def preprocessor_randomfouriergausspreproc_modular(fm_train_real=traindat,
fm_test_real=testdat,
width=1.4,
size_cache=10):
from shogun.Kernel import Chi2Kernel
from shogun.Features import RealFeatures
from shogun.Preprocessor import RandomFourierGaussPreproc
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
preproc = RandomFourierGaussPreproc()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel = Chi2Kernel(feats_train, feats_train, width, size_cache)
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 preprocessor_logplusone_modular(fm_train_real=traindat,
fm_test_real=testdat,
width=1.4,
size_cache=10):
from shogun.Kernel import Chi2Kernel
from shogun.Features import RealFeatures
from shogun.Preprocessor import LogPlusOne
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
preproc = LogPlusOne()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel = Chi2Kernel(feats_train, feats_train, width, size_cache)
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 preprocessor_prunevarsubmean_modular(fm_train_real=traindat,
fm_test_real=testdat,
width=1.4,
size_cache=10):
from shogun.Kernel import Chi2Kernel
from shogun.Features import RealFeatures
from shogun.Preprocessor import PruneVarSubMean
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
preproc = PruneVarSubMean()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel = Chi2Kernel(feats_train, feats_train, width, size_cache)
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 preprocessor_randomfouriergausspreproc_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10): from shogun.Kernel import Chi2Kernel from shogun.Features import RealFeatures from shogun.Preprocessor import RandomFourierGaussPreproc feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) preproc=RandomFourierGaussPreproc() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() kernel=Chi2Kernel(feats_train, feats_train, width, size_cache) 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 preprocessor_prunevarsubmean_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10): from shogun.Kernel import Chi2Kernel from shogun.Features import RealFeatures from shogun.Preprocessor import PruneVarSubMean feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) preproc=PruneVarSubMean() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() kernel=Chi2Kernel(feats_train, feats_train, width, size_cache) 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 preprocessor_normone_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10): from shogun.Kernel import Chi2Kernel from shogun.Features import RealFeatures from shogun.Preprocessor import NormOne feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) preprocessor=NormOne() preprocessor.init(feats_train) feats_train.add_preprocessor(preprocessor) feats_train.apply_preprocessor() feats_test.add_preprocessor(preprocessor) feats_test.apply_preprocessor() kernel=Chi2Kernel(feats_train, feats_train, width, size_cache) 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 serialization_complex_example(num=5, dist=1, dim=10, C=2.0, width=10):
import os
from numpy import concatenate, zeros, ones
from numpy.random import randn, seed
from shogun.Features import RealFeatures, MulticlassLabels
from shogun.Classifier import GMNPSVM
from shogun.Kernel import GaussianKernel
from shogun.IO import SerializableHdf5File,SerializableAsciiFile, \
SerializableJsonFile,SerializableXmlFile,MSG_DEBUG
from shogun.Preprocessor import NormOne, LogPlusOne
seed(17)
data = concatenate(
(randn(dim, num), randn(dim, num) + dist, randn(dim, num) + 2 * dist,
randn(dim, num) + 3 * dist),
axis=1)
lab = concatenate((zeros(num), ones(num), 2 * ones(num), 3 * ones(num)))
feats = RealFeatures(data)
#feats.io.set_loglevel(MSG_DEBUG)
kernel = GaussianKernel(feats, feats, width)
labels = MulticlassLabels(lab)
svm = GMNPSVM(C, kernel, labels)
feats.add_preprocessor(NormOne())
feats.add_preprocessor(LogPlusOne())
feats.set_preprocessed(1)
svm.train(feats)
#svm.print_serializable()
fstream = SerializableHdf5File("blaah.h5", "w")
status = svm.save_serializable(fstream)
check_status(status, 'h5')
fstream = SerializableAsciiFile("blaah.asc", "w")
status = svm.save_serializable(fstream)
check_status(status, 'asc')
fstream = SerializableJsonFile("blaah.json", "w")
status = svm.save_serializable(fstream)
check_status(status, 'json')
fstream = SerializableXmlFile("blaah.xml", "w")
status = svm.save_serializable(fstream)
check_status(status, 'xml')
fstream = SerializableHdf5File("blaah.h5", "r")
new_svm = GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status, 'h5')
new_svm.train()
fstream = SerializableAsciiFile("blaah.asc", "r")
new_svm = GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status, 'asc')
new_svm.train()
fstream = SerializableJsonFile("blaah.json", "r")
new_svm = GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status, 'json')
new_svm.train()
fstream = SerializableXmlFile("blaah.xml", "r")
new_svm = GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status, 'xml')
new_svm.train()
os.unlink("blaah.h5")
os.unlink("blaah.asc")
os.unlink("blaah.json")
os.unlink("blaah.xml")
return svm, new_svm
def serialization_complex_example(num=5, dist=1, dim=10, C=2.0, width=10):
import os
from numpy import concatenate, zeros, ones
from numpy.random import randn, seed
from shogun.Features import RealFeatures, Labels
from shogun.Classifier import GMNPSVM
from shogun.Kernel import GaussianKernel
from shogun.IO import SerializableHdf5File,SerializableAsciiFile, \
SerializableJsonFile,SerializableXmlFile,MSG_DEBUG
from shogun.Preprocessor import NormOne, LogPlusOne
seed(17)
data=concatenate((randn(dim, num), randn(dim, num) + dist,
randn(dim, num) + 2*dist,
randn(dim, num) + 3*dist), axis=1)
lab=concatenate((zeros(num), ones(num), 2*ones(num), 3*ones(num)))
feats=RealFeatures(data)
#feats.io.set_loglevel(MSG_DEBUG)
kernel=GaussianKernel(feats, feats, width)
labels=Labels(lab)
svm = GMNPSVM(C, kernel, labels)
feats.add_preprocessor(NormOne())
feats.add_preprocessor(LogPlusOne())
feats.set_preprocessed(1)
svm.train(feats)
#svm.print_serializable()
fstream = SerializableHdf5File("blaah.h5", "w")
status = svm.save_serializable(fstream)
check_status(status)
fstream = SerializableAsciiFile("blaah.asc", "w")
status = svm.save_serializable(fstream)
check_status(status)
fstream = SerializableJsonFile("blaah.json", "w")
status = svm.save_serializable(fstream)
check_status(status)
fstream = SerializableXmlFile("blaah.xml", "w")
status = svm.save_serializable(fstream)
check_status(status)
fstream = SerializableHdf5File("blaah.h5", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status)
new_svm.train()
fstream = SerializableAsciiFile("blaah.asc", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status)
new_svm.train()
fstream = SerializableJsonFile("blaah.json", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status)
new_svm.train()
fstream = SerializableXmlFile("blaah.xml", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status)
new_svm.train()
os.unlink("blaah.h5")
os.unlink("blaah.asc")
os.unlink("blaah.json")
os.unlink("blaah.xml")
return svm,new_svm
