def parse_function()

in Dassl.pytorch/tools/parse_test_res.py [0:0]

• 46 lines of code
• 15 McCabe index (conditional complexity)

def parse_function(*metrics, directory="", args=None, end_signal=None):
    print("===")
    print(f"Parsing files in {directory}")
    subdirs = listdir_nohidden(directory, sort=True)

    outputs = []

    for subdir in subdirs:
        fpath = osp.join(directory, subdir, "log.txt")
        assert check_isfile(fpath)
        good_to_go = False
        output = OrderedDict()

        with open(fpath, "r") as f:
            lines = f.readlines()

            for line in lines:
                line = line.strip()

                if line == end_signal:
                    good_to_go = True

                for metric in metrics:
                    match = metric["regex"].search(line)
                    if match and good_to_go:
                        if "file" not in output:
                            output["file"] = fpath
                        num = float(match.group(1))
                        name = metric["name"]
                        output[name] = num

        if output:
            outputs.append(output)

    assert len(outputs) > 0, f"Nothing found in {directory}"

    metrics_results = defaultdict(list)
    for output in outputs:
        msg = ""
        for key, value in output.items():
            if isinstance(value, float):
                msg += f"{key}: {value:.1f}%. "
            else:
                msg += f"{key}: {value}. "
            if key != "file":
                metrics_results[key].append(value)
        print(msg)

    output_results = OrderedDict()
    for key, values in metrics_results.items():
        avg = np.mean(values)
        std = compute_ci95(values) if args.ci95 else np.std(values)
        print(f"* average {key}: {avg:.1f}% +- {std:.1f}%")
        output_results[key] = avg
    print("===")

    return output_results