Chess_Predictor.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HamburgermanA3445/chesspredictionmodels/blob/main/Chess_Predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "chessData.csv can be downloaded through https://www.kaggle.com/datasets/ronakbadhe/chess-evaluations?resource=download"
      ],
      "metadata": {
        "id": "cS0NqOXXZIa8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uqsqcHdyAOrw"
      },
      "outputs": [],
      "source": [
        "import copy\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import fetch_california_housing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('chessData.csv')"
      ],
      "metadata": {
        "id": "FzCRK4ajt7QW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "f9e277f9-2c4c-4919-b534-19a000ff24a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-7ae8adb400ab>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'chessData.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'chessData.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "itrqthicvt1U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evals = df['Evaluation'].head(100)\n",
        "pos = df['FEN'].head(100)\n",
        "d=0\n",
        "b={\n",
        "    'p': 1,\n",
        "    'b': 3,\n",
        "    'n': 3,\n",
        "    'r': 5,\n",
        "    'q': 9,\n",
        "    'k': 100,\n",
        "    'P': -1,\n",
        "    'B': -3,\n",
        "    'N': -3,\n",
        "    'R': -5,\n",
        "    'Q': -9,\n",
        "    'K': -100\n",
        "}\n",
        "\n",
        "for c in range(len(evals)):\n",
        "  evals[c]=int(evals[c].replace('#',''))\n",
        "\n",
        "for x in range(len(pos)):\n",
        "  temp=[]\n",
        "  for z in list(filter(None, pos[x].split(' ')[0].split('/'))):\n",
        "      for y in z:\n",
        "          if y.isnumeric():\n",
        "              for a in list('0'*int(y)):\n",
        "                  d+=int(a)\n",
        "          else:\n",
        "              d+=b[y]\n",
        "      temp.append(d)\n",
        "      d=0\n",
        "  pos[x]=temp"
      ],
      "metadata": {
        "id": "7eQ1wT4myqY7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = pos.tolist(), evals.tolist()"
      ],
      "metadata": {
        "id": "570m5XMQ1lBK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, shuffle=True)\n",
        "X_train = torch.tensor(X_train, dtype=torch.float32)\n",
        "y_train = torch.tensor(y_train, dtype=torch.float32).reshape(-1, 1)\n",
        "X_test = torch.tensor(X_test, dtype=torch.float32)\n",
        "y_test = torch.tensor(y_test, dtype=torch.float32).reshape(-1, 1)"
      ],
      "metadata": {
        "id": "LU3KaiDqKvBt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = nn.Sequential(\n",
        "    nn.Linear(8, 24),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(24, 12),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(12, 6),\n",
        "    nn.ReLU(),\n",
        "    nn.Linear(6, 1)\n",
        ")\n",
        "\n",
        "loss_fn = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.0001)\n",
        "\n",
        "n_epochs = 10000\n",
        "batch_size = 10\n",
        "batch_start = torch.arange(0, len(X_train), batch_size)\n",
        "\n",
        "best_mse = np.inf\n",
        "best_weights = None\n",
        "history = []"
      ],
      "metadata": {
        "id": "xCNef4EAKvLx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "outputId": "69605e12-307d-43ba-cc07-033bbe4ed3fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-796b9bed479b>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mn_epochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mbatch_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mbest_mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(n_epochs):\n",
        "    model.train()\n",
        "    with tqdm.tqdm(batch_start, unit=\"batch\", mininterval=0, disable=True) as bar:\n",
        "        bar.set_description(f\"Epoch {epoch}\")\n",
        "        for start in bar:\n",
        "            X_batch = X_train[start:start+batch_size]\n",
        "            y_batch = y_train[start:start+batch_size]\n",
        "            y_pred = model(X_batch)\n",
        "            loss = loss_fn(y_pred, y_batch)\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            bar.set_postfix(mse=float(loss))\n",
        "    model.eval()\n",
        "    y_pred = model(X_test)\n",
        "    mse = loss_fn(y_pred, y_test)\n",
        "    mse = float(mse)\n",
        "    history.append(mse)\n",
        "    if mse < best_mse:\n",
        "        best_mse = mse\n",
        "        best_weights = copy.deepcopy(model.state_dict())"
      ],
      "metadata": {
        "id": "U3vPzKbSKvTP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(best_weights)\n",
        "print(\"MSE: %.2f\" % best_mse)\n",
        "print(\"RMSE: %.2f\" % np.sqrt(best_mse))\n",
        "plt.plot(history)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "S5jFRfPzKvXA",
        "outputId": "530aab1c-605d-4f82-c60a-3c63ea07f44b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 28776.35\n",
            "RMSE: 169.64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "u7UgKw_aaDl8",
        "outputId": "c72c2b9d-b7f8-45ef-80fe-0825ba5d106c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-1f8b88459e61>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    }
  ]
}