wehub-resource-sync
200 lines
6.7 KiB
Python
200 lines
6.7 KiB
Python
# Copyright (c) 2021 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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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, 'total_count', 'probs'),
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[
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('one-dim', 5, parameterize.xrand((3,))),
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('multi-dim', 9, parameterize.xrand((2, 3))),
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('prob-sum-one', 5, np.array([0.5, 0.2, 0.3])),
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('prob-sum-non-one', 5, np.array([2.0, 3.0, 5.0])),
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],
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)
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class TestMultinomial(unittest.TestCase):
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def setUp(self):
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(main_program, startup_program):
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probs = paddle.static.data(
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'probs', self.probs.shape, self.probs.dtype
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)
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dist = paddle.distribution.Multinomial(self.total_count, probs)
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mean = dist.mean
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var = dist.variance
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entropy = dist.entropy()
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mini_samples = dist.sample(shape=(6,))
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large_samples = dist.sample(shape=(5000,))
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fetch_list = [mean, var, entropy, mini_samples, large_samples]
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feed = {'probs': self.probs}
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executor.run(startup_program)
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[
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self.mean,
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self.var,
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self.entropy,
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self.mini_samples,
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self.large_samples,
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] = executor.run(main_program, feed=feed, fetch_list=fetch_list)
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def test_mean(self):
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self.assertEqual(str(self.mean.dtype).split('.')[-1], self.probs.dtype)
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np.testing.assert_allclose(
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self.mean,
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self._np_mean(),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.dtype)),
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)
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def test_variance(self):
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self.assertEqual(str(self.var.dtype).split('.')[-1], self.probs.dtype)
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np.testing.assert_allclose(
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self.var,
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self._np_variance(),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.dtype)),
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)
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def test_entropy(self):
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self.assertEqual(
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str(self.entropy.dtype).split('.')[-1], self.probs.dtype
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)
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np.testing.assert_allclose(
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self.entropy,
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self._np_entropy(),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.dtype)),
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)
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def test_sample(self):
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self.assertEqual(
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str(self.mini_samples.dtype).split('.')[-1], self.probs.dtype
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)
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self.assertTrue(np.all(self.mini_samples.sum(-1) == self.total_count))
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sample_mean = self.large_samples.mean(axis=0)
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np.testing.assert_allclose(sample_mean, self.mean, atol=0, rtol=0.20)
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def _np_variance(self):
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probs = self.probs / self.probs.sum(-1, keepdims=True)
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return self.total_count * probs * (1 - probs)
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def _np_mean(self):
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probs = self.probs / self.probs.sum(-1, keepdims=True)
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return self.total_count * probs
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def _np_entropy(self):
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probs = self.probs / self.probs.sum(-1, keepdims=True)
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return scipy.stats.multinomial.entropy(self.total_count, probs)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'total_count', 'probs', 'value'),
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[
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(
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'value-float',
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5,
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np.array([0.2, 0.3, 0.5]),
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np.array([1.0, 1.0, 3.0]),
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),
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('value-int', 5, np.array([0.2, 0.3, 0.5]), np.array([2, 2, 1])),
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(
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'value-multi-dim',
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5,
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np.array([[0.3, 0.7], [0.5, 0.5]]),
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np.array([[1.0, 4.0], [2.0, 3.0]]),
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),
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# ('value-sum-non-n', 10, np.array([0.5, 0.2, 0.3]), np.array([4,5,2])),
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],
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)
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class TestMultinomialPmf(unittest.TestCase):
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def setUp(self):
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(main_program, startup_program):
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probs = paddle.static.data(
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'probs', self.probs.shape, self.probs.dtype
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)
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value = paddle.static.data(
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'value', self.value.shape, self.value.dtype
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)
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dist = paddle.distribution.Multinomial(self.total_count, probs)
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pmf = dist.prob(value)
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feed = {'probs': self.probs, 'value': self.value}
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fetch_list = [pmf]
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executor.run(startup_program)
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[self.pmf] = executor.run(
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main_program, feed=feed, fetch_list=fetch_list
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)
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def test_prob(self):
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np.testing.assert_allclose(
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self.pmf,
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scipy.stats.multinomial.pmf(
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self.value, self.total_count, self.probs
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),
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rtol=config.RTOL.get(str(self.probs.dtype)),
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atol=config.ATOL.get(str(self.probs.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, 'total_count', 'probs'),
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[
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('total_count_le_one', 0, np.array([0.3, 0.7])),
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('total_count_float', np.array([0.3, 0.7])),
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('probs_zero_dim', np.array(0)),
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],
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)
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class TestMultinomialException(unittest.TestCase):
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def setUp(self):
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startup_program = paddle.static.Program()
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self.main_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.main_program, startup_program):
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probs = paddle.static.data(
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'probs', self.probs.shape, self.probs.dtype
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)
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dist = paddle.distribution.Multinomial(self.total_count, probs)
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self.feed = {'probs': self.probs}
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self.executor.run(startup_program)
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def TestInit(self):
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with self.assertRaises(ValueError):
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self.executor.run(self.main_program, feed=self.feed, fetch=[])
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if __name__ == '__main__':
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unittest.main()
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