def structure_hmsvm_bmrm(m_data_dict=data_dict):
from shogun.Features import RealMatrixFeatures
from shogun.Loss import HingeLoss
from shogun.Structure import HMSVMLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE
from shogun.Evaluation import StructuredAccuracy
from shogun.Structure import DualLibQPBMSOSVM
labels_array = m_data_dict['label'][0]
idxs = numpy.nonzero(labels_array == -1)
labels_array[idxs] = 0
labels = HMSVMLabels(labels_array, 250, 500, 2)
features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250,
500)
loss = HingeLoss()
model = HMSVMModel(features, labels, SMT_TWO_STATE, 4)
sosvm = DualLibQPBMSOSVM(model, loss, labels, 5000.0)
sosvm.train()
print sosvm.get_w()
predicted = sosvm.apply()
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
print('Accuracy = %.4f' % acc)
def so_multiclass(fm_train_real=traindat,
label_train_multiclass=label_traindat):
try:
from shogun.Features import RealFeatures
from shogun.Loss import HingeLoss
from shogun.Structure 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)
loss = HingeLoss()
sosvm = PrimalMosekSOSVM(model, loss, 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 structure_hmsvm_mosek(m_data_dict=data_dict):
from shogun.Features import RealMatrixFeatures
from shogun.Loss import HingeLoss
from shogun.Structure import HMSVMLabels, HMSVMModel, Sequence, TwoStateModel, SMT_TWO_STATE
from shogun.Evaluation import StructuredAccuracy
try:
from shogun.Structure import PrimalMosekSOSVM
except ImportError:
print "Mosek not available"
import sys
sys.exit(0)
labels_array = m_data_dict['label'][0]
idxs = numpy.nonzero(labels_array == -1)
labels_array[idxs] = 0
labels = HMSVMLabels(labels_array, 250, 500, 2)
features = RealMatrixFeatures(m_data_dict['signal'].astype(float), 250,
500)
loss = HingeLoss()
model = HMSVMModel(features, labels, SMT_TWO_STATE, 4)
sosvm = PrimalMosekSOSVM(model, loss, labels)
sosvm.train()
print sosvm.get_w()
predicted = sosvm.apply()
evaluator = StructuredAccuracy()
acc = evaluator.evaluate(predicted, labels)
print('Accuracy = %.4f' % acc)
def so_multiclass(fm_train_real=traindat,
label_train_multiclass=label_traindat):
labels = MulticlassSOLabels(label_train_multiclass)
features = RealFeatures(fm_train_real.T)
model = MulticlassModel(features, labels)
loss = HingeLoss()
sosvm = PrimalMosekSOSVM(model, loss, 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())
# Number of samples of each class
N = 1000
# Dimension of the data
dim = 2
X, y = gen_data()
cnt = 250
X2, y2 = fill_data(cnt, np.min(X), np.max(X))
labels = MulticlassSOLabels(y)
features = RealFeatures(X.T)
model = MulticlassModel(features, labels)
loss = HingeLoss()
lambda_ = 1e1
sosvm = DualLibQPBMSOSVM(model, loss, labels, lambda_)
sosvm.set_cleanAfter(
10
) # number of iterations that cutting plane has to be inactive for to be removed
sosvm.set_cleanICP(True) # enables inactive cutting plane removal feature
sosvm.set_TolRel(0.001) # set relative tolerance
sosvm.set_verbose(True) # enables verbosity of the solver
sosvm.set_cp_models(16) # set number of cutting plane models
sosvm.set_solver(BMRM) # select training algorithm
#sosvm.set_solver(PPBMRM)
#sosvm.set_solver(P3BMRM)
import toy_datasets as toy
import matplotlib.pyplot as plt
import pdb
import sys
from modshogun import MSG_DEBUG, MSG_INFO
from shogun.Loss import HingeLoss
from shogun.Structure import StructuredAccuracy
from subgradient_sosvm import SubgradientSOSVM, StochasticSubgradientSOSVM
Plot = True
SaveFigs = False
SaveLatexFigs = False
dummy = HingeLoss()
if len(sys.argv) > 1:
if sys.argv[1] == 'info':
print 'Info mode set'
dummy.io.set_loglevel(MSG_INFO)
elif sys.argv[1] == 'debug':
print 'Debug mode set'
dummy.io.set_loglevel(MSG_DEBUG)
elif sys.argv[1] == 'silent':
print 'Silent mode set'
else:
print 'Option %s not available, silent mode activated.'
# Training data formed by n_samples examples
n_samples = 20
# Square grid CRFs of size times size