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tidied model_tests

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James Hensman 2015-08-13 09:27:03 +01:00
parent affd538687
commit 90df33af50
1 changed files with 4 additions and 23 deletions
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27
GPy/testing/model_tests.py
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@ -361,14 +361,12 @@ class GradientTests(np.testing.TestCase):
rbflin = GPy.kern.RBF(2) + GPy.kern.Linear(2)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=2)
# @unittest.expectedFailure
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'''
rbflin = GPy.kern.RBF(2) + GPy.kern.Linear(2)
raise unittest.SkipTest("This is not implemented yet!")
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):
''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs'''
rbflin = GPy.kern.RBF(1) + GPy.kern.Linear(1)
@ -410,23 +408,8 @@ class GradientTests(np.testing.TestCase):
Z = np.linspace(0, 15, 4)[:, None]
kernel = GPy.kern.RBF(1)
m = GPy.models.SparseGPClassification(X, Y, kernel=kernel, Z=Z)
# distribution = GPy.likelihoods.likelihood_functions.Bernoulli()
# likelihood = GPy.likelihoods.EP(Y, distribution)
# m = GPy.core.SparseGP(X, likelihood, kernel, Z)
# m.ensure_default_constraints()
self.assertTrue(m.checkgrad())
@unittest.expectedFailure
def test_generalized_FITC(self):
N = 20
X = np.hstack([np.random.rand(N / 2) + 1, np.random.rand(N / 2) - 1])[:, None]
k = GPy.kern.RBF(1) + GPy.kern.White(1)
Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
m = GPy.models.FITCClassification(X, Y, kernel=k)
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
@unittest.expectedFailure
def test_multioutput_regression_1D(self):
X1 = np.random.rand(50, 1) * 8
X2 = np.random.rand(30, 1) * 5
@ -436,12 +419,11 @@ class GradientTests(np.testing.TestCase):
Y = np.vstack((Y1, Y2))
k1 = GPy.kern.RBF(1)
m = GPy.models.GPMultioutputRegression(X_list=[X1, X2], Y_list=[Y1, Y2], kernel_list=[k1])
import ipdb;ipdb.set_trace()
m.constrain_fixed('.*rbf_var', 1.)
m = GPy.models.GPCoregionalizedRegression(X_list=[X1, X2], Y_list=[Y1, Y2], kernel=k1)
#import ipdb;ipdb.set_trace()
#m.constrain_fixed('.*rbf_var', 1.)
self.assertTrue(m.checkgrad())
@unittest.expectedFailure
def test_multioutput_sparse_regression_1D(self):
X1 = np.random.rand(500, 1) * 8
X2 = np.random.rand(300, 1) * 5
@ -451,8 +433,7 @@ class GradientTests(np.testing.TestCase):
Y = np.vstack((Y1, Y2))
k1 = GPy.kern.RBF(1)
m = GPy.models.SparseGPMultioutputRegression(X_list=[X1, X2], Y_list=[Y1, Y2], kernel_list=[k1])
m.constrain_fixed('.*rbf_var', 1.)
m = GPy.models.SparseGPCoregionalizedRegression(X_list=[X1, X2], Y_list=[Y1, Y2], kernel=k1)
self.assertTrue(m.checkgrad())
def test_gp_heteroscedastic_regression(self):