src/populate.py

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 AutoEvalColumn, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results


def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    raw_data = get_raw_eval_results(results_path, requests_path)
    all_data_json = [v.to_dict() for v in raw_data]

    df = pd.DataFrame.from_records(all_data_json)
    # df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False))
    # df = df.sort_values(by=[AutoEvalColumn.task3.name], ascending=True)


    df[AutoEvalColumn.task0.name] = pd.Series(
        np.stack(
            np.array(df[AutoEvalColumn.task0.name].values)
        ).squeeze()
    )
    df[AutoEvalColumn.task1.name] = pd.Series(
        np.stack(
            np.array(df[AutoEvalColumn.task1.name].values)
        ).squeeze()
    )
    df[AutoEvalColumn.task2.name] = pd.Series(
        np.stack(
            np.array(df[AutoEvalColumn.task2.name].values)
        ).squeeze()
    )

    en_cer_rank = df[AutoEvalColumn.task0.name].rank(method="min", numeric_only=True, ascending=True)
    ml_cer_rank = df[AutoEvalColumn.task1.name].rank(method="min", numeric_only=True, ascending=True)
    bitrate_rank = df[AutoEvalColumn.task2.name].rank(method="min", numeric_only=True, ascending=True)
    df["Ranking"] = pd.Series((en_cer_rank + ml_cer_rank + bitrate_rank)/3)
    df = df.sort_values(by=["Ranking", AutoEvalColumn.task1.name], ascending=True)
    df["Rank"] = df.groupby("Precision").cumcount() + 1
    df.pop("Ranking")

    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]:
    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]