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 AutoEvalColumnQA, AutoEvalColumnLongDoc, EvalQueueColumn
from src.leaderboard.read_evals import get_raw_eval_results, EvalResult, FullEvalResult
from typing import Tuple, List


def get_leaderboard_df(raw_data: List[FullEvalResult], cols: list, benchmark_cols: list, task: str, metric: str) -> pd.DataFrame:
    """Creates a dataframe from all the individual experiment results"""
    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)
    print(f'dataframe created: {df.shape}')

    # calculate the average score for selected benchmarks
    _benchmark_cols = frozenset(benchmark_cols).intersection(frozenset(df.columns.to_list()))
    if task == 'qa':
        df[AutoEvalColumnQA.average.name] = df[list(_benchmark_cols)].mean(axis=1).round(decimals=2)
        df = df.sort_values(by=[AutoEvalColumnQA.average.name], ascending=False)
    elif task == "long_doc":
        df[AutoEvalColumnLongDoc.average.name] = df[list(_benchmark_cols)].mean(axis=1).round(decimals=2)
        df = df.sort_values(by=[AutoEvalColumnLongDoc.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 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]