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 modshogun import RealFeatures, MulticlassLabels
from modshogun 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 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 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)
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 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 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 modshogun import RealFeatures, MulticlassLabels
from modshogun 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 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 run_classification(train, test, labels):
lin = LibLinear(L2R_L2LOSS_SVC)
lin.set_bias_enabled(True)
lin.set_C(5., 5.)
machine = LinearMulticlassMachine(MulticlassOneVsRestStrategy(), train, lin, labels)
machine.train()
pred = machine.apply_multiclass(test)
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 modshogun import RealFeatures, MulticlassLabels
from modshogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun import ECOCStrategy, ECOCRandomSparseEncoder, ECOCRandomDenseEncoder, ECOCHDDecoder
from modshogun import Math_init_random
Math_init_random(12345)
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 modshogun 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_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 modshogun import RealFeatures, MulticlassLabels
from modshogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun 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 modshogun 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_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)
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 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 classifier_liblinear_modular (train_fname, test_fname, label_fname, C, epsilon): from modshogun import RealFeatures, SparseRealFeatures, BinaryLabels from modshogun import LibLinear, L2R_L2LOSS_SVC_DUAL from modshogun import Math_init_random, CSVFile Math_init_random(17) feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) labels=BinaryLabels(CSVFile(label_fname)) svm=LibLinear(C, feats_train, labels) svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL) svm.set_epsilon(epsilon) svm.set_bias_enabled(True) svm.train() predictions = svm.apply(feats_test) return predictions, svm, predictions.get_labels()
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 modshogun import RealFeatures, MulticlassLabels
from modshogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun 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 modshogun 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 modshogun import RealFeatures, MulticlassLabels
from modshogun import LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun import ECOCStrategy, ECOCRandomSparseEncoder, ECOCRandomDenseEncoder, ECOCHDDecoder
from modshogun import Math_init_random;
Math_init_random(12345);
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 modshogun 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_multiclass_ecoc(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):
import shogun.Classifier as Classifier
from modshogun import ECOCStrategy, LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun import MulticlassAccuracy
from modshogun import RealFeatures, MulticlassLabels
def nonabstract_class(name):
try:
getattr(Classifier, name)()
except TypeError:
return False
return True
encoders = [
x for x in dir(Classifier)
if re.match(r'ECOC.+Encoder', x) and nonabstract_class(x)
]
decoders = [
x for x in dir(Classifier)
if re.match(r'ECOC.+Decoder', x) and nonabstract_class(x)
]
fea_train = RealFeatures(fm_train_real)
fea_test = RealFeatures(fm_test_real)
gnd_train = MulticlassLabels(label_train_multiclass)
if label_test_multiclass is None:
gnd_test = None
else:
gnd_test = MulticlassLabels(label_test_multiclass)
base_classifier = LibLinear(L2R_L2LOSS_SVC)
base_classifier.set_bias_enabled(True)
#print('Testing with %d encoders and %d decoders' % (len(encoders), len(decoders)))
#print('-' * 70)
#format_str = '%%15s + %%-10s %%-10%s %%-10%s %%-10%s'
#print((format_str % ('s', 's', 's')) % ('encoder', 'decoder', 'codelen', 'time', 'accuracy'))
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)
for ier in range(len(encoders)):
for idr in range(len(decoders)):
t_begin = time.clock()
(codelen, acc) = run_ecoc(ier, idr)
if acc is None:
acc_fmt = 's'
acc = 'N/A'
else:
acc_fmt = '.4f'
t_elapse = time.clock() - t_begin
def classifier_multiclass_ecoc (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):
import modshogun
from modshogun import ECOCStrategy, LibLinear, L2R_L2LOSS_SVC, LinearMulticlassMachine
from modshogun import MulticlassAccuracy
from modshogun import RealFeatures, MulticlassLabels
def nonabstract_class(name):
try:
getattr(modshogun, name)()
except TypeError:
return False
return True
encoders = [x for x in dir(modshogun)
if re.match(r'ECOC.+Encoder', x) and nonabstract_class(x)]
decoders = [x for x in dir(modshogun)
if re.match(r'ECOC.+Decoder', x) and nonabstract_class(x)]
fea_train = RealFeatures(fm_train_real)
fea_test = RealFeatures(fm_test_real)
gnd_train = MulticlassLabels(label_train_multiclass)
if label_test_multiclass is None:
gnd_test = None
else:
gnd_test = MulticlassLabels(label_test_multiclass)
base_classifier = LibLinear(L2R_L2LOSS_SVC)
base_classifier.set_bias_enabled(True)
#print('Testing with %d encoders and %d decoders' % (len(encoders), len(decoders)))
#print('-' * 70)
#format_str = '%%15s + %%-10s %%-10%s %%-10%s %%-10%s'
#print((format_str % ('s', 's', 's')) % ('encoder', 'decoder', 'codelen', 'time', 'accuracy'))
def run_ecoc(ier, idr):
encoder = getattr(modshogun, encoders[ier])()
decoder = getattr(modshogun, 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)
for ier in range(len(encoders)):
for idr in range(len(decoders)):
t_begin = time.clock()
(codelen, acc) = run_ecoc(ier, idr)
if acc is None:
acc_fmt = 's'
acc = 'N/A'
else:
acc_fmt = '.4f'
t_elapse = time.clock() - t_begin
from modshogun import RealFeatures, BinaryLabels
from modshogun import LibLinear, L2R_L2LOSS_SVC_DUAL
from numpy import random, mean
X_train = RealFeatures(random.randn(30, 100))
Y_train = BinaryLabels(random.randn(X_train.get_num_vectors()))
results = []
for C1_pow in range(-3, 1):
for C2_pow in range(-3, 1):
svm = LibLinear()
svm.set_bias_enabled(False)
svm.set_liblinear_solver_type(L2R_L2LOSS_SVC_DUAL)
svm.set_C(10**C1_pow, 10**C2_pow)
svm.set_features(X_train)
svm.set_labels(Y_train)
svm.train()
Y_pred = svm.apply_binary(X_train)
accuracy = mean(Y_train.get_labels() == Y_pred.get_labels())
print 10**C1_pow, 10**C2_pow, accuracy
results.append({"accuracy":accuracy, "svm":svm})
results.sort(key=lambda x:x["accuracy"], reverse=True)
