src/populate.py

import json
import os

import pandas as pd
import numpy as np

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, get_raw_model_results




def get_model_leaderboard_df(results_path: str, requests_path: str="", cols: list=[], benchmark_cols: list=[], rank_col: list=[]) -> pd.DataFrame:
    """Creates a dataframe from all the individual experiment results"""
    raw_data = get_raw_model_results(results_path)
    all_data_json = [v.to_dict() for v in raw_data]
    # assert len(rank_col) <= 1, "Only one column can be selected for ranking"

    df = pd.DataFrame.from_records(all_data_json)
    
    df = df[benchmark_cols]
    # print(df.head())
    
    # if there is one col in rank_col, this is an isolated dimension to rank by
    # sort by that selected column and remove NaN values
    if rank_col and rank_col[0] not in ["sort_by_score", "sort_by_rank"]: 
        # df = df.dropna(subset=benchmark_cols)
        df = df.dropna(subset=rank_col)
        df = df.fillna(0.00)
        # print(df[rank_col[0]])
        df = df.sort_values(by=[rank_col[0]], ascending=True)
        # print(rank_col, benchmark_cols)
        # print(df.head())
        
        for col in benchmark_cols:
            if 'Std dev' in col or 'Score' in col:
                df[col] = (df[col]).map('{:.2f}'.format)
                df[col] = df[col].round(decimals=2)
                
    elif rank_col and rank_col[0] == "sort_by_score": # sorting by averaging all benchmark cols, except cols before offset_idx
        start_idx = rank_col[1]
        end_idx = rank_col[2]
        avg_scores = df.iloc[:, start_idx:end_idx].mean(axis=1)
        df.insert(1, "Average Score", avg_scores) 
        
        df["Average Score"] = avg_scores.round(decimals=4)
        df = df.sort_values(by=["Average Score"], ascending=False)
        df["Average Score"] = df["Average Score"].map('{:.2f}'.format)
        
        # df = df.drop(columns=benchmark_cols[offset_idx:])
        # print(benchmark_cols)
        # print(df.head())
        # insert a rank column
        rank = np.arange(1, len(df)+1)
        df.insert(0, 'Rank', rank) 

        for col in benchmark_cols:
            if 'Std dev' in col or 'Score' in col:
                df[col] = (df[col]).map('{:.2f}'.format)
                df[col] = df[col].round(decimals=2)

        # df = df.fillna('--')
        df.replace("nan", '--', inplace=True)
        
    elif rank_col and rank_col[0] == "sort_by_rank":
    # else:  # when rank_col, the first in benchmark_cols is empty, sort by averaging all the benchmarks, except the first one
        start_idx = rank_col[1]
        end_idx = rank_col[2]
        avg_rank = df.iloc[:, start_idx:end_idx].mean(axis=1)
        df.insert(1, "Average Rank", avg_rank) 
        
        df["Average Rank"] = avg_rank.round(decimals=4)
        df = df.sort_values(by=["Average Rank"], ascending=True)        
        df["Average Rank"] = df["Average Rank"].map('{:.2f}'.format)

        # we'll skip NaN, instrad of deleting the whole row
        df = df.fillna('--')
        # insert a rank column
        rank = np.arange(1, len(df)+1)
        df.insert(0, 'Rank', rank)
        
        df.style.background_gradient(cmap='coolwarm', subset=benchmark_cols)
        
    
                        
            
    # for col in benchmark_cols:
    #     # print(col)
    #     # if 'Std dev' in col or 'Score' in col:
    #     if 'Std dev' in col or 'Score' in col:
    #     #     if set(['Chemistry', 'Reasoning']).intersection(set(col.split())): 
    #     #         df[col] = (df[col]).map('{:.2f}'.format)
    #     #     else:   
    #     #         df[col] = (df[col]*100).map('{:.2f}'.format)
    #         # if "Chemistry" in col or "C++" in col:
    #         if "Chemistry" in col or "C++" in col or "Overall" in col or "Probability" in col or "Logical" in col:
    #             df[col] = (df[col]).map('{:.2f}'.format)
    #         else:
    #             df[col] = (df[col]*100).map('{:.2f}'.format)
    #         df[col] = df[col].round(decimals=2)

    # df = df.sort_values(by=[AutoEvalColumn.score.name], ascending=True)
    # df[AutoEvalColumn.rank.name] = df[AutoEvalColumn.score.name].rank(ascending=True, method="min")
    # print(cols) # []
    # print(df.columns) # ['eval_name', 'Model', 'Hub License', 'Organization', 'Knowledge cutoff', 'Overall']
    # exit()
    # only keep the columns that are in the cols list
    
    # for col in cols:
    #     if col not in df.columns:
    #         df[col] = None
    #     else:
    #         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_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)
    # raw_data = get_raw_model_results(results_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)
    for col in cols:
        if col not in df.columns:
            df[col] = None
        else:
            df[col] = df[col].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 requestes"""
    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 os.path.isfile(e) and 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]