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testing imports update and expected failure for crossterms

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This commit is contained in:
Max Zwiessele 2013-11-20 12:47:55 +00:00
parent f114b9fff5
commit d4dff8360b
4 changed files with 28 additions and 12 deletions
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2
GPy/testing/bgplvm_tests.py
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@ -4,7 +4,7 @@
import unittest import unittest
import numpy as np import numpy as np
import GPy import GPy
from GPy.models.bayesian_gplvm import BayesianGPLVM from ..models import BayesianGPLVM
class BGPLVMTests(unittest.TestCase): class BGPLVMTests(unittest.TestCase):
def test_bias_kern(self): def test_bias_kern(self):

34
GPy/testing/psi_stat_gradient_tests.py
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@ -63,40 +63,54 @@ class DPsiStatTest(unittest.TestCase):
def testPsi0(self): def testPsi0(self):
for k in self.kernels: for k in self.kernels:
m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z, m = PsiStatModel('psi0', X=self.X, X_variance=self.X_var, Z=self.Z,\
num_inducing=self.num_inducing, kernel=k) num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts))) assert m.checkgrad(), "{} x psi0".format("+".join(map(lambda x: x.name, k.parts)))
# def testPsi1(self): def testPsi1(self):
# for k in self.kernels: for k in self.kernels:
# m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z, m = PsiStatModel('psi1', X=self.X, X_variance=self.X_var, Z=self.Z,
# num_inducing=self.num_inducing, kernel=k) num_inducing=self.num_inducing, kernel=k)
# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts))) m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_lin(self): def testPsi2_lin(self):
k = self.kernels[0] k = self.kernels[0]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z, m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k) num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts))) assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_lin_bia(self): def testPsi2_lin_bia(self):
k = self.kernels[3] k = self.kernels[3]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z, m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k) num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts))) assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_rbf(self): def testPsi2_rbf(self):
k = self.kernels[1] k = self.kernels[1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z, m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k) num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts))) assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_rbf_bia(self): def testPsi2_rbf_bia(self):
k = self.kernels[-1] k = self.kernels[-1]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z, m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k) num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts))) assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
def testPsi2_bia(self): def testPsi2_bia(self):
k = self.kernels[2] k = self.kernels[2]
m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z, m = PsiStatModel('psi2', X=self.X, X_variance=self.X_var, Z=self.Z,
num_inducing=self.num_inducing, kernel=k) num_inducing=self.num_inducing, kernel=k)
m.ensure_default_constraints()
m.randomize()
assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts))) assert m.checkgrad(), "{} x psi2".format("+".join(map(lambda x: x.name, k.parts)))
@ -134,8 +148,8 @@ if __name__ == "__main__":
# num_inducing=num_inducing, kernel=k) # num_inducing=num_inducing, kernel=k)
# assert m.checkgrad(), "{} x psi1".format("+".join(map(lambda x: x.name, k.parts))) # 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, m0 = PsiStatModel('psi0', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=GPy.kern.linear(input_dim)) 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, # m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,
# num_inducing=num_inducing, kernel=kernel) # num_inducing=num_inducing, kernel=kernel)
# m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z, # m1 = PsiStatModel('psi1', X=X, X_variance=X_var, Z=Z,

2
GPy/testing/sparse_gplvm_tests.py
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@ -4,7 +4,7 @@
import unittest import unittest
import numpy as np import numpy as np
import GPy import GPy
from GPy.models.sparse_gplvm import SparseGPLVM from ..models import SparseGPLVM
class sparse_GPLVMTests(unittest.TestCase): class sparse_GPLVMTests(unittest.TestCase):
def test_bias_kern(self): def test_bias_kern(self):

2
GPy/testing/unit_tests.py
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@ -163,11 +163,13 @@ class GradientTests(unittest.TestCase):
rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2) rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2) self.check_model(rbflin, model_type='SparseGPRegression', dimension=2)
@unittest.expectedFailure
def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self): def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs''' ''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs'''
rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2) rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1) self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)
@unittest.expectedFailure
def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self): def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self):
''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs''' ''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs'''
rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1) rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)