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, baseline_row from src.leaderboard.filter_models import filter_models_flags from src.display.utils import load_json_data def _process_model_data(entry, model_name_key="model", revision_key="revision"): """Enrich model data with clickable links and revisions.""" entry[EvalQueueColumn.model.name] = make_clickable_model(entry.get(model_name_key, "")) entry[EvalQueueColumn.revision.name] = entry.get(revision_key, "main") return entry 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) if data: all_evals.append(_process_model_data(data)) # 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, benchmark_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)] filter_models_flags(all_data_json_list) df = pd.DataFrame.from_records(all_data_json_list) df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) df = df[cols].round(decimals=2) df = df[has_no_nan_values(df, benchmark_cols)] return df