chore: import upstream snapshot with attribution

test/distribution/test_distribution_binomial_static.py

@@ -0,0 +1,256 @@
+# 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
+
+paddle.enable_static()
+
+
+paddle.enable_static()
+
+
+@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',
+            np.array([1000]),
+            parameterize.xrand((1,), dtype='float32', min=0, max=1),
+        ),
+        (
+            'multi-dim',
+            np.array([100]),
+            parameterize.xrand((1, 3), dtype='float64', min=0, max=1),
+        ),
+    ],
+)
+class TestBinomial(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
+            )
+            total_count = paddle.static.data(
+                'total_count', self.total_count.shape, self.total_count.dtype
+            )
+            dist = Binomial(total_count, probs)
+            mean = dist.mean
+            var = dist.variance
+            entropy = dist.entropy()
+            large_samples = dist.sample(shape=(1000,))
+        fetch_list = [mean, var, entropy, large_samples]
+        feed = {
+            'probs': self.probs,
+            'total_count': self.total_count,
+        }
+
+        executor.run(startup_program)
+        [
+            self.mean,
+            self.var,
+            self.entropy,
+            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.large_samples.dtype).split('.')[-1], self.probs.dtype
+        )
+        sample_mean = self.large_samples.mean(axis=0)
+        sample_variance = self.large_samples.var(axis=0)
+        np.testing.assert_allclose(sample_mean, self.mean, atol=0, rtol=0.20)
+        np.testing.assert_allclose(sample_variance, self.var, 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'),
+    [
+        (
+            'zero-dim',
+            np.array(10),
+            np.array(0.6).astype('float64'),
+            np.array([2.0, 3.0, 5.0]).astype('float64'),
+        ),
+        (
+            'value-same-shape',
+            np.array([10]).astype('int64'),
+            np.array([0.2, 0.3, 0.5]).astype('float64'),
+            np.array([2.0, 3.0, 5.0]).astype('float64'),
+        ),
+        (
+            'value-broadcast-shape',
+            np.array([10]),
+            np.array([[0.3, 0.7], [0.5, 0.5]]),
+            np.array([[[4.0, 6.0], [8.0, 2.0]], [[2.0, 4.0], [9.0, 7.0]]]),
+        ),
+    ],
+)
+class TestBinomialProbs(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):
+            total_count = paddle.static.data(
+                'total_count', self.total_count.shape, self.total_count.dtype
+            )
+            probs = paddle.static.data(
+                'probs', self.probs.shape, self.probs.dtype
+            )
+            value = paddle.static.data(
+                'value', self.value.shape, self.value.dtype
+            )
+            dist = Binomial(total_count, probs)
+            pmf = dist.prob(value)
+        feed = {
+            'total_count': self.total_count,
+            '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.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)),
+        )
+
+
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'n_1', 'p_1', 'n_2', 'p_2'),
+    [
+        (
+            'multi-dim-probability',
+            np.array([32]),
+            parameterize.xrand((1, 2), dtype='float64', min=0, max=1),
+            np.array([32]),
+            parameterize.xrand((1, 2), dtype='float64', min=0, max=1),
+        ),
+    ],
+)
+class TestBinomialKL(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):
+            n_1 = paddle.static.data('n_1', self.n_1.shape, self.n_1.dtype)
+            p_1 = paddle.static.data('p_1', self.p_1.shape, self.p_1.dtype)
+            n_2 = paddle.static.data('n_2', self.n_2.shape, self.n_2.dtype)
+            p_2 = paddle.static.data('p_2', self.p_2.shape, self.p_2.dtype)
+            dist1 = Binomial(n_1, p_1)
+            dist2 = Binomial(n_2, p_2)
+            kl_dist1_dist2 = dist1.kl_divergence(dist2)
+        feed = {
+            'n_1': self.n_1,
+            'p_1': self.p_1,
+            'n_2': self.n_2,
+            'p_2': self.p_2,
+        }
+        fetch_list = [kl_dist1_dist2]
+
+        executor.run(startup_program)
+        [self.kl_dist1_dist2] = executor.run(
+            main_program, feed=feed, fetch_list=fetch_list
+        )
+
+    def test_kl_divergence(self):
+        kl0 = self.kl_dist1_dist2
+        kl1 = self.kl_divergence_scipy()
+
+        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_scipy(self):
+        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, self.n_1, self.p_1)
+        log_prob_2 = scipy.stats.binom.logpmf(support, self.n_2, self.p_2)
+        return (np.exp(log_prob_1) * (log_prob_1 - log_prob_2)).sum(0)
+
+
+if __name__ == '__main__':
+    unittest.main()