# Copyright 2017 the GPflow authors.
#
# 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.
import itertools
import tensorflow as tf
import numpy as np
from numpy.testing import assert_allclose
import gpflow
from gpflow.test_util import GPflowTestCase
from gpflow import settings
class TestMeanFuncs(GPflowTestCase):
"""
Test the output shape for basic and compositional mean functions, also
check that the combination of mean functions returns the correct clas
"""
input_dim = 3
output_dim = 2
N = 20
def mfs1(self):
rng = np.random.RandomState(0)
return [gpflow.mean_functions.Zero(),
gpflow.mean_functions.Linear(
rng.randn(self.input_dim, self.output_dim).astype(settings.float_type),
rng.randn(self.output_dim).astype(settings.float_type)),
gpflow.mean_functions.Constant(
rng.randn(self.output_dim).astype(settings.float_type))]
def mfs2(self):
rng = np.random.RandomState(0)
return [gpflow.mean_functions.Zero(),
gpflow.mean_functions.Linear(
rng.randn(self.input_dim, self.output_dim).astype(settings.float_type),
rng.randn(self.output_dim).astype(settings.float_type)),
gpflow.mean_functions.Constant(
rng.randn(self.output_dim).astype(settings.float_type))]
def composition_mfs_add(self):
composition_mfs_add = []
for (mean_f1, mean_f2) in itertools.product(self.mfs1(), self.mfs2()):
composition_mfs_add.extend([mean_f1 + mean_f2])
return composition_mfs_add
def composition_mfs_mult(self):
composition_mfs_mult = []
for (mean_f1, mean_f2) in itertools.product(self.mfs1(), self.mfs2()):
composition_mfs_mult.extend([mean_f1 * mean_f2])
return composition_mfs_mult
def composition_mfs(self):
return self.composition_mfs_add() + self.composition_mfs_mult()
def test_basic_output_shape(self):
with self.test_context() as sess:
X = tf.placeholder(settings.float_type, shape=[self.N, self.input_dim])
X_data = np.random.randn(self.N, self.input_dim).astype(settings.float_type)
for mf in self.mfs1():
mf.compile()
Y = sess.run(mf(X), feed_dict={X: X_data})
self.assertTrue(Y.shape in [(self.N, self.output_dim), (self.N, 1)])
def test_add_output_shape(self):
with self.test_context() as sess:
X = tf.placeholder(settings.float_type, [self.N, self.input_dim])
X_data = np.random.randn(self.N, self.input_dim).astype(settings.float_type)
for comp_mf in self.composition_mfs_add():
comp_mf.compile()
Y = sess.run(comp_mf(X), feed_dict={X: X_data})
self.assertTrue(Y.shape in [(self.N, self.output_dim), (self.N, 1)])
def test_mult_output_shape(self):
with self.test_context() as sess:
X = tf.placeholder(settings.float_type, [self.N, self.input_dim])
X_data = np.random.randn(self.N, self.input_dim).astype(settings.float_type)
for comp_mf in self.composition_mfs_mult():
comp_mf.compile()
Y = sess.run(comp_mf(X), feed_dict={X: X_data})
self.assertTrue(Y.shape in [(self.N, self.output_dim), (self.N, 1)])
def test_composition_output_shape(self):
with self.test_context() as sess:
X = tf.placeholder(settings.float_type, [self.N, self.input_dim])
X_data = np.random.randn(self.N, self.input_dim).astype(settings.float_type)
comp_mf = self.composition_mfs()[1]
comp_mf.compile()
# for comp_mf in self.composition_mfs:
Y = sess.run(comp_mf(X), feed_dict={X: X_data})
self.assertTrue(Y.shape in [(self.N, self.output_dim), (self.N, 1)])
def test_combination_types(self):
with self.test_context():
self.assertTrue(all(isinstance(mf, gpflow.mean_functions.Additive)
for mf in self.composition_mfs_add()))
self.assertTrue(all(isinstance(mf, gpflow.mean_functions.Product)
for mf in self.composition_mfs_mult()))
class TestModelCompositionOperations(GPflowTestCase):
"""
Tests that operator precedence is correct and zero unary operations, i.e.
adding 0, multiplying by 1, adding x and then subtracting etc. do not
change the mean function
"""
input_dim = 3
output_dim = 2
N = 20
rng = np.random.RandomState(0)
Xtest = rng.randn(30, 3).astype(settings.float_type)
def initials(self):
# Need two copies of the linear1_1 (l1, l2) since we can't add the
# same parameter twice to a single tree.
self.rng.seed(seed=0)
linear1_1 = gpflow.mean_functions.Linear(
self.rng.randn(self.input_dim, self.output_dim).astype(settings.float_type),
self.rng.randn(self.output_dim).astype(settings.float_type))
self.rng.seed(seed=0)
linear1_2 = gpflow.mean_functions.Linear(
self.rng.randn(self.input_dim, self.output_dim).astype(settings.float_type),
self.rng.randn(self.output_dim).astype(settings.float_type))
self.rng.seed(seed=1)
linear2 = gpflow.mean_functions.Linear(
self.rng.randn(self.input_dim, self.output_dim).astype(settings.float_type),
self.rng.randn(self.output_dim).astype(settings.float_type))
linear3 = gpflow.mean_functions.Linear(
self.rng.randn(self.input_dim, self.output_dim).astype(settings.float_type),
self.rng.randn(self.output_dim).astype(settings.float_type))
linears = (linear1_1, linear1_2, linear2, linear3)
# Need two copies of the const1 since we can't add the same parameter
# twice to a single tree
self.rng.seed(seed=2)
const1_1 = gpflow.mean_functions.Constant(
self.rng.randn(self.output_dim).astype(settings.float_type))
self.rng.seed(seed=2)
const1_2 = gpflow.mean_functions.Constant(
self.rng.randn(self.output_dim).astype(settings.float_type))
self.rng.seed(seed=3)
const2 = gpflow.mean_functions.Constant(
self.rng.randn(self.output_dim).astype(settings.float_type))
const3 = gpflow.mean_functions.Constant(
self.rng.randn(self.output_dim).astype(settings.float_type))
consts = (const1_1, const1_2, const2, const3)
const1inv = gpflow.mean_functions.Constant(const1_1.c.read_value() * -1)
linear1inv = gpflow.mean_functions.Linear(
A=(linear1_1.A.read_value() * -1.),
b=(linear1_2.b.read_value() * -1.))
invs = (linear1inv, const1inv)
return linears, consts, invs
def a_b_plus_c(self):
# a * (b + c)
linears, consts, _ = self.initials()
linear1_1, _linear1_2, linear2, linear3 = linears
const1_1, _const1_2, const2, const3 = consts
const_set1 = gpflow.mean_functions.Product(
const1_1, gpflow.mean_functions.Additive(const2, const3))
linear_set1 = gpflow.mean_functions.Product(
linear1_1, gpflow.mean_functions.Additive(linear2, linear3))
return const_set1, linear_set1
def ab_plus_ac(self):
linears, consts, _ = self.initials()
linear1_1, linear1_2, linear2, linear3 = linears
const1_1, const1_2, const2, const3 = consts
# ab + ac
const_set2 = gpflow.mean_functions.Additive(
gpflow.mean_functions.Product(const1_1, const2),
gpflow.mean_functions.Product(const1_2, const3))
linear_set2 = gpflow.mean_functions.Additive(
gpflow.mean_functions.Product(linear1_1, linear2),
gpflow.mean_functions.Product(linear1_2, linear3))
return const_set2, linear_set2
# a-a = 0,
def a_minus_a(self):
linears, consts, invs = self.initials()
linear1_1, _linear1_2, _linear2, _linear3 = linears
const1_1, _const1_2, _const2, _const3 = consts
linear1inv, const1inv = invs
linear1_minus_linear1 = gpflow.mean_functions.Additive(linear1_1, linear1inv)
const1_minus_const1 = gpflow.mean_functions.Additive(const1_1, const1inv)
return linear1_minus_linear1, const1_minus_const1
def comp_minus_constituent1(self):
# (a + b) - a = b = a + (b - a)
linears, _consts, invs = self.initials()
linear1_1, _linear1_2, linear2, _linear3 = linears
linear1inv, _const1inv = invs
comp_minus_constituent1 = gpflow.mean_functions.Additive(
gpflow.mean_functions.Additive(linear1_1, linear2), linear1inv)
return comp_minus_constituent1
def comp_minus_constituent2(self):
linears, _consts, invs = self.initials()
linear1_1, _linear1_2, linear2, _linear3 = linears
linear1inv, _const1inv = invs
comp_minus_constituent2 = gpflow.mean_functions.Additive(
linear1_1, gpflow.mean_functions.Additive(linear2, linear1inv))
return comp_minus_constituent2
def setUp(self):
self.test_graph = tf.Graph()
with self.test_context():
zero = gpflow.mean_functions.Zero()
k = gpflow.kernels.Bias(self.input_dim)
const_set1, linear_set1 = self.a_b_plus_c()
const_set2, linear_set2 = self.ab_plus_ac()
linear1_minus_linear1, const1_minus_const1 = self.a_minus_a()
comp_minus_constituent1 = self.comp_minus_constituent1()
comp_minus_constituent2 = self.comp_minus_constituent2()
_linear1_1, _linear1_2, linear2, _linear3 = self.initials()[0]
X = self.rng.randn(self.N, self.input_dim).astype(settings.float_type)
Y = self.rng.randn(self.N, self.output_dim).astype(settings.float_type)
self.m_linear_set1 = gpflow.models.GPR(X, Y, mean_function=linear_set1, kern=k)
self.m_linear_set2 = gpflow.models.GPR(X, Y, mean_function=linear_set2, kern=k)
self.m_const_set1 = gpflow.models.GPR(X, Y, mean_function=const_set1, kern=k)
self.m_const_set2 = gpflow.models.GPR(X, Y, mean_function=const_set2, kern=k)
self.m_linear_min_linear = gpflow.models.GPR(
X, Y, mean_function=linear1_minus_linear1, kern=k)
self.m_const_min_const = gpflow.models.GPR(
X, Y, mean_function=const1_minus_const1, kern=k)
self.m_constituent = gpflow.models.GPR(X, Y, mean_function=linear2, kern=k)
self.m_zero = gpflow.models.GPR(X, Y, mean_function=zero, kern=k)
self.m_comp_minus_constituent1 = gpflow.models.GPR(
X, Y, mean_function=comp_minus_constituent1, kern=k)
self.m_comp_minus_constituent2 = gpflow.models.GPR(
X, Y, mean_function=comp_minus_constituent2, kern=k)
def test_precedence(self):
with self.test_context():
mu1_lin, v1_lin = self.m_linear_set1.predict_f(self.Xtest)
mu2_lin, v2_lin = self.m_linear_set2.predict_f(self.Xtest)
mu1_const, v1_const = self.m_const_set1.predict_f(self.Xtest)
mu2_const, v2_const = self.m_const_set2.predict_f(self.Xtest)
assert_allclose(v1_lin, v1_lin)
assert_allclose(mu1_lin, mu2_lin)
assert_allclose(v1_const, v2_const)
assert_allclose(mu1_const, mu2_const)
def test_inverse_operations(self):
with self.test_context():
mu1_lin_min_lin, v1_lin_min_lin = self.m_linear_min_linear.predict_f(self.Xtest)
mu1_const_min_const, v1_const_min_const = self.m_const_min_const.predict_f(self.Xtest)
mu1_comp_min_constituent1, v1_comp_min_constituent1 = self.m_comp_minus_constituent1.predict_f(self.Xtest)
mu1_comp_min_constituent2, v1_comp_min_constituent2 = self.m_comp_minus_constituent2.predict_f(self.Xtest)
mu_const, _ = self.m_constituent.predict_f(self.Xtest)
mu_zero, v_zero = self.m_zero.predict_f(self.Xtest)
self.assertTrue(np.all(np.isclose(mu1_lin_min_lin, mu_zero)))
self.assertTrue(np.all(np.isclose(mu1_const_min_const, mu_zero)))
assert_allclose(mu1_comp_min_constituent1, mu_const)
assert_allclose(mu1_comp_min_constituent2, mu_const)
assert_allclose(mu1_comp_min_constituent1, mu1_comp_min_constituent2)
class TestModelsWithMeanFuncs(GPflowTestCase):
"""
Simply check that all models have a higher prediction with a constant mean
function than with a zero mean function.
For compositions of mean functions check that multiplication/ addition of
a constant results in a higher prediction, whereas addition of zero/
mutliplication with one does not.
"""
def setUp(self):
self.input_dim = 3
self.output_dim = 2
self.N = 20
self.Ntest = 30
self.M = 5
rng = np.random.RandomState(0)
X, Y, Z, self.Xtest = (
rng.randn(self.N, self.input_dim).astype(settings.float_type),
rng.randn(self.N, self.output_dim).astype(settings.float_type),
rng.randn(self.M, self.input_dim).astype(settings.float_type),
rng.randn(self.Ntest, self.input_dim).astype(settings.float_type))
with self.test_context():
k = lambda: gpflow.kernels.Matern32(self.input_dim)
lik = lambda: gpflow.likelihoods.Gaussian()
mf0 = lambda: gpflow.mean_functions.Zero()
mf1 = lambda: gpflow.mean_functions.Constant(np.ones(self.output_dim) * 10)
self.models_with, self.models_without = ([
[gpflow.models.GPR(X, Y, mean_function=mf(), kern=k()),
gpflow.models.SGPR(X, Y, mean_function=mf(), Z=Z, kern=k()),
gpflow.models.GPRFITC(X, Y, mean_function=mf(), Z=Z, kern=k()),
gpflow.models.SVGP(X, Y, mean_function=mf(), Z=Z, kern=k(), likelihood=lik()),
gpflow.models.VGP(X, Y, mean_function=mf(), kern=k(), likelihood=lik()),
gpflow.models.VGP(X, Y, mean_function=mf(), kern=k(), likelihood=lik()),
gpflow.models.GPMC(X, Y, mean_function=mf(), kern=k(), likelihood=lik()),
gpflow.models.SGPMC(X, Y, mean_function=mf(), kern=k(), likelihood=lik(), Z=Z)]
for mf in (mf0, mf1)])
def test_basic_mean_function(self):
with self.test_context():
for m_with, m_without in zip(self.models_with, self.models_without):
m_with.compile()
m_without.compile()
mu1, v1 = m_with.predict_f(self.Xtest)
mu2, v2 = m_without.predict_f(self.Xtest)
self.assertTrue(np.all(v1 == v2))
self.assertFalse(np.all(mu1 == mu2))
class TestSwitchedMeanFunction(GPflowTestCase):
"""
Test for the SwitchedMeanFunction.
"""
def test(self):
with self.test_context() as sess:
rng = np.random.RandomState(0)
X = np.hstack([rng.randn(10, 3), 1.0 * rng.randint(0, 2, 10).reshape(-1, 1)])
zeros = gpflow.mean_functions.Constant(np.zeros(1))
ones = gpflow.mean_functions.Constant(np.ones(1))
switched_mean = gpflow.mean_functions.SwitchedMeanFunction([zeros, ones])
switched_mean.compile()
result = sess.run(switched_mean(X))
np_list = np.array([0., 1.])
result_ref = (np_list[X[:, 3].astype(np.int)]).reshape(-1, 1)
assert_allclose(result, result_ref)
class TestBug277Regression(GPflowTestCase):
"""
See github issue #277. This is a regression test.
"""
def test(self):
with self.test_context():
m1 = gpflow.mean_functions.Linear()
m2 = gpflow.mean_functions.Linear()
self.assertTrue(m1.b.read_value() == m2.b.read_value())
m1.b = [1.]
self.assertFalse(m1.b.read_value() == m2.b.read_value())
if __name__ == "__main__":
tf.test.main()
