def driver(): input_directory = "../test" output_directory = "prediction" # Find files. files = [] for f in os.listdir(input_directory): if os.path.isfile( os.path.join(input_directory, f) ) and not f.lower().startswith('.') and f.lower().endswith('psv'): files.append(f) if not os.path.isdir(output_directory): os.mkdir(output_directory) # Load model. model = load_sepsis_model() # model.eval() # Iterate over files. for f in files: # Load data. input_file = os.path.join(input_directory, f) data = load_challenge_data(input_file) # Make predictions. num_rows = len(data) scores = np.zeros(num_rows) labels = np.zeros(num_rows) # for t in range(num_rows): # current_data = data[:t + 1] current_data = data current_score, current_label = get_sepsis_score(current_data, model) # current_score, current_label = get_sepsis_score2(current_data, model) # current_score, current_label = get_sepsis_score3(current_data, model) scores = current_score labels = current_label # Save results. output_file = os.path.join(output_directory, f) save_challenge_predictions(output_file, scores, labels) ##### Main Evaluation Utility Score label_directory = "../outputtest" prediction_directory = "prediction" result = evaluate_sepsis_score(label_directory, prediction_directory) # auroc, auprc, accuracy, f_measure, normalized_observed_utility print('auroc, auprc, accuracy, f_measure, normalized_observed_utility') print(result) return result
input_directory = sys.argv[1] output_directory = sys.argv[2] # Find files. files = [] for f in os.listdir(input_directory): if os.path.isfile( os.path.join(input_directory, f) ) and not f.lower().startswith('.') and f.lower().endswith('psv'): files.append(f) if not os.path.isdir(output_directory): os.mkdir(output_directory) # Load model. model = load_sepsis_model() # Iterate over files. for f in files: # Load data. input_file = os.path.join(input_directory, f) data = load_challenge_data(input_file) # Make predictions. num_rows = len(data) scores = np.zeros(num_rows) labels = np.zeros(num_rows) for t in range(num_rows): current_data = data[:t + 1] current_score, current_label = get_sepsis_score( current_data, model)
output_directory = sys.argv[2] # Find files. files = [] for f in os.listdir(input_directory): if os.path.isfile( os.path.join(input_directory, f) ) and not f.lower().startswith('.') and f.lower().endswith('psv'): files.append(f) if not os.path.isdir(output_directory): os.mkdir(output_directory) # Load model. print('Loading sepsis model...') ssvm, cols, Dmean, Dstd = load_sepsis_model() # Iterate over files. print('Predicting sepsis labels...') num_files = len(files) for i, f in enumerate(files): print(' {}/{}...'.format(i + 1, num_files)) # Load data. input_file = os.path.join(input_directory, f) data = load_challenge_data(input_file) # Make predictions. num_rows = len(data) scores = np.zeros(num_rows) labels = np.zeros(num_rows)