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https://github.com/SheffieldML/GPy.git
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pickling and caching
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Max Zwiessele
parent
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commit
f3b74fa85f
28 changed files with 481 additions and 686 deletions
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@ -5,6 +5,7 @@ import unittest
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import numpy as np
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import GPy
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import sys
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from GPy.core.parameterization.param import Param
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verbose = 0
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@ -30,7 +31,7 @@ class Kern_check_model(GPy.core.Model):
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dL_dK = np.ones((X.shape[0], X2.shape[0]))
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self.kernel = kernel
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self.X = GPy.core.parameterization.Param('X',X)
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self.X = X
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self.X2 = X2
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self.dL_dK = dL_dK
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@ -77,10 +78,11 @@ class Kern_check_dK_dX(Kern_check_model):
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"""This class allows gradient checks for the gradient of a kernel with respect to X. """
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def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
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Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
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self.X = Param('X',X)
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self.add_parameter(self.X)
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def parameters_changed(self):
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self.X.gradient = self.kernel.gradients_X(self.dL_dK, self.X, self.X2)
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self.X.gradient[:] = self.kernel.gradients_X(self.dL_dK, self.X, self.X2)
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class Kern_check_dKdiag_dX(Kern_check_dK_dX):
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"""This class allows gradient checks for the gradient of a kernel diagonal with respect to X. """
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@ -91,7 +93,7 @@ class Kern_check_dKdiag_dX(Kern_check_dK_dX):
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return (np.diag(self.dL_dK)*self.kernel.Kdiag(self.X)).sum()
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def parameters_changed(self):
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self.X.gradient = self.kernel.gradients_X_diag(self.dL_dK.diagonal(), self.X)
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self.X.gradient[:] = self.kernel.gradients_X_diag(self.dL_dK.diagonal(), self.X)
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@ -127,6 +129,7 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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if not result:
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print("Positive definite check failed for " + kern.name + " covariance function.")
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pass_checks = False
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assert(result)
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return False
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if verbose:
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@ -138,6 +141,7 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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print("Gradient of K(X, X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
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Kern_check_dK_dtheta(kern, X=X, X2=None).checkgrad(verbose=True)
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pass_checks = False
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assert(result)
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return False
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if verbose:
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@ -149,6 +153,7 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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print("Gradient of K(X, X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
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Kern_check_dK_dtheta(kern, X=X, X2=X2).checkgrad(verbose=True)
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pass_checks = False
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assert(result)
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return False
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if verbose:
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@ -165,6 +170,7 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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print("Gradient of Kdiag(X) wrt theta failed for " + kern.name + " covariance function. Gradient values as follows:")
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Kern_check_dKdiag_dtheta(kern, X=X).checkgrad(verbose=True)
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pass_checks = False
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assert(result)
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return False
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if verbose:
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@ -183,6 +189,8 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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if not result:
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print("Gradient of K(X, X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
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testmodel.checkgrad(verbose=True)
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import ipdb;ipdb.set_trace()
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assert(result)
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pass_checks = False
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return False
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@ -202,6 +210,7 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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if not result:
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print("Gradient of K(X, X2) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
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testmodel.checkgrad(verbose=True)
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assert(result)
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pass_checks = False
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return False
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@ -219,6 +228,7 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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print("Gradient of Kdiag(X) wrt X failed for " + kern.name + " covariance function. Gradient values as follows:")
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Kern_check_dKdiag_dX(kern, X=X).checkgrad(verbose=True)
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pass_checks = False
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assert(result)
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return False
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return pass_checks
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@ -227,7 +237,7 @@ def check_kernel_gradient_functions(kern, X=None, X2=None, output_ind=None, verb
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class KernelGradientTestsContinuous(unittest.TestCase):
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def setUp(self):
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self.N, self.D = 100, 5
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self.N, self.D = 10, 5
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self.X = np.random.randn(self.N,self.D)
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self.X2 = np.random.randn(self.N+10,self.D)
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@ -339,59 +349,59 @@ class KernelTestsMiscellaneous(unittest.TestCase):
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self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=[self.linear, self.rbf]), self.linear.K(self.X)+self.rbf.K(self.X)))
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self.assertTrue(np.allclose(self.sumkern.K(self.X, which_parts=self.sumkern.parts[0]), self.rbf.K(self.X)))
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class KernelTestsNonContinuous(unittest.TestCase):
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def setUp(self):
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N0 = 3
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N1 = 9
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N2 = 4
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N = N0+N1+N2
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self.D = 3
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self.X = np.random.randn(N, self.D+1)
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indices = np.random.random_integers(0, 2, size=N)
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self.X[indices==0, -1] = 0
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self.X[indices==1, -1] = 1
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self.X[indices==2, -1] = 2
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#self.X = self.X[self.X[:, -1].argsort(), :]
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self.X2 = np.random.randn((N0+N1)*2, self.D+1)
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self.X2[:(N0*2), -1] = 0
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self.X2[(N0*2):, -1] = 1
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def test_IndependentOutputs(self):
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k = GPy.kern.RBF(self.D)
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kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single')
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self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
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k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(self.D, name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')]
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kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
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self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
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def test_ODE_UY(self):
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kern = GPy.kern.ODE_UY(2, active_dims=[0, self.D])
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X = self.X[self.X[:,-1]!=2]
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X2 = self.X2[self.X2[:,-1]!=2]
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self.assertTrue(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
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# class KernelTestsNonContinuous(unittest.TestCase):
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# def setUp(self):
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# N0 = 3
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# N1 = 9
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# N2 = 4
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# N = N0+N1+N2
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# self.D = 3
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# self.X = np.random.randn(N, self.D+1)
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# indices = np.random.random_integers(0, 2, size=N)
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# self.X[indices==0, -1] = 0
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# self.X[indices==1, -1] = 1
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# self.X[indices==2, -1] = 2
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# #self.X = self.X[self.X[:, -1].argsort(), :]
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# self.X2 = np.random.randn((N0+N1)*2, self.D+1)
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# self.X2[:(N0*2), -1] = 0
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# self.X2[(N0*2):, -1] = 1
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#
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# def test_IndependentOutputs(self):
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# k = GPy.kern.RBF(self.D)
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# kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single')
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# self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
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# k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(self.D, name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')]
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# kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
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# self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
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#
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# def test_ODE_UY(self):
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# kern = GPy.kern.ODE_UY(2, active_dims=[0, self.D])
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# X = self.X[self.X[:,-1]!=2]
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# X2 = self.X2[self.X2[:,-1]!=2]
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# self.assertTrue(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
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if __name__ == "__main__":
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print "Running unit tests, please be (very) patient..."
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#unittest.main()
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np.random.seed(0)
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N0 = 3
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N1 = 9
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N2 = 4
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N = N0+N1+N2
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D = 3
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X = np.random.randn(N, D+1)
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indices = np.random.random_integers(0, 2, size=N)
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X[indices==0, -1] = 0
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X[indices==1, -1] = 1
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X[indices==2, -1] = 2
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#X = X[X[:, -1].argsort(), :]
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X2 = np.random.randn((N0+N1)*2, D+1)
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X2[:(N0*2), -1] = 0
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X2[(N0*2):, -1] = 1
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k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(D, name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')]
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kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
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assert(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
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k = GPy.kern.RBF(D)
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kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single')
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assert(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
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unittest.main()
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# np.random.seed(0)
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# N0 = 3
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# N1 = 9
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# N2 = 4
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# N = N0+N1+N2
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# D = 3
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# X = np.random.randn(N, D+1)
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# indices = np.random.random_integers(0, 2, size=N)
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# X[indices==0, -1] = 0
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# X[indices==1, -1] = 1
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# X[indices==2, -1] = 2
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# #X = X[X[:, -1].argsort(), :]
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# X2 = np.random.randn((N0+N1)*2, D+1)
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# X2[:(N0*2), -1] = 0
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# X2[(N0*2):, -1] = 1
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# k = [GPy.kern.RBF(1, active_dims=[1], name='rbf1'), GPy.kern.RBF(D, name='rbf012'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf02')]
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# kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
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# assert(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
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# k = GPy.kern.RBF(D)
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# kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single')
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# assert(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
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@ -93,12 +93,12 @@ class Test(unittest.TestCase):
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def test_set_params(self):
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self.assertEqual(self.par.params_changed_count, 0, 'no params changed yet')
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self.par._param_array_[:] = 1
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self.par.param_array[:] = 1
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self.par._trigger_params_changed()
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self.assertEqual(self.par.params_changed_count, 1, 'now params changed')
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self.assertEqual(self.parent.parent_changed_count, self.par.params_changed_count)
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self.par._param_array_[:] = 2
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self.par.param_array[:] = 2
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self.par._trigger_params_changed()
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self.assertEqual(self.par.params_changed_count, 2, 'now params changed')
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self.assertEqual(self.parent.parent_changed_count, self.par.params_changed_count)
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@ -86,9 +86,9 @@ class ParameterizedTest(unittest.TestCase):
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self.assertListEqual(self.test1.constraints[Logexp()].tolist(), range(self.param.size, self.param.size+self.rbf.size))
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def test_remove_parameter_param_array_grad_array(self):
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val = self.test1.kern._param_array_.copy()
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val = self.test1.kern.param_array.copy()
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self.test1.kern.remove_parameter(self.white)
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self.assertListEqual(self.test1.kern._param_array_.tolist(), val[:2].tolist())
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self.assertListEqual(self.test1.kern.param_array.tolist(), val[:2].tolist())
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def test_add_parameter_already_in_hirarchy(self):
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self.assertRaises(HierarchyError, self.test1.add_parameter, self.white._parameters_[0])
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