def variational_classifier_modular(kl_inference,train_fname=traindat,test_fname=testdat, label_fname=label_binary_traindat,kernel_log_sigma=0,kernel_log_scale=0,noise_factor=1e-5, min_coeff_kernel=1e-2,max_attempt=0): from math import exp features_train=RealFeatures(CSVFile(train_fname)) labels_train=BinaryLabels(CSVFile(label_fname)) likelihood=LogitDVGLikelihood() error_eval=ErrorRateMeasure() mean_func=ConstMean() kernel_sigma=2*exp(2*kernel_log_sigma); kernel_func=GaussianKernel(10, kernel_sigma) inf=kl_inference(kernel_func, features_train, mean_func, labels_train, likelihood) try: inf.set_noise_factor(noise_factor) inf.set_min_coeff_kernel(min_coeff_kernel) inf.set_max_attempt(max_attempt) except: pass inf.set_scale(exp(kernel_log_scale)) gp=GaussianProcessClassification(inf) gp.train() pred_labels_train=gp.apply_binary(features_train) error_train=error_eval.evaluate(pred_labels_train, labels_train) #print "\nInference name:%s"%inf.get_name(), #print "marginal likelihood:%.10f"%inf.get_negative_log_marginal_likelihood(), #print "Training error %.4f"%error_train return pred_labels_train, gp, pred_labels_train.get_labels()
def variational_classifier_modular(kl_inference,
train_fname=traindat,
test_fname=testdat,
label_fname=label_binary_traindat,
kernel_log_sigma=0,
kernel_log_scale=0,
noise_factor=1e-5,
min_coeff_kernel=1e-2,
max_attempt=0):
from math import exp
features_train = RealFeatures(CSVFile(train_fname))
labels_train = BinaryLabels(CSVFile(label_fname))
likelihood = LogitDVGLikelihood()
error_eval = ErrorRateMeasure()
mean_func = ConstMean()
kernel_sigma = 2 * exp(2 * kernel_log_sigma)
kernel_func = GaussianKernel(10, kernel_sigma)
inf = kl_inference(kernel_func, features_train, mean_func, labels_train,
likelihood)
try:
inf.set_noise_factor(noise_factor)
inf.set_min_coeff_kernel(min_coeff_kernel)
inf.set_max_attempt(max_attempt)
except:
pass
inf.set_scale(exp(kernel_log_scale))
gp = GaussianProcessClassification(inf)
gp.train()
pred_labels_train = gp.apply_binary(features_train)
error_train = error_eval.evaluate(pred_labels_train, labels_train)
#print "\nInference name:%s"%inf.get_name(),
#print "marginal likelihood:%.10f"%inf.get_negative_log_marginal_likelihood(),
#print "Training error %.4f"%error_train
return pred_labels_train, gp, pred_labels_train.get_labels()
def gaussian_process_binary_classification_laplace(X_train,
y_train,
n_test=50):
# import all necessary modules from Shogun (some of them require Eigen3)
try:
from modshogun import RealFeatures, BinaryLabels, GaussianKernel, \
LogitLikelihood, ProbitLikelihood, ZeroMean, LaplacianInferenceMethod, \
EPInferenceMethod, GaussianProcessClassification
except ImportError:
print('Eigen3 needed for Gaussian Processes')
return
# convert training data into Shogun representation
train_features = RealFeatures(X_train)
train_labels = BinaryLabels(y_train)
# generate all pairs in 2d range of testing data
x1 = linspace(X_train[0, :].min() - 1, X_train[0, :].max() + 1, n_test)
x2 = linspace(X_train[1, :].min() - 1, X_train[1, :].max() + 1, n_test)
X_test = asarray(list(product(x1, x2))).T
# convert testing features into Shogun representation
test_features = RealFeatures(X_test)
# create Gaussian kernel with width = 2.0
kernel = GaussianKernel(10, 2.0)
# create zero mean function
mean = ZeroMean()
# you can easily switch between probit and logit likelihood models
# by uncommenting/commenting the following lines:
# create probit likelihood model
# lik = ProbitLikelihood()
# create logit likelihood model
lik = LogitLikelihood()
# you can easily switch between Laplace and EP approximation by
# uncommenting/commenting the following lines:
# specify Laplace approximation inference method
# inf = LaplacianInferenceMethod(kernel, train_features, mean, train_labels, lik)
# specify EP approximation inference method
inf = EPInferenceMethod(kernel, train_features, mean, train_labels, lik)
# create and train GP classifier, which uses Laplace approximation
gp = GaussianProcessClassification(inf)
gp.train()
# get probabilities p(y*=1|x*) for each testing feature x*
p_test = gp.get_probabilities(test_features)
# create figure
figure()
title('Training examples, predictive probability and decision boundary')
# plot training data
plot(X_train[0, argwhere(y_train == 1)], X_train[1,
argwhere(y_train == 1)],
'ro')
plot(X_train[0, argwhere(y_train == -1)], X_train[1,
argwhere(y_train == -1)],
'bo')
# plot decision boundary
contour(x1,
x2,
reshape(p_test, (n_test, n_test)),
levels=[0.5],
colors=('black'))
# plot probabilities
pcolor(x1, x2, reshape(p_test, (n_test, n_test)))
# show color bar
colorbar()
# show figure
show()
def gaussian_process_binary_classification_laplace(X_train, y_train, n_test=50):
# import all necessary modules from Shogun (some of them require Eigen3)
try:
from modshogun import RealFeatures, BinaryLabels, GaussianKernel, \
LogitLikelihood, ProbitLikelihood, ZeroMean, SingleLaplacianInferenceMethod, \
EPInferenceMethod, GaussianProcessClassification
except ImportError:
print('Eigen3 needed for Gaussian Processes')
return
# convert training data into Shogun representation
train_features = RealFeatures(X_train)
train_labels = BinaryLabels(y_train)
# generate all pairs in 2d range of testing data
x1 = linspace(X_train[0,:].min()-1, X_train[0,:].max()+1, n_test)
x2 = linspace(X_train[1,:].min()-1, X_train[1,:].max()+1, n_test)
X_test = asarray(list(product(x1, x2))).T
# convert testing features into Shogun representation
test_features = RealFeatures(X_test)
# create Gaussian kernel with width = 2.0
kernel = GaussianKernel(10, 2.0)
# create zero mean function
mean = ZeroMean()
# you can easily switch between probit and logit likelihood models
# by uncommenting/commenting the following lines:
# create probit likelihood model
# lik = ProbitLikelihood()
# create logit likelihood model
lik = LogitLikelihood()
# you can easily switch between Laplace and EP approximation by
# uncommenting/commenting the following lines:
# specify Laplace approximation inference method
# inf = SingleLaplacianInferenceMethod(kernel, train_features, mean, train_labels, lik)
# specify EP approximation inference method
inf = EPInferenceMethod(kernel, train_features, mean, train_labels, lik)
# create and train GP classifier, which uses Laplace approximation
gp = GaussianProcessClassification(inf)
gp.train()
# get probabilities p(y*=1|x*) for each testing feature x*
p_test = gp.get_probabilities(test_features)
# create figure
figure()
title('Training examples, predictive probability and decision boundary')
# plot training data
plot(X_train[0, argwhere(y_train == 1)], X_train[1, argwhere(y_train == 1)], 'ro')
plot(X_train[0, argwhere(y_train == -1)], X_train[1, argwhere(y_train == -1)], 'bo')
# plot decision boundary
contour(x1, x2, reshape(p_test, (n_test, n_test)), levels=[0.5], colors=('black'))
# plot probabilities
pcolor(x1, x2, reshape(p_test, (n_test, n_test)))
# show color bar
colorbar()
# show figure
show()