chore: import upstream snapshot with attribution

test/distribution/test_distribution_multinomial_static.py

@@ -0,0 +1,199 @@
+# 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
+
+paddle.enable_static()
+
+
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'total_count', 'probs'),
+    [
+        ('one-dim', 5, parameterize.xrand((3,))),
+        ('multi-dim', 9, parameterize.xrand((2, 3))),
+        ('prob-sum-one', 5, np.array([0.5, 0.2, 0.3])),
+        ('prob-sum-non-one', 5, np.array([2.0, 3.0, 5.0])),
+    ],
+)
+class TestMultinomial(unittest.TestCase):
+    def setUp(self):
+        startup_program = paddle.static.Program()
+        main_program = paddle.static.Program()
+        executor = paddle.static.Executor(self.place)
+        with paddle.static.program_guard(main_program, startup_program):
+            probs = paddle.static.data(
+                'probs', self.probs.shape, self.probs.dtype
+            )
+            dist = paddle.distribution.Multinomial(self.total_count, probs)
+            mean = dist.mean
+            var = dist.variance
+            entropy = dist.entropy()
+            mini_samples = dist.sample(shape=(6,))
+            large_samples = dist.sample(shape=(5000,))
+        fetch_list = [mean, var, entropy, mini_samples, large_samples]
+        feed = {'probs': self.probs}
+
+        executor.run(startup_program)
+        [
+            self.mean,
+            self.var,
+            self.entropy,
+            self.mini_samples,
+            self.large_samples,
+        ] = executor.run(main_program, feed=feed, fetch_list=fetch_list)
+
+    def test_mean(self):
+        self.assertEqual(str(self.mean.dtype).split('.')[-1], self.probs.dtype)
+        np.testing.assert_allclose(
+            self.mean,
+            self._np_mean(),
+            rtol=config.RTOL.get(str(self.probs.dtype)),
+            atol=config.ATOL.get(str(self.probs.dtype)),
+        )
+
+    def test_variance(self):
+        self.assertEqual(str(self.var.dtype).split('.')[-1], self.probs.dtype)
+        np.testing.assert_allclose(
+            self.var,
+            self._np_variance(),
+            rtol=config.RTOL.get(str(self.probs.dtype)),
+            atol=config.ATOL.get(str(self.probs.dtype)),
+        )
+
+    def test_entropy(self):
+        self.assertEqual(
+            str(self.entropy.dtype).split('.')[-1], self.probs.dtype
+        )
+        np.testing.assert_allclose(
+            self.entropy,
+            self._np_entropy(),
+            rtol=config.RTOL.get(str(self.probs.dtype)),
+            atol=config.ATOL.get(str(self.probs.dtype)),
+        )
+
+    def test_sample(self):
+        self.assertEqual(
+            str(self.mini_samples.dtype).split('.')[-1], self.probs.dtype
+        )
+        self.assertTrue(np.all(self.mini_samples.sum(-1) == self.total_count))
+
+        sample_mean = self.large_samples.mean(axis=0)
+        np.testing.assert_allclose(sample_mean, self.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',
+            5,
+            np.array([0.2, 0.3, 0.5]),
+            np.array([1.0, 1.0, 3.0]),
+        ),
+        ('value-int', 5, np.array([0.2, 0.3, 0.5]), np.array([2, 2, 1])),
+        (
+            'value-multi-dim',
+            5,
+            np.array([[0.3, 0.7], [0.5, 0.5]]),
+            np.array([[1.0, 4.0], [2.0, 3.0]]),
+        ),
+        # ('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):
+        startup_program = paddle.static.Program()
+        main_program = paddle.static.Program()
+        executor = paddle.static.Executor(self.place)
+
+        with paddle.static.program_guard(main_program, startup_program):
+            probs = paddle.static.data(
+                'probs', self.probs.shape, self.probs.dtype
+            )
+            value = paddle.static.data(
+                'value', self.value.shape, self.value.dtype
+            )
+            dist = paddle.distribution.Multinomial(self.total_count, probs)
+            pmf = dist.prob(value)
+        feed = {'probs': self.probs, 'value': self.value}
+        fetch_list = [pmf]
+
+        executor.run(startup_program)
+        [self.pmf] = executor.run(
+            main_program, feed=feed, fetch_list=fetch_list
+        )
+
+    def test_prob(self):
+        np.testing.assert_allclose(
+            self.pmf,
+            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(
+    (parameterize.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 setUp(self):
+        startup_program = paddle.static.Program()
+        self.main_program = paddle.static.Program()
+        self.executor = paddle.static.Executor(self.place)
+
+        with paddle.static.program_guard(self.main_program, startup_program):
+            probs = paddle.static.data(
+                'probs', self.probs.shape, self.probs.dtype
+            )
+            dist = paddle.distribution.Multinomial(self.total_count, probs)
+        self.feed = {'probs': self.probs}
+
+        self.executor.run(startup_program)
+
+    def TestInit(self):
+        with self.assertRaises(ValueError):
+            self.executor.run(self.main_program, feed=self.feed, fetch=[])
+
+
+if __name__ == '__main__':
+    unittest.main()