wehub-resource-sync
286 lines
9.4 KiB
Python
286 lines
9.4 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 math
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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 import DistributionNumpy
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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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class LogNormalNumpy(DistributionNumpy):
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def __init__(self, loc, scale):
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self.loc = np.array(loc)
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self.scale = np.array(scale)
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if str(self.loc.dtype) not in ['float32', 'float64']:
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self.loc = self.loc.astype('float32')
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self.scale = self.scale.astype('float32')
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@property
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def mean(self):
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var = self.scale * self.scale
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return np.exp(self.loc + var / 2)
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@property
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def variance(self):
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var = self.scale * self.scale
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return (np.exp(var) - 1) * np.exp(2 * self.loc + var)
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def log_prob(self, value):
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var = self.scale * self.scale
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log_scale = np.log(self.scale)
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return (
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-((np.log(value) - self.loc) * (np.log(value) - self.loc))
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/ (2.0 * var)
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- log_scale
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- math.log(math.sqrt(2.0 * math.pi))
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- np.log(value)
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)
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def probs(self, value):
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var = self.scale * self.scale
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return np.exp(
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-1.0
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* ((np.log(value) - self.loc) * (np.log(value) - self.loc))
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/ (2.0 * var)
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) / (math.sqrt(2 * math.pi) * self.scale * value)
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def entropy(self):
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return (
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0.5
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+ self.loc
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+ 0.5 * np.log(np.array(2.0 * math.pi).astype(self.loc.dtype))
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+ np.log(self.scale)
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)
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def kl_divergence(self, other):
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var_ratio = self.scale / other.scale
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var_ratio = var_ratio * var_ratio
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t1 = (self.loc - other.loc) / other.scale
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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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@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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)
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class LogNormalTest(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.paddle_lognormal = LogNormal(
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loc=paddle.to_tensor(self.loc), scale=paddle.to_tensor(self.scale)
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)
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self.np_lognormal = LogNormalNumpy(self.loc, self.scale)
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def test_mean(self):
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mean = self.paddle_lognormal.mean
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np_mean = self.np_lognormal.mean
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self.assertEqual(mean.numpy().dtype, np_mean.dtype)
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np.testing.assert_allclose(
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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_variance(self):
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var = self.paddle_lognormal.variance
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np_var = self.np_lognormal.variance
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self.assertEqual(var.numpy().dtype, np_var.dtype)
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np.testing.assert_allclose(
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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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entropy = self.paddle_lognormal.entropy()
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np_entropy = self.np_lognormal.entropy()
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self.assertEqual(entropy.numpy().dtype, np_entropy.dtype)
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np.testing.assert_allclose(
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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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with paddle.base.dygraph.guard(self.place):
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probs = self.paddle_lognormal.probs(paddle.to_tensor(self.value))
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np_probs = self.np_lognormal.probs(self.value)
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np.testing.assert_allclose(
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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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with paddle.base.dygraph.guard(self.place):
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log_prob = self.paddle_lognormal.log_prob(
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paddle.to_tensor(self.value)
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)
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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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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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)
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class TestLogNormalSample(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.paddle_lognormal = LogNormal(loc=self.loc, scale=self.scale)
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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.samples = self.paddle_lognormal.sample(self.sample_shape)
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self.rsamples = self.paddle_lognormal.rsample(self.rsample_shape)
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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(
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samples_mean, self.paddle_lognormal.mean, rtol=0.1, atol=0
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)
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np.testing.assert_allclose(
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samples_var, self.paddle_lognormal.variance, rtol=0.1, atol=0
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)
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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(
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rsamples_mean, self.paddle_lognormal.mean, rtol=0.1, atol=0
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)
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np.testing.assert_allclose(
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rsamples_var, self.paddle_lognormal.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(
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self.samples.shape, list(self.sample_shape + batch_shape)
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)
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self.assertEqual(
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self.rsamples.shape, list(self.rsample_shape + batch_shape)
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)
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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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)
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class TestLogNormalKL(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.ln_a = LogNormal(
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loc=paddle.to_tensor(self.loc1), scale=paddle.to_tensor(self.scale1)
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)
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self.ln_b = LogNormal(
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loc=paddle.to_tensor(self.loc2), scale=paddle.to_tensor(self.scale2)
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)
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self.normal_a = Normal(
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loc=paddle.to_tensor(self.loc1), scale=paddle.to_tensor(self.scale1)
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)
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self.normal_b = Normal(
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loc=paddle.to_tensor(self.loc2), scale=paddle.to_tensor(self.scale2)
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)
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def test_kl_divergence(self):
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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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self.assertEqual(tuple(kl0.shape), self.scale1.shape)
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self.assertEqual(tuple(kl1.shape), self.scale1.shape)
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np.testing.assert_allclose(
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kl0,
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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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kl1,
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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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kl_normal,
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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 = np.array(dist1.loc)
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loc2 = np.array(dist2.loc)
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scale1 = np.array(dist1.scale)
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scale2 = np.array(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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