wehub-resource-sync
286 lines
9.0 KiB
Python
286 lines
9.0 KiB
Python
# Copyright (c) 2024 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 chi2
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paddle.enable_static()
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np.random.seed(2024)
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paddle.seed(2024)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'df'),
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[
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(
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'one-dim',
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parameterize.xrand(
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(4,),
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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, 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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'broadcast',
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parameterize.xrand(
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(2, 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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),
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],
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)
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class TestChi2(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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df = paddle.static.data('df', self.df.shape, self.df.dtype)
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self._paddle_chi2 = chi2.Chi2(df)
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self.feeds = {'df': self.df}
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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_chi2.mean],
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)
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np.testing.assert_allclose(
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mean,
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scipy.stats.chi2.mean(self.df),
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rtol=config.RTOL.get(str(self.df.dtype)),
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atol=config.ATOL.get(str(self.df.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_chi2.variance],
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)
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np.testing.assert_allclose(
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variance,
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scipy.stats.chi2.var(self.df),
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rtol=config.RTOL.get(str(self.df.dtype)),
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atol=config.ATOL.get(str(self.df.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_chi2.entropy()],
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)
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np.testing.assert_allclose(
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entropy,
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scipy.stats.chi2.entropy(self.df),
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rtol=config.RTOL.get(str(self.df.dtype)),
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atol=config.ATOL.get(str(self.df.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_chi2.df.shape,
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self._paddle_chi2.df.dtype,
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)
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prob = self._paddle_chi2.prob(value)
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random_number = np.random.rand(*self._paddle_chi2.df.shape).astype(
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self.df.dtype
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)
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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.chi2.pdf(random_number, self.df),
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rtol=config.RTOL.get(str(self.df.dtype)),
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atol=config.ATOL.get(str(self.df.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_chi2.df.shape,
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self._paddle_chi2.df.dtype,
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)
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log_prob = self._paddle_chi2.log_prob(value)
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random_number = np.random.rand(*self._paddle_chi2.df.shape).astype(
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self.df.dtype
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)
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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.chi2.logpdf(random_number, self.df),
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rtol=config.RTOL.get(str(self.df.dtype)),
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atol=config.ATOL.get(str(self.df.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, 'df'),
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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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),
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(
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'multi-dim',
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parameterize.xrand(
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(2, 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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)
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class TestChi2Sample(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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df = paddle.static.data('df', self.df.shape, self.df.dtype)
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self._paddle_chi2 = chi2.Chi2(df)
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self.feeds = {'df': self.df}
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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.df).shape),
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},
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{
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'input': (2, 2),
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'expect': (2, 2, *np.squeeze(self.df).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_chi2.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_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_chi2.sample(sample_shape),
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)
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except_shape = sample_shape + np.squeeze(self.df).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.chi2.mean(self.df),
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rtol=0.1,
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atol=config.ATOL.get(str(self.df.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.chi2.var(self.df),
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rtol=0.1,
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atol=config.ATOL.get(str(self.df.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, 'df'),
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[
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('0-dim', 0.4),
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],
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)
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class TestChi2SampleKS(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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df = paddle.static.data('df', (), 'float')
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self._paddle_chi2 = chi2.Chi2(df)
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self.feeds = {'df': self.df}
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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_chi2.sample(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(samples, scipy.stats.chi2(self.df).cdf)
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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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(parameterize.TEST_CASE_NAME),
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[
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('chi2_test_err'),
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],
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)
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class Chi2TestError(unittest.TestCase):
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def setUp(self):
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self.program = paddle.static.Program()
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@parameterize.parameterize_func([(10,)]) # not sequence object sample shape
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def test_bad_sample_shape(self, shape):
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with paddle.static.program_guard(self.program):
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_chi2 = chi2.Chi2(1.0)
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self.assertRaises(TypeError, _chi2.sample, shape)
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if __name__ == '__main__':
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unittest.main()
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