wehub-resource-sync
257 lines
8.4 KiB
Python
257 lines
8.4 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.binomial import Binomial
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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, 'total_count', 'probs'),
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[
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(
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'zero-dim',
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np.array(1000),
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np.array(0.6),
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),
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(
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'one-dim',
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np.array([1000]),
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parameterize.xrand((1,), dtype='float32', min=0, max=1),
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),
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(
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'multi-dim',
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np.array([100]),
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parameterize.xrand((1, 3), dtype='float64', min=0, max=1),
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),
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],
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)
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class TestBinomial(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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total_count = paddle.static.data(
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'total_count', self.total_count.shape, self.total_count.dtype
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)
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dist = Binomial(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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large_samples = dist.sample(shape=(1000,))
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fetch_list = [mean, var, entropy, large_samples]
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feed = {
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'probs': self.probs,
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'total_count': self.total_count,
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}
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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.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.large_samples.dtype).split('.')[-1], self.probs.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 scipy.stats.binom.var(self.total_count, self.probs)
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def _np_mean(self):
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return scipy.stats.binom.mean(self.total_count, self.probs)
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def _np_entropy(self):
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return scipy.stats.binom.entropy(self.total_count, self.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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'zero-dim',
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np.array(10),
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np.array(0.6).astype('float64'),
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np.array([2.0, 3.0, 5.0]).astype('float64'),
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),
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(
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'value-same-shape',
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np.array([10]).astype('int64'),
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np.array([0.2, 0.3, 0.5]).astype('float64'),
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np.array([2.0, 3.0, 5.0]).astype('float64'),
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),
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(
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'value-broadcast-shape',
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np.array([10]),
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np.array([[0.3, 0.7], [0.5, 0.5]]),
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np.array([[[4.0, 6.0], [8.0, 2.0]], [[2.0, 4.0], [9.0, 7.0]]]),
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),
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],
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)
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class TestBinomialProbs(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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total_count = paddle.static.data(
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'total_count', self.total_count.shape, self.total_count.dtype
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)
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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 = Binomial(total_count, probs)
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pmf = dist.prob(value)
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feed = {
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'total_count': self.total_count,
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'probs': self.probs,
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'value': self.value,
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}
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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.binom.pmf(self.value, self.total_count, self.probs),
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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, 'n_1', 'p_1', 'n_2', 'p_2'),
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[
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(
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'multi-dim-probability',
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np.array([32]),
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parameterize.xrand((1, 2), dtype='float64', min=0, max=1),
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np.array([32]),
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parameterize.xrand((1, 2), dtype='float64', min=0, max=1),
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),
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],
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)
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class TestBinomialKL(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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n_1 = paddle.static.data('n_1', self.n_1.shape, self.n_1.dtype)
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p_1 = paddle.static.data('p_1', self.p_1.shape, self.p_1.dtype)
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n_2 = paddle.static.data('n_2', self.n_2.shape, self.n_2.dtype)
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p_2 = paddle.static.data('p_2', self.p_2.shape, self.p_2.dtype)
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dist1 = Binomial(n_1, p_1)
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dist2 = Binomial(n_2, p_2)
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kl_dist1_dist2 = dist1.kl_divergence(dist2)
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feed = {
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'n_1': self.n_1,
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'p_1': self.p_1,
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'n_2': self.n_2,
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'p_2': self.p_2,
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}
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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.p_1.shape)
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self.assertEqual(tuple(kl1.shape), self.p_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.p_1.dtype)),
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atol=config.ATOL.get(str(self.p_1.dtype)),
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)
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def kl_divergence_scipy(self):
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support = np.arange(1 + self.n_1.max(), dtype=self.p_1.dtype)
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support = support.reshape((-1,) + (1,) * len(self.p_1.shape))
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log_prob_1 = scipy.stats.binom.logpmf(support, self.n_1, self.p_1)
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log_prob_2 = scipy.stats.binom.logpmf(support, self.n_2, self.p_2)
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return (np.exp(log_prob_1) * (log_prob_1 - log_prob_2)).sum(0)
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if __name__ == '__main__':
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unittest.main()
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