https://github.com/GPflow/GPflow
Tip revision: 6fd1a26809a4c754b73c1a645b48d7cda35b2cd6 authored by John Bradshaw on 24 October 2017, 10:29:09 UTC
Merge remote-tracking branch 'origin/GPflow-1.0-RC' into john-bradshaw/linear-features-for-kernels-gpflow1.0
Merge remote-tracking branch 'origin/GPflow-1.0-RC' into john-bradshaw/linear-features-for-kernels-gpflow1.0
Tip revision: 6fd1a26
test_model.py
# Copyright 2016 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.from __future__ import print_function
from __future__ import print_function
import tensorflow as tf
import numpy as np
import unittest
import gpflow
from gpflow import test_util
class TestOptimize(test_util.GPflowTestCase):
def setUp(self):
rng = np.random.RandomState(0)
class Quadratic(gpflow.models.Model):
def __init__(self):
gpflow.models.Model.__init__(self)
self.x = gpflow.Param(rng.randn(10))
@gpflow.params_as_tensors
def _build_likelihood(self):
return tf.negative(tf.reduce_sum(tf.square(self.x)))
self.m = Quadratic()
def test_adam(self):
with self.test_context():
m = self.m
opt = gpflow.train.AdamOptimizer(0.01)
m.compile()
opt.minimize(m, maxiter=5000)
self.assertTrue(m.x.read_value().max() < 1e-2)
def test_lbfgsb(self):
with self.test_context():
m = self.m
m.compile()
opt = gpflow.train.ScipyOptimizer(options={'disp': False, 'maxiter': 1000})
opt.minimize(m)
self.assertTrue(m.x.read_value().max() < 1e-6)
class KeyboardRaiser:
"""
This wraps a function and makes it raise a KeyboardInterrupt after some number of calls
"""
def __init__(self, iters_to_raise):
self.iters_to_raise = iters_to_raise
self.count = 0
def __call__(self, *a, **kw):
self.count += 1
if self.count >= self.iters_to_raise:
raise KeyboardInterrupt
class TestKeyboardCatching(test_util.GPflowTestCase):
def setUp(self):
X = np.random.randn(1000, 3)
Y = np.random.randn(1000, 3)
Z = np.random.randn(100, 3)
self.m = gpflow.models.SGPR(X, Y, Z=Z, kern=gpflow.kernels.RBF(3))
def test_optimize_np(self):
with self.test_context():
m = self.m
m.compile()
x_before = m.read_trainables()
options = {'maxiter': 1000, 'gtol': 0, 'ftol': 0}
opt = gpflow.train.ScipyOptimizer(options=options)
step = 15
raiser = KeyboardRaiser(step)
opt.minimize(m, step_callback=raiser)
self.assertEqual(raiser.count, step)
x_after = m.read_trainables()
before = np.hstack([np.hstack(np.hstack([x])) for x in x_before])
after = np.hstack([np.hstack(np.hstack([x])) for x in x_after])
self.assertFalse(np.allclose(before, after))
# TODO(@awav)
#def test_optimize_tf(self):
# with self.test_context():
# x0 = self.m.get_free_state()
# callback = KeyboardRaiser(5, lambda x: None)
# o = tf.train.AdamOptimizer()
# self.m.optimize(o, maxiter=10, callback=callback)
# x1 = self.m.get_free_state()
# self.assertFalse(np.allclose(x0, x1))
class TestLikelihoodAutoflow(test_util.GPflowTestCase):
def setUp(self):
X = np.random.randn(1000, 3)
Y = np.random.randn(1000, 3)
Z = np.random.randn(100, 3)
self.m = gpflow.models.SGPR(X, Y, Z=Z, kern=gpflow.kernels.RBF(3))
def test_lik_and_prior(self):
m = self.m
with self.test_context():
m.compile()
l0 = m.compute_log_likelihood()
p0 = m.compute_log_prior()
m.clear()
with self.test_context():
m.kern.variance.prior = gpflow.priors.Gamma(1.4, 1.6)
m.compile()
l1 = m.compute_log_likelihood()
p1 = m.compute_log_prior()
m.clear()
self.assertEqual(p0, 0.0)
self.assertNotEqual(p0, p1)
self.assertEqual(l0, l1)
class TestName(test_util.GPflowTestCase):
def test_name(self):
m1 = gpflow.models.Model()
self.assertEqual(m1.name, 'Model')
m2 = gpflow.models.Model(name='foo')
self.assertEqual(m2.name, 'foo')
# class TestNoRecompileThroughNewModelInstance(test_util.GPflowTestCase):
# """ Regression tests for Bug #454 """
# def setUp(self):
# self.X = np.random.rand(10, 2)
# self.Y = np.random.rand(10, 1)
# def test_gpr(self):
# with self.test_context():
# m1 = gpflow.models.GPR(self.X, self.Y, gpflow.kernels.Matern32(2))
# m1.compile()
# m2 = gpflow.models.GPR(self.X, self.Y, gpflow.kernels.Matern32(2))
# self.assertFalse(m1._needs_recompile)
# def test_sgpr(self):
# with self.test_context():
# m1 = gpflow.models.SGPR(self.X, self.Y, gpflow.kernels.Matern32(2), Z=self.X)
# m1.compile()
# m2 = gpflow.models.SGPR(self.X, self.Y, gpflow.kernels.Matern32(2), Z=self.X)
# self.assertFalse(m1._needs_recompile)
# def test_gpmc(self):
# with self.test_context():
# m1 = gpflow.models.GPMC(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT())
# m1.compile()
# m2 = gpflow.models.GPMC(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT())
# self.assertFalse(m1._needs_recompile)
# def test_sgpmc(self):
# with self.test_context():
# m1 = gpflow.models.SGPMC(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT(),
# Z=self.X)
# m1.compile()
# m2 = gpflow.models.SGPMC(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT(),
# Z=self.X)
# self.assertFalse(m1._needs_recompile)
# def test_svgp(self):
# with self.test_context():
# m1 = gpflow.models.SVGP(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT(),
# Z=self.X)
# m1.compile()
# m2 = gpflow.models.SVGP(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT(),
# Z=self.X)
# self.assertFalse(m1._needs_recompile)
# def test_vgp(self):
# with self.test_context():
# m1 = gpflow.models.VGP(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT())
# m1.compile()
# m2 = gpflow.models.VGP(
# self.X, self.Y,
# gpflow.kernels.Matern32(2),
# likelihood=gpflow.likelihoods.StudentT())
# self.assertFalse(m1._needs_recompile)
if __name__ == "__main__":
unittest.main()