wehub-resource-sync
341 lines
11 KiB
Python
341 lines
11 KiB
Python
# Copyright (c) 2024 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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from parameterize import (
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TEST_CASE_NAME,
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parameterize_cls,
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parameterize_func,
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)
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import paddle
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from paddle.distribution.student_t import StudentT
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'df', 'loc', 'scale'),
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[
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(
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'one-dim',
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10.0,
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1.0,
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2.0,
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),
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(
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'multi-dim',
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parameterize.xrand((2, 1), dtype='float32', min=4, max=30),
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parameterize.xrand((2, 3), dtype='float32', min=1, max=10),
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parameterize.xrand((2, 3), dtype='float32', min=0.1, max=3),
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),
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(
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'multi-dim2',
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parameterize.xrand((2, 1), dtype='float64', min=4, max=30),
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parameterize.xrand((2, 3), dtype='float64', min=-10, max=-1),
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parameterize.xrand((2, 3), dtype='float64', min=0.1, max=3),
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),
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],
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)
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class TestStudentT(unittest.TestCase):
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def setUp(self):
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df = (
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self.df if isinstance(self.df, float) else paddle.to_tensor(self.df)
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)
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loc = (
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self.loc
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if isinstance(self.loc, float)
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else paddle.to_tensor(self.loc)
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)
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scale = (
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self.scale
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if isinstance(self.scale, float)
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else paddle.to_tensor(self.scale)
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)
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self._dist = StudentT(df, loc, scale)
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def test_mean(self):
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mean = self._dist.mean
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target_dtype = (
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"float32" if isinstance(self.df, float) else self.df.dtype
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)
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self.assertEqual(mean.numpy().dtype, target_dtype)
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np.testing.assert_allclose(
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mean,
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self._np_mean(),
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rtol=config.RTOL.get(str(target_dtype)),
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atol=config.ATOL.get(str(target_dtype)),
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)
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def test_variance(self):
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var = self._dist.variance
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target_dtype = (
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"float32" if isinstance(self.df, float) else self.df.dtype
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)
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self.assertEqual(var.numpy().dtype, target_dtype)
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np.testing.assert_allclose(
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var,
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self._np_variance(),
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rtol=config.RTOL.get(str(target_dtype)),
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atol=config.ATOL.get(str(target_dtype)),
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)
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def test_entropy(self):
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entropy = self._dist.entropy()
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target_dtype = (
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"float32" if isinstance(self.df, float) else self.df.dtype
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)
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self.assertEqual(entropy.numpy().dtype, target_dtype)
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np.testing.assert_allclose(
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entropy,
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self._np_entropy(),
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rtol=config.RTOL.get(str(target_dtype)),
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atol=config.ATOL.get(str(target_dtype)),
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)
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def test_sample(self):
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sample_shape = ()
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samples = self._dist.sample(sample_shape)
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self.assertEqual(
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tuple(samples.shape),
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sample_shape + self._dist.batch_shape + self._dist.event_shape,
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)
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sample_shape = (10000,)
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samples = self._dist.sample(sample_shape)
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sample_mean = samples.mean(axis=0)
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sample_variance = samples.var(axis=0)
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# Tolerance value 0.1 is empirical value which is consistent with
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# TensorFlow
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np.testing.assert_allclose(
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sample_mean, self._dist.mean, atol=0, rtol=0.10
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)
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# Tolerance value 0.1 is empirical value which is consistent with
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# TensorFlow
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np.testing.assert_allclose(
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sample_variance, self._dist.variance, atol=0, rtol=0.10
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)
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def _np_variance(self):
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if isinstance(self.df, np.ndarray) and self.df.dtype == np.float32:
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df = self.df.astype("float64")
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else:
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df = self.df
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if isinstance(self.loc, np.ndarray) and self.loc.dtype == np.float32:
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loc = self.loc.astype("float64")
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else:
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loc = self.loc
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if (
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isinstance(self.scale, np.ndarray)
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and self.scale.dtype == np.float32
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):
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scale = self.scale.astype("float64")
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else:
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scale = self.scale
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return scipy.stats.t.var(df, loc, scale)
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def _np_mean(self):
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if isinstance(self.df, np.ndarray) and self.df.dtype == np.float32:
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df = self.df.astype("float64")
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else:
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df = self.df
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if isinstance(self.loc, np.ndarray) and self.loc.dtype == np.float32:
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loc = self.loc.astype("float64")
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else:
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loc = self.loc
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if (
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isinstance(self.scale, np.ndarray)
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and self.scale.dtype == np.float32
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):
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scale = self.scale.astype("float64")
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else:
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scale = self.scale
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return scipy.stats.t.mean(df, loc, scale)
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def _np_entropy(self):
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if isinstance(self.df, np.ndarray) and self.df.dtype == np.float32:
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df = self.df.astype("float64")
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else:
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df = self.df
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if isinstance(self.loc, np.ndarray) and self.loc.dtype == np.float32:
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loc = self.loc.astype("float64")
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else:
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loc = self.loc
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if (
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isinstance(self.scale, np.ndarray)
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and self.scale.dtype == np.float32
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):
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scale = self.scale.astype("float64")
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else:
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scale = self.scale
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return scipy.stats.t.entropy(df, loc, scale)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'df', 'loc', 'scale'),
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[
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(
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'float-tensor',
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10.0,
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paddle.to_tensor(1.0),
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2.0,
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),
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(
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'float-tensor1',
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10.0,
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parameterize.xrand((2, 3), dtype='float32', min=1, max=10),
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2.0,
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),
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(
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'float-tensor2',
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parameterize.xrand((2, 1), dtype='float64', min=4, max=30),
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parameterize.xrand((2, 3), dtype='float64', min=1, max=10),
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2.0,
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),
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(
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'float-tensor3',
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parameterize.xrand((2, 1), dtype='float64', min=4, max=30),
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1.0,
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parameterize.xrand((2, 1), dtype='float64', min=0.1, max=3),
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),
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(
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'float-tensor4',
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5.0,
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parameterize.xrand((2, 1), dtype='float32', min=-1, max=-10),
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parameterize.xrand((2, 3), dtype='float32', min=0.1, max=3),
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),
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],
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)
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class TestStudentT2(TestStudentT):
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def setUp(self):
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self._dist = StudentT(self.df, self.loc, 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, 'df', 'loc', 'scale', 'value'),
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[
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(
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'one-dim',
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10.0,
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0.0,
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1.0,
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np.array(3.3).astype("float32"),
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),
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(
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'value-broadcast-shape',
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parameterize.xrand((2, 1), dtype='float64', min=4, max=30),
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parameterize.xrand((2, 1), dtype='float64', min=-10, max=10),
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parameterize.xrand((2, 1), dtype='float64', min=0.1, max=5),
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parameterize.xrand((2, 4), dtype='float64', min=-10, max=10),
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),
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],
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)
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class TestStudentTProbs(unittest.TestCase):
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def setUp(self):
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df = (
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self.df if isinstance(self.df, float) else paddle.to_tensor(self.df)
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)
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loc = (
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self.loc
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if isinstance(self.loc, float)
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else paddle.to_tensor(self.loc)
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)
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scale = (
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self.scale
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if isinstance(self.scale, float)
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else paddle.to_tensor(self.scale)
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)
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self._dist = StudentT(df, loc, scale)
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def test_prob(self):
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target_dtype = (
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"float32" if isinstance(self.df, float) else self.df.dtype
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)
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np.testing.assert_allclose(
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self._dist.prob(paddle.to_tensor(self.value)),
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scipy.stats.t.pdf(self.value, self.df, self.loc, self.scale),
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rtol=config.RTOL.get(str(target_dtype)),
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atol=config.ATOL.get(str(target_dtype)),
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)
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def test_log_prob(self):
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target_dtype = (
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"float32" if isinstance(self.df, float) else self.df.dtype
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)
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np.testing.assert_allclose(
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self._dist.log_prob(paddle.to_tensor(self.value)),
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scipy.stats.t.logpdf(self.value, self.df, self.loc, self.scale),
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rtol=config.RTOL.get(str(target_dtype)),
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atol=config.ATOL.get(str(target_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, 'df', 'loc', 'scale', 'value'),
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[
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(
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'float-tensor1',
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10.0,
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parameterize.xrand((2, 1), dtype='float32', min=-10, max=10),
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1.0,
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np.array(3.3).astype("float32"),
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),
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(
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'float-tensor2',
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parameterize.xrand((2, 1), dtype='float64', min=4, max=30),
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1.0,
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parameterize.xrand((2, 1), dtype='float64', min=0.1, max=5),
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parameterize.xrand((2, 4), dtype='float64', min=-10, max=10),
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),
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],
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)
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class TestStudentTProbs2(TestStudentTProbs):
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def setUp(self):
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self._dist = StudentT(self.df, self.loc, self.scale)
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@parameterize.place(config.DEVICES)
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@parameterize_cls([TEST_CASE_NAME], ['StudentTTestError'])
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class StudentTTestError(unittest.TestCase):
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def setUp(self):
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paddle.disable_static(self.place)
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@parameterize_func(
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[
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(-5.0, 0.0, 1.0, ValueError), # negative df
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(5.0, 0.0, -1.0, ValueError), # negative scale
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]
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)
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def test_bad_parameter(self, df, loc, scale, error):
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with paddle.base.dygraph.guard(self.place):
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self.assertRaises(error, StudentT, df, loc, scale)
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@parameterize_func([(10,)]) # not sequence object sample shape
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def test_bad_sample_shape(self, shape):
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with paddle.base.dygraph.guard(self.place):
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t = StudentT(5.0, 0.0, 1.0)
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self.assertRaises(TypeError, t.sample, shape)
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if __name__ == '__main__':
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unittest.main()
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