src/populate.py

import json
import os
from ast import literal_eval
import pandas as pd
import re

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.about import (
    nc_tasks,
    nr_tasks,
    lp_tasks,
)

def sanitize_string(input_string):
    # Remove leading and trailing whitespace
    input_string = input_string.strip()
    
    # Remove leading whitespace on each line
    sanitized_string = re.sub(r'(?m)^\s+', '', input_string)
    
    return sanitized_string
'''
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[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
'''

# Function to extract the numerical part before '+'
def extract_x(value):
    return float(value.split('+')[0])

# Function to highlight the highest (or lowest) value based on X
def make_bold(df, cols, ascending):
    df_highlight = df.copy()
    
    def apply_highlight(s):
        if ascending:
            max_idx = s.apply(extract_x).idxmin()
        else:
            max_idx = s.apply(extract_x).idxmax()
        
        return ['font-weight: bold' if i == max_idx else '' for i in range(len(s))]
    
    styler = df_highlight.style.apply(lambda x: apply_highlight(x) if x.name in cols else ['']*len(x), axis=0)
    return styler

def format_number(num):
    return f"{num:.3f}"
def get_leaderboard_df(EVAL_REQUESTS_PATH, task_type) -> pd.DataFrame:
    if task_type in ['Node Classification', 'Entity Classification']:
        ascending = False
        tasks = nc_tasks
        task_type = ['Node Classification', 'Entity Classification']
    elif task_type in ['Node Regression', 'Entity Regression']:
        ascending = True
        tasks = nr_tasks
        task_type = ['Node Regression', 'Entity Regression']
    elif task_type in ['Link Prediction', 'Recommendation']:
        ascending = False
        tasks = lp_tasks
        task_type = ['Link Prediction', 'Recommendation']

    model_result_filepaths = []
    for root,_, files in os.walk(EVAL_REQUESTS_PATH):
        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
            continue
        for file in files:
            model_result_filepaths.append(os.path.join(root, file))
    
    model_res = []
    for model in model_result_filepaths:
        import json
        with open(model) as f:
            out = json.load(f)
            if ('task' in out) and (out['task'] in task_type):
                model_res.append(out)

    for model in model_res:
        model["test"] = literal_eval(model["test"].split('}')[0]+'}')
        model["valid"] = literal_eval(model["valid"].split('}')[0]+'}')
        #model["params"] = int(model["params"])
        model['submitted_time'] = model['submitted_time'].split('T')[0]
        #model['paper_url'] = '[Link](' + model['paper_url'] + ')'
        #model['github_url'] = '[Link](' + model['github_url'] + ')'

    name2short_name = {task.value.benchmark: task.value.benchmark for task in tasks}
    for model in model_res:
        model.update({
            name2short_name[i]: (f"{format_number(model['test'][i][0])} ± {format_number(model['test'][i][1])}" if i in model['test'] else '-')
            for i in name2short_name
        })
    columns_to_show = ['model', 'author', 'email', 'paper_url', 'github_url', 'submitted_time'] + list(name2short_name.values())

    # Check if model_res is empty
    if len(model_res) > 0:
        df_res = pd.DataFrame([{col: model[col] for col in columns_to_show} for model in model_res])
    else:
        # Initialize an empty DataFrame with the desired columns
        df_res = pd.DataFrame(columns=columns_to_show)

    #df_res = pd.DataFrame([{col: model[col] for col in columns_to_show} for model in model_res])
    
    ranks = df_res[list(name2short_name.values())].rank(ascending = ascending)
    df_res.rename(columns={'model': 'Model', 'author': 'Author', 'email': 'Email', 'paper_url': 'Paper URL', 'github_url': 'Github URL', 'submitted_time': 'Time'}, inplace=True)
    df_res.Model.replace('Relbench User Study', 'Human Data Scientist', inplace=True)
    df_res['Average Rank⬆️'] = ranks.mean(axis=1)
    df_res.sort_values(by='Average Rank⬆️', ascending=True, inplace=True)
    #df_res = make_bold(df_res, list(name2short_name.values()), ascending = ascending)
    print(df_res)
    return df_res

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 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]