#!/usr/bin/env python
from tools.multiclass_shared import prepare_data
[traindat, label_traindat, testdat, label_testdat] = prepare_data()
parameter_list = [[traindat,testdat,label_traindat,2.1,1,1e-5],[traindat,testdat,label_traindat,2.2,1,1e-5]]
def classifier_multiclassmachine (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
from shogun import RealFeatures, MulticlassLabels
from shogun import GaussianKernel
from shogun import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
kernel=GaussianKernel(feats_train, feats_train, width)
labels=MulticlassLabels(label_train_multiclass)
classifier = LibSVM()
classifier.set_epsilon(epsilon)
#print labels.get_labels()
mc_classifier = KernelMulticlassMachine(MulticlassOneVsRestStrategy(),kernel,classifier,labels)
mc_classifier.train()
kernel.init(feats_train, feats_test)
out = mc_classifier.apply().get_labels()
return out
if __name__=='__main__':
print('MulticlassMachine')
classifier_multiclassmachine(*parameter_list[0])
#!/usr/bin/env python
from tools.multiclass_shared import prepare_data
[traindat, label_traindat, testdat, label_testdat] = prepare_data(False)
parameter_list = [[
traindat, testdat, label_traindat, label_testdat, 2.1, 1, 1e-5
], [traindat, testdat, label_traindat, label_testdat, 2.2, 1, 1e-5]]
def classifier_multiclass_ecoc_discriminant(
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 LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun import ECOCStrategy, ECOCDiscriminantEncoder, ECOCHDDecoder
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = LibLinear(L2R_L2LOSS_SVC)
classifier.set_epsilon(epsilon)
classifier.set_bias_enabled(True)
#!/usr/bin/env python
from tools.multiclass_shared import prepare_data
[traindat, label_traindat, testdat, label_testdat] = prepare_data(True)
parameter_list = [[
traindat, testdat, label_traindat, label_testdat, 2.1, 1, 1e-5
], [traindat, testdat, label_traindat, label_testdat, 2.2, 1, 1e-5]]
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 shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import RelaxedTree, MulticlassLibLinear
from shogun.Kernel 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)
