wehub-resource-sync
247 lines
8.1 KiB
Python
247 lines
8.1 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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from paddle.distribution import Poisson
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paddle.enable_static()
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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, 'rate'),
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[
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('zero-dim', np.array(1000.0).astype('float32')),
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('one-dim', np.array([1000.0]).astype('float32')),
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(
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'multi-dim',
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parameterize.xrand((2,), min=1, max=20)
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.astype('int32')
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.astype('float64'),
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),
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],
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)
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class TestPoisson(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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rate = paddle.static.data('rate', self.rate.shape, self.rate.dtype)
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dist = Poisson(rate)
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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=())
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large_samples = dist.sample(shape=(1000,))
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fetch_list = [mean, var, entropy, mini_samples, large_samples]
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feed = {'rate': self.rate}
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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.rate.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.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.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.rate.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.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.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.rate.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.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.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.rate.dtype
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)
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sample_mean = self.large_samples.mean(axis=0)
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sample_variance = self.large_samples.var(axis=0)
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np.testing.assert_allclose(sample_mean, self.mean, atol=0, rtol=0.20)
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np.testing.assert_allclose(sample_variance, self.var, atol=0, rtol=0.20)
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def _np_variance(self):
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return self.rate
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def _np_mean(self):
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return self.rate
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def _np_entropy(self):
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return scipy.stats.poisson.entropy(self.rate)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'rate', 'value'),
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[
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(
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'value-same-shape',
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np.array(1000).astype('float32'),
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np.array(1100).astype('float32'),
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),
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(
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'value-broadcast-shape',
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np.array(10).astype('float64'),
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np.array([2.0, 3.0]).astype('float64'),
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),
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],
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)
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class TestPoissonProbs(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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rate = paddle.static.data('rate', self.rate.shape, self.rate.dtype)
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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 = Poisson(rate)
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pmf = dist.prob(value)
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feed = {'rate': self.rate, '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.poisson.pmf(self.value, self.rate),
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rtol=config.RTOL.get(str(self.rate.dtype)),
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atol=config.ATOL.get(str(self.rate.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, 'rate_1', 'rate_2'),
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[
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(
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'zero-dim',
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parameterize.xrand((1,), min=1, max=20)
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.astype('int32')
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.astype('float32')
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.reshape([]),
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parameterize.xrand((1,), min=1, max=20)
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.astype('int32')
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.astype('float32')
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.reshape([]),
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),
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(
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'multi-dim',
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parameterize.xrand((2, 3), min=1, max=20)
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.astype('int32')
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.astype('float32'),
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parameterize.xrand((2, 3), min=1, max=20)
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.astype('int32')
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.astype('float32'),
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),
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],
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)
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class TestPoissonKL(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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rate_1 = paddle.static.data('rate_1', self.rate_1.shape)
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rate_2 = paddle.static.data('rate_2', self.rate_2.shape)
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dist1 = Poisson(rate_1)
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dist2 = Poisson(rate_2)
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kl_dist1_dist2 = dist1.kl_divergence(dist2)
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feed = {'rate_1': self.rate_1, 'rate_2': self.rate_2}
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fetch_list = [kl_dist1_dist2]
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executor.run(startup_program)
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[self.kl_dist1_dist2] = executor.run(
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main_program, feed=feed, fetch_list=fetch_list
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)
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def test_kl_divergence(self):
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kl0 = self.kl_dist1_dist2
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kl1 = self.kl_divergence_scipy()
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self.assertEqual(tuple(kl0.shape), self.rate_1.shape)
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self.assertEqual(tuple(kl1.shape), self.rate_1.shape)
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np.testing.assert_allclose(
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kl0,
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kl1,
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rtol=config.RTOL.get(str(self.rate_1.dtype)),
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atol=config.ATOL.get(str(self.rate_1.dtype)),
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)
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def kl_divergence_scipy(self):
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rate_max = np.max(np.maximum(self.rate_1, self.rate_2))
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rate_min = np.min(np.minimum(self.rate_1, self.rate_2))
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support_max = self.enumerate_bounded_support(rate_max)
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support_min = self.enumerate_bounded_support(rate_min)
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a_min = np.min(support_min)
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a_max = np.max(support_max)
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common_support = np.arange(
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a_min, a_max, dtype=self.rate_1.dtype
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).reshape((-1,) + (1,) * len(self.rate_1.shape))
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log_prob_1 = scipy.stats.poisson.logpmf(common_support, self.rate_1)
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log_prob_2 = scipy.stats.poisson.logpmf(common_support, self.rate_2)
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return (np.exp(log_prob_1) * (log_prob_1 - log_prob_2)).sum(0)
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def enumerate_bounded_support(self, rate):
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s = np.sqrt(rate)
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upper = int(rate + 30 * s)
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lower = int(np.clip(rate - 30 * s, a_min=0, a_max=rate))
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values = np.arange(lower, upper, dtype=self.rate_1.dtype)
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return values
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if __name__ == '__main__':
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unittest.main()
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