wehub-resource-sync
344 lines
12 KiB
Python
344 lines
12 KiB
Python
# Copyright (c) 2022 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, 'loc', 'scale'),
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[
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('one-dim', parameterize.xrand((2,)), parameterize.xrand((2,))),
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('multi-dim', parameterize.xrand((5, 5)), parameterize.xrand((5, 5))),
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],
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test_pir=True,
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)
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class TestLaplace(unittest.TestCase):
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def build_program(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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loc = paddle.static.data('loc', self.loc.shape, self.loc.dtype)
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scale = paddle.static.data(
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'scale', self.scale.shape, self.scale.dtype
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)
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self._dist = paddle.distribution.Laplace(loc=loc, scale=scale)
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self.sample_shape = (50000,)
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mean = self._dist.mean
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var = self._dist.variance
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stddev = self._dist.stddev
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entropy = self._dist.entropy()
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samples = self._dist.sample(self.sample_shape)
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fetch_list = [mean, var, stddev, entropy, samples]
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self.feeds = {'loc': self.loc, 'scale': self.scale}
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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.stddev,
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self.entropy,
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self.samples,
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] = executor.run(main_program, feed=self.feeds, fetch_list=fetch_list)
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def setUp(self):
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if self.test_pir:
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with paddle.pir_utils.IrGuard():
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self.build_program()
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else:
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self.build_program()
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def test_mean(self):
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self.assertEqual(str(self.mean.dtype).split('.')[-1], self.scale.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.scale.dtype)),
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atol=config.ATOL.get(str(self.scale.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.scale.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.scale.dtype)),
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atol=config.ATOL.get(str(self.scale.dtype)),
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)
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def test_stddev(self):
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self.assertEqual(
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str(self.stddev.dtype).split('.')[-1], self.scale.dtype
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)
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np.testing.assert_allclose(
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self.stddev,
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self._np_stddev(),
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rtol=config.RTOL.get(str(self.scale.dtype)),
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atol=config.ATOL.get(str(self.scale.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.scale.dtype
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)
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def test_sample(self):
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self.assertEqual(self.samples.dtype, self.scale.dtype)
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self.assertEqual(
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tuple(self.samples.shape),
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tuple(self._dist._extend_shape(self.sample_shape)),
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)
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self.assertEqual(self.samples.shape, self.sample_shape + self.loc.shape)
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self.assertEqual(self.samples.shape, self.sample_shape + self.loc.shape)
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np.testing.assert_allclose(
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self.samples.mean(axis=0),
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scipy.stats.laplace.mean(self.loc, scale=self.scale),
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rtol=0.2,
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atol=0.0,
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)
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np.testing.assert_allclose(
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self.samples.var(axis=0),
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scipy.stats.laplace.var(self.loc, scale=self.scale),
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rtol=0.1,
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atol=0.0,
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)
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def _np_mean(self):
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return self.loc
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def _np_stddev(self):
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return (2**0.5) * self.scale
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def _np_variance(self):
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stddev = (2**0.5) * self.scale
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return np.power(stddev, 2)
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def _np_entropy(self):
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return scipy.stats.laplace.entropy(loc=self.loc, scale=self.scale)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'loc', 'scale', 'value'),
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[
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(
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'value-float',
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np.array([0.2, 0.3]),
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np.array([2.0, 3.0]),
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np.array([2.0, 5.0]),
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),
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(
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'value-int',
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np.array([0.2, 0.3]),
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np.array([2.0, 3.0]),
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np.array([2, 5]),
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),
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(
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'value-multi-dim',
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np.array([0.2, 0.3]),
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np.array([2.0, 3.0]),
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np.array([[4.0, 6], [8, 2]]),
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),
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],
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test_pir=True,
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)
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class TestLaplacePDF(unittest.TestCase):
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def build_program(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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loc = paddle.static.data('loc', self.loc.shape, self.loc.dtype)
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scale = paddle.static.data(
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'scale', self.scale.shape, self.scale.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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self._dist = paddle.distribution.Laplace(loc=loc, scale=scale)
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prob = self._dist.prob(value)
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log_prob = self._dist.log_prob(value)
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cdf = self._dist.cdf(value)
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icdf = self._dist.icdf(value)
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fetch_list = [prob, log_prob, cdf, icdf]
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self.feeds = {'loc': self.loc, 'scale': self.scale, 'value': self.value}
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executor.run(startup_program)
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[self.prob, self.log_prob, self.cdf, self.icdf] = executor.run(
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main_program, feed=self.feeds, fetch_list=fetch_list
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)
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def setUp(self):
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if self.test_pir:
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with paddle.pir_utils.IrGuard():
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self.build_program()
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else:
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self.build_program()
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def test_prob(self):
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np.testing.assert_allclose(
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self.prob,
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scipy.stats.laplace.pdf(self.value, self.loc, self.scale),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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def test_log_prob(self):
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np.testing.assert_allclose(
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self.log_prob,
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scipy.stats.laplace.logpdf(self.value, self.loc, self.scale),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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def test_cdf(self):
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np.testing.assert_allclose(
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self.cdf,
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scipy.stats.laplace.cdf(self.value, self.loc, self.scale),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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def test_icdf(self):
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np.testing.assert_allclose(
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self.icdf,
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scipy.stats.laplace.ppf(self.value, self.loc, self.scale),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.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, 'loc1', 'scale1', 'loc2', 'scale2'),
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[
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(
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'kl',
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np.array([0.0]),
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np.array([1.0]),
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np.array([1.0]),
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np.array([0.5]),
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)
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],
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test_pir=True,
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)
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class TestLaplaceAndLaplaceKL(unittest.TestCase):
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def build_program(self):
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self.mp = paddle.static.Program()
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self.sp = paddle.static.Program()
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self.executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(self.mp, self.sp):
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loc1 = paddle.static.data('loc1', self.loc1.shape, self.loc1.dtype)
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scale1 = paddle.static.data(
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'scale1', self.scale1.shape, self.scale1.dtype
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)
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loc2 = paddle.static.data('loc2', self.loc2.shape, self.loc2.dtype)
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scale2 = paddle.static.data(
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'scale2', self.scale2.shape, self.scale2.dtype
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)
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self._dist_1 = paddle.distribution.Laplace(loc=loc1, scale=scale1)
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self._dist_2 = paddle.distribution.Laplace(loc=loc2, scale=scale2)
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self.feeds = {
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'loc1': self.loc1,
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'scale1': self.scale1,
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'loc2': self.loc2,
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'scale2': self.scale2,
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}
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def setUp(self):
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if self.test_pir:
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with paddle.pir_utils.IrGuard():
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self.build_program()
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else:
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self.build_program()
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def add_kl_divergence(self):
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with paddle.static.program_guard(self.mp, self.sp):
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out = paddle.distribution.kl_divergence(self._dist_1, self._dist_2)
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self.executor.run(self.sp)
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[out] = self.executor.run(
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self.mp, feed=self.feeds, fetch_list=[out]
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)
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np.testing.assert_allclose(out, self._np_kl(), atol=0, rtol=0.50)
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def test_kl_divergence(self):
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if self.test_pir:
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with paddle.pir_utils.IrGuard():
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self.add_kl_divergence()
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else:
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self.add_kl_divergence()
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def _np_kl(self):
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x = np.linspace(
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scipy.stats.laplace.ppf(0.01), scipy.stats.laplace.ppf(0.99), 1000
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)
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d1 = scipy.stats.laplace.pdf(x, loc=0.0, scale=1.0)
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d2 = scipy.stats.laplace.pdf(x, loc=1.0, scale=0.5)
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return scipy.stats.entropy(d1, d2)
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"""
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# Note: Zero dimension of a Tensor is not supported by static graph mode of paddle;
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# therefore, ks test below cannot be conducted temporarily.
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'loc', 'scale', 'sample_shape'), [
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('one-dim', np.array(4.0), np.array(3.0), np.array([3000]))])
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class TestLaplaceKS(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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loc = paddle.static.data('loc', self.loc.shape,
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self.loc.dtype)
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scale = paddle.static.data('scale', self.scale.shape,
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self.scale.dtype)
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self.sample = paddle.static.data('sample_shape', self.sample_shape.shape,
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self.sample_shape.dtype)
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self._dist = paddle.distribution.Laplace(loc=loc, scale=scale)
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self.feeds = {'loc': self.loc, 'scale': self.scale, 'sample_shape': self.sample_shape}
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def test_sample(self):
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with paddle.static.program_guard(self.program):
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[sample_values] = self.executor.run(self.program,
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feed=self.feeds,
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fetch_list=self._dist.sample((3000,)))
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self.assertTrue(self._kstest(self.loc, self.scale, sample_values))
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def _kstest(self, loc, scale, samples):
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# Uses the Kolmogorov-Smirnov test for goodness of fit.
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ks, p_value = scipy.stats.kstest(
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samples,
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scipy.stats.laplace(loc, scale=scale).cdf)
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return ks < 0.02
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"""
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if __name__ == '__main__':
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unittest.main()
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