GPy/GPy/testing/parameter_testing.py

141 lines
7 KiB
Python

'''
Created on 4 Sep 2013
@author: maxz
'''
import unittest
from GPy.kern.constructors import rbf, linear, white
import numpy
from GPy.models.bayesian_gplvm import BayesianGPLVM
from GPy.likelihoods.gaussian import Gaussian
import pickle
import os
from GPy.core.parameterized import Parameterized
from GPy.core.parameter import Param
class Test(unittest.TestCase):
N, D, Q = 10, 6, 4
def setUp(self):
self.rbf_variance = numpy.random.rand()
self.rbf_lengthscale = numpy.random.rand(self.Q)
self.linear_variance = numpy.random.rand(self.Q)
self.noise_variance = numpy.random.rand(1)
self.kern = (rbf(self.Q, self.rbf_variance, self.rbf_lengthscale, ARD=True)
+ linear(self.Q, self.linear_variance, ARD=True)
+ white(self.Q, self.noise_variance))
self.X = numpy.random.rand(self.N, self.Q) + 10
self.X_variance = numpy.random.rand(self.N, self.Q) * .2
K = self.kern.K(self.X)
self.Y = numpy.random.multivariate_normal(numpy.zeros(self.N), K + numpy.eye(self.N) * .2, self.D).T
# self.bgplvm = BayesianGPLVM(Gaussian(self.Y, variance=self.noise_variance), self.Q, self.X, self.X_variance, kernel=self.kern)
# self.bgplvm.ensure_default_constraints(warning=False)
# self.bgplvm.tie_params("noise_variance|white_variance")
# self.bgplvm.constrain_fixed("rbf_var", warning=False)
self.parameter = Parameterized([
Parameterized([
Param('X', self.X),
Param('X_variance', self.X_variance),
]),
Param('iip', self.bgplvm.Z),
Parameterized([
Param('rbf_variance', self.rbf_variance),
Param('rbf_lengthscale', self.rbf_lengthscale)
]),
Param('linear_variance', self.linear_variance),
Param('white_variance', self.noise_variance),
Param('noise_variance', self.noise_variance),
])
self.parameter['.*variance'].constrain_positive(False)
self.parameter['.*length'].constrain_positive(False)
self.parameter.white.tie_to(self.parameter.noise)
self.parameter.rbf_var.constrain_fixed(False)
def tearDown(self):
pass
# def testGrepParamNamesTest(self):
# assert(self.bgplvm.grep_param_names('X_\d') == self.parameter.grep_param_names('X_\d'))
# assert(self.bgplvm.grep_param_names('X_\d+_1') == self.parameter.grep_param_names('X_\d+_1'))
# assert(self.bgplvm.grep_param_names('X_\d_1') == self.parameter.grep_param_names('X_\d_1'))
# assert(self.bgplvm.grep_param_names('X_.+_1') == self.parameter.grep_param_names('X_.+_1'))
# assert(self.bgplvm.grep_param_names('X_1_1') == self.parameter.grep_param_names('X_1_1'))
# assert(self.bgplvm.grep_param_names('X') == self.parameter.grep_param_names('X'))
# assert(self.bgplvm.grep_param_names('rbf') == self.parameter.grep_param_names('rbf'))
# assert(self.bgplvm.grep_param_names('rbf_l.*_1') == self.parameter.grep_param_names('rbf_l.*_1'))
# assert(self.bgplvm.grep_param_names('l') == self.parameter.grep_param_names('l'))
# assert(self.bgplvm.grep_param_names('dont_match') == self.parameter.grep_param_names('dont_match'))
# assert(self.bgplvm.grep_param_names('.*') == self.parameter.grep_param_names('.*'))
def testGetParams(self):
assert(numpy.allclose(self.bgplvm._get_params(), self.parameter._get_params()))
assert(numpy.allclose(self.bgplvm._get_params_transformed(), self.parameter._get_params_transformed()))
def testSetParams(self):
self.bgplvm.randomize()
self.parameter._set_params(self.bgplvm._get_params())
assert(numpy.allclose(self.bgplvm._get_params(), self.parameter._get_params()))
assert(numpy.allclose(self.bgplvm._get_params_transformed(), self.parameter._get_params_transformed()))
self.bgplvm.randomize()
self.parameter._set_params_transformed(self.bgplvm._get_params_transformed())
assert(numpy.allclose(self.bgplvm._get_params(), self.parameter._get_params()))
assert(numpy.allclose(self.bgplvm._get_params_transformed(), self.parameter._get_params_transformed()))
def testSlicing(self):
assert(numpy.allclose(self.parameter.X[:,1], self.X[:,1]))
assert(numpy.allclose(self.parameter.X[:,1], self.X[:,1]))
assert(numpy.allclose(self.parameter.X_variance[1,1], self.X_variance[1,1]))
assert(numpy.allclose(self.parameter.X_variance[:], self.X_variance[:]))
assert(numpy.allclose(self.parameter.X[:,:][:,0:2][:,1], self.X[:,1]))
assert(numpy.allclose(self.parameter.X[:,1], self.X[:,1]))
assert(numpy.allclose(self.parameter.X_variance[1,1], self.X_variance[1,1]))
assert(numpy.allclose(self.parameter.X_variance[:], self.X_variance[:]))
def testSlicingSet(self):
self.parameter['.*variance'] = 1.
assert(numpy.alltrue(self.parameter['.*variance'] == 1.))
self.parameter.X[0,:3] = 2
assert(numpy.alltrue(self.parameter.X[0,:3] == 2))
X = self.parameter.X.copy()
self.parameter.X[[0,4,9],[0,1,3]] -= 1
assert(numpy.alltrue((X[[0,4,9],[0,1,3]] - 1) == self.parameter.X[[0,4,9],[0,1,3]]))
self.parameter[''] = 10
assert(numpy.alltrue(self.parameter[''] == 10))
def testConstraints(self):
self.parameter[''].unconstrain()
self.parameter.X.constrain_positive()
self.parameter.X[:,numpy.s_[0::2]].unconstrain_positive()
assert(numpy.alltrue(self.parameter.constraints.indices()[0] == numpy.r_[1:self.N*self.Q:2]))
def testNdarrayFunc(self):
assert(numpy.alltrue(self.parameter.X * self.parameter.X == self.X * self.X))
assert(numpy.alltrue(self.parameter.X[0,:] * self.parameter.X[1,:] == self.X[0,:] * self.X[1,:]))
def testPickle(self):
fname = '/tmp/GPy_io_test.pickle'
m = self.parameter
m.X.fix()
self.parameter.pickle(fname)
with open(fname, 'r') as f:
m2 = pickle.load(f)
self.assertEqual(m.__str__(), m2.__str__())
self.assertEqual(m.X_v.__str__(), m2.X_v.__str__())
os.remove(fname)
if __name__ == "__main__":
import sys;sys.argv = ['',
'Test.testSlicing',
'Test.testGetParams',
'Test.testNdarrayFunc',
'Test.testSetParams',
'Test.testConstraints',
'Test.testSlicingSet',
'Test.testPickle',
]
unittest.main()