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):