if not args.no_accuracy:
try:
print('accuracy: %f' % accuracy(classifier, test_feats))
except ZeroDivisionError:
print('accuracy: 0')
if args.multi and args.binary and not args.no_masi_distance:
print('average masi distance: %f' %
(scoring.avg_masi_distance(classifier, test_feats)))
if not args.no_precision or not args.no_recall or not args.no_fmeasure:
if args.multi and args.binary:
refsets, testsets = scoring.multi_ref_test_sets(
classifier, test_feats)
else:
refsets, testsets = scoring.ref_test_sets(classifier, test_feats)
for label in labels:
ref = refsets[label]
test = testsets[label]
if not args.no_precision:
print('%s precision: %f' % (label, precision(ref, test) or 0))
if not args.no_recall:
print('%s recall: %f' % (label, recall(ref, test) or 0))
if not args.no_fmeasure:
print('%s f-measure: %f' % (label, f_measure(ref, test) or 0))
if args.show_most_informative and hasattr(
################ ## evaluation ## ################ if not args.no_eval: if not args.no_accuracy: print 'accuracy: %f' % accuracy(classifier, test_feats) if args.multi and args.binary and not args.no_masi_distance: print 'average masi distance: %f' % (scoring.avg_masi_distance(classifier, test_feats)) if not args.no_precision or not args.no_recall or not args.no_fmeasure: if args.multi and args.binary: refsets, testsets = scoring.multi_ref_test_sets(classifier, test_feats) else: refsets, testsets = scoring.ref_test_sets(classifier, test_feats) for label in labels: ref = refsets[label] test = testsets[label] if not args.no_precision: print '%s precision: %f' % (label, precision(ref, test) or 0) if not args.no_recall: print '%s recall: %f' % (label, recall(ref, test) or 0) if not args.no_fmeasure: print '%s f-measure: %f' % (label, f_measure(ref, test) or 0) if args.show_most_informative and args.algorithm != 'DecisionTree' and not (args.multi and args.binary):