analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33624bb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No game.pgn in games/game34fad7dd-e598-4969-9ac2-ab745885f0ee\n",
      "No game.pgn in games/gameb134bc87-b178-4031-a7ef-5d63e85830f6\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "No game.pgn in games/game1503b2e5-e815-49e1-b1bf-14d9b02e3067\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "No game.pgn in games/game1a570601-11a1-4bb2-bf14-ba8b4eeef40f\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "No game.pgn in games/gameda5b7902-6ab2-43f9-8236-79bc9a2e0633\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
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      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "No game.pgn in games/gameb8966a66-5767-4b1d-be36-388a6f669efa\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n",
      "warning: UnknownSAN in game\n"
     ]
    }
   ],
   "source": [
    "import chess \n",
    "import chess.pgn\n",
    "import glob\n",
    "import pandas as pd\n",
    "import os \n",
    "import re \n",
    "\n",
    "# list each folder of folder \"games\"\n",
    "# get all folders\n",
    "folders = glob.glob(\"games/*\")\n",
    "\n",
    "# sort folders by date\n",
    "folders.sort(key=os.path.getmtime)\n",
    "\n",
    "df = pd.DataFrame(columns=[\"white\", \"black\", \"white_elo\", \"black_elo\", \"result\", \"nmoves\", \"nstarting_move\", \"pgn_base\", \"temperature\", \"random_engine\", \"has_illegal\", \"illegal_move\", \"folder_name\"])\n",
    "\n",
    "for folder in folders:\n",
    "\n",
    "    # for each folder:\n",
    "    # read PGN file \"game.pgn\"\n",
    "\n",
    "    # check that \"game.pgn\" exists\n",
    "    if not os.path.exists(folder + \"/game.pgn\"):\n",
    "        print(\"No game.pgn in \" + folder) # TODO\n",
    "        continue\n",
    "\n",
    "    with open(folder + \"/game.pgn\") as pgn:\n",
    "        game = chess.pgn.read_game(pgn)\n",
    "\n",
    "    # read metainformation.txt\n",
    "    # get the GPT model and the engine (SF or random)\n",
    "    nmove_value = None\n",
    "    with open(folder + \"/metainformation.txt\") as f:\n",
    "        lines = f.readlines()\n",
    "\n",
    "    # Iterate over each line in the file\n",
    "    for line in lines:\n",
    "        # Check if the line contains the string 'nmove:'\n",
    "        if 'nmove:' in line:\n",
    "            # Split the line at 'nmove:' and take the second part\n",
    "            # Then strip leading and trailing whitespaces and convert to integer\n",
    "            nmove_value = int(line.split('nmove:')[1].strip())\n",
    "            # Print the extracted value\n",
    "            break\n",
    "    \n",
    "    if nmove_value is None:\n",
    "        nmove_value = 1 # default value\n",
    "    \n",
    "    \n",
    "   \n",
    "\n",
    "    # number of moves\n",
    "    game_length = len(list(game.mainline_moves()))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # for each PGN:\n",
    "    # get the GPT model and the engine (SF or random)\n",
    "    white = game.headers[\"White\"]\n",
    "    black = game.headers[\"Black\"]\n",
    "\n",
    "\n",
    "    # get the Elo of the engine (if any)\n",
    "\n",
    "    # get the Elo of the player (if any)\n",
    "    white_elo = game.headers[\"WhiteElo\"]\n",
    "    black_elo = game.headers[\"BlackElo\"]\n",
    "\n",
    "    # get the result (or infer based on checkmates) # special case: no mate, or unifinished game due to wrong move\n",
    "    result = game.headers[\"Result\"]\n",
    "\n",
    "    has_illegal = False\n",
    "    illegal_move = ''\n",
    "    # check that UnknownSAN key is in game\n",
    "    # if not, continue\n",
    "    if 'UnknownSAN' in game.headers:\n",
    "        has_illegal = True\n",
    "        illegal_move = game.headers[\"UnknownSAN\"]\n",
    "        print(\"warning: UnknownSAN in game\")\n",
    "        # continue\n",
    "\n",
    "    with open(folder + \"/metainformation.txt\") as file:\n",
    "        content = file.read()\n",
    "\n",
    "    # find the content between 'temperature:' and ends of line\n",
    "    match_random = re.search(r'random_engine:([\\s\\S]+?)(\\n)', content, re.MULTILINE)\n",
    "    random_engine = None\n",
    "    if match_random:\n",
    "        random_engine = match_random.group(1).strip()\n",
    "        if 'True' in random_engine:\n",
    "            random_engine = True\n",
    "        elif 'False' in random_engine:\n",
    "            random_engine = False\n",
    "        else:   \n",
    "            print(\"random engine value unclear/unknwon\")\n",
    "                \n",
    "    else:\n",
    "        random_engine = False\n",
    "        # print('No random engine found') # default value: False (note: should not happen)\n",
    "\n",
    "    with open(folder + \"/metainformation.txt\") as file:\n",
    "        content = file.read()\n",
    "\n",
    "    # find the content between 'temperature:' and ends of line\n",
    "    match_temperature = re.search(r'temperature:([\\s\\S]+?)(\\n)', content, re.MULTILINE)\n",
    "    temperature = None\n",
    "    if match_temperature:\n",
    "        temperature = match_temperature.group(1).strip()\n",
    "        # print(extracted_content)\n",
    "    else:\n",
    "        temperature = 0.0\n",
    "        # print('No temperature found') # default value: 0\n",
    "\n",
    "    with open(folder + \"/metainformation.txt\") as file:\n",
    "        content = file.read()\n",
    "\n",
    "    # Find the content between 'base_pgn:' and another term at the start of a new line followed by ':'\n",
    "    match = re.search(r'base_pgn:([\\s\\S]+?)(^\\w+:)', content, re.MULTILINE)\n",
    "    pgn_base = ''\n",
    "    if match:\n",
    "        # Extract and clean up the matched content\n",
    "        pgn_base = match.group(1).strip()\n",
    "        # print(extracted_content)\n",
    "    else:\n",
    "        print('No base pgn found')\n",
    "\n",
    "    # put in dfframe\n",
    "    # columns: white, black, white_elo, black_elo, result\n",
    "    # append to dfframe\n",
    "\n",
    "    df = df.append({\"white\": white, \"black\": black, \"white_elo\": white_elo, \"black_elo\": black_elo, \"result\": result, \"nmoves\" : game_length, \"nstarting_move\": nmove_value, \"pgn_base\" : pgn_base, \"temperature\" : temperature, \"random_engine\" : random_engine, \"has_illegal\" : has_illegal, \"illegal_move\" : illegal_move, \"folder_name\" : folder}, ignore_index=True)\n",
    "\n",
    "\n",
    "    # compute stats\n",
    "    # scores in general, per Elo and chess engine, per GPT model\n",
    "    # ability to finish a game (with weaker models)\n",
    "\n",
    "    # first: Elo = 1700, GPT=3.5 instruct\n",
    "    # second: Elo = 1800, GPT=3.5 instruct\n",
    "    # ...\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ee48e12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Open and read the content of metainformation.txt\n",
    "with open(\"games/gamea7e73056-951a-417d-b671-0a60fba939f7/\" + 'metainformation.txt', 'r') as file:\n",
    "    content = file.read()\n",
    "\n",
    "# Find the content between 'base_pgn:' and another term at the start of a new line followed by ':'\n",
    "match = re.search(r'base_pgn:([\\s\\S]+?)(^\\w+:)', content, re.MULTILINE)\n",
    "match = re.search(r'temperature:([\\s\\S]+?)(\\n)', content, re.MULTILINE)\n",
    "\n",
    "if match:\n",
    "    # Extract and clean up the matched content\n",
    "    extracted_content = match.group(1).strip()\n",
    "    print(extracted_content)\n",
    "else:\n",
    "    print('No match found')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb1b28bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "878 games compiled in the database\n"
     ]
    }
   ],
   "source": [
    "print(len(df), \"games compiled in the database\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "46e66f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import chess\n",
    "import chess.pgn\n",
    "import io \n",
    "\n",
    "\n",
    "# for column \"pgn_base\", I'd like to transform the string into a list of moves\n",
    "\n",
    "def pgn_to_list(pgn):\n",
    "    g = chess.pgn.read_game(io.StringIO(pgn))\n",
    "    g.mainline_moves()\n",
    "    return str(g.mainline_moves())\n",
    "\n",
    "def is_base_prompt(pgn):\n",
    "    g = chess.pgn.read_game(io.StringIO(pgn))\n",
    "    # [Event \"FIDE World Championship Match 2024\"]\\n[Site \"Los Angeles, USA\"]\n",
    "    return g.headers[\"Event\"] == \"FIDE World Championship Match 2024\" and g.headers[\"Site\"] == \"Los Angeles, USA\"\n",
    "\n",
    "def has_illegal_moves(pgn):\n",
    "    # exist g.headers[\"UnknownSAN\"] \n",
    "    g = chess.pgn.read_game(io.StringIO(pgn))\n",
    "    # key in array\n",
    "    return \"UnknownSAN\" in g.headers\n",
    "\n",
    "df[\"pgn_base_moves\"] = df[\"pgn_base\"].apply(pgn_to_list) # extract only moves\n",
    "df[\"base_pgn_prompt\"] = df[\"pgn_base\"].apply(is_base_prompt) # extract only prompt\n",
    "# df.sort_values(by=['nstarting_move'])\n",
    "df['temperature'] = pd.to_numeric(df['temperature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3118f825",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * gpt-3.5-turbo-instruct : 573 games, among 401 with white piece and 172 with black pieces\n",
      " * gpt-4 : 179 games, among 116 with white piece and 63 with black pieces\n",
      " * gpt-3.5-turbo : 53 games, among 53 with white piece and 0 with black pieces\n",
      " * text-davinci-003 : 73 games, among 43 with white piece and 30 with black pieces\n"
     ]
    }
   ],
   "source": [
    "# Frequencies based on piece colors\n",
    "players = ['gpt-3.5-turbo-instruct', 'gpt-4', 'gpt-3.5-turbo', 'text-davinci-003']\n",
    "color_breakdown = {}\n",
    "for player in players:\n",
    "    white_count = df[df['white'] == player].shape[0]\n",
    "    black_count = df[df['black'] == player].shape[0]\n",
    "    color_breakdown[player] = {'white': white_count, 'black': black_count}\n",
    "\n",
    "for player in players:\n",
    "    nwhite = color_breakdown[player]['white']\n",
    "    nblack = color_breakdown[player]['black']\n",
    "    print(\" *\", player, \":\", nwhite+nblack, \"games, among\", nwhite, \"with white piece and\", nblack, \"with black pieces\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c098cd6",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game42dd235a-0b46-4237-9fb2-06e1bccf0f41</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game657b48b9-9437-498a-adee-c2efcf753e5b</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game8614d5d1-2fb8-4c6b-984e-621c73664d00</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game77d6c5e7-fd9e-4cea-977b-3686d76b521b</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game37ba7154-6423-44a0-b9db-c52e9db003e2</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    white      black white_elo black_elo result nmoves  \\\n",
       "0  gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0     65   \n",
       "1  gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0     81   \n",
       "2  gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0     75   \n",
       "3  gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0    137   \n",
       "4  gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0     51   \n",
       "\n",
       "  nstarting_move                                           pgn_base  \\\n",
       "0              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "1              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "2              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "3              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "4              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "\n",
       "   temperature random_engine has_illegal illegal_move  \\\n",
       "0          0.0         False       False                \n",
       "1          0.0         False       False                \n",
       "2          0.0         False       False                \n",
       "3          0.0         False       False                \n",
       "4          0.0         False       False                \n",
       "\n",
       "                                      folder_name pgn_base_moves  \\\n",
       "0  games/game42dd235a-0b46-4237-9fb2-06e1bccf0f41                  \n",
       "1  games/game657b48b9-9437-498a-adee-c2efcf753e5b                  \n",
       "2  games/game8614d5d1-2fb8-4c6b-984e-621c73664d00                  \n",
       "3  games/game77d6c5e7-fd9e-4cea-977b-3686d76b521b                  \n",
       "4  games/game37ba7154-6423-44a0-b9db-c52e9db003e2                  \n",
       "\n",
       "   base_pgn_prompt  \n",
       "0             True  \n",
       "1             True  \n",
       "2             True  \n",
       "3             True  \n",
       "4             True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27a680b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13b053b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"games_db.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28c35c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_score(df, model_name='gpt-3.5-turbo-instruct', percentage=True):\n",
    "    # Count the number of wins, losses, and draws for gpt-3.5-turbo-instruct\n",
    "    wins_as_white = df[(df['white'] == model_name) & (df['result'] == '1-0')].shape[0]\n",
    "    wins_as_black = df[(df['black'] == model_name) & (df['result'] == '0-1')].shape[0]\n",
    "    losses_as_white = df[(df['white'] == model_name) & (df['result'] == '0-1')].shape[0]\n",
    "    losses_as_black = df[(df['black'] == model_name) & (df['result'] == '1-0')].shape[0]\n",
    "    draws_as_white = df[(df['white'] == model_name) & (df['result'] == '1/2-1/2')].shape[0]\n",
    "    draws_as_black = df[(df['black'] == model_name) & (df['result'] == '1/2-1/2')].shape[0]\n",
    "\n",
    "    # Calculate total wins, losses, and draws\n",
    "    total_wins = wins_as_white + wins_as_black\n",
    "    total_losses = losses_as_white + losses_as_black\n",
    "    total_draws = draws_as_white + draws_as_black\n",
    "\n",
    "    if percentage:\n",
    "        return (total_wins + (total_draws * 0.5)) / (total_wins + total_losses + total_draws)\n",
    "    else:\n",
    "        return (total_wins + (total_draws * 0.5), total_wins + total_losses + total_draws)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d668c8a9",
   "metadata": {},
   "source": [
    "text-davinci-003 (completion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c08a9942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73 games with davinci\n",
      "with white pieces 43\n",
      "with black pieces 30\n",
      "21 games against random chess engine\n",
      "72 games with illegal move\n",
      "score 0.0\n",
      "the only game with no illegal move is\n",
      "The longest game was 20.0 moves\n",
      "The shortest game was 4.5 moves\n",
      "The average game length was 12.027397260273972 moves\n",
      "The median game length was 11.5 moves\n"
     ]
    }
   ],
   "source": [
    "df_davinci = df.query(\"white == 'text-davinci-003' or black == 'text-davinci-003'\")\n",
    "print(len(df_davinci), \"games with davinci\")\n",
    "print(\"with white pieces\", len(df.query(\"white == 'text-davinci-003'\")))\n",
    "print(\"with black pieces\", len(df.query(\"black == 'text-davinci-003'\")))\n",
    "\n",
    "print(len(df_davinci.query(\"white == 'RANDOM chess engine' or black == 'RANDOM chess engine'\")), \"games against random chess engine\")\n",
    "# print(compute_score(df_davinci.query(\"white == 'RANDOM chess engine' or black == 'RANDOM chess engine'\"), \"text-davinci-003\"))\n",
    "\n",
    "\n",
    "print(len(df_davinci.query(\"has_illegal == True\")), \"games with illegal move\")\n",
    "print(\"score\", compute_score(df_davinci, \"text-davinci-003\"))\n",
    "\n",
    "print(\"the only game with no illegal move is\")\n",
    "df_davinci.query(\"has_illegal == False\")\n",
    "\n",
    "\n",
    "# box plot of df_davinci['nmoves']\n",
    "print(\"The longest game was\", df_davinci['nmoves'].max()/2, \"moves\")\n",
    "print(\"The shortest game was\", df_davinci['nmoves'].min()/2, \"moves\")\n",
    "print(\"The average game length was\", df_davinci['nmoves'].mean()/2, \"moves\")\n",
    "print(\"The median game length was\", df_davinci['nmoves'].median()/2, \"moves\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eed048b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f965e556",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>white</th>\n",
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       "      <th>pgn_base_moves</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>gpt-4</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxc8#</td>\n",
       "      <td>games/gamed4ecce8c-6c03-462d-a631-2a02ca21d421</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td></td>\n",
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       "      <td>1. h3 g6 2. g3 c6 3. Bg2 e6 4. e4 d5 5. Nc3 Qb...</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
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       "      <td>0-1</td>\n",
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       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1. h4 Na6 2. b4 c5 3. Nf3 g5 4. Rh3 d6 5. d3 N...</td>\n",
       "      <td>True</td>\n",
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       "      <td></td>\n",
       "      <td>games/gamea70f1483-9cf1-424e-955b-128c772335c1</td>\n",
       "      <td>1. h4 g6 2. Nf3 h5 3. Nc3 Bh6 4. Na4 Nc6 5. b3...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>878 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      white                black white_elo black_elo result  \\\n",
       "877                   gpt-4            Stockfish         ?      1597      *   \n",
       "729                   gpt-4  RANDOM chess engine         ?         ?    1-0   \n",
       "728                   gpt-4  RANDOM chess engine         ?         ?    1-0   \n",
       "727                   gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "726                   gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "..                      ...                  ...       ...       ...    ...   \n",
       "397  gpt-3.5-turbo-instruct            Stockfish         ?      1871    0-1   \n",
       "393  gpt-3.5-turbo-instruct            Stockfish         ?      1871    0-1   \n",
       "363  gpt-3.5-turbo-instruct            Stockfish         ?      1785    0-1   \n",
       "386  gpt-3.5-turbo-instruct            Stockfish         ?      1871    0-1   \n",
       "378  gpt-3.5-turbo-instruct            Stockfish         ?      1871    1-0   \n",
       "\n",
       "    nmoves nstarting_move                                           pgn_base  \\\n",
       "877     80              1  It is your turn! You have white pieces. Please...   \n",
       "729     35              1  It is your turn! You have white pieces. Please...   \n",
       "728     29              1  It is your turn! You have white pieces. Please...   \n",
       "727     24              1  It is your turn! You have white pieces. Please...   \n",
       "726     42              1  It is your turn! You have white pieces. Please...   \n",
       "..     ...            ...                                                ...   \n",
       "397     50             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "393     50             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "363    130             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "386    138             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "378     85             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "\n",
       "     temperature random_engine has_illegal illegal_move  \\\n",
       "877          0.0         False        True         Bxa7   \n",
       "729          0.0          True       False                \n",
       "728          0.0          True       False                \n",
       "727          0.0          True        True        Qxc8#   \n",
       "726          0.0          True        True         Qh4#   \n",
       "..           ...           ...         ...          ...   \n",
       "397          0.0         False       False                \n",
       "393          0.0         False       False                \n",
       "363          0.0         False       False                \n",
       "386          0.0         False       False                \n",
       "378          0.0         False       False                \n",
       "\n",
       "                                        folder_name  \\\n",
       "877  games/game4b840b06-d4f7-4f35-a0d9-71591040c3e2   \n",
       "729  games/game88a92eb0-6166-47d7-aec2-ecdf247b7716   \n",
       "728  games/game7355eb01-74bf-4d3c-8fb2-00dd70818186   \n",
       "727  games/gamed4ecce8c-6c03-462d-a631-2a02ca21d421   \n",
       "726  games/game19858450-f0c8-470c-b05e-e2a7d94f5f10   \n",
       "..                                              ...   \n",
       "397  games/game7c6ca350-54c6-4ec0-856d-3a69cd708779   \n",
       "393  games/game794ba31b-dd5d-4db5-8578-3ef1e3024f61   \n",
       "363  games/game68a6373b-7275-4517-9efc-1baf7205ed7e   \n",
       "386  games/game9e225b87-958f-42c7-862c-e8cf2c331a39   \n",
       "378  games/gamea70f1483-9cf1-424e-955b-128c772335c1   \n",
       "\n",
       "                                        pgn_base_moves  base_pgn_prompt  \n",
       "877                                                               False  \n",
       "729                                                               False  \n",
       "728                                                               False  \n",
       "727                                                               False  \n",
       "726                                                               False  \n",
       "..                                                 ...              ...  \n",
       "397  1. h3 Nf6 2. a3 Na6 3. d4 h6 4. Bd2 g5 5. g3 R...             True  \n",
       "393  1. h3 e6 2. d4 Ke7 3. Bd2 f6 4. d5 Kd6 5. Be3 ...             True  \n",
       "363  1. h3 g6 2. g3 c6 3. Bg2 e6 4. e4 d5 5. Nc3 Qb...             True  \n",
       "386  1. h4 Na6 2. b4 c5 3. Nf3 g5 4. Rh3 d6 5. d3 N...             True  \n",
       "378  1. h4 g6 2. Nf3 h5 3. Nc3 Bh6 4. Na4 Nc6 5. b3...             True  \n",
       "\n",
       "[878 rows x 15 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['pgn_base'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd2edd56",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>[Event \"Chess tournament\"]\\n[Site \"Rennes FRA\"...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Kc6</td>\n",
       "      <td>games/game57c05bf7-d0c9-4ab3-9e3f-532aeeb8a226</td>\n",
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       "      <td>gpt-3.5-turbo-instruct</td>\n",
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       "      <td>0-1</td>\n",
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       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
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       "      <td>games/game27033b17-cec7-421b-8547-1692989bcca8</td>\n",
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       "      <td>It is your turn! You have white pieces. Please...</td>\n",
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      "text/plain": [
       "                      white             black white_elo black_elo   result  \\\n",
       "412               Stockfish  text-davinci-003      1694         ?        *   \n",
       "177               Stockfish  text-davinci-003      1694         ?        *   \n",
       "175               Stockfish  text-davinci-003      1694         ?        *   \n",
       "445               Stockfish  text-davinci-003      1694         ?        *   \n",
       "413               Stockfish  text-davinci-003      1694         ?        *   \n",
       "..                      ...               ...       ...       ...      ...   \n",
       "295  gpt-3.5-turbo-instruct         Stockfish         ?      1785      0-1   \n",
       "296  gpt-3.5-turbo-instruct         Stockfish         ?      1785  1/2-1/2   \n",
       "297  gpt-3.5-turbo-instruct         Stockfish         ?      1785      0-1   \n",
       "288  gpt-3.5-turbo-instruct         Stockfish         ?      1785      0-1   \n",
       "877                   gpt-4         Stockfish         ?      1597        *   \n",
       "\n",
       "    nmoves nstarting_move                                           pgn_base  \\\n",
       "412     29              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "177     19              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "175     23              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "445     29              1  [Event \"Chess tournament\"]\\n[Site \"Rennes FRA\"...   \n",
       "413     23              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "..     ...            ...                                                ...   \n",
       "295    104              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "296    168              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "297     92              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "288     62              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "877     80              1  It is your turn! You have white pieces. Please...   \n",
       "\n",
       "     temperature random_engine has_illegal illegal_move  \\\n",
       "412          0.0         False        True          Qg5   \n",
       "177          0.0         False        True          Bd7   \n",
       "175          0.0         False        True         Nbd7   \n",
       "445          0.0         False        True          Kc6   \n",
       "413          0.0         False        True         Bxd4   \n",
       "..           ...           ...         ...          ...   \n",
       "295          0.8         False       False                \n",
       "296          0.8         False       False                \n",
       "297          0.8         False       False                \n",
       "288          0.8         False       False                \n",
       "877          0.0         False        True         Bxa7   \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "412  games/game845e0126-8c7a-4806-9e30-9b9e841aa321                  \n",
       "177  games/game640f7c9e-c6e5-4ed8-b5f5-61e9b77d6973                  \n",
       "175  games/game3dc25955-c098-400c-ab6a-e0cb5d1535b9                  \n",
       "445  games/game57c05bf7-d0c9-4ab3-9e3f-532aeeb8a226                  \n",
       "413  games/game59c83e3f-5ad3-4d64-b697-6ea009548d2a                  \n",
       "..                                              ...            ...   \n",
       "295  games/gamede46833d-5915-4d4c-9bdf-97c520c238f8                  \n",
       "296  games/game1801ac0a-fa0a-4007-80e1-61089f165c93                  \n",
       "297  games/gamebefce8aa-7c78-4f39-bb1c-e5fb504b4e18                  \n",
       "288  games/game27033b17-cec7-421b-8547-1692989bcca8                  \n",
       "877  games/game4b840b06-d4f7-4f35-a0d9-71591040c3e2                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "412             True  \n",
       "177             True  \n",
       "175             True  \n",
       "445            False  \n",
       "413             True  \n",
       "..               ...  \n",
       "295             True  \n",
       "296             True  \n",
       "297             True  \n",
       "288             True  \n",
       "877            False  \n",
       "\n",
       "[878 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "df.sort_values(by=['white_elo'])"
   ]
  },
  {
   "attachments": {},
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   "source": [
    "GPTs against random engine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf38750b",
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   "outputs": [
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gameab7de814-fdb5-450a-a1cc-e78b989c3b24</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game827a2550-740e-4006-91ea-fd36f32a8cb1</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game8189b1d5-812f-4894-ab9a-678ecd211389</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1/2-1/2</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game18d6ab87-e8c6-4d5c-8176-82cd72e9d542</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamee04d1e84-9913-433a-928d-c5bdf052777b</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Nxf6#</td>\n",
       "      <td>games/game1e6a52b2-e8ff-421a-a3e0-a35f85b6b26e</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>gxh8=Q</td>\n",
       "      <td>games/gamed22f5ad7-ecc6-4bdd-94e8-14c746d3be83</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxd8+</td>\n",
       "      <td>games/game8dd34ec0-8485-4776-8e85-12daa8e5aa84</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamec005da68-771e-4002-8394-015dab24e403</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      white                black white_elo black_elo   result  \\\n",
       "122        text-davinci-003  RANDOM chess engine         ?         ?        *   \n",
       "405  gpt-3.5-turbo-instruct  RANDOM chess engine         ?         ?      1-0   \n",
       "407  gpt-3.5-turbo-instruct  RANDOM chess engine         ?         ?      1-0   \n",
       "408  gpt-3.5-turbo-instruct  RANDOM chess engine         ?         ?      1-0   \n",
       "409  gpt-3.5-turbo-instruct  RANDOM chess engine         ?         ?  1/2-1/2   \n",
       "..                      ...                  ...       ...       ...      ...   \n",
       "752           gpt-3.5-turbo  RANDOM chess engine         ?         ?      1-0   \n",
       "753           gpt-3.5-turbo  RANDOM chess engine         ?         ?        *   \n",
       "754           gpt-3.5-turbo  RANDOM chess engine         ?         ?        *   \n",
       "756           gpt-3.5-turbo  RANDOM chess engine         ?         ?        *   \n",
       "761           gpt-3.5-turbo  RANDOM chess engine         ?         ?      1-0   \n",
       "\n",
       "    nmoves nstarting_move                                           pgn_base  \\\n",
       "122     16              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "405     17              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "407     29              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "408     35              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "409     47              1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "..     ...            ...                                                ...   \n",
       "752     27              1  It is your turn! You have white pieces. Please...   \n",
       "753     22              1  It is your turn! You have white pieces. Please...   \n",
       "754     12              1  It is your turn! You have white pieces. Please...   \n",
       "756     14              1  It is your turn! You have white pieces. Please...   \n",
       "761      9              1  It is your turn! You have white pieces. Please...   \n",
       "\n",
       "     temperature random_engine has_illegal illegal_move  \\\n",
       "122          0.0          True        True          Nh2   \n",
       "405          0.0          True       False                \n",
       "407          0.0          True       False                \n",
       "408          0.0          True       False                \n",
       "409          0.0          True       False                \n",
       "..           ...           ...         ...          ...   \n",
       "752          0.0          True       False                \n",
       "753          0.0          True        True        Nxf6#   \n",
       "754          0.0          True        True       gxh8=Q   \n",
       "756          0.0          True        True        Qxd8+   \n",
       "761          0.0          True       False                \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "122  games/game433a71de-c5f7-4b11-bcd1-d82f8692ae13                  \n",
       "405  games/gameab7de814-fdb5-450a-a1cc-e78b989c3b24                  \n",
       "407  games/game827a2550-740e-4006-91ea-fd36f32a8cb1                  \n",
       "408  games/game8189b1d5-812f-4894-ab9a-678ecd211389                  \n",
       "409  games/game18d6ab87-e8c6-4d5c-8176-82cd72e9d542                  \n",
       "..                                              ...            ...   \n",
       "752  games/gamee04d1e84-9913-433a-928d-c5bdf052777b                  \n",
       "753  games/game1e6a52b2-e8ff-421a-a3e0-a35f85b6b26e                  \n",
       "754  games/gamed22f5ad7-ecc6-4bdd-94e8-14c746d3be83                  \n",
       "756  games/game8dd34ec0-8485-4776-8e85-12daa8e5aa84                  \n",
       "761  games/gamec005da68-771e-4002-8394-015dab24e403                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "122             True  \n",
       "405             True  \n",
       "407             True  \n",
       "408             True  \n",
       "409             True  \n",
       "..               ...  \n",
       "752            False  \n",
       "753            False  \n",
       "754            False  \n",
       "756            False  \n",
       "761            False  \n",
       "\n",
       "[190 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random = df.query(\"random_engine == True\")\n",
    "df_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "48693dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score 98.89 % for games with only legal moves\n",
      "Score 44.5  for games with only legal moves ( 45 games)\n",
      "The only not won game is: https://lichess.org/bE99x52y is due to repetition\n",
      "45 legal games and 16 illegal games (out of 61 total games)\n",
      "Illegal moves are           45\n",
      "hxg8=      4\n",
      "gxh8=      3\n",
      "{[%clk     2\n",
      "cxd8=      1\n",
      "exf8=      1\n",
      "gxf8=      1\n",
      "bxc1=      1\n",
      "dxc8=      1\n",
      "gxh1=      1\n",
      "{The       1\n",
      "Name: illegal_move, dtype: int64\n",
      "Number of moves against random chess engine\n",
      "The longest game was 33.0 moves\n",
      "The shortest game was 2.0 moves\n",
      "The average game length was 14.4 moves\n",
      "The median game length was 14.0 moves\n",
      "530    games/game53ab030a-eac5-4e07-b9ac-b5bea986a4db\n",
      "522    games/game1c647bc0-c1c2-4ad9-8768-7855ae92409f\n",
      "514    games/game16e5fb79-e763-41a4-a44c-d41a01dde2b8\n",
      "487    games/gamec425a3ab-71c9-4da3-a8c8-ea448ac49ee4\n",
      "518    games/game2d2ca7b6-6776-4dca-9514-edaa8b061e44\n",
      "504    games/gamed9d33ce5-eb88-4196-a26d-02c9006851af\n",
      "532    games/game86c3db5b-1bf9-48c6-a5e8-cfaf57e749ff\n",
      "405    games/gameab7de814-fdb5-450a-a1cc-e78b989c3b24\n",
      "491    games/gameb00f9f35-a114-48cc-ae01-3cd8476a1617\n",
      "521    games/gameeff6abb2-7da3-47e6-a271-a44dfbeacb1c\n",
      "410    games/gamef852f558-6691-4007-841c-05ee3e605b2e\n",
      "486    games/game1468a652-da4b-4fae-afe8-b785088641d4\n",
      "505    games/game949bfc8a-c05c-4934-a953-1da8bf98972d\n",
      "513    games/game5e93eae3-35b4-4d8a-9fbf-873b3ef39311\n",
      "498    games/game78d1fcf3-48f9-49ff-9c3c-3425f19bd2ef\n",
      "507    games/gamee962903c-7c2c-414f-9835-d64a3c39e942\n",
      "506    games/game68ea4c73-29b2-4ba8-b708-bc30e63bf7d1\n",
      "488    games/gamed137473f-ae3f-4116-9958-c1829be5c200\n",
      "500    games/gameecb5a092-8ba3-46eb-9295-66947722e239\n",
      "501    games/game199b51a3-dc6d-4a2b-9519-b83a269bf291\n",
      "499    games/game1c863745-2c56-4006-a59e-681362cb2f32\n",
      "537    games/gamee0feb1a7-b1b9-4c51-8586-95fd2c88faed\n",
      "538    games/gamef385c3d6-e1b1-4c99-8ca1-e00cd3e2033d\n",
      "407    games/game827a2550-740e-4006-91ea-fd36f32a8cb1\n",
      "411    games/game03666498-31a9-4429-a48a-b930447ea411\n",
      "527    games/game5894639a-47e3-4a96-bdbe-7dab13be2903\n",
      "489    games/game42d79729-78f4-4cf7-9a6c-3bfe458ed897\n",
      "516    games/gamebd58a964-3313-48d4-95e3-dd00ec814849\n",
      "539    games/gamef17b9ebb-ed63-4eec-89fa-a6d8da8aecaa\n",
      "503    games/game7d48d3c7-c30f-4d8d-84e8-b5966ae7f1f1\n",
      "523    games/gameb9549172-540c-4bce-9bae-ca75fc6d14f8\n",
      "492    games/game0ef565cf-24b7-4514-b8f6-286a0c205e15\n",
      "408    games/game8189b1d5-812f-4894-ab9a-678ecd211389\n",
      "493    games/gamee061c7dc-e2c7-4947-ba40-9b7253252f07\n",
      "535    games/game20d14bea-98b9-4adb-8fc2-bba18051a569\n",
      "536    games/game44e75ed6-439a-4303-b313-459e5410b206\n",
      "524    games/gameb584b73f-c1e4-4f51-89a4-933ba581ce07\n",
      "519    games/game9ae69985-1e85-4109-83fd-9eae80dea0ca\n",
      "515    games/game711f2aed-5707-405a-82ac-98263bad2500\n",
      "526    games/game50b8e50c-8645-495c-8136-74f0e4c0c582\n",
      "495    games/game95681c1a-56ac-4cc1-ba7a-160893146373\n",
      "409    games/game18d6ab87-e8c6-4d5c-8176-82cd72e9d542\n",
      "525    games/game90197117-5f3f-4ed1-8c8a-f2790fa63dd3\n",
      "497    games/game97c3a0ae-3f9e-4f3a-91a6-1e3326cb4223\n",
      "528    games/game0e45383a-fb8d-4edd-ba6b-ce3812a2647e\n",
      "Name: folder_name, dtype: object\n",
      "Score 100.0 % for games with only legal moves\n",
      "6 legal games and 17 illegal games (out of 23 total games)\n",
      "Illegal moves are           6\n",
      "dxe6      1\n",
      "Qxd8+     1\n",
      "Qd5       1\n",
      "hxg8=Q    1\n",
      "fxg6      1\n",
      "Bxf8      1\n",
      "exf6      1\n",
      "Qe2#      1\n",
      "fxg7      1\n",
      "Nxf6#     1\n",
      "gxh8=Q    1\n",
      "Nf7+      1\n",
      "Bxd6      1\n",
      "Qxf8#     1\n",
      "fxg8=Q    1\n",
      "Bxg8      1\n",
      "cxd6      1\n",
      "Name: illegal_move, dtype: int64\n",
      "Quite severe example: https://lichess.org/FMoHsC7m with Bxd6\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gpt35_instruct_legal_games = df_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and has_illegal == False\")\n",
    "score35 = compute_score(gpt35_instruct_legal_games, model_name='gpt-3.5-turbo-instruct') #['illegal_move'].value_counts()\n",
    "print(\"Score\", round(100*score35, 2), \"% for games with only legal moves\")\n",
    "\n",
    "score, t = compute_score(gpt35_instruct_legal_games, model_name='gpt-3.5-turbo-instruct', percentage=False)\n",
    "print(\"Score\", score, \" for games with only legal moves (\", t, \"games)\")\n",
    "\n",
    "print(\"The only not won game is:\", \"https://lichess.org/bE99x52y\", \"is due to repetition\")\n",
    "\n",
    "vals35 = df_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct')\")['has_illegal'].value_counts()\n",
    "print(vals35[0], \"legal games and\", vals35[1], \"illegal games\", \"(out of\", vals35[0] + vals35[1], \"total games)\")\n",
    "\n",
    "print(\"Illegal moves are\", df_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct')\")['illegal_move'].value_counts())\n",
    "\n",
    "print(\"Number of moves against random chess engine\")\n",
    "print(\"The longest game was\", gpt35_instruct_legal_games['nmoves'].max()/2, \"moves\")\n",
    "print(\"The shortest game was\", gpt35_instruct_legal_games['nmoves'].min()/2, \"moves\")\n",
    "print(\"The average game length was\", gpt35_instruct_legal_games['nmoves'].mean()/2, \"moves\")\n",
    "print(\"The median game length was\", gpt35_instruct_legal_games['nmoves'].median()/2, \"moves\")\n",
    "\n",
    "print(gpt35_instruct_legal_games.sort_values(by=\"nmoves\", ascending=True)['folder_name'])\n",
    "\n",
    "(gpt35_instruct_legal_games['nmoves'] / 2).plot(kind='box')\n",
    "\n",
    "\n",
    "#### same for gpt-3.5-turbo \n",
    "gpt35_legal_games = df_random.query(\"(white == 'gpt-3.5-turbo' or black == 'gpt-3.5-turbo') and has_illegal == False\")\n",
    "score35 = compute_score(gpt35_legal_games, model_name='gpt-3.5-turbo') #['illegal_move'].value_counts()\n",
    "print(\"Score\", round(100*score35, 2), \"% for games with only legal moves\")\n",
    "\n",
    "vals35 = df_random.query(\"(white == 'gpt-3.5-turbo' or black == 'gpt-3.5-turbo')\")['has_illegal'].value_counts()\n",
    "print(vals35[0], \"legal games and\", vals35[1], \"illegal games\", \"(out of\", vals35[0] + vals35[1], \"total games)\")\n",
    "\n",
    "print(\"Illegal moves are\", df_random.query(\"(white == 'gpt-3.5-turbo' or black == 'gpt-3.5-turbo')\")['illegal_move'].value_counts())\n",
    "print(\"Quite severe example:\", \"https://lichess.org/FMoHsC7m\", \"with Bxd6\") # gameb5a17c54-fc9b-4a55-867b-ca61800ba228)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9eee201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis against SF (no random engine, no random first moves) for model: gpt-3.5-turbo\n",
      "Score 100.0 % for games with only legal moves\n",
      "Score 6.0 for games with only legal moves ( 6 games)\n",
      "Score 26.09 % for all games, being legal or illegal\n",
      "Score 6.0 for all games ( 23 games)\n",
      "Out of 23 games against SF, 6 were legal games and 17 were illegal games, hence 74 % of illegal games.\n",
      "6 legal games and 17 illegal games (out of 23 total games)\n",
      "74 % of illegal games\n",
      "Illegal moves are:\n",
      "|        |   illegal_move |\n",
      "|:-------|---------------:|\n",
      "| dxe6   |              1 |\n",
      "| Qe2#   |              1 |\n",
      "| Qxd8+  |              1 |\n",
      "| Qd5    |              1 |\n",
      "| hxg8=Q |              1 |\n",
      "| fxg6   |              1 |\n",
      "| Bxf8   |              1 |\n",
      "| exf6   |              1 |\n",
      "| fxg7   |              1 |\n",
      "| Bxg8   |              1 |\n",
      "| Nxf6#  |              1 |\n",
      "| gxh8=Q |              1 |\n",
      "| Nf7+   |              1 |\n",
      "| Bxd6   |              1 |\n",
      "| Qxf8#  |              1 |\n",
      "| fxg8=Q |              1 |\n",
      "| cxd6   |              1 |\n",
      "Illegal moves with n first random move are:\n",
      "| illegal_move   |\n",
      "|----------------|\n"
     ]
    }
   ],
   "source": [
    "df_non_random = df.query(\"random_engine == False and nstarting_move <= 2\")\n",
    "\n",
    "def analyze_model_performance(df_m, model_gpt_name):    \n",
    "\n",
    "    print(f\"Analysis against SF (no random engine, no random first moves) for model: {model_gpt_name}\")\n",
    "\n",
    "    model_games = df_m.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}') and has_illegal == False\")\n",
    "    score_model = compute_score(model_games, model_name=model_gpt_name)\n",
    "    print(\"Score\", round(100*score_model, 2), \"% for games with only legal moves\")\n",
    "    score, t = compute_score(model_games, model_name=model_gpt_name, percentage=False)\n",
    "    print(\"Score\", score, \"for games with only legal moves (\", t, \"games)\")\n",
    "\n",
    "    tot = len(df_m.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}')\"))\n",
    "    print(\"Score\", round(100*(score/tot), 2), \"% for all games, being legal or illegal\")\n",
    "    print(\"Score\", score, \"for all games (\", tot, \"games)\")\n",
    "\n",
    "    vals_model = df_m.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}')\")['has_illegal'].value_counts()\n",
    "    ntot = vals_model.get(0, 0) + vals_model.get(1, 0)\n",
    "\n",
    "    print(\"Out of\", ntot ,\"games against SF,\", vals_model.get(0, 0), \"were legal games and\", vals_model.get(1, 0), \"were illegal games, hence\", round((vals_model.get(1, 0)/ntot)*100), \"% of illegal games.\")\n",
    "    print(vals_model.get(0, 0), \"legal games and\", vals_model.get(1, 0), \"illegal games\", \"(out of\", ntot, \"total games)\")\n",
    "    print(round((vals_model.get(1, 0)/ntot)*100), \"% of illegal games\")\n",
    "\n",
    "    print(\"Illegal moves are:\")\n",
    "    print(df_m.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}') and has_illegal == True\")['illegal_move'].value_counts().to_markdown())\n",
    "\n",
    "    print(\"Illegal moves with n first random move are:\")\n",
    "    print(df_m.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}') and has_illegal == True and nstarting_move > 2\")['illegal_move'].value_counts().to_markdown())\n",
    "\n",
    "# Example usage:\n",
    "# analyze_model_performance(df_non_random, 'gpt-3.5-turbo-instruct')\n",
    "# analyze_model_performance(df_non_random, 'gpt-4')\n",
    "\n",
    "# analyze_model_performance(df_random, 'gpt-4')\n",
    "\n",
    "analyze_model_performance(df_random, 'gpt-3.5-turbo')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb299e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis against SF (no random engine, random first moves) for gpt-3.5-turbo-instruct\n",
      "Score 9.15% for games with only legal moves\n",
      "Score 6.5 for games with only legal moves (71 games)\n",
      "Score 8.9% for all games, being legal or illegal\n",
      "Score 6.5 for all games (73 games)\n",
      "Out of 73 games against SF, 71 were legal games and 2 were illegal games, hence 3% of illegal games.\n",
      "71 legal games and 2 illegal games (out of 73 total games)\n",
      "3% of illegal games\n",
      "Illegal moves are:\n",
      "|       |   illegal_move |\n",
      "|:------|---------------:|\n",
      "| Qxc6+ |              1 |\n",
      "| Bc5   |              1 |\n",
      "Illegal moves with n first random move are:\n",
      "|       |   illegal_move |\n",
      "|:------|---------------:|\n",
      "| Qxc6+ |              1 |\n",
      "| Bc5   |              1 |\n"
     ]
    }
   ],
   "source": [
    "def analyze_model_performance(df, gpt_model_name):\n",
    "    df_non_random_with_random_first_moves = df.query(\"random_engine == False and nstarting_move > 2\")\n",
    "\n",
    "    print(f\"Analysis against SF (no random engine, random first moves) for {gpt_model_name}\")\n",
    "\n",
    "    gpt_instruct_legal_games_with_random_first_moves = df_non_random_with_random_first_moves.query(f\"(white == '{gpt_model_name}' or black == '{gpt_model_name}') and has_illegal == False\")\n",
    "    score = compute_score(gpt_instruct_legal_games_with_random_first_moves, model_name=gpt_model_name)\n",
    "    print(f\"Score {round(100*score, 2)}% for games with only legal moves\")\n",
    "    \n",
    "    score, t = compute_score(gpt_instruct_legal_games_with_random_first_moves, model_name=gpt_model_name, percentage=False)\n",
    "    print(f\"Score {score} for games with only legal moves ({t} games)\")\n",
    "\n",
    "    tot = len(df_non_random_with_random_first_moves.query(f\"(white == '{gpt_model_name}' or black == '{gpt_model_name}')\"))\n",
    "    print(f\"Score {round(100*(score/tot), 2)}% for all games, being legal or illegal\")\n",
    "    print(f\"Score {score} for all games ({tot} games)\")\n",
    "\n",
    "    vals = df_non_random_with_random_first_moves.query(f\"(white == '{gpt_model_name}' or black == '{gpt_model_name}')\")['has_illegal'].value_counts()\n",
    "    ntot = vals.get(0, 0) + vals.get(1, 0)\n",
    "    illegal_percentage = round((vals.get(1, 0)/ntot)*100) if ntot else 0\n",
    "\n",
    "    print(f\"Out of {ntot} games against SF, {vals.get(0, 0)} were legal games and {vals.get(1, 0)} were illegal games, hence {illegal_percentage}% of illegal games.\")\n",
    "    print(f\"{vals.get(0, 0)} legal games and {vals.get(1, 0)} illegal games (out of {ntot} total games)\")\n",
    "    print(f\"{illegal_percentage}% of illegal games\")\n",
    "\n",
    "    print(\"Illegal moves are:\")\n",
    "    print(df_non_random_with_random_first_moves.query(f\"(white == '{gpt_model_name}' or black == '{gpt_model_name}') and has_illegal == True\")['illegal_move'].value_counts().to_markdown())\n",
    "\n",
    "    print(f\"Illegal moves with n first random move are:\")\n",
    "    print(df_non_random_with_random_first_moves.query(f\"(white == '{gpt_model_name}' or black == '{gpt_model_name}') and has_illegal == True and nstarting_move > 2\")['illegal_move'].value_counts().to_markdown())\n",
    "\n",
    "# Example usage:\n",
    "analyze_model_performance(df, 'gpt-3.5-turbo-instruct')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e5b8c4b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00a4a2b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "439"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct')\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "afa8aafa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 illegal moves with white\n",
      "54\n",
      "40\n",
      "15 illegal moves with black\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(15, 54, 40, 15)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_moves_and_illegals(df_l, gpt_model_name):\n",
    "    illegal_white_moves = len(df_l.query(f\"(white == '{gpt_model_name}') and has_illegal == True\"))\n",
    "    total_white_moves = len(df_l.query(f\"(white == '{gpt_model_name}')\"))\n",
    "    total_black_moves = len(df_l.query(f\"(black == '{gpt_model_name}')\"))\n",
    "    illegal_black_moves = len(df_l.query(f\"(black == '{gpt_model_name}') and has_illegal == True\"))\n",
    "\n",
    "    print(illegal_white_moves, \"illegal moves with white\")\n",
    "    print(total_white_moves)\n",
    "    print(total_black_moves)\n",
    "    print(illegal_black_moves, \"illegal moves with black\")\n",
    "\n",
    "    return illegal_white_moves, total_white_moves, total_black_moves, illegal_black_moves\n",
    "\n",
    "# count_moves_and_illegals(df_non_random, 'gpt-3.5-turbo-instruct')\n",
    "count_moves_and_illegals(df_non_random, 'gpt-4')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6b3ffaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temperature\n",
      "0.0            41\n",
      "0.8            29\n",
      "dtype: int64\n",
      "0.0 % of games with temperature 0.8\n",
      "| illegal_move   |\n",
      "|----------------|\n"
     ]
    }
   ],
   "source": [
    "mask = (df_non_random[\"white\"] == 'gpt-3.5-turbo-instruct') | (df_non_random[\"black\"] == 'gpt-3.5-turbo-instruct')\n",
    "illegal_gpt35vsSF_instruct = df_non_random.loc[mask & (df_non_random[\"has_illegal\"] == True)].copy()\n",
    "illegal_gpt35vsSF_instruct[\"temperature\"] = illegal_gpt35vsSF_instruct[\"temperature\"].astype(float)\n",
    "\n",
    "print(illegal_gpt35vsSF_instruct[[\"temperature\"]].value_counts())\n",
    "\n",
    "t8 = len(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and `temperature` == '0.8'\")) / len(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct')\"))\n",
    "print(round(100*t8, 2), \"% of games with temperature 0.8\")\n",
    "\n",
    "print(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and `temperature` == '0.8' and has_illegal == True\")['illegal_move'].value_counts().to_markdown())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bb8fe079",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99 games with altered prompt\n",
      "22.55125284738041 % games with altered prompt\n",
      "19 illegal games with altered prompts and 51 illegal games with original prompts\n",
      "1-0      16\n",
      "bxc3      1\n",
      "bxc8=     1\n",
      "f7        1\n",
      "Name: illegal_move, dtype: int64\n",
      "|    |   nstarting_move |\n",
      "|---:|-----------------:|\n",
      "|  1 |               70 |\n"
     ]
    }
   ],
   "source": [
    "illegal_gpt35vsSF_instruct = df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and has_illegal == True\") # [['pgn_base']].value_counts()\n",
    "n = 0\n",
    "m = 0\n",
    "for i, row in illegal_gpt35vsSF_instruct.iterrows():\n",
    "    if \"Rennes FRA\" in row[\"pgn_base\"]:\n",
    "        n = n + 1\n",
    "    else:\n",
    "        m = m + 1\n",
    "\n",
    "print(len(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and pgn_base.str.contains('Rennes FRA')\")), \"games with altered prompt\")\n",
    "print(len(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and pgn_base.str.contains('Rennes FRA')\")) / len(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct')\")) * 100, \"% games with altered prompt\")\n",
    "\n",
    "print(n, \"illegal games with altered prompts and\", m, \"illegal games with original prompts\")\n",
    "print(illegal_gpt35vsSF_instruct.query(\"pgn_base.str.contains('Rennes FRA')\")['illegal_move'].value_counts())\n",
    "\n",
    "print(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and has_illegal == True\")['nstarting_move'].value_counts().to_markdown())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b7ee51b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Does random first moves impact?\n",
      "|    |   nstarting_move |\n",
      "|---:|-----------------:|\n",
      "|  1 |              435 |\n",
      "|  2 |                4 |\n"
     ]
    }
   ],
   "source": [
    "print(\"Does random first moves impact?\")\n",
    "print(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct')\")['nstarting_move'].value_counts().to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "93c63170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nxa3 games/game2adc58cf-0608-41b3-9b1c-523f74fb1e93\n",
      "Kb3 games/game7f1ea428-7829-4cc2-ba9f-7c27810e7db9\n",
      "Rxh5 games/game5f02a530-c7dd-4960-b182-4b5c844fa526\n",
      "Qa7 games/game359d97d4-0d8b-4494-9623-6ee8aad6f27b\n",
      "bxc3 games/game44815a7f-14ab-4be7-acf2-5b4dd20e3791\n",
      "f7 games/game6a45d413-1654-4995-957a-9219c82d2d74\n",
      "If we consider \"1-0\", \"comments\", and \"unspecified promotions\" as fixable, then 1.4000000000000001 % are non valid games\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.4000000000000001"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_invalid_games(df_l, model_gpt_name):\n",
    "    query_str = f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}') and has_illegal == True and illegal_move != '1-0' and illegal_move != '1-'\"\n",
    "    \n",
    "    x = 0\n",
    "    for _, r in df_l.query(query_str).iterrows():\n",
    "        if '=' in r['illegal_move'] or '{' in r['illegal_move']:\n",
    "            continue\n",
    "\n",
    "        print(r['illegal_move'], r['folder_name'])\n",
    "        x += 1 \n",
    "\n",
    "    percentage = round(x / len(df_l.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}')\")), 3) * 100\n",
    "    print(f'If we consider \"1-0\", \"comments\", and \"unspecified promotions\" as fixable, then {percentage} % are non valid games')\n",
    "\n",
    "    return percentage\n",
    "\n",
    "count_invalid_games(df_non_random, 'gpt-3.5-turbo-instruct')\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f2b5d116",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Qualitative analysis of illegal moves:\n",
      "|    | GPT Color   | Assessment   |\n",
      "|---:|:------------|:-------------|\n",
      "|  0 | Black       | Mate in 1    |\n",
      "|  1 | Black       | 6.39         |\n",
      "|  2 | Black       | 34.74        |\n",
      "|  3 | Black       | 10.52        |\n",
      "|  4 | Black       | 6.74         |\n",
      "|  5 | Black       | 5.64         |\n",
      "|  6 | Black       | Mate in 2    |\n",
      "|  7 | Black       | 3.73         |\n",
      "|  8 | Black       | 6.17         |\n",
      "|  9 | White       | Mate in -1   |\n",
      "| 10 | Black       | 7.71         |\n",
      "| 11 | Black       | Mate in 3    |\n",
      "| 12 | Black       | Mate in 1    |\n",
      "| 13 | Black       | -1.54        |\n",
      "| 14 | Black       | 6.81         |\n",
      "| 15 | Black       | 35.81        |\n",
      "| 16 | Black       | 5.48         |\n",
      "| 17 | Black       | 6.34         |\n",
      "| 18 | Black       | Mate in 9    |\n",
      "| 19 | Black       | 7.79         |\n",
      "| 20 | Black       | 3.63         |\n",
      "| 21 | Black       | 10.46        |\n",
      "| 22 | Black       | 5.83         |\n",
      "| 23 | Black       | Mate in 1    |\n",
      "| 24 | Black       | 7.16         |\n",
      "| 25 | Black       | 6.93         |\n",
      "| 26 | Black       | Mate in 1    |\n",
      "| 27 | Black       | 6.23         |\n",
      "| 28 | Black       | Mate in 1    |\n",
      "| 29 | Black       | Mate in 1    |\n",
      "| 30 | Black       | Mate in 5    |\n",
      "| 31 | Black       | Mate in 4    |\n",
      "| 32 | Black       | Mate in 2    |\n",
      "| 33 | Black       | 6.64         |\n",
      "| 34 | Black       | Mate in 3    |\n",
      "| 35 | Black       | 0.0          |\n",
      "| 36 | Black       | -0.41        |\n",
      "| 37 | Black       | 6.79         |\n",
      "| 38 | Black       | Mate in 4    |\n",
      "| 39 | Black       | Mate in 1    |\n",
      "| 40 | Black       | Mate in 7    |\n",
      "| 41 | Black       | 6.39         |\n",
      "| 42 | Black       | 6.38         |\n",
      "| 43 | Black       | 5.72         |\n",
      "| 44 | Black       | Mate in 2    |\n",
      "| 45 | Black       | 5.22         |\n",
      "| 46 | Black       | 6.97         |\n",
      "| 47 | Black       | 5.51         |\n",
      "| 48 | Black       | Mate in 1    |\n"
     ]
    }
   ],
   "source": [
    "print(\"Qualitative analysis of illegal moves:\")\n",
    "\n",
    "import pandas as pd\n",
    "from stockfish import Stockfish\n",
    "\n",
    "# A list to store your data\n",
    "data_list = []\n",
    "\n",
    "illegal_moves10 = df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and illegal_move == '1-0'\")\n",
    "\n",
    "for index, row in illegal_moves10.iterrows():\n",
    "    if row['white'] == 'gpt-3.5-turbo-instruct':\n",
    "        color = \"White\"\n",
    "    else:\n",
    "        color = \"Black\"\n",
    "\n",
    "    # read the PGN file\n",
    "    with open(row['folder_name'] + \"/game.pgn\") as pgn:\n",
    "        game = chess.pgn.read_game(pgn)\n",
    "        board = game.board()\n",
    "        for move in game.mainline_moves():\n",
    "            board.push(move)\n",
    "\n",
    "        stockfish = Stockfish(\"./stockfish/stockfish/stockfish-ubuntu-x86-64-avx2\")\n",
    "        stockfish.set_position([str(m) for m in game.mainline_moves()])\n",
    "        # stockfish._go_time(5000)\n",
    "        ev = stockfish.get_evaluation()\n",
    "\n",
    "        # Create an evaluation string\n",
    "        if ev['type'] == 'cp':\n",
    "            evaluation = str(ev['value']/100)\n",
    "        else:\n",
    "            evaluation = \"Mate in \" + str(ev['value'])\n",
    "\n",
    "        # Append the dictionary to your list\n",
    "        data_list.append({\n",
    "            \"GPT Color\": color,\n",
    "            \"Assessment\": evaluation\n",
    "        })\n",
    "\n",
    "# Convert the list of dictionaries into a dataframe\n",
    "df_results = pd.DataFrame(data_list)\n",
    "\n",
    "# If you want to see the first few rows of your dataframe:\n",
    "print(df_results.to_markdown())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a6d7509c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>*</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"Chess tournament\"]\\n[Site \"Rennes FRA\"...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>f7</td>\n",
       "      <td>games/game6a45d413-1654-4995-957a-9219c82d2d74</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>1/2-1/2</td>\n",
       "      <td>290</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"Chess tournament\"]\\n[Site \"Rennes FRA\"...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game6ceeed32-b952-4496-a5ce-b8142eda01d2</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1/2-1/2</td>\n",
       "      <td>275</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"Chess tournament\"]\\n[Site \"Rennes FRA\"...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game240bf989-a2c2-426a-9104-95b534b34b09</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1954</td>\n",
       "      <td>0-1</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game82b3bf36-96cc-4667-8331-3c221b9e7d61</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>0-1</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game332c5a22-657d-48da-8c76-8e582629daaf</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game369351ce-475e-47b8-9881-45ed2f033efc</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1871</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>{(Leko-G</td>\n",
       "      <td>games/gameb34046f8-b640-49e0-9ef5-94f35774746b</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1785</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>{M.</td>\n",
       "      <td>games/game051d6c13-e880-49d1-9aac-6f19b81a0c62</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1871</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>{[%clk</td>\n",
       "      <td>games/game2ce2d311-6259-4bb9-bd60-db1b5d701042</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1871</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>{This</td>\n",
       "      <td>games/gamef0fc0dd7-c1ee-483f-b56c-8d359ae390e9</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      white                   black white_elo black_elo  \\\n",
       "616  gpt-3.5-turbo-instruct               Stockfish         ?      1785   \n",
       "610  gpt-3.5-turbo-instruct               Stockfish         ?      1871   \n",
       "619  gpt-3.5-turbo-instruct               Stockfish         ?      1785   \n",
       "39   gpt-3.5-turbo-instruct               Stockfish         ?      1954   \n",
       "285  gpt-3.5-turbo-instruct               Stockfish         ?      1785   \n",
       "..                      ...                     ...       ...       ...   \n",
       "14   gpt-3.5-turbo-instruct               Stockfish         ?      1871   \n",
       "322               Stockfish  gpt-3.5-turbo-instruct      1871         ?   \n",
       "319               Stockfish  gpt-3.5-turbo-instruct      1785         ?   \n",
       "339               Stockfish  gpt-3.5-turbo-instruct      1871         ?   \n",
       "326               Stockfish  gpt-3.5-turbo-instruct      1871         ?   \n",
       "\n",
       "      result nmoves nstarting_move  \\\n",
       "616        *    348              1   \n",
       "610  1/2-1/2    290              1   \n",
       "619  1/2-1/2    275              1   \n",
       "39       0-1    244              1   \n",
       "285      0-1    244              1   \n",
       "..       ...    ...            ...   \n",
       "14       0-1     28              1   \n",
       "322        *      1              1   \n",
       "319        *      1              1   \n",
       "339        *      1              1   \n",
       "326        *      1              1   \n",
       "\n",
       "                                              pgn_base  temperature  \\\n",
       "616  [Event \"Chess tournament\"]\\n[Site \"Rennes FRA\"...          0.0   \n",
       "610  [Event \"Chess tournament\"]\\n[Site \"Rennes FRA\"...          0.0   \n",
       "619  [Event \"Chess tournament\"]\\n[Site \"Rennes FRA\"...          0.0   \n",
       "39   [Event \"FIDE World Championship Match 2024\"]\\n...          0.0   \n",
       "285  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "..                                                 ...          ...   \n",
       "14   [Event \"FIDE World Championship Match 2024\"]\\n...          0.0   \n",
       "322  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "319  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "339  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "326  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "\n",
       "    random_engine has_illegal illegal_move  \\\n",
       "616         False        True           f7   \n",
       "610         False       False                \n",
       "619         False       False                \n",
       "39          False       False                \n",
       "285         False       False                \n",
       "..            ...         ...          ...   \n",
       "14          False       False                \n",
       "322         False        True     {(Leko-G   \n",
       "319         False        True          {M.   \n",
       "339         False        True       {[%clk   \n",
       "326         False        True        {This   \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "616  games/game6a45d413-1654-4995-957a-9219c82d2d74                  \n",
       "610  games/game6ceeed32-b952-4496-a5ce-b8142eda01d2                  \n",
       "619  games/game240bf989-a2c2-426a-9104-95b534b34b09                  \n",
       "39   games/game82b3bf36-96cc-4667-8331-3c221b9e7d61                  \n",
       "285  games/game332c5a22-657d-48da-8c76-8e582629daaf                  \n",
       "..                                              ...            ...   \n",
       "14   games/game369351ce-475e-47b8-9881-45ed2f033efc                  \n",
       "322  games/gameb34046f8-b640-49e0-9ef5-94f35774746b                  \n",
       "319  games/game051d6c13-e880-49d1-9aac-6f19b81a0c62                  \n",
       "339  games/game2ce2d311-6259-4bb9-bd60-db1b5d701042                  \n",
       "326  games/gamef0fc0dd7-c1ee-483f-b56c-8d359ae390e9                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "616            False  \n",
       "610            False  \n",
       "619            False  \n",
       "39              True  \n",
       "285             True  \n",
       "..               ...  \n",
       "14              True  \n",
       "322             True  \n",
       "319             True  \n",
       "339             True  \n",
       "326             True  \n",
       "\n",
       "[346 rows x 15 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct' and has_illegal == True)\").sort_values(by=\"nmoves\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb15fb4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19 games with mate out of 49 games\n",
      "25 games with cp > 5.0 out of 49 games\n",
      "5 games with cp < 5.0 out of 49 games\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "cp_g = 0\n",
    "for index, row in df_results.iterrows():\n",
    "    if 'Mate' in row['Assessment']:\n",
    "        j = j + 1\n",
    "    elif float(row['Assessment']) > 5.0:\n",
    "        cp_g = cp_g + 1\n",
    "\n",
    "print(j, \"games with mate\", \"out of\", len(df_results), \"games\")\n",
    "print(cp_g, \"games with cp > 5.0\", \"out of\", len(df_results), \"games\")\n",
    "print(len(df_results) - (j+cp_g), \"games with cp < 5.0\", \"out of\", len(df_results), \"games\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c3d4c710",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "illegal moves 0.6629999999999999 %\n",
      "illegal_moves without 1-0: 0.6629999999999999 %\n",
      "Total number of moves played by gpt-4 : 4527.0\n",
      "White played 2672.5 moves\n",
      "Black played 1854.5 moves\n",
      "Number of moves against SF\n",
      "The longest game was 128.5 moves\n",
      "The shortest game was 13.0 moves\n",
      "The average game length was 48.159574468085104 moves\n",
      "The median game length was 45.75 moves\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-25-ae6575508112>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  model_games['nmoves'] = model_games['nmoves'] / 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def analyze_games(df_l, model_name):\n",
    "    # Query for games played by the given model\n",
    "    model_games = df_l.query(f\"(white == '{model_name}' or black == '{model_name}')\")\n",
    "    model_games['nmoves'] = model_games['nmoves'] / 2\n",
    "    \n",
    "    # Sum of moves played by the model\n",
    "    white_nmoves = df_l.query(f\"white == '{model_name}'\")['nmoves'].sum() / 2\n",
    "    black_nmoves = df_l.query(f\"black == '{model_name}'\")['nmoves'].sum() / 2\n",
    "    t_moves = model_games['nmoves'].sum()\n",
    "\n",
    "    # Count of illegal moves\n",
    "    illegal_moves = len(df_l.query(f\"(white == '{model_name}' or black == '{model_name}') and has_illegal == True\"))\n",
    "    illegal_moves_10 = len(df_l.query(f\"(white == '{model_name}' or black == '{model_name}') and has_illegal == True and illegal_move != '1-0'\"))\n",
    "\n",
    "    # Printing results\n",
    "    print(\"illegal moves\", round(illegal_moves/t_moves, 5)*100, \"%\")\n",
    "    print(\"illegal_moves without 1-0:\", round(illegal_moves_10/t_moves, 5)*100, \"%\")\n",
    "    print(\"Total number of moves played by\", model_name, \":\", t_moves)\n",
    "    print(\"White played\", white_nmoves, \"moves\")\n",
    "    print(\"Black played\", black_nmoves, \"moves\")\n",
    "    print(\"Number of moves against SF\")\n",
    "    print(\"The longest game was\", model_games['nmoves'].max(), \"moves\")\n",
    "    print(\"The shortest game was\", model_games['nmoves'].min(), \"moves\")\n",
    "    print(\"The average game length was\", model_games['nmoves'].mean(), \"moves\")\n",
    "    print(\"The median game length was\", model_games['nmoves'].median(), \"moves\")\n",
    "\n",
    "    # Plotting box plot\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.boxplot(model_games['nmoves'])\n",
    "    plt.title(f\"Number of moves per game against SF\")\n",
    "    plt.ylabel(\"Number of moves\")\n",
    "    plt.xlabel(f\"{model_name}\")\n",
    "    plt.savefig(f\"{model_name}_games_nmoves.png\", dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "# analyze_games(df_non_random, 'gpt-3.5-turbo-instruct')\n",
    "analyze_games(df_non_random, 'gpt-4')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3acae5ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score 0.0 % for games with only legal moves\n",
      "2 legal games and 28 illegal games (out of 30 total games)\n",
      "Illegal moves are          2\n",
      "Qxe4     2\n",
      "cxb7     2\n",
      "Bxh6     1\n",
      "Qxf5     1\n",
      "Kxe1     1\n",
      "Nxd1     1\n",
      "Rd8      1\n",
      "Qxf6     1\n",
      "Bg8      1\n",
      "Rxf7+    1\n",
      "Nxe5+    1\n",
      "Qxg5     1\n",
      "Bxf6     1\n",
      "c4       1\n",
      "Bxd6+    1\n",
      "Bh4      1\n",
      "Rxh6     1\n",
      "cxd6     1\n",
      "Nxe1     1\n",
      "Rxf6     1\n",
      "Rxh1     1\n",
      "exf6     1\n",
      "Bxc6     1\n",
      "Nxf7+    1\n",
      "O-O      1\n",
      "exd6     1\n",
      "Name: illegal_move, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#### same for gpt-3.5-turbo\n",
    "gpt35_legal_games = df_non_random.query(\"(white == 'gpt-3.5-turbo' or black == 'gpt-3.5-turbo') and has_illegal == False\")\n",
    "score35 = compute_score(gpt35_legal_games, model_name='gpt-3.5-turbo') #['illegal_move'].value_counts()\n",
    "print(\"Score\", round(100*score35, 2), \"% for games with only legal moves\")\n",
    "\n",
    "vals35 = df_non_random.query(\"(white == 'gpt-3.5-turbo' or black == 'gpt-3.5-turbo')\")['has_illegal'].value_counts()\n",
    "print(vals35[0], \"legal games and\", vals35[1], \"illegal games\", \"(out of\", vals35[0] + vals35[1], \"total games)\")\n",
    "\n",
    "print(\"Illegal moves are\", df_non_random.query(\"(white == 'gpt-3.5-turbo' or black == 'gpt-3.5-turbo')\")['illegal_move'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "57be0777",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "439 games played against SF\n",
      "369 legal games played against SF\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gpt35 = df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct')\")\n",
    "print(len(df_gpt35), \"games played against SF\")\n",
    "print(len(df_gpt35.query(\"has_illegal == False\")), \"legal games played against SF\")\n",
    "\n",
    "# Extracting elo values for games where Stockfish is playing against gpt-3.5-turbo-instruct\n",
    "stockfish_black_elo_vs_gpt3_5_instruct = df_gpt35['black_elo']\n",
    "# remove values with '?' value\n",
    "stockfish_black_elo_vs_gpt3_5_instruct = stockfish_black_elo_vs_gpt3_5_instruct[stockfish_black_elo_vs_gpt3_5_instruct != '?']\n",
    "\n",
    "stockfish_white_elo_vs_gpt3_5_instruct = df_gpt35['white_elo']\n",
    "# remove values with '?' value\n",
    "stockfish_white_elo_vs_gpt3_5_instruct = stockfish_white_elo_vs_gpt3_5_instruct[stockfish_white_elo_vs_gpt3_5_instruct != '?']\n",
    "\n",
    "\n",
    "# Combining Elo ratings of Stockfish as both white and black player\n",
    "combined_stockfish_elo = pd.concat([stockfish_white_elo_vs_gpt3_5_instruct, stockfish_black_elo_vs_gpt3_5_instruct])\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have already computed value counts\n",
    "value_counts = combined_stockfish_elo.value_counts().sort_index()\n",
    "\n",
    "# Plotting the histogram for combined Elo ratings\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(value_counts.index, value_counts.values)\n",
    "plt.xlabel('Elo rating')\n",
    "plt.ylabel('Number of games')\n",
    "plt.title('Elo rating distribution of Stockfish for games of gpt-3.5-turbo-instruct against Stockfish')\n",
    "plt.savefig(\"stockfish_elo_distribution.png\", dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b621c14c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score 99.15 % for games with only legal moves\n",
      "The only not won game is: https://lichess.org/1A1IrIjO#74 leads to stalemate in a completely winning position\n",
      "59 legal games and 26 illegal games (out of 85 total games)\n",
      "Illegal moves are          59\n",
      "12...     2\n",
      "Qd4       1\n",
      "6.        1\n",
      "Bxf8      1\n",
      "Nxg4      1\n",
      "gxh8      1\n",
      "16...     1\n",
      "Qd7       1\n",
      "Qd5+      1\n",
      "fxg7      1\n",
      "9...      1\n",
      "Rxg8      1\n",
      "Qh4#      1\n",
      "Qxd8      1\n",
      "cxd8      1\n",
      "Qxc8#     1\n",
      "14...     1\n",
      "Qae8#     1\n",
      "Qh5       1\n",
      "17...     1\n",
      "Bxa6      1\n",
      "gxh7+     1\n",
      "5.        1\n",
      "exd2+     1\n",
      "exd8      1\n",
      "Name: illegal_move, dtype: int64\n",
      "Illegal moves are much more severe here! ie they are really illegal moves see eg https://lichess.org/k2MhW32K or https://lichess.org/lVIF2hvE#48\n",
      "This https://lichess.org/wFwTWWgh is more about ambigous move\n"
     ]
    }
   ],
   "source": [
    "gpt4_legal_games = df_random.query(\"(white == 'gpt-4' or black == 'gpt-4') and has_illegal == False\")\n",
    "score4 = compute_score(gpt4_legal_games, model_name='gpt-4') #['illegal_move'].value_counts()\n",
    "print(\"Score\", round(100*score4, 2), \"% for games with only legal moves\")\n",
    "\n",
    "print(\"The only not won game is:\", \"https://lichess.org/1A1IrIjO#74\", \"leads to stalemate in a completely winning position\")\n",
    "\n",
    "vals4 = df_random.query(\"(white == 'gpt-4' or black == 'gpt-4')\")['has_illegal'].value_counts()\n",
    "print(vals4[0], \"legal games and\", vals4[1], \"illegal games\", \"(out of\", vals4[0] + vals4[1], \"total games)\")\n",
    "\n",
    "print(\"Illegal moves are\", df_random.query(\"(white == 'gpt-4' or black == 'gpt-4')\")['illegal_move'].value_counts())\n",
    "print(\"Illegal moves are much more severe here! ie they are really illegal moves\", \"see eg https://lichess.org/k2MhW32K or https://lichess.org/lVIF2hvE#48\")\n",
    "print(\"This https://lichess.org/wFwTWWgh is more about ambigous move\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "93722364",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set the style of seaborn for better visualization\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "def plot_histogram(df):\n",
    "    # Initialize the matplotlib figure\n",
    "    plt.figure(figsize=(12, 6))\n",
    "\n",
    "    # Plot histogram of nmoves\n",
    "    sns.histplot(df['nmoves'], bins=30, kde=True, color=\"skyblue\")\n",
    "\n",
    "    # Set plot labels and title\n",
    "    plt.xlabel('Number of Moves', fontsize=14)\n",
    "    plt.ylabel('Frequency', fontsize=14)\n",
    "    plt.title('Distribution of Number of Moves in Chess Games', fontsize=16)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "def plot_boxplot(df):\n",
    "    # Initialize the matplotlib figure\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Create a boxplot of nmoves\n",
    "    sns.boxplot(x=df['nmoves'], color=\"lightblue\")\n",
    "\n",
    "    # Set plot labels and title\n",
    "    plt.xlabel('Number of Moves', fontsize=14)\n",
    "    plt.title('Boxplot of Number of Moves in Chess Games', fontsize=16)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a1f1abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(gpt4_legal_games)\n",
    "plot_boxplot(gpt4_legal_games)\n",
    "plot_histogram(gpt35_instruct_legal_games)\n",
    "plot_boxplot(gpt35_instruct_legal_games)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92f1f217",
   "metadata": {},
   "source": [
    "GPT4 is weaker than GPT35 instruct for legal moves\n",
    "Yet GPT4 and GPT35 instruct are capable of winning a significant number of games (99%) when legal moves are played all along. \n",
    "GPT4 and GPT35 can play quite long games (resp. 28 moves and 35 moves on average)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d1701256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gpt-4                     62\n",
       "RANDOM chess engine       53\n",
       "gpt-3.5-turbo-instruct    41\n",
       "gpt-3.5-turbo             23\n",
       "text-davinci-003          11\n",
       "Name: white, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_random.query(\"(white == 'gpt-4' or black == 'gpt-4') and has_illegal == False\")\n",
    "df_random['white'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7549abf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game6f846bce-1290-400b-bd36-dea4b8f52101</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game4005dd14-1a8d-4f81-8444-33223730406c</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamed0d94f4b-fba4-4561-acfd-076023c5b9d3</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game2869bc09-4bb0-42fa-af2f-c8780ba3fa4b</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxd8</td>\n",
       "      <td>games/game15deea9a-1f36-4793-8277-e46fc6a4af7c</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game88a92eb0-6166-47d7-aec2-ecdf247b7716</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamee8b4784b-db75-4007-9eb5-1d5ac6528fd5</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamed3c3a6f1-2494-48eb-b75a-813f1e11c6fc</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qd5+</td>\n",
       "      <td>games/game67c52316-8f1d-4e4d-8f68-f10e83d8eff0</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gameb1086888-c2a9-4316-989c-28b7e4bc9fc8</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     white                black white_elo black_elo result nmoves  \\\n",
       "634  gpt-4  RANDOM chess engine         ?         ?    1-0     41   \n",
       "635  gpt-4  RANDOM chess engine         ?         ?    1-0     37   \n",
       "636  gpt-4  RANDOM chess engine         ?         ?    1-0     39   \n",
       "637  gpt-4  RANDOM chess engine         ?         ?    1-0     39   \n",
       "638  gpt-4  RANDOM chess engine         ?         ?      *     34   \n",
       "..     ...                  ...       ...       ...    ...    ...   \n",
       "729  gpt-4  RANDOM chess engine         ?         ?    1-0     35   \n",
       "730  gpt-4  RANDOM chess engine         ?         ?    1-0     15   \n",
       "731  gpt-4  RANDOM chess engine         ?         ?    1-0     37   \n",
       "732  gpt-4  RANDOM chess engine         ?         ?      *     20   \n",
       "739  gpt-4  RANDOM chess engine         ?         ?    1-0     37   \n",
       "\n",
       "    nstarting_move                                           pgn_base  \\\n",
       "634              1  It is your turn! You have white pieces. Please...   \n",
       "635              1  It is your turn! You have white pieces. Please...   \n",
       "636              1  It is your turn! You have white pieces. Please...   \n",
       "637              1  It is your turn! You have white pieces. Please...   \n",
       "638              1  It is your turn! You have white pieces. Please...   \n",
       "..             ...                                                ...   \n",
       "729              1  It is your turn! You have white pieces. Please...   \n",
       "730              1  It is your turn! You have white pieces. Please...   \n",
       "731              1  It is your turn! You have white pieces. Please...   \n",
       "732              1  It is your turn! You have white pieces. Please...   \n",
       "739              1  It is your turn! You have white pieces. Please...   \n",
       "\n",
       "     temperature random_engine has_illegal illegal_move  \\\n",
       "634          0.0          True       False                \n",
       "635          0.0          True       False                \n",
       "636          0.0          True       False                \n",
       "637          0.0          True       False                \n",
       "638          0.0          True        True         Qxd8   \n",
       "..           ...           ...         ...          ...   \n",
       "729          0.0          True       False                \n",
       "730          0.0          True       False                \n",
       "731          0.0          True       False                \n",
       "732          0.0          True        True         Qd5+   \n",
       "739          0.0          True       False                \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "634  games/game6f846bce-1290-400b-bd36-dea4b8f52101                  \n",
       "635  games/game4005dd14-1a8d-4f81-8444-33223730406c                  \n",
       "636  games/gamed0d94f4b-fba4-4561-acfd-076023c5b9d3                  \n",
       "637  games/game2869bc09-4bb0-42fa-af2f-c8780ba3fa4b                  \n",
       "638  games/game15deea9a-1f36-4793-8277-e46fc6a4af7c                  \n",
       "..                                              ...            ...   \n",
       "729  games/game88a92eb0-6166-47d7-aec2-ecdf247b7716                  \n",
       "730  games/gamee8b4784b-db75-4007-9eb5-1d5ac6528fd5                  \n",
       "731  games/gamed3c3a6f1-2494-48eb-b75a-813f1e11c6fc                  \n",
       "732  games/game67c52316-8f1d-4e4d-8f68-f10e83d8eff0                  \n",
       "739  games/gameb1086888-c2a9-4316-989c-28b7e4bc9fc8                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "634            False  \n",
       "635            False  \n",
       "636            False  \n",
       "637            False  \n",
       "638            False  \n",
       "..               ...  \n",
       "729            False  \n",
       "730            False  \n",
       "731            False  \n",
       "732            False  \n",
       "739            False  \n",
       "\n",
       "[85 rows x 15 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random.query(\"white == 'gpt-4' or black == 'gpt-4'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8ace2bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True     54\n",
       " False    51\n",
       " Name: has_illegal, dtype: int64,\n",
       " False    59\n",
       " True     26\n",
       " Name: has_illegal, dtype: int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random.query(\"white != 'gpt-4' and black != 'gpt-4'\").has_illegal.value_counts(), df_random.query(\"white == 'gpt-4' or black == 'gpt-4'\").has_illegal.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a575c38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxd8</td>\n",
       "      <td>games/game15deea9a-1f36-4793-8277-e46fc6a4af7c</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Bxf8</td>\n",
       "      <td>games/gameff98520e-021f-4ef1-bb36-4807dbc168f1</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>gxh8</td>\n",
       "      <td>games/gamec7fca954-8b14-4636-a2a1-d7c8089013d2</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>cxd8</td>\n",
       "      <td>games/game0eff4138-41f7-49bc-a4b4-80b44da285cc</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>exd8</td>\n",
       "      <td>games/game687739e3-5136-498b-a7b2-a887e1286a2c</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qd7</td>\n",
       "      <td>games/gamef7856584-6c16-4284-86a6-64bd609541ef</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Rxg8</td>\n",
       "      <td>games/game1ab6fd05-977b-4411-ad66-afa6a230d232</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>14...</td>\n",
       "      <td>games/gamebb2911c8-a412-44c5-9f66-16a69f61c2d2</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>12...</td>\n",
       "      <td>games/game1e393a37-6e35-4067-a108-a741a46edbf4</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>exd2+</td>\n",
       "      <td>games/gamebb7e6927-ad0c-4734-8ac2-7f6a3b3b8e3e</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>12...</td>\n",
       "      <td>games/game62ef65ca-1c4c-473d-8bf3-5ef603b5c90c</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>9...</td>\n",
       "      <td>games/game0570bbdc-b110-41c2-83fd-19126a6fe9ec</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>16...</td>\n",
       "      <td>games/gameace06f90-87eb-40d6-a258-e4bada39bf3c</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>17...</td>\n",
       "      <td>games/gamedd90ac78-1f2f-4346-b68b-2b727c00da46</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>gpt-4</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Nxg4</td>\n",
       "      <td>games/game2b596bb9-a92f-4e8b-a657-e856bfd9cb2b</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>games/game789ed89f-1225-41e5-9c14-6e0810ef90df</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>697</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6.0</td>\n",
       "      <td>games/game843ca850-a82d-48d1-940a-83e04e8b6c59</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qh5</td>\n",
       "      <td>games/game1932a9b8-216b-4815-9011-6977debf64b9</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>fxg7</td>\n",
       "      <td>games/game341d35dd-e01c-4641-b34b-3bebc94202d0</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Bxa6</td>\n",
       "      <td>games/game793dad3a-d033-4962-97ef-5646fda1eed1</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>gxh7+</td>\n",
       "      <td>games/game58fbbe46-4b86-4f6e-b7da-0b38c73acd0e</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qae8#</td>\n",
       "      <td>games/gamea49150b6-28d8-4c6c-aa11-4ff0c1eea95b</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qd4</td>\n",
       "      <td>games/game1cedf899-6cb3-444f-b65c-c08a9495d221</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qh4#</td>\n",
       "      <td>games/game19858450-f0c8-470c-b05e-e2a7d94f5f10</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxc8#</td>\n",
       "      <td>games/gamed4ecce8c-6c03-462d-a631-2a02ca21d421</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>RANDOM chess engine</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Qd5+</td>\n",
       "      <td>games/game67c52316-8f1d-4e4d-8f68-f10e83d8eff0</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   white                black white_elo black_elo result  \\\n",
       "638                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "641                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "643                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "644                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "646                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "654                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "671  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "675  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "679  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "680  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "682  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "683  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "685  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "686  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "691  RANDOM chess engine                gpt-4         ?         ?      *   \n",
       "696                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "697                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "700                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "703                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "705                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "710                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "711                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "717                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "726                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "727                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "732                gpt-4  RANDOM chess engine         ?         ?      *   \n",
       "\n",
       "    nmoves nstarting_move                                           pgn_base  \\\n",
       "638     34              1  It is your turn! You have white pieces. Please...   \n",
       "641     12              1  It is your turn! You have white pieces. Please...   \n",
       "643     20              1  It is your turn! You have white pieces. Please...   \n",
       "644     16              1  It is your turn! You have white pieces. Please...   \n",
       "646     16              1  It is your turn! You have white pieces. Please...   \n",
       "654     36              1  It is your turn! You have white pieces. Please...   \n",
       "671     33              1  It is your turn! You have white pieces. Please...   \n",
       "675     27              1  It is your turn! You have white pieces. Please...   \n",
       "679     23              1  It is your turn! You have white pieces. Please...   \n",
       "680     25              1  It is your turn! You have white pieces. Please...   \n",
       "682     23              1  It is your turn! You have white pieces. Please...   \n",
       "683     17              1  It is your turn! You have white pieces. Please...   \n",
       "685     31              1  It is your turn! You have white pieces. Please...   \n",
       "686     33              1  It is your turn! You have white pieces. Please...   \n",
       "691      9              1  It is your turn! You have white pieces. Please...   \n",
       "696      8              1  It is your turn! You have white pieces. Please...   \n",
       "697     10              1  It is your turn! You have white pieces. Please...   \n",
       "700     30              1  It is your turn! You have white pieces. Please...   \n",
       "703     20              1  It is your turn! You have white pieces. Please...   \n",
       "705     48              1  It is your turn! You have white pieces. Please...   \n",
       "710     26              1  It is your turn! You have white pieces. Please...   \n",
       "711     34              1  It is your turn! You have white pieces. Please...   \n",
       "717     28              1  It is your turn! You have white pieces. Please...   \n",
       "726     42              1  It is your turn! You have white pieces. Please...   \n",
       "727     24              1  It is your turn! You have white pieces. Please...   \n",
       "732     20              1  It is your turn! You have white pieces. Please...   \n",
       "\n",
       "     temperature random_engine has_illegal illegal_move  \\\n",
       "638          0.0          True        True         Qxd8   \n",
       "641          0.0          True        True         Bxf8   \n",
       "643          0.0          True        True         gxh8   \n",
       "644          0.0          True        True         cxd8   \n",
       "646          0.0          True        True         exd8   \n",
       "654          0.0          True        True          Qd7   \n",
       "671          0.0          True        True         Rxg8   \n",
       "675          0.0          True        True        14...   \n",
       "679          0.0          True        True        12...   \n",
       "680          0.0          True        True        exd2+   \n",
       "682          0.0          True        True        12...   \n",
       "683          0.0          True        True         9...   \n",
       "685          0.0          True        True        16...   \n",
       "686          0.0          True        True        17...   \n",
       "691          0.0          True        True         Nxg4   \n",
       "696          0.0          True        True          5.0   \n",
       "697          0.0          True        True          6.0   \n",
       "700          0.0          True        True          Qh5   \n",
       "703          0.0          True        True         fxg7   \n",
       "705          0.0          True        True         Bxa6   \n",
       "710          0.0          True        True        gxh7+   \n",
       "711          0.0          True        True        Qae8#   \n",
       "717          0.0          True        True          Qd4   \n",
       "726          0.0          True        True         Qh4#   \n",
       "727          0.0          True        True        Qxc8#   \n",
       "732          0.0          True        True         Qd5+   \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "638  games/game15deea9a-1f36-4793-8277-e46fc6a4af7c                  \n",
       "641  games/gameff98520e-021f-4ef1-bb36-4807dbc168f1                  \n",
       "643  games/gamec7fca954-8b14-4636-a2a1-d7c8089013d2                  \n",
       "644  games/game0eff4138-41f7-49bc-a4b4-80b44da285cc                  \n",
       "646  games/game687739e3-5136-498b-a7b2-a887e1286a2c                  \n",
       "654  games/gamef7856584-6c16-4284-86a6-64bd609541ef                  \n",
       "671  games/game1ab6fd05-977b-4411-ad66-afa6a230d232                  \n",
       "675  games/gamebb2911c8-a412-44c5-9f66-16a69f61c2d2                  \n",
       "679  games/game1e393a37-6e35-4067-a108-a741a46edbf4                  \n",
       "680  games/gamebb7e6927-ad0c-4734-8ac2-7f6a3b3b8e3e                  \n",
       "682  games/game62ef65ca-1c4c-473d-8bf3-5ef603b5c90c                  \n",
       "683  games/game0570bbdc-b110-41c2-83fd-19126a6fe9ec                  \n",
       "685  games/gameace06f90-87eb-40d6-a258-e4bada39bf3c                  \n",
       "686  games/gamedd90ac78-1f2f-4346-b68b-2b727c00da46                  \n",
       "691  games/game2b596bb9-a92f-4e8b-a657-e856bfd9cb2b                  \n",
       "696  games/game789ed89f-1225-41e5-9c14-6e0810ef90df                  \n",
       "697  games/game843ca850-a82d-48d1-940a-83e04e8b6c59                  \n",
       "700  games/game1932a9b8-216b-4815-9011-6977debf64b9                  \n",
       "703  games/game341d35dd-e01c-4641-b34b-3bebc94202d0                  \n",
       "705  games/game793dad3a-d033-4962-97ef-5646fda1eed1                  \n",
       "710  games/game58fbbe46-4b86-4f6e-b7da-0b38c73acd0e                  \n",
       "711  games/gamea49150b6-28d8-4c6c-aa11-4ff0c1eea95b                  \n",
       "717  games/game1cedf899-6cb3-444f-b65c-c08a9495d221                  \n",
       "726  games/game19858450-f0c8-470c-b05e-e2a7d94f5f10                  \n",
       "727  games/gamed4ecce8c-6c03-462d-a631-2a02ca21d421                  \n",
       "732  games/game67c52316-8f1d-4e4d-8f68-f10e83d8eff0                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "638            False  \n",
       "641            False  \n",
       "643            False  \n",
       "644            False  \n",
       "646            False  \n",
       "654            False  \n",
       "671            False  \n",
       "675            False  \n",
       "679            False  \n",
       "680            False  \n",
       "682            False  \n",
       "683            False  \n",
       "685            False  \n",
       "686            False  \n",
       "691            False  \n",
       "696            False  \n",
       "697            False  \n",
       "700            False  \n",
       "703            False  \n",
       "705            False  \n",
       "710            False  \n",
       "711            False  \n",
       "717            False  \n",
       "726            False  \n",
       "727            False  \n",
       "732            False  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_random.query(\"white != 'gpt-4' and black != 'gpt-4' and has_illegal == True\")\n",
    "df_random.query(\"white == 'gpt-3.5-turbo' or black == 'gpt-3.5-turbo'\")\n",
    "df_random.query(\"(white == 'gpt-4' or black == 'gpt-4') and has_illegal == True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8715692d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9888888888888889"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compute_score(df_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e7445721",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.query(\"random_engine == False\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d40acc82",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de5a2b46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.2, 0.8])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temperature'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dc0a104c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game42dd235a-0b46-4237-9fb2-06e1bccf0f41</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game657b48b9-9437-498a-adee-c2efcf753e5b</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game8614d5d1-2fb8-4c6b-984e-621c73664d00</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game77d6c5e7-fd9e-4cea-977b-3686d76b521b</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>1-0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game37ba7154-6423-44a0-b9db-c52e9db003e2</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>*</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Kxe6</td>\n",
       "      <td>games/gamec1ca5fc0-2939-4cf5-9f12-96967340d450</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>0-1</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game647f59c4-6047-427f-93d7-d242494400fe</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>*</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Rd7</td>\n",
       "      <td>games/gamea335db59-7428-4233-8cb1-5e787af0f074</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>0-1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game3cbe09f5-5d97-4528-94ab-181b0ba74f01</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>*</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bxa7</td>\n",
       "      <td>games/game4b840b06-d4f7-4f35-a0d9-71591040c3e2</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>513 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      white      black white_elo black_elo result nmoves  \\\n",
       "0    gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0     65   \n",
       "1    gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0     81   \n",
       "2    gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0     75   \n",
       "3    gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0    137   \n",
       "4    gpt-3.5-turbo-instruct  Stockfish         ?      1785    1-0     51   \n",
       "..                      ...        ...       ...       ...    ...    ...   \n",
       "873                   gpt-4  Stockfish         ?      1597      *     96   \n",
       "874                   gpt-4  Stockfish         ?      1597    0-1    132   \n",
       "875                   gpt-4  Stockfish         ?      1597      *     54   \n",
       "876                   gpt-4  Stockfish         ?      1597    0-1     60   \n",
       "877                   gpt-4  Stockfish         ?      1597      *     80   \n",
       "\n",
       "    nstarting_move                                           pgn_base  \\\n",
       "0                1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "1                1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "2                1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "3                1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "4                1  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "..             ...                                                ...   \n",
       "873              1  It is your turn! You have white pieces. Please...   \n",
       "874              1  It is your turn! You have white pieces. Please...   \n",
       "875              1  It is your turn! You have white pieces. Please...   \n",
       "876              1  It is your turn! You have white pieces. Please...   \n",
       "877              1  It is your turn! You have white pieces. Please...   \n",
       "\n",
       "     temperature random_engine has_illegal illegal_move  \\\n",
       "0            0.0         False       False                \n",
       "1            0.0         False       False                \n",
       "2            0.0         False       False                \n",
       "3            0.0         False       False                \n",
       "4            0.0         False       False                \n",
       "..           ...           ...         ...          ...   \n",
       "873          0.0         False        True         Kxe6   \n",
       "874          0.0         False       False                \n",
       "875          0.0         False        True          Rd7   \n",
       "876          0.0         False       False                \n",
       "877          0.0         False        True         Bxa7   \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "0    games/game42dd235a-0b46-4237-9fb2-06e1bccf0f41                  \n",
       "1    games/game657b48b9-9437-498a-adee-c2efcf753e5b                  \n",
       "2    games/game8614d5d1-2fb8-4c6b-984e-621c73664d00                  \n",
       "3    games/game77d6c5e7-fd9e-4cea-977b-3686d76b521b                  \n",
       "4    games/game37ba7154-6423-44a0-b9db-c52e9db003e2                  \n",
       "..                                              ...            ...   \n",
       "873  games/gamec1ca5fc0-2939-4cf5-9f12-96967340d450                  \n",
       "874  games/game647f59c4-6047-427f-93d7-d242494400fe                  \n",
       "875  games/gamea335db59-7428-4233-8cb1-5e787af0f074                  \n",
       "876  games/game3cbe09f5-5d97-4528-94ab-181b0ba74f01                  \n",
       "877  games/game4b840b06-d4f7-4f35-a0d9-71591040c3e2                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "0               True  \n",
       "1               True  \n",
       "2               True  \n",
       "3               True  \n",
       "4               True  \n",
       "..               ...  \n",
       "873            False  \n",
       "874            False  \n",
       "875            False  \n",
       "876            False  \n",
       "877            False  \n",
       "\n",
       "[513 rows x 15 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('nstarting_move <= 2 and temperature == 0.0') # right now nmoves with 1 corresponds to traditional stuff... with 2 corresponds to basic, well-known, random openings and is fair... n > 2 corresponds to k random moves (and is a bit unfair for either side)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a044c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "804c2f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>1-0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game304ec1bf-b9a9-4a3b-916d-7409826aa88f</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game4c361571-15f6-44db-8bc0-43c714154e4c</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>0-1</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gameb60af328-5b43-4c0a-ba5c-e6a5f093fb74</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>1-0</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game84a60b18-215a-45ff-844b-357c69a7f2cc</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>0-1</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamea4b3d755-0e42-443b-8ea1-518cf0bd8147</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1871</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamee3774df5-84af-4ac4-8d2f-5ab85fe24366</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1871</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>{[%clk</td>\n",
       "      <td>games/game2ce2d311-6259-4bb9-bd60-db1b5d701042</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1871</td>\n",
       "      <td>?</td>\n",
       "      <td>1-0</td>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game66a32f90-7d48-4edc-a5e0-f30c35b4692c</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1871</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1-0</td>\n",
       "      <td>games/game93e2cca4-186e-48a5-ad24-fa4b830cf3ff</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Stockfish</td>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>1871</td>\n",
       "      <td>?</td>\n",
       "      <td>*</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1-0</td>\n",
       "      <td>games/game7b1e6340-9143-4d43-9758-917a5b2b60ec</td>\n",
       "      <td></td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      white                   black white_elo black_elo  \\\n",
       "242  gpt-3.5-turbo-instruct               Stockfish         ?      1694   \n",
       "243  gpt-3.5-turbo-instruct               Stockfish         ?      1871   \n",
       "244  gpt-3.5-turbo-instruct               Stockfish         ?      1694   \n",
       "245  gpt-3.5-turbo-instruct               Stockfish         ?      1694   \n",
       "246  gpt-3.5-turbo-instruct               Stockfish         ?      1694   \n",
       "..                      ...                     ...       ...       ...   \n",
       "338               Stockfish  gpt-3.5-turbo-instruct      1871         ?   \n",
       "339               Stockfish  gpt-3.5-turbo-instruct      1871         ?   \n",
       "340               Stockfish  gpt-3.5-turbo-instruct      1871         ?   \n",
       "341               Stockfish  gpt-3.5-turbo-instruct      1871         ?   \n",
       "342               Stockfish  gpt-3.5-turbo-instruct      1871         ?   \n",
       "\n",
       "    result nmoves nstarting_move  \\\n",
       "242    1-0     65              1   \n",
       "243    0-1    102              1   \n",
       "244    0-1    150              1   \n",
       "245    1-0     79              1   \n",
       "246    0-1    164              1   \n",
       "..     ...    ...            ...   \n",
       "338    1-0     79              1   \n",
       "339      *      1              1   \n",
       "340    1-0     83              1   \n",
       "341      *     55              1   \n",
       "342      *     63              1   \n",
       "\n",
       "                                              pgn_base  temperature  \\\n",
       "242  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "243  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "244  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "245  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "246  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "..                                                 ...          ...   \n",
       "338  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "339  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "340  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "341  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "342  [Event \"FIDE World Championship Match 2024\"]\\n...          0.8   \n",
       "\n",
       "    random_engine has_illegal illegal_move  \\\n",
       "242         False       False                \n",
       "243         False       False                \n",
       "244         False       False                \n",
       "245         False       False                \n",
       "246         False       False                \n",
       "..            ...         ...          ...   \n",
       "338         False       False                \n",
       "339         False        True       {[%clk   \n",
       "340         False       False                \n",
       "341         False        True          1-0   \n",
       "342         False        True          1-0   \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "242  games/game304ec1bf-b9a9-4a3b-916d-7409826aa88f                  \n",
       "243  games/game4c361571-15f6-44db-8bc0-43c714154e4c                  \n",
       "244  games/gameb60af328-5b43-4c0a-ba5c-e6a5f093fb74                  \n",
       "245  games/game84a60b18-215a-45ff-844b-357c69a7f2cc                  \n",
       "246  games/gamea4b3d755-0e42-443b-8ea1-518cf0bd8147                  \n",
       "..                                              ...            ...   \n",
       "338  games/gamee3774df5-84af-4ac4-8d2f-5ab85fe24366                  \n",
       "339  games/game2ce2d311-6259-4bb9-bd60-db1b5d701042                  \n",
       "340  games/game66a32f90-7d48-4edc-a5e0-f30c35b4692c                  \n",
       "341  games/game93e2cca4-186e-48a5-ad24-fa4b830cf3ff                  \n",
       "342  games/game7b1e6340-9143-4d43-9758-917a5b2b60ec                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "242             True  \n",
       "243             True  \n",
       "244             True  \n",
       "245             True  \n",
       "246             True  \n",
       "..               ...  \n",
       "338             True  \n",
       "339             True  \n",
       "340             True  \n",
       "341             True  \n",
       "342             True  \n",
       "\n",
       "[101 rows x 15 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('nstarting_move <= 2 and temperature == 0.8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3073946e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1d9da2c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plot_histogram(df)\n",
    "plot_histogram(df.query('nstarting_move > 2'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f8c60a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plot_boxplot(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1215102",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_average(df, model_name='gpt-3.5-turbo-instruct'):\n",
    "    # Create a copy of the DataFrame to avoid modifying the original\n",
    "    df_copy = df.copy()\n",
    "    \n",
    "    # Convert Elo columns to numeric, setting errors='coerce' to handle non-numeric values\n",
    "    df_copy['white_elo'] = pd.to_numeric(df_copy['white_elo'], errors='coerce')\n",
    "    df_copy['black_elo'] = pd.to_numeric(df_copy['black_elo'], errors='coerce')\n",
    "\n",
    "    # Filter out rows where gpt-3.5-turbo-instruct is the player and get the opponent's Elo\n",
    "    opponent_elo_white = df_copy[df_copy['white'] == model_name]['black_elo']\n",
    "    opponent_elo_black = df_copy[df_copy['black'] == model_name]['white_elo']\n",
    "\n",
    "    # Concatenate the Elo ratings of opponents when gpt-3.5-turbo-instruct played as white and black\n",
    "    all_opponent_elo = pd.concat([opponent_elo_white, opponent_elo_black])\n",
    "\n",
    "    # Calculate the average Elo rating of the opponents, excluding missing or NaN values\n",
    "    average_opponent_elo = all_opponent_elo.mean()\n",
    "\n",
    "    return average_opponent_elo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f4ff0c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1747.8626258554725, 1785.4345248666352, 1584.2638713688007, 1433.464744474366)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import fsolve\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def computation_Elo(df,  initial_guess = 1700, model_name='gpt-3.5-turbo-instruct', K=400):\n",
    "    # Given values\n",
    "    S_A = compute_score(df, model_name)  # The score of Player A\n",
    "    R_B = compute_average(df, model_name)  # The rating of Player \n",
    "\n",
    "    # Define the equation to solve for R_A\n",
    "    def equation(R_A, *data):\n",
    "        S_A, R_B = data\n",
    "        return S_A - 1 / (1 + 10**((R_B - R_A) / K)) \n",
    "\n",
    "    # Solve the equation for R_A\n",
    "    R_A_solution = fsolve(equation, initial_guess, args=(S_A, R_B))\n",
    "\n",
    "    # Extract the calculated R_A value\n",
    "    R_A_calculated = float(R_A_solution[0])\n",
    "    return R_A_calculated\n",
    "\n",
    "computation_Elo(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct')\")), computation_Elo(df.query('nstarting_move <= 2 and temperature == 0.0')), computation_Elo(df.query('nstarting_move <= 2 and temperature == 0.8')), computation_Elo(df.query('nstarting_move > 2 and temperature == 0.0'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8cec0f29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1743.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves\n",
      "1696.0 Elo for gpt-3.5-turbo-instruct against SF and with all games\n",
      "1371.0 Elo for gpt-4 against SF and only with legal games/moves\n",
      "1305.0 Elo for gpt-4 against SF and with all games\n",
      "With temperature=0\n",
      "1789.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves\n",
      "1742.0 Elo for gpt-3.5-turbo-instruct against SF and with all games\n",
      "With temperature=0.8\n",
      "1573.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves\n",
      "1519.0 Elo for gpt-3.5-turbo-instruct against SF and with all games\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# rs = []\n",
    "# for i in range(0, 1000):\n",
    "#     rs.append(compute_elo(df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and `has_illegal` == False\").sample(), candidate_name=\"gpt-3.5-turbo-instruct\", starting_elo=1500, K=400))\n",
    "\n",
    "\n",
    "def lookup_fide_table(score):\n",
    "        # Data here: https://handbook.fide.com/chapter/B022017\n",
    "        data = {\n",
    "            'p': [1.0, .99, .98, .97, .96, .95, .94, .93, .92, .91, .90, .89, .88, .87, .86, .85, .84,\n",
    "                .83, .82, .81, .80, .79, .78, .77, .76, .75, .74, .73, .72, .71, .70, .69, .68, .67,\n",
    "                .66, .65, .64, .63, .62, .61, .60, .59, .58, .57, .56, .55, .54, .53, .52, .51, .50,\n",
    "                .49, .48, .47, .46, .45, .44, .43, .42, .41, .40, .39, .38, .37, .36, .35, .34, .33,\n",
    "                .32, .31, .30, .29, .28, .27, .26, .25, .24, .23, .22, .21, .20, .19, .18, .17, .16,\n",
    "                .15, .14, .13, .12, .11, .10, .09, .08, .07, .06, .05, .04, .03, .02, .01],\n",
    "            'dp': [800, 677, 589, 538, 501, 470, 444, 422, 401, 383, 366, 351, 336, 322, 309, 296, 284,\n",
    "                273, 262, 251, 240, 230, 220, 211, 202, 193, 184, 175, 166, 158, 149, 141, 133, 125,\n",
    "                117, 110, 102, 95, 87, 80, 72, 65, 57, 50, 43, 36, 29, 21, 14, 7, 0, -7, -14, -21, \n",
    "                -29, -36, -43, -50, -57, -65, -72, -80, -87, -95, -102, -110, -117, -125, -133, -141,\n",
    "                -149, -158, -166, -175, -184, -193, -202, -211, -220, -230, -240, -251, -262, -273, \n",
    "                -284, -296, -309, -322, -336, -351, -366, -383, -401, -444, -470, -501, -538, -589, -677, -800]\n",
    "        }\n",
    "\n",
    "        # Convert to DataFrame\n",
    "        df_fide = pd.DataFrame(data)\n",
    "\n",
    "        rscore = round(score, 2)\n",
    "\n",
    "        # lookup score in table\n",
    "        dp = df_fide.loc[df_fide['p'] == rscore, 'dp'].iloc[0]\n",
    "        return dp\n",
    "\n",
    "def fide_elo_computation(dfe, model_name, score=None):\n",
    "    average_opponents_ratings = compute_average(dfe, model_name)\n",
    "    if score is None:\n",
    "        score = compute_score(dfe, model_name)\n",
    "    dp = lookup_fide_table(score)\n",
    "\n",
    "\n",
    "    return average_opponents_ratings + dp\n",
    "\n",
    "                    \n",
    "def compute_elo(df_l, model_gpt_name):\n",
    "    df_elo = df_l.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}') and `has_illegal` == False\")\n",
    "    fide_elo_value = round(fide_elo_computation(df_elo, model_gpt_name), 0)\n",
    "    print(f\"{fide_elo_value} Elo for {model_gpt_name} against SF and only with legal games/moves\")\n",
    "    \n",
    "    df_elo_withillegal = df_l.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}')\")\n",
    "    s, t = compute_score(df_elo_withillegal, model_gpt_name, percentage=False)\n",
    "    sc = s / len(df_elo_withillegal)\n",
    "    fide_elo_with_illegal_value = round(fide_elo_computation(df_elo_withillegal, model_gpt_name, sc), 0)\n",
    "    print(f\"{fide_elo_with_illegal_value} Elo for {model_gpt_name} against SF and with all games\")\n",
    "\n",
    "# Usage:\n",
    "compute_elo(df_non_random, 'gpt-3.5-turbo-instruct')\n",
    "compute_elo(df_non_random, 'gpt-4')\n",
    "\n",
    "print(\"With temperature=0\")\n",
    "df_non_random['temperature'] = df_non_random['temperature'].astype(float)\n",
    "compute_elo(df_non_random.query(\"temperature == 0.0\"), 'gpt-3.5-turbo-instruct')\n",
    "print(\"With temperature=0.8\")\n",
    "compute_elo(df_non_random.query(\"temperature != 0\"), 'gpt-3.5-turbo-instruct')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "756f697b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elo with altered prompt\n",
      "1808.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves\n",
      "1752.0 Elo for gpt-3.5-turbo-instruct against SF and with all games\n",
      "we don't have interactions temperature/altered prompt\n",
      "Elo with non altered prompt\n",
      "1729.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves\n",
      "1681.0 Elo for gpt-3.5-turbo-instruct against SF and with all games\n",
      "Elo with non altered prompt, t=0\n",
      "1778.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves\n",
      "1744.0 Elo for gpt-3.5-turbo-instruct against SF and with all games\n",
      "Elo with non altered prompt, t=0.8\n",
      "1572.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves\n",
      "1518.0 Elo for gpt-3.5-turbo-instruct against SF and with all games\n"
     ]
    }
   ],
   "source": [
    "print(\"Elo with altered prompt\")\n",
    "compute_elo(df_non_random.query(\"pgn_base.str.contains('Rennes FRA')\"), 'gpt-3.5-turbo-instruct')\n",
    "# compute_elo(df_non_random.query(\"pgn_base.str.contains('Rennes FRA')\").query(\"temperature == 0.0\"), 'gpt-3.5-turbo-instruct')\n",
    "print(\"we don't have interactions temperature/altered prompt\")\n",
    "# compute_elo(df_non_random.query(\"pgn_base.str.contains('Rennes FRA')\").query(\"temperature == 0.8\"), 'gpt-3.5-turbo-instruct')\n",
    "\n",
    "print(\"Elo with non altered prompt\")\n",
    "compute_elo(df_non_random.query(\"not pgn_base.str.contains('Rennes FRA')\"), 'gpt-3.5-turbo-instruct')\n",
    "print(\"Elo with non altered prompt, t=0\")\n",
    "compute_elo(df_non_random.query(\"not pgn_base.str.contains('Rennes FRA')\").query(\"temperature == 0.0\"), 'gpt-3.5-turbo-instruct')\n",
    "print(\"Elo with non altered prompt, t=0.8\")\n",
    "compute_elo(df_non_random.query(\"not pgn_base.str.contains('Rennes FRA')\").query(\"temperature == 0.8\"), 'gpt-3.5-turbo-instruct')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1b23fa17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1725.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves and only with white pieces\n",
      "1715.0 Elo for gpt-3.5-turbo-instruct against SF and with all games and only with white pieces\n",
      "1671.0 Elo for gpt-3.5-turbo-instruct against SF and with all games and only with black pieces\n",
      "1809.0 Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves and only with black pieces\n"
     ]
    }
   ],
   "source": [
    "df_elo_onlywhite = df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct') and `has_illegal` == False\")\n",
    "print(round(fide_elo_computation(df_elo_onlywhite, 'gpt-3.5-turbo-instruct'), 0), \"Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves and only with white pieces\")\n",
    "\n",
    "df_elo_onlywhite_withillegal = df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct')\")\n",
    "mname = 'gpt-3.5-turbo-instruct'\n",
    "s, t = compute_score(df_elo_onlywhite_withillegal, mname, percentage=False)\n",
    "sc = s / len(df_elo_onlywhite_withillegal)\n",
    "print(round(fide_elo_computation(df_elo_onlywhite_withillegal, mname, sc), 0), \"Elo for\", mname, \"against SF and with all games and only with white pieces\")\n",
    "\n",
    "# only black\n",
    "df_elo_onlyblack_withillegal = df_non_random.query(\"(black == 'gpt-3.5-turbo-instruct')\")\n",
    "mname = 'gpt-3.5-turbo-instruct'\n",
    "s, t = compute_score(df_elo_onlyblack_withillegal, mname, percentage=False)\n",
    "sc = s / len(df_elo_onlyblack_withillegal)\n",
    "print(round(fide_elo_computation(df_elo_onlyblack_withillegal, mname, sc), 0), \"Elo for\", mname, \"against SF and with all games and only with black pieces\")\n",
    "\n",
    "# only black\n",
    "df_elo_onlyblack = df_non_random.query(\"(black == 'gpt-3.5-turbo-instruct') and `has_illegal` == False\")\n",
    "print(round(fide_elo_computation(df_elo_onlyblack, 'gpt-3.5-turbo-instruct'), 0), \"Elo for gpt-3.5-turbo-instruct against SF and only with legal games/moves and only with black pieces\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c9c01b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def extract_elo_ratings(df_l, model_gpt_name):\n",
    "    wrs = [] # winning games \n",
    "    lrs = [] # losing games\n",
    "    drs = [] # drawing games\n",
    "    \n",
    "    # Filter and iterate over the dataframe\n",
    "    for _, row in df_l.query(f\"(white == '{model_gpt_name}' or black == '{model_gpt_name}') and `has_illegal` == False\").iterrows():\n",
    "        if row['white'] == model_gpt_name and row['result'] == '1-0':\n",
    "            wrs.append(row['black_elo'])\n",
    "        elif row['black'] == model_gpt_name and row['result'] == '0-1':\n",
    "            wrs.append(row['white_elo'])\n",
    "        elif row['black'] == model_gpt_name and row['result'] == '1-0':\n",
    "            lrs.append(row['white_elo'])\n",
    "        elif row['white'] == model_gpt_name and row['result'] == '0-1':\n",
    "            lrs.append(row['black_elo'])\n",
    "        elif row['result'] == '1/2-1/2':\n",
    "            if row['white'] == model_gpt_name:\n",
    "                drs.append(row['black_elo'])\n",
    "            else:\n",
    "                drs.append(row['white_elo'])\n",
    "        else:\n",
    "            print(\"warning\")\n",
    "            print(row)\n",
    "            continue\n",
    "\n",
    "    wrs = [float(val) for val in wrs]\n",
    "    lrs = [float(val) for val in lrs]\n",
    "    drs = [float(val) for val in drs]\n",
    "    \n",
    "    return wrs, lrs, drs\n",
    "\n",
    "def plot_elo_distribution(wrs, lrs, drs, model_gpt_name):\n",
    "    # Compute unique Elos and their counts\n",
    "    unique_elos = sorted(np.unique(wrs))\n",
    "    win_counts = [wrs.count(elo) for elo in unique_elos]\n",
    "    draw_counts = [drs.count(elo) for elo in unique_elos]\n",
    "    lose_counts = [lrs.count(elo) for elo in unique_elos]\n",
    "\n",
    "    # Plotting\n",
    "    bar_width = 0.3\n",
    "    index = np.arange(len(unique_elos))\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    plt.bar(index, win_counts, bar_width, alpha=0.7, color='green', label='Winning Games')\n",
    "    plt.bar(index + bar_width, draw_counts, bar_width, alpha=0.7, color='blue', label='Drawing Games')\n",
    "    plt.bar(index + 2 * bar_width, lose_counts, bar_width, alpha=0.7, color='red', label='Losing Games')\n",
    "\n",
    "    plt.xlabel('Elo Rating')\n",
    "    plt.ylabel('Number of Games')\n",
    "    plt.title(f'Distribution of Elo Ratings by Game Result for {model_gpt_name}')\n",
    "\n",
    "    plt.xticks(index + bar_width, unique_elos, rotation=45)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "    plt.savefig(f\"elo_distribution-scores-{model_gpt_name}.png\", dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "def analyze_and_plot_elo_distribution(df_l, model_gpt_name):\n",
    "    wrs, lrs, drs = extract_elo_ratings(df_l, model_gpt_name)\n",
    "    plot_elo_distribution(wrs, lrs, drs, model_gpt_name)\n",
    "\n",
    "\n",
    "\n",
    "# Return the functions for further use\n",
    "analyze_and_plot_elo_distribution(df_non_random, \"gpt-3.5-turbo-instruct\")\n",
    "analyze_and_plot_elo_distribution(df_non_random, \"gpt-4\")\n",
    "# df_non_random\n",
    "\n",
    "\n",
    "analyze_and_plot_elo_distribution(df_non_random.query(\"not pgn_base.str.contains('Rennes FRA')\"), 'gpt-3.5-turbo-instruct')\n",
    "analyze_and_plot_elo_distribution(df_non_random.query(\"pgn_base.str.contains('Rennes FRA')\"), 'gpt-3.5-turbo-instruct')\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f7bfc171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Elo of gpt-3.5-turbo-instruct with white: 1787.4658753709198\n"
     ]
    }
   ],
   "source": [
    "final_elo = fide_elo_computation(df.query('nstarting_move <= 2 and temperature == 0.0'), 'gpt-3.5-turbo-instruct') # starting_elo=1700, K=40)\n",
    "print(f\"Final Elo of gpt-3.5-turbo-instruct with white: {final_elo}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b17adf86",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in df_non_random.query(\"(white == 'gpt-3.5-turbo-instruct' or black == 'gpt-3.5-turbo-instruct') and `has_illegal` == False\").iterrows():\n",
    "    if v['white'] == \"gpt-3.5-turbo-instruct\" and v['black_elo'] != '?':\n",
    "            opponent_elo = float(v['black_elo'])\n",
    "    elif v['black'] == \"gpt-3.5-turbo-instruct\" and v['white_elo'] != '?':\n",
    "        opponent_elo = float(v['white_elo'])\n",
    "    else:\n",
    "        print(\"warning: no elo found for opponent\", k, v['folder_name'])\n",
    "        continue\n",
    "    result = v['result']\n",
    "    if result == '*':\n",
    "        print(\"warning: no result found for opponent\", k, v['folder_name'])\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "93e78377",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.query('nstarting_move > 2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "23faccf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>0-1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game25c2d76d-f2e0-47e0-808f-a18c546a97d4</td>\n",
       "      <td>1. d4 g5 2. Nc3 f6 3. Kd2 c6 4. Nf3 Bh6 5. Ke3...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>0-1</td>\n",
       "      <td>72</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game41920ed0-d6b6-423e-946a-54aa5832a6f1</td>\n",
       "      <td>1. g3 e6 2. Nh3 Bb4 3. f3 Qg5 4. Ng1 Ne7 5. Nh...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>0-1</td>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game66b23efd-001d-4342-8144-d000024c5bc3</td>\n",
       "      <td>1. Na3 a6 2. h3 f6 3. Nb1 h5 4. d4 Rh6 5. Qd3 ...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>0-1</td>\n",
       "      <td>38</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game762b6a90-356a-4a30-9e00-e8a5bb05fd68</td>\n",
       "      <td>1. f3 c5 2. b3 Qb6 3. b4 Qa5 4. d3 Qb6 5. e4 N...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1785</td>\n",
       "      <td>0-1</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game7f234286-8554-4440-a3fb-b5e0730486ff</td>\n",
       "      <td>1. g3 f6 2. a4 g6 3. c4 Kf7 4. Ra3 Bh6 5. f4 e...</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>62</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>games/game80abc05e-cf5d-437e-b9b5-5f910e3ed15c</td>\n",
       "      <td>1. d3 h6 2. b3 d5 3. Nh3 h5 4. a3 h4 5. Nc3 a5...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>1-0</td>\n",
       "      <td>117</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game04015b27-2567-40a6-9d07-85aaab9f2877</td>\n",
       "      <td>1. c3 g6 2. Nh3 a6 3. e4 Bg7 4. Rg1 e5 5. Na3 ...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>138</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamea9ee7c9d-89b5-47c9-8c38-be409e5ebd4e</td>\n",
       "      <td>1. g3 f6 2. Bg2 c5 3. e3 c4 4. e4 h6 5. Kf1 e6...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game19079542-6cb0-47e3-83af-f56e3afd09a2</td>\n",
       "      <td>1. b4 d5 2. h4 Bd7 3. f3 Bf5 4. Nc3 e6 5. Rh2 ...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>gpt-3.5-turbo-instruct</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>[Event \"FIDE World Championship Match 2024\"]\\n...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game52d0e78b-e22f-40b4-b50b-81da4155df26</td>\n",
       "      <td>1. f4 h5 2. Na3 g5 3. b3 a5 4. c4 Na6 5. Kf2 h...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      white      black white_elo black_elo result nmoves  \\\n",
       "174  gpt-3.5-turbo-instruct  Stockfish         ?      1785    0-1     38   \n",
       "178  gpt-3.5-turbo-instruct  Stockfish         ?      1785    0-1     72   \n",
       "179  gpt-3.5-turbo-instruct  Stockfish         ?      1785    0-1     42   \n",
       "180  gpt-3.5-turbo-instruct  Stockfish         ?      1785    0-1     38   \n",
       "181  gpt-3.5-turbo-instruct  Stockfish         ?      1785    0-1     22   \n",
       "..                      ...        ...       ...       ...    ...    ...   \n",
       "399  gpt-3.5-turbo-instruct  Stockfish         ?      1871    0-1     62   \n",
       "400  gpt-3.5-turbo-instruct  Stockfish         ?      1871    1-0    117   \n",
       "401  gpt-3.5-turbo-instruct  Stockfish         ?      1871    0-1    138   \n",
       "403  gpt-3.5-turbo-instruct  Stockfish         ?      1871    0-1     34   \n",
       "404  gpt-3.5-turbo-instruct  Stockfish         ?      1871    0-1     34   \n",
       "\n",
       "    nstarting_move                                           pgn_base  \\\n",
       "174             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "178             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "179             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "180             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "181             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "..             ...                                                ...   \n",
       "399             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "400             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "401             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "403             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "404             11  [Event \"FIDE World Championship Match 2024\"]\\n...   \n",
       "\n",
       "     temperature random_engine has_illegal illegal_move  \\\n",
       "174          0.0         False       False                \n",
       "178          0.0         False       False                \n",
       "179          0.0         False       False                \n",
       "180          0.0         False       False                \n",
       "181          0.0         False       False                \n",
       "..           ...           ...         ...          ...   \n",
       "399          0.0         False       False                \n",
       "400          0.0         False       False                \n",
       "401          0.0         False       False                \n",
       "403          0.0         False       False                \n",
       "404          0.0         False       False                \n",
       "\n",
       "                                        folder_name  \\\n",
       "174  games/game25c2d76d-f2e0-47e0-808f-a18c546a97d4   \n",
       "178  games/game41920ed0-d6b6-423e-946a-54aa5832a6f1   \n",
       "179  games/game66b23efd-001d-4342-8144-d000024c5bc3   \n",
       "180  games/game762b6a90-356a-4a30-9e00-e8a5bb05fd68   \n",
       "181  games/game7f234286-8554-4440-a3fb-b5e0730486ff   \n",
       "..                                              ...   \n",
       "399  games/game80abc05e-cf5d-437e-b9b5-5f910e3ed15c   \n",
       "400  games/game04015b27-2567-40a6-9d07-85aaab9f2877   \n",
       "401  games/gamea9ee7c9d-89b5-47c9-8c38-be409e5ebd4e   \n",
       "403  games/game19079542-6cb0-47e3-83af-f56e3afd09a2   \n",
       "404  games/game52d0e78b-e22f-40b4-b50b-81da4155df26   \n",
       "\n",
       "                                        pgn_base_moves  base_pgn_prompt  \n",
       "174  1. d4 g5 2. Nc3 f6 3. Kd2 c6 4. Nf3 Bh6 5. Ke3...             True  \n",
       "178  1. g3 e6 2. Nh3 Bb4 3. f3 Qg5 4. Ng1 Ne7 5. Nh...             True  \n",
       "179  1. Na3 a6 2. h3 f6 3. Nb1 h5 4. d4 Rh6 5. Qd3 ...             True  \n",
       "180  1. f3 c5 2. b3 Qb6 3. b4 Qa5 4. d3 Qb6 5. e4 N...             True  \n",
       "181  1. g3 f6 2. a4 g6 3. c4 Kf7 4. Ra3 Bh6 5. f4 e...             True  \n",
       "..                                                 ...              ...  \n",
       "399  1. d3 h6 2. b3 d5 3. Nh3 h5 4. a3 h4 5. Nc3 a5...             True  \n",
       "400  1. c3 g6 2. Nh3 a6 3. e4 Bg7 4. Rg1 e5 5. Na3 ...             True  \n",
       "401  1. g3 f6 2. Bg2 c5 3. e3 c4 4. e4 h6 5. Kf1 e6...             True  \n",
       "403  1. b4 d5 2. h4 Bd7 3. f3 Bf5 4. Nc3 e6 5. Rh2 ...             True  \n",
       "404  1. f4 h5 2. Na3 g5 3. b3 a5 4. c4 Na6 5. Kf2 h...             True  \n",
       "\n",
       "[73 rows x 15 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('nstarting_move > 2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cce4efc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, True], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['has_illegal'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5922ffd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         507\n",
       "1-0       49\n",
       "1-         4\n",
       "O-O        3\n",
       "Rxe8       2\n",
       "        ... \n",
       "Nbd2       1\n",
       "exf6       1\n",
       "Bxh6       1\n",
       "Qe5+       1\n",
       "{This      1\n",
       "Name: illegal_move, Length: 117, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['illegal_move'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6fa9ed56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     445\n",
       "False    243\n",
       "Name: base_pgn_prompt, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['base_pgn_prompt'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "861eb3f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1814.3735994559265"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computation_Elo(df.query('base_pgn_prompt == False'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c76112",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8f4c9e45",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>124</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game720a4b7d-5a2d-44ca-bb26-323a1cc5ebce</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game1ca03199-45fb-4c5e-b5e5-0e23d4f0984f</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game2da141a2-804b-463f-82cb-59658a06aa97</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamefb896b24-5f50-4c10-8159-2eb4eb665340</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game9b1c3725-388f-453c-b8a4-c953eb05819b</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>*</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Kxe6</td>\n",
       "      <td>games/gamec1ca5fc0-2939-4cf5-9f12-96967340d450</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>0-1</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game647f59c4-6047-427f-93d7-d242494400fe</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>*</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Rd7</td>\n",
       "      <td>games/gamea335db59-7428-4233-8cb1-5e787af0f074</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>0-1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/game3cbe09f5-5d97-4528-94ab-181b0ba74f01</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>gpt-4</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1597</td>\n",
       "      <td>*</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bxa7</td>\n",
       "      <td>games/game4b840b06-d4f7-4f35-a0d9-71591040c3e2</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     white      black white_elo black_elo result nmoves nstarting_move  \\\n",
       "631  gpt-4  Stockfish         ?      1871    0-1    124              1   \n",
       "632  gpt-4  Stockfish         ?      1871    0-1    176              1   \n",
       "633  gpt-4  Stockfish         ?      1871    0-1    164              1   \n",
       "783  gpt-4  Stockfish         ?      1871    0-1    148              1   \n",
       "788  gpt-4  Stockfish         ?      1871    0-1    136              1   \n",
       "..     ...        ...       ...       ...    ...    ...            ...   \n",
       "873  gpt-4  Stockfish         ?      1597      *     96              1   \n",
       "874  gpt-4  Stockfish         ?      1597    0-1    132              1   \n",
       "875  gpt-4  Stockfish         ?      1597      *     54              1   \n",
       "876  gpt-4  Stockfish         ?      1597    0-1     60              1   \n",
       "877  gpt-4  Stockfish         ?      1597      *     80              1   \n",
       "\n",
       "                                              pgn_base  temperature  \\\n",
       "631  It is your turn! You have white pieces. Please...          0.0   \n",
       "632  It is your turn! You have white pieces. Please...          0.0   \n",
       "633  It is your turn! You have white pieces. Please...          0.0   \n",
       "783  It is your turn! You have white pieces. Please...          0.0   \n",
       "788  It is your turn! You have white pieces. Please...          0.0   \n",
       "..                                                 ...          ...   \n",
       "873  It is your turn! You have white pieces. Please...          0.0   \n",
       "874  It is your turn! You have white pieces. Please...          0.0   \n",
       "875  It is your turn! You have white pieces. Please...          0.0   \n",
       "876  It is your turn! You have white pieces. Please...          0.0   \n",
       "877  It is your turn! You have white pieces. Please...          0.0   \n",
       "\n",
       "    random_engine has_illegal illegal_move  \\\n",
       "631         False       False                \n",
       "632         False       False                \n",
       "633         False       False                \n",
       "783         False       False                \n",
       "788         False       False                \n",
       "..            ...         ...          ...   \n",
       "873         False        True         Kxe6   \n",
       "874         False       False                \n",
       "875         False        True          Rd7   \n",
       "876         False       False                \n",
       "877         False        True         Bxa7   \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "631  games/game720a4b7d-5a2d-44ca-bb26-323a1cc5ebce                  \n",
       "632  games/game1ca03199-45fb-4c5e-b5e5-0e23d4f0984f                  \n",
       "633  games/game2da141a2-804b-463f-82cb-59658a06aa97                  \n",
       "783  games/gamefb896b24-5f50-4c10-8159-2eb4eb665340                  \n",
       "788  games/game9b1c3725-388f-453c-b8a4-c953eb05819b                  \n",
       "..                                              ...            ...   \n",
       "873  games/gamec1ca5fc0-2939-4cf5-9f12-96967340d450                  \n",
       "874  games/game647f59c4-6047-427f-93d7-d242494400fe                  \n",
       "875  games/gamea335db59-7428-4233-8cb1-5e787af0f074                  \n",
       "876  games/game3cbe09f5-5d97-4528-94ab-181b0ba74f01                  \n",
       "877  games/game4b840b06-d4f7-4f35-a0d9-71591040c3e2                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "631            False  \n",
       "632            False  \n",
       "633            False  \n",
       "783            False  \n",
       "788            False  \n",
       "..               ...  \n",
       "873            False  \n",
       "874            False  \n",
       "875            False  \n",
       "876            False  \n",
       "877            False  \n",
       "\n",
       "[94 rows x 15 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"(white == 'gpt-4' or black == 'gpt-4')\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "edac6af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>white</th>\n",
       "      <th>black</th>\n",
       "      <th>white_elo</th>\n",
       "      <th>black_elo</th>\n",
       "      <th>result</th>\n",
       "      <th>nmoves</th>\n",
       "      <th>nstarting_move</th>\n",
       "      <th>pgn_base</th>\n",
       "      <th>temperature</th>\n",
       "      <th>random_engine</th>\n",
       "      <th>has_illegal</th>\n",
       "      <th>illegal_move</th>\n",
       "      <th>folder_name</th>\n",
       "      <th>pgn_base_moves</th>\n",
       "      <th>base_pgn_prompt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxe4</td>\n",
       "      <td>games/game5f552356-e569-481a-8bdf-1dafa5cad6b5</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Rxh1</td>\n",
       "      <td>games/game0646c65e-786d-4c0a-8cb0-46883c81cee7</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Rxh6</td>\n",
       "      <td>games/gamee2ab9315-21e3-4d4a-89fc-1dbce2060cb7</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bxh6</td>\n",
       "      <td>games/gameaec9eb9a-3ce6-4e51-af85-b2d1b5432a58</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bg8</td>\n",
       "      <td>games/game8db0c871-75c4-4782-b6dc-ef44e1513eba</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamedf9ad5a3-e5f3-4f77-a7d2-816e16df32ab</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Rxf6</td>\n",
       "      <td>games/gamea60d049e-a949-45ba-8f8d-e0da62892c99</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>c4</td>\n",
       "      <td>games/game000df145-c8f1-4570-a57f-d8d79807d0fa</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>cxd6</td>\n",
       "      <td>games/game9a01a28b-0b9b-4ac7-a27a-635ddcac67cb</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxg5</td>\n",
       "      <td>games/gamefd422ced-0c4f-46f2-b8b8-4ce40f5f0b6b</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Rxf7+</td>\n",
       "      <td>games/game8634bbb1-7a3a-46fd-acad-7b9d2829cd9e</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Nxd1</td>\n",
       "      <td>games/game80c7e9bf-3159-41e6-a10b-7cdce2a013bc</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Nxe1</td>\n",
       "      <td>games/gameb71e67b4-7463-482a-a951-a77d9254865a</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>0-1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>games/gamea9168c1b-a985-4794-b8cf-f68cdbf2122b</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxe4</td>\n",
       "      <td>games/game5cae331f-043b-488f-8240-3833e30db899</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bh4</td>\n",
       "      <td>games/game3af69992-0ddc-41d3-b867-c394dbc2ba5b</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxf6</td>\n",
       "      <td>games/game28a77f57-e350-43e2-a9d9-ae9b0bf98f6e</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1871</td>\n",
       "      <td>*</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>exf6</td>\n",
       "      <td>games/game2ad399e6-0fa0-41c8-9aec-0ee92bf5b84a</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Nxe5+</td>\n",
       "      <td>games/gamea88f3e66-8175-4613-bd56-bea0241e3418</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bxc6</td>\n",
       "      <td>games/gamefdb72f62-aaf6-40dd-98de-7f6f4bc6a405</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bxf6</td>\n",
       "      <td>games/gamebe4015b3-16f0-45ba-bf2d-f66d531974be</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>cxb7</td>\n",
       "      <td>games/game19160e40-7636-410a-a798-33a9ce14279e</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Kxe1</td>\n",
       "      <td>games/gamee93d33af-f08a-4b80-99bc-abb3ac93a30a</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Rd8</td>\n",
       "      <td>games/gamee378cea2-80da-49b4-9308-72657a59b7c9</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>O-O</td>\n",
       "      <td>games/gameab89e6f2-378c-4e94-b94e-027d55a90472</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>cxb7</td>\n",
       "      <td>games/game7259eae1-35fb-4f8d-9ebd-fa71766c23a1</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Bxd6+</td>\n",
       "      <td>games/gamea1f9d208-d8cb-4239-8a05-cc137bd61181</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Qxf5</td>\n",
       "      <td>games/game11506edb-6818-4ac6-9928-5413243c2984</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Nxf7+</td>\n",
       "      <td>games/game350da87f-85d8-4190-ac52-0ab54ede330a</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>gpt-3.5-turbo</td>\n",
       "      <td>Stockfish</td>\n",
       "      <td>?</td>\n",
       "      <td>1694</td>\n",
       "      <td>*</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>It is your turn! You have white pieces. Please...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>exd6</td>\n",
       "      <td>games/game39b35c3c-e08e-4d1e-a7a0-b5af1188c32f</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             white      black white_elo black_elo result nmoves  \\\n",
       "755  gpt-3.5-turbo  Stockfish         ?      1871      *     30   \n",
       "757  gpt-3.5-turbo  Stockfish         ?      1871      *     26   \n",
       "758  gpt-3.5-turbo  Stockfish         ?      1871      *     54   \n",
       "759  gpt-3.5-turbo  Stockfish         ?      1871      *     38   \n",
       "760  gpt-3.5-turbo  Stockfish         ?      1871      *     86   \n",
       "762  gpt-3.5-turbo  Stockfish         ?      1871    0-1     44   \n",
       "763  gpt-3.5-turbo  Stockfish         ?      1871      *     40   \n",
       "764  gpt-3.5-turbo  Stockfish         ?      1871      *     46   \n",
       "765  gpt-3.5-turbo  Stockfish         ?      1871      *     14   \n",
       "766  gpt-3.5-turbo  Stockfish         ?      1871      *     52   \n",
       "767  gpt-3.5-turbo  Stockfish         ?      1871      *     40   \n",
       "768  gpt-3.5-turbo  Stockfish         ?      1871      *     24   \n",
       "769  gpt-3.5-turbo  Stockfish         ?      1871      *     44   \n",
       "770  gpt-3.5-turbo  Stockfish         ?      1871    0-1     50   \n",
       "771  gpt-3.5-turbo  Stockfish         ?      1694      *     58   \n",
       "772  gpt-3.5-turbo  Stockfish         ?      1694      *     22   \n",
       "773  gpt-3.5-turbo  Stockfish         ?      1871      *     30   \n",
       "774  gpt-3.5-turbo  Stockfish         ?      1871      *     12   \n",
       "775  gpt-3.5-turbo  Stockfish         ?      1694      *     26   \n",
       "776  gpt-3.5-turbo  Stockfish         ?      1694      *     16   \n",
       "777  gpt-3.5-turbo  Stockfish         ?      1694      *     64   \n",
       "778  gpt-3.5-turbo  Stockfish         ?      1694      *     16   \n",
       "779  gpt-3.5-turbo  Stockfish         ?      1694      *     48   \n",
       "780  gpt-3.5-turbo  Stockfish         ?      1694      *     54   \n",
       "781  gpt-3.5-turbo  Stockfish         ?      1694      *     22   \n",
       "782  gpt-3.5-turbo  Stockfish         ?      1694      *     24   \n",
       "784  gpt-3.5-turbo  Stockfish         ?      1694      *     30   \n",
       "785  gpt-3.5-turbo  Stockfish         ?      1694      *     50   \n",
       "786  gpt-3.5-turbo  Stockfish         ?      1694      *     42   \n",
       "787  gpt-3.5-turbo  Stockfish         ?      1694      *     16   \n",
       "\n",
       "    nstarting_move                                           pgn_base  \\\n",
       "755              1  It is your turn! You have white pieces. Please...   \n",
       "757              1  It is your turn! You have white pieces. Please...   \n",
       "758              1  It is your turn! You have white pieces. Please...   \n",
       "759              1  It is your turn! You have white pieces. Please...   \n",
       "760              1  It is your turn! You have white pieces. Please...   \n",
       "762              1  It is your turn! You have white pieces. Please...   \n",
       "763              1  It is your turn! You have white pieces. Please...   \n",
       "764              1  It is your turn! You have white pieces. Please...   \n",
       "765              1  It is your turn! You have white pieces. Please...   \n",
       "766              1  It is your turn! You have white pieces. Please...   \n",
       "767              1  It is your turn! You have white pieces. Please...   \n",
       "768              1  It is your turn! You have white pieces. Please...   \n",
       "769              1  It is your turn! You have white pieces. Please...   \n",
       "770              1  It is your turn! You have white pieces. Please...   \n",
       "771              1  It is your turn! You have white pieces. Please...   \n",
       "772              1  It is your turn! You have white pieces. Please...   \n",
       "773              1  It is your turn! You have white pieces. Please...   \n",
       "774              1  It is your turn! You have white pieces. Please...   \n",
       "775              1  It is your turn! You have white pieces. Please...   \n",
       "776              1  It is your turn! You have white pieces. Please...   \n",
       "777              1  It is your turn! You have white pieces. Please...   \n",
       "778              1  It is your turn! You have white pieces. Please...   \n",
       "779              1  It is your turn! You have white pieces. Please...   \n",
       "780              1  It is your turn! You have white pieces. Please...   \n",
       "781              1  It is your turn! You have white pieces. Please...   \n",
       "782              1  It is your turn! You have white pieces. Please...   \n",
       "784              1  It is your turn! You have white pieces. Please...   \n",
       "785              1  It is your turn! You have white pieces. Please...   \n",
       "786              1  It is your turn! You have white pieces. Please...   \n",
       "787              1  It is your turn! You have white pieces. Please...   \n",
       "\n",
       "     temperature random_engine has_illegal illegal_move  \\\n",
       "755          0.0         False        True         Qxe4   \n",
       "757          0.0         False        True         Rxh1   \n",
       "758          0.0         False        True         Rxh6   \n",
       "759          0.0         False        True         Bxh6   \n",
       "760          0.0         False        True          Bg8   \n",
       "762          0.0         False       False                \n",
       "763          0.0         False        True         Rxf6   \n",
       "764          0.0         False        True           c4   \n",
       "765          0.0         False        True         cxd6   \n",
       "766          0.0         False        True         Qxg5   \n",
       "767          0.0         False        True        Rxf7+   \n",
       "768          0.0         False        True         Nxd1   \n",
       "769          0.0         False        True         Nxe1   \n",
       "770          0.0         False       False                \n",
       "771          0.0         False        True         Qxe4   \n",
       "772          0.0         False        True          Bh4   \n",
       "773          0.0         False        True         Qxf6   \n",
       "774          0.0         False        True         exf6   \n",
       "775          0.0         False        True        Nxe5+   \n",
       "776          0.0         False        True         Bxc6   \n",
       "777          0.0         False        True         Bxf6   \n",
       "778          0.0         False        True         cxb7   \n",
       "779          0.0         False        True         Kxe1   \n",
       "780          0.0         False        True          Rd8   \n",
       "781          0.0         False        True          O-O   \n",
       "782          0.0         False        True         cxb7   \n",
       "784          0.0         False        True        Bxd6+   \n",
       "785          0.0         False        True         Qxf5   \n",
       "786          0.0         False        True        Nxf7+   \n",
       "787          0.0         False        True         exd6   \n",
       "\n",
       "                                        folder_name pgn_base_moves  \\\n",
       "755  games/game5f552356-e569-481a-8bdf-1dafa5cad6b5                  \n",
       "757  games/game0646c65e-786d-4c0a-8cb0-46883c81cee7                  \n",
       "758  games/gamee2ab9315-21e3-4d4a-89fc-1dbce2060cb7                  \n",
       "759  games/gameaec9eb9a-3ce6-4e51-af85-b2d1b5432a58                  \n",
       "760  games/game8db0c871-75c4-4782-b6dc-ef44e1513eba                  \n",
       "762  games/gamedf9ad5a3-e5f3-4f77-a7d2-816e16df32ab                  \n",
       "763  games/gamea60d049e-a949-45ba-8f8d-e0da62892c99                  \n",
       "764  games/game000df145-c8f1-4570-a57f-d8d79807d0fa                  \n",
       "765  games/game9a01a28b-0b9b-4ac7-a27a-635ddcac67cb                  \n",
       "766  games/gamefd422ced-0c4f-46f2-b8b8-4ce40f5f0b6b                  \n",
       "767  games/game8634bbb1-7a3a-46fd-acad-7b9d2829cd9e                  \n",
       "768  games/game80c7e9bf-3159-41e6-a10b-7cdce2a013bc                  \n",
       "769  games/gameb71e67b4-7463-482a-a951-a77d9254865a                  \n",
       "770  games/gamea9168c1b-a985-4794-b8cf-f68cdbf2122b                  \n",
       "771  games/game5cae331f-043b-488f-8240-3833e30db899                  \n",
       "772  games/game3af69992-0ddc-41d3-b867-c394dbc2ba5b                  \n",
       "773  games/game28a77f57-e350-43e2-a9d9-ae9b0bf98f6e                  \n",
       "774  games/game2ad399e6-0fa0-41c8-9aec-0ee92bf5b84a                  \n",
       "775  games/gamea88f3e66-8175-4613-bd56-bea0241e3418                  \n",
       "776  games/gamefdb72f62-aaf6-40dd-98de-7f6f4bc6a405                  \n",
       "777  games/gamebe4015b3-16f0-45ba-bf2d-f66d531974be                  \n",
       "778  games/game19160e40-7636-410a-a798-33a9ce14279e                  \n",
       "779  games/gamee93d33af-f08a-4b80-99bc-abb3ac93a30a                  \n",
       "780  games/gamee378cea2-80da-49b4-9308-72657a59b7c9                  \n",
       "781  games/gameab89e6f2-378c-4e94-b94e-027d55a90472                  \n",
       "782  games/game7259eae1-35fb-4f8d-9ebd-fa71766c23a1                  \n",
       "784  games/gamea1f9d208-d8cb-4239-8a05-cc137bd61181                  \n",
       "785  games/game11506edb-6818-4ac6-9928-5413243c2984                  \n",
       "786  games/game350da87f-85d8-4190-ac52-0ab54ede330a                  \n",
       "787  games/game39b35c3c-e08e-4d1e-a7a0-b5af1188c32f                  \n",
       "\n",
       "     base_pgn_prompt  \n",
       "755            False  \n",
       "757            False  \n",
       "758            False  \n",
       "759            False  \n",
       "760            False  \n",
       "762            False  \n",
       "763            False  \n",
       "764            False  \n",
       "765            False  \n",
       "766            False  \n",
       "767            False  \n",
       "768            False  \n",
       "769            False  \n",
       "770            False  \n",
       "771            False  \n",
       "772            False  \n",
       "773            False  \n",
       "774            False  \n",
       "775            False  \n",
       "776            False  \n",
       "777            False  \n",
       "778            False  \n",
       "779            False  \n",
       "780            False  \n",
       "781            False  \n",
       "782            False  \n",
       "784            False  \n",
       "785            False  \n",
       "786            False  \n",
       "787            False  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"(white == 'gpt-3.5-turbo' or black == 'gpt-3.5-turbo')\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "04183c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1366.1344692187176"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computation_Elo(df.query(\"(white == 'gpt-4' or black == 'gpt-4') and has_illegal == False\"), model_name='gpt-4')"
   ]
  }
 ],
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