parser_run-no-LLM.py

from utils.parser_utils import *

from run_parser import LILAC, LogBatcher, DivLog, LogPrompt, SelfLog, OpenLogParser, LLM_TD
from run_parser import Drain, ULP, Brain, SPELL, AEL

parsers = {
    # baseline
    "Drain": Drain,
    "ULP": ULP,
    "Brain": Brain,
    "SPELL": SPELL,
    "AEL": AEL,
    # unsupervised parsers
    # "OpenLogParser": OpenLogParser,
    # # "LogPrompt": LogPrompt,
    # "LLM_TD": LLM_TD,
    # "LogBatcher": LogBatcher,
    # # supervised parsers
    # "SelfLog": SelfLog,
    # "LILAC-2": LILAC,
    # "LILAC-4": LILAC,
    # "DivLog-2": DivLog,
    # "DivLog-4": DivLog,
}

multiple_runs_list = list(parsers.keys())

datasets = [
    'Android',
    'Apache',
    'BGL',
    'HDFS',
    'HPC',
    'Hadoop',
    'HealthApp',
    'Linux',
    'Mac',
    'OpenSSH',
    'OpenStack',
    'Proxifier',
    'Spark',
    'Thunderbird',
    'Windows',
    'Zookeeper',
    "Audit" # custom
]

model = "no-LLM"
# model = "gpt-3.5-turbo" # openai api
# model="deepseek-ai/DeepSeek-R1" # togetherai api
# model = "deepseek-reasoner" # deepseek api
# model = "codellama:7b-instruct" # ollama local api

dataset_type = "2k"

total_runs = 3

params = {
    #"in_dir": DATA_FOLDER + "2k/",
    "in_dir": DATA_FOLDER + f"{dataset_type}/",
    "settings": settings,
    "dataset_type": dataset_type,
    "model": model,
    "log_format": True,
    "corrected_LH": True ### ATTENTION !!!!!! ###
}

if __name__ == "__main__":
    output_folder = OUTPUT_FOLDER[:-1] + "-full" if dataset_type == "full" else OUTPUT_FOLDER
    times_path = os.path.join(output_folder, model, "times.csv")
    error_log = {}
    for dataset in datasets: # per dataset
        params["dataset"] = dataset
        for parser_name, parser in parsers.items(): # per parser
            runs = 1
            if parser_name in multiple_runs_list:
                runs = total_runs # run supervised parsers multiple times
                try:
                    params["n_candidates"] = int(parser_name[-1])
                except:
                    pass
            for i in range(1, runs+1): # per sampling if supervised
                run_dir = ""
                if parser_name in multiple_runs_list:
                    params["run"] = i
                    run_dir = f"run{i}"
                print(f"Running {parser_name} on {dataset}")
                out_dir = os.path.join(output_folder, model, parser_name, run_dir)
                params["out_dir"] = out_dir
                if not os.path.exists(out_dir):
                    os.makedirs(out_dir)
                
                # skip if the output file already exists
                # if os.path.exists(os.path.join(out_dir, dataset + "_" + params["dataset_type"] + ".log_structured.csv")):
                #     continue

                runtime, invoc_time = parser.parse(**params)
                dict_time = {"parser": parser_name, "dataset": dataset, "run": i, "total_runtime":runtime, "invocation_time": invoc_time}
                df_time = pd.DataFrame(dict_time, index=[0])
                if not os.path.exists(times_path):
                    df_time.to_csv(times_path, index=False)
                else:
                    df_time.to_csv(times_path, mode="a", header=False, index=False)