wehub-resource-sync
347 lines
11 KiB
Python
347 lines
11 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import scipy.stats
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from distribution.config import ATOL, DEVICES, RTOL
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from parameterize import TEST_CASE_NAME, parameterize_cls, place, xrand
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import paddle
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from paddle.distribution import geometric
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np.random.seed(2023)
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paddle.enable_static()
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@place(DEVICES)
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@parameterize_cls(
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(TEST_CASE_NAME, 'probs'),
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[
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(
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'one-dim',
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xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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),
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(
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'multi-dim',
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xrand(
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(2, 3),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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),
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],
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)
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class TestGeometric(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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# scale no need convert to tensor for scale input unittest
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probs = paddle.static.data(
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'probs', self.probs.shape, self.probs.dtype
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)
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self._paddle_geometric = geometric.Geometric(probs)
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self.feeds = {'probs': self.probs}
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def test_mean(self):
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with paddle.static.program_guard(self.program):
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[mean] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_geometric.mean],
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)
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np.testing.assert_allclose(
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mean,
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scipy.stats.geom.mean(self.probs, loc=-1),
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rtol=RTOL.get(str(self.probs.dtype)),
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atol=ATOL.get(str(self.probs.dtype)),
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)
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def test_variance(self):
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with paddle.static.program_guard(self.program):
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[variance] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_geometric.variance],
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)
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np.testing.assert_allclose(
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variance,
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scipy.stats.geom.var(self.probs, loc=-1),
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rtol=RTOL.get(str(self.probs.dtype)),
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atol=ATOL.get(str(self.probs.dtype)),
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)
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def test_stddev(self):
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with paddle.static.program_guard(self.program):
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[stddev] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_geometric.stddev],
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)
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np.testing.assert_allclose(
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stddev,
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scipy.stats.geom.std(self.probs, loc=-1),
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rtol=RTOL.get(str(self.probs.dtype)),
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atol=ATOL.get(str(self.probs.dtype)),
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)
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def test_sample(self):
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with paddle.static.program_guard(self.program):
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[data] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_geometric.sample(),
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)
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self.assertTrue(
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data.shape == np.broadcast_arrays(self.probs)[0].shape
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)
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def test_rsample(self):
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with paddle.static.program_guard(self.program):
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[data] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=self._paddle_geometric.rsample(),
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)
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self.assertTrue(
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data.shape == np.broadcast_arrays(self.probs)[0].shape
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)
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def test_entropy(self):
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with paddle.static.program_guard(self.program):
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[entropy] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_geometric.entropy()],
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)
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np.testing.assert_allclose(
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entropy,
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scipy.stats.geom.entropy(self.probs, loc=-1),
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rtol=RTOL.get(str(self.probs.dtype)),
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atol=ATOL.get(str(self.probs.dtype)),
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)
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def test_init_prob_type_error(self):
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with self.assertRaises(TypeError):
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paddle.distribution.geometric.Geometric([0.5])
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@place(DEVICES)
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@parameterize_cls(
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(TEST_CASE_NAME, 'probs', 'value'),
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[
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(
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'one-dim',
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xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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5,
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),
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(
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'mult-dim',
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xrand(
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(2, 2),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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5,
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),
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(
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'mult-dim',
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xrand(
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(2, 2, 2),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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5,
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),
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],
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)
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class TestGeometricPMF(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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probs = paddle.static.data(
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'probs', self.probs.shape, self.probs.dtype
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)
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self._paddle_geometric = geometric.Geometric(probs)
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self.feeds = {'probs': self.probs, 'value': self.value}
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def test_pmf(self):
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with paddle.static.program_guard(self.program):
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[pmf] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_geometric.pmf(self.value)],
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)
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np.testing.assert_allclose(
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pmf,
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scipy.stats.geom.pmf(self.value, self.probs, loc=-1),
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rtol=RTOL.get(str(self.probs.dtype)),
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atol=ATOL.get(str(self.probs.dtype)),
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)
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def test_log_pmf(self):
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with paddle.static.program_guard(self.program):
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[log_pmf] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_geometric.log_pmf(self.value)],
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)
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np.testing.assert_allclose(
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log_pmf,
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scipy.stats.geom.logpmf(self.value, self.probs, loc=-1),
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rtol=RTOL.get(str(self.probs.dtype)),
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atol=ATOL.get(str(self.probs.dtype)),
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)
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def test_cdf(self):
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with paddle.static.program_guard(self.program):
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[cdf] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_geometric.cdf(self.value)],
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)
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np.testing.assert_allclose(
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cdf,
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scipy.stats.geom.cdf(self.value, self.probs, loc=-1),
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rtol=RTOL.get(str(self.probs.dtype)),
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atol=ATOL.get(str(self.probs.dtype)),
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)
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def test_pmf_error(self):
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self.assertRaises(TypeError, self._paddle_geometric.pmf, [1, 2])
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def test_log_pmf_error(self):
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self.assertRaises(TypeError, self._paddle_geometric.log_pmf, [1, 2])
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def test_cdf_error(self):
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self.assertRaises(TypeError, self._paddle_geometric.cdf, [1, 2])
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@place(DEVICES)
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@parameterize_cls(
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(TEST_CASE_NAME, 'probs1', 'probs2'),
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[
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(
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'one-dim',
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xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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xrand(
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(2,),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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),
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(
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'multi-dim',
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xrand(
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(2, 2),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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xrand(
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(2, 2),
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dtype='float32',
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min=np.finfo(dtype='float32').tiny,
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max=1.0,
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),
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),
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],
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)
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class TestGeometricKL(unittest.TestCase):
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def setUp(self):
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paddle.enable_static()
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self.program_p = paddle.static.Program()
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self.program_q = 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_p, self.program_q):
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probs_p = paddle.static.data(
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'probs1', self.probs1.shape, self.probs1.dtype
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)
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probs_q = paddle.static.data(
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'probs2', self.probs2.shape, self.probs2.dtype
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)
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self._paddle_geomP = geometric.Geometric(probs_p)
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self._paddle_geomQ = geometric.Geometric(probs_q)
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self.feeds = {
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'probs1': self.probs1,
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'probs2': self.probs2,
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}
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def test_kl_divergence(self):
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with paddle.static.program_guard(self.program_p, self.program_q):
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self.executor.run(self.program_q)
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[kl_diver] = self.executor.run(
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self.program_p,
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feed=self.feeds,
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fetch_list=[
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self._paddle_geomP.kl_divergence(self._paddle_geomQ)
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],
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)
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np.testing.assert_allclose(
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kl_diver,
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self._kl(),
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rtol=RTOL.get(str(self.probs1.dtype)),
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atol=ATOL.get(str(self.probs1.dtype)),
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)
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def test_kl1_error(self):
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self.assertRaises(
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TypeError,
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self._paddle_geomP.kl_divergence,
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paddle.distribution.beta.Beta,
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)
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def test_kl2_error(self):
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self.assertRaises(
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TypeError,
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self._paddle_geomQ.kl_divergence,
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paddle.distribution.beta.Beta,
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)
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def _kl(self):
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return self.probs1 * np.log(self.probs1 / self.probs2) + (
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1.0 - self.probs1
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) * np.log((1.0 - self.probs1) / (1.0 - self.probs2))
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if __name__ == '__main__':
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unittest.main()
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