def classifier_multiclass_ecoc_ovr(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()
n_diff = (out_mc != out_ecoc).sum()
if n_diff == 0:
print("Same results for OvR and ECOCOvR")
else:
print(
"Different results for OvR and ECOCOvR (%d out of %d are different)"
% (n_diff, len(out_mc)))
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc_mc = evaluator.evaluate(label_mc, labels_test)
acc_ecoc = evaluator.evaluate(label_ecoc, labels_test)
print('Normal OVR Accuracy = %.4f' % acc_mc)
print('ECOC OVR Accuracy = %.4f' % acc_ecoc)
return out_ecoc, out_mc
def classifier_multiclass_ecoc_random(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, ECOCRandomSparseEncoder, ECOCRandomDenseEncoder, 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)
rnd_dense_strategy = ECOCStrategy(ECOCRandomDenseEncoder(),
ECOCHDDecoder())
rnd_sparse_strategy = ECOCStrategy(ECOCRandomSparseEncoder(),
ECOCHDDecoder())
dense_classifier = LinearMulticlassMachine(rnd_dense_strategy, feats_train,
classifier, labels)
dense_classifier.train()
label_dense = dense_classifier.apply(feats_test)
out_dense = label_dense.get_labels()
sparse_classifier = LinearMulticlassMachine(rnd_sparse_strategy,
feats_train, classifier,
labels)
sparse_classifier.train()
label_sparse = sparse_classifier.apply(feats_test)
out_sparse = label_sparse.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc_dense = evaluator.evaluate(label_dense, labels_test)
acc_sparse = evaluator.evaluate(label_sparse, labels_test)
print('Random Dense Accuracy = %.4f' % acc_dense)
print('Random Sparse Accuracy = %.4f' % acc_sparse)
return out_sparse, out_dense
def classifier_multiclasslogisticregression_modular(
fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
label_test_multiclass=label_testdat,
z=1,
epsilon=1e-5):
from shogun.Features import RealFeatures, MulticlassLabels
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = MulticlassLabels(label_train_multiclass)
classifier = MulticlassLogisticRegression(z, feats_train, labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def classifier_multiclass_shareboost(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, RealSubsetFeatures, MulticlassLabels
from shogun.Classifier import ShareBoost
#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)
shareboost = ShareBoost(feats_train, labels,
min(fm_train_real.shape[0] - 1, 30))
shareboost.train()
#print(shareboost.get_activeset())
feats_test = RealSubsetFeatures(RealFeatures(fm_test_real),
shareboost.get_activeset())
label_pred = shareboost.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
#print('Accuracy = %.4f' % acc)
return out
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)
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 shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def classifier_multiclasslinearmachine_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 shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine, MulticlassOneVsOneStrategy, 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(MulticlassOneVsOneStrategy(), feats_train, classifier, labels)
mc_classifier.train()
label_pred = mc_classifier.apply()
out = label_pred.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
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)
encoder = ECOCDiscriminantEncoder()
encoder.set_features(feats_train)
encoder.set_labels(labels)
encoder.set_sffs_iterations(50)
strategy = ECOCStrategy(encoder, ECOCHDDecoder())
classifier = LinearMulticlassMachine(strategy, feats_train, classifier, labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
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)
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 shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def classifier_multiclasslinearmachine_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 shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine, MulticlassOneVsOneStrategy, 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(MulticlassOneVsOneStrategy(),
feats_train, classifier, labels)
mc_classifier.train()
label_pred = mc_classifier.apply()
out = label_pred.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def classifier_multiclass_shareboost (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, RealSubsetFeatures, MulticlassLabels
from shogun.Classifier import ShareBoost
#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)
shareboost = ShareBoost(feats_train, labels, min(fm_train_real.shape[0]-1, 30))
shareboost.train();
#print(shareboost.get_activeset())
feats_test = RealSubsetFeatures(RealFeatures(fm_test_real), shareboost.get_activeset())
label_pred = shareboost.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation 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_multiclassaccuracy_modular (ground_truth, predicted): from shogun.Features import MulticlassLabels from shogun.Evaluation import MulticlassAccuracy ground_truth_labels = MulticlassLabels(ground_truth) predicted_labels = MulticlassLabels(predicted) evaluator = MulticlassAccuracy() accuracy = evaluator.evaluate(predicted_labels,ground_truth_labels) return accuracy
def classifier_multiclass_ecoc_ovr (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()
n_diff = (out_mc != out_ecoc).sum()
if n_diff == 0:
print("Same results for OvR and ECOCOvR")
else:
print("Different results for OvR and ECOCOvR (%d out of %d are different)" % (n_diff, len(out_mc)))
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc_mc = evaluator.evaluate(label_mc, labels_test)
acc_ecoc = evaluator.evaluate(label_ecoc, labels_test)
print('Normal OVR Accuracy = %.4f' % acc_mc)
print('ECOC OVR Accuracy = %.4f' % acc_ecoc)
return out_ecoc, out_mc
def evaluation_multiclassovrevaluation_modular(ground_truth): from shogun.Features import MulticlassLabels from shogun.Evaluation import MulticlassAccuracy,ROCEvaluation ground_truth_labels = MulticlassLabels(ground_truth) predicted_labels = MulticlassLabels(ground_truth) binary_evaluator = ROCEvaluation() evaluator = MulticlassAccuracy(binary_evaluator) mean_roc = evaluator.evaluate(predicted_labels,ground_truth_labels) print mean_roc return mean_roc
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, ECOCRandomSparseEncoder, ECOCRandomDenseEncoder, 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)
rnd_dense_strategy = ECOCStrategy(ECOCRandomDenseEncoder(), ECOCHDDecoder())
rnd_sparse_strategy = ECOCStrategy(ECOCRandomSparseEncoder(), ECOCHDDecoder())
dense_classifier = LinearMulticlassMachine(rnd_dense_strategy, feats_train, classifier, labels)
dense_classifier.train()
label_dense = dense_classifier.apply(feats_test)
out_dense = label_dense.get_labels()
sparse_classifier = LinearMulticlassMachine(rnd_sparse_strategy, feats_train, classifier, labels)
sparse_classifier.train()
label_sparse = sparse_classifier.apply(feats_test)
out_sparse = label_sparse.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc_dense = evaluator.evaluate(label_dense, labels_test)
acc_sparse = evaluator.evaluate(label_sparse, labels_test)
print('Random Dense Accuracy = %.4f' % acc_dense)
print('Random Sparse Accuracy = %.4f' % acc_sparse)
return out_sparse, out_dense
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)
encoder = ECOCDiscriminantEncoder()
encoder.set_features(feats_train)
encoder.set_labels(labels)
encoder.set_sffs_iterations(50)
strategy = ECOCStrategy(encoder, ECOCHDDecoder())
classifier = LinearMulticlassMachine(strategy, feats_train, classifier,
labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
def run_ecoc(ier, idr): encoder = getattr(Classifier, encoders[ier])() decoder = getattr(Classifier, decoders[idr])() # whether encoder is data dependent if hasattr(encoder, 'set_labels'): encoder.set_labels(gnd_train) encoder.set_features(fea_train) strategy = ECOCStrategy(encoder, decoder) classifier = LinearMulticlassMachine(strategy, fea_train, base_classifier, gnd_train) classifier.train() label_pred = classifier.apply(fea_test) if gnd_test is not None: evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, gnd_test) else: acc = None return (classifier.get_num_machines(), acc)
def classifier_multiclasslogisticregression_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,z=1,epsilon=1e-5):
from shogun.Features import RealFeatures, MulticlassLabels
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
labels=MulticlassLabels(label_train_multiclass)
classifier = MulticlassLogisticRegression(z,feats_train,labels)
classifier.train()
label_pred = classifier.apply(feats_test)
out = label_pred.get_labels()
if label_test_multiclass is not None:
from shogun.Evaluation import MulticlassAccuracy
labels_test = MulticlassLabels(label_test_multiclass)
evaluator = MulticlassAccuracy()
acc = evaluator.evaluate(label_pred, labels_test)
print('Accuracy = %.4f' % acc)
return out
