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| import json | |
| import os | |
| from tqdm import tqdm | |
| import copy | |
| import pandas as pd | |
| from src.display.formatting import has_no_nan_values, make_clickable_model | |
| from src.display.utils import AutoEvalColumn, EvalQueueColumn | |
| from src.leaderboard.filter_models import filter_models | |
| from src.leaderboard.read_evals import get_raw_eval_results, EvalResult, update_model_type_with_open_llm_request_file | |
| from src.backend.envs import Tasks as BackendTasks | |
| from src.display.utils import Tasks | |
| factuality_tasks = [ | |
| "NQ Open/EM", | |
| "TriviaQA/EM", | |
| "PopQA/EM", | |
| "FEVER/Acc", | |
| "TrueFalse/Acc", | |
| "TruthQA MC2/Acc", | |
| ] | |
| faithfulness_tasks = [ | |
| "MemoTrap/Acc", | |
| "IFEval/Acc", | |
| "NQ-Swap/EM", | |
| "RACE/Acc", | |
| "SQuADv2/EM", | |
| "CNN-DM/ROUGE", | |
| "XSum/ROUGE", | |
| "HaluQA/Acc", | |
| "FaithDial/Acc", | |
| ] | |
| def get_leaderboard_df(results_path: str, | |
| requests_path: str, | |
| requests_path_open_llm: str, | |
| cols: list, | |
| benchmark_cols: list, | |
| is_backend: bool = False) -> tuple[list[EvalResult], pd.DataFrame]: | |
| # Returns a list of EvalResult | |
| raw_data: list[EvalResult] = get_raw_eval_results(results_path, requests_path, requests_path_open_llm) | |
| if requests_path_open_llm != "": | |
| for result_idx in tqdm(range(len(raw_data)), desc="updating model type with open llm leaderboard"): | |
| raw_data[result_idx] = update_model_type_with_open_llm_request_file(raw_data[result_idx], requests_path_open_llm) | |
| all_data_json_ = [v.to_dict() for v in raw_data if v.is_complete()] | |
| name_to_bm_map = {} | |
| task_iterator = Tasks | |
| if is_backend is True: | |
| task_iterator = BackendTasks | |
| for task in task_iterator: | |
| task = task.value | |
| name = task.col_name | |
| bm = (task.benchmark, task.metric) | |
| name_to_bm_map[name] = bm | |
| # bm_to_name_map = {bm: name for name, bm in name_to_bm_map.items()} | |
| all_data_json = [] | |
| for entry in all_data_json_: | |
| new_entry = copy.deepcopy(entry) | |
| for k, v in entry.items(): | |
| if k in name_to_bm_map: | |
| benchmark, metric = name_to_bm_map[k] | |
| new_entry[k] = entry[k][metric] | |
| all_data_json += [new_entry] | |
| # all_data_json.append(baseline_row) | |
| filter_models(all_data_json) | |
| df = pd.DataFrame.from_records(all_data_json) | |
| # if AutoEvalColumn.average.name in df: | |
| # df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) | |
| cols_mod = copy.deepcopy(cols) | |
| cols_mod.remove('Faithfulness') | |
| cols_mod.remove('Factuality') | |
| df = df[cols_mod]#.round(decimals=2) | |
| # filter out if any of the benchmarks have not been produced | |
| df = df[has_no_nan_values(df, benchmark_cols)] | |
| Factuality_score = df[factuality_tasks].mean(axis=1) | |
| Faithfulness_score = df[faithfulness_tasks].mean(axis=1) | |
| df.insert(2, 'Factuality', Factuality_score) | |
| df.insert(2, 'Faithfulness', Faithfulness_score) | |
| df = df.round(decimals=2) | |
| return raw_data, df | |
| def get_evaluation_queue_df(save_path: str, cols: list) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: | |
| entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")] | |
| all_evals = [] | |
| for entry in entries: | |
| if ".json" in entry: | |
| file_path = os.path.join(save_path, entry) | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) | |
| data[EvalQueueColumn.revision.name] = data.get("revision", "main") | |
| all_evals.append(data) | |
| elif ".md" not in entry: | |
| # this is a folder | |
| sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")] | |
| for sub_entry in sub_entries: | |
| file_path = os.path.join(save_path, entry, sub_entry) | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) | |
| data[EvalQueueColumn.revision.name] = data.get("revision", "main") | |
| all_evals.append(data) | |
| pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]] | |
| running_list = [e for e in all_evals if e["status"] == "RUNNING"] | |
| finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"] | |
| df_pending = pd.DataFrame.from_records(pending_list, columns=cols) | |
| df_running = pd.DataFrame.from_records(running_list, columns=cols) | |
| df_finished = pd.DataFrame.from_records(finished_list, columns=cols) | |
| return df_finished[cols], df_running[cols], df_pending[cols] | |