def main(dataset, output, epsilon, capacity, width, kernel_type):
LOGGER.info("SVM Multiclass classifier")
LOGGER.info("Epsilon: %s" % epsilon)
LOGGER.info("Capacity: %s" % capacity)
LOGGER.info("Gaussian width: %s" % width)
# Get features
feats, labels = get_features_and_labels(LibSVMFile(dataset))
# Create kernel
try:
kernel = KERNELS[kernel_type](feats, width)
except KeyError:
LOGGER.error("Kernel %s not available. try Gaussian or Linear" % kernel_type)
# Initialize and train Multiclass SVM
svm = MulticlassLibSVM(capacity, kernel, labels)
svm.set_epsilon(epsilon)
with track_execution():
svm.train()
# Serialize to file
writable_file = SerializableHdf5File(output, 'w')
with closing(writable_file):
svm.save_serializable(writable_file)
LOGGER.info("Serialized classifier saved in: '%s'" % output)
def classifier_multiclasslibsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from modshogun import RealFeatures, MulticlassLabels from modshogun import GaussianKernel from modshogun import MulticlassLibSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=MulticlassLabels(label_train_multiclass) svm=MulticlassLibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) out = svm.apply().get_labels() predictions = svm.apply() return predictions, svm, predictions.get_labels()
def classifier_multiclasslibsvm_modular(fm_train_real=traindat,
fm_test_real=testdat,
label_train_multiclass=label_traindat,
width=2.1,
C=1,
epsilon=1e-5):
from modshogun import RealFeatures, MulticlassLabels
from modshogun import GaussianKernel
from modshogun import MulticlassLibSVM
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
kernel = GaussianKernel(feats_train, feats_train, width)
labels = MulticlassLabels(label_train_multiclass)
svm = MulticlassLibSVM(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.train()
kernel.init(feats_train, feats_test)
out = svm.apply().get_labels()
predictions = svm.apply()
return predictions, svm, predictions.get_labels()