wehub-resource-sync
371 lines
12 KiB
Python
371 lines
12 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 exponential, 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, '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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),
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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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(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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)
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class TestExponential(unittest.TestCase):
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def setUp(self):
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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, dtype=paddle.float32)
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self.scale = 1 / rate
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self._paddle_expon = exponential.Exponential(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_expon.mean,
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scipy.stats.expon.mean(scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_expon.rate.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_expon.variance,
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scipy.stats.expon.var(scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_expon.rate.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_expon.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_expon.prob(paddle.to_tensor(value)),
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scipy.stats.expon.pdf(value, scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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)
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def test_cdf(self):
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value = np.random.rand(*self._paddle_expon.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_expon.cdf(paddle.to_tensor(value)),
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scipy.stats.expon.cdf(value, scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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)
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def test_icdf(self):
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value = np.random.rand(*self._paddle_expon.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_expon.icdf(paddle.to_tensor(value)),
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scipy.stats.expon.ppf(value, scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_expon.rate.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_expon.entropy(),
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scipy.stats.expon.entropy(scale=self.scale),
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rtol=config.RTOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_expon.rate.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, '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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),
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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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),
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],
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)
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class TestExponentialSample(unittest.TestCase):
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def setUp(self):
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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, dtype=paddle.float32)
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self.scale = 1 / rate
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self._paddle_expon = exponential.Exponential(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_expon.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_expon.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_expon.sample(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 = (20000,)
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samples = self._paddle_expon.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.expon.mean(scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self._paddle_expon.rate.numpy().dtype)),
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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.expon.var(scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self._paddle_expon.rate.numpy().dtype)),
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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_expon.rate).shape),
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},
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{
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'input': (2, 5),
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'expect': (
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2,
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5,
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*paddle.squeeze(self._paddle_expon.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_expon.rsample(case.get('input')).shape)
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== case.get('expect')
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)
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def test_rsample(self):
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sample_shape = (20000,)
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samples = self._paddle_expon.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.expon.mean(scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self._paddle_expon.rate.numpy().dtype)),
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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.expon.var(scale=self.scale),
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rtol=0.1,
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atol=config.ATOL.get(str(self._paddle_expon.rate.numpy().dtype)),
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)
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def test_rsample_backpropagation(self):
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sample_shape = (1000, 2)
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with paddle.base.dygraph.guard(self.place):
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self._paddle_expon.rate.stop_gradient = False
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samples = self._paddle_expon.rsample(sample_shape)
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grads = paddle.grad([samples], [self._paddle_expon.rate])
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self.assertEqual(len(grads), 1)
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self.assertEqual(grads[0].dtype, self._paddle_expon.rate.dtype)
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self.assertEqual(grads[0].shape, self._paddle_expon.rate.shape)
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axis = list(range(len(sample_shape)))
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np.testing.assert_allclose(
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-samples.sum(axis) / self._paddle_expon.rate,
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grads[0],
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rtol=config.RTOL.get(
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str(self._paddle_expon.rate.numpy().dtype)
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),
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atol=config.ATOL.get(
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str(self._paddle_expon.rate.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, 'rate'),
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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 TestExponentialSampleKS(unittest.TestCase):
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def setUp(self):
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rate = paddle.to_tensor(self.rate, dtype=paddle.float32)
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self.scale = rate.reciprocal()
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self._paddle_expon = exponential.Exponential(rate)
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def test_sample_ks(self):
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sample_shape = (10000,)
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samples = self._paddle_expon.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 = (10000,)
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samples = self._paddle_expon.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.expon(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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(parameterize.TEST_CASE_NAME, 'rate1', 'rate2'),
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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 TestExponentialKL(unittest.TestCase):
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def setUp(self):
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self._expon1 = exponential.Exponential(paddle.to_tensor(self.rate1))
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self._expon2 = exponential.Exponential(paddle.to_tensor(self.rate2))
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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._expon1, self._expon2),
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self._kl(),
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rtol=config.RTOL.get(str(self._expon1.rate.numpy().dtype)),
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atol=config.ATOL.get(str(self._expon1.rate.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._expon1.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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rate_ratio = self.rate2 / self.rate1
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t1 = -np.log(rate_ratio)
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return t1 + rate_ratio - 1
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if __name__ == '__main__':
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unittest.main()
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