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()
#
# labels_predict = svm.apply(features_test)
# train_pred = svm.apply(features_train)
# alphas = svm.get_alphas()
# b = svm.get_bias()
#
eval = AccuracyMeasure()
# train_eval = AccuracyMeasure()
# # accuracy = eval.evaluate(labels_predict.get_labels(), labels_test)
# # print(accuracy)
# train_eval.evaluate(train_pred, labels_train)
# train_accuracy = train_eval.get_accuracy() * 100
eval.evaluate(labels_predict, labels_test)
accuracy = eval.get_accuracy() * 100
roc = ROCEvaluation()
roc_pred = roc.evaluate(labels_predict, labels_test)
test = roc.get_ROC()
plt.plot(test[0], test[1], marker='.')
plt.show()
print('Concatenated')
print('Alphas:', alpha)
print('Beta:', beta)
print('AUC:', roc_pred)
#
# print('Train Accuracy(%):', train_accuracy)
print('Accuracy(%):', accuracy)
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)
