optimize = True
mds_home= '../datasets/MDS'
dataset_home='../datasets/Webis-CLS-10'
nfolds=5
outfile = './DCI.varpivot.dat'
if exists(outfile):
rperf = Result.load(outfile, False)
else:
rperf = Result(['dataset', 'task', 'method', 'fold', 'npivots', 'acc', 'dci_time', 'svm_time'])
pivot_range = [10,25,50,100,250,500,1000,1500,2000,2500,5000]
for source, target, fold, taskname in MDS_task_generator(abspath(mds_home), nfolds=nfolds):
s_pivots, t_pivots = pivot_selection(max(pivot_range), source.X, source.y, source.U, target.U,
source.V, target.V,
phi=1, cross=True)
for npivots in pivot_range:
for dcf in ['cosine','linear']:
dci = DCI(dcf=dcf, unify=False, post='normal')
acc, dci_time, svm_time, _ = DCIclassify(source, target, s_pivots[:npivots], t_pivots[:npivots], dci, optimize=True)
rperf.add(dataset='MDS', task=taskname, method=str(dci), fold=fold, npivots=npivots, acc=acc, dci_time=dci_time, svm_time=svm_time)
rperf.pivot(index=['dataset', 'task','npivots'], values=['acc', 'dci_time', 'svm_time'])
rperf.dump(outfile)
for source, target, oracle, taskname in WebisCLS10_task_generator(abspath(dataset_home)):
s_pivots, t_pivots = pivot_selection(max(pivot_range), source.X, source.y, source.U, target.U,
source.V, target.V,
dataset_home='../datasets/MDS'
svmlight_home='../../svm_light'
nfolds=5
results = Result(['dataset', 'task', 'method', 'acc'])
for domain in UpperMDS_task_generator(abspath(dataset_home)):
isvm = SVMlight(svmlightbase=svmlight_home, verbose=0, transduction=None)
score = cross_val_score(isvm, domain.X, domain.y, cv=nfolds).mean()
results.add(dataset='MDS', task=domain.domain, method='UPPER', acc=score)
results.pivot(grand_totals=True)
results = Result(['dataset', 'task', 'method', 'fold', 'acc'])
for source, target, fold, task in MDS_task_generator(abspath(dataset_home), nfolds=nfolds):
source_name = source.domain
target_name = target.domain
source, target = unify_feat_space(source, target)
isvm = SVMlight(svmlightbase=svmlight_home, verbose=0, transduction=None).fit(source.X, source.y)
tsvm = SVMlight(svmlightbase=svmlight_home, verbose=0, transduction=target.X).fit(source.X, source.y)
yte_ = isvm.predict(target.X)
tyte_ = tsvm.transduced_labels
iacc = (yte_ == target.y).mean()
tacc = (tyte_ == target.y).mean()
results.add(dataset='MDS', task=task, method='ISVM', fold=fold, acc=iacc)
results.add(dataset='MDS', task=task, method='TSVM', fold=fold, acc=tacc)