chore: import upstream snapshot with attribution

test/distribution/test_distribution_binomial.py

@@ -0,0 +1,213 @@
+# Copyright (c) 2021 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 unittest
+
+import numpy as np
+import parameterize
+import scipy.stats
+from distribution import config
+
+import paddle
+from paddle.distribution.binomial import Binomial
+
+
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'total_count', 'probs'),
+    [
+        (
+            'zero-dim',
+            np.array(1000),
+            np.array(0.6),
+        ),
+        (
+            'one-dim',
+            1000,
+            np.array([0.4]).astype('float32'),
+        ),
+        (
+            'multi-dim-total_count-probability',
+            parameterize.xrand((2, 1), min=1, max=100).astype('int32'),
+            parameterize.xrand((2, 3), dtype='float64', min=0.3, max=1),
+        ),
+    ],
+)
+class TestBinomial(unittest.TestCase):
+    def setUp(self):
+        self._dist = Binomial(
+            total_count=paddle.to_tensor(self.total_count),
+            probs=paddle.to_tensor(self.probs),
+        )
+
+    def test_mean(self):
+        mean = self._dist.mean
+        self.assertEqual(mean.numpy().dtype, self.probs.dtype)
+        np.testing.assert_allclose(
+            mean,
+            self._np_mean(),
+            rtol=config.RTOL.get(str(self.probs.dtype)),
+            atol=config.ATOL.get(str(self.probs.dtype)),
+        )
+
+    def test_variance(self):
+        var = self._dist.variance
+        self.assertEqual(var.numpy().dtype, self.probs.dtype)
+        np.testing.assert_allclose(
+            var,
+            self._np_variance(),
+            rtol=config.RTOL.get(str(self.probs.dtype)),
+            atol=config.ATOL.get(str(self.probs.dtype)),
+        )
+
+    def test_entropy(self):
+        entropy = self._dist.entropy()
+        self.assertEqual(entropy.numpy().dtype, self.probs.dtype)
+        np.testing.assert_allclose(
+            entropy,
+            self._np_entropy(),
+            rtol=config.RTOL.get(str(self.probs.dtype)),
+            atol=config.ATOL.get(str(self.probs.dtype)),
+        )
+
+    def test_sample(self):
+        sample_shape = ()
+        samples = self._dist.sample(sample_shape)
+        self.assertEqual(
+            tuple(samples.shape),
+            sample_shape + self._dist.batch_shape + self._dist.event_shape,
+        )
+
+        sample_shape = (5000,)
+        samples = self._dist.sample(sample_shape)
+        sample_mean = samples.mean(axis=0)
+        sample_variance = samples.var(axis=0)
+
+        np.testing.assert_allclose(
+            sample_mean, self._dist.mean, atol=0, rtol=0.20
+        )
+        np.testing.assert_allclose(
+            sample_variance, self._dist.variance, atol=0, rtol=0.20
+        )
+
+    def _np_variance(self):
+        return scipy.stats.binom.var(self.total_count, self.probs)
+
+    def _np_mean(self):
+        return scipy.stats.binom.mean(self.total_count, self.probs)
+
+    def _np_entropy(self):
+        return scipy.stats.binom.entropy(self.total_count, self.probs)
+
+
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'total_count', 'probs', 'value'),
+    [
+        (
+            'value-same-shape',
+            1000,
+            np.array([0.12, 0.3, 0.85]).astype('float64'),
+            np.array([2.0, 55.0, 999.0]).astype('float64'),
+        ),
+        (
+            'value-broadcast-shape',
+            10,
+            np.array([[0.3, 0.7], [0.5, 0.5]]),
+            np.array([[[4.0, 6], [8, 2]], [[2.0, 4], [9, 7]]]),
+        ),
+    ],
+)
+class TestBinomialProbs(unittest.TestCase):
+    def setUp(self):
+        self._dist = Binomial(
+            total_count=paddle.to_tensor(self.total_count),
+            probs=paddle.to_tensor(self.probs),
+        )
+
+    def test_prob(self):
+        np.testing.assert_allclose(
+            self._dist.prob(paddle.to_tensor(self.value)),
+            scipy.stats.binom.pmf(self.value, self.total_count, self.probs),
+            rtol=config.RTOL.get(str(self.probs.dtype)),
+            atol=config.ATOL.get(str(self.probs.dtype)),
+        )
+
+    def test_log_prob(self):
+        np.testing.assert_allclose(
+            self._dist.log_prob(paddle.to_tensor(self.value)),
+            scipy.stats.binom.logpmf(self.value, self.total_count, self.probs),
+            rtol=config.RTOL.get(str(self.probs.dtype)),
+            atol=config.ATOL.get(str(self.probs.dtype)),
+        )
+
+
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'n_1', 'p_1', 'n_2', 'p_2'),
+    [
+        (
+            'one-dim-probability',
+            np.array([3333]),
+            parameterize.xrand((1,), dtype='float32', min=0, max=1),
+            np.array([3333]),
+            parameterize.xrand((1,), dtype='float32', min=0, max=1),
+        ),
+        (
+            'multi-dim-probability',
+            np.array([25, 25, 25]),
+            parameterize.xrand((2, 3), dtype='float64', min=0, max=1),
+            np.array([25, 25, 25]),
+            parameterize.xrand((2, 3), dtype='float64', min=0, max=1),
+        ),
+    ],
+)
+class TestBinomialKL(unittest.TestCase):
+    def setUp(self):
+        self._dist1 = Binomial(
+            total_count=paddle.to_tensor(self.n_1),
+            probs=paddle.to_tensor(self.p_1),
+        )
+        self._dist2 = Binomial(
+            total_count=paddle.to_tensor(self.n_2),
+            probs=paddle.to_tensor(self.p_2),
+        )
+
+    def test_kl_divergence(self):
+        kl0 = self._dist1.kl_divergence(self._dist2)
+        kl1 = self.kl_divergence(self._dist1, self._dist2)
+
+        self.assertEqual(tuple(kl0.shape), self.p_1.shape)
+        self.assertEqual(tuple(kl1.shape), self.p_1.shape)
+        np.testing.assert_allclose(
+            kl0,
+            kl1,
+            rtol=config.RTOL.get(str(self.p_1.dtype)),
+            atol=config.ATOL.get(str(self.p_1.dtype)),
+        )
+
+    def kl_divergence(self, dist1, dist2):
+        support = np.arange(1 + self.n_1.max(), dtype=self.p_1.dtype)
+        support = support.reshape((-1,) + (1,) * len(self.p_1.shape))
+        log_prob_1 = scipy.stats.binom.logpmf(
+            support, dist1.total_count, dist1.probs
+        )
+        log_prob_2 = scipy.stats.binom.logpmf(
+            support, dist2.total_count, dist2.probs
+        )
+        return (np.exp(log_prob_1) * (log_prob_1 - log_prob_2)).sum(0)
+
+
+if __name__ == '__main__':
+    unittest.main()