apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-18 13:55:14 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Tests setup but not fitting properly yet

Browse source
This commit is contained in:
Alan Saul 2013-09-19 15:56:18 +01:00
parent ca09051a56
commit 9d7b670160
1 changed files with 65 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

87
GPy/examples/laplace_approximations.py
View file

@ -659,9 +659,10 @@ def boston_example():
Y = data['Y'].copy() Y = data['Y'].copy()
Y = Y-Y.mean() Y = Y-Y.mean()
Y = Y/Y.std() Y = Y/Y.std()
num_folds = 2 import ipdb; ipdb.set_trace() # XXX BREAKPOINT
num_folds = 10
kf = KFold(len(Y), n_folds=num_folds, indices=True) kf = KFold(len(Y), n_folds=num_folds, indices=True)
score_folds = np.zeros((3, num_folds)) score_folds = np.zeros((4, num_folds))
def rmse(Y, Ystar): def rmse(Y, Ystar):
return np.sqrt(np.mean((Y-Ystar)**2)) return np.sqrt(np.mean((Y-Ystar)**2))
#for train, test in kf: #for train, test in kf:
@ -673,56 +674,98 @@ def boston_example():
#Gaussian GP #Gaussian GP
print "Gauss GP" print "Gauss GP"
kernelgp = GPy.kern.rbf(X.shape[1]) #+ GPy.kern.white(X.shape[1]) kernelgp = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1], variance=0.01)
mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp) mgp = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelgp)
mgp.ensure_default_constraints() mgp.ensure_default_constraints()
mgp['noise'] = noise mgp['noise'] = noise
mgp.constrain_fixed('white', 0.01)
print mgp
mgp.optimize(messages=1) mgp.optimize(messages=1)
Y_test_pred = mgp.predict(X_test) Y_test_pred = mgp.predict(X_test)
score_folds[0, n] = rmse(Y_test, Y_test_pred[0]) score_folds[0, n] = rmse(Y_test, Y_test_pred[0])
plt.figure() print mgp
plt.scatter(X_test[:, 0], Y_test_pred[0])
plt.scatter(X_test[:, 0], Y_test, c='r', marker='x')
print score_folds print score_folds
plt.title('GP gauss') #plt.figure()
#plt.scatter(X_test[:, 0], Y_test_pred[0])
#plt.scatter(X_test[:, 0], Y_test, c='r', marker='x')
#plt.title('GP gauss')
print "Gaussian Laplace GP" print "Gaussian Laplace GP"
sigma2_start = 1 sigma2_start = 1
kernelstu = GPy.kern.rbf(X.shape[1]) #+ GPy.kern.white(X.shape[1], variance=0.01) kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1], variance=0.1)
N, D = Y_train.shape N, D = Y_train.shape
g_distribution = GPy.likelihoods.functions.Gaussian(variance=noise, N=N, D=D) g_distribution = GPy.likelihoods.functions.Gaussian(variance=noise, N=N, D=D)
g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution, opt='rasm') g_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), g_distribution, opt='rasm')
mg = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=g_likelihood) mg = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=g_likelihood)
mg.ensure_default_constraints() mg.ensure_default_constraints()
mg.constrain_positive('noise_variance') mg.constrain_positive('noise_variance')
mg.optimize(messages=1) mg.constrain_fixed('white', 0.01)
mg['noise'] = noise
print mg
try:
mg.optimize(messages=1)
except Exception:
print "Blew up"
Y_test_pred = mg.predict(X_test) Y_test_pred = mg.predict(X_test)
score_folds[1, n] = rmse(Y_test, Y_test_pred[0]) score_folds[1, n] = rmse(Y_test, Y_test_pred[0])
print score_folds print score_folds
plt.figure() print mg
plt.scatter(X_test[:, 0], Y_test_pred[0]) #plt.figure()
plt.scatter(X_test[:, 0], Y_test, c='r', marker='x') #plt.scatter(X_test[:, 0], Y_test_pred[0])
plt.title('Lap gauss') #plt.scatter(X_test[:, 0], Y_test, c='r', marker='x')
#plt.title('Lap gauss')
#Student t likelihood #Student t likelihood
print "Student-T GP"
deg_free = 5 deg_free = 5
kernelstu = GPy.kern.rbf(X.shape[1]) #+ GPy.kern.white(X.shape[1], variance=0.01) print "Student-T GP {}df".format(deg_free)
kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1], variance=0.1)
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise) t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise)
stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm') stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm')
mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood) mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood)
mstu_t.ensure_default_constraints() mstu_t.ensure_default_constraints()
mstu_t.constrain_fixed('white', 0.01)
#mstu_t.constrain_positive('t_noise') #mstu_t.constrain_positive('t_noise')
mstu_t.constrain_bounded('t_noise', 0.01, 1000) mstu_t.constrain_bounded('t_noise', 0.001, 1000)
mstu_t.optimize(messages=1) mstu_t['t_noise'] = noise
print mstu_t
try:
mstu_t.optimize(messages=1)
except Exception:
print "Blew up"
Y_test_pred = mstu_t.predict(X_test) Y_test_pred = mstu_t.predict(X_test)
score_folds[2, n] = rmse(Y_test, Y_test_pred[0]) score_folds[2, n] = rmse(Y_test, Y_test_pred[0])
print score_folds print score_folds
plt.figure() print mstu_t
plt.scatter(X_test[:, 0], Y_test_pred[0]) #plt.figure()
plt.scatter(X_test[:, 0], Y_test, c='r', marker='x') #plt.scatter(X_test[:, 0], Y_test_pred[0])
plt.title('Stu t') #plt.scatter(X_test[:, 0], Y_test, c='r', marker='x')
import ipdb; ipdb.set_trace() # XXX BREAKPOINT #plt.title('Stu t {}df'.format(deg_free))
deg_free = 3
print "Student-T GP {}df".format(deg_free)
kernelstu = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1], variance=0.1)
t_distribution = GPy.likelihoods.functions.StudentT(deg_free, sigma2=noise)
stu_t_likelihood = GPy.likelihoods.Laplace(Y_train.copy(), t_distribution, opt='rasm')
mstu_t = GPy.models.GPRegression(X_train.copy(), Y_train.copy(), kernel=kernelstu, likelihood=stu_t_likelihood)
mstu_t.ensure_default_constraints()
mstu_t.constrain_fixed('white', 0.01)
#mstu_t.constrain_positive('t_noise')
mstu_t.constrain_bounded('t_noise', 0.001, 1000)
mstu_t['t_noise'] = noise
print mstu_t
try:
mstu_t.optimize(messages=1)
except Exception:
print "Blew up"
mstu_t.optimize(messages=1)
Y_test_pred = mstu_t.predict(X_test)
score_folds[3, n] = rmse(Y_test, Y_test_pred[0])
print score_folds
print mstu_t
#plt.figure()
#plt.scatter(X_test[:, 0], Y_test_pred[0])
#plt.scatter(X_test[:, 0], Y_test, c='r', marker='x')
#plt.title('Stu t {}df'.format(deg_free))
def plot_f_approx(model): def plot_f_approx(model):