Half Normal Distribution (and inverse error function)

tensorflow/contrib/distributions/BUILD

@@ -176,6 +176,24 @@ cuda_py_test(
     ],
 )
 
+cuda_py_test(
+    name = "half_normal_test",
+    size = "medium",
+    srcs = ["python/kernel_tests/half_normal_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//third_party/py/numpy",
+        "//tensorflow/python:client",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:framework_for_generated_wrappers",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:gradients",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:variables",
+    ],
+)
+
 cuda_py_test(
     name = "inverse_gamma_test",
     srcs = ["python/kernel_tests/inverse_gamma_test.py"],

tensorflow/contrib/distributions/__init__.py

@@ -35,6 +35,7 @@
 from tensorflow.contrib.distributions.python.ops.distribution_util import tridiag
 from tensorflow.contrib.distributions.python.ops.estimator import *
 from tensorflow.contrib.distributions.python.ops.geometric import *
+from tensorflow.contrib.distributions.python.ops.half_normal import *
 from tensorflow.contrib.distributions.python.ops.independent import *
 from tensorflow.contrib.distributions.python.ops.inverse_gamma import *
 from tensorflow.contrib.distributions.python.ops.logistic import *
@@ -105,6 +106,7 @@
     'Gamma',
     'GammaWithSoftplusConcentrationRate',
     'Geometric',
+    'HalfNormal',
     'Independent',
     'InverseGamma',
     'InverseGammaWithSoftplusConcentrationRate',

tensorflow/contrib/distributions/python/kernel_tests/half_normal_test.py

@@ -0,0 +1,320 @@
+# Copyright 2017 The TensorFlow 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.
+# ==============================================================================
+"""Tests for initializers."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import importlib
+import numpy as np
+
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gradients_impl
+from tensorflow.python.ops import variables
+from tensorflow.contrib.distributions.python.ops import half_normal as hn_lib
+from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging
+
+
+def try_import(name):  # pylint: disable=invalid-name
+  module = None
+  try:
+    module = importlib.import_module(name)
+  except ImportError as e:
+    tf_logging.warning("Could not import %s: %s" % (name, str(e)))
+  return module
+
+stats = try_import("scipy.stats")
+
+
+class HalfNormalTest(test.TestCase):
+
+  def setUp(self):
+    self._rng = np.random.RandomState(123)
+
+  def assertAllFinite(self, tensor):
+    is_finite = np.isfinite(tensor.eval())
+    all_true = np.ones_like(is_finite, dtype=np.bool)
+    self.assertAllEqual(all_true, is_finite)
+
+  def _testParamShapes(self, sample_shape, expected):
+    with self.test_session():
+      param_shapes = hn_lib.HalfNormal.param_shapes(sample_shape)
+      scale_shape = param_shapes["scale"]
+      self.assertAllEqual(expected, scale_shape.eval())
+      scale = array_ops.ones(scale_shape)
+      self.assertAllEqual(
+          expected,
+          array_ops.shape(hn_lib.HalfNormal(scale).sample()).eval())
+
+  def _testParamStaticShapes(self, sample_shape, expected):
+    param_shapes = hn_lib.HalfNormal.param_static_shapes(sample_shape)
+    scale_shape = param_shapes["scale"]
+    self.assertEqual(expected, scale_shape)
+
+  def _testBatchShapes(self, dist, tensor):
+    self.assertAllEqual(dist.batch_shape_tensor().eval(), tensor.shape)
+    self.assertAllEqual(dist.batch_shape_tensor().eval(), tensor.eval().shape)
+    self.assertAllEqual(dist.batch_shape, tensor.shape)
+    self.assertAllEqual(dist.batch_shape, tensor.eval().shape)
+
+  def testParamShapes(self):
+    sample_shape = [10, 3, 4]
+    self._testParamShapes(sample_shape, sample_shape)
+    self._testParamShapes(constant_op.constant(sample_shape), sample_shape)
+
+  def testParamStaticShapes(self):
+    sample_shape = [10, 3, 4]
+    self._testParamStaticShapes(sample_shape, sample_shape)
+    self._testParamStaticShapes(
+        tensor_shape.TensorShape(sample_shape), sample_shape)
+
+  def testHalfNormalLogPDF(self):
+    with self.test_session():
+      batch_size = 6
+      scale = constant_op.constant([3.0] * batch_size)
+      x = np.array([-2.5, 2.5, 4.0, 0.0, -1.0, 2.0], dtype=np.float32)
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+
+      log_pdf = halfnorm.log_prob(x)
+      self._testBatchShapes(halfnorm, log_pdf)
+
+      pdf = halfnorm.prob(x)
+      self._testBatchShapes(halfnorm, pdf)
+
+      if not stats:
+        return
+      expected_log_pdf = stats.halfnorm(scale=scale.eval()).logpdf(x)
+      self.assertAllClose(expected_log_pdf, log_pdf.eval())
+      self.assertAllClose(np.exp(expected_log_pdf), pdf.eval())
+
+  def testHalfNormalLogPDFMultidimensional(self):
+    with self.test_session():
+      batch_size = 6
+      scale = constant_op.constant([[3.0, 1.0]] * batch_size)
+      x = np.array([[-2.5, 2.5, 4.0, 0.0, -1.0, 2.0]], dtype=np.float32).T
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+
+      log_pdf = halfnorm.log_prob(x)
+      self._testBatchShapes(halfnorm, log_pdf)
+
+      pdf = halfnorm.prob(x)
+      self._testBatchShapes(halfnorm, pdf)
+
+      if not stats:
+        return
+      expected_log_pdf = stats.halfnorm(scale=scale.eval()).logpdf(x)
+      self.assertAllClose(expected_log_pdf, log_pdf.eval())
+      self.assertAllClose(np.exp(expected_log_pdf), pdf.eval())
+
+  def testHalfNormalCDF(self):
+    with self.test_session():
+      batch_size = 50
+      scale = self._rng.rand(batch_size) + 1.0
+      x = np.linspace(-8.0, 8.0, batch_size).astype(np.float64)
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+
+      cdf = halfnorm.cdf(x)
+      self._testBatchShapes(halfnorm, cdf)
+
+      log_cdf = halfnorm.log_cdf(x)
+      self._testBatchShapes(halfnorm, log_cdf)
+
+      if not stats:
+        return
+      expected_logcdf = stats.halfnorm(scale=scale).logcdf(x)
+      self.assertAllClose(expected_logcdf, log_cdf.eval(), atol=0)
+      self.assertAllClose(np.exp(expected_logcdf), cdf.eval(), atol=0)
+
+  def testHalfNormalSurvivalFunction(self):
+    with self.test_session():
+      batch_size = 50
+      scale = self._rng.rand(batch_size) + 1.0
+      x = np.linspace(-8.0, 8.0, batch_size).astype(np.float64)
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+
+      sf = halfnorm.survival_function(x)
+      self._testBatchShapes(halfnorm, sf)
+
+      log_sf = halfnorm.log_survival_function(x)
+      self._testBatchShapes(halfnorm, log_sf)
+
+      if not stats:
+        return
+      expected_logsf = stats.halfnorm(scale=scale).logsf(x)
+      self.assertAllClose(expected_logsf, log_sf.eval(), atol=0)
+      self.assertAllClose(np.exp(expected_logsf), sf.eval(), atol=0)
+
+  def testHalfNormalQuantile(self):
+    with self.test_session():
+      batch_size = 50
+      scale = self._rng.rand(batch_size) + 1.0
+      p = np.linspace(0., 1.0, batch_size).astype(np.float64)
+
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+      x = halfnorm.quantile(p)
+      self._testBatchShapes(halfnorm, x)
+
+      if not stats:
+        return
+      expected_x = stats.halfnorm(scale=scale).ppf(p)
+      self.assertAllClose(expected_x, x.eval(), atol=0)
+
+  def testFiniteGradients(self):
+    for dtype in [np.float32, np.float64]:
+      g = ops.Graph()
+      with g.as_default():
+        scale = variables.Variable(dtype(3.0))
+        dist = hn_lib.HalfNormal(scale=scale)
+        x = np.array([0.01, 0.1, 1., 5., 10.]).astype(dtype)
+        for func in [
+            dist.cdf, dist.log_cdf, dist.survival_function,
+            dist.log_prob, dist.prob, dist.log_survival_function,
+        ]:
+          print(func.__name__)
+          value = func(x)
+          grads = gradients_impl.gradients(value, [scale])
+          with self.test_session(graph=g):
+            variables.global_variables_initializer().run()
+            self.assertAllFinite(value)
+            self.assertAllFinite(grads[0])
+
+  def testHalfNormalEntropy(self):
+    with self.test_session():
+      scale = np.array([[1.0, 2.0, 3.0]])
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+
+      # See https://en.wikipedia.org/wiki/Half-normal_distribution for the
+      # entropy formula used here.
+      expected_entropy = 0.5 * np.log(np.pi * scale ** 2.0 / 2.0) + 0.5
+
+      entropy = halfnorm.entropy()
+      self._testBatchShapes(halfnorm, entropy)
+      self.assertAllClose(expected_entropy, entropy.eval())
+
+  def testHalfNormalMeanAndMode(self):
+    with self.test_session():
+      scale = np.array([11., 12., 13.])
+
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+      expected_mean = scale * np.sqrt(2.0) / np.sqrt(np.pi)
+
+      self.assertAllEqual((3,), halfnorm.mean().eval().shape)
+      self.assertAllEqual(expected_mean, halfnorm.mean().eval())
+
+      self.assertAllEqual((3,), halfnorm.mode().eval().shape)
+      self.assertAllEqual([0., 0., 0.], halfnorm.mode().eval())
+
+  def testHalfNormalVariance(self):
+    with self.test_session():
+      scale = np.array([7., 7., 7.])
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+      expected_variance = scale ** 2.0 * (1.0 - 2.0 / np.pi)
+
+      self.assertAllEqual((3,), halfnorm.variance().eval().shape)
+      self.assertAllEqual(expected_variance, halfnorm.variance().eval())
+
+  def testHalfNormalStandardDeviation(self):
+    with self.test_session():
+      scale = np.array([7., 7., 7.])
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+      expected_variance = scale ** 2.0 * (1.0 - 2.0 / np.pi)
+
+      self.assertAllEqual((3,), halfnorm.stddev().shape)
+      self.assertAllEqual(np.sqrt(expected_variance), halfnorm.stddev().eval())
+
+  def testHalfNormalSample(self):
+    with self.test_session():
+      scale = constant_op.constant(3.0)
+      n = constant_op.constant(100000)
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+
+      sample = halfnorm.sample(n)
+
+      self.assertEqual(sample.eval().shape, (100000,))
+      self.assertAllClose(sample.eval().mean(),
+                          3.0 * np.sqrt(2.0) / np.sqrt(np.pi), atol=1e-1)
+
+      expected_shape = tensor_shape.TensorShape([n.eval()]).concatenate(
+          tensor_shape.TensorShape(halfnorm.batch_shape_tensor().eval()))
+      self.assertAllEqual(expected_shape, sample.shape)
+      self.assertAllEqual(expected_shape, sample.eval().shape)
+
+      expected_shape_static = (tensor_shape.TensorShape(
+          [n.eval()]).concatenate(halfnorm.batch_shape))
+      self.assertAllEqual(expected_shape_static, sample.shape)
+      self.assertAllEqual(expected_shape_static, sample.eval().shape)
+
+  def testHalfNormalSampleMultiDimensional(self):
+    with self.test_session():
+      batch_size = 2
+      scale = constant_op.constant([[2.0, 3.0]] * batch_size)
+      n = constant_op.constant(100000)
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+
+      sample = halfnorm.sample(n)
+      self.assertEqual(sample.shape, (100000, batch_size, 2))
+      self.assertAllClose(sample.eval()[:, 0, 0].mean(),
+                          2.0 * np.sqrt(2.0) / np.sqrt(np.pi), atol=1e-1)
+      self.assertAllClose(sample.eval()[:, 0, 1].mean(),
+                          3.0 * np.sqrt(2.0) / np.sqrt(np.pi), atol=1e-1)
+
+      expected_shape = tensor_shape.TensorShape([n.eval()]).concatenate(
+          tensor_shape.TensorShape(halfnorm.batch_shape_tensor().eval()))
+      self.assertAllEqual(expected_shape, sample.shape)
+      self.assertAllEqual(expected_shape, sample.eval().shape)
+
+      expected_shape_static = (tensor_shape.TensorShape(
+          [n.eval()]).concatenate(halfnorm.batch_shape))
+      self.assertAllEqual(expected_shape_static, sample.shape)
+      self.assertAllEqual(expected_shape_static, sample.eval().shape)
+
+  def testNegativeSigmaFails(self):
+    with self.test_session():
+      halfnorm = hn_lib.HalfNormal(scale=[-5.], validate_args=True, name="G")
+      with self.assertRaisesOpError("Condition x > 0 did not hold"):
+        halfnorm.mean().eval()
+
+  def testHalfNormalShape(self):
+    with self.test_session():
+      scale = constant_op.constant([6.0] * 5)
+      halfnorm = hn_lib.HalfNormal(scale=scale)
+
+      self.assertEqual(halfnorm.batch_shape_tensor().eval(), [5])
+      self.assertEqual(halfnorm.batch_shape, tensor_shape.TensorShape([5]))
+      self.assertAllEqual(halfnorm.event_shape_tensor().eval(), [])
+      self.assertEqual(halfnorm.event_shape, tensor_shape.TensorShape([]))
+
+  def testHalfNormalShapeWithPlaceholders(self):
+    scale = array_ops.placeholder(dtype=dtypes.float32)
+    halfnorm = hn_lib.HalfNormal(scale=scale)
+
+    with self.test_session() as sess:
+      # get_batch_shape should return an "<unknown>" tensor.
+      self.assertEqual(halfnorm.batch_shape, tensor_shape.TensorShape(None))
+      self.assertEqual(halfnorm.event_shape, ())
+      self.assertAllEqual(halfnorm.event_shape_tensor().eval(), [])
+      self.assertAllEqual(
+          sess.run(halfnorm.batch_shape_tensor(),
+                   feed_dict={scale: [1.0, 2.0]}), [2])
+
+
+if __name__ == "__main__":
+  test.main()