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all tests are now to check
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Max Zwiessele
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183
GPy/testing/old_tests/psi_stat_gradient_tests.py
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183
GPy/testing/old_tests/psi_stat_gradient_tests.py
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'''
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Created on 22 Apr 2013
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@author: maxz
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'''
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import unittest
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import numpy
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import GPy
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import itertools
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from GPy.core import Model
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from GPy.core.parameterization.param import Param
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from GPy.core.parameterization.transformations import Logexp
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from GPy.core.parameterization.variational import NormalPosterior
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class PsiStatModel(Model):
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def __init__(self, which, X, X_variance, Z, num_inducing, kernel):
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super(PsiStatModel, self).__init__(name='psi stat test')
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self.which = which
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self.X = Param("X", X)
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self.X_variance = Param('X_variance', X_variance, Logexp())
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self.q = NormalPosterior(self.X, self.X_variance)
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self.Z = Param("Z", Z)
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self.N, self.input_dim = X.shape
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self.num_inducing, input_dim = Z.shape
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assert self.input_dim == input_dim, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
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self.kern = kernel
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self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.q)
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self.add_parameters(self.q, self.Z, self.kern)
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def log_likelihood(self):
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return self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance).sum()
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def parameters_changed(self):
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psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.q)
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self.X.gradient = psimu
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self.X_variance.gradient = psiS
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#psimu, psiS = numpy.ones(self.N * self.input_dim), numpy.ones(self.N * self.input_dim)
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try: psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.q)
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except AttributeError: psiZ = numpy.zeros_like(self.Z)
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self.Z.gradient = psiZ
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#psiZ = numpy.ones(self.num_inducing * self.input_dim)
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N,M = self.X.shape[0], self.Z.shape[0]
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dL_dpsi0, dL_dpsi1, dL_dpsi2 = numpy.zeros([N]), numpy.zeros([N,M]), numpy.zeros([N,M,M])
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if self.which == 'psi0': dL_dpsi0 += 1
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if self.which == 'psi1': dL_dpsi1 += 1
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if self.which == 'psi2': dL_dpsi2 += 1
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self.kern.update_gradients_variational(numpy.zeros([1,1]),
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dL_dpsi0,
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dL_dpsi1,
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dL_dpsi2, self.X, self.X_variance, self.Z)
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class DPsiStatTest(unittest.TestCase):
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input_dim = 5
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N = 50
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num_inducing = 10
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input_dim = 20
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X = numpy.random.randn(N, input_dim)
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X_var = .5 * numpy.ones_like(X) + .4 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
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Z = numpy.random.permutation(X)[:num_inducing]
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Y = X.dot(numpy.random.randn(input_dim, input_dim))
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# 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)]
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kernels = [
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GPy.kern.Linear(input_dim),
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GPy.kern.RBF(input_dim),
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#GPy.kern.Bias(input_dim),
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#GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim),
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#GPy.kern.RBF(input_dim) + GPy.kern.Bias(input_dim)
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]
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def testPsi0(self):
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for k in self.kernels:
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m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
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num_inducing=self.num_inducing, kernel=k)
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m.randomize()
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assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k._parameters_)))
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def testPsi1(self):
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for k in self.kernels:
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m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.randomize()
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assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k._parameters_)))
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def testPsi2_lin(self):
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k = self.kernels[0]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
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def testPsi2_lin_bia(self):
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k = self.kernels[3]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
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def testPsi2_rbf(self):
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k = self.kernels[1]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
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def testPsi2_rbf_bia(self):
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k = self.kernels[-1]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
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def testPsi2_bia(self):
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k = self.kernels[2]
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m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
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num_inducing=self.num_inducing, kernel=k)
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m.randomize()
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k._parameters_)))
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if __name__ == "__main__":
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import sys
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interactive = 'i' in sys.argv
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if interactive:
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# N, num_inducing, input_dim, input_dim = 30, 5, 4, 30
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# X = numpy.random.rand(N, input_dim)
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# k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
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# K = k.K(X)
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# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, input_dim).T
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# Y -= Y.mean(axis=0)
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# k = GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim) + GPy.kern.White(input_dim, 0.00001)
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# m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
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# m.randomize()
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# # self.assertTrue(m.checkgrad())
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numpy.random.seed(0)
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input_dim = 3
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N = 3
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num_inducing = 2
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D = 15
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X = numpy.random.randn(N, input_dim)
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X_var = .5 * numpy.ones_like(X) + .1 * numpy.clip(numpy.random.randn(*X.shape), 0, 1)
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Z = numpy.random.permutation(X)[:num_inducing]
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Y = X.dot(numpy.random.randn(input_dim, D))
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# kernel = GPy.kern.Bias(input_dim)
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#
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# kernels = [GPy.kern.Linear(input_dim), GPy.kern.RBF(input_dim), GPy.kern.Bias(input_dim),
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# GPy.kern.Linear(input_dim) + GPy.kern.Bias(input_dim),
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# GPy.kern.RBF(input_dim) + GPy.kern.Bias(input_dim)]
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# for k in kernels:
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# m = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=k)
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# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
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#
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m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
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num_inducing=num_inducing, kernel=GPy.kern.RBF(input_dim)+GPy.kern.Bias(input_dim))
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# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=kernel)
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# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=kernel)
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# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.RBF(input_dim))
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# m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing, kernel=GPy.kern.Linear(input_dim, ARD=True, variances=numpy.random.rand(input_dim)))
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# + GPy.kern.Bias(input_dim))
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# m = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# num_inducing=num_inducing,
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# kernel=(
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# GPy.kern.RBF(input_dim, ARD=1)
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# +GPy.kern.Linear(input_dim, ARD=1)
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# +GPy.kern.Bias(input_dim))
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# )
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# m.ensure_default_constraints()
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m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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num_inducing=num_inducing, kernel=(
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GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.Linear(input_dim, numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(input_dim), ARD=1)
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#+GPy.kern.RBF(input_dim, numpy.random.rand(), numpy.random.rand(), ARD=0)
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+GPy.kern.Bias(input_dim)
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+GPy.kern.White(input_dim)
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)
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)
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#m2.ensure_default_constraints()
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else:
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unittest.main()
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