Revision 01527ae03564cc4fe5ed5c5e01599e89bf60b151 authored by Markus Kaiser on 03 April 2017, 11:55:06 UTC, committed by Markus Kaiser on 03 April 2017, 13:08:33 UTC
1 parent ec37e76
test_kerns.py
from __future__ import absolute_import, print_function
import GPflow
import tensorflow as tf
import numpy as np
import unittest
from .reference import referenceRbfKernel, referenceArcCosineKernel, referencePeriodicKernel
class TestRbf(unittest.TestCase):
def test_1d(self):
lengthScale = 1.4
variance = 2.3
kernel = GPflow.kernels.RBF(1)
kernel.lengthscales = lengthScale
kernel.variance = variance
rng = np.random.RandomState(1)
x_free = tf.placeholder('float64')
kernel.make_tf_array(x_free)
X = tf.placeholder('float64')
X_data = rng.randn(3, 1)
reference_gram_matrix = referenceRbfKernel(X_data, lengthScale, variance)
with kernel.tf_mode():
gram_matrix = tf.Session().run(kernel.K(X), feed_dict={x_free: kernel.get_free_state(), X: X_data})
self.assertTrue(np.allclose(gram_matrix, reference_gram_matrix))
class TestArcCosine(unittest.TestCase):
def evalKernelError(self, D, variance, weight_variances,
bias_variance, order, ARD, X_data):
kernel = GPflow.kernels.ArcCosine(D,
order=order,
variance=variance,
weight_variances=weight_variances,
bias_variance=bias_variance,
ARD=ARD)
rng = np.random.RandomState(1)
x_free = tf.placeholder('float64')
kernel.make_tf_array(x_free)
X = tf.placeholder('float64')
if weight_variances is None:
weight_variances = 1.
reference_gram_matrix = referenceArcCosineKernel(X_data, order,
weight_variances,
bias_variance,
variance)
with kernel.tf_mode():
gram_matrix = tf.Session().run(kernel.K(X), feed_dict={x_free: kernel.get_free_state(), X: X_data})
self.assertTrue(np.allclose(gram_matrix, reference_gram_matrix))
def test_1d(self):
D = 1
N = 3
weight_variances = 1.7
bias_variance = 0.6
variance = 2.3
ARD = False
orders = GPflow.kernels.ArcCosine.implemented_orders
rng = np.random.RandomState(1)
X_data = rng.randn(N, D)
for order in orders:
self.evalKernelError(D, variance, weight_variances,
bias_variance, order, ARD, X_data)
def test_3d(self):
D = 3
N = 8
weight_variances = np.array([0.4, 4.2, 2.3])
bias_variance = 1.9
variance = 1e-2
ARD = True
orders = GPflow.kernels.ArcCosine.implemented_orders
rng = np.random.RandomState(1)
X_data = rng.randn(N, D)
for order in orders:
self.evalKernelError(D, variance, weight_variances,
bias_variance, order, ARD, X_data)
def test_non_implemented_order(self):
with self.assertRaises(ValueError):
GPflow.kernels.ArcCosine(1, order=42)
def test_weight_initializations(self):
D = 1
N = 3
weight_variances = None
bias_variance = 1.
variance = 1.
ARDs = {False, True}
order = 0
rng = np.random.RandomState(1)
X_data = rng.randn(N, D)
for ARD in ARDs:
self.evalKernelError(D, variance, weight_variances,
bias_variance, order, ARD, X_data)
class TestPeriodic(unittest.TestCase):
def evalKernelError(self, D, lengthscale, variance, period, X_data):
kernel = GPflow.kernels.PeriodicKernel(D, period=period, variance=variance, lengthscales=lengthscale)
x_free = tf.placeholder('float64')
kernel.make_tf_array(x_free)
X = tf.placeholder('float64')
reference_gram_matrix = referencePeriodicKernel(X_data, lengthscale, variance, period)
with kernel.tf_mode():
gram_matrix = tf.Session().run(kernel.K(X),
feed_dict={x_free: kernel.get_free_state(), X: X_data})
self.assertTrue(np.allclose(gram_matrix, reference_gram_matrix))
def test_1d(self):
D = 1
lengthScale = 2
variance = 2.3
period = 2
rng = np.random.RandomState(1)
X_data = rng.randn(3, 1)
self.evalKernelError(D, lengthScale, variance, period, X_data)
def test_2d(self):
D = 2
N = 5
lengthScale = 11.5
variance = 1.3
period = 20
rng = np.random.RandomState(1)
X_data = rng.multivariate_normal(np.zeros(D), np.eye(D), N)
self.evalKernelError(D, lengthScale, variance, period, X_data)
class TestCoregion(unittest.TestCase):
def setUp(self):
self.rng = np.random.RandomState(0)
self.k = GPflow.kernels.Coregion(1, output_dim=3, rank=2)
self.k.W = self.rng.randn(3, 2)
self.k.kappa = self.rng.rand(3) + 1.
self.X = np.random.randint(0, 3, (10, 1))
self.X2 = np.random.randint(0, 3, (12, 1))
def test_shape(self):
K = self.k.compute_K(self.X, self.X2)
self.assertTrue(K.shape == (10, 12))
K = self.k.compute_K_symm(self.X)
self.assertTrue(K.shape == (10, 10))
def test_diag(self):
K = self.k.compute_K_symm(self.X)
Kdiag = self.k.compute_Kdiag(self.X)
self.assertTrue(np.allclose(np.diag(K), Kdiag))
def test_slice(self):
# compute another kernel with additinoal inputs, make sure out kernel is still okay.
X = np.hstack((self.X, self.rng.randn(10, 1)))
k1 = GPflow.kernels.Coregion(1, 3, 2, active_dims=[0])
k2 = GPflow.kernels.RBF(1, active_dims=[1])
k = k1 * k2
K1 = k.compute_K_symm(X)
K2 = k1.compute_K_symm(X) * k2.compute_K_symm(X) # slicing happens inside kernel
self.assertTrue(np.allclose(K1, K2))
class TestKernSymmetry(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
self.kernels = GPflow.kernels.Stationary.__subclasses__() + [GPflow.kernels.Constant, GPflow.kernels.Linear,
GPflow.kernels.Polynomial, GPflow.kernels.ArcCosine]
self.rng = np.random.RandomState()
def test_1d(self):
kernels = [K(1) for K in self.kernels]
x_free = tf.placeholder('float64')
[k.make_tf_array(x_free) for k in kernels]
X = tf.placeholder('float64')
X_data = self.rng.randn(10, 1)
for k in kernels:
with k.tf_mode():
Errors = tf.Session().run(k.K(X) - k.K(X, X),
feed_dict={x_free: k.get_free_state(), X: X_data})
self.assertTrue(np.allclose(Errors, 0))
def test_5d(self):
kernels = [K(5) for K in self.kernels]
x_free = tf.placeholder('float64')
[k.make_tf_array(x_free) for k in kernels]
X = tf.placeholder('float64')
X_data = self.rng.randn(10, 5)
for k in kernels:
with k.tf_mode():
Errors = tf.Session().run(k.K(X) - k.K(X, X),
feed_dict={x_free: k.get_free_state(), X: X_data})
self.assertTrue(np.allclose(Errors, 0))
class TestKernDiags(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
inputdim = 3
rng = np.random.RandomState(1)
self.X = tf.placeholder(tf.float64, [30, inputdim])
self.X_data = rng.randn(30, inputdim)
self.kernels = [k(inputdim) for k in GPflow.kernels.Stationary.__subclasses__() +
[GPflow.kernels.Constant, GPflow.kernels.Linear, GPflow.kernels.Polynomial]]
self.kernels.append(GPflow.kernels.RBF(inputdim) + GPflow.kernels.Linear(inputdim))
self.kernels.append(GPflow.kernels.RBF(inputdim) * GPflow.kernels.Linear(inputdim))
self.kernels.append(GPflow.kernels.RBF(inputdim) +
GPflow.kernels.Linear(inputdim, ARD=True, variance=rng.rand(inputdim)))
self.kernels.append(GPflow.kernels.PeriodicKernel(inputdim))
self.kernels.extend(GPflow.kernels.ArcCosine(inputdim, order=order)
for order in GPflow.kernels.ArcCosine.implemented_orders)
self.x_free = tf.placeholder('float64')
[k.make_tf_array(self.x_free) for k in self.kernels]
def test(self):
for k in self.kernels:
with k.tf_mode():
k1 = k.Kdiag(self.X)
k2 = tf.diag_part(k.K(self.X))
k1, k2 = tf.Session().run([k1, k2],
feed_dict={self.x_free: k.get_free_state(), self.X: self.X_data})
self.assertTrue(np.allclose(k1, k2))
class TestAdd(unittest.TestCase):
"""
add a rbf and linear kernel, make sure the result is the same as adding
the result of the kernels separaetely
"""
def setUp(self):
tf.reset_default_graph()
self.rbf = GPflow.kernels.RBF(1)
self.lin = GPflow.kernels.Linear(1)
self.k = GPflow.kernels.RBF(1) + GPflow.kernels.Linear(1)
self.rng = np.random.RandomState(0)
def test_sym(self):
x_free = tf.placeholder('float64')
[k.make_tf_array(x_free) for k in (self.rbf, self.lin, self.k)]
X = tf.placeholder('float64')
X_data = self.rng.randn(10, 1)
for k in [self.rbf, self.lin, self.k]:
with k.tf_mode():
k._K = tf.Session().run(k.K(X), feed_dict={x_free: k.get_free_state(), X: X_data})
self.assertTrue(np.allclose(self.rbf._K + self.lin._K, self.k._K))
def test_asym(self):
x_free = tf.placeholder('float64')
[k.make_tf_array(x_free) for k in (self.rbf, self.lin, self.k)]
X = tf.placeholder('float64')
Z = tf.placeholder('float64')
X_data = self.rng.randn(10, 1)
Z_data = self.rng.randn(12, 1)
for k in [self.rbf, self.lin, self.k]:
with k.tf_mode():
k._K = tf.Session().run(k.K(X), feed_dict={x_free: k.get_free_state(), X: X_data, Z: Z_data})
self.assertTrue(np.allclose(self.rbf._K + self.lin._K, self.k._K))
class TestWhite(unittest.TestCase):
"""
The white kernel should not give the same result when called with k(X) and
k(X, X)
"""
def setUp(self):
tf.reset_default_graph()
self.k = GPflow.kernels.White(1)
self.rng = np.random.RandomState(0)
def test(self):
x_free = tf.placeholder('float64')
self.k.make_tf_array(x_free)
X = tf.placeholder('float64')
X_data = self.rng.randn(10, 1)
with self.k.tf_mode():
K_sym = tf.Session().run(self.k.K(X), feed_dict={x_free: self.k.get_free_state(), X: X_data})
K_asym = tf.Session().run(self.k.K(X, X), feed_dict={x_free: self.k.get_free_state(), X: X_data})
self.assertFalse(np.allclose(K_sym, K_asym))
class TestSlice(unittest.TestCase):
"""
Make sure the results of a sliced kernel is the same as an unsliced kernel
with correctly sliced data...
"""
def setUp(self):
self.rng = np.random.RandomState(0)
tf.reset_default_graph()
self.X = self.rng.randn(20, 2)
self.Z = self.rng.randn(10, 2)
kernels = GPflow.kernels.Stationary.__subclasses__() + [GPflow.kernels.Constant, GPflow.kernels.Linear,
GPflow.kernels.Polynomial]
self.kernels = []
for kernclass in kernels:
k1 = kernclass(1, active_dims=[0])
k2 = kernclass(1, active_dims=[1])
k3 = kernclass(1, active_dims=slice(0, 1))
self.kernels.append([k1, k2, k3])
def test_symm(self):
for k1, k2, k3 in self.kernels:
K1 = k1.compute_K_symm(self.X)
K2 = k2.compute_K_symm(self.X)
K3 = k3.compute_K_symm(self.X[:, :1])
K4 = k3.compute_K_symm(self.X[:, 1:])
self.assertTrue(np.allclose(K1, K3))
self.assertTrue(np.allclose(K2, K4))
def test_asymm(self):
for k1, k2, k3 in self.kernels:
K1 = k1.compute_K(self.X, self.Z)
K2 = k2.compute_K(self.X, self.Z)
K3 = k3.compute_K(self.X[:, :1], self.Z[:, :1])
K4 = k3.compute_K(self.X[:, 1:], self.Z[:, 1:])
self.assertTrue(np.allclose(K1, K3))
self.assertTrue(np.allclose(K2, K4))
class TestProd(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
self.k1 = GPflow.kernels.Matern32(2)
self.k2 = GPflow.kernels.Matern52(2, lengthscales=0.3)
self.k3 = self.k1 * self.k2
self.x_free = tf.placeholder(tf.float64)
self.X = tf.placeholder(tf.float64, [30, 2])
self.X_data = np.random.randn(30, 2)
def test_prod(self):
with self.k1.tf_mode():
with self.k2.tf_mode():
with self.k3.tf_mode():
self.k1.make_tf_array(self.x_free)
K1 = self.k1.K(self.X)
K1 = tf.Session().run(K1, feed_dict={self.X: self.X_data, self.x_free: self.k1.get_free_state()})
self.k2.make_tf_array(self.x_free)
K2 = self.k2.K(self.X)
K2 = tf.Session().run(K2, feed_dict={self.X: self.X_data, self.x_free: self.k2.get_free_state()})
self.k3.make_tf_array(self.x_free)
K3 = self.k3.K(self.X)
K3 = tf.Session().run(K3, feed_dict={self.X: self.X_data, self.x_free: self.k3.get_free_state()})
self.assertTrue(np.allclose(K1 * K2, K3))
class TestARDActiveProd(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
self.rng = np.random.RandomState(0)
# k3 = k1 * k2
self.k1 = GPflow.kernels.RBF(3, active_dims=[0, 1, 3], ARD=True)
self.k2 = GPflow.kernels.RBF(1, active_dims=[2], ARD=True)
self.k3 = GPflow.kernels.RBF(4, ARD=True)
self.k1.lengthscales = np.array([3.4, 4.5, 5.6])
self.k2.lengthscales = 6.7
self.k3.lengthscales = np.array([3.4, 4.5, 6.7, 5.6])
self.k3a = self.k1 * self.k2
# make kernel functions in python
self.x_free = tf.placeholder('float64')
self.k3.make_tf_array(self.x_free)
self.k3a.make_tf_array(self.x_free)
self.X = tf.placeholder('float64', [50, 4])
self.X_data = np.random.randn(50, 4)
def test(self):
with self.k3.tf_mode():
with self.k3a.tf_mode():
K1 = self.k3.K(self.X)
K2 = self.k3a.K(self.X)
K1 = tf.Session().run(K1, feed_dict={self.X: self.X_data, self.x_free: self.k3.get_free_state()})
K2 = tf.Session().run(K2, feed_dict={self.X: self.X_data, self.x_free: self.k3a.get_free_state()})
self.assertTrue(np.allclose(K1, K2))
class TestKernNaming(unittest.TestCase):
def test_no_nesting_1(self):
k1 = GPflow.kernels.RBF(1)
k2 = GPflow.kernels.Linear(2)
k3 = k1 + k2
k4 = GPflow.kernels.Matern32(1)
k5 = k3 + k4
self.assertTrue(k5.rbf is k1)
self.assertTrue(k5.linear is k2)
self.assertTrue(k5.matern32 is k4)
def test_no_nesting_2(self):
k1 = GPflow.kernels.RBF(1) + GPflow.kernels.Linear(2)
k2 = GPflow.kernels.Matern32(1) + GPflow.kernels.Matern52(2)
k = k1 + k2
self.assertTrue(hasattr(k, 'rbf'))
self.assertTrue(hasattr(k, 'linear'))
self.assertTrue(hasattr(k, 'matern32'))
self.assertTrue(hasattr(k, 'matern52'))
def test_simple(self):
k1 = GPflow.kernels.RBF(1)
k2 = GPflow.kernels.Linear(2)
k = k1 + k2
self.assertTrue(k.rbf is k1)
self.assertTrue(k.linear is k2)
def test_duplicates_1(self):
k1 = GPflow.kernels.Matern32(1)
k2 = GPflow.kernels.Matern32(43)
k = k1 + k2
self.assertTrue(k.matern32_1 is k1)
self.assertTrue(k.matern32_2 is k2)
def test_duplicates_2(self):
k1 = GPflow.kernels.Matern32(1)
k2 = GPflow.kernels.Matern32(2)
k3 = GPflow.kernels.Matern32(3)
k = k1 + k2 + k3
self.assertTrue(k.matern32_1 is k1)
self.assertTrue(k.matern32_2 is k2)
self.assertTrue(k.matern32_3 is k3)
class TestKernNamingProduct(unittest.TestCase):
def test_no_nesting_1(self):
k1 = GPflow.kernels.RBF(1)
k2 = GPflow.kernels.Linear(2)
k3 = k1 * k2
k4 = GPflow.kernels.Matern32(1)
k5 = k3 * k4
self.assertTrue(k5.rbf is k1)
self.assertTrue(k5.linear is k2)
self.assertTrue(k5.matern32 is k4)
def test_no_nesting_2(self):
k1 = GPflow.kernels.RBF(1) * GPflow.kernels.Linear(2)
k2 = GPflow.kernels.Matern32(1) * GPflow.kernels.Matern52(2)
k = k1 * k2
self.assertTrue(hasattr(k, 'rbf'))
self.assertTrue(hasattr(k, 'linear'))
self.assertTrue(hasattr(k, 'matern32'))
self.assertTrue(hasattr(k, 'matern52'))
def test_simple(self):
k1 = GPflow.kernels.RBF(1)
k2 = GPflow.kernels.Linear(2)
k = k1 * k2
self.assertTrue(k.rbf is k1)
self.assertTrue(k.linear is k2)
def test_duplicates_1(self):
k1 = GPflow.kernels.Matern32(1)
k2 = GPflow.kernels.Matern32(43)
k = k1 * k2
self.assertTrue(k.matern32_1 is k1)
self.assertTrue(k.matern32_2 is k2)
def test_duplicates_2(self):
k1 = GPflow.kernels.Matern32(1)
k2 = GPflow.kernels.Matern32(2)
k3 = GPflow.kernels.Matern32(3)
k = k1 * k2 * k3
self.assertTrue(k.matern32_1 is k1)
self.assertTrue(k.matern32_2 is k2)
self.assertTrue(k.matern32_3 is k3)
class TestARDInit(unittest.TestCase):
"""
For ARD kernels, make sure that kernels can be instantiated with a single
lengthscale or a suitable array of lengthscales
"""
def test_scalar(self):
k1 = GPflow.kernels.RBF(3, lengthscales=2.3)
k2 = GPflow.kernels.RBF(3, lengthscales=np.ones(3) * 2.3)
self.assertTrue(np.all(k1.lengthscales.value == k2.lengthscales.value))
def test_MLP(self):
k1 = GPflow.kernels.ArcCosine(3, weight_variances=1.23, ARD=True)
k2 = GPflow.kernels.ArcCosine(3, weight_variances=np.ones(3) * 1.23, ARD=True)
self.assertTrue(np.all(k1.weight_variances.value == k2.weight_variances.value))
if __name__ == "__main__":
unittest.main()
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