) parser.add_argument('--node-classifier', metavar='FILE', help='Train and output a node split classifier.' ) args = parser.parse_args() feature_map_function = eval(args.feature_map_function) if args.load_classifier is not None: mpf = classifier_probability(eval(args.feature_map_function), args.load_classifier) else: mpf = eval(args.objective_function) wsg = Rag(args.ws, args.probs, mpf) features, labels, weights, history, ave_sizes = \ wsg.learn_agglomerate(args.gt, feature_map_function) print 'shapes: ', features.shape, labels.shape if args.load_classifier is not None: try: f = h5py.File(args.save_training_data) old_features = array(f['samples']) old_labels = array(f['labels']) features = concatenate((features, old_features), 0) labels = concatenate((labels, old_labels), 0) except: pass print "fitting classifier of size, pos: ", labels.size, (labels==1).sum() if args.balance_classes: cw = 'auto'
help='Save node features and labels to FILE.') parser.add_argument('--node-classifier', metavar='FILE', help='Train and output a node split classifier.') args = parser.parse_args() feature_map_function = eval(args.feature_map_function) if args.load_classifier is not None: mpf = classifier_probability(eval(args.feature_map_function), args.load_classifier) else: mpf = eval(args.objective_function) wsg = Rag(args.ws, args.probs, mpf) features, labels, weights, history, ave_sizes = \ wsg.learn_agglomerate(args.gt, feature_map_function) print 'shapes: ', features.shape, labels.shape if args.load_classifier is not None: try: f = h5py.File(args.save_training_data) old_features = array(f['samples']) old_labels = array(f['labels']) features = concatenate((features, old_features), 0) labels = concatenate((labels, old_labels), 0) except: pass print "fitting classifier of size, pos: ", labels.size, (labels == 1).sum() if args.balance_classes: cw = 'auto'