aboutsummaryrefslogtreecommitdiffhomepage
path: root/tensorflow/contrib/distributions/python/kernel_tests/laplace_test.py
diff options
context:
space:
mode:
Diffstat (limited to 'tensorflow/contrib/distributions/python/kernel_tests/laplace_test.py')
-rw-r--r--tensorflow/contrib/distributions/python/kernel_tests/laplace_test.py130
1 files changed, 72 insertions, 58 deletions
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/laplace_test.py b/tensorflow/contrib/distributions/python/kernel_tests/laplace_test.py
index 32a3ce4afe..312b733f3d 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/laplace_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/laplace_test.py
@@ -19,31 +19,36 @@ from __future__ import print_function
import numpy as np
from scipy import stats
-import tensorflow as tf
+from tensorflow.contrib.distributions.python.ops import laplace as laplace_lib
+from tensorflow.python.client import session
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.platform import test
-class LaplaceTest(tf.test.TestCase):
+class LaplaceTest(test.TestCase):
def testLaplaceShape(self):
with self.test_session():
- loc = tf.constant([3.0] * 5)
- scale = tf.constant(11.0)
- laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)
+ loc = constant_op.constant([3.0] * 5)
+ scale = constant_op.constant(11.0)
+ laplace = laplace_lib.Laplace(loc=loc, scale=scale)
self.assertEqual(laplace.batch_shape().eval(), (5,))
- self.assertEqual(laplace.get_batch_shape(), tf.TensorShape([5]))
+ self.assertEqual(laplace.get_batch_shape(), tensor_shape.TensorShape([5]))
self.assertAllEqual(laplace.event_shape().eval(), [])
- self.assertEqual(laplace.get_event_shape(), tf.TensorShape([]))
+ self.assertEqual(laplace.get_event_shape(), tensor_shape.TensorShape([]))
def testLaplaceLogPDF(self):
with self.test_session():
batch_size = 6
- loc = tf.constant([2.0] * batch_size)
- scale = tf.constant([3.0] * batch_size)
+ loc = constant_op.constant([2.0] * batch_size)
+ scale = constant_op.constant([3.0] * batch_size)
loc_v = 2.0
scale_v = 3.0
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
- laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)
+ laplace = laplace_lib.Laplace(loc=loc, scale=scale)
expected_log_pdf = stats.laplace.logpdf(x, loc_v, scale=scale_v)
log_pdf = laplace.log_pdf(x)
self.assertEqual(log_pdf.get_shape(), (6,))
@@ -56,12 +61,12 @@ class LaplaceTest(tf.test.TestCase):
def testLaplaceLogPDFMultidimensional(self):
with self.test_session():
batch_size = 6
- loc = tf.constant([[2.0, 4.0]] * batch_size)
- scale = tf.constant([[3.0, 4.0]] * batch_size)
+ loc = constant_op.constant([[2.0, 4.0]] * batch_size)
+ scale = constant_op.constant([[3.0, 4.0]] * batch_size)
loc_v = np.array([2.0, 4.0])
scale_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
- laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)
+ laplace = laplace_lib.Laplace(loc=loc, scale=scale)
expected_log_pdf = stats.laplace.logpdf(x, loc_v, scale=scale_v)
log_pdf = laplace.log_pdf(x)
log_pdf_values = log_pdf.eval()
@@ -76,12 +81,12 @@ class LaplaceTest(tf.test.TestCase):
def testLaplaceLogPDFMultidimensionalBroadcasting(self):
with self.test_session():
batch_size = 6
- loc = tf.constant([[2.0, 4.0]] * batch_size)
- scale = tf.constant(3.0)
+ loc = constant_op.constant([[2.0, 4.0]] * batch_size)
+ scale = constant_op.constant(3.0)
loc_v = np.array([2.0, 4.0])
scale_v = 3.0
x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T
- laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)
+ laplace = laplace_lib.Laplace(loc=loc, scale=scale)
expected_log_pdf = stats.laplace.logpdf(x, loc_v, scale=scale_v)
log_pdf = laplace.log_pdf(x)
log_pdf_values = log_pdf.eval()
@@ -96,13 +101,13 @@ class LaplaceTest(tf.test.TestCase):
def testLaplaceCDF(self):
with self.test_session():
batch_size = 6
- loc = tf.constant([2.0] * batch_size)
- scale = tf.constant([3.0] * batch_size)
+ loc = constant_op.constant([2.0] * batch_size)
+ scale = constant_op.constant([3.0] * batch_size)
loc_v = 2.0
scale_v = 3.0
x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)
- laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)
+ laplace = laplace_lib.Laplace(loc=loc, scale=scale)
expected_cdf = stats.laplace.cdf(x, loc_v, scale=scale_v)
cdf = laplace.cdf(x)
@@ -113,7 +118,7 @@ class LaplaceTest(tf.test.TestCase):
with self.test_session():
loc_v = np.array([1.0, 3.0, 2.5])
scale_v = np.array([1.0, 4.0, 5.0])
- laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)
+ laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
expected_means = stats.laplace.mean(loc_v, scale=scale_v)
self.assertEqual(laplace.mean().get_shape(), (3,))
self.assertAllClose(laplace.mean().eval(), expected_means)
@@ -122,7 +127,7 @@ class LaplaceTest(tf.test.TestCase):
with self.test_session():
loc_v = np.array([0.5, 3.0, 2.5])
scale_v = np.array([1.0, 4.0, 5.0])
- laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)
+ laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
self.assertEqual(laplace.mode().get_shape(), (3,))
self.assertAllClose(laplace.mode().eval(), loc_v)
@@ -130,7 +135,7 @@ class LaplaceTest(tf.test.TestCase):
with self.test_session():
loc_v = np.array([1.0, 3.0, 2.5])
scale_v = np.array([1.0, 4.0, 5.0])
- laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)
+ laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
expected_variances = stats.laplace.var(loc_v, scale=scale_v)
self.assertEqual(laplace.variance().get_shape(), (3,))
self.assertAllClose(laplace.variance().eval(), expected_variances)
@@ -139,7 +144,7 @@ class LaplaceTest(tf.test.TestCase):
with self.test_session():
loc_v = np.array([1.0, 3.0, 2.5])
scale_v = np.array([1.0, 4.0, 5.0])
- laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)
+ laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
expected_std = stats.laplace.std(loc_v, scale=scale_v)
self.assertEqual(laplace.std().get_shape(), (3,))
self.assertAllClose(laplace.std().eval(), expected_std)
@@ -149,35 +154,40 @@ class LaplaceTest(tf.test.TestCase):
loc_v = np.array([1.0, 3.0, 2.5])
scale_v = np.array([1.0, 4.0, 5.0])
expected_entropy = stats.laplace.entropy(loc_v, scale=scale_v)
- laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)
+ laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
self.assertEqual(laplace.entropy().get_shape(), (3,))
self.assertAllClose(laplace.entropy().eval(), expected_entropy)
def testLaplaceSample(self):
- with tf.Session():
+ with session.Session():
loc_v = 4.0
scale_v = 3.0
- loc = tf.constant(loc_v)
- scale = tf.constant(scale_v)
+ loc = constant_op.constant(loc_v)
+ scale = constant_op.constant(scale_v)
n = 100000
- laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)
+ laplace = laplace_lib.Laplace(loc=loc, scale=scale)
samples = laplace.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n,))
self.assertEqual(sample_values.shape, (n,))
- self.assertAllClose(sample_values.mean(),
- stats.laplace.mean(loc_v, scale=scale_v),
- rtol=0.05, atol=0.)
- self.assertAllClose(sample_values.var(),
- stats.laplace.var(loc_v, scale=scale_v),
- rtol=0.05, atol=0.)
+ self.assertAllClose(
+ sample_values.mean(),
+ stats.laplace.mean(
+ loc_v, scale=scale_v),
+ rtol=0.05,
+ atol=0.)
+ self.assertAllClose(
+ sample_values.var(),
+ stats.laplace.var(loc_v, scale=scale_v),
+ rtol=0.05,
+ atol=0.)
self.assertTrue(self._kstest(loc_v, scale_v, sample_values))
def testLaplaceSampleMultiDimensional(self):
- with tf.Session():
+ with session.Session():
loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100
scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1
- laplace = tf.contrib.distributions.Laplace(loc=loc_v, scale=scale_v)
+ laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v)
n = 10000
samples = laplace.sample(n, seed=137)
sample_values = samples.eval()
@@ -188,12 +198,15 @@ class LaplaceTest(tf.test.TestCase):
scale_bc = scale_v + zeros
self.assertAllClose(
sample_values.mean(axis=0),
- stats.laplace.mean(loc_bc, scale=scale_bc),
- rtol=0.35, atol=0.)
+ stats.laplace.mean(
+ loc_bc, scale=scale_bc),
+ rtol=0.35,
+ atol=0.)
self.assertAllClose(
sample_values.var(axis=0),
stats.laplace.var(loc_bc, scale=scale_bc),
- rtol=0.10, atol=0.)
+ rtol=0.10,
+ atol=0.)
fails = 0
trials = 0
for ai, a in enumerate(np.reshape(loc_v, [-1])):
@@ -210,9 +223,8 @@ class LaplaceTest(tf.test.TestCase):
return ks < 0.02
def testLaplacePdfOfSampleMultiDims(self):
- with tf.Session() as sess:
- laplace = tf.contrib.distributions.Laplace(
- loc=[7., 11.], scale=[[5.], [6.]])
+ with session.Session() as sess:
+ laplace = laplace_lib.Laplace(loc=[7., 11.], scale=[[5.], [6.]])
num = 50000
samples = laplace.sample(num, seed=137)
pdfs = laplace.pdf(samples)
@@ -220,15 +232,17 @@ class LaplaceTest(tf.test.TestCase):
self.assertEqual(samples.get_shape(), (num, 2, 2))
self.assertEqual(pdfs.get_shape(), (num, 2, 2))
self.assertAllClose(
- stats.laplace.mean([[7., 11.], [7., 11.]],
- scale=np.array([[5., 5.], [6., 6.]])),
+ stats.laplace.mean(
+ [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])),
sample_vals.mean(axis=0),
- rtol=0.05, atol=0.)
+ rtol=0.05,
+ atol=0.)
self.assertAllClose(
stats.laplace.var([[7., 11.], [7., 11.]],
scale=np.array([[5., 5.], [6., 6.]])),
sample_vals.var(axis=0),
- rtol=0.05, atol=0.)
+ rtol=0.05,
+ atol=0.)
self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02)
self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02)
self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02)
@@ -246,27 +260,27 @@ class LaplaceTest(tf.test.TestCase):
def testLaplaceNonPositiveInitializationParamsRaises(self):
with self.test_session():
- loc_v = tf.constant(0.0, name="loc")
- scale_v = tf.constant(-1.0, name="scale")
- laplace = tf.contrib.distributions.Laplace(
+ loc_v = constant_op.constant(0.0, name="loc")
+ scale_v = constant_op.constant(-1.0, name="scale")
+ laplace = laplace_lib.Laplace(
loc=loc_v, scale=scale_v, validate_args=True)
with self.assertRaisesOpError("scale"):
laplace.mean().eval()
- loc_v = tf.constant(1.0, name="loc")
- scale_v = tf.constant(0.0, name="scale")
- laplace = tf.contrib.distributions.Laplace(
+ loc_v = constant_op.constant(1.0, name="loc")
+ scale_v = constant_op.constant(0.0, name="scale")
+ laplace = laplace_lib.Laplace(
loc=loc_v, scale=scale_v, validate_args=True)
with self.assertRaisesOpError("scale"):
laplace.mean().eval()
def testLaplaceWithSoftplusScale(self):
with self.test_session():
- loc_v = tf.constant([0.0, 1.0], name="loc")
- scale_v = tf.constant([-1.0, 2.0], name="scale")
- laplace = tf.contrib.distributions.LaplaceWithSoftplusScale(
- loc=loc_v, scale=scale_v)
- self.assertAllClose(tf.nn.softplus(scale_v).eval(), laplace.scale.eval())
+ loc_v = constant_op.constant([0.0, 1.0], name="loc")
+ scale_v = constant_op.constant([-1.0, 2.0], name="scale")
+ laplace = laplace_lib.LaplaceWithSoftplusScale(loc=loc_v, scale=scale_v)
+ self.assertAllClose(nn_ops.softplus(scale_v).eval(), laplace.scale.eval())
self.assertAllClose(loc_v.eval(), laplace.loc.eval())
+
if __name__ == "__main__":
- tf.test.main()
+ test.main()
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-29 12:36:10 +0000