concentration, rate)
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self.feeds = {
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'concentration': self.concentration,
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'rate': self.rate,
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}
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def test_sample(self):
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sample_shape = (15000,)
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with paddle.static.program_guard(self.program):
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[samples] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_gamma.sample(sample_shape),
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)
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self.assertTrue(self._kstest(samples))
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def test_rsample(self):
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sample_shape = (15000,)
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with paddle.static.program_guard(self.program):
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[samples] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_gamma.rsample(sample_shape),
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)
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self.assertTrue(self._kstest(samples))
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def _kstest(self, samples):
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# Uses the Kolmogorov-Smirnov test for goodness of fit.
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ks, _ = scipy.stats.kstest(
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samples, scipy.stats.gamma(self.concentration, scale=self.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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(
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parameterize.TEST_CASE_NAME,
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'concentration1',
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'rate1',
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'concentration2',
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'rate2',
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),
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[
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(
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'one-dim',
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parameterize.xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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),
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(
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'multi-dim',
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parameterize.xrand(
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(2, 3),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(2, 3),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(2, 3),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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parameterize.xrand(
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(2, 3),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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),
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),
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],
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)
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class TestGammaKL(unittest.TestCase):
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def setUp(self):
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self.program1 = paddle.static.Program()
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self.program2 = paddle.static.Program()
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self.executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(self.program1, self.program2):
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concentration1 = paddle.static.data(
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'concentration1',
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self.concentration1.shape,
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self.concentration1.dtype,
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)
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concentration2 = paddle.static.data(
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'concentration2',
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self.concentration2.shape,
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self.concentration2.dtype,
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)
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rate1 = paddle.static.data(
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'rate1', self.rate1.shape, self.rate1.dtype
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)
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rate2 = paddle.static.data(
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'rate2', self.rate2.shape, self.rate2.dtype
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)
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self._gamma1 = gamma.Gamma(concentration1, rate1)
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self._gamma2 = gamma.Gamma(concentration2, rate2)
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self.feeds = {
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'concentration1': self.concentration1,
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'concentration2': self.concentration2,
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'rate1': self.rate1,
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'rate2': self.rate2,
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}
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def test_kl_divergence(self):
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with paddle.static.program_guard(self.program1, self.program2):
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self.executor.run(self.program2)
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[kl] = self.executor.run(
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self.program1,
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feed=self.feeds,
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fetch_list=[self._gamma1.kl_divergence(self._gamma2)],
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)
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np.testing.assert_allclose(
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kl,
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self._kl(),
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rtol=config.RTOL.get(str(self.concentration1.dtype)),
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atol=config.ATOL.get(str(self.concentration1.dtype)),
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)
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def test_kl1_error(self):
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self.assertRaises(
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TypeError,
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self._gamma1.kl_divergence,
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paddle.distribution.beta.Beta,
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)
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def _kl(self):
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concentration1 = self.concentration1
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concentration2 = self.concentration2
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rate1 = self.rate1
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rate2 = self.rate2
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t1 = concentration2 * np.log(rate1 / rate2)
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t2 = scipy.special.gammaln(concentration2) - scipy.special.gammaln(
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concentration1
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)
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t3 = (concentration1 - concentration2) * scipy.special.digamma(
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concentration1
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)
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t4 = (rate2 - rate1) * (concentration1 / rate1)
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return t1 + t2 + t3 + t4
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if __name__ == '__main__':
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unittest.main()
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