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 argmax(self, w, feat_idx, training):
feature_vector = self.get_features().get_feature_vector(feat_idx)
label = None
if training == True:
label = int(
RealNumber.obtain_from_generic(
self.get_labels().get_label(feat_idx)).value)
ypred = 0
max_score = -1e10
for c in xrange(self.n_classes):
score = 0.0
for i in xrange(self.n_feats):
score += w[i + self.n_feats * c] * feature_vector[i]
if training == True:
score += (c != label)
if score > max_score:
max_score = score
ypred = c
res = ResultSet()
res.score = max_score
res.psi_pred = RealVector(self.dim)
res.psi_pred.zero()
for i in xrange(self.n_feats):
res.psi_pred[i + self.n_feats * ypred] = feature_vector[i]
res.argmax = RealNumber(ypred)
if training == True:
res.delta = (label != ypred)
res.psi_truth = RealVector(self.dim)
res.psi_truth.zero()
for i in xrange(self.n_feats):
res.psi_truth[i + self.n_feats * label] = feature_vector[i]
for i in xrange(self.n_feats):
res.score -= w[i + self.n_feats * label] * feature_vector[i]
return res
def argmax(self,w,feat_idx,training): feature_vector = self.get_features().get_feature_vector(feat_idx) label = None if training == True: label = int(RealNumber.obtain_from_generic(self.get_labels().get_label(feat_idx)).value) ypred = 0 max_score = -1e10 for c in xrange(self.n_classes): score = 0.0 for i in xrange(self.n_feats): score += w[i+self.n_feats*c]*feature_vector[i] if training == True: score += (c!=label) if score > max_score: max_score = score ypred = c res = ResultSet() res.score = max_score res.psi_pred = RealVector(self.dim) res.psi_pred.zero() for i in xrange(self.n_feats): res.psi_pred[i+self.n_feats*ypred] = feature_vector[i] res.argmax = RealNumber(ypred) if training == True: res.delta = (label!=ypred) res.psi_truth = RealVector(self.dim) res.psi_truth.zero() for i in xrange(self.n_feats): res.psi_truth[i+self.n_feats*label] = feature_vector[i] for i in xrange(self.n_feats): res.score -= w[i+self.n_feats*label]*feature_vector[i] return res
def so_multiclass(fm_train_real=traindat,
label_train_multiclass=label_traindat):
try:
from modshogun import RealFeatures
from modshogun import MulticlassModel, MulticlassSOLabels, PrimalMosekSOSVM, RealNumber
except ImportError:
print("Mosek not available")
return
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
model = MulticlassModel(features, labels)
sosvm = PrimalMosekSOSVM(model, labels)
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("Correct classification rate: %0.2f" %
(100.0 * count / out.get_num_labels()))
def so_multiclass (fm_train_real=traindat,label_train_multiclass=label_traindat):
try:
from modshogun import RealFeatures
from modshogun import MulticlassModel, MulticlassSOLabels, PrimalMosekSOSVM, RealNumber
except ImportError:
print("Mosek not available")
return
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
model = MulticlassModel(features, labels)
sosvm = PrimalMosekSOSVM(model, labels)
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("Correct classification rate: %0.2f" % ( 100.0*count/out.get_num_labels() ))
def get_so_labels(out):
N = out.get_num_labels()
l = np.zeros(N)
for i in xrange(N):
l[i] = RealNumber.obtain_from_generic(out.get_label(i)).value
return l
def get_so_labels(out):
N = out.get_num_labels()
l = np.zeros(N)
for i in xrange(N):
l[i] = RealNumber.obtain_from_generic(out.get_label(i)).value
return l
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