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s/sample_n/sample/ in most unittests.

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Change: 138670422
This commit is contained in:
Joshua V. Dillon 2016-11-09 12:24:31 -08:00 committed by TensorFlower Gardener
parent 641894039e
commit 56b09d13c8
14 changed files with 59 additions and 59 deletions

18
tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py
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@ -89,7 +89,7 @@ class BernoulliTest(tf.test.TestCase):
def testDtype(self):
dist = make_bernoulli([])
self.assertEqual(dist.dtype, tf.int32)
self.assertEqual(dist.dtype, dist.sample_n(5).dtype)
self.assertEqual(dist.dtype, dist.sample(5).dtype)
self.assertEqual(dist.dtype, dist.mode().dtype)
self.assertEqual(dist.p.dtype, dist.mean().dtype)
self.assertEqual(dist.p.dtype, dist.variance().dtype)
@ -100,7 +100,7 @@ class BernoulliTest(tf.test.TestCase):
dist64 = make_bernoulli([], tf.int64)
self.assertEqual(dist64.dtype, tf.int64)
self.assertEqual(dist64.dtype, dist64.sample_n(5).dtype)
self.assertEqual(dist64.dtype, dist64.sample(5).dtype)
self.assertEqual(dist64.dtype, dist64.mode().dtype)
def _testPmf(self, **kwargs):
@ -186,7 +186,7 @@ class BernoulliTest(tf.test.TestCase):
p = [0.2, 0.6]
dist = tf.contrib.distributions.Bernoulli(p=p)
n = 100000
samples = dist.sample_n(n)
samples = dist.sample(n)
samples.set_shape([n, 2])
self.assertEqual(samples.dtype, tf.int32)
sample_values = samples.eval()
@ -201,7 +201,7 @@ class BernoulliTest(tf.test.TestCase):
# owing to mismatched types. b/30940152
dist = tf.contrib.distributions.Bernoulli(np.log([.2, .4]))
self.assertAllEqual(
(1, 2), dist.sample_n(1, seed=42).get_shape().as_list())
(1, 2), dist.sample(1, seed=42).get_shape().as_list())
def testSampleActsLikeSampleN(self):
with self.test_session() as sess:
@ -210,12 +210,12 @@ class BernoulliTest(tf.test.TestCase):
n = 1000
seed = 42
self.assertAllEqual(dist.sample(n, seed).eval(),
dist.sample_n(n, seed).eval())
dist.sample(n, seed).eval())
n = tf.placeholder(tf.int32)
sample, sample_n = sess.run([dist.sample(n, seed),
dist.sample_n(n, seed)],
feed_dict={n: 1000})
self.assertAllEqual(sample, sample_n)
sample, sample = sess.run([dist.sample(n, seed),
dist.sample(n, seed)],
feed_dict={n: 1000})
self.assertAllEqual(sample, sample)
def testMean(self):
with self.test_session():

8
tensorflow/contrib/distributions/python/kernel_tests/beta_test.py
View File

@ -228,7 +228,7 @@ class BetaTest(tf.test.TestCase):
b = 2.
beta = tf.contrib.distributions.Beta(a, b)
n = tf.constant(100000)
samples = beta.sample_n(n)
samples = beta.sample(n)
sample_values = samples.eval()
self.assertEqual(sample_values.shape, (100000,))
self.assertFalse(np.any(sample_values < 0.0))
@ -254,11 +254,11 @@ class BetaTest(tf.test.TestCase):
tf.set_random_seed(654321)
beta1 = tf.contrib.distributions.Beta(a=a_val, b=b_val, name="beta1")
samples1 = beta1.sample_n(n_val, seed=123456).eval()
samples1 = beta1.sample(n_val, seed=123456).eval()
tf.set_random_seed(654321)
beta2 = tf.contrib.distributions.Beta(a=a_val, b=b_val, name="beta2")
samples2 = beta2.sample_n(n_val, seed=123456).eval()
samples2 = beta2.sample(n_val, seed=123456).eval()
self.assertAllClose(samples1, samples2)
@ -268,7 +268,7 @@ class BetaTest(tf.test.TestCase):
b = np.random.rand(3, 2, 2).astype(np.float32)
beta = tf.contrib.distributions.Beta(a, b)
n = tf.constant(100000)
samples = beta.sample_n(n)
samples = beta.sample(n)
sample_values = samples.eval()
self.assertEqual(sample_values.shape, (100000, 3, 2, 2))
self.assertFalse(np.any(sample_values < 0.0))

6
tensorflow/contrib/distributions/python/kernel_tests/categorical_test.py
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@ -73,11 +73,11 @@ class CategoricalTest(tf.test.TestCase):
def testDtype(self):
dist = make_categorical([], 5, dtype=tf.int32)
self.assertEqual(dist.dtype, tf.int32)
self.assertEqual(dist.dtype, dist.sample_n(5).dtype)
self.assertEqual(dist.dtype, dist.sample(5).dtype)
self.assertEqual(dist.dtype, dist.mode().dtype)
dist = make_categorical([], 5, dtype=tf.int64)
self.assertEqual(dist.dtype, tf.int64)
self.assertEqual(dist.dtype, dist.sample_n(5).dtype)
self.assertEqual(dist.dtype, dist.sample(5).dtype)
self.assertEqual(dist.dtype, dist.mode().dtype)
self.assertEqual(dist.p.dtype, tf.float32)
self.assertEqual(dist.logits.dtype, tf.float32)
@ -140,7 +140,7 @@ class CategoricalTest(tf.test.TestCase):
histograms = [[[0.2, 0.8], [0.4, 0.6]]]
dist = tf.contrib.distributions.Categorical(tf.log(histograms) - 50.)
n = 10000
samples = dist.sample_n(n, seed=123)
samples = dist.sample(n, seed=123)
samples.set_shape([n, 1, 2])
self.assertEqual(samples.dtype, tf.int32)
sample_values = samples.eval()

2
tensorflow/contrib/distributions/python/kernel_tests/dirichlet_test.py
View File

@ -182,7 +182,7 @@ class DirichletTest(tf.test.TestCase):
alpha = [1., 2]
dirichlet = tf.contrib.distributions.Dirichlet(alpha)
n = tf.constant(100000)
samples = dirichlet.sample_n(n)
samples = dirichlet.sample(n)
sample_values = samples.eval()
self.assertEqual(sample_values.shape, (100000, 2))
self.assertTrue(np.all(sample_values > 0.0))

4
tensorflow/contrib/distributions/python/kernel_tests/exponential_test.py
View File

@ -87,7 +87,7 @@ class ExponentialTest(tf.test.TestCase):
n = tf.constant(100000)
exponential = tf.contrib.distributions.Exponential(lam=lam)
samples = exponential.sample_n(n, seed=137)
samples = exponential.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(sample_values.shape, (100000, 2))
self.assertFalse(np.any(sample_values < 0.0))
@ -106,7 +106,7 @@ class ExponentialTest(tf.test.TestCase):
exponential = tf.contrib.distributions.Exponential(lam=lam)
n = 100000
samples = exponential.sample_n(n, seed=138)
samples = exponential.sample(n, seed=138)
self.assertEqual(samples.get_shape(), (n, batch_size, 2))
sample_values = samples.eval()

8
tensorflow/contrib/distributions/python/kernel_tests/gamma_test.py
View File

@ -185,7 +185,7 @@ class GammaTest(tf.test.TestCase):
beta = tf.constant(beta_v)
n = 100000
gamma = tf.contrib.distributions.Gamma(alpha=alpha, beta=beta)
samples = gamma.sample_n(n, seed=137)
samples = gamma.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n,))
self.assertEqual(sample_values.shape, (n,))
@ -208,7 +208,7 @@ class GammaTest(tf.test.TestCase):
beta = tf.constant(beta_v)
n = 100000
gamma = tf.contrib.distributions.Gamma(alpha=alpha, beta=beta)
samples = gamma.sample_n(n, seed=137)
samples = gamma.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n,))
self.assertEqual(sample_values.shape, (n,))
@ -228,7 +228,7 @@ class GammaTest(tf.test.TestCase):
beta_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1
gamma = tf.contrib.distributions.Gamma(alpha=alpha_v, beta=beta_v)
n = 10000
samples = gamma.sample_n(n, seed=137)
samples = gamma.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n, 10, 100))
self.assertEqual(sample_values.shape, (n, 10, 100))
@ -263,7 +263,7 @@ class GammaTest(tf.test.TestCase):
with tf.Session() as sess:
gamma = tf.contrib.distributions.Gamma(alpha=[7., 11.], beta=[[5.], [6.]])
num = 50000
samples = gamma.sample_n(num, seed=137)
samples = gamma.sample(num, seed=137)
pdfs = gamma.pdf(samples)
sample_vals, pdf_vals = sess.run([samples, pdfs])
self.assertEqual(samples.get_shape(), (num, 2, 2))

6
tensorflow/contrib/distributions/python/kernel_tests/inverse_gamma_test.py
View File

@ -203,7 +203,7 @@ class InverseGammaTest(tf.test.TestCase):
beta = tf.constant(beta_v)
n = 100000
inv_gamma = tf.contrib.distributions.InverseGamma(alpha=alpha, beta=beta)
samples = inv_gamma.sample_n(n, seed=137)
samples = inv_gamma.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n,))
self.assertEqual(sample_values.shape, (n,))
@ -222,7 +222,7 @@ class InverseGammaTest(tf.test.TestCase):
inv_gamma = tf.contrib.distributions.InverseGamma(alpha=alpha_v,
beta=beta_v)
n = 10000
samples = inv_gamma.sample_n(n, seed=137)
samples = inv_gamma.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n, 10, 100))
self.assertEqual(sample_values.shape, (n, 10, 100))
@ -257,7 +257,7 @@ class InverseGammaTest(tf.test.TestCase):
inv_gamma = tf.contrib.distributions.InverseGamma(alpha=[7., 11.],
beta=[[5.], [6.]])
num = 50000
samples = inv_gamma.sample_n(num, seed=137)
samples = inv_gamma.sample(num, seed=137)
pdfs = inv_gamma.pdf(samples)
sample_vals, pdf_vals = sess.run([samples, pdfs])
self.assertEqual(samples.get_shape(), (num, 2, 2))

6
tensorflow/contrib/distributions/python/kernel_tests/laplace_test.py
View File

@ -161,7 +161,7 @@ class LaplaceTest(tf.test.TestCase):
scale = tf.constant(scale_v)
n = 100000
laplace = tf.contrib.distributions.Laplace(loc=loc, scale=scale)
samples = laplace.sample_n(n, seed=137)
samples = laplace.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n,))
self.assertEqual(sample_values.shape, (n,))
@ -179,7 +179,7 @@ class LaplaceTest(tf.test.TestCase):
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)
n = 10000
samples = laplace.sample_n(n, seed=137)
samples = laplace.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (n, 10, 100))
self.assertEqual(sample_values.shape, (n, 10, 100))
@ -214,7 +214,7 @@ class LaplaceTest(tf.test.TestCase):
laplace = tf.contrib.distributions.Laplace(
loc=[7., 11.], scale=[[5.], [6.]])
num = 50000
samples = laplace.sample_n(num, seed=137)
samples = laplace.sample(num, seed=137)
pdfs = laplace.pdf(samples)
sample_vals, pdf_vals = sess.run([samples, pdfs])
self.assertEqual(samples.get_shape(), (num, 2, 2))

10
tensorflow/contrib/distributions/python/kernel_tests/mvn_test.py
View File

@ -111,7 +111,7 @@ class MultivariateNormalDiagTest(tf.test.TestCase):
diag = [1.0, 2.0]
with self.test_session():
dist = distributions.MultivariateNormalDiag(mu, diag)
samps = dist.sample_n(1000, seed=0).eval()
samps = dist.sample(1000, seed=0).eval()
cov_mat = tf.matrix_diag(diag).eval()**2
self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
@ -122,7 +122,7 @@ class MultivariateNormalDiagTest(tf.test.TestCase):
diag = [-1.0, -2.0]
with self.test_session():
dist = distributions.MultivariateNormalDiagWithSoftplusStDev(mu, diag)
samps = dist.sample_n(1000, seed=0).eval()
samps = dist.sample(1000, seed=0).eval()
cov_mat = tf.matrix_diag(tf.nn.softplus(diag)).eval()**2
self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
@ -177,7 +177,7 @@ class MultivariateNormalDiagPlusVDVTTest(tf.test.TestCase):
with self.test_session():
dist = distributions.MultivariateNormalDiagPlusVDVT(mu, diag_large, v)
samps = dist.sample_n(1000, seed=0).eval()
samps = dist.sample(1000, seed=0).eval()
cov_mat = dist.sigma.eval()
self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
@ -318,7 +318,7 @@ class MultivariateNormalCholeskyTest(tf.test.TestCase):
n = tf.constant(100000)
mvn = distributions.MultivariateNormalCholesky(mu, chol)
samples = mvn.sample_n(n, seed=137)
samples = mvn.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (100000, 2))
self.assertAllClose(sample_values.mean(axis=0), mu, atol=1e-2)
@ -355,7 +355,7 @@ class MultivariateNormalCholeskyTest(tf.test.TestCase):
mvn = distributions.MultivariateNormalCholesky(mu, chol)
n = tf.constant(100000)
samples = mvn.sample_n(n, seed=137)
samples = mvn.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (100000, 3, 5, 2))

4
tensorflow/contrib/distributions/python/kernel_tests/normal_test.py
View File

@ -290,7 +290,7 @@ class NormalTest(tf.test.TestCase):
sigma_v = np.sqrt(3.0)
n = tf.constant(100000)
normal = tf.contrib.distributions.Normal(mu=mu, sigma=sigma)
samples = normal.sample_n(n)
samples = normal.sample(n)
sample_values = samples.eval()
# Note that the standard error for the sample mean is ~ sigma / sqrt(n).
# The sample variance similarly is dependent on sigma and n.
@ -323,7 +323,7 @@ class NormalTest(tf.test.TestCase):
sigma_v = [np.sqrt(2.0), np.sqrt(3.0)]
n = tf.constant(100000)
normal = tf.contrib.distributions.Normal(mu=mu, sigma=sigma)
samples = normal.sample_n(n)
samples = normal.sample(n)
sample_values = samples.eval()
# Note that the standard error for the sample mean is ~ sigma / sqrt(n).
# The sample variance similarly is dependent on sigma and n.

16
tensorflow/contrib/distributions/python/kernel_tests/student_t_test.py
View File

@ -110,7 +110,7 @@ class StudentTTest(tf.test.TestCase):
sigma_v = np.sqrt(10.0)
n = tf.constant(200000)
student = tf.contrib.distributions.StudentT(df=df, mu=mu, sigma=sigma)
samples = student.sample_n(n)
samples = student.sample(n)
sample_values = samples.eval()
n_val = 200000
self.assertEqual(sample_values.shape, (n_val,))
@ -134,12 +134,12 @@ class StudentTTest(tf.test.TestCase):
tf.set_random_seed(654321)
student = tf.contrib.distributions.StudentT(
df=df, mu=mu, sigma=sigma, name="student_t1")
samples1 = student.sample_n(n, seed=123456).eval()
samples1 = student.sample(n, seed=123456).eval()
tf.set_random_seed(654321)
student2 = tf.contrib.distributions.StudentT(
df=df, mu=mu, sigma=sigma, name="student_t2")
samples2 = student2.sample_n(n, seed=123456).eval()
samples2 = student2.sample(n, seed=123456).eval()
self.assertAllClose(samples1, samples2)
@ -149,7 +149,7 @@ class StudentTTest(tf.test.TestCase):
df = tf.constant(df_v)
n = tf.constant(200000)
student = tf.contrib.distributions.StudentT(df=df, mu=1.0, sigma=1.0)
samples = student.sample_n(n)
samples = student.sample(n)
sample_values = samples.eval()
n_val = 200000
self.assertEqual(sample_values.shape, (n_val, 4))
@ -166,7 +166,7 @@ class StudentTTest(tf.test.TestCase):
sigma_v = [np.sqrt(10.0), np.sqrt(15.0)]
n = tf.constant(200000)
student = tf.contrib.distributions.StudentT(df=df, mu=mu, sigma=sigma)
samples = student.sample_n(n)
samples = student.sample(n)
sample_values = samples.eval()
self.assertEqual(samples.get_shape(), (200000, batch_size, 2))
self.assertAllClose(
@ -208,7 +208,7 @@ class StudentTTest(tf.test.TestCase):
self.assertEqual(student.entropy().get_shape(), (3,))
self.assertEqual(student.log_pdf(2.).get_shape(), (3,))
self.assertEqual(student.pdf(2.).get_shape(), (3,))
self.assertEqual(student.sample_n(37).get_shape(), (37, 3,))
self.assertEqual(student.sample(37).get_shape(), (37, 3,))
_check(tf.contrib.distributions.StudentT(df=[2., 3., 4.,], mu=2., sigma=1.))
_check(tf.contrib.distributions.StudentT(df=7., mu=[2., 3., 4.,], sigma=1.))
@ -377,7 +377,7 @@ class StudentTTest(tf.test.TestCase):
with self.test_session() as sess:
student = tf.contrib.distributions.StudentT(df=3., mu=np.pi, sigma=1.)
num = 20000
samples = student.sample_n(num)
samples = student.sample(num)
pdfs = student.pdf(samples)
mean = student.mean()
mean_pdf = student.pdf(student.mean())
@ -398,7 +398,7 @@ class StudentTTest(tf.test.TestCase):
mu=[[5.], [6.]],
sigma=3.)
num = 50000
samples = student.sample_n(num)
samples = student.sample(num)
pdfs = student.pdf(samples)
sample_vals, pdf_vals = sess.run([samples, pdfs])
self.assertEqual(samples.get_shape(), (num, 2, 2))

6
tensorflow/contrib/distributions/python/kernel_tests/transformed_distribution_test.py
View File

@ -70,7 +70,7 @@ class TransformedDistributionTest(tf.test.TestCase):
sp_dist = stats.lognorm(s=sigma, scale=np.exp(mu))
# sample
sample = log_normal.sample_n(100000, seed=235)
sample = log_normal.sample(100000, seed=235)
with self.test_session(graph=g):
self.assertAllClose(sp_dist.mean(), np.mean(sample.eval()),
atol=0.0, rtol=0.05)
@ -98,7 +98,7 @@ class TransformedDistributionTest(tf.test.TestCase):
distribution=distributions.Normal(mu=mu, sigma=sigma),
bijector=bijectors.Exp(event_ndims=0))
sample = log_normal.sample_n(1)
sample = log_normal.sample(1)
sample_val, log_pdf_val = sess.run([sample, log_normal.log_pdf(sample)])
self.assertAllClose(
stats.lognorm.logpdf(sample_val, s=sigma,
@ -113,7 +113,7 @@ class TransformedDistributionTest(tf.test.TestCase):
bijector=_ChooseLocation(loc=[-100., 100.]))
z = [-1, +1, -1, -1, +1]
self.assertAllClose(
np.sign(conditional_normal.sample_n(
np.sign(conditional_normal.sample(
5, bijector_kwargs={"z": z}).eval()), z)
def testShapeChangingBijector(self):

6
tensorflow/contrib/distributions/python/kernel_tests/uniform_test.py
View File

@ -135,7 +135,7 @@ class UniformTest(tf.test.TestCase):
n = tf.constant(100000)
uniform = tf.contrib.distributions.Uniform(a=a, b=b)
samples = uniform.sample_n(n, seed=137)
samples = uniform.sample(n, seed=137)
sample_values = samples.eval()
self.assertEqual(sample_values.shape, (100000, 2))
self.assertAllClose(sample_values[::, 0].mean(), (b_v + a1_v) / 2,
@ -160,7 +160,7 @@ class UniformTest(tf.test.TestCase):
n_v = 100000
n = tf.constant(n_v)
samples = uniform.sample_n(n)
samples = uniform.sample(n)
self.assertEqual(samples.get_shape(), (n_v, batch_size, 2))
sample_values = samples.eval()
@ -221,7 +221,7 @@ class UniformTest(tf.test.TestCase):
a = 10.0
b = [11.0, 100.0]
uniform = tf.contrib.distributions.Uniform(a, b)
self.assertTrue(tf.reduce_all(uniform.pdf(uniform.sample_n(10)) > 0).eval(
self.assertTrue(tf.reduce_all(uniform.pdf(uniform.sample(10)) > 0).eval(
))
def testUniformBroadcasting(self):

18
tensorflow/contrib/distributions/python/kernel_tests/wishart_test.py
View File

@ -106,23 +106,23 @@ class WishartCholeskyTest(tf.test.TestCase):
chol_w = distributions.WishartCholesky(
df, chol(scale), cholesky_input_output_matrices=False)
x = chol_w.sample_n(1, seed=42).eval()
x = chol_w.sample(1, seed=42).eval()
chol_x = [chol(x[0])]
full_w = distributions.WishartFull(
df, scale, cholesky_input_output_matrices=False)
self.assertAllClose(x, full_w.sample_n(1, seed=42).eval())
self.assertAllClose(x, full_w.sample(1, seed=42).eval())
chol_w_chol = distributions.WishartCholesky(
df, chol(scale), cholesky_input_output_matrices=True)
self.assertAllClose(chol_x, chol_w_chol.sample_n(1, seed=42).eval())
eigen_values = tf.matrix_diag_part(chol_w_chol.sample_n(1000, seed=42))
self.assertAllClose(chol_x, chol_w_chol.sample(1, seed=42).eval())
eigen_values = tf.matrix_diag_part(chol_w_chol.sample(1000, seed=42))
np.testing.assert_array_less(0., eigen_values.eval())
full_w_chol = distributions.WishartFull(
df, scale, cholesky_input_output_matrices=True)
self.assertAllClose(chol_x, full_w_chol.sample_n(1, seed=42).eval())
eigen_values = tf.matrix_diag_part(full_w_chol.sample_n(1000, seed=42))
self.assertAllClose(chol_x, full_w_chol.sample(1, seed=42).eval())
eigen_values = tf.matrix_diag_part(full_w_chol.sample(1000, seed=42))
np.testing.assert_array_less(0., eigen_values.eval())
# Check first and second moments.
@ -131,7 +131,7 @@ class WishartCholeskyTest(tf.test.TestCase):
df=df,
scale=chol(make_pd(1., 3)),
cholesky_input_output_matrices=False)
x = chol_w.sample_n(10000, seed=42)
x = chol_w.sample(10000, seed=42)
self.assertAllEqual((10000, 3, 3), x.get_shape())
moment1_estimate = tf.reduce_mean(x, reduction_indices=[0]).eval()
@ -161,7 +161,7 @@ class WishartCholeskyTest(tf.test.TestCase):
scale=chol(make_pd(1., 3)),
cholesky_input_output_matrices=False,
name="wishart1")
samples1 = chol_w1.sample_n(n_val, seed=123456).eval()
samples1 = chol_w1.sample(n_val, seed=123456).eval()
tf.set_random_seed(654321)
chol_w2 = distributions.WishartCholesky(
@ -169,7 +169,7 @@ class WishartCholeskyTest(tf.test.TestCase):
scale=chol(make_pd(1., 3)),
cholesky_input_output_matrices=False,
name="wishart2")
samples2 = chol_w2.sample_n(n_val, seed=123456).eval()
samples2 = chol_w2.sample(n_val, seed=123456).eval()
self.assertAllClose(samples1, samples2)