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[tests] skip on climin import error

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mzwiessele 2016-07-29 09:48:57 +01:00
parent 40fe3d213a
commit 06497922f7
1 changed files with 26 additions and 22 deletions
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48
GPy/testing/minibatch_tests.py
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@ -127,28 +127,32 @@ class SparseGPMinibatchTest(unittest.TestCase):
def test_sparsegp_init(self):
# Test if the different implementations give the exact same likelihood as the full model.
# All of the following settings should give the same likelihood and gradients as the full model:
np.random.seed(1234)
Z = self.X[np.random.choice(self.X.shape[0], replace=False, size=10)].copy()
Q = Z.shape[1]
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=False)
assert(m.checkgrad())
m.optimize('adadelta', max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=True)
assert(m.checkgrad())
m.optimize('rprop', max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=False)
assert(m.checkgrad())
m.optimize('rprop', max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=True)
assert(m.checkgrad())
m.optimize('adadelta', max_iters=10)
assert(m.checkgrad())
try:
np.random.seed(1234)
Z = self.X[np.random.choice(self.X.shape[0], replace=False, size=10)].copy()
Q = Z.shape[1]
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=False)
assert(m.checkgrad())
m.optimize('adadelta', max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=True)
assert(m.checkgrad())
m.optimize('rprop', max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=False)
assert(m.checkgrad())
m.optimize('rprop', max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=True)
assert(m.checkgrad())
m.optimize('adadelta', max_iters=10)
assert(m.checkgrad())
except ImportError:
from nose import SkipTest
raise SkipTest('climin not installed, skipping stochastic gradients')
def test_predict_missing_data(self):
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=True, batchsize=self.Y.shape[1])