{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "kBcnQ90G8_o-"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Import libaries\n"
      ],
      "metadata": {
        "id": "38OGDbSI84N-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install skops"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "XfXjrEjx6dOM",
        "outputId": "e93b1a5f-c269-4c3c-e6db-c6ece761c7c4"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting skops\n",
            "  Downloading skops-0.10.0-py3-none-any.whl.metadata (5.8 kB)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.10/dist-packages (from skops) (1.5.2)\n",
            "Requirement already satisfied: huggingface-hub>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from skops) (0.26.2)\n",
            "Requirement already satisfied: tabulate>=0.8.8 in /usr/local/lib/python3.10/dist-packages (from skops) (0.9.0)\n",
            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from skops) (24.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (3.16.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (2024.10.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (6.0.2)\n",
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            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (4.66.6)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (4.12.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (3.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (2024.8.30)\n",
            "Downloading skops-0.10.0-py3-none-any.whl (121 kB)\n",
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/121.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━\u001b[0m \u001b[32m112.6/121.9 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.9/121.9 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: skops\n",
            "Successfully installed skops-0.10.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zrmBieIvzkwr"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from pathlib import Path\n",
        "from tempfile import mkdtemp, mkstemp\n",
        "\n",
        "import skops.io as sio\n",
        "from skops import card, hub_utils"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load datasets"
      ],
      "metadata": {
        "id": "kBcnQ90G8_o-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mpl_df = pd.read_csv(\"/content/mpl_id_s14.csv\")\n",
        "attributes_df = pd.read_csv(\"/content/mlbb_heroes_attribute.csv\")"
      ],
      "metadata": {
        "id": "c4JKbn5x7_JW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Drop unused columns"
      ],
      "metadata": {
        "id": "wTu-lqvP9EaI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# drop unused columns\n",
        "mpl_df.drop(columns=['no','week','date','match','game','match_length','blue_team','red_team'], inplace=True)\n",
        "attributes_df.drop(columns=['id','main_role','secondary_role','main_damage_type'], inplace=True)"
      ],
      "metadata": {
        "id": "1t6Viu528I1t"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Def functions to feature enginering"
      ],
      "metadata": {
        "id": "f_hLrJ1O9H_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to add sum attributes for each side\n",
        "def calculate_side_features(mpl_df, attribute_df):\n",
        "    # Create a hero mapping dictionary for fast lookups\n",
        "    attribute_dict = attribute_df.set_index('hero')[['durability', 'offense', 'control_effects', 'difficulty', 'early', 'mid', 'late']].to_dict('index')\n",
        "\n",
        "    # Internal function to calculate the sum of attributes from one side (blue or red)\n",
        "    def sum_attributes(row, side):\n",
        "        positions = ['explaner', 'jungler', 'midlaner', 'goldlaner', 'roamer']\n",
        "        features = ['durability', 'offense', 'control_effects', 'difficulty', 'early', 'mid', 'late']\n",
        "        side_sums = {f\"{side}_{feature}\": 0 for feature in features}\n",
        "\n",
        "        for pos in positions:\n",
        "            hero = row[f\"{side}_{pos}\"]\n",
        "            if hero in attribute_dict:\n",
        "                for feature in features:\n",
        "                    side_sums[f\"{side}_{feature}\"] += attribute_dict[hero][feature]\n",
        "\n",
        "        return pd.Series(side_sums)\n",
        "\n",
        "    # Apply the above function to each row in mpl_df\n",
        "    blue_features = mpl_df.apply(lambda row: sum_attributes(row, 'blue'), axis=1)\n",
        "    red_features = mpl_df.apply(lambda row: sum_attributes(row, 'red'), axis=1)\n",
        "\n",
        "    # Merge the results into mpl_df\n",
        "    mpl_df = pd.concat([mpl_df, blue_features, red_features], axis=1)\n",
        "\n",
        "    # Calculate the total power spike for each side\n",
        "    mpl_df['blue_total_power_spike'] = mpl_df[['blue_early', 'blue_mid', 'blue_late']].sum(axis=1)\n",
        "    mpl_df['red_total_power_spike'] = mpl_df[['red_early', 'red_mid', 'red_late']].sum(axis=1)\n",
        "\n",
        "    return mpl_df"
      ],
      "metadata": {
        "id": "EUVghJRP8LvX"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Functions for feature engineering on DataFrame\n",
        "def perform_feature_engineering(mpl_df):\n",
        "    # Feature engineering based on attribute differences\n",
        "    mpl_df['durability_diff'] = mpl_df['blue_durability'] - mpl_df['red_durability']\n",
        "    mpl_df['offense_diff'] = mpl_df['blue_offense'] - mpl_df['red_offense']\n",
        "    mpl_df['control_effects_diff'] = mpl_df['blue_control_effects'] - mpl_df['red_control_effects']\n",
        "    mpl_df['difficulty_diff'] = mpl_df['blue_difficulty'] - mpl_df['red_difficulty']\n",
        "    mpl_df['power_spike_diff'] = mpl_df['blue_total_power_spike'] - mpl_df['red_total_power_spike']\n",
        "\n",
        "    # Average value for role per team\n",
        "    mpl_df['blue_avg_durability'] = mpl_df['blue_durability'] / 5\n",
        "    mpl_df['red_avg_durability'] = mpl_df['red_durability'] / 5\n",
        "    mpl_df['blue_avg_offense'] = mpl_df['blue_offense'] / 5\n",
        "    mpl_df['red_avg_offense'] = mpl_df['red_offense'] / 5\n",
        "\n",
        "    # Aggressiveness score\n",
        "    mpl_df['blue_aggressiveness_score'] = mpl_df['blue_offense'] / mpl_df['blue_durability']\n",
        "    mpl_df['red_aggressiveness_score'] = mpl_df['red_offense'] / mpl_df['red_durability']\n",
        "\n",
        "    # Early-mid and mid-late comparison for the blue team\n",
        "    mpl_df['blue_early_mid_ratio'] = np.where(mpl_df['blue_mid'] == 0, 1, mpl_df['blue_early'] / mpl_df['blue_mid'])\n",
        "    mpl_df['blue_mid_late_ratio'] = np.where(mpl_df['blue_late'] == 0, 1, mpl_df['blue_mid'] / mpl_df['blue_late'])\n",
        "\n",
        "    # Early-mid and mid-late comparison for the red team\n",
        "    mpl_df['red_early_mid_ratio'] = np.where(mpl_df['red_mid'] == 0, 1, mpl_df['red_early'] / mpl_df['red_mid'])\n",
        "    mpl_df['red_mid_late_ratio'] = np.where(mpl_df['red_late'] == 0, 1, mpl_df['red_mid'] / mpl_df['red_late'])\n",
        "\n",
        "    # Drop unused position columns\n",
        "    mpl_df.drop(columns=[\n",
        "        'blue_explaner', 'blue_jungler', 'blue_midlaner', 'blue_goldlaner', 'blue_roamer',\n",
        "        'red_explaner', 'red_jungler', 'red_midlaner', 'red_goldlaner', 'red_roamer'\n",
        "    ], inplace=True)\n",
        "\n",
        "    # Drop unnecessary columns for the model\n",
        "    mpl_df = mpl_df.drop(columns=[\n",
        "        'blue_durability', 'blue_offense', 'blue_control_effects', 'blue_difficulty',\n",
        "        'red_durability', 'red_offense', 'red_control_effects', 'red_difficulty',\n",
        "        'blue_total_power_spike',\n",
        "        'blue_early', 'blue_mid', 'blue_late',\n",
        "        'red_total_power_spike',\n",
        "        'red_early', 'red_mid', 'red_late'\n",
        "    ])\n",
        "\n",
        "    return mpl_df"
      ],
      "metadata": {
        "id": "3IdkY9Qh8P32"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature enginering"
      ],
      "metadata": {
        "id": "R2zkJH4n9O9r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate mpl_df and attributes_df\n",
        "mpl_attr_df = calculate_side_features(mpl_df, attributes_df)\n",
        "mpl_attr_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "ROqemk878N8_",
        "outputId": "db58df78-28c3-40cd-fbb9-c66aaeb321e5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  blue_explaner blue_jungler blue_midlaner blue_goldlaner blue_roamer  \\\n",
              "0         Edith       Julian        Luo Yi         Moskov    Minotaur   \n",
              "1        Thamuz        Nolan     Valentina         Harith    Minotaur   \n",
              "2         Hylos       Julian     Valentina         Claude       Edith   \n",
              "3        X.Borg       Julian     Valentina         Moskov      Arlott   \n",
              "4       Paquito       Julian     Valentina         Moskov       Hylos   \n",
              "\n",
              "  red_explaner red_jungler red_midlaner red_goldlaner red_roamer  ...  \\\n",
              "0       Arlott       Roger    Valentina        Harith       Chou  ...   \n",
              "1       Arlott         Joy       Vexana         Roger      Edith  ...   \n",
              "2         Ruby       Nolan       Vexana        Moskov     Khufra  ...   \n",
              "3        Hylos       Roger        Zhask        Claude  Guinevere  ...   \n",
              "4       X.Borg       Roger      Novaria        Harith  Guinevere  ...   \n",
              "\n",
              "  blue_late  red_durability  red_offense  red_control_effects  red_difficulty  \\\n",
              "0         3              26           27                   23              30   \n",
              "1         1              24           29                   22              23   \n",
              "2         2              25           25                   32              21   \n",
              "3         2              24           27                   21              22   \n",
              "4         1              25           26                   13              24   \n",
              "\n",
              "   red_early  red_mid  red_late  blue_total_power_spike  red_total_power_spike  \n",
              "0          3        3         2                       8                      8  \n",
              "1          2        4         2                       8                      8  \n",
              "2          3        3         1                       9                      7  \n",
              "3          2        4         3                       8                      9  \n",
              "4          1        5         4                       7                     10  \n",
              "\n",
              "[5 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-897a9488-b977-41bb-b15e-13011eb27010\" class=\"colab-df-container\">\n",
              "    <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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>blue_explaner</th>\n",
              "      <th>blue_jungler</th>\n",
              "      <th>blue_midlaner</th>\n",
              "      <th>blue_goldlaner</th>\n",
              "      <th>blue_roamer</th>\n",
              "      <th>red_explaner</th>\n",
              "      <th>red_jungler</th>\n",
              "      <th>red_midlaner</th>\n",
              "      <th>red_goldlaner</th>\n",
              "      <th>red_roamer</th>\n",
              "      <th>...</th>\n",
              "      <th>blue_late</th>\n",
              "      <th>red_durability</th>\n",
              "      <th>red_offense</th>\n",
              "      <th>red_control_effects</th>\n",
              "      <th>red_difficulty</th>\n",
              "      <th>red_early</th>\n",
              "      <th>red_mid</th>\n",
              "      <th>red_late</th>\n",
              "      <th>blue_total_power_spike</th>\n",
              "      <th>red_total_power_spike</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Edith</td>\n",
              "      <td>Julian</td>\n",
              "      <td>Luo Yi</td>\n",
              "      <td>Moskov</td>\n",
              "      <td>Minotaur</td>\n",
              "      <td>Arlott</td>\n",
              "      <td>Roger</td>\n",
              "      <td>Valentina</td>\n",
              "      <td>Harith</td>\n",
              "      <td>Chou</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Thamuz</td>\n",
              "      <td>Nolan</td>\n",
              "      <td>Valentina</td>\n",
              "      <td>Harith</td>\n",
              "      <td>Minotaur</td>\n",
              "      <td>Arlott</td>\n",
              "      <td>Joy</td>\n",
              "      <td>Vexana</td>\n",
              "      <td>Roger</td>\n",
              "      <td>Edith</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>29</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hylos</td>\n",
              "      <td>Julian</td>\n",
              "      <td>Valentina</td>\n",
              "      <td>Claude</td>\n",
              "      <td>Edith</td>\n",
              "      <td>Ruby</td>\n",
              "      <td>Nolan</td>\n",
              "      <td>Vexana</td>\n",
              "      <td>Moskov</td>\n",
              "      <td>Khufra</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>32</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>X.Borg</td>\n",
              "      <td>Julian</td>\n",
              "      <td>Valentina</td>\n",
              "      <td>Moskov</td>\n",
              "      <td>Arlott</td>\n",
              "      <td>Hylos</td>\n",
              "      <td>Roger</td>\n",
              "      <td>Zhask</td>\n",
              "      <td>Claude</td>\n",
              "      <td>Guinevere</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>24</td>\n",
              "      <td>27</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Paquito</td>\n",
              "      <td>Julian</td>\n",
              "      <td>Valentina</td>\n",
              "      <td>Moskov</td>\n",
              "      <td>Hylos</td>\n",
              "      <td>X.Borg</td>\n",
              "      <td>Roger</td>\n",
              "      <td>Novaria</td>\n",
              "      <td>Harith</td>\n",
              "      <td>Guinevere</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>13</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-897a9488-b977-41bb-b15e-13011eb27010')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-897a9488-b977-41bb-b15e-13011eb27010 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-897a9488-b977-41bb-b15e-13011eb27010');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c9832f8d-9419-4ef8-ae65-0bcede1a4201\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c9832f8d-9419-4ef8-ae65-0bcede1a4201')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c9832f8d-9419-4ef8-ae65-0bcede1a4201 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mpl_attr_df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# feature enginering\n",
        "mpl_df_transformed = perform_feature_engineering(mpl_attr_df)\n",
        "mpl_df_transformed = mpl_df_transformed.round(3)\n",
        "mpl_df_transformed.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "IvGErtHo8Sk3",
        "outputId": "f5c666ff-9150-46d8-eec6-15be90cecf5b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  result  durability_diff  offense_diff  control_effects_diff  \\\n",
              "0    RED                1            -1                    12   \n",
              "1   BLUE                5            -4                    -1   \n",
              "2   BLUE                6             1                     2   \n",
              "3    RED                3             3                     6   \n",
              "4   BLUE                4             2                    20   \n",
              "\n",
              "   difficulty_diff  power_spike_diff  blue_avg_durability  red_avg_durability  \\\n",
              "0              -11                 0                  5.4                 5.2   \n",
              "1                4                 0                  5.8                 4.8   \n",
              "2                1                 2                  6.2                 5.0   \n",
              "3                2                -1                  5.4                 4.8   \n",
              "4                2                -3                  5.8                 5.0   \n",
              "\n",
              "   blue_avg_offense  red_avg_offense  blue_aggressiveness_score  \\\n",
              "0               5.2              5.4                      0.963   \n",
              "1               5.0              5.8                      0.862   \n",
              "2               5.2              5.0                      0.839   \n",
              "3               6.0              5.4                      1.111   \n",
              "4               5.6              5.2                      0.966   \n",
              "\n",
              "   red_aggressiveness_score  blue_early_mid_ratio  blue_mid_late_ratio  \\\n",
              "0                     1.038                 0.667                  1.0   \n",
              "1                     1.208                 0.750                  4.0   \n",
              "2                     1.000                 0.750                  2.0   \n",
              "3                     1.125                 1.000                  1.5   \n",
              "4                     1.040                 1.000                  3.0   \n",
              "\n",
              "   red_early_mid_ratio  red_mid_late_ratio  \n",
              "0                  1.0               1.500  \n",
              "1                  0.5               2.000  \n",
              "2                  1.0               3.000  \n",
              "3                  0.5               1.333  \n",
              "4                  0.2               1.250  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f29c7a75-8ca3-4bbc-9b0d-b4ab96087fe4\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>result</th>\n",
              "      <th>durability_diff</th>\n",
              "      <th>offense_diff</th>\n",
              "      <th>control_effects_diff</th>\n",
              "      <th>difficulty_diff</th>\n",
              "      <th>power_spike_diff</th>\n",
              "      <th>blue_avg_durability</th>\n",
              "      <th>red_avg_durability</th>\n",
              "      <th>blue_avg_offense</th>\n",
              "      <th>red_avg_offense</th>\n",
              "      <th>blue_aggressiveness_score</th>\n",
              "      <th>red_aggressiveness_score</th>\n",
              "      <th>blue_early_mid_ratio</th>\n",
              "      <th>blue_mid_late_ratio</th>\n",
              "      <th>red_early_mid_ratio</th>\n",
              "      <th>red_mid_late_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RED</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>12</td>\n",
              "      <td>-11</td>\n",
              "      <td>0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>5.2</td>\n",
              "      <td>5.2</td>\n",
              "      <td>5.4</td>\n",
              "      <td>0.963</td>\n",
              "      <td>1.038</td>\n",
              "      <td>0.667</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BLUE</td>\n",
              "      <td>5</td>\n",
              "      <td>-4</td>\n",
              "      <td>-1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.8</td>\n",
              "      <td>0.862</td>\n",
              "      <td>1.208</td>\n",
              "      <td>0.750</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BLUE</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.839</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.750</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RED</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>5.4</td>\n",
              "      <td>4.8</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.4</td>\n",
              "      <td>1.111</td>\n",
              "      <td>1.125</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BLUE</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>2</td>\n",
              "      <td>-3</td>\n",
              "      <td>5.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.6</td>\n",
              "      <td>5.2</td>\n",
              "      <td>0.966</td>\n",
              "      <td>1.040</td>\n",
              "      <td>1.000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f29c7a75-8ca3-4bbc-9b0d-b4ab96087fe4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f29c7a75-8ca3-4bbc-9b0d-b4ab96087fe4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-69e85242-a49e-44c4-b4fc-3022ec5ccf84\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69e85242-a49e-44c4-b4fc-3022ec5ccf84')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-69e85242-a49e-44c4-b4fc-3022ec5ccf84 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mpl_df_transformed",
              "summary": "{\n  \"name\": \"mpl_df_transformed\",\n  \"rows\": 212,\n  \"fields\": [\n    {\n      \"column\": \"result\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"BLUE\",\n          \"RED\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"durability_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": -14,\n        \"max\": 11,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          -3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"offense_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": -12,\n        \"max\": 19,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          5,\n          -8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"control_effects_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": -22,\n        \"max\": 20,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          -14,\n          -2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"difficulty_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": -16,\n        \"max\": 18,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          16,\n          17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"power_spike_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": -4,\n        \"max\": 4,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blue_avg_durability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6214503060388611,\n        \"min\": 3.6,\n        \"max\": 7.4,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.4,\n          4.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"red_avg_durability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6430055511285113,\n        \"min\": 3.8,\n        \"max\": 7.4,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          5.2,\n          4.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blue_avg_offense\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6480312945415349,\n        \"min\": 3.2,\n        \"max\": 7.2,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          5.2,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"red_avg_offense\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6736092644226174,\n        \"min\": 2.8,\n        \"max\": 7.2,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          5.4,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blue_aggressiveness_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21542079891962987,\n        \"min\": 0.571,\n        \"max\": 2.0,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          1.259,\n          1.318\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"red_aggressiveness_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2080560009696417,\n        \"min\": 0.452,\n        \"max\": 1.565,\n        \"num_unique_values\": 109,\n        \"samples\": [\n          0.571,\n          0.774\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blue_early_mid_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39883052760267895,\n        \"min\": 0.2,\n        \"max\": 2.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          0.333,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"blue_mid_late_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9611989055114264,\n        \"min\": 0.667,\n        \"max\": 5.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1.25,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"red_early_mid_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3949263231878436,\n        \"min\": 0.2,\n        \"max\": 2.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1.25,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"red_mid_late_ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0457086449633677,\n        \"min\": 0.5,\n        \"max\": 5.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          5.0,\n          0.667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split dataset"
      ],
      "metadata": {
        "id": "2cgtljuR9SFN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = mpl_df_transformed.drop(columns=['result'])  # All columns except 'result'\n",
        "y = mpl_df_transformed['result']  # target column"
      ],
      "metadata": {
        "id": "ChhFq-XCINNy"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split dataset 75/25\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)"
      ],
      "metadata": {
        "id": "qWReEl_f9Wm9"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the size of each dataset\n",
        "print(f\"Training set size: {X_train.shape[0]}\")\n",
        "print(f\"Test set size: {X_test.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2PsEW4l7--cQ",
        "outputId": "d60c7d5b-e89a-4b9f-fbc6-8c8f11a9e9d1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 159\n",
            "Test set size: 53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "F7NBmeyTCTfR",
        "outputId": "3b27b220-1615-46bd-e726-9239284bd40d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "result\n",
              "RED     80\n",
              "BLUE    79\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>result</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RED</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BLUE</th>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train model"
      ],
      "metadata": {
        "id": "MTr1h0Ts9WH4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train model with Random Forest\n",
        "model = RandomForestClassifier(\n",
        "    random_state=91,\n",
        "    n_estimators=50,\n",
        "    max_depth=2,\n",
        "    min_samples_split=2,\n",
        "    min_samples_leaf=8,\n",
        ")\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "hsrMfXt59gWP",
        "outputId": "32d56ac8-797b-44b3-a090-c8eebf143696"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=2, min_samples_leaf=8, n_estimators=50,\n",
              "                       random_state=91)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=2, min_samples_leaf=8, n_estimators=50,\n",
              "                       random_state=91)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=2, min_samples_leaf=8, n_estimators=50,\n",
              "                       random_state=91)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference"
      ],
      "metadata": {
        "id": "Y6IkbI5C9wUv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on test data\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "7zPF9MGyCIBf"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred)\n",
        "print(y_test.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfgEB06c9lyW",
        "outputId": "a0320a3f-3fc9-4a41-a681-807b70c9f0be"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['BLUE' 'RED' 'BLUE' 'RED' 'BLUE' 'RED' 'RED' 'RED' 'RED' 'RED' 'RED'\n",
            " 'RED' 'RED' 'RED' 'RED' 'BLUE' 'RED' 'BLUE' 'BLUE' 'BLUE' 'BLUE' 'BLUE'\n",
            " 'RED' 'RED' 'BLUE' 'BLUE' 'BLUE' 'BLUE' 'RED' 'BLUE' 'BLUE' 'RED' 'BLUE'\n",
            " 'BLUE' 'BLUE' 'RED' 'RED' 'RED' 'RED' 'BLUE' 'RED' 'RED' 'RED' 'BLUE'\n",
            " 'BLUE' 'BLUE' 'RED' 'RED' 'BLUE' 'BLUE' 'BLUE' 'RED' 'RED']\n",
            "['BLUE' 'RED' 'BLUE' 'RED' 'BLUE' 'RED' 'BLUE' 'RED' 'BLUE' 'RED' 'BLUE'\n",
            " 'RED' 'RED' 'BLUE' 'RED' 'BLUE' 'RED' 'BLUE' 'RED' 'BLUE' 'BLUE' 'RED'\n",
            " 'BLUE' 'RED' 'BLUE' 'RED' 'RED' 'BLUE' 'RED' 'BLUE' 'RED' 'RED' 'BLUE'\n",
            " 'RED' 'RED' 'BLUE' 'BLUE' 'RED' 'RED' 'BLUE' 'RED' 'RED' 'BLUE' 'BLUE'\n",
            " 'BLUE' 'BLUE' 'BLUE' 'RED' 'BLUE' 'BLUE' 'BLUE' 'RED' 'RED']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuration\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "print()\n",
        "\n",
        "# Confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred,labels=model.classes_)\n",
        "print('Confusion Matrix:')\n",
        "print(cm)\n",
        "print()\n",
        "\n",
        "# Classification report\n",
        "report = classification_report(y_test, y_pred)\n",
        "print('Classification Report:')\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57LnREhz-I-X",
        "outputId": "064695ae-967c-4e0c-e81f-56c56708d360"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.70\n",
            "\n",
            "Confusion Matrix:\n",
            "[[18  9]\n",
            " [ 7 19]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        BLUE       0.72      0.67      0.69        27\n",
            "         RED       0.68      0.73      0.70        26\n",
            "\n",
            "    accuracy                           0.70        53\n",
            "   macro avg       0.70      0.70      0.70        53\n",
            "weighted avg       0.70      0.70      0.70        53\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialize a local repository"
      ],
      "metadata": {
        "id": "Xj4NjZshI8Z5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# _, pkl_name = mkstemp(prefix=\"skops-\", suffix=\".pkl\")\n",
        "# local_repo = mkdtemp(prefix=\"skops-\")\n",
        "\n",
        "# with open(pkl_name, mode=\"bw\") as f:\n",
        "#     sio.dump(model, file=f)\n",
        "\n",
        "pkl_name = \"model.pkl\"\n",
        "local_repo = \"mpl-id-s14-prediction\"\n",
        "\n",
        "with open(pkl_name, mode=\"bw\") as f:\n",
        "    sio.dump(model, file=f)\n",
        "\n",
        "hub_utils.init(\n",
        "    model=pkl_name,\n",
        "    requirements=[f\"scikit-learn={sklearn.__version__}\"],\n",
        "    dst=local_repo,\n",
        "    task=\"tabular-classification\",\n",
        "    data=X_test,\n",
        ")\n",
        "\n",
        "if \"__file__\" in locals():  # __file__ not defined during docs built\n",
        "    # Add this script itself to the files to be uploaded for reproducibility\n",
        "    hub_utils.add_files(__file__, dst=local_repo)"
      ],
      "metadata": {
        "id": "7ajeBC7hI3uj"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create model card"
      ],
      "metadata": {
        "id": "AJhkAc9NRzL0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))"
      ],
      "metadata": {
        "id": "HZFv0iwiR00h"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "limitations = (\"This model is only trained with MPL ID Season 14 data\")\n",
        "model_description = (\n",
        "    \"This is a RandomForestClassifier model trained on mpl_id_s14 dataset.\"\n",
        ")\n",
        "model_card_authors = \"z4fL\"\n",
        "model_card.add(\n",
        "    folded=False,\n",
        "    **{\n",
        "        \"Model Card Authors\": model_card_authors,\n",
        "        \"Limitations\": limitations,\n",
        "        \"Model description\": model_description,\n",
        "    },\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Fx0zmHXR41H",
        "outputId": "de418a76-ed81-4259-8e3b-fa75eb30925b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Card(\n",
              "  model=RandomForestClassifier(max_depth=2..., n_estimators=50, random_state=91),\n",
              "  metadata.library_name=sklearn,\n",
              "  metadata.tags=['sklearn', 'skops', 'tabular-classification'],\n",
              "  metadata.model_format=pickle,\n",
              "  metadata.model_file=model.pkl,\n",
              "  metadata.widget=[{...}],\n",
              "  Model description=This is a RandomForestClassi...rained on mpl_id_s14 dataset.,\n",
              "  Model description/Training Procedure/Hyperparameters=TableSection(19x2),\n",
              "  Model description/Training Procedure/Model Plot=<style>#sk-co...v></div></div>,\n",
              "  Model Card Authors=z4fL,\n",
              "  Limitations=This model is only trained with MPL ID Season 14 data,\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a confusion matrix\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
        "disp.plot()\n",
        "\n",
        "# save the plot\n",
        "plt.savefig(Path(local_repo) / \"confusion_matrix.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "evvbXoFob7jY",
        "outputId": "08b5f9fb-2a25-46ca-c0bf-3532496a657e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f18c4ca9900>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the plot will be written to the model card under the name confusion_matrix\n",
        "# we pass the path of the plot itself\n",
        "model_card.add_plot(confusion_matrix=\"confusion_matrix.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "10pfrWyqdAgS",
        "outputId": "49d6d8a3-2ee9-4983-9370-e72feb5fb0b6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Card(\n",
              "  model=RandomForestClassifier(max_depth=2..., n_estimators=50, random_state=91),\n",
              "  metadata.library_name=sklearn,\n",
              "  metadata.tags=['sklearn', 'skops', 'tabular-classification'],\n",
              "  metadata.model_format=pickle,\n",
              "  metadata.model_file=model.pkl,\n",
              "  metadata.widget=[{...}],\n",
              "  Model description=This is a RandomForestClassi...rained on mpl_id_s14 dataset.,\n",
              "  Model description/Training Procedure/Hyperparameters=TableSection(19x2),\n",
              "  Model description/Training Procedure/Model Plot=<style>#sk-co...v></div></div>,\n",
              "  Model Card Authors=z4fL,\n",
              "  Limitations=This model is only trained with MPL ID Season 14 data,\n",
              "  confusion_matrix=PlotSection(confusion_matrix.png),\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the card\n",
        "model_card.save(Path(local_repo) / \"README.md\")"
      ],
      "metadata": {
        "id": "ANOK0vridJ8l"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Push repository"
      ],
      "metadata": {
        "id": "ih5BTPsmehkR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# if the repository doesn't exist remotely on the Hugging Face Hub, it will be created when we set create_remote to True\n",
        "repo_id = \"z4fL/prediction-win-mpl-id-s14\"\n",
        "hub_utils.push(\n",
        "    repo_id=repo_id,\n",
        "    source=local_repo,\n",
        "    token=\"\",\n",
        "    commit_message=\"initial commit\",\n",
        ")"
      ],
      "metadata": {
        "id": "OnY7ehGteHAX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}