print __doc__
# Load the dataset from scikits' data sets
diabetes = datasets.load_diabetes()
X, y = diabetes['data'], diabetes['target']
# Instanciate a GP model
gp = GaussianProcess(regr='constant', corr='absolute_exponential',
theta0=[1e-4] * 10, thetaL=[1e-12] * 10,
thetaU=[1e-2] * 10, nugget=1e-2, optimizer='Welch',
verbose=False)
# Fit the GP model to the data
gp.fit(X, y)
gp.theta0 = gp.theta
gp.thetaL = None
gp.thetaU = None
gp.verbose = False
# Estimate the leave-one-out predictions using the cross_val module
n_jobs = 2 # the distributing capacity available on the machine
y_pred = y + cross_val.cross_val_score(gp, X, y=y,
cv=cross_val.LeaveOneOut(y.size),
n_jobs=n_jobs,
).ravel()
# Compute the empirical explained variance
Q2 = metrics.explained_variance_score(y, y_pred)
# Goodness-of-fit plot
pl.figure()
from scikits.learn.gaussian_process import GaussianProcess
from scikits.learn.cross_val import cross_val_score, KFold
from scikits.learn.metrics import r2_score
# Load the dataset from scikits' data sets
diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target
# Instanciate a GP model
gp = GaussianProcess(regr='constant',
corr='absolute_exponential',
theta0=[1e-4] * 10,
thetaL=[1e-12] * 10,
thetaU=[1e-2] * 10,
nugget=1e-2,
optimizer='Welch')
# Fit the GP model to the data performing maximum likelihood estimation
gp.fit(X, y)
# Deactivate maximum likelihood estimation for the cross-validation loop
gp.theta0 = gp.theta # Given correlation parameter = MLE
gp.thetaL, gp.thetaU = None, None # None bounds deactivate MLE
# Perform a cross-validation estimate of the coefficient of determination using
# the cross_val module using all CPUs available on the machine
K = 20 # folds
R2 = cross_val_score(gp, X, y=y, cv=KFold(y.size, K), n_jobs=-1).mean()
print("The %d-Folds estimate of the coefficient of determination is R2 = %s" %
(K, R2))
# Author: Vincent Dubourg <*****@*****.**>
# License: BSD style
from scikits.learn import datasets
from scikits.learn.gaussian_process import GaussianProcess
from scikits.learn.cross_val import cross_val_score, KFold
# Load the dataset from scikits' data sets
diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target
# Instanciate a GP model
gp = GaussianProcess(regr='constant', corr='absolute_exponential',
theta0=[1e-4] * 10, thetaL=[1e-12] * 10,
thetaU=[1e-2] * 10, nugget=1e-2, optimizer='Welch')
# Fit the GP model to the data performing maximum likelihood estimation
gp.fit(X, y)
# Deactivate maximum likelihood estimation for the cross-validation loop
gp.theta0 = gp.theta # Given correlation parameter = MLE
gp.thetaL, gp.thetaU = None, None # None bounds deactivate MLE
# Perform a cross-validation estimate of the coefficient of determination using
# the cross_val module using all CPUs available on the machine
K = 20 # folds
R2 = cross_val_score(gp, X, y=y, cv=KFold(y.size, K), n_jobs=-1).mean()
print("The %d-Folds estimate of the coefficient of determination is R2 = %s"
% (K, R2))
