Revision 0c7c67eaaec0b062efea92f91c532c6dadc668d0 authored by Rasmus Bonnevie on 05 May 2017, 06:27:09 UTC, committed by Rasmus Bonnevie on 05 May 2017, 06:27:09 UTC
1 parent e9275c2
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 GPflow
import tensorflow as tf
import numpy as np
import unittest
class TestOptimize(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
rng = np.random.RandomState(0)
class Quadratic(GPflow.model.Model):
def __init__(self):
GPflow.model.Model.__init__(self)
self.x = GPflow.param.Param(rng.randn(10))
def build_likelihood(self):
return -tf.reduce_sum(tf.square(self.x))
self.m = Quadratic()
def test_adam(self):
o = tf.train.AdamOptimizer()
self.m.optimize(o, maxiter=5000)
self.assertTrue(self.m.x.value.max() < 1e-2)
def test_lbfgsb(self):
self.m.optimize(disp=False)
self.assertTrue(self.m.x.value.max() < 1e-6)
def test_feval_counter(self):
self.m._compile()
self.m.num_fevals = 0
for _ in range(10):
self.m._objective(self.m.get_free_state())
self.assertTrue(self.m.num_fevals == 10)
class TestNeedsRecompile(unittest.TestCase):
def setUp(self):
self.m = GPflow.model.Model()
self.m.p = GPflow.param.Param(1.0)
def test_fix(self):
self.m._needs_recompile = False
self.m.p.fixed = True
self.assertTrue(self.m._needs_recompile)
def test_replace_param(self):
self.m._needs_recompile = False
new_p = GPflow.param.Param(3.0)
self.m.p = new_p
self.assertTrue(self.m._needs_recompile)
def test_set_prior(self):
self.m._needs_recompile = False
self.m.p.prior = GPflow.priors.Gaussian(0, 1)
self.assertTrue(self.m._needs_recompile)
def test_set_transform(self):
self.m._needs_recompile = False
self.m.p.transform = GPflow.transforms.Identity()
self.assertTrue(self.m._needs_recompile)
def test_replacement(self):
m = GPflow.model.Model()
m.p = GPflow.param.Parameterized()
m.p.p = GPflow.param.Param(1.0)
m._needs_recompile = False
# replace Parameterized
new_p = GPflow.param.Parameterized()
new_p.p = GPflow.param.Param(1.0)
m.p = new_p
self.assertTrue(m._needs_recompile is True)
class KeyboardRaiser:
"""
This wraps a function and makes it raise a KeyboardInterrupt after some number of calls
"""
def __init__(self, iters_to_raise, f):
self.iters_to_raise, self.f = iters_to_raise, f
self.count = 0
def __call__(self, *a, **kw):
self.count += 1
if self.count >= self.iters_to_raise:
raise KeyboardInterrupt
return self.f(*a, **kw)
class TestKeyboardCatching(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
X = np.random.randn(1000, 3)
Y = np.random.randn(1000, 3)
Z = np.random.randn(100, 3)
self.m = GPflow.sgpr.SGPR(X, Y, Z=Z, kern=GPflow.kernels.RBF(3))
def test_optimize_np(self):
x0 = self.m.get_free_state()
self.m._compile()
self.m._objective = KeyboardRaiser(15, self.m._objective)
self.m.optimize(disp=0, maxiter=10000, ftol=0, gtol=0)
x1 = self.m.get_free_state()
self.assertFalse(np.allclose(x0, x1))
def test_optimize_tf(self):
x0 = self.m.get_free_state()
callback = KeyboardRaiser(5, lambda x: None)
o = tf.train.AdamOptimizer()
self.m.optimize(o, maxiter=15, callback=callback)
x1 = self.m.get_free_state()
self.assertFalse(np.allclose(x0, x1))
class TestLikelihoodAutoflow(unittest.TestCase):
def setUp(self):
tf.reset_default_graph()
X = np.random.randn(1000, 3)
Y = np.random.randn(1000, 3)
Z = np.random.randn(100, 3)
self.m = GPflow.sgpr.SGPR(X, Y, Z=Z, kern=GPflow.kernels.RBF(3))
def test_lik_and_prior(self):
l0 = self.m.compute_log_likelihood()
p0 = self.m.compute_log_prior()
self.m.kern.variance.prior = GPflow.priors.Gamma(1.4, 1.6)
l1 = self.m.compute_log_likelihood()
p1 = self.m.compute_log_prior()
self.assertTrue(p0 == 0.0)
self.assertFalse(p0 == p1)
self.assertTrue(l0 == l1)
class TestName(unittest.TestCase):
def test_name(self):
m = GPflow.model.Model(name='foo')
assert m.name == 'foo'
if __name__ == "__main__":
unittest.main()
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