import pathlib import pandas as pd from datasets import Dataset from src.display.formatting import has_no_nan_values, make_clickable_model from src.display.utils import AutoEvalColumn, EvalQueueColumn from src.display.utils import load_json_data, column_map, type_map, moe_map, NUMERIC_INTERVALS def get_evaluation_queue_df(save_path, cols): """Generate dataframes for pending, running, and finished evaluation entries.""" save_path = pathlib.Path(save_path) all_evals = [] for path in save_path.rglob("*.json"): data = load_json_data(path) # Organizing data by status status_map = { "PENDING": ["PENDING", "RERUN"], "RUNNING": ["RUNNING"], "FINISHED": ["FINISHED", "PENDING_NEW_EVAL"], } status_dfs = {status: [] for status in status_map} for eval_data in all_evals: for status, extra_statuses in status_map.items(): if eval_data["status"] in extra_statuses: status_dfs[status].append(eval_data) return tuple(pd.DataFrame(status_dfs[status], columns=cols) for status in ["FINISHED", "RUNNING", "PENDING"]) def get_leaderboard_df(leaderboard_dataset: Dataset, cols: list): """Retrieve and process leaderboard data.""" all_data_json = leaderboard_dataset.to_dict() num_items = leaderboard_dataset.num_rows all_data_json_list = [{k: all_data_json[k][ix] for k in all_data_json.keys()} for ix in range(num_items)] df = pd.DataFrame.from_records(all_data_json_list) # replace df.moe true to false, false to true # map column names df = df.rename(columns=column_map) df[AutoEvalColumn.moe.name] = df[AutoEvalColumn.moe.name].map(moe_map) df[AutoEvalColumn.T.name] = df[AutoEvalColumn.type.name] df[AutoEvalColumn.type.name] = df[AutoEvalColumn.type.name].map(type_map) df[AutoEvalColumn.average.name] = df.apply(lambda x: round((x[AutoEvalColumn.complete.name] + x[AutoEvalColumn.instruct.name]) / 2, 1) if not pd.isna(x[AutoEvalColumn.complete.name]) and not pd.isna(x[AutoEvalColumn.instruct.name]) else None, axis=1) df[AutoEvalColumn.size_range.name] = df[AutoEvalColumn.size.name].apply(lambda x: next((k for k, v in NUMERIC_INTERVALS.items() if x in v), "?")) df = make_clickable_model(df, AutoEvalColumn.model.name, AutoEvalColumn.link.name) df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) df = df[cols].round(decimals=2) return df