def main()

in prediction_generation/old-code/kcpa_ecp.py [0:0]
69 lines of code
2 McCabe index (conditional complexity)

def main():
    args = parse_args()
    
    # Load dataset
    data = load_dataset(args.input)
    time_series = np.array(data["series"][0]["raw"]).reshape(-1, 1)  # Convert to numpy array
    dataset_md5 = get_md5(args.input)
    
    # Set `maxcp` to a reasonable value if it's 'max'
    if args.maxcp == 'max':
        args.maxcp = min(len(time_series) // 2, 100)  # Limit L to at most 100 or half the length of the series
    
    start_time = time.time()
    try:
        # Run the appropriate algorithm
        if args.algorithm == 'e.divisive':
            result = e_divisive(time_series, alpha=args.alpha, runs=args.runs, minsize=args.minsize, siglvl=args.siglvl)
        elif args.algorithm == 'kcpa':
            result = kcpa(time_series, L=args.maxcp, cost=args.cost)
        
        runtime = time.time() - start_time
        
        # If result is None or empty, raise an error to handle it as a failure
        if not result:
            raise ValueError("No valid change points detected.")
        
        # Construct success output
        output_data = {
            "error": None,
            "command": f"python3.9 /TCPDBench/execs/python/kcpa_ecp.py -i {args.input} -a {args.algorithm} --alpha {args.alpha} --minsize {args.minsize} --runs {args.runs} --siglvl {args.siglvl}",
            "script": "/TCPDBench/execs/python/kcpa_ecp.py",
            "script_md5": get_md5('/TCPDBench/execs/python/kcpa_ecp.py'),
            "hostname": socket.gethostname(),
            "dataset": args.input.split('/')[-1].split('.')[0],
            "dataset_md5": dataset_md5,
            "status": "SUCCESS",
            "parameters": {
                "alpha": args.alpha,
                "minsize": args.minsize,
                "max_cp": args.maxcp,
                "method": args.algorithm,
                "runs": args.runs,
                "siglvl": args.siglvl
            },
            "arguments": vars(args),
            "result": {
                "cplocations": list(map(int, result)),
                "runtime": runtime
            }
        }

    except Exception as e:
        # Handle any errors and produce a failure output
        runtime = time.time() - start_time
        output_data = {
            "error": str(e),
            "command": f"python3.9 /TCPDBench/execs/python/kcpa_ecp.py -i {args.input} -a {args.algorithm} --alpha {args.alpha} --minsize {args.minsize} --runs {args.runs} --siglvl {args.siglvl}",
            "script": "/TCPDBench/execs/python/kcpa_ecp.py",
            "script_md5": get_md5('/TCPDBench/execs/python/kcpa_ecp.py'),
            "hostname": socket.gethostname(),
            "dataset": args.input.split('/')[-1].split('.')[0],
            "dataset_md5": dataset_md5,
            "status": "FAIL",
            "parameters": {
                "alpha": args.alpha,
                "minsize": args.minsize,
                "max_cp": args.maxcp,
                "method": args.algorithm,
                "runs": args.runs,
                "siglvl": args.siglvl
            },
            "arguments": vars(args),
            "result": {
                "cplocations": None,
                "runtime": None
            }
        }
    
    # Write output to file or print to console
    if args.output:
        with open(args.output, 'w') as outfile:
            json.dump(output_data, outfile, indent=4)
    else:
        print(json.dumps(output_data, indent=4))