mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-10 12:32:40 +02:00
format on save
This commit is contained in:
Martin Bubel
parent
779f31da9c
commit
0b92d3a57c
1 changed files with 169 additions and 68 deletions
|
|
@ -8,18 +8,21 @@ The test cases for various inference algorithms
|
|||
import unittest
|
||||
import numpy as np
|
||||
import GPy
|
||||
#np.seterr(invalid='raise')
|
||||
|
||||
# np.seterr(invalid='raise')
|
||||
|
||||
|
||||
class InferenceXTestCase(unittest.TestCase):
|
||||
|
||||
def genData(self):
|
||||
np.random.seed(1111)
|
||||
Ylist = GPy.examples.dimensionality_reduction._simulate_matern(5, 1, 1, 10, 3, False)[0]
|
||||
Ylist = GPy.examples.dimensionality_reduction._simulate_matern(
|
||||
5, 1, 1, 10, 3, False
|
||||
)[0]
|
||||
return Ylist[0]
|
||||
|
||||
def test_inferenceX_BGPLVM_Linear(self):
|
||||
Ys = self.genData()
|
||||
m = GPy.models.BayesianGPLVM(Ys,3,kernel=GPy.kern.Linear(3,ARD=True))
|
||||
m = GPy.models.BayesianGPLVM(Ys, 3, kernel=GPy.kern.Linear(3, ARD=True))
|
||||
m.optimize()
|
||||
x, mi = m.infer_newX(m.Y, optimize=True)
|
||||
np.testing.assert_array_almost_equal(m.X.mean, mi.X.mean, decimal=2)
|
||||
|
|
@ -27,8 +30,9 @@ class InferenceXTestCase(unittest.TestCase):
|
|||
|
||||
def test_inferenceX_BGPLVM_RBF(self):
|
||||
Ys = self.genData()
|
||||
m = GPy.models.BayesianGPLVM(Ys,3,kernel=GPy.kern.RBF(3,ARD=True))
|
||||
m = GPy.models.BayesianGPLVM(Ys, 3, kernel=GPy.kern.RBF(3, ARD=True))
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
m.optimize()
|
||||
|
|
@ -38,67 +42,110 @@ class InferenceXTestCase(unittest.TestCase):
|
|||
|
||||
def test_inferenceX_GPLVM_Linear(self):
|
||||
Ys = self.genData()
|
||||
m = GPy.models.GPLVM(Ys,3,kernel=GPy.kern.Linear(3,ARD=True))
|
||||
m = GPy.models.GPLVM(Ys, 3, kernel=GPy.kern.Linear(3, ARD=True))
|
||||
m.optimize()
|
||||
x, mi = m.infer_newX(m.Y, optimize=True)
|
||||
np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
|
||||
|
||||
def test_inferenceX_GPLVM_RBF(self):
|
||||
Ys = self.genData()
|
||||
m = GPy.models.GPLVM(Ys,3,kernel=GPy.kern.RBF(3,ARD=True))
|
||||
m = GPy.models.GPLVM(Ys, 3, kernel=GPy.kern.RBF(3, ARD=True))
|
||||
m.optimize()
|
||||
x, mi = m.infer_newX(m.Y, optimize=True)
|
||||
np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
|
||||
|
||||
class InferenceGPEP(unittest.TestCase):
|
||||
|
||||
class InferenceGPEP(unittest.TestCase):
|
||||
def genData(self):
|
||||
np.random.seed(1)
|
||||
k = GPy.kern.RBF(1, variance=7., lengthscale=0.2)
|
||||
X = np.random.rand(200,1)
|
||||
f = np.random.multivariate_normal(np.zeros(200), k.K(X) + 1e-5 * np.eye(X.shape[0]))
|
||||
k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2)
|
||||
X = np.random.rand(200, 1)
|
||||
f = np.random.multivariate_normal(
|
||||
np.zeros(200), k.K(X) + 1e-5 * np.eye(X.shape[0])
|
||||
)
|
||||
lik = GPy.likelihoods.Bernoulli()
|
||||
p = lik.gp_link.transf(f) # squash the latent function
|
||||
Y = lik.samples(f).reshape(-1,1)
|
||||
p = lik.gp_link.transf(f) # squash the latent function
|
||||
Y = lik.samples(f).reshape(-1, 1)
|
||||
return X, Y
|
||||
|
||||
def genNoisyData(self):
|
||||
np.random.seed(1)
|
||||
X = np.random.rand(100,1)
|
||||
X = np.random.rand(100, 1)
|
||||
self.real_std = 0.1
|
||||
noise = np.random.randn(*X[:, 0].shape)*self.real_std
|
||||
Y = (np.sin(X[:, 0]*2*np.pi) + noise)[:, None]
|
||||
self.f = np.random.rand(X.shape[0],1)
|
||||
noise = np.random.randn(*X[:, 0].shape) * self.real_std
|
||||
Y = (np.sin(X[:, 0] * 2 * np.pi) + noise)[:, None]
|
||||
self.f = np.random.rand(X.shape[0], 1)
|
||||
Y_extra_noisy = Y.copy()
|
||||
Y_extra_noisy[50] += 4.
|
||||
Y_extra_noisy[50] += 4.0
|
||||
# Y_extra_noisy[80:83] -= 2.
|
||||
return X, Y, Y_extra_noisy
|
||||
|
||||
def test_inference_EP(self):
|
||||
from paramz import ObsAr
|
||||
|
||||
X, Y = self.genData()
|
||||
lik = GPy.likelihoods.Bernoulli()
|
||||
k = GPy.kern.RBF(1, variance=7., lengthscale=0.2)
|
||||
inf = GPy.inference.latent_function_inference.expectation_propagation.EP(max_iters=30, delta=0.5)
|
||||
self.model = GPy.core.GP(X=X,
|
||||
Y=Y,
|
||||
kernel=k,
|
||||
inference_method=inf,
|
||||
likelihood=lik)
|
||||
k = GPy.kern.RBF(1, variance=7.0, lengthscale=0.2)
|
||||
inf = GPy.inference.latent_function_inference.expectation_propagation.EP(
|
||||
max_iters=30, delta=0.5
|
||||
)
|
||||
self.model = GPy.core.GP(
|
||||
X=X, Y=Y, kernel=k, inference_method=inf, likelihood=lik
|
||||
)
|
||||
K = self.model.kern.K(X)
|
||||
mean_prior = np.zeros(K.shape[0])
|
||||
post_params, ga_approx, cav_params, log_Z_tilde = self.model.inference_method.expectation_propagation(mean_prior, K, ObsAr(Y), lik, None)
|
||||
(
|
||||
post_params,
|
||||
ga_approx,
|
||||
cav_params,
|
||||
log_Z_tilde,
|
||||
) = self.model.inference_method.expectation_propagation(
|
||||
mean_prior, K, ObsAr(Y), lik, None
|
||||
)
|
||||
|
||||
mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
|
||||
p, m, d = self.model.inference_method._inference(Y, mean_prior, K, ga_approx, cav_params, lik, Y_metadata=None, Z_tilde=log_Z_tilde)
|
||||
p0, m0, d0 = super(GPy.inference.latent_function_inference.expectation_propagation.EP, inf).inference(k, X,lik ,mu_tilde[:,None], mean_function=None, variance=1./ga_approx.tau, K=K, Z_tilde=log_Z_tilde + np.sum(- 0.5*np.log(ga_approx.tau) + 0.5*(ga_approx.v*ga_approx.v*1./ga_approx.tau)))
|
||||
p, m, d = self.model.inference_method._inference(
|
||||
Y,
|
||||
mean_prior,
|
||||
K,
|
||||
ga_approx,
|
||||
cav_params,
|
||||
lik,
|
||||
Y_metadata=None,
|
||||
Z_tilde=log_Z_tilde,
|
||||
)
|
||||
p0, m0, d0 = super(
|
||||
GPy.inference.latent_function_inference.expectation_propagation.EP, inf
|
||||
).inference(
|
||||
k,
|
||||
X,
|
||||
lik,
|
||||
mu_tilde[:, None],
|
||||
mean_function=None,
|
||||
variance=1.0 / ga_approx.tau,
|
||||
K=K,
|
||||
Z_tilde=log_Z_tilde
|
||||
+ np.sum(
|
||||
-0.5 * np.log(ga_approx.tau)
|
||||
+ 0.5 * (ga_approx.v * ga_approx.v * 1.0 / ga_approx.tau)
|
||||
),
|
||||
)
|
||||
|
||||
assert (np.sum(np.array([m - m0,
|
||||
np.sum(d['dL_dK'] - d0['dL_dK']),
|
||||
np.sum(d['dL_dthetaL'] - d0['dL_dthetaL']),
|
||||
np.sum(d['dL_dm'] - d0['dL_dm']),
|
||||
np.sum(p._woodbury_vector - p0._woodbury_vector),
|
||||
np.sum(p.woodbury_inv - p0.woodbury_inv)])) < 1e6)
|
||||
assert (
|
||||
np.sum(
|
||||
np.array(
|
||||
[
|
||||
m - m0,
|
||||
np.sum(d["dL_dK"] - d0["dL_dK"]),
|
||||
np.sum(d["dL_dthetaL"] - d0["dL_dthetaL"]),
|
||||
np.sum(d["dL_dm"] - d0["dL_dm"]),
|
||||
np.sum(p._woodbury_vector - p0._woodbury_vector),
|
||||
np.sum(p.woodbury_inv - p0.woodbury_inv),
|
||||
]
|
||||
)
|
||||
)
|
||||
< 1e6
|
||||
)
|
||||
|
||||
# NOTE: adding a test like above for parameterized likelihood- the above test is
|
||||
# only for probit likelihood which does not have any tunable hyperparameter which is why
|
||||
|
|
@ -110,70 +157,124 @@ class InferenceGPEP(unittest.TestCase):
|
|||
# and it is possible that any error might creep up because of quadrature implementation.
|
||||
def test_inference_EP_non_classification(self):
|
||||
from paramz import ObsAr
|
||||
|
||||
X, Y, Y_extra_noisy = self.genNoisyData()
|
||||
deg_freedom = 5.
|
||||
deg_freedom = 5.0
|
||||
init_noise_var = 0.08
|
||||
lik_studentT = GPy.likelihoods.StudentT(deg_free=deg_freedom, sigma2=init_noise_var)
|
||||
lik_studentT = GPy.likelihoods.StudentT(
|
||||
deg_free=deg_freedom, sigma2=init_noise_var
|
||||
)
|
||||
# like_gaussian_noise = GPy.likelihoods.MixedNoise()
|
||||
k = GPy.kern.RBF(1, variance=2., lengthscale=1.1)
|
||||
ep_inf_alt = GPy.inference.latent_function_inference.expectation_propagation.EP(max_iters=4, delta=0.5)
|
||||
k = GPy.kern.RBF(1, variance=2.0, lengthscale=1.1)
|
||||
ep_inf_alt = GPy.inference.latent_function_inference.expectation_propagation.EP(
|
||||
max_iters=4, delta=0.5
|
||||
)
|
||||
# ep_inf_nested = GPy.inference.latent_function_inference.expectation_propagation.EP(ep_mode='nested', max_iters=100, delta=0.5)
|
||||
m = GPy.core.GP(X=X,Y=Y_extra_noisy,kernel=k,likelihood=lik_studentT,inference_method=ep_inf_alt)
|
||||
m = GPy.core.GP(
|
||||
X=X,
|
||||
Y=Y_extra_noisy,
|
||||
kernel=k,
|
||||
likelihood=lik_studentT,
|
||||
inference_method=ep_inf_alt,
|
||||
)
|
||||
K = m.kern.K(X)
|
||||
mean_prior = np.zeros(K.shape[0])
|
||||
post_params, ga_approx, cav_params, log_Z_tilde = m.inference_method.expectation_propagation(mean_prior, K, ObsAr(Y_extra_noisy), lik_studentT, None)
|
||||
(
|
||||
post_params,
|
||||
ga_approx,
|
||||
cav_params,
|
||||
log_Z_tilde,
|
||||
) = m.inference_method.expectation_propagation(
|
||||
mean_prior, K, ObsAr(Y_extra_noisy), lik_studentT, None
|
||||
)
|
||||
|
||||
mu_tilde = ga_approx.v / ga_approx.tau.astype(float)
|
||||
p, m, d = m.inference_method._inference(Y_extra_noisy, mean_prior, K, ga_approx, cav_params, lik_studentT, Y_metadata=None, Z_tilde=log_Z_tilde)
|
||||
p0, m0, d0 = super(GPy.inference.latent_function_inference.expectation_propagation.EP, ep_inf_alt).inference(k, X,lik_studentT ,mu_tilde[:,None], mean_function=None, variance=1./ga_approx.tau, K=K, Z_tilde=log_Z_tilde + np.sum(- 0.5*np.log(ga_approx.tau) + 0.5*(ga_approx.v*ga_approx.v*1./ga_approx.tau)))
|
||||
p, m, d = m.inference_method._inference(
|
||||
Y_extra_noisy,
|
||||
mean_prior,
|
||||
K,
|
||||
ga_approx,
|
||||
cav_params,
|
||||
lik_studentT,
|
||||
Y_metadata=None,
|
||||
Z_tilde=log_Z_tilde,
|
||||
)
|
||||
p0, m0, d0 = super(
|
||||
GPy.inference.latent_function_inference.expectation_propagation.EP,
|
||||
ep_inf_alt,
|
||||
).inference(
|
||||
k,
|
||||
X,
|
||||
lik_studentT,
|
||||
mu_tilde[:, None],
|
||||
mean_function=None,
|
||||
variance=1.0 / ga_approx.tau,
|
||||
K=K,
|
||||
Z_tilde=log_Z_tilde
|
||||
+ np.sum(
|
||||
-0.5 * np.log(ga_approx.tau)
|
||||
+ 0.5 * (ga_approx.v * ga_approx.v * 1.0 / ga_approx.tau)
|
||||
),
|
||||
)
|
||||
|
||||
assert (
|
||||
np.sum(
|
||||
np.array(
|
||||
[
|
||||
m - m0,
|
||||
np.sum(d["dL_dK"] - d0["dL_dK"]),
|
||||
np.sum(d["dL_dthetaL"] - d0["dL_dthetaL"]),
|
||||
np.sum(d["dL_dm"] - d0["dL_dm"]),
|
||||
np.sum(p._woodbury_vector - p0._woodbury_vector),
|
||||
np.sum(p.woodbury_inv - p0.woodbury_inv),
|
||||
]
|
||||
)
|
||||
)
|
||||
< 1e6
|
||||
)
|
||||
|
||||
assert (np.sum(np.array([m - m0,
|
||||
np.sum(d['dL_dK'] - d0['dL_dK']),
|
||||
np.sum(d['dL_dthetaL'] - d0['dL_dthetaL']),
|
||||
np.sum(d['dL_dm'] - d0['dL_dm']),
|
||||
np.sum(p._woodbury_vector - p0._woodbury_vector),
|
||||
np.sum(p.woodbury_inv - p0.woodbury_inv)])) < 1e6)
|
||||
|
||||
class VarDtcTest(unittest.TestCase):
|
||||
|
||||
def test_var_dtc_inference_with_mean(self):
|
||||
""" Check dL_dm in var_dtc is calculated correctly"""
|
||||
"""Check dL_dm in var_dtc is calculated correctly"""
|
||||
np.random.seed(1)
|
||||
x = np.linspace(0.,2*np.pi,100)[:,None]
|
||||
y = -np.cos(x)+np.random.randn(*x.shape)*0.3+1
|
||||
m = GPy.models.SparseGPRegression(x,y, mean_function=GPy.mappings.Linear(input_dim=1, output_dim=1))
|
||||
x = np.linspace(0.0, 2 * np.pi, 100)[:, None]
|
||||
y = -np.cos(x) + np.random.randn(*x.shape) * 0.3 + 1
|
||||
m = GPy.models.SparseGPRegression(
|
||||
x, y, mean_function=GPy.mappings.Linear(input_dim=1, output_dim=1)
|
||||
)
|
||||
self.assertTrue(m.checkgrad())
|
||||
|
||||
|
||||
class HMCSamplerTest(unittest.TestCase):
|
||||
|
||||
def test_sampling(self):
|
||||
np.random.seed(1)
|
||||
x = np.linspace(0.,2*np.pi,100)[:,None]
|
||||
y = -np.cos(x)+np.random.randn(*x.shape)*0.3+1
|
||||
x = np.linspace(0.0, 2 * np.pi, 100)[:, None]
|
||||
y = -np.cos(x) + np.random.randn(*x.shape) * 0.3 + 1
|
||||
|
||||
m = GPy.models.GPRegression(x,y)
|
||||
m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
|
||||
m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
|
||||
m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
|
||||
m = GPy.models.GPRegression(x, y)
|
||||
m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
|
||||
m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
|
||||
m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
|
||||
|
||||
hmc = GPy.inference.mcmc.HMC(m,stepsize=1e-2)
|
||||
hmc = GPy.inference.mcmc.HMC(m, stepsize=1e-2)
|
||||
s = hmc.sample(num_samples=3)
|
||||
|
||||
class MCMCSamplerTest(unittest.TestCase):
|
||||
|
||||
class MCMCSamplerTest(unittest.TestCase):
|
||||
def test_sampling(self):
|
||||
np.random.seed(1)
|
||||
x = np.linspace(0.,2*np.pi,100)[:,None]
|
||||
y = -np.cos(x)+np.random.randn(*x.shape)*0.3+1
|
||||
x = np.linspace(0.0, 2 * np.pi, 100)[:, None]
|
||||
y = -np.cos(x) + np.random.randn(*x.shape) * 0.3 + 1
|
||||
|
||||
m = GPy.models.GPRegression(x,y)
|
||||
m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
|
||||
m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
|
||||
m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.,10.))
|
||||
m = GPy.models.GPRegression(x, y)
|
||||
m.kern.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
|
||||
m.kern.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
|
||||
m.likelihood.variance.set_prior(GPy.priors.Gamma.from_EV(1.0, 10.0))
|
||||
|
||||
mcmc = GPy.inference.mcmc.Metropolis_Hastings(m)
|
||||
mcmc.sample(Ntotal=100, Nburn=10)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue