chore: import upstream snapshot with attribution

test/distribution/test_distribution_multinomial.py

@@ -0,0 +1,168 @@
+# 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
+
+
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'total_count', 'probs'),
+    [
+        ('one-dim', 10, parameterize.xrand((3,))),
+        ('multi-dim', 9, parameterize.xrand((10, 20))),
+        ('prob-sum-one', 10, np.array([0.5, 0.2, 0.3])),
+        ('prob-sum-non-one', 10, np.array([2.0, 3.0, 5.0])),
+    ],
+)
+class TestMultinomial(unittest.TestCase):
+    def setUp(self):
+        self._dist = paddle.distribution.Multinomial(
+            total_count=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(samples.numpy().dtype, self.probs.dtype)
+        self.assertEqual(
+            tuple(samples.shape),
+            sample_shape + self._dist.batch_shape + self._dist.event_shape,
+        )
+
+        sample_shape = (6,)
+        samples = self._dist.sample(sample_shape)
+        self.assertEqual(samples.numpy().dtype, self.probs.dtype)
+        self.assertEqual(
+            tuple(samples.shape),
+            sample_shape + self._dist.batch_shape + self._dist.event_shape,
+        )
+        self.assertTrue(
+            np.all(samples.sum(-1).numpy() == self._dist.total_count)
+        )
+
+        sample_shape = (5000,)
+        samples = self._dist.sample(sample_shape)
+        sample_mean = samples.mean(axis=0)
+        # Tolerance value 0.2 is empirical value which is consistent with
+        # TensorFlow
+        np.testing.assert_allclose(
+            sample_mean, self._dist.mean, atol=0, rtol=0.20
+        )
+
+    def _np_variance(self):
+        probs = self.probs / self.probs.sum(-1, keepdims=True)
+        return self.total_count * probs * (1 - probs)
+
+    def _np_mean(self):
+        probs = self.probs / self.probs.sum(-1, keepdims=True)
+        return self.total_count * probs
+
+    def _np_entropy(self):
+        probs = self.probs / self.probs.sum(-1, keepdims=True)
+        return scipy.stats.multinomial.entropy(self.total_count, probs)
+
+
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'total_count', 'probs', 'value'),
+    [
+        (
+            'value-float',
+            10,
+            np.array([0.2, 0.3, 0.5]),
+            np.array([2.0, 3.0, 5.0]),
+        ),
+        ('value-int', 10, np.array([0.2, 0.3, 0.5]), np.array([2, 3, 5])),
+        (
+            'value-multi-dim',
+            10,
+            np.array([[0.3, 0.7], [0.5, 0.5]]),
+            np.array([[4.0, 6], [8, 2]]),
+        ),
+        # ('value-sum-non-n', 10, np.array([0.5, 0.2, 0.3]), np.array([4,5,2])),
+    ],
+)
+class TestMultinomialPmf(unittest.TestCase):
+    def setUp(self):
+        self._dist = paddle.distribution.Multinomial(
+            total_count=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.multinomial.pmf(
+                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(
+    (config.TEST_CASE_NAME, 'total_count', 'probs'),
+    [
+        ('total_count_le_one', 0, np.array([0.3, 0.7])),
+        ('total_count_float', np.array([0.3, 0.7])),
+        ('probs_zero_dim', np.array(0)),
+    ],
+)
+class TestMultinomialException(unittest.TestCase):
+    def TestInit(self):
+        with self.assertRaises(ValueError):
+            paddle.distribution.Multinomial(
+                self.total_count, paddle.to_tensor(self.probs)
+            )
+
+
+if __name__ == '__main__':
+    unittest.main()