def evaluation_thresholds_modular(index):
from modshogun import Labels, ROCEvaluation
import numpy
numpy.random.seed(17)
output = numpy.arange(-1, 1, 0.001)
output = (0.3 * output + 0.7 * (numpy.random.rand(len(output)) - 0.5))
label = [-1.0] * (len(output) // 2)
label.extend([1.0] * (len(output) // 2))
label = numpy.array(label)
pred = Labels(output)
truth = Labels(label)
evaluator = ROCEvaluation()
evaluator.evaluate(pred, truth)
[fp, tp] = evaluator.get_ROC()
thresh = evaluator.get_thresholds()
b = thresh[index]
#print("tpr", numpy.mean(output[label>0]>b), tp[index])
#print("fpr", numpy.mean(output[label<0]>b), fp[index])
return tp[index], fp[index], numpy.mean(output[label > 0] > b), numpy.mean(
output[label < 0] > b)
def evaluation_thresholds_modular(index): from modshogun import Labels, ROCEvaluation import numpy numpy.random.seed(17) output=numpy.arange(-1,1,0.001) output=(0.3*output+0.7*(numpy.random.rand(len(output))-0.5)) label=[-1.0]*(len(output)/2) label.extend([1.0]*(len(output)/2)) label=numpy.array(label) pred=Labels(output) truth=Labels(label) evaluator=ROCEvaluation() evaluator.evaluate(pred, truth) [fp,tp]=evaluator.get_ROC() thresh=evaluator.get_thresholds() b=thresh[index] #print "tpr", numpy.mean(output[label>0]>b), tp[index] #print "fpr", numpy.mean(output[label<0]>b), fp[index] return tp[index],fp[index],numpy.mean(output[label>0]>b),numpy.mean(output[label<0]>b)
def evaluation_rocevaluation_modular (ground_truth, predicted): from modshogun import BinaryLabels from modshogun import ROCEvaluation ground_truth_labels = BinaryLabels(ground_truth) predicted_labels = BinaryLabels(predicted) evaluator = ROCEvaluation() evaluator.evaluate(predicted_labels,ground_truth_labels) return evaluator.get_ROC(), evaluator.get_auROC()
def evaluation_rocevaluation_modular(ground_truth, predicted):
from modshogun import BinaryLabels
from modshogun import ROCEvaluation
ground_truth_labels = BinaryLabels(ground_truth)
predicted_labels = BinaryLabels(predicted)
evaluator = ROCEvaluation()
evaluator.evaluate(predicted_labels, ground_truth_labels)
return evaluator.get_ROC(), evaluator.get_auROC()
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()
plot(roc[0], roc[1])
fill_between(roc[0],roc[1],0,alpha=0.1)