Python prepare_data Examples

Example #1

File: classifier_multiclassmachine_modular.py Project: lgatto/shogun

import classifier_multiclass_shared

[traindat, label_traindat, testdat, label_testdat] = classifier_multiclass_shared.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_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
	from shogun.Features import RealFeatures, MulticlassLabels
	from shogun.Kernel import GaussianKernel
	from shogun.Classifier 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_modular(*parameter_list[0])

Example #2

import classifier_multiclass_shared

[traindat, label_traindat, testdat,
 label_testdat] = classifier_multiclass_shared.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_multiclasslinearmachine_modular(
        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 LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
    from shogun.Classifier 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)

Example #3

File: classifier_multiclass_ecoc_ovr.py Project: vinodrajendran001/ASP

import classifier_multiclass_shared
# run with toy data
[traindat, label_traindat, testdat, label_testdat] = classifier_multiclass_shared.prepare_data()

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_multiclasslinearmachine_modular (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 LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
	from shogun.Classifier import ECOCStrategy, ECOCOVREncoder, ECOCLLBDecoder, MulticlassOneVsRestStrategy

	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)

	mc_classifier = LinearMulticlassMachine(MulticlassOneVsRestStrategy(), feats_train, classifier, labels)
	mc_classifier.train()
	label_mc = mc_classifier.apply(feats_test)
	out_mc = label_mc.get_labels()

	ecoc_strategy = ECOCStrategy(ECOCOVREncoder(), ECOCLLBDecoder())
	ecoc_classifier = LinearMulticlassMachine(ecoc_strategy, feats_train, classifier, labels)
	ecoc_classifier.train()
	label_ecoc = ecoc_classifier.apply(feats_test)
	out_ecoc = label_ecoc.get_labels()