{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_path = '../results'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = \"/home/jovyan/rmt/babilong-leaderboard/data/BABILong NeurIPS24 Figs - leaderboard.csv\"\n",
    "res_df = pd.read_csv(p)\n",
    "res_df = res_df[res_df.task.isin(['qa1', 'qa2', 'qa3', 'qa4', 'qa5'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "lens = [0, 1000, 2000, 4000, 8000, 16000, 32000, 64000, 128000, 500000, 1000000, 10000000]\n",
    "len_names = ['0K', '1K', '2K', '4K', '8K', '16K', '32K', '64K', '128K', '512K', '1M', '10M']\n",
    "\n",
    "for model_name in res_df.Model.unique():\n",
    "    model_df = res_df[res_df.Model == model_name]\n",
    "    for i, row in model_df.iterrows():\n",
    "        for l, ln in zip(lens, len_names):\n",
    "            score = row[ln]\n",
    "            # print(score)\n",
    "            if not pd.isna(score):\n",
    "                os.makedirs(os.path.join(res_path, model_name), exist_ok=True)\n",
    "                os.makedirs(os.path.join(res_path, model_name,  row.task), exist_ok=True)\n",
    "                path = os.path.join(res_path, model_name, row.task, f'{l}.csv')\n",
    "                df = pd.DataFrame([{'result': score}])\n",
    "                df.to_csv(path, index=False)"
   ]
  }
 ],
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