init_params.py

import os
import argparse
from uuid import uuid4
import sys


def init_main_args():
    """Init command line args used for configuration."""

    parser = argparse.ArgumentParser(
        description="Runs experiment using LogClass Framework",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(
        "--raw_logs",
        metavar="raw_logs",
        type=str,
        nargs=1,
        help="input raw logs file path",
    )
    base_dir_default = os.path.join(
        os.path.dirname(os.path.realpath(__file__)), "output"
    )
    parser.add_argument(
        "--base_dir",
        metavar="base_dir",
        type=str,
        nargs=1,
        default=[base_dir_default],
        help="base output directory for pipeline output files",
    )
    parser.add_argument(
        "--logs",
        metavar="logs",
        type=str,
        nargs=1,
        help="input logs file path and output for raw logs preprocessing",
    )
    parser.add_argument(
        "--models_dir",
        metavar="models_dir",
        type=str,
        nargs=1,
        help="trained models input/output directory path",
    )
    parser.add_argument(
        "--features_dir",
        metavar="features_dir",
        type=str,
        nargs=1,
        help="trained features_dir input/output directory path",
    )
    parser.add_argument(
        "--logs_type",
        metavar="logs_type",
        type=str,
        nargs=1,
        default=["open_Apache"],
        choices=[
            "bgl",
            "open_Apache",
            "open_bgl",
            "open_hadoop",
            "open_hdfs",
            "open_hpc",
            "open_proxifier",
            "open_zookeeper",
            ],
        help="Input type of logs.",
    )
    parser.add_argument(
        "--kfold",
        metavar="kfold",
        type=int,
        nargs=1,
        help="kfold crossvalidation",
    )
    parser.add_argument(
        "--healthy_label",
        metavar='healthy_label',
        type=str,
        nargs=1,
        default=["unlabeled"],
        help="the labels of unlabeled logs",
    )
    parser.add_argument(
        "--features",
        metavar="features",
        type=str,
        nargs='+',
        default=["tfilf"],
        choices=["tfidf", "tfilf", "length", "tf"],
        help="Features to be extracted from the logs messages.",
    )
    parser.add_argument(
        "--report",
        metavar="report",
        type=str,
        nargs='+',
        default=["confusion_matrix"],
        choices=["confusion_matrix",
                 "acc",
                 "multi_acc",
                 "top_k_svm",
                 "micro",
                 "macro"
                 ],
        help="Reports to be generated from the model and its predictions.",
    )
    parser.add_argument(
        "--binary_classifier",
        metavar="binary_classifier",
        type=str,
        nargs=1,
        default=["pu_learning"],
        choices=["pu_learning", "regular"],
        help="Binary classifier to be used as anomaly detector.",
    )
    parser.add_argument(
        "--multi_classifier",
        metavar="multi_classifier",
        type=str,
        nargs=1,
        default=["svm"],
        choices=["svm"],
        help="Multi-clas classifier to classify anomalies.",
    )
    parser.add_argument(
        "--train",
        action="store_true",
        default=False,
        help="If set, logclass will train on the given data. Otherwise"
             + "it will run inference on it.",
    )
    parser.add_argument(
        "--force",
        action="store_true",
        default=False,
        help="Force training overwriting previous output with same id.",
    )
    parser.add_argument(
        "--id",
        metavar="id",
        type=str,
        nargs=1,
        help="Experiment id. Automatically generated if not specified.",
    )
    parser.add_argument(
        "--swap",
        action="store_true",
        default=False,
        help="Swap testing/training data in kfold cross validation.",
    )

    return parser


def parse_main_args(args):
    """Parse provided args for runtime configuration."""
    params = {
        "report": args.report,
        "train": args.train,
        "force": args.force,
        "base_dir": args.base_dir[0],
        "logs_type": args.logs_type[0],
        "healthy_label": args.healthy_label[0],
        "features": args.features,
        "binary_classifier": args.binary_classifier[0],
        "multi_classifier": args.multi_classifier[0],
        "swap": args.swap,
    }
    if args.raw_logs:
        params["raw_logs"] = os.path.normpath(args.raw_logs[0])
    if args.kfold:
        params["kfold"] = args.kfold[0]
    if args.logs:
        params['logs'] = os.path.normpath(args.logs[0])
    else:
        params['logs'] = os.path.join(
            params['base_dir'],
            "preprocessed_logs",
            f"{params['logs_type']}.txt"
        )
    if args.id:
        params['id'] = args.id[0]
    else:
        params['id'] = str(uuid4().time_low)
    print(f"\nExperiment ID: {params['id']}")
    # Creating experiments results folder with the format
    # {experiment_module_name}_{logs_type}_{id}
    experiment_name = os.path.basename(sys.argv[0]).split('.')[0]
    params['id_dir'] = os.path.join(
            params['base_dir'],
            '_'.join((
                experiment_name, params['logs_type'], params['id']
                ))
        )
    if args.models_dir:
        params['models_dir'] = os.path.normpath(args.models_dir[0])
    else:
        params['models_dir'] = os.path.join(
            params['id_dir'],
            "models",
        )
    if args.features_dir:
        params['features_dir'] = os.path.normpath(args.features_dir[0])
    else:
        params['features_dir'] = os.path.join(
            params['id_dir'],
            "features",
        )
    params['results_dir'] = os.path.join(params['id_dir'], "results")

    return params