'training set and a test set, stored in the provided tables, ' 'and evaluates the performance of a collection of logistic ' 'regression classifiers, one for each ICD9 label.') parser.add_argument('train_table_name') parser.add_argument('test_table_name') parser.add_argument('--top100_labels', action='store_true', default=False) parser.add_argument('--dont_normalize', action='store_true', default=False) args = parser.parse_args() db = DatabaseManager() start = datetime.datetime.now() time_str = start.strftime("%m%d_%H%M%S") config = vars(args) experiment_id = db.classifier_experiment_create(config, start, 'logistic_regression', args.train_table_name, None, args.test_table_name) log_filename = '{}_logistic_regression.log'.format(experiment_id) db.classifier_experiment_insert_log_file(experiment_id, log_filename) logger = logging_utils.build_logger(log_filename).getLogger( 'logistic_regression') logger.info('Program start, classifier experiment id = %s', experiment_id) logger.info(args) X_train, Y_train = load_X_Y( args.train_table_name, top100_labels=args.top100_labels, normalize_by_npatients=(False if args.dont_normalize else True)) n_features = X_train.shape[
'and evaluates the performance of a fully connected ' 'feed forward neural network.') parser.add_argument('train_table_name') parser.add_argument('val_table_name') parser.add_argument('test_table_name') parser.add_argument('--top100_labels', action='store_true', default=False) parser.add_argument('--no_gpu', action='store_true', default=False) args = parser.parse_args() db = DatabaseManager() start = datetime.datetime.now() time_str = start.strftime("%m%d_%H%M%S") config = vars(args) experiment_id = db.classifier_experiment_create(config, start, 'nnff', args.train_table_name, args.val_table_name, args.test_table_name) log_filename = '{}_nnff.log'.format(experiment_id) db.classifier_experiment_insert_log_file(experiment_id, log_filename) logger = logging_utils.build_logger(log_filename).getLogger('feed_forward') logger.info('Program start, classifier experiment id = %s', experiment_id) logger.info(args) X_train, Y_train = tensor_loader.load_X_Y(logger, args.train_table_name, args.no_gpu) X_val, Y_val = tensor_loader.load_X_Y(logger, args.val_table_name, args.no_gpu, validation_set=True)