def features_io_modular (fm_train_real, label_train_twoclass):
import numpy
from modshogun import SparseRealFeatures, RealFeatures, MulticlassLabels
from modshogun import GaussianKernel
from modshogun import LibSVMFile, CSVFile, BinaryFile, HDF5File
feats=SparseRealFeatures(fm_train_real)
feats2=SparseRealFeatures()
f=BinaryFile("tmp/fm_train_sparsereal.bin","w")
feats.save(f)
f=LibSVMFile("tmp/fm_train_sparsereal.ascii","w")
feats.save(f)
f=BinaryFile("tmp/fm_train_sparsereal.bin")
feats2.load(f)
f=LibSVMFile("tmp/fm_train_sparsereal.ascii")
feats2.load(f)
feats=RealFeatures(fm_train_real)
feats2=RealFeatures()
f=BinaryFile("tmp/fm_train_real.bin","w")
feats.save(f)
f=HDF5File("tmp/fm_train_real.h5","w", "/data/doubles")
feats.save(f)
f=CSVFile("tmp/fm_train_real.ascii","w")
feats.save(f)
f=BinaryFile("tmp/fm_train_real.bin")
feats2.load(f)
#print("diff binary", numpy.max(numpy.abs(feats2.get_feature_matrix().flatten()-fm_train_real.flatten())))
f=CSVFile("tmp/fm_train_real.ascii")
feats2.load(f)
#print("diff ascii", numpy.max(numpy.abs(feats2.get_feature_matrix().flatten()-fm_train_real.flatten())))
lab=MulticlassLabels(numpy.array([0.0,1.0,2.0,3.0]))
lab2=MulticlassLabels()
f=CSVFile("tmp/label_train_twoclass.ascii","w")
lab.save(f)
f=BinaryFile("tmp/label_train_twoclass.bin","w")
lab.save(f)
f=HDF5File("tmp/label_train_real.h5","w", "/data/labels")
lab.save(f)
f=CSVFile("tmp/label_train_twoclass.ascii")
lab2.load(f)
f=BinaryFile("tmp/label_train_twoclass.bin")
lab2.load(f)
f=HDF5File("tmp/fm_train_real.h5","r", "/data/doubles")
feats2.load(f)
#print(feats2.get_feature_matrix())
f=HDF5File("tmp/label_train_real.h5","r", "/data/labels")
lab2.load(f)
#print(lab2.get_labels())
#clean up
import os
for f in ['tmp/fm_train_sparsereal.bin','tmp/fm_train_sparsereal.ascii',
'tmp/fm_train_real.bin','tmp/fm_train_real.h5','tmp/fm_train_real.ascii',
'tmp/label_train_real.h5', 'tmp/label_train_twoclass.ascii','tmp/label_train_twoclass.bin']:
os.unlink(f)
return feats, feats2, lab, lab2
def features_io_modular(fm_train_real, label_train_twoclass):
import numpy
from modshogun import SparseRealFeatures, RealFeatures, MulticlassLabels
from modshogun import GaussianKernel
from modshogun import LibSVMFile, CSVFile, BinaryFile, HDF5File
feats = SparseRealFeatures(fm_train_real)
feats2 = SparseRealFeatures()
f = BinaryFile("fm_train_sparsereal.bin", "w")
feats.save(f)
f = LibSVMFile("fm_train_sparsereal.ascii", "w")
feats.save(f)
f = BinaryFile("fm_train_sparsereal.bin")
feats2.load(f)
f = LibSVMFile("fm_train_sparsereal.ascii")
feats2.load(f)
feats = RealFeatures(fm_train_real)
feats2 = RealFeatures()
f = BinaryFile("fm_train_real.bin", "w")
feats.save(f)
f = HDF5File("fm_train_real.h5", "w", "/data/doubles")
feats.save(f)
f = CSVFile("fm_train_real.ascii", "w")
feats.save(f)
f = BinaryFile("fm_train_real.bin")
feats2.load(f)
#print("diff binary", numpy.max(numpy.abs(feats2.get_feature_matrix().flatten()-fm_train_real.flatten())))
f = CSVFile("fm_train_real.ascii")
feats2.load(f)
#print("diff ascii", numpy.max(numpy.abs(feats2.get_feature_matrix().flatten()-fm_train_real.flatten())))
lab = MulticlassLabels(numpy.array([0.0, 1.0, 2.0, 3.0]))
lab2 = MulticlassLabels()
f = CSVFile("label_train_twoclass.ascii", "w")
lab.save(f)
f = BinaryFile("label_train_twoclass.bin", "w")
lab.save(f)
f = HDF5File("label_train_real.h5", "w", "/data/labels")
lab.save(f)
f = CSVFile("label_train_twoclass.ascii")
lab2.load(f)
f = BinaryFile("label_train_twoclass.bin")
lab2.load(f)
f = HDF5File("fm_train_real.h5", "r", "/data/doubles")
feats2.load(f)
#print(feats2.get_feature_matrix())
f = HDF5File("label_train_real.h5", "r", "/data/labels")
lab2.load(f)
#print(lab2.get_labels())
#clean up
import os
for f in [
'fm_train_sparsereal.bin', 'fm_train_sparsereal.ascii',
'fm_train_real.bin', 'fm_train_real.h5', 'fm_train_real.ascii',
'label_train_real.h5', 'label_train_twoclass.ascii',
'label_train_twoclass.bin'
]:
os.unlink(f)
return feats, feats2, lab, lab2
