# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numbers import unittest import numpy as np import scipy.stats from distribution.config import ATOL, DEVICES, RTOL from parameterize import TEST_CASE_NAME, parameterize_cls, place, xrand import paddle from paddle.distribution import geometric, kl from paddle.nn.functional import log_softmax np.random.seed(2023) @place(DEVICES) @parameterize_cls( (TEST_CASE_NAME, 'probs'), [ ( 'one-dim', xrand( (2,), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), ), ( 'multi-dim', xrand( (2, 3), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), ), ], ) class TestGeometric(unittest.TestCase): def setUp(self): probs = self.probs if not isinstance(self.probs, numbers.Real): probs = paddle.to_tensor(self.probs, dtype=paddle.float32) self._paddle_geom = geometric.Geometric(probs) def test_mean(self): with paddle.base.dygraph.guard(self.place): np.testing.assert_allclose( self._paddle_geom.mean, scipy.stats.geom.mean(self.probs, loc=-1), rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)), atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) def test_variance(self): with paddle.base.dygraph.guard(self.place): np.testing.assert_allclose( self._paddle_geom.variance, scipy.stats.geom.var(self.probs, loc=-1), rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)), atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) def test_stddev(self): with paddle.base.dygraph.guard(self.place): np.testing.assert_allclose( self._paddle_geom.stddev, scipy.stats.geom.std(self.probs, loc=-1), rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)), atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) def test_entropy(self): with paddle.base.dygraph.guard(self.place): np.testing.assert_allclose( self._paddle_geom.entropy(), scipy.stats.geom.entropy(self.probs, loc=-1), rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)), atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) def test_init_prob_type_error(self): with self.assertRaises(TypeError): paddle.distribution.geometric.Geometric([2]) def test_sample_shape(self): cases = [ { 'input': (), 'expect': tuple(paddle.squeeze(self._paddle_geom.probs).shape), }, { 'input': (4, 2), 'expect': ( 4, 2, *paddle.squeeze(self._paddle_geom.probs).shape, ), }, ] for case in cases: self.assertTrue( tuple(self._paddle_geom.sample(case.get('input')).shape) == case.get('expect') ) def test_sample(self): sample_shape = (100000,) samples = self._paddle_geom.sample(sample_shape) sample_values = samples.numpy() self.assertEqual(sample_values.dtype, self.probs.dtype) np.testing.assert_allclose( sample_values.mean(axis=0), scipy.stats.geom.mean(self.probs, loc=-1), rtol=0.1, atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) np.testing.assert_allclose( sample_values.var(axis=0), scipy.stats.geom.var(self.probs, loc=-1), rtol=0.1, atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) def test_rsample_shape(self): cases = [ { 'input': (), 'expect': tuple(paddle.squeeze(self._paddle_geom.probs).shape), }, { 'input': (2, 5), 'expect': ( 2, 5, *paddle.squeeze(self._paddle_geom.probs).shape, ), }, ] for case in cases: self.assertTrue( tuple(self._paddle_geom.rsample(case.get('input')).shape) == case.get('expect') ) def test_rsample(self): sample_shape = (100000,) samples = self._paddle_geom.rsample(sample_shape) sample_values = samples.numpy() self.assertEqual(sample_values.dtype, self.probs.dtype) np.testing.assert_allclose( sample_values.mean(axis=0), scipy.stats.geom.mean(self.probs, loc=-1), rtol=0.1, atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) np.testing.assert_allclose( sample_values.var(axis=0), scipy.stats.geom.var(self.probs, loc=-1), rtol=0.1, atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) def test_back_rsample(self): sample_shape = (100000,) with paddle.base.dygraph.guard(self.place): self._paddle_geom.probs.stop_gradient = False rs_value = self._paddle_geom.rsample(sample_shape) softmax_rs = log_softmax(rs_value) grads = paddle.grad([softmax_rs], [self._paddle_geom.probs]) self.assertEqual(len(grads), 1) self.assertEqual(grads[0].dtype, self._paddle_geom.probs.dtype) self.assertEqual(grads[0].shape, self._paddle_geom.probs.shape) @place(DEVICES) @parameterize_cls( (TEST_CASE_NAME, 'probs', 'value'), [ ( 'one-dim', xrand( (2,), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), 5, ), ( 'mult-dim', xrand( (2, 2), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), 5, ), ( 'mult-dim', xrand( (2, 2, 2), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), 5, ), ], ) class TestGeometricPMF(unittest.TestCase): def setUp(self): self._paddle_geom = geometric.Geometric( probs=paddle.to_tensor(self.probs) ) def test_pmf(self): with paddle.base.dygraph.guard(self.place): np.testing.assert_allclose( self._paddle_geom.pmf(self.value), scipy.stats.geom.pmf(self.value, self.probs, loc=-1), rtol=RTOL.get(str(self.probs.dtype)), atol=ATOL.get(str(self.probs.dtype)), ) def test_log_pmf(self): with paddle.base.dygraph.guard(self.place): np.testing.assert_allclose( self._paddle_geom.log_pmf(self.value), scipy.stats.geom.logpmf(self.value, self.probs, loc=-1), rtol=RTOL.get(str(self.probs.dtype)), atol=ATOL.get(str(self.probs.dtype)), ) def test_cdf(self): with paddle.base.dygraph.guard(self.place): np.testing.assert_allclose( self._paddle_geom.cdf(self.value), scipy.stats.geom.cdf(self.value, self.probs, loc=-1), rtol=RTOL.get(str(self._paddle_geom.probs.numpy().dtype)), atol=ATOL.get(str(self._paddle_geom.probs.numpy().dtype)), ) def test_pmf_error(self): self.assertRaises(TypeError, self._paddle_geom.pmf, [1, 2]) def test_log_pmf_error(self): self.assertRaises(TypeError, self._paddle_geom.log_pmf, [1, 2]) def test_cdf_error(self): self.assertRaises(TypeError, self._paddle_geom.cdf, [1, 2]) @place(DEVICES) @parameterize_cls( (TEST_CASE_NAME, 'probs1', 'probs2'), [ ( 'one-dim', xrand( (2,), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), xrand( (2,), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), ), ( 'multi-dim', xrand( (2, 2), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), xrand( (2, 2), dtype='float32', min=np.finfo(dtype='float32').tiny, max=1.0, ), ), ], ) class TestGeometricKL(unittest.TestCase): def setUp(self): paddle.disable_static() self._geometric1 = geometric.Geometric( probs=paddle.to_tensor(self.probs1) ) self._geometric2 = geometric.Geometric( probs=paddle.to_tensor(self.probs2) ) def test_kl_divergence(self): np.testing.assert_allclose( kl.kl_divergence(self._geometric1, self._geometric2), self._kl(), rtol=RTOL.get(str(self._geometric1.probs.numpy().dtype)), atol=ATOL.get(str(self._geometric1.probs.numpy().dtype)), ) def test_kl1_error(self): self.assertRaises( TypeError, self._geometric1.kl_divergence, paddle.distribution.beta.Beta, ) def test_kl2_error(self): self.assertRaises( TypeError, self._geometric2.kl_divergence, paddle.distribution.beta.Beta, ) def _kl(self): return self.probs1 * np.log(self.probs1 / self.probs2) + ( 1.0 - self.probs1 ) * np.log((1.0 - self.probs1) / (1.0 - self.probs2)) if __name__ == '__main__': unittest.main()