Gamma, Chi2 and Exponential Distributions for Tensorflow

Change: 122546445

tensorflow/contrib/distributions/BUILD

@@ -27,6 +27,33 @@ cuda_py_tests(
     ],
 )
 
+cuda_py_tests(
+    name = "gamma_test",
+    srcs = ["python/kernel_tests/gamma_test.py"],
+    additional_deps = [
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
+cuda_py_tests(
+    name = "chi2_test",
+    srcs = ["python/kernel_tests/chi2_test.py"],
+    additional_deps = [
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
+cuda_py_tests(
+    name = "exponential_test",
+    srcs = ["python/kernel_tests/exponential_test.py"],
+    additional_deps = [
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_tests(
     name = "gaussian_test",
     size = "small",
@@ -65,7 +92,6 @@ cuda_py_tests(
     srcs = ["python/kernel_tests/gaussian_conjugate_posteriors_test.py"],
     additional_deps = [
         ":distributions_py",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:platform_test",
     ],
 )

tensorflow/contrib/distributions/__init__.py

@@ -27,6 +27,9 @@
 
 ### Univariate (scalar) distributions
 
+@@Chi2
+@@Exponential
+@@Gamma
 @@Gaussian
 @@Uniform
 
@@ -50,8 +53,12 @@
 from __future__ import print_function
 
 # pylint: disable=unused-import,wildcard-import,line-too-long
+
+from tensorflow.contrib.distributions.python.ops.chi2 import *
 from tensorflow.contrib.distributions.python.ops.dirichlet_multinomial import *
 from tensorflow.contrib.distributions.python.ops.distribution import *
+from tensorflow.contrib.distributions.python.ops.exponential import *
+from tensorflow.contrib.distributions.python.ops.gamma import *
 from tensorflow.contrib.distributions.python.ops.gaussian import *
 from tensorflow.contrib.distributions.python.ops.gaussian_conjugate_posteriors import *
 from tensorflow.contrib.distributions.python.ops.mvn import *

tensorflow/contrib/distributions/python/kernel_tests/chi2_test.py

@@ -0,0 +1,85 @@
+# Copyright 2016 Google Inc. 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 numpy as np
+from scipy import stats
+import tensorflow as tf
+
+
+class Chi2Test(tf.test.TestCase):
+
+  def testChi2LogPDF(self):
+    with tf.Session():
+      batch_size = 6
+      df = tf.constant([2.0] * batch_size, dtype=np.float64)
+      df_v = 2.0
+      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float64)
+      chi2 = tf.contrib.distributions.Chi2(df=df)
+      expected_log_pdf = stats.chi2.logpdf(x, df_v)
+
+      log_pdf = chi2.log_pdf(x)
+      self.assertEqual(log_pdf.get_shape(), (6,))
+      self.assertAllClose(log_pdf.eval(), expected_log_pdf)
+
+      pdf = chi2.pdf(x)
+      self.assertEqual(pdf.get_shape(), (6,))
+      self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf))
+
+  def testChi2CDF(self):
+    with tf.Session():
+      batch_size = 6
+      df = tf.constant([2.0] * batch_size, dtype=np.float64)
+      df_v = 2.0
+      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float64)
+
+      chi2 = tf.contrib.distributions.Chi2(df=df)
+      expected_cdf = stats.chi2.cdf(x, df_v)
+
+      cdf = chi2.cdf(x)
+      self.assertEqual(cdf.get_shape(), (6,))
+      self.assertAllClose(cdf.eval(), expected_cdf)
+
+  def testChi2Mean(self):
+    with tf.Session():
+      df_v = np.array([1., 3, 5], dtype=np.float64)
+      expected_mean = stats.chi2.mean(df_v)
+      chi2 = tf.contrib.distributions.Chi2(df=df_v)
+      self.assertEqual(chi2.mean.get_shape(), (3,))
+      self.assertAllClose(chi2.mean.eval(), expected_mean)
+
+  def testChi2Variance(self):
+    with tf.Session():
+      df_v = np.array([1., 3, 5], np.float64)
+      expected_variances = stats.chi2.var(df_v)
+      chi2 = tf.contrib.distributions.Chi2(df=df_v)
+      self.assertEqual(chi2.variance.get_shape(), (3,))
+      self.assertAllClose(chi2.variance.eval(), expected_variances)
+
+  def testChi2Entropy(self):
+    with tf.Session():
+      df_v = np.array([1., 3, 5], dtype=np.float64)
+      expected_entropy = stats.chi2.entropy(df_v)
+      chi2 = tf.contrib.distributions.Chi2(df=df_v)
+      self.assertEqual(chi2.entropy().get_shape(), (3,))
+      self.assertAllClose(chi2.entropy().eval(), expected_entropy)
+
+
+if __name__ == '__main__':
+  tf.test.main()

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

@@ -0,0 +1,85 @@
+# Copyright 2016 Google Inc. 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 numpy as np
+from scipy import stats
+import tensorflow as tf
+
+
+class ExponentialTest(tf.test.TestCase):
+
+  def testExponentialLogPDF(self):
+    with tf.Session():
+      batch_size = 6
+      lam = tf.constant([2.0] * batch_size)
+      lam_v = 2.0
+      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
+      exponential = tf.contrib.distributions.Exponential(lam=lam)
+      expected_log_pdf = stats.expon.logpdf(x, scale=1 / lam_v)
+
+      log_pdf = exponential.log_pdf(x)
+      self.assertEqual(log_pdf.get_shape(), (6,))
+      self.assertAllClose(log_pdf.eval(), expected_log_pdf)
+
+      pdf = exponential.pdf(x)
+      self.assertEqual(pdf.get_shape(), (6,))
+      self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf))
+
+  def testExponentialCDF(self):
+    with tf.Session():
+      batch_size = 6
+      lam = tf.constant([2.0] * batch_size)
+      lam_v = 2.0
+      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
+
+      exponential = tf.contrib.distributions.Exponential(lam=lam)
+      expected_cdf = stats.expon.cdf(x, scale=1 / lam_v)
+
+      cdf = exponential.cdf(x)
+      self.assertEqual(cdf.get_shape(), (6,))
+      self.assertAllClose(cdf.eval(), expected_cdf)
+
+  def testExponentialMean(self):
+    with tf.Session():
+      lam_v = np.array([1.0, 4.0, 2.5])
+      expected_mean = stats.expon.mean(scale=1 / lam_v)
+      exponential = tf.contrib.distributions.Exponential(lam=lam_v)
+      self.assertEqual(exponential.mean.get_shape(), (3,))
+      self.assertAllClose(exponential.mean.eval(), expected_mean)
+
+  def testExponentialVariance(self):
+    with tf.Session():
+      lam_v = np.array([1.0, 4.0, 2.5])
+      expected_variance = stats.expon.var(scale=1 / lam_v)
+      exponential = tf.contrib.distributions.Exponential(lam=lam_v)
+      self.assertEqual(exponential.variance.get_shape(), (3,))
+      self.assertAllClose(exponential.variance.eval(), expected_variance)
+
+  def testExponentialEntropy(self):
+    with tf.Session():
+      lam_v = np.array([1.0, 4.0, 2.5])
+      expected_entropy = stats.expon.entropy(scale=1 / lam_v)
+      exponential = tf.contrib.distributions.Exponential(lam=lam_v)
+      self.assertEqual(exponential.entropy().get_shape(), (3,))
+      self.assertAllClose(exponential.entropy().eval(), expected_entropy)
+
+
+if __name__ == '__main__':
+  tf.test.main()

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

@@ -0,0 +1,142 @@
+# Copyright 2016 Google Inc. 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 numpy as np
+from scipy import stats
+import tensorflow as tf
+
+
+class GammaTest(tf.test.TestCase):
+
+  def testGammaShape(self):
+    with tf.Session():
+      alpha = tf.constant([3.0] * 5)
+      beta = tf.constant(11.0)
+      gamma = tf.contrib.distributions.Gamma(alpha=alpha, beta=beta)
+
+      self.assertEqual(gamma.batch_shape().eval(), (5,))
+      self.assertEqual(gamma.get_batch_shape(), tf.TensorShape([5]))
+      self.assertEqual(gamma.event_shape().eval(), 1)
+      self.assertEqual(gamma.get_event_shape(), tf.TensorShape([]))
+
+  def testGammaLogPDF(self):
+    with tf.Session():
+      batch_size = 6
+      alpha = tf.constant([2.0] * batch_size)
+      beta = tf.constant([3.0] * batch_size)
+      alpha_v = 2.0
+      beta_v = 3.0
+      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
+      gamma = tf.contrib.distributions.Gamma(alpha=alpha, beta=beta)
+      expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v)
+      log_pdf = gamma.log_pdf(x)
+      self.assertEqual(log_pdf.get_shape(), (6,))
+      self.assertAllClose(log_pdf.eval(), expected_log_pdf)
+
+      pdf = gamma.pdf(x)
+      self.assertEqual(pdf.get_shape(), (6,))
+      self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf))
+
+  def testGammaLogPDFMultidimensional(self):
+    with tf.Session():
+      batch_size = 6
+      alpha = tf.constant([[2.0, 4.0]] * batch_size)
+      beta = tf.constant([[3.0, 4.0]] * batch_size)
+      alpha_v = np.array([2.0, 4.0])
+      beta_v = np.array([3.0, 4.0])
+      x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T
+      gamma = tf.contrib.distributions.Gamma(alpha=alpha, beta=beta)
+      expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v)
+      log_pdf = gamma.log_pdf(x)
+      log_pdf_values = log_pdf.eval()
+      self.assertEqual(log_pdf.get_shape(), (6, 2))
+      self.assertAllClose(log_pdf_values, expected_log_pdf)
+
+      pdf = gamma.pdf(x)
+      pdf_values = pdf.eval()
+      self.assertEqual(pdf.get_shape(), (6, 2))
+      self.assertAllClose(pdf_values, np.exp(expected_log_pdf))
+
+  def testGammaLogPDFMultidimensionalBroadcasting(self):
+    with tf.Session():
+      batch_size = 6
+      alpha = tf.constant([[2.0, 4.0]] * batch_size)
+      beta = tf.constant(3.0)
+      alpha_v = np.array([2.0, 4.0])
+      beta_v = 3.0
+      x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T
+      gamma = tf.contrib.distributions.Gamma(alpha=alpha, beta=beta)
+      expected_log_pdf = stats.gamma.logpdf(x, alpha_v, scale=1 / beta_v)
+      log_pdf = gamma.log_pdf(x)
+      log_pdf_values = log_pdf.eval()
+      self.assertEqual(log_pdf.get_shape(), (6, 2))
+      self.assertAllClose(log_pdf_values, expected_log_pdf)
+
+      pdf = gamma.pdf(x)
+      pdf_values = pdf.eval()
+      self.assertEqual(pdf.get_shape(), (6, 2))
+      self.assertAllClose(pdf_values, np.exp(expected_log_pdf))
+
+  def testGammaCDF(self):
+    with tf.Session():
+      batch_size = 6
+      alpha = tf.constant([2.0] * batch_size)
+      beta = tf.constant([3.0] * batch_size)
+      alpha_v = 2.0
+      beta_v = 3.0
+      x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
+
+      gamma = tf.contrib.distributions.Gamma(alpha=alpha, beta=beta)
+      expected_cdf = stats.gamma.cdf(x, alpha_v, scale=1 / beta_v)
+
+      cdf = gamma.cdf(x)
+      self.assertEqual(cdf.get_shape(), (6,))
+      self.assertAllClose(cdf.eval(), expected_cdf)
+
+  def testGammaMean(self):
+    with tf.Session():
+      alpha_v = np.array([1.0, 3.0, 2.5])
+      beta_v = np.array([1.0, 4.0, 5.0])
+      gamma = tf.contrib.distributions.Gamma(alpha=alpha_v, beta=beta_v)
+      expected_means = stats.gamma.mean(alpha_v, scale=1 / beta_v)
+      self.assertEqual(gamma.mean.get_shape(), (3,))
+      self.assertAllClose(gamma.mean.eval(), expected_means)
+
+  def testGammaVariance(self):
+    with tf.Session():
+      alpha_v = np.array([1.0, 3.0, 2.5])
+      beta_v = np.array([1.0, 4.0, 5.0])
+      gamma = tf.contrib.distributions.Gamma(alpha=alpha_v, beta=beta_v)
+      expected_variances = stats.gamma.var(alpha_v, scale=1 / beta_v)
+      self.assertEqual(gamma.variance.get_shape(), (3,))
+      self.assertAllClose(gamma.variance.eval(), expected_variances)
+
+  def testGammaEntropy(self):
+    with tf.Session():
+      alpha_v = np.array([1.0, 3.0, 2.5])
+      beta_v = np.array([1.0, 4.0, 5.0])
+      expected_entropy = stats.gamma.entropy(alpha_v, scale=1 / beta_v)
+      gamma = tf.contrib.distributions.Gamma(alpha=alpha_v, beta=beta_v)
+      self.assertEqual(gamma.entropy().get_shape(), (3,))
+      self.assertAllClose(gamma.entropy().eval(), expected_entropy)
+
+
+if __name__ == '__main__':
+  tf.test.main()