def testKumaraswamyModeInvalid(self):
with session.Session():
a = np.array([1., 2, 3])
b = np.array([2., 4, 1.2])
dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False)
with self.assertRaisesOpError("Condition x < y.*"):
dist.mode().eval()
a = np.array([2., 2, 3])
b = np.array([1., 4, 1.2])
dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False)
with self.assertRaisesOpError("Condition x < y.*"):
dist.mode().eval()
def testKumaraswamyModeInvalid(self):
with session.Session():
a = np.array([1., 2, 3])
b = np.array([2., 4, 1.2])
dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False)
with self.assertRaisesOpError(
"Mode undefined for concentration1 <= 1."):
dist.mode().eval()
a = np.array([2., 2, 3])
b = np.array([1., 4, 1.2])
dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=False)
with self.assertRaisesOpError(
"Mode undefined for concentration0 <= 1."):
dist.mode().eval()
def testKumaraswamySample(self):
with self.test_session():
a = 1.
b = 2.
kumaraswamy = kumaraswamy_lib.Kumaraswamy(a, b)
n = constant_op.constant(100000)
samples = kumaraswamy.sample(n)
sample_values = samples.eval()
self.assertEqual(sample_values.shape, (100000,))
self.assertFalse(np.any(sample_values < 0.0))
if not stats:
return
self.assertLess(
stats.kstest(
# Kumaraswamy is a univariate distribution.
sample_values,
lambda x: _kumaraswamy_cdf(1., 2., x))[0],
0.01)
# The standard error of the sample mean is 1 / (sqrt(18 * n))
expected_mean = _kumaraswamy_moment(a, b, 1)
self.assertAllClose(sample_values.mean(axis=0), expected_mean, atol=1e-2)
expected_variance = _kumaraswamy_moment(a, b, 2) - _kumaraswamy_moment(
a, b, 1)**2
self.assertAllClose(
np.cov(sample_values, rowvar=0), expected_variance, atol=1e-1)
def testBProperty(self):
a = [[1., 2, 3]]
b = [[2., 4, 3]]
with self.test_session():
dist = kumaraswamy_lib.Kumaraswamy(a, b)
self.assertEqual([1, 3], dist.concentration0.get_shape())
self.assertAllClose(b, dist.concentration0.eval())
def testKumaraswamyMode(self):
with session.Session():
a = np.array([1.1, 2, 3])
b = np.array([2., 4, 1.2])
expected_mode = _kumaraswamy_mode(a, b)
dist = kumaraswamy_lib.Kumaraswamy(a, b)
self.assertEqual(dist.mode().get_shape(), (3,))
self.assertAllClose(expected_mode, dist.mode().eval())
def testKumaraswamySampleMultipleTimes(self):
with self.test_session():
a_val = 1.
b_val = 2.
n_val = 100
random_seed.set_random_seed(654321)
kumaraswamy1 = kumaraswamy_lib.Kumaraswamy(
concentration1=a_val, concentration0=b_val, name="kumaraswamy1")
samples1 = kumaraswamy1.sample(n_val, seed=123456).eval()
random_seed.set_random_seed(654321)
kumaraswamy2 = kumaraswamy_lib.Kumaraswamy(
concentration1=a_val, concentration0=b_val, name="kumaraswamy2")
samples2 = kumaraswamy2.sample(n_val, seed=123456).eval()
self.assertAllClose(samples1, samples2)
def testPdfXStretchedInBroadcastWhenLowerRank(self):
with self.test_session():
a = [[1., 2], [2., 3]]
b = [[1., 2], [2., 3]]
x = [.5, .5]
pdf = kumaraswamy_lib.Kumaraswamy(a, b).prob(x)
expected_pdf = _kumaraswamy_pdf(a, b, x)
self.assertAllClose(expected_pdf, pdf.eval())
self.assertEqual((2, 2), pdf.get_shape())
def testSimpleShapes(self):
with self.test_session():
a = np.random.rand(3)
b = np.random.rand(3)
dist = kumaraswamy_lib.Kumaraswamy(a, b)
self.assertAllEqual([], dist.event_shape_tensor().eval())
self.assertAllEqual([3], dist.batch_shape_tensor().eval())
self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape)
self.assertEqual(tensor_shape.TensorShape([3]), dist.batch_shape)
def testComplexShapesBroadcast(self):
with self.test_session():
a = np.random.rand(3, 2, 2)
b = np.random.rand(2, 2)
dist = kumaraswamy_lib.Kumaraswamy(a, b)
self.assertAllEqual([], dist.event_shape_tensor().eval())
self.assertAllEqual([3, 2, 2], dist.batch_shape_tensor().eval())
self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape)
self.assertEqual(tensor_shape.TensorShape([3, 2, 2]), dist.batch_shape)
def testKumaraswamyModeEnableAllowNanStats(self):
with session.Session():
a = np.array([1., 2, 3])
b = np.array([2., 4, 1.2])
dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=True)
expected_mode = _kumaraswamy_mode(a, b)
expected_mode[0] = np.nan
self.assertEqual((3,), dist.mode().get_shape())
self.assertAllClose(expected_mode, dist.mode().eval())
a = np.array([2., 2, 3])
b = np.array([1., 4, 1.2])
dist = kumaraswamy_lib.Kumaraswamy(a, b, allow_nan_stats=True)
expected_mode = _kumaraswamy_mode(a, b)
expected_mode[0] = np.nan
self.assertEqual((3,), dist.mode().get_shape())
self.assertAllClose(expected_mode, dist.mode().eval())
def testKumaraswamyMean(self):
with session.Session():
a = [1., 2, 3]
b = [2., 4, 1.2]
dist = kumaraswamy_lib.Kumaraswamy(a, b)
self.assertEqual(dist.mean().get_shape(), (3,))
if not stats:
return
expected_mean = _kumaraswamy_moment(a, b, 1)
self.assertAllClose(expected_mean, dist.mean().eval())
def testPdfAStretchedInBroadcastWhenSameRank(self):
with self.test_session():
a = [[1., 2]]
b = [[1., 2]]
x = [[.5, .5], [.3, .7]]
dist = kumaraswamy_lib.Kumaraswamy(a, b)
pdf = dist.prob(x)
expected_pdf = _kumaraswamy_pdf(a, b, x)
self.assertAllClose(expected_pdf, pdf.eval())
self.assertEqual((2, 2), pdf.get_shape())
def testPdfTwoBatchesNontrivialX(self):
with self.test_session():
a = [1., 2]
b = [1., 2]
x = [.3, .7]
dist = kumaraswamy_lib.Kumaraswamy(a, b)
pdf = dist.prob(x)
expected_pdf = _kumaraswamy_pdf(a, b, x)
self.assertAllClose(expected_pdf, pdf.eval())
self.assertEqual((2,), pdf.get_shape())
def testKumaraswamyEntropy(self):
with session.Session():
a = np.array([1., 2, 3])
b = np.array([2., 4, 1.2])
dist = kumaraswamy_lib.Kumaraswamy(a, b)
self.assertEqual(dist.entropy().get_shape(), (3,))
if not stats:
return
expected_entropy = (1 - 1. / a) + (
1 - 1. / b) * _harmonic_number(b) + np.log(a * b)
self.assertAllClose(expected_entropy, dist.entropy().eval())
def testPdfUniformZeroBatch(self):
with self.test_session():
# This is equivalent to a uniform distribution
a = 1.
b = 1.
x = np.array([.1, .2, .3, .5, .8], dtype=np.float32)
dist = kumaraswamy_lib.Kumaraswamy(a, b)
pdf = dist.prob(x)
expected_pdf = _kumaraswamy_pdf(a, b, x)
self.assertAllClose(expected_pdf, pdf.eval())
self.assertEqual((5,), pdf.get_shape())
def testKumaraswamyVariance(self):
with session.Session():
a = [1., 2, 3]
b = [2., 4, 1.2]
dist = kumaraswamy_lib.Kumaraswamy(a, b)
self.assertEqual(dist.variance().get_shape(), (3,))
if not stats:
return
expected_variance = _kumaraswamy_moment(a, b, 2) - _kumaraswamy_moment(
a, b, 1)**2
self.assertAllClose(expected_variance, dist.variance().eval())
def testPdfXProper(self):
a = [[1., 2, 3]]
b = [[2., 4, 3]]
with self.test_session():
dist = kumaraswamy_lib.Kumaraswamy(a, b, validate_args=True)
dist.prob([.1, .3, .6]).eval()
dist.prob([.2, .3, .5]).eval()
# Either condition can trigger.
with self.assertRaisesOpError("sample must be non-negative"):
dist.prob([-1., 0.1, 0.5]).eval()
with self.assertRaisesOpError("sample must be no larger than `1`"):
dist.prob([.1, .2, 1.2]).eval()
def testKumaraswamyCdf(self):
with self.test_session():
shape = (30, 40, 50)
for dt in (np.float32, np.float64):
a = 10. * np.random.random(shape).astype(dt)
b = 10. * np.random.random(shape).astype(dt)
x = np.random.random(shape).astype(dt)
actual = kumaraswamy_lib.Kumaraswamy(a, b).cdf(x).eval()
self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x)
self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x)
if not stats:
return
self.assertAllClose(
_kumaraswamy_cdf(a, b, x), actual, rtol=1e-4, atol=0)
def testKumaraswamySampleMultidimensional(self):
with self.test_session():
a = np.random.rand(3, 2, 2).astype(np.float32)
b = np.random.rand(3, 2, 2).astype(np.float32)
kumaraswamy = kumaraswamy_lib.Kumaraswamy(a, b)
n = constant_op.constant(100000)
samples = kumaraswamy.sample(n)
sample_values = samples.eval()
self.assertEqual(sample_values.shape, (100000, 3, 2, 2))
self.assertFalse(np.any(sample_values < 0.0))
if not stats:
return
self.assertAllClose(sample_values[:, 1, :].mean(axis=0),
_kumaraswamy_moment(a, b, 1)[1, :],
atol=1e-1)