wehub-resource-sync
468 lines
15 KiB
Python
468 lines
15 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 numbers
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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, kl
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np.random.seed(2023)
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paddle.seed(2023)
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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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'0-dim',
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0.5,
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0.5,
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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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concentration = self.concentration
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if not isinstance(self.concentration, numbers.Real):
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concentration = paddle.to_tensor(self.concentration)
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rate = self.rate
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if not isinstance(self.rate, numbers.Real):
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rate = paddle.to_tensor(self.rate)
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self.scale = 1 / rate
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self._paddle_gamma = gamma.Gamma(concentration, rate)
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def test_mean(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_gamma.mean,
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scipy.stats.gamma.mean(self.concentration, scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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)
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def test_variance(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_gamma.variance,
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scipy.stats.gamma.var(self.concentration, scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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)
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def test_prob(self):
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value = np.random.rand(*self._paddle_gamma.rate.shape)
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_gamma.prob(paddle.to_tensor(value)),
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scipy.stats.gamma.pdf(
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value, self.concentration, scale=self.scale
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),
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rtol=config.RTOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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)
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def test_log_prob(self):
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value = np.random.rand(*self._paddle_gamma.rate.shape)
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_gamma.log_prob(paddle.to_tensor(value)),
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scipy.stats.gamma.logpdf(
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value, self.concentration, scale=self.scale
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),
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rtol=config.RTOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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)
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def test_entropy(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_gamma.entropy(),
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scipy.stats.gamma.entropy(self.concentration, scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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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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concentration = self.concentration
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if not isinstance(self.concentration, numbers.Real):
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concentration = paddle.to_tensor(self.concentration)
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rate = self.rate
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if not isinstance(self.rate, numbers.Real):
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rate = paddle.to_tensor(self.rate)
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self.scale = 1 / rate
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self._paddle_gamma = gamma.Gamma(concentration, rate)
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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(paddle.squeeze(self._paddle_gamma.rate).shape),
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},
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{
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'input': (3, 2),
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'expect': (
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3,
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2,
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*paddle.squeeze(self._paddle_gamma.rate).shape,
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),
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},
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]
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for case in cases:
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self.assertTrue(
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tuple(self._paddle_gamma.sample(case.get('input')).shape)
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== case.get('expect')
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)
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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(paddle.squeeze(self._paddle_gamma.rate).shape),
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},
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{
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'input': (3, 2),
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'expect': (
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3,
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2,
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*paddle.squeeze(self._paddle_gamma.rate).shape,
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),
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},
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]
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for case in cases:
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self.assertTrue(
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tuple(self._paddle_gamma.rsample(case.get('input')).shape)
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== case.get('expect')
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)
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def test_sample(self):
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sample_shape = (30000,)
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samples = self._paddle_gamma.sample(sample_shape)
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sample_values = samples.numpy()
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self.assertEqual(sample_values.dtype, self.rate.dtype)
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np.testing.assert_allclose(
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sample_values.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(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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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.gamma.var(self.concentration, scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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)
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def test_rsample(self):
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sample_shape = (30000,)
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samples = self._paddle_gamma.rsample(sample_shape)
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sample_values = samples.numpy()
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self.assertEqual(sample_values.dtype, self.rate.dtype)
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np.testing.assert_allclose(
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sample_values.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(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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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.gamma.var(self.concentration, scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(
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str(self._paddle_gamma.concentration.numpy().dtype)
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),
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)
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@unittest.skip("TODO: implement standard_gamma grad op.")
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def test_rsample_backpropagation(self):
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sample_shape = (1000,)
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with paddle.base.dygraph.guard(self.place):
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self._paddle_gamma.concentration.stop_gradient = False
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self._paddle_gamma.rate.stop_gradient = False
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samples = self._paddle_gamma.rsample(sample_shape)
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grads = paddle.grad(
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[samples],
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[self._paddle_gamma.concentration, self._paddle_gamma.rate],
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)
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self.assertEqual(len(grads), 2)
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self.assertEqual(
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grads[0].dtype, self._paddle_gamma.concentration.dtype
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)
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self.assertEqual(
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grads[0].shape, self._paddle_gamma.concentration.shape
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)
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self.assertEqual(grads[1].dtype, self._paddle_gamma.rate.dtype)
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self.assertEqual(grads[1].shape, self._paddle_gamma.rate.shape)
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samples.backward()
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self.assertEqual(
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list(self._paddle_gamma.concentration.gradient().shape),
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self._paddle_gamma.concentration.shape,
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)
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self.assertEqual(
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list(self._paddle_gamma.rate.gradient().shape),
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self._paddle_gamma.rate.shape,
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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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concentration = self.concentration
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if not isinstance(self.concentration, numbers.Real):
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concentration = paddle.to_tensor(self.concentration)
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rate = self.rate
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if not isinstance(self.rate, numbers.Real):
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rate = paddle.to_tensor(self.rate)
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self.scale = 1 / rate
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self._paddle_gamma = gamma.Gamma(concentration, rate)
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def test_sample_ks(self):
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sample_shape = (15000,)
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samples = self._paddle_gamma.sample(sample_shape)
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self.assertTrue(self._kstest(samples))
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def test_rsample_ks(self):
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sample_shape = (15000,)
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samples = self._paddle_gamma.rsample(sample_shape)
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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._gamma1 = gamma.Gamma(
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paddle.to_tensor(self.concentration1), paddle.to_tensor(self.rate1)
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)
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self._gamma2 = gamma.Gamma(
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paddle.to_tensor(self.concentration2), paddle.to_tensor(self.rate2)
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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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kl.kl_divergence(self._gamma1, self._gamma2),
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self._kl(),
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rtol=config.RTOL.get(str(self._gamma1.concentration.numpy().dtype)),
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atol=config.ATOL.get(str(self._gamma1.concentration.numpy().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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