'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)
