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GPy/GPy/old_tests/psi_stat_gradient_tests.py

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'''
Created on 22 Apr 2013
@author: maxz
'''
import unittest
import numpy
import GPy
import itertools
from GPy.core import Model
from GPy.core.parameterization.param import Param
from GPy.core.parameterization.transformations import Logexp
from GPy.core.parameterization.variational import NormalPosterior
class PsiStatModel(Model):
def __init__(self, which, X, X_variance, Z, num_inducing, kernel):
super(PsiStatModel, self).__init__(name='psi stat test')
self.which = which
self.X = Param("X", X)
self.X_variance = Param('X_variance', X_variance, Logexp())
self.q = NormalPosterior(self.X, self.X_variance)
self.Z = Param("Z", Z)
self.N, self.input_dim = X.shape
self.num_inducing, input_dim = Z.shape
assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
self.kern = kernel
self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.q)
self.add_parameters(self.q, self.Z, self.kern)
def log_likelihood(self):
return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
def parameters_changed(self):
psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.q)
self.X.gradient = psimu
self.X_variance.gradient = psiS
#psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim)
try: psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.q)
except AttributeError: psiZ = numpy.zeros_like(self.Z)
self.Z.gradient = psiZ
#psiZ = numpy.ones(self.num_inducing * self.input_dim)
N,M = self.X.shape[0], self.Z.shape[0]
dL_dpsi0, dL_dpsi1, dL_dpsi2 = numpy.zeros([N]), numpy.zeros([N,M]), numpy.zeros([N,M,M])
if self.which == 'psi0': dL_dpsi0 += 1
if self.which == 'psi1': dL_dpsi1 += 1
if self.which == 'psi2': dL_dpsi2 += 1
self.kern.update_gradients_variational(numpy.zeros([1,1]),
dL_dpsi0,
dL_dpsi1,
dL_dpsi2, self.X, self.X_variance, self.Z)
class DPsiStatTest(unittest.TestCase):
input_dim = 5
N = 50
num_inducing = 10
input_dim = 20
X = numpy.random.randn(N, input_dim)
X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
Z = numpy.random.permutation(X)[:num_inducing]
Y = X.dot(numpy.random.randn(input_dim, input_dim))
# kernels = [GPy.kern.Linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)), GPy.kern.RBF(input_dim, ARD=True), GPy.kern.Bias(input_dim)]
kernels = [
GPy.kern.Linear(input_dim),
GPy.kern.RBF(input_dim),
#GPy.kern.Bias(input_dim),
#GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim),
#GPy.kern.RBF(input_dim) + GPy.kern.Bias(input_dim)
]
def testPsi0(self):
for k in self.kernels:
m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
num_inducing=self.num_inducing, kernel=k)
m.randomize()
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi1(self):
for k in self.kernels:
m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.randomize()
assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_lin(self):
k = self.kernels[0]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_lin_bia(self):
k = self.kernels[3]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_rbf(self):
k = self.kernels[1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_rbf_bia(self):
k = self.kernels[-1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
def testPsi2_bia(self):
k = self.kernels[2]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k)
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
if __name__ == "__main__":
import sys
interactive = 'i' in sys.argv
if interactive:
# N, num_inducing, input_dim, input_dim = 30, 5, 4, 30
# X = numpy.random.rand(N, input_dim)
# k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
# K = k.K(X)
# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, input_dim).T
# Y -= Y.mean(axis=0)
# k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
# m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
# m.randomize()
# # self.assertTrue(m.checkgrad())
numpy.random.seed(0)
input_dim = 3
N = 3
num_inducing = 2
D = 15
X = numpy.random.randn(N, input_dim)
X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
Z = numpy.random.permutation(X)[:num_inducing]
Y = X.dot(numpy.random.randn(input_dim, D))
# kernel = GPy.kern.Bias(input_dim)
#
# kernels = [GPy.kern.Linear(input_dim), GPy.kern.RBF(input_dim), GPy.kern.Bias(input_dim),
# GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim),
# GPy.kern.RBF(input_dim) + GPy.kern.Bias(input_dim)]
# for k in kernels:
# m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=k)
# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
#
m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
num_inducing=num_inducing, kernel=GPy.kern.RBF(input_dim)+GPy.kern.Bias(input_dim))
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=kernel)
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=kernel)
# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=GPy.kern.RBF(input_dim))
# m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=GPy.kern.Linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
# + GPy.kern.Bias(input_dim))
# m = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing,
# kernel=(
# GPy.kern.RBF(input_dim, ARD=1)
# +GPy.kern.Linear(input_dim, ARD=1)
# +GPy.kern.Bias(input_dim))
# )
# m.ensure_default_constraints()
m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
num_inducing=num_inducing, kernel=(
GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1)
#+GPy.kern.Linear(input_dim, numpy.random.rand(input_dim), ARD=1)
#+GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1)
#+GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(), ARD=0)
+GPy.kern.Bias(input_dim)
+GPy.kern.White(input_dim)
)
)
#m2.ensure_default_constraints()
else:
unittest.main()
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