print('%d training feats, %d testing feats' %
(len(train_feats), len(test_feats)))
##############
## training ##
##############
trainf = nltk_trainer.classification.args.make_classifier_builder(args)
if args.cross_fold:
if args.multi and args.binary:
raise NotImplementedError(
"cross-fold is not supported for multi-binary classifiers")
scoring.cross_fold(train_feats,
trainf,
accuracy,
folds=args.cross_fold,
trace=args.trace,
metrics=not args.no_eval,
informative=args.show_most_informative)
sys.exit(0)
if args.multi and args.binary:
if args.trace:
print('training multi-binary %s classifier' % args.classifier)
classifier = MultiBinaryClassifier.train(labels, train_feats, trainf)
else:
classifier = trainf(train_feats)
################
## evaluation ##
################
# if there were no instances reserved for testing, test over the whole training set
if not test_feats:
test_feats = train_feats
if args.trace:
print '%d training feats, %d testing feats' % (len(train_feats), len(test_feats))
##############
## training ##
##############
trainf = nltk_trainer.classification.args.make_classifier_builder(args)
if args.cross_fold:
if args.multi and args.binary:
raise NotImplementedError ("cross-fold is not supported for multi-binary classifiers")
scoring.cross_fold(train_feats, trainf, accuracy, folds=args.cross_fold,
trace=args.trace, metrics=not args.no_eval, informative=args.show_most_informative)
sys.exit(0)
if args.multi and args.binary:
if args.trace:
print 'training multi-binary %s classifier' % args.classifier
classifier = MultiBinaryClassifier.train(labels, train_feats, trainf)
else:
classifier = trainf(train_feats)
################
## evaluation ##
################
if not args.no_eval:
if not args.no_accuracy:
try: