wehub-resource-sync
351 lines
11 KiB
Python
351 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 numbers
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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, kl
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from paddle.nn.functional import log_softmax
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np.random.seed(2023)
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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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probs = self.probs
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if not isinstance(self.probs, numbers.Real):
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probs = paddle.to_tensor(self.probs, dtype=paddle.float32)
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self._paddle_geom = geometric.Geometric(probs)
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def test_mean(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_geom.mean,
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scipy.stats.geom.mean(self.probs, loc=-1),
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rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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)
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def test_variance(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_geom.variance,
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scipy.stats.geom.var(self.probs, loc=-1),
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rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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)
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def test_stddev(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_geom.stddev,
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scipy.stats.geom.std(self.probs, loc=-1),
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rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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)
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def test_entropy(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_geom.entropy(),
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scipy.stats.geom.entropy(self.probs, loc=-1),
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rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().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([2])
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def test_sample_shape(self):
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cases = [
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{
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'input': (),
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'expect': tuple(paddle.squeeze(self._paddle_geom.probs).shape),
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},
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{
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'input': (4, 2),
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'expect': (
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4,
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2,
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*paddle.squeeze(self._paddle_geom.probs).shape,
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),
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},
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]
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for case in cases:
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self.assertTrue(
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tuple(self._paddle_geom.sample(case.get('input')).shape)
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== case.get('expect')
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)
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def test_sample(self):
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sample_shape = (100000,)
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samples = self._paddle_geom.sample(sample_shape)
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sample_values = samples.numpy()
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self.assertEqual(sample_values.dtype, self.probs.dtype)
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np.testing.assert_allclose(
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sample_values.mean(axis=0),
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scipy.stats.geom.mean(self.probs, loc=-1),
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rtol=0.1,
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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)
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np.testing.assert_allclose(
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sample_values.var(axis=0),
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scipy.stats.geom.var(self.probs, loc=-1),
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rtol=0.1,
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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)
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def test_rsample_shape(self):
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cases = [
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{
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'input': (),
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'expect': tuple(paddle.squeeze(self._paddle_geom.probs).shape),
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},
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{
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'input': (2, 5),
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'expect': (
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2,
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5,
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*paddle.squeeze(self._paddle_geom.probs).shape,
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),
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},
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]
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for case in cases:
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self.assertTrue(
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tuple(self._paddle_geom.rsample(case.get('input')).shape)
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== case.get('expect')
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)
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def test_rsample(self):
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sample_shape = (100000,)
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samples = self._paddle_geom.rsample(sample_shape)
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sample_values = samples.numpy()
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self.assertEqual(sample_values.dtype, self.probs.dtype)
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np.testing.assert_allclose(
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sample_values.mean(axis=0),
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scipy.stats.geom.mean(self.probs, loc=-1),
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rtol=0.1,
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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)
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np.testing.assert_allclose(
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sample_values.var(axis=0),
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scipy.stats.geom.var(self.probs, loc=-1),
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rtol=0.1,
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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)
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def test_back_rsample(self):
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sample_shape = (100000,)
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with paddle.base.dygraph.guard(self.place):
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self._paddle_geom.probs.stop_gradient = False
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rs_value = self._paddle_geom.rsample(sample_shape)
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softmax_rs = log_softmax(rs_value)
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grads = paddle.grad([softmax_rs], [self._paddle_geom.probs])
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self.assertEqual(len(grads), 1)
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self.assertEqual(grads[0].dtype, self._paddle_geom.probs.dtype)
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self.assertEqual(grads[0].shape, self._paddle_geom.probs.shape)
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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._paddle_geom = geometric.Geometric(
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probs=paddle.to_tensor(self.probs)
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)
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def test_pmf(self):
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with paddle.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_geom.pmf(self.value),
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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.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_geom.log_pmf(self.value),
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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.base.dygraph.guard(self.place):
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np.testing.assert_allclose(
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self._paddle_geom.cdf(self.value),
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scipy.stats.geom.cdf(self.value, self.probs, loc=-1),
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rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)),
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)
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def test_pmf_error(self):
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self.assertRaises(TypeError, self._paddle_geom.pmf, [1, 2])
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def test_log_pmf_error(self):
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self.assertRaises(TypeError, self._paddle_geom.log_pmf, [1, 2])
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def test_cdf_error(self):
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self.assertRaises(TypeError, self._paddle_geom.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.disable_static()
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self._geometric1 = geometric.Geometric(
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probs=paddle.to_tensor(self.probs1)
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)
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self._geometric2 = geometric.Geometric(
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probs=paddle.to_tensor(self.probs2)
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)
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def test_kl_divergence(self):
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np.testing.assert_allclose(
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kl.kl_divergence(self._geometric1, self._geometric2),
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self._kl(),
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rtol=RTOL.get(str(self._geometric1.probs.numpy().dtype)),
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atol=ATOL.get(str(self._geometric1.probs.numpy().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._geometric1.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._geometric2.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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