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psi_stat tests renamed
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Max Zwiessele
parent
b7de50b5b3
commit
1f5a7d0053
3 changed files with 33 additions and 21 deletions
157
GPy/testing/psi_stat_gradient_tests.py
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157
GPy/testing/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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class PsiStatModel(model):
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def __init__(self, which, X, X_variance, Z, M, kernel):
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self.which = which
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self.X = X
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self.X_variance = X_variance
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self.Z = Z
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self.N, self.Q = X.shape
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self.M, Q = Z.shape
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assert self.Q == Q, "shape missmatch: Z:{!s} X:{!s}".format(Z.shape, X.shape)
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self.kern = kernel
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super(PsiStatModel, self).__init__()
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self.psi_ = self.kern.__getattribute__(self.which)(self.Z, self.X, self.X_variance)
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def _get_param_names(self):
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Xnames = ["{}_{}_{}".format(what, i, j) for what, i, j in itertools.product(['X', 'X_variance'], range(self.N), range(self.Q))]
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Znames = ["Z_{}_{}".format(i, j) for i, j in itertools.product(range(self.M), range(self.Q))]
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return Xnames + Znames + self.kern._get_param_names()
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def _get_params(self):
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return numpy.hstack([self.X.flatten(), self.X_variance.flatten(), self.Z.flatten(), self.kern._get_params()])
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def _set_params(self, x, save_old=True, save_count=0):
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start, end = 0, self.X.size
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self.X = x[start:end].reshape(self.N, self.Q)
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start, end = end, end + self.X_variance.size
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self.X_variance = x[start: end].reshape(self.N, self.Q)
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start, end = end, end + self.Z.size
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self.Z = x[start: end].reshape(self.M, self.Q)
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self.kern._set_params(x[end:])
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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 _log_likelihood_gradients(self):
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psimu, psiS = self.kern.__getattribute__("d" + self.which + "_dmuS")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
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try:
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psiZ = self.kern.__getattribute__("d" + self.which + "_dZ")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance)
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except AttributeError:
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psiZ = numpy.zeros(self.M * self.Q)
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thetagrad = self.kern.__getattribute__("d" + self.which + "_dtheta")(numpy.ones_like(self.psi_), self.Z, self.X, self.X_variance).flatten()
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return numpy.hstack((psimu.flatten(), psiS.flatten(), psiZ.flatten(), thetagrad))
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class DPsiStatTest(unittest.TestCase):
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Q = 5
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N = 50
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M = 10
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D = 20
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X = numpy.random.randn(N, Q)
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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)[:M]
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Y = X.dot(numpy.random.randn(Q, D))
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# kernels = [GPy.kern.linear(Q, ARD=True, variances=numpy.random.rand(Q)), GPy.kern.rbf(Q, ARD=True), GPy.kern.bias(Q)]
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kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q),
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GPy.kern.linear(Q) + GPy.kern.bias(Q),
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GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
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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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M=self.M, kernel=k)
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try:
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assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
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except:
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import ipdb;ipdb.set_trace()
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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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# M=self.M, kernel=k)
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# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
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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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M=self.M, kernel=k)
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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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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M=self.M, kernel=k)
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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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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M=self.M, kernel=k)
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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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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M=self.M, kernel=k)
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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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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M=self.M, kernel=k)
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assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
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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, M, Q, D = 30, 5, 4, 30
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# X = numpy.random.rand(N, Q)
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# k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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# K = k.K(X)
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# Y = numpy.random.multivariate_normal(numpy.zeros(N), K, D).T
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# Y -= Y.mean(axis=0)
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# k = GPy.kern.linear(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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# m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
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# m.ensure_default_constraints()
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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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Q = 5
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N = 50
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M = 10
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D = 15
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X = numpy.random.randn(N, Q)
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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)[:M]
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Y = X.dot(numpy.random.randn(Q, D))
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# kernel = GPy.kern.bias(Q)
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#
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# kernels = [GPy.kern.linear(Q), GPy.kern.rbf(Q), GPy.kern.bias(Q),
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# GPy.kern.linear(Q) + GPy.kern.bias(Q),
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# GPy.kern.rbf(Q) + GPy.kern.bias(Q)]
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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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# M=M, 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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# M=M, kernel=GPy.kern.linear(Q))
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# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
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# M=M, kernel=kernel)
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# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
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# M=M, kernel=kernel)
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# m2 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# M=M, kernel=GPy.kern.rbf(Q))
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m3 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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M=M, kernel=GPy.kern.linear(Q, ARD=True, variances=numpy.random.rand(Q)))
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m3.ensure_default_constraints()
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# + GPy.kern.bias(Q))
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# m4 = PsiStatModel('psi2', X=X, X_variance=X_var, Z=Z,
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# M=M, kernel=GPy.kern.rbf(Q) + GPy.kern.bias(Q))
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else:
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unittest.main()
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