https://github.com/GPflow/GPflow
Tip revision: af90c6e97f09f0b9a77d2fcc796f8a031ad097e8 authored by alexggmatthews on 06 June 2016, 17:06:36 UTC
Building up cone.
Building up cone.
Tip revision: af90c6e
test_model.py
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, max_iters=5000)
self.failUnless(self.m.x._array.max() < 1e-2)
def test_lbfgsb(self):
self.m.optimize(display=False)
self.failUnless(self.m.x._array.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, 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(display=0, max_iters=10000, ftol=0, gtol=0)
x1 = self.m.get_free_state()
self.failIf(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, max_iters=15, callback=callback)
x1 = self.m.get_free_state()
self.failIf(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.failUnless(p0==0.0)
self.failIf(p0==p1)
self.failUnless(l0==l1)
if __name__ == "__main__":
unittest.main()