wehub-resource-sync
299 lines
10 KiB
Python
299 lines
10 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 scipy.stats
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from distribution import config
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from parameterize import TEST_CASE_NAME, parameterize_cls, place, xrand
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from test_distribution_lognormal import LogNormalNumpy
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import paddle
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from paddle.distribution.kl import kl_divergence
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from paddle.distribution.lognormal import LogNormal
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from paddle.distribution.normal import Normal
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@place(config.DEVICES)
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@parameterize_cls(
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(TEST_CASE_NAME, 'loc', 'scale', 'value'),
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[
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('one-dim', xrand((2,)), xrand((2,)), xrand((2,))),
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('multi-dim', xrand((3, 3)), xrand((3, 3)), xrand((3, 3))),
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],
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test_pir=True,
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)
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class TestLogNormal(unittest.TestCase):
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def run_program(self):
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paddle.enable_static()
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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.paddle_lognormal = LogNormal(loc=loc, scale=scale)
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self.np_lognormal = LogNormalNumpy(loc=self.loc, scale=self.scale)
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mean = self.paddle_lognormal.mean
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var = self.paddle_lognormal.variance
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entropy = self.paddle_lognormal.entropy()
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probs = self.paddle_lognormal.probs(value)
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log_prob = self.paddle_lognormal.log_prob(value)
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fetch_list = [mean, var, entropy, probs, log_prob]
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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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[
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self.mean,
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self.var,
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self.entropy,
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self.probs,
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self.log_prob,
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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.run_program()
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else:
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self.run_program()
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def test_mean(self):
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np_mean = self.np_lognormal.mean
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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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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_var(self):
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np_var = self.np_lognormal.variance
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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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np_var,
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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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np_entropy = self.np_lognormal.entropy()
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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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np.testing.assert_allclose(
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self.entropy,
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np_entropy,
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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_probs(self):
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np_probs = self.np_lognormal.probs(self.value)
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np.testing.assert_allclose(
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self.probs,
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np_probs,
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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_log_prob(self):
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np_log_prob = self.np_lognormal.log_prob(self.value)
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np.testing.assert_allclose(
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self.log_prob,
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np_log_prob,
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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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@place(config.DEVICES)
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@parameterize_cls(
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(TEST_CASE_NAME, 'loc', 'scale'),
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[('sample', xrand((4,)), xrand((4,), min=0, max=1))],
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test_pir=True,
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)
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class TestLogNormalSample(unittest.TestCase):
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def run_program(self):
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paddle.enable_static()
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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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n = 1000000
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self.sample_shape = (n,)
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self.rsample_shape = (n,)
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self.paddle_lognormal = LogNormal(loc=loc, scale=scale)
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mean = self.paddle_lognormal.mean
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variance = self.paddle_lognormal.variance
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samples = self.paddle_lognormal.sample(self.sample_shape)
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rsamples = self.paddle_lognormal.rsample(self.rsample_shape)
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fetch_list = [mean, variance, samples, rsamples]
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self.feeds = {'loc': self.loc, 'scale': self.scale}
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executor.run(startup_program)
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[self.mean, self.variance, self.samples, self.rsamples] = 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.run_program()
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else:
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self.run_program()
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def test_sample(self):
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samples_mean = self.samples.mean(axis=0)
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samples_var = self.samples.var(axis=0)
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np.testing.assert_allclose(samples_mean, self.mean, rtol=0.1, atol=0)
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np.testing.assert_allclose(samples_var, self.variance, rtol=0.1, atol=0)
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rsamples_mean = self.rsamples.mean(axis=0)
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rsamples_var = self.rsamples.var(axis=0)
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np.testing.assert_allclose(rsamples_mean, self.mean, rtol=0.1, atol=0)
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np.testing.assert_allclose(
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rsamples_var, self.variance, rtol=0.1, atol=0
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)
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batch_shape = (self.loc + self.scale).shape
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self.assertEqual(self.samples.shape, self.sample_shape + batch_shape)
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self.assertEqual(self.rsamples.shape, self.rsample_shape + batch_shape)
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for i in range(len(self.scale)):
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self.assertTrue(
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self._kstest(self.loc[i], self.scale[i], self.samples[:, i])
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)
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self.assertTrue(
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self._kstest(self.loc[i], self.scale[i], self.rsamples[:, i])
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)
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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, _ = scipy.stats.kstest(
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samples, scipy.stats.lognorm(s=scale, scale=np.exp(loc)).cdf
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)
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return ks < 0.02
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@place(config.DEVICES)
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@parameterize_cls(
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(TEST_CASE_NAME, 'loc1', 'scale1', 'loc2', 'scale2'),
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[
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('one-dim', xrand((2,)), xrand((2,)), xrand((2,)), xrand((2,))),
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(
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'multi-dim',
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xrand((2, 2)),
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xrand((2, 2)),
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xrand((2, 2)),
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xrand((2, 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 TestLogNormalKL(unittest.TestCase):
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def run_program(self):
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paddle.enable_static()
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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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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.ln_a = LogNormal(loc=loc1, scale=scale1)
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self.ln_b = LogNormal(loc=loc2, scale=scale2)
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self.normal_a = Normal(loc=loc1, scale=scale1)
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self.normal_b = Normal(loc=loc2, scale=scale2)
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kl0 = self.ln_a.kl_divergence(self.ln_b)
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kl1 = kl_divergence(self.ln_a, self.ln_b)
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kl_normal = kl_divergence(self.normal_a, self.normal_b)
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kl_formula = self._kl(self.ln_a, self.ln_b)
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fetch_list = [kl0, kl1, kl_normal, kl_formula]
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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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executor.run(startup_program)
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[self.kl0, self.kl1, self.kl_normal, self.kl_formula] = 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.run_program()
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else:
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self.run_program()
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def test_kl_divergence(self):
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np.testing.assert_allclose(
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self.kl0,
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self.kl_formula,
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rtol=config.RTOL.get(str(self.scale1.dtype)),
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atol=config.ATOL.get(str(self.scale1.dtype)),
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)
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np.testing.assert_allclose(
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self.kl1,
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self.kl_formula,
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rtol=config.RTOL.get(str(self.scale1.dtype)),
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atol=config.ATOL.get(str(self.scale1.dtype)),
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)
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np.testing.assert_allclose(
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self.kl_normal,
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self.kl_formula,
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rtol=config.RTOL.get(str(self.scale1.dtype)),
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atol=config.ATOL.get(str(self.scale1.dtype)),
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)
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def _kl(self, dist1, dist2):
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loc1 = dist1.loc
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loc2 = dist2.loc
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scale1 = dist1.scale
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scale2 = dist2.scale
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var_ratio = scale1 / scale2
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var_ratio = var_ratio * var_ratio
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t1 = (loc1 - loc2) / scale2
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t1 = t1 * t1
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return 0.5 * (var_ratio + t1 - 1 - np.log(var_ratio))
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if __name__ == '__main__':
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unittest.main()
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