def evaluation_rocevaluation_modular(ground_truth, predicted): from shogun.Features import Labels from shogun.Evaluation import ROCEvaluation ground_truth_labels = Labels(ground_truth) predicted_labels = Labels(predicted) evaluator = ROCEvaluation() evaluator.evaluate(predicted_labels,ground_truth_labels) return evaluator.get_ROC(), evaluator.get_auROC()
def evaluation_multiclassovrevaluation_modular(traindat, label_traindat,
testdat, label_testdat):
from shogun.Features import MulticlassLabels
from shogun.Evaluation import MulticlassOVREvaluation, ROCEvaluation
from modshogun import MulticlassLibLinear, RealFeatures, ContingencyTableEvaluation, ACCURACY
from shogun.Mathematics import Math
Math.init_random(1)
ground_truth_labels = MulticlassLabels(label_traindat)
svm = MulticlassLibLinear(1.0, RealFeatures(traindat),
MulticlassLabels(label_traindat))
svm.train()
predicted_labels = svm.apply()
binary_evaluator = ROCEvaluation()
evaluator = MulticlassOVREvaluation(binary_evaluator)
mean_roc = evaluator.evaluate(predicted_labels, ground_truth_labels)
#print mean_roc
binary_evaluator = ContingencyTableEvaluation(ACCURACY)
evaluator = MulticlassOVREvaluation(binary_evaluator)
mean_accuracy = evaluator.evaluate(predicted_labels, ground_truth_labels)
#print mean_accuracy
return mean_roc, mean_accuracy
def evaluation_rocevaluation_modular(ground_truth, predicted): from shogun.Features import Labels from shogun.Evaluation import ROCEvaluation ground_truth_labels = Labels(ground_truth) predicted_labels = Labels(predicted) evaluator = ROCEvaluation() evaluator.evaluate(predicted_labels,ground_truth_labels) return evaluator.get_ROC(), evaluator.get_auROC()
gk=GaussianKernel(features, features, 1.0)
svm = LibSVM(1000.0, gk, labels)
svm.train()
lda=LDA(1,features,labels)
lda.train()
## plot points
subplot(211)
plot(pos[0,:], pos[1,:], "r.")
plot(neg[0,:], neg[1,:], "b.")
grid(True)
title('Data',size=10)
# plot ROC for SVM
subplot(223)
ROC_evaluation=ROCEvaluation()
ROC_evaluation.evaluate(svm.apply(),labels)
roc = ROC_evaluation.get_ROC()
print roc
plot(roc[0], roc[1])
fill_between(roc[0],roc[1],0,alpha=0.1)
text(mean(roc[0])/2,mean(roc[1])/2,'auROC = %.5f' % ROC_evaluation.get_auROC())
grid(True)
xlabel('FPR')
ylabel('TPR')
title('LibSVM (Gaussian kernel, C=%.3f) ROC curve' % svm.get_C1(),size=10)
# plot ROC for LDA
subplot(224)
ROC_evaluation.evaluate(lda.apply(),labels)
roc = ROC_evaluation.get_ROC()
model_selection=GridSearchModelSelection(param_tree_root, cross_validation)
best_parameters=model_selection.select_model(True)
print "Best parameters: ",
best_parameters.print_tree()
best_parameters.apply_to_machine(classifier)
classifier.train()
A = test_labels
B = BinaryLabels(classifier.apply(feats_test).get_labels())
#print evaluation_contingencytableevaluation_modular((trainlab), (classifier.apply(feats_train).get_labels()))
#print evaluation_rocevaluation_modular((trainlab), (classifier.apply(feats_train).get_labels()))
# Run the SVM
subplot(111)
ROC_evaluation=ROCEvaluation()
ROC_evaluation.evaluate(A, B)
roc = ROC_evaluation.get_ROC()
plot(roc[0], roc[1])
fill_between(roc[0],roc[1],0,alpha=0.1)
grid(True)
xlabel('FPR')
ylabel('TPR')
title('ROC (Width=%.3f, C1=%.3f, C2=%.3f) ROC curve = %.3f' % (kernel.get_width(), classifier.get_C1(), classifier.get_C2(), ROC_evaluation.get_auROC()),size=10)
savefig("unamed.png")
"""
subplot(222)
ROC_evaluation=ROCEvaluation()
ROC_evaluation.evaluate(classifier.apply(feats_test),Labels(testlab))
roc = ROC_evaluation.get_ROC()
gk=GaussianKernel(features, features, 1.0)
svm = LibSVM(1000.0, gk, labels)
svm.train()
lda=LDA(1,features,labels)
lda.train()
## plot points
subplot(211)
plot(pos[0,:], pos[1,:], "r.")
plot(neg[0,:], neg[1,:], "b.")
grid(True)
title('Data',size=10)
# plot ROC for SVM
subplot(223)
ROC_evaluation=ROCEvaluation()
ROC_evaluation.evaluate(svm.classify(),labels)
roc = ROC_evaluation.get_ROC()
plot(roc[:,0], roc[:,1])
fill_between(roc[:,0],roc[:,1],0,alpha=0.1)
text(mean(roc[:,0])/2,mean(roc[:,1])/2,'auROC = %.5f' % ROC_evaluation.get_auROC())
grid(True)
xlabel('FPR')
ylabel('TPR')
title('LibSVM (Gaussian kernel, C=%.3f) ROC curve' % svm.get_C1(),size=10)
# plot ROC for LDA
subplot(224)
ROC_evaluation.evaluate(lda.classify(),labels)
roc = ROC_evaluation.get_ROC()
plot(roc[:,0], roc[:,1])
