src/populate.py

import json
import os
import copy

import pandas as pd

from src.display.formatting import has_no_nan_values, make_requests_clickable_model
from src.display.utils import AutoEvalColumn, EvalQueueColumn, baseline_row, proprietary_rows
from src.leaderboard.filter_models import filter_models_flags
from src.leaderboard.read_evals import get_raw_eval_results


def get_leaderboard_df(results_path: str, requests_path: str, dynamic_path: str, cols: list, benchmark_cols: list, show_incomplete=False) -> pd.DataFrame:
    raw_data = get_raw_eval_results(results_path=results_path, requests_path=requests_path, dynamic_path=dynamic_path)
    all_data_json = [v.to_dict() for v in raw_data]
    all_data_json.append(baseline_row)
    for proprietary_row in proprietary_rows:
        all_data_json.append(proprietary_row)
    filter_models_flags(all_data_json)

    df = pd.DataFrame.from_records(all_data_json)
    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
    
    df = df[cols].round(decimals=2)

    # filter out if any of the benchmarks have not been produced
    if not show_incomplete:
        df = df[has_no_nan_values(df, benchmark_cols)]
    return raw_data, df


def get_evaluation_queue_df(save_path: str, cols: list, show_incomplete=False) -> list[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_requests_clickable_model(data["model"], entry)
            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_requests_clickable_model(data["model"], os.path.join(entry, sub_entry))
                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
                all_evals.append(data)

    cols_pending = copy.deepcopy(cols)
    cols_pending.append('source')
    cols_pending.append('submitted_time')

    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN", "PENDING_NEW_EVAL"]]
    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
    finished_list = [e for e in all_evals if e["status"] in ["FINISHED", "PENDING_NEW_EVAL" if show_incomplete else "FINISHED"]]
    failed_list = [e for e in all_evals if e["status"] == "FAILED"]
    df_pending = pd.DataFrame.from_records(pending_list, columns=cols_pending)
    df_running = pd.DataFrame.from_records(running_list, columns=cols)
    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
    df_failed = pd.DataFrame.from_records(failed_list, columns=cols)

    df_pending['source_priority'] = df_pending["source"].apply(lambda x: {"manual": 0, "leaderboard": 1, "script": 2}.get(x, 3))
    df_pending['status_priority'] = df_pending["status"].apply(lambda x: {"PENDING": 2, "RERUN": 0, "PENDING_NEW_EVAL": 1}.get(x, 3))
    
    df_pending = df_pending.sort_values(['source_priority', 'status_priority', 'submitted_time'])
    df_pending = df_pending.drop(['source_priority', 'status_priority', 'submitted_time', 'source'], axis=1)

    return df_finished[cols], df_running[cols], df_pending[cols], df_failed[cols]