def structure_multiclass_bmrm(fm_train_real=traindat,
label_train_multiclass=label_traindat):
from modshogun import RealFeatures
from modshogun import SOSVMHelper
from modshogun import BMRM, PPBMRM, P3BMRM
from modshogun import MulticlassModel, MulticlassSOLabels, DualLibQPBMSOSVM, RealNumber
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
model = MulticlassModel(features, labels)
sosvm = DualLibQPBMSOSVM(model, labels, 1.0)
# BMRM
sosvm.set_solver(BMRM)
sosvm.set_verbose(True)
sosvm.train()
out = sosvm.apply()
count = 0
for i in xrange(out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
#print("BMRM: Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() ))
#hp = sosvm.get_helper()
#print hp.get_primal_values()
#print hp.get_train_errors()
# PPBMRM
w = np.zeros(model.get_dim())
sosvm.set_w(w)
sosvm.set_solver(PPBMRM)
sosvm.set_verbose(True)
sosvm.train()
out = sosvm.apply()
count = 0
for i in xrange(out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
#print("PPBMRM: Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() ))
# P3BMRM
w = np.zeros(model.get_dim())
sosvm.set_w(w)
sosvm.set_solver(P3BMRM)
sosvm.set_verbose(True)
sosvm.train()
out = sosvm.apply()
count = 0
for i in xrange(out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
def structure_multiclass_bmrm(fm_train_real=traindat,label_train_multiclass=label_traindat):
from modshogun import RealFeatures
from modshogun import SOSVMHelper
from modshogun import BMRM, PPBMRM, P3BMRM
from modshogun import MulticlassModel, MulticlassSOLabels, DualLibQPBMSOSVM, RealNumber
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
model = MulticlassModel(features, labels)
sosvm = DualLibQPBMSOSVM(model, labels, 1.0)
# BMRM
sosvm.set_solver(BMRM)
sosvm.set_verbose(True)
sosvm.train()
out = sosvm.apply()
count = 0
for i in xrange(out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
#print("BMRM: Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() ))
#hp = sosvm.get_helper()
#print hp.get_primal_values()
#print hp.get_train_errors()
# PPBMRM
w = np.zeros(model.get_dim())
sosvm.set_w(w)
sosvm.set_solver(PPBMRM)
sosvm.set_verbose(True)
sosvm.train()
out = sosvm.apply()
count = 0
for i in xrange(out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
#print("PPBMRM: Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() ))
# P3BMRM
w = np.zeros(model.get_dim())
sosvm.set_w(w)
sosvm.set_solver(P3BMRM)
sosvm.set_verbose(True)
sosvm.train()
out = sosvm.apply()
count = 0
for i in xrange(out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
def structure_multiclass_bmrm(fm_train_real=traindat,
label_train_multiclass=label_traindat):
from modshogun import MulticlassSOLabels, LabelsFactory
from modshogun import RealFeatures
from modshogun import SOSVMHelper
try:
from modshogun import BMRM, PPBMRM, P3BMRM, DualLibQPBMSOSVM
except ImportError:
print(
"At least one of BMRM, PPBMRM, P3BMRM, DualLibQPBMSOSVM not available"
)
exit(0)
from modshogun import MulticlassModel, RealNumber
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
model = MulticlassModel(features, labels)
sosvm = DualLibQPBMSOSVM(model, labels, 1.0)
# BMRM
sosvm.set_solver(BMRM)
sosvm.set_verbose(True)
sosvm.train()
bmrm_out = LabelsFactory.to_multiclass_structured(sosvm.apply())
count = 0
for i in range(bmrm_out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(bmrm_out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
#print("BMRM: Correct classification rate: %0.2f" % ( 100.0*count/bmrm_out.get_num_labels() ))
#hp = sosvm.get_helper()
#print hp.get_primal_values()
#print hp.get_train_errors()
# PPBMRM
w = np.zeros(model.get_dim())
sosvm.set_w(w)
sosvm.set_solver(PPBMRM)
sosvm.set_verbose(True)
sosvm.train()
ppbmrm_out = LabelsFactory.to_multiclass_structured(sosvm.apply())
count = 0
for i in range(ppbmrm_out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(ppbmrm_out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
#print("PPBMRM: Correct classification rate: %0.2f" % ( 100.0*count/ppbmrm_out.get_num_labels() ))
# P3BMRM
w = np.zeros(model.get_dim())
sosvm.set_w(w)
sosvm.set_solver(P3BMRM)
sosvm.set_verbose(True)
sosvm.train()
p3bmrm_out = LabelsFactory.to_multiclass_structured(sosvm.apply())
count = 0
for i in range(p3bmrm_out.get_num_labels()):
yi_pred = RealNumber.obtain_from_generic(p3bmrm_out.get_label(i))
if yi_pred.value == label_train_multiclass[i]:
count = count + 1
#print("P3BMRM: Correct classification rate: %0.2f" % ( 100.0*count/p3bmrm_out.get_num_labels() ))
return bmrm_out, ppbmrm_out, p3bmrm_out