import json import os import pandas as pd from src.display.formatting import has_no_nan_values, make_clickable_model from src.display.utils import AutoEvalColumnQA, EvalQueueColumn from src.leaderboard.read_evals import get_raw_eval_results, EvalResult from typing import Tuple def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list, task: str, metric: str) -> Tuple[list[EvalResult], pd.DataFrame]: """Creates a dataframe from all the individual experiment results""" raw_data = get_raw_eval_results(results_path, requests_path) all_data_json = [] for v in raw_data: all_data_json += v.to_dict(task=task, metric=metric) df = pd.DataFrame.from_records(all_data_json) _benchmark_cols = frozenset(benchmark_cols).intersection(frozenset(df.columns.to_list())) df[AutoEvalColumnQA.average.name] = df[list(_benchmark_cols)].mean(axis=1) df = df.sort_values(by=[AutoEvalColumnQA.average.name], ascending=False) df.reset_index(inplace=True) _cols = frozenset(cols).intersection(frozenset(df.columns.to_list())) df = df[_cols].round(decimals=2) # filter out if any of the benchmarks have not been produced df = df[has_no_nan_values(df, _benchmark_cols)] return raw_data, df def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]: """Creates the different dataframes for the evaluation queues requests""" 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]