src/populate.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# flake8: noqa E501

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
from src.utils import get_model_name_from_filepath


def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
    """Creates a dataframe from all the individual experiment results"""
    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.soliditybench.name], ascending=False)
    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_requests_df(save_path: str, cols: list) -> list[pd.DataFrame]:
    """Creates the different dataframes for the evaluation requestss requested."""
    all_evals = []

    def process_file(file_path):
        try:
            with open(file_path, 'r', encoding='utf-8') as fp:
                data = json.load(fp)
        except (json.JSONDecodeError, UnicodeDecodeError) as e:
            print(f"Error reading or decoding {file_path}: {e}")
            return None

        model_name = get_model_name_from_filepath(file_path)
        data[EvalQueueColumn.model.name] = make_clickable_model(model_name)
        data[EvalQueueColumn.revision.name] = data.get("revision", "main")
        return data

    for root, _, files in os.walk(save_path):
        for file in files:
            if file.endswith('.json'):
                file_path = os.path.join(root, file)
                data = process_file(file_path)
                if data:
                    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]