def classifier_multiclass_relaxedtree(fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
label_test_multiclass=label_testdat,
lawidth=2.1,
C=1,
epsilon=1e-5):
from modshogun import RealFeatures, MulticlassLabels
from modshogun import RelaxedTree, MulticlassLibLinear
from modshogun import GaussianKernel
#print('Working on a problem of %d features and %d samples' % fm_train_real.shape)
feats_train = RealFeatures(fm_train_real)
labels = MulticlassLabels(label_train_multiclass)
machine = RelaxedTree()
machine.set_machine_for_confusion_matrix(MulticlassLibLinear())
machine.set_kernel(GaussianKernel())
machine.set_labels(labels)
machine.train(feats_train)
label_pred = machine.apply_multiclass(RealFeatures(fm_test_real))
out = label_pred.get_labels()
if label_test_multiclass is not None:
from modshogun import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
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 classifier_multiclassliblinear_modular(
fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
label_test_multiclass=label_testdat,
width=2.1,
C=1,
epsilon=1e-5):
from modshogun import RealFeatures, MulticlassLabels
from modshogun import MulticlassLibLinear
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = MulticlassLibLinear(C, feats_train, labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from modshogun import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def load_svm(self, filepath):
from modshogun import SerializableAsciiFile
print("Attempting to load a multiclass liblinear svm from \"" +
filepath +"\"")
self.svm = MulticlassLibLinear()
loader = SerializableAsciiFile(filepath, "r")
self.svm.load_serializable(loader)
print("Svm succesfully loaded")
def classifier_multilabeloutputliblinear_modular(
fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
label_test_multiclass=label_testdat,
width=2.1,
C=1,
epsilon=1e-5):
from modshogun import RealFeatures, MulticlassLabels, MultilabelLabels
from modshogun import MulticlassLibLinear
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = MulticlassLibLinear(C, feats_train, labels)
classifier.train()
label_pred = classifier.apply_multilabel_output(feats_test, 2)
out = label_pred.get_labels()
#print out
return out
