def regression_svrlight (fm_train=traindat,fm_test=testdat,label_train=label_traindat, \
width=1.2,C=1,epsilon=1e-5,tube_epsilon=1e-2,num_threads=3):
from shogun import RegressionLabels, RealFeatures
from shogun import GaussianKernel
try:
from shogun import SVRLight
except ImportError:
print('No support for SVRLight available.')
return
feats_train=RealFeatures(fm_train)
feats_test=RealFeatures(fm_test)
kernel=GaussianKernel(feats_train, feats_train, width)
labels=RegressionLabels(label_train)
svr=SVRLight(C, epsilon, kernel, labels)
svr.set_tube_epsilon(tube_epsilon)
svr.parallel.set_num_threads(num_threads)
svr.train()
kernel.init(feats_train, feats_test)
out = svr.apply().get_labels()
return out, kernel
def kernel_gaussian(train_fname=traindat, test_fname=testdat, width=1.3):
from shogun import RealFeatures, GaussianKernel, CSVFile
feats_train = RealFeatures(CSVFile(train_fname))
feats_test = RealFeatures(CSVFile(test_fname))
kernel = GaussianKernel(feats_train, feats_train, width)
km_train = kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def kernel_gaussian (train_fname=traindat,test_fname=testdat, width=1.3): from shogun import RealFeatures, GaussianKernel, CSVFile feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) kernel=GaussianKernel(feats_train, feats_train, width) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_sparse_gaussian(fm_train_real=traindat,
fm_test_real=testdat,
width=1.1):
from shogun import SparseRealFeatures
from shogun import GaussianKernel
feats_train = SparseRealFeatures(fm_train_real)
feats_test = SparseRealFeatures(fm_test_real)
kernel = GaussianKernel(feats_train, feats_train, width)
km_train = kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def classifier_multiclassmachine (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from shogun import RealFeatures, MulticlassLabels from shogun import GaussianKernel from shogun import LibSVM, KernelMulticlassMachine, MulticlassOneVsRestStrategy feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=MulticlassLabels(label_train_multiclass) classifier = LibSVM() classifier.set_epsilon(epsilon) #print labels.get_labels() mc_classifier = KernelMulticlassMachine(MulticlassOneVsRestStrategy(),kernel,classifier,labels) mc_classifier.train() kernel.init(feats_train, feats_test) out = mc_classifier.apply().get_labels() return out
def kernel_io(train_fname=traindat, test_fname=testdat, width=1.9):
from shogun import RealFeatures, GaussianKernel, CSVFile
from tempfile import NamedTemporaryFile
feats_train = RealFeatures(CSVFile(train_fname))
feats_test = RealFeatures(CSVFile(test_fname))
kernel = GaussianKernel(feats_train, feats_train, width)
km_train = kernel.get_kernel_matrix()
tmp_train_csv = NamedTemporaryFile(suffix='train.csv')
f = CSVFile(tmp_train_csv.name, "w")
kernel.save(f)
del f
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
tmp_test_csv = NamedTemporaryFile(suffix='test.csv')
f = CSVFile(tmp_test_csv.name, "w")
kernel.save(f)
del f
return km_train, km_test, kernel
def kernel_io (train_fname=traindat,test_fname=testdat,width=1.9): from shogun import RealFeatures, GaussianKernel, CSVFile from tempfile import NamedTemporaryFile feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) kernel=GaussianKernel(feats_train, feats_train, width) km_train=kernel.get_kernel_matrix() tmp_train_csv = NamedTemporaryFile(suffix='train.csv') f=CSVFile(tmp_train_csv.name, "w") kernel.save(f) del f kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() tmp_test_csv = NamedTemporaryFile(suffix='test.csv') f=CSVFile(tmp_test_csv.name,"w") kernel.save(f) del f return km_train, km_test, kernel
