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[sparsegplvm] added sparsegplvm and tests for minibatch sparsegplvm

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This commit is contained in:
mzwiessele 2016-03-07 13:39:32 +00:00
parent 99caca6702
commit b0347c5108
4 changed files with 107 additions and 2 deletions
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91
GPy/testing/bgplvm_minibatch_tests.py → GPy/testing/minibatch_tests.py
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@ -103,6 +103,97 @@ class BGPLVMTest(unittest.TestCase):
np.testing.assert_allclose(m.gradient, self.m_full.gradient)
assert(m.checkgrad())
class SparseGPMinibatchTest(unittest.TestCase):
def setUp(self):
np.random.seed(12345)
X, W = np.random.normal(0,1,(100,6)), np.random.normal(0,1,(6,13))
Y = X.dot(W) + np.random.normal(0, .1, (X.shape[0], W.shape[1]))
self.inan = np.random.binomial(1, .1, Y.shape).astype(bool)
self.X, self.W, self.Y = X,W,Y
self.Q = 3
self.m_full = GPy.models.SparseGPLVM(Y, self.Q, kernel=GPy.kern.RBF(self.Q, ARD=True))
def test_lik_comparisons_m1_s0(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:
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=False)
m[:] = self.m_full[:]
np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
np.testing.assert_allclose(m.gradient, self.m_full.gradient)
assert(m.checkgrad())
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])
m[:] = self.m_full[:]
np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
np.testing.assert_allclose(m.gradient, self.m_full.gradient)
mu1, var1 = m.predict(m.X, full_cov=False)
mu2, var2 = self.m_full.predict(self.m_full.X, full_cov=False)
np.testing.assert_allclose(mu1, mu2)
for i in range(var1.shape[1]):
np.testing.assert_allclose(var1[:,[i]], var2)
mu1, var1 = m.predict(m.X, full_cov=True)
mu2, var2 = self.m_full.predict(self.m_full.X, full_cov=True)
np.testing.assert_allclose(mu1, mu2)
for i in range(var1.shape[2]):
np.testing.assert_allclose(var1[:,:,i], var2)
def test_lik_comparisons_m0_s0(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:
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=False, stochastic=False)
m[:] = self.m_full[:]
np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
np.testing.assert_allclose(m.gradient, self.m_full.gradient)
assert(m.checkgrad())
def test_lik_comparisons_m1_s1(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:
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=True, batchsize=self.Y.shape[1])
m[:] = self.m_full[:]
np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
np.testing.assert_allclose(m.gradient, self.m_full.gradient)
assert(m.checkgrad())
def test_lik_comparisons_m0_s1(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:
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=False, stochastic=True, batchsize=self.Y.shape[1])
m[:] = self.m_full[:]
np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
np.testing.assert_allclose(m.gradient, self.m_full.gradient)
assert(m.checkgrad())
def test_gradients_missingdata(self):
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=False, batchsize=self.Y.shape[1])
assert(m.checkgrad())
def test_gradients_missingdata_stochastics(self):
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=True, batchsize=1)
assert(m.checkgrad())
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=True, batchsize=4)
assert(m.checkgrad())
def test_gradients_stochastics(self):
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=False, stochastic=True, batchsize=1)
assert(m.checkgrad())
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=False, stochastic=True, batchsize=4)
assert(m.checkgrad())
def test_predict(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:
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=True, batchsize=self.Y.shape[1])
m[:] = self.m_full[:]
np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
np.testing.assert_allclose(m.gradient, self.m_full.gradient)
assert(m.checkgrad())
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']

13
GPy/testing/model_tests.py
View file

@ -515,16 +515,27 @@ class GradientTests(np.testing.TestCase):
rbflin = GPy.kern.RBF(1) + GPy.kern.White(1)
self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1)
def test_GPLVM_rbf_bias_white_kern_2D(self):
""" Testing GPLVM with rbf + bias kernel """
N, input_dim, D = 50, 1, 2
X = np.random.rand(N, input_dim)
k = GPy.kern.RBF(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05)
k = GPy.kern.RBF(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05) + GPy.kern.Matern32(input_dim) + GPy.kern.Matern52(input_dim)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
m = GPy.models.GPLVM(Y, input_dim, kernel=k)
self.assertTrue(m.checkgrad())
def test_SparseGPLVM_rbf_bias_white_kern_2D(self):
""" Testing GPLVM with rbf + bias kernel """
N, input_dim, D = 50, 1, 2
X = np.random.rand(N, input_dim)
k = GPy.kern.RBF(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.Bias(input_dim, 0.1) + GPy.kern.White(input_dim, 0.05) + GPy.kern.Matern32(input_dim) + GPy.kern.Matern52(input_dim)
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
m = GPy.models.SparseGPLVM(Y, input_dim, kernel=k)
self.assertTrue(m.checkgrad())
def test_BCGPLVM_rbf_bias_white_kern_2D(self):
""" Testing GPLVM with rbf + bias kernel """
N, input_dim, D = 50, 1, 2