C_Edwards_Data_Drag_final_project.ipynb

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      "source": [
        "# Data is a Drag\n",
        "## ICS 635 Machine Learning: Final Project\n",
        "### by: Candace Edwards\n",
        "Github: https://github.com/CS-Edwards/ml_final <br>\n",
        "Notebook: [Link](https://colab.research.google.com/drive/1LilcQYvIRcnSTwTYoAQsbQidPFrZrLcR?usp=sharing)"
      ],
      "metadata": {
        "id": "fi5qmFgyFH7E"
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    {
      "cell_type": "markdown",
      "source": [
        "### Notebook Setup"
      ],
      "metadata": {
        "id": "x3PaLgaZH-fq"
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    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade scikit-learn"
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      "execution_count": 1,
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.1.0)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.22.4)\n"
          ]
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      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "UlB4zK8cqsNp"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, LeaveOneOut, StratifiedKFold\n",
        "from sklearn.metrics import make_scorer, log_loss, f1_score, confusion_matrix, ConfusionMatrixDisplay,accuracy_score, precision_score, recall_score, roc_curve, auc\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn import tree\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "import warnings\n",
        "from sklearn.exceptions import UndefinedMetricWarning, FitFailedWarning #, UserWarning\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# Read in dataset\n",
        "#\n",
        "data = pd.read_csv('data/rpdr_data_final.csv')"
      ],
      "metadata": {
        "id": "SahjfK4aq4R0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Prep:"
      ],
      "metadata": {
        "id": "YBM-pDH_VdV6"
      }
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      "cell_type": "code",
      "source": [
        "data.head(10)"
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              "  season                contestant  age         dob                  hometown  \\\n",
              "0    S01         BEBE ZAHARA BENET   28  1981-03-20    Minneapolis, Minnesota   \n",
              "1    S01              NINA FLOWERS   34  1974-02-22      Bayamón, Puerto Rico   \n",
              "2    S01         REBECCA GLASSCOCK   26  1983-05-25  Fort Lauderdale, Florida   \n",
              "3    S01                   SHANNEL   26  1979-07-03         Las Vegas, Nevada   \n",
              "4    S01                    ONGINA   26  1982-01-06   Los Angeles, California   \n",
              "5    S01                      JADE   32  1984-11-18         Chicago, Illinois   \n",
              "6    S01                   AKASHIA   32  1985-02-19           Cleveland, Ohio   \n",
              "7    S01              TAMMIE BROWN   36  1980-09-15   Los Angeles, California   \n",
              "8    S01  VICTORIA PORKCHOP PARKER   39  1969-01-16   Raleigh, North Carolina   \n",
              "9    S02              TYRA SANCHEZ   21  1988-04-22          Orlando, Florida   \n",
              "\n",
              "   LOW  WIN  HIGH  BTM  SAFE  OUT  total_mcwins  total_mc  avg_rank  \\\n",
              "0    0    3     1    1     2    0             0         4       1.0   \n",
              "1    1    1     4    0     1    0             1         4       2.0   \n",
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          "metadata": {},
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        "data.info()"
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        "id": "Ki51x4xpr_vh",
        "outputId": "73b75019-4dd7-4929-d4d1-d9a71b05e63d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 184 entries, 0 to 183\n",
            "Data columns (total 15 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   season         184 non-null    object \n",
            " 1   contestant     184 non-null    object \n",
            " 2   age            184 non-null    int64  \n",
            " 3   dob            184 non-null    object \n",
            " 4   hometown       184 non-null    object \n",
            " 5   LOW            184 non-null    int64  \n",
            " 6   WIN            184 non-null    int64  \n",
            " 7   HIGH           184 non-null    int64  \n",
            " 8   BTM            184 non-null    int64  \n",
            " 9   SAFE           184 non-null    int64  \n",
            " 10  OUT            184 non-null    int64  \n",
            " 11  total_mcwins   184 non-null    int64  \n",
            " 12  total_mc       184 non-null    int64  \n",
            " 13  avg_rank       183 non-null    float64\n",
            " 14  season_winner  184 non-null    int64  \n",
            "dtypes: float64(1), int64(10), object(4)\n",
            "memory usage: 21.7+ KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "print(data.isna().sum())"
      ],
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        },
        "id": "stWkN2SIB6aL",
        "outputId": "675e4754-2602-4597-82b3-62600f2b7354"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "season           0\n",
            "contestant       0\n",
            "age              0\n",
            "dob              0\n",
            "hometown         0\n",
            "LOW              0\n",
            "WIN              0\n",
            "HIGH             0\n",
            "BTM              0\n",
            "SAFE             0\n",
            "OUT              0\n",
            "total_mcwins     0\n",
            "total_mc         0\n",
            "avg_rank         1\n",
            "season_winner    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['avg_rank'] = data['avg_rank'].fillna(-1)\n",
        "print(data.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FlLyGPuCCfO",
        "outputId": "7bd7cae3-e703-4eb5-993b-847d56c80c9c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "season           0\n",
            "contestant       0\n",
            "age              0\n",
            "dob              0\n",
            "hometown         0\n",
            "LOW              0\n",
            "WIN              0\n",
            "HIGH             0\n",
            "BTM              0\n",
            "SAFE             0\n",
            "OUT              0\n",
            "total_mcwins     0\n",
            "total_mc         0\n",
            "avg_rank         0\n",
            "season_winner    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1. calculate mini challenge win percentage\n",
        "#2. calulate  safe to top ratio\n",
        "#3. train,test,split; y_pred is the winner "
      ],
      "metadata": {
        "id": "TBSBkh5Eu2X5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "#Normalize mini challenge wins as percentage\n",
        "#\n",
        "data['mcwins_pct'] = data['total_mcwins']/data['total_mc']\n",
        "data['mcwins_pct'] = data['mcwins_pct'].fillna(-1)"
      ],
      "metadata": {
        "id": "8UkF9uz-ts2b"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#NEW METRIC: draft notes\n",
        "\n",
        "#top to safe proportion: top placements relative to safe placements\n",
        "# scale 0-1: 0 = never in the top but safe,  1 = never safe but in the top ; -1 to handle NaN values = never in the top and never safe\n",
        "#initially tried ratio with smoothing parameter but that didn't work June Jumbalaya who had 0 safe and 0 high, had higher score than Shannel 2 safe 1 high\n",
        "#smoothing = 0.1\n",
        "#data['top_safe_ratio']= (data['WIN']+data['HIGH']+smoothing)/(data['SAFE'] + smoothing)\n",
        "\n",
        "data['top_safe_ratio']= (data['WIN']+data['HIGH'])/(data['SAFE']+ data['WIN']+data['HIGH'])\n",
        "data['top_safe_ratio'] = data['top_safe_ratio'].fillna(-1)\n",
        "\n"
      ],
      "metadata": {
        "id": "_J2RBpjFuMMh"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "C-45pFiKxReq",
        "outputId": "1b167251-318c-4a80-99f5-374dc53a5712"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    season                contestant  age         dob  \\\n",
              "0      S01         BEBE ZAHARA BENET   28  1981-03-20   \n",
              "1      S01              NINA FLOWERS   34  1974-02-22   \n",
              "2      S01         REBECCA GLASSCOCK   26  1983-05-25   \n",
              "3      S01                   SHANNEL   26  1979-07-03   \n",
              "4      S01                    ONGINA   26  1982-01-06   \n",
              "..     ...                       ...  ...         ...   \n",
              "179    S14           MADDY MORPHOSIS   26  1994-09-02   \n",
              "180    S14               ORION STORY   25  1996-06-25   \n",
              "181    S14  KORNBREAD THE SNACK JETE   29  1992-01-14   \n",
              "182    S14             ALYSSA HUNTER   26  1995-02-19   \n",
              "183    S14            JUNE JAMBALAYA   29           0   \n",
              "\n",
              "                     hometown  LOW  WIN  HIGH  BTM  SAFE  OUT  total_mcwins  \\\n",
              "0      Minneapolis, Minnesota    0    3     1    1     2    0             0   \n",
              "1        Bayamón, Puerto Rico    1    1     4    0     1    0             1   \n",
              "2    Fort Lauderdale, Florida    1    1     1    3     1    0             1   \n",
              "3           Las Vegas, Nevada    1    0     1    2     2    1             0   \n",
              "4     Los Angeles, California    0    2     2    1     0    2             1   \n",
              "..                        ...  ...  ...   ...  ...   ...  ...           ...   \n",
              "179    Fayetteville, Arkansas    0    0     0    2     3    9             0   \n",
              "180    Grand Rapids, Michigan    0    0     0    2     2   10             0   \n",
              "181   Los Angeles, California    0    1     0    0     2   10             0   \n",
              "182       Catano, Puerto Rico    0    0     0    1     2   11             0   \n",
              "183   Los Angeles, California    0    0     0    2     0   12             0   \n",
              "\n",
              "     total_mc  avg_rank  season_winner  mcwins_pct  top_safe_ratio  \n",
              "0           4       1.0              1        0.00        0.666667  \n",
              "1           4       2.0              0        0.25        0.833333  \n",
              "2           4       3.0              0        0.25        0.666667  \n",
              "3           4       4.0              0        0.00        0.333333  \n",
              "4           4       5.0              0        0.25        1.000000  \n",
              "..        ...       ...            ...         ...             ...  \n",
              "179         6      10.0              0        0.00        0.000000  \n",
              "180         6      11.0              0        0.00        0.000000  \n",
              "181         6      12.0              0        0.00        0.333333  \n",
              "182         6      13.0              0        0.00        0.000000  \n",
              "183         6      14.0              0        0.00       -1.000000  \n",
              "\n",
              "[184 rows x 17 columns]"
            ],
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              "\n",
              "  <div id=\"df-2934237a-ce5f-4039-ba53-3c1a7f2e5a8a\">\n",
              "    <div 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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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>contestant</th>\n",
              "      <th>age</th>\n",
              "      <th>dob</th>\n",
              "      <th>hometown</th>\n",
              "      <th>LOW</th>\n",
              "      <th>WIN</th>\n",
              "      <th>HIGH</th>\n",
              "      <th>BTM</th>\n",
              "      <th>SAFE</th>\n",
              "      <th>OUT</th>\n",
              "      <th>total_mcwins</th>\n",
              "      <th>total_mc</th>\n",
              "      <th>avg_rank</th>\n",
              "      <th>season_winner</th>\n",
              "      <th>mcwins_pct</th>\n",
              "      <th>top_safe_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>S01</td>\n",
              "      <td>BEBE ZAHARA BENET</td>\n",
              "      <td>28</td>\n",
              "      <td>1981-03-20</td>\n",
              "      <td>Minneapolis, Minnesota</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S01</td>\n",
              "      <td>NINA FLOWERS</td>\n",
              "      <td>34</td>\n",
              "      <td>1974-02-22</td>\n",
              "      <td>Bayamón, Puerto Rico</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>S01</td>\n",
              "      <td>REBECCA GLASSCOCK</td>\n",
              "      <td>26</td>\n",
              "      <td>1983-05-25</td>\n",
              "      <td>Fort Lauderdale, Florida</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>S01</td>\n",
              "      <td>SHANNEL</td>\n",
              "      <td>26</td>\n",
              "      <td>1979-07-03</td>\n",
              "      <td>Las Vegas, Nevada</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>S01</td>\n",
              "      <td>ONGINA</td>\n",
              "      <td>26</td>\n",
              "      <td>1982-01-06</td>\n",
              "      <td>Los Angeles, California</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>S14</td>\n",
              "      <td>MADDY MORPHOSIS</td>\n",
              "      <td>26</td>\n",
              "      <td>1994-09-02</td>\n",
              "      <td>Fayetteville, Arkansas</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>S14</td>\n",
              "      <td>ORION STORY</td>\n",
              "      <td>25</td>\n",
              "      <td>1996-06-25</td>\n",
              "      <td>Grand Rapids, Michigan</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>S14</td>\n",
              "      <td>KORNBREAD THE SNACK JETE</td>\n",
              "      <td>29</td>\n",
              "      <td>1992-01-14</td>\n",
              "      <td>Los Angeles, California</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>S14</td>\n",
              "      <td>ALYSSA HUNTER</td>\n",
              "      <td>26</td>\n",
              "      <td>1995-02-19</td>\n",
              "      <td>Catano, Puerto Rico</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>S14</td>\n",
              "      <td>JUNE JAMBALAYA</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>Los Angeles, California</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>184 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2934237a-ce5f-4039-ba53-3c1a7f2e5a8a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2934237a-ce5f-4039-ba53-3c1a7f2e5a8a');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# Remove all non-numeric data\n",
        "#\n",
        "model_data = data.drop(columns=['season','contestant','dob','hometown'])"
      ],
      "metadata": {
        "id": "BTSqZxFGxWEB"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_data.info()"
      ],
      "metadata": {
        "id": "14lk10geFKXU",
        "outputId": "7592d500-2937-459a-a0db-b8b73cdea064",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 184 entries, 0 to 183\n",
            "Data columns (total 13 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             184 non-null    int64  \n",
            " 1   LOW             184 non-null    int64  \n",
            " 2   WIN             184 non-null    int64  \n",
            " 3   HIGH            184 non-null    int64  \n",
            " 4   BTM             184 non-null    int64  \n",
            " 5   SAFE            184 non-null    int64  \n",
            " 6   OUT             184 non-null    int64  \n",
            " 7   total_mcwins    184 non-null    int64  \n",
            " 8   total_mc        184 non-null    int64  \n",
            " 9   avg_rank        184 non-null    float64\n",
            " 10  season_winner   184 non-null    int64  \n",
            " 11  mcwins_pct      184 non-null    float64\n",
            " 12  top_safe_ratio  184 non-null    float64\n",
            "dtypes: float64(3), int64(10)\n",
            "memory usage: 18.8 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print(model_data.isna().sum())"
      ],
      "metadata": {
        "id": "J6oU3_ZvCbcc"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target = 'season_winner'\n",
        "X = model_data.drop(target, axis=1)\n",
        "y = model_data[target]"
      ],
      "metadata": {
        "id": "qq5LsZLm7Fqp"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_state = 33"
      ],
      "metadata": {
        "id": "bR4NYPMCukvw"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train,  y_test = train_test_split(X,y,test_size=0.30,random_state= random_state)"
      ],
      "metadata": {
        "id": "9MEu55kQ6pvN"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Experiment 1: Vanilla Models\n"
      ],
      "metadata": {
        "id": "DOL-Yn-r6oAt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def metrics_summary(y_test,y_pred,y_prob):\n",
        "  '''\n",
        "  Calculate metrics to evaluate the performance of a binary classification model.\n",
        "\n",
        "  Returns:\n",
        "      A tuple: first object, contains the values of\n",
        "      various evaluation metrics. second object, the confusion matrix.\n",
        "  '''\n",
        "  tn,fp,fn,tp = confusion_matrix(y_test,y_pred).ravel()\n",
        "  accuracy = (tp + tn)/(tp+tn+fp+fn)\n",
        "  recall = tp/(tp+fn)\n",
        "  precision = tp/(tp+fp)\n",
        "  f1 = 2 * (precision * recall) / (precision + recall)\n",
        "  logloss = log_loss(y_test, y_prob)\n",
        "\n",
        "  metrics = {'Accuracy': accuracy, 'Recall': recall, 'Precision': precision,'F1': f1, 'Log-loss': logloss}\n",
        "  \n",
        "\n",
        "  return metrics, confusion_matrix(y_test,y_pred)\n",
        "\n",
        "  "
      ],
      "metadata": {
        "id": "2xXvnh842zwf"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# Train and Test vanilla classifers in loop.\n",
        "# Create 2D dictionary for metrics and confusion matrix\n",
        "#\n",
        "classifiers = {'log_reg':LogisticRegression(random_state=random_state), \n",
        "               'd_tree': tree.DecisionTreeClassifier(random_state=random_state),\n",
        "               'n_bayes': GaussianNB(),\n",
        "               'r_forest': RandomForestClassifier(random_state=random_state) }\n",
        "\n",
        "metrics_dict ={}\n",
        "confusion_matrix_dict = {}\n",
        "\n",
        "for key,value in classifiers.items():\n",
        "  clf = value\n",
        "  clf.fit(X_train,y_train)\n",
        "  y_prob = clf.predict_proba(X_test)[:,1]\n",
        "  y_pred = clf.predict(X_test)\n",
        "  metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "  metrics_dict[key] = metrics\n",
        "  confusion_matrix_dict[key] = matrix\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yn-H4X6Ux7IW",
        "outputId": "20683e66-8e19-4026-99a3-560879e9e99a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_metrics = pd.DataFrame(metrics_dict)\n",
        "print('Results: Experiment 1 - Vanilla Classifers ')\n",
        "print(df_metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDfr59GsFuOg",
        "outputId": "f48b9a56-795a-4d5e-f808-98dd60c1d733"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results: Experiment 1 - Vanilla Classifers \n",
            "            log_reg        d_tree       n_bayes  r_forest\n",
            "Accuracy   0.946429  1.000000e+00  1.000000e+00  1.000000\n",
            "Recall     0.250000  1.000000e+00  1.000000e+00  1.000000\n",
            "Precision  1.000000  1.000000e+00  1.000000e+00  1.000000\n",
            "F1         0.400000  1.000000e+00  1.000000e+00  1.000000\n",
            "Log-loss   0.070506  2.220446e-16  7.649225e-08  0.039307\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udl8aHRrTIRH",
        "outputId": "52e7fd8e-dc74-4a17-db9e-82c11da72ca3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'log_reg': array([[52,  0],\n",
              "        [ 3,  1]]),\n",
              " 'd_tree': array([[52,  0],\n",
              "        [ 0,  4]]),\n",
              " 'n_bayes': array([[52,  0],\n",
              "        [ 0,  4]]),\n",
              " 'r_forest': array([[52,  0],\n",
              "        [ 0,  4]])}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'log_reg'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix_dict[key])\n",
        "disp.plot(cmap =plt.cm.RdPu )\n",
        "plt.title(f'Confusion Matrix: {key}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UqkBsZTQKUKP",
        "outputId": "80267280-b794-4ceb-fe0a-7ccdfe032e64"
      },
      "execution_count": 22,
      "outputs": [
        {
          "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": [
        "key = 'd_tree'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix_dict[key])\n",
        "disp.plot(cmap =plt.cm.RdPu )\n",
        "plt.title(f'Confusion Matrix: {key}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "VGe5FG8BFC-S",
        "outputId": "d18a0337-2135-4588-e92d-685609a9518b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "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": [
        "key = 'n_bayes'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix_dict[key])\n",
        "disp.plot(cmap =plt.cm.RdPu )\n",
        "plt.title(f'Confusion Matrix: {key}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "4nxqhewtFJcm",
        "outputId": "de561fc1-413f-4529-f525-3a3afaa3bda4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "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": [
        "key = 'r_forest'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix_dict[key])\n",
        "disp.plot(cmap =plt.cm.RdPu )\n",
        "plt.title(f'Confusion Matrix: {key}')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HATQaIWr373H",
        "outputId": "29a6eb0a-2bda-4979-a88a-77b81ff7c0b7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "3BqoTVZTx3Jw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiment 2: Cross Validation and Optimization\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "MZ1j7wNAYTFM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# Results Log:per model\n",
        "#\n",
        "\n",
        "logistic_reg_all_dict = {}\n",
        "n_bayes_all_dict = {}\n",
        "d_tree_all_dict = {}\n",
        "r_forest_all_dict = {}\n",
        "\n",
        "#Experiment 1\n",
        "logistic_reg_all_dict['log_reg_1'] = metrics_dict['log_reg']\n",
        "n_bayes_all_dict['n_bayes_1'] = metrics_dict['n_bayes']\n",
        "d_tree_all_dict['d_tree_1'] = metrics_dict['d_tree']\n",
        "r_forest_all_dict['r_forest_1'] = metrics_dict['r_forest']"
      ],
      "metadata": {
        "id": "HcrUOV-qV6Ru"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(logistic_reg_all_dict)"
      ],
      "metadata": {
        "id": "FKX1d6jgXzBt"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# Results Log: Experiment 2a\n",
        "#\n",
        "\n",
        "experiment_2a_best_dict = {}"
      ],
      "metadata": {
        "id": "Dy-1TLiMYGL0"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings(\"ignore\", category=UndefinedMetricWarning)\n",
        "warnings.filterwarnings(\"ignore\", category=FitFailedWarning)\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)"
      ],
      "metadata": {
        "id": "8sOA9Y9vGzqF"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2a. K-Fold Cross Validation"
      ],
      "metadata": {
        "id": "XicZJ7GwZYro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_splits = 5\n",
        "kf = StratifiedKFold(n_splits=n_splits)"
      ],
      "metadata": {
        "id": "nO3xwouhgAia"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Model: Logistic Regression"
      ],
      "metadata": {
        "id": "C4IUh3VCzk_d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#### Logistic Regression\n",
        "#Docs: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
        "\n",
        "clf = LogisticRegression(random_state=random_state)\n",
        "\n",
        "param_grid ={ \n",
        "    'penalty':['l1', 'l2', 'elasticnet'],  #Regularization  \n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],   #Reg strength  \n",
        "    'class_weight':['balanced', None],           #Imbalanced class: weighted \n",
        "    'solver': ['liblinear']\n",
        "   }\n",
        "\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=kf, scoring='f1')\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "print(\"Best F1: {:.4f}\".format(grid_search.best_score_))\n",
        "print(grid_search.best_estimator_)\n",
        "\n",
        "y_prob = grid_search.best_estimator_.predict_proba(X_test)[:,1]\n",
        "y_pred = grid_search.best_estimator_.predict(X_test)\n",
        "metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "print('Results: Experiment 2a_1.: Logistic Regression ')\n",
        "print(pd.DataFrame(metrics,index=[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PPsX_81vZSk8",
        "outputId": "6f3c6afd-6a19-43b1-87ba-54e605b113a8"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'C': 0.01, 'class_weight': 'balanced', 'penalty': 'l1', 'solver': 'liblinear'}\n",
            "Best F1: 0.8076\n",
            "LogisticRegression(C=0.01, class_weight='balanced', penalty='l1',\n",
            "                   random_state=33, solver='liblinear')\n",
            "Results: Experiment 2a_1.: Logistic Regression \n",
            "   Accuracy  Recall  Precision        F1  Log-loss\n",
            "0  0.910714     1.0   0.444444  0.615385  0.407539\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Results\n",
        "logistic_reg_all_dict['log_reg_2a'] = metrics\n",
        "experiment_2a_best_dict['log_reg_2a'] = metrics"
      ],
      "metadata": {
        "id": "72BKsaodS5w1"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'log_reg_2a'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=matrix)\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot confusion matrix\n",
        "disp.plot(cmap=plt.cm.RdPu, ax=axs[0])\n",
        "axs[0].set_title(f'Confusion Matrix: {key}')\n",
        "\n",
        "#ROC and AUROC calculations\n",
        "fpr_1, tpr_1, thresholds = roc_curve(y_test, y_prob)\n",
        "log_reg_2a_roc_auc = auc(fpr_1, tpr_1)\n",
        "\n",
        "# Plot ROC curve\n",
        "axs[1].plot(fpr_1, tpr_1, color='darkorange', label=f'{key} ROC curve (area = {log_reg_2a_roc_auc :.2f})')\n",
        "axs[1].set_xlabel('False Positive Rate')\n",
        "axs[1].set_ylabel('True Positive Rate')\n",
        "axs[1].set_title('Receiver Operating Characteristic (ROC) Curve')\n",
        "axs[1].legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "m1tFP_0pR8cD",
        "outputId": "6385c6fb-837b-4229-cb40-e5d1bcfa88b8"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Model: Naive Bayes"
      ],
      "metadata": {
        "id": "-lr9ojyBzg--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Naive Bayes\n",
        "### Docs: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html\n",
        "\n",
        "clf = GaussianNB()\n",
        "\n",
        "param_grid ={ \n",
        "    'priors':[[0.0815,0.9184], None],  #Regularization  \n",
        "   }\n",
        "\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=kf, scoring='f1')\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "print(\"Best F1: {:.4f}\".format(grid_search.best_score_))\n",
        "print(grid_search.best_estimator_)\n",
        "\n",
        "y_prob = grid_search.best_estimator_.predict_proba(X_test)[:,1]\n",
        "y_pred = grid_search.best_estimator_.predict(X_test)\n",
        "metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "print('Results: Experiment 2a_2.: Naive Bayes ')\n",
        "print(pd.DataFrame(metrics,index=[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7hfby5b4Yl4U",
        "outputId": "d63bc771-315d-43bc-8be5-b5f9136c2bbc"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'priors': None}\n",
            "Best F1: 0.9333\n",
            "GaussianNB()\n",
            "Results: Experiment 2a_2.: Naive Bayes \n",
            "   Accuracy  Recall  Precision   F1      Log-loss\n",
            "0       1.0     1.0        1.0  1.0  7.649225e-08\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_bayes_all_dict['n_bayes_2a'] = metrics\n",
        "experiment_2a_best_dict['n_bayes_2a'] = metrics"
      ],
      "metadata": {
        "id": "609_DfvSj5sf"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'n_bayes_2a'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=matrix)\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot confusion matrix\n",
        "disp.plot(cmap=plt.cm.RdPu, ax=axs[0])\n",
        "axs[0].set_title(f'Confusion Matrix: {key}')\n",
        "\n",
        "#ROC and AUROC calculations\n",
        "fpr_2, tpr_2, thresholds = roc_curve(y_test, y_prob)\n",
        "n_bayes_2a_roc_auc = auc(fpr_2, tpr_2)\n",
        "\n",
        "# Plot ROC curve\n",
        "axs[1].plot(fpr_2, tpr_2, color='#9C3587', label=f'{key} ROC curve (area = {n_bayes_2a_roc_auc :.2f})')\n",
        "axs[1].set_xlabel('False Positive Rate')\n",
        "axs[1].set_ylabel('True Positive Rate')\n",
        "axs[1].set_title('Receiver Operating Characteristic (ROC) Curve')\n",
        "axs[1].legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "3LF_bidFkHZQ",
        "outputId": "2d4925e0-22ac-4077-882b-b4671ef2a666"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Model: Decision Tree"
      ],
      "metadata": {
        "id": "pQEUvEAozalh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Decision Trees\n",
        "### Docs: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
        "\n",
        "clf = tree.DecisionTreeClassifier(random_state=random_state)\n",
        "\n",
        "param_grid ={ \n",
        "    'criterion':[\"gini\", \"entropy\"],  #splitting criterion\n",
        "    'class_weight':[{0:0.9184,1: 0.0815},'balanced', None],    #Imbalanced class: weighted \n",
        "   }\n",
        "\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=kf, scoring='f1')\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "print(\"Best F1: {:.4f}\".format(grid_search.best_score_))\n",
        "print(grid_search.best_estimator_)\n",
        "\n",
        "y_prob = grid_search.best_estimator_.predict_proba(X_test)[:,1]\n",
        "y_pred = grid_search.best_estimator_.predict(X_test)\n",
        "metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "print('Results: Experiment 2a_3.: Decision Tree Classifier ')\n",
        "print(pd.DataFrame(metrics,index=[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEbmyCLrk1yM",
        "outputId": "a8df1680-dcc0-4d16-8a4c-c6a60b3d1b55"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'class_weight': {0: 0.9184, 1: 0.0815}, 'criterion': 'gini'}\n",
            "Best F1: 0.9600\n",
            "DecisionTreeClassifier(class_weight={0: 0.9184, 1: 0.0815}, random_state=33)\n",
            "Results: Experiment 2a_3.: Decision Tree Classifier \n",
            "   Accuracy  Recall  Precision   F1      Log-loss\n",
            "0       1.0     1.0        1.0  1.0  2.220446e-16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d_tree_all_dict['d_tree_2a'] = metrics\n",
        "experiment_2a_best_dict['d_tree_2a'] = metrics"
      ],
      "metadata": {
        "id": "2BEMdBjCmnsC"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'd_tree_2a'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=matrix)\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot confusion matrix\n",
        "disp.plot(cmap=plt.cm.RdPu, ax=axs[0])\n",
        "axs[0].set_title(f'Confusion Matrix: {key}')\n",
        "\n",
        "#ROC and AUROC calculations\n",
        "fpr_3, tpr_3, thresholds = roc_curve(y_test, y_prob)\n",
        "d_trees_2a_roc_auc = auc(fpr_3, tpr_3)\n",
        "\n",
        "# Plot ROC curve\n",
        "axs[1].plot(fpr_3, tpr_3, color='#E53F71', label=f'{key} ROC curve (area = {d_trees_2a_roc_auc :.2f})')\n",
        "axs[1].set_xlabel('False Positive Rate')\n",
        "axs[1].set_ylabel('True Positive Rate')\n",
        "axs[1].set_title('Receiver Operating Characteristic (ROC) Curve')\n",
        "axs[1].legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "zBBAH7amms4B",
        "outputId": "f0e9ee5c-3201-4850-db56-009b479de3c0"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Model: Random Forest"
      ],
      "metadata": {
        "id": "XbEnlz93zU0X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Random Forest\n",
        "### Docs: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
        "\n",
        "clf = RandomForestClassifier(random_state=random_state)\n",
        "\n",
        "param_grid ={ \n",
        "    'criterion':[\"gini\", \"entropy\"],  #splitting criterion\n",
        "    'class_weight':[{0:0.9184,1: 0.0815},'balanced','balanced_subsample', None],    #Imbalanced class: weighted\n",
        "    'bootstrap':[True,False] \n",
        "   }\n",
        "\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=kf, scoring='f1')\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "print(\"Best F1: {:.4f}\".format(grid_search.best_score_))\n",
        "print(grid_search.best_estimator_)\n",
        "\n",
        "y_prob = grid_search.best_estimator_.predict_proba(X_test)[:,1]\n",
        "y_pred = grid_search.best_estimator_.predict(X_test)\n",
        "metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "print('Results: Experiment 2a_4.: Random Forest Classifier ')\n",
        "print(pd.DataFrame(metrics,index=[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xnM3bJnznMlw",
        "outputId": "64e23308-7400-423b-d8e0-e338db3cb87a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'bootstrap': True, 'class_weight': {0: 0.9184, 1: 0.0815}, 'criterion': 'entropy'}\n",
            "Best F1: 0.9333\n",
            "RandomForestClassifier(class_weight={0: 0.9184, 1: 0.0815}, criterion='entropy',\n",
            "                       random_state=33)\n",
            "Results: Experiment 2a_4.: Random Forest Classifier \n",
            "   Accuracy  Recall  Precision   F1  Log-loss\n",
            "0       1.0     1.0        1.0  1.0  0.037536\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r_forest_all_dict['r_forest_2a'] = metrics\n",
        "experiment_2a_best_dict['r_forest_2a'] = metrics"
      ],
      "metadata": {
        "id": "sr5guwozp1ty"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'r_forest_2a'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=matrix)\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot confusion matrix\n",
        "disp.plot(cmap=plt.cm.RdPu, ax=axs[0])\n",
        "axs[0].set_title(f'Confusion Matrix: {key}')\n",
        "\n",
        "#ROC and AUROC calculations\n",
        "fpr_4, tpr_4, thresholds = roc_curve(y_test, y_prob)\n",
        "r_forest_2a_roc_auc = auc(fpr_4, tpr_4)\n",
        "\n",
        "# Plot ROC curve\n",
        "axs[1].plot(fpr_4, tpr_4, color='#3F1651', label=f'{key} ROC curve (area = {r_forest_2a_roc_auc :.2f})')\n",
        "axs[1].set_xlabel('False Positive Rate')\n",
        "axs[1].set_ylabel('True Positive Rate')\n",
        "axs[1].set_title('Receiver Operating Characteristic (ROC) Curve')\n",
        "axs[1].legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Ydy29_LgqAdR",
        "outputId": "de2eb44d-6636-448f-b066-8a2cff4aafb3"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Experiment 2a: Summary Results"
      ],
      "metadata": {
        "id": "quaiOzM72o9a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#print('Results: Experiment 2a')\n",
        "#experiment_2a_best_dict"
      ],
      "metadata": {
        "id": "Ac71y1SuAsdF"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exp_2_results = pd.DataFrame(experiment_2a_best_dict)\n",
        "print('Experiment 2a: Stratified K-Fold Cross Validation')\n",
        "print(exp_2_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45djahcHqjzp",
        "outputId": "e7710630-264a-472a-ff7a-9c672c5fe0d6"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment 2a: Stratified K-Fold Cross Validation\n",
            "           log_reg_2a    n_bayes_2a     d_tree_2a  r_forest_2a\n",
            "Accuracy     0.910714  1.000000e+00  1.000000e+00     1.000000\n",
            "Recall       1.000000  1.000000e+00  1.000000e+00     1.000000\n",
            "Precision    0.444444  1.000000e+00  1.000000e+00     1.000000\n",
            "F1           0.615385  1.000000e+00  1.000000e+00     1.000000\n",
            "Log-loss     0.407539  7.649225e-08  2.220446e-16     0.037536\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Summary ROC curve\n",
        "plt.plot(fpr_1, tpr_1, color='darkorange', label=f'log_reg_2a ROC curve (area = {log_reg_2a_roc_auc :.2f})')\n",
        "plt.plot(fpr_2, tpr_2, color='#9C3587', label=f'n_bayes_2a ROC curve (area = {n_bayes_2a_roc_auc :.2f})')\n",
        "plt.plot(fpr_3, tpr_3, color='#E53F71', label=f'd_trees_2a ROC curve (area = {d_trees_2a_roc_auc :.2f})')\n",
        "plt.plot(fpr_4, tpr_4, color='#3F1651', label=f'r_forest_2a ROC curve (area = {r_forest_2a_roc_auc :.2f})')\n",
        "plt.plot([0, 1], [0, 1], 'k--') #random classifier line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "M_y6VkQXq_RU",
        "outputId": "fae557e8-1c42-419f-8ae3-59af63425fe9"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2b. Leave One Out Cross Validation (LOOCV)"
      ],
      "metadata": {
        "id": "nW_JzgmXZco4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# Results Log: Experiment 2b\n",
        "#\n",
        "\n",
        "experiment_2b_best_dict = {}"
      ],
      "metadata": {
        "id": "MiA5uzNXZmnY"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Model: Logistic Regression"
      ],
      "metadata": {
        "id": "OK9caPNSzPqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Logistic Regression\n",
        "clf = LogisticRegression(random_state=random_state)\n",
        "\n",
        "param_grid ={ \n",
        "    'penalty':['l1', 'l2', 'elasticnet'],  #Regularization  \n",
        "    'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],   #Reg strength  \n",
        "    'class_weight':['balanced', None],           #Imbalanced class: weighted \n",
        "    'solver': ['liblinear']\n",
        "   }\n",
        "\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=LeaveOneOut(), scoring='f1')\n",
        "\n",
        "\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "\n",
        "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "print(\"Best F1: {:.4f}\".format(grid_search.best_score_))\n",
        "print(grid_search.best_estimator_)\n",
        "\n",
        "\n",
        "\n",
        "y_prob = grid_search.best_estimator_.predict_proba(X_test)[:,1]\n",
        "y_pred = grid_search.best_estimator_.predict(X_test)\n",
        "metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "print('Results: Experiment 2b_1.: Logistic Regression ')\n",
        "print(pd.DataFrame(metrics,index=[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7R-DwkT2Zm3h",
        "outputId": "4db42927-f88c-461f-870c-9b3ccfd9ddf1"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'C': 0.001, 'class_weight': 'balanced', 'penalty': 'l2', 'solver': 'liblinear'}\n",
            "Best F1: 0.0781\n",
            "LogisticRegression(C=0.001, class_weight='balanced', random_state=33,\n",
            "                   solver='liblinear')\n",
            "Results: Experiment 2b_1.: Logistic Regression \n",
            "   Accuracy  Recall  Precision        F1  Log-loss\n",
            "0     0.875     1.0   0.363636  0.533333  0.397526\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_reg_all_dict['log_reg_2b'] = metrics\n",
        "experiment_2b_best_dict['log_reg_2b'] = metrics"
      ],
      "metadata": {
        "id": "40wganmCbpVo"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'log_reg_2b'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=matrix)\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot confusion matrix\n",
        "disp.plot(cmap=plt.cm.RdPu, ax=axs[0])\n",
        "axs[0].set_title(f'Confusion Matrix: {key}')\n",
        "\n",
        "#ROC and AUROC calculations\n",
        "fpr_5, tpr_5, thresholds = roc_curve(y_test, y_prob)\n",
        "log_reg_2b_roc_auc = auc(fpr_5, tpr_5)\n",
        "\n",
        "# Plot ROC curve\n",
        "axs[1].plot(fpr_5, tpr_5, color='darkorange', label=f'{key} ROC curve (area = {log_reg_2b_roc_auc :.2f})')\n",
        "axs[1].set_xlabel('False Positive Rate')\n",
        "axs[1].set_ylabel('True Positive Rate')\n",
        "axs[1].set_title('Receiver Operating Characteristic (ROC) Curve')\n",
        "axs[1].legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "P1YDKbhtxSGZ",
        "outputId": "65e5a980-b918-4a4c-b9ff-b9205a443a8f"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Model: Naive Bayes"
      ],
      "metadata": {
        "id": "BEiFQ0brzE6t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Naive Bayes\n",
        "### Docs: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html\n",
        "\n",
        "clf = GaussianNB()\n",
        "\n",
        "param_grid ={ \n",
        "    'priors':[[0.0815,0.9184], None],  #Regularization  \n",
        "   }\n",
        "\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=LeaveOneOut(), scoring='f1')\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "print(\"Best F1: {:.4f}\".format(grid_search.best_score_))\n",
        "print(grid_search.best_estimator_)\n",
        "\n",
        "y_prob = grid_search.best_estimator_.predict_proba(X_test)[:,1]\n",
        "y_pred = grid_search.best_estimator_.predict(X_test)\n",
        "metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "print('Results: Experiment 2a_2.: Naive Bayes ')\n",
        "print(pd.DataFrame(metrics,index=[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vU_1tcCCy2qr",
        "outputId": "029d5fcd-c745-4afd-901e-56527381d8cd"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'priors': None}\n",
            "Best F1: 0.0703\n",
            "GaussianNB()\n",
            "Results: Experiment 2a_2.: Naive Bayes \n",
            "   Accuracy  Recall  Precision   F1      Log-loss\n",
            "0       1.0     1.0        1.0  1.0  7.649225e-08\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_bayes_all_dict['n_bayes_2b'] = metrics\n",
        "experiment_2b_best_dict['n_bayes_2b'] = metrics"
      ],
      "metadata": {
        "id": "4CLAmUxMz7cn"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'n_bayes_2b'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=matrix)\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot confusion matrix\n",
        "disp.plot(cmap=plt.cm.RdPu, ax=axs[0])\n",
        "axs[0].set_title(f'Confusion Matrix: {key}')\n",
        "\n",
        "#ROC and AUROC calculations\n",
        "fpr_6, tpr_6, thresholds = roc_curve(y_test, y_prob)\n",
        "n_bayes_2b_roc_auc = auc(fpr_6, tpr_6)\n",
        "\n",
        "# Plot ROC curve\n",
        "axs[1].plot(fpr_6, tpr_6, color='#9C3587', label=f'{key} ROC curve (area = {n_bayes_2b_roc_auc :.2f})')\n",
        "axs[1].set_xlabel('False Positive Rate')\n",
        "axs[1].set_ylabel('True Positive Rate')\n",
        "axs[1].set_title('Receiver Operating Characteristic (ROC) Curve')\n",
        "axs[1].legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "EQ7HoW8n0Cdv",
        "outputId": "7afcc6dc-42fd-4648-d075-4fcfd6cf168d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Model: Decision Tree"
      ],
      "metadata": {
        "id": "Xfkq5-Hx0fbe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Decision Trees\n",
        "### Docs: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
        "\n",
        "clf = tree.DecisionTreeClassifier(random_state=random_state)\n",
        "\n",
        "param_grid ={ \n",
        "    'criterion':[\"gini\", \"entropy\"],  #splitting criterion\n",
        "    'class_weight':[{0:0.9184,1: 0.0815},'balanced', None],    #Imbalanced class: weighted \n",
        "   }\n",
        "\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=LeaveOneOut(), scoring='f1')\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "print(\"Best F1: {:.4f}\".format(grid_search.best_score_))\n",
        "print(grid_search.best_estimator_)\n",
        "\n",
        "y_prob = grid_search.best_estimator_.predict_proba(X_test)[:,1]\n",
        "y_pred = grid_search.best_estimator_.predict(X_test)\n",
        "metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "print('Results: Experiment 2a_3.: Decision Tree Classifier ')\n",
        "print(pd.DataFrame(metrics,index=[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TXTK01HL0rv1",
        "outputId": "3644eb8d-f9a2-4a87-e4da-e84bbb8409ef"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'class_weight': {0: 0.9184, 1: 0.0815}, 'criterion': 'entropy'}\n",
            "Best F1: 0.0781\n",
            "DecisionTreeClassifier(class_weight={0: 0.9184, 1: 0.0815}, criterion='entropy',\n",
            "                       random_state=33)\n",
            "Results: Experiment 2a_3.: Decision Tree Classifier \n",
            "   Accuracy  Recall  Precision   F1      Log-loss\n",
            "0       1.0     1.0        1.0  1.0  2.220446e-16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d_tree_all_dict['d_tree_2b'] = metrics\n",
        "experiment_2b_best_dict['d_tree_2b'] = metrics"
      ],
      "metadata": {
        "id": "Jz3i2CwI0r3p"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'd_tree_2b'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=matrix)\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot confusion matrix\n",
        "disp.plot(cmap=plt.cm.RdPu, ax=axs[0])\n",
        "axs[0].set_title(f'Confusion Matrix: {key}')\n",
        "\n",
        "#ROC and AUROC calculations\n",
        "fpr_7, tpr_7, thresholds = roc_curve(y_test, y_prob)\n",
        "d_trees_2b_roc_auc = auc(fpr_7, tpr_7)\n",
        "\n",
        "# Plot ROC curve\n",
        "axs[1].plot(fpr_7, tpr_7, color='#E53F71', label=f'{key} ROC curve (area = {d_trees_2b_roc_auc :.2f})')\n",
        "axs[1].set_xlabel('False Positive Rate')\n",
        "axs[1].set_ylabel('True Positive Rate')\n",
        "axs[1].set_title('Receiver Operating Characteristic (ROC) Curve')\n",
        "axs[1].legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jrTv4lDr0r-4",
        "outputId": "9b1fc6d9-d942-4202-9908-678174cc6512"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Model: Random Forest"
      ],
      "metadata": {
        "id": "vSnXF2DG0s-Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Random Forest\n",
        "### Docs: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
        "\n",
        "clf = RandomForestClassifier(random_state=random_state)\n",
        "\n",
        "param_grid ={ \n",
        "    'criterion':[\"gini\", \"entropy\"],  #splitting criterion\n",
        "    'class_weight':[{0:0.9184,1: 0.0815},'balanced','balanced_subsample', None],    #Imbalanced class: weighted\n",
        "    'bootstrap':[True,False] \n",
        "   }\n",
        "\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=LeaveOneOut(), scoring='f1')\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
        "print(\"Best F1: {:.4f}\".format(grid_search.best_score_))\n",
        "print(grid_search.best_estimator_)\n",
        "\n",
        "y_prob = grid_search.best_estimator_.predict_proba(X_test)[:,1]\n",
        "y_pred = grid_search.best_estimator_.predict(X_test)\n",
        "metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "print('Results: Experiment 2b_4.: Random Forest Classifier ')\n",
        "print(pd.DataFrame(metrics,index=[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJmkalKp0zFa",
        "outputId": "fef31517-9a4d-455b-aa97-96403cdf9848"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters:  {'bootstrap': True, 'class_weight': {0: 0.9184, 1: 0.0815}, 'criterion': 'gini'}\n",
            "Best F1: 0.0703\n",
            "RandomForestClassifier(class_weight={0: 0.9184, 1: 0.0815}, random_state=33)\n",
            "Results: Experiment 2b_4.: Random Forest Classifier \n",
            "   Accuracy  Recall  Precision        F1  Log-loss\n",
            "0  0.982143    0.75        1.0  0.857143  0.040056\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r_forest_all_dict['r_forest_2b'] = metrics\n",
        "experiment_2b_best_dict['r_forest_2b'] = metrics"
      ],
      "metadata": {
        "id": "aZM_Ikmi0zQh"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "key = 'r_forest_2b'\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=matrix)\n",
        "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot confusion matrix\n",
        "disp.plot(cmap=plt.cm.RdPu, ax=axs[0])\n",
        "axs[0].set_title(f'Confusion Matrix: {key}')\n",
        "\n",
        "#ROC and AUROC calculations\n",
        "fpr_8, tpr_8, thresholds = roc_curve(y_test, y_prob)\n",
        "r_forest_2b_roc_auc = auc(fpr_8, tpr_8)\n",
        "\n",
        "# Plot ROC curve\n",
        "axs[1].plot(fpr_8, tpr_8, color='#3F1651', label=f'{key} ROC curve (area = {r_forest_2b_roc_auc :.2f})')\n",
        "axs[1].set_xlabel('False Positive Rate')\n",
        "axs[1].set_ylabel('True Positive Rate')\n",
        "axs[1].set_title('Receiver Operating Characteristic (ROC) Curve')\n",
        "axs[1].legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "AWaoj7nf0zbk",
        "outputId": "c0955560-69c3-47d9-ddaa-7e0b72e1d407"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Experiment 2b: Summary Results"
      ],
      "metadata": {
        "id": "-b6iheW63KXj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#print('Results: Experiment 2b')\n",
        "#experiment_2b_best_dict"
      ],
      "metadata": {
        "id": "tNVYWY-hBFhe"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exp_2b_results = pd.DataFrame(experiment_2b_best_dict)\n",
        "print('Experiment 2b: Leave One Out Cross Validation')\n",
        "print(exp_2b_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLnXWhfl3U75",
        "outputId": "ad155dd7-2829-482e-a483-5091a77ad84d"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment 2b: Leave One Out Cross Validation\n",
            "           log_reg_2b    n_bayes_2b     d_tree_2b  r_forest_2b\n",
            "Accuracy     0.875000  1.000000e+00  1.000000e+00     0.982143\n",
            "Recall       1.000000  1.000000e+00  1.000000e+00     0.750000\n",
            "Precision    0.363636  1.000000e+00  1.000000e+00     1.000000\n",
            "F1           0.533333  1.000000e+00  1.000000e+00     0.857143\n",
            "Log-loss     0.397526  7.649225e-08  2.220446e-16     0.040056\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Summary ROC curve\n",
        "plt.plot(fpr_5, tpr_5, color='darkorange', label=f'log_reg_2a ROC curve (area = {log_reg_2b_roc_auc :.2f})')\n",
        "plt.plot(fpr_6, tpr_6, color='#9C3587', label=f'n_bayes_2a ROC curve (area = {n_bayes_2b_roc_auc :.2f})')\n",
        "plt.plot(fpr_7, tpr_7, color='#E53F71', label=f'd_trees_2a ROC curve (area = {d_trees_2b_roc_auc :.2f})')\n",
        "plt.plot(fpr_8, tpr_8, color='#3F1651', label=f'r_forest_2a ROC curve (area = {r_forest_2b_roc_auc :.2f})')\n",
        "plt.plot([0, 1], [0, 1], 'k--') #random classifier line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "8fZqrwyL3VEw",
        "outputId": "bc614092-8a1a-4d62-8b27-bb502d9a65a3"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Experiment 2: Results by Model"
      ],
      "metadata": {
        "id": "JLYuwRV930Rt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#view log_r dcict ##delete later/or move\n",
        "log_reg_results_all = pd.DataFrame(logistic_reg_all_dict)\n",
        "print('Results by Model: Logistic Regression')\n",
        "print(log_reg_results_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQTO4RAWcILW",
        "outputId": "0fe8909e-48ad-461d-b15b-a90f9295722a"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results by Model: Logistic Regression\n",
            "           log_reg_1  log_reg_2a  log_reg_2b\n",
            "Accuracy    0.946429    0.910714    0.875000\n",
            "Recall      0.250000    1.000000    1.000000\n",
            "Precision   1.000000    0.444444    0.363636\n",
            "F1          0.400000    0.615385    0.533333\n",
            "Log-loss    0.070506    0.407539    0.397526\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_bayes_results_all = pd.DataFrame(n_bayes_all_dict)\n",
        "print('Results by Model:  Naive Bayes ')\n",
        "print(n_bayes_results_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uax7yWH36md",
        "outputId": "4559e84d-f44c-45ea-a1e1-bb45c24814fa"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results by Model:  Naive Bayes \n",
            "              n_bayes_1    n_bayes_2a    n_bayes_2b\n",
            "Accuracy   1.000000e+00  1.000000e+00  1.000000e+00\n",
            "Recall     1.000000e+00  1.000000e+00  1.000000e+00\n",
            "Precision  1.000000e+00  1.000000e+00  1.000000e+00\n",
            "F1         1.000000e+00  1.000000e+00  1.000000e+00\n",
            "Log-loss   7.649225e-08  7.649225e-08  7.649225e-08\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d_tree_results_all = pd.DataFrame(d_tree_all_dict)\n",
        "print('Results by Model: Decision Trees')\n",
        "print(d_tree_results_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6Ty_K5X4BUb",
        "outputId": "bdf6e413-a616-4fbf-a131-df7d3ddcb36a"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results by Model: Decision Trees\n",
            "               d_tree_1     d_tree_2a     d_tree_2b\n",
            "Accuracy   1.000000e+00  1.000000e+00  1.000000e+00\n",
            "Recall     1.000000e+00  1.000000e+00  1.000000e+00\n",
            "Precision  1.000000e+00  1.000000e+00  1.000000e+00\n",
            "F1         1.000000e+00  1.000000e+00  1.000000e+00\n",
            "Log-loss   2.220446e-16  2.220446e-16  2.220446e-16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r_forest_results_all = pd.DataFrame(r_forest_all_dict)\n",
        "print('Results by Model: Random Forest')\n",
        "print(r_forest_results_all)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBbTu27i4JNp",
        "outputId": "3763ab89-422b-448e-b1b9-e1d74f451084"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results by Model: Random Forest\n",
            "           r_forest_1  r_forest_2a  r_forest_2b\n",
            "Accuracy     1.000000     1.000000     0.982143\n",
            "Recall       1.000000     1.000000     0.750000\n",
            "Precision    1.000000     1.000000     1.000000\n",
            "F1           1.000000     1.000000     0.857143\n",
            "Log-loss     0.039307     0.037536     0.040056\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Experiment 3: Top Model"
      ],
      "metadata": {
        "id": "6wi-j0ZsWn9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# RESET: Train Test Split, and random state, test_size\n",
        "#\n",
        "random_state = 18\n",
        "X_train, X_test, y_train,  y_test = train_test_split(X,y,test_size=0.45,random_state= random_state)"
      ],
      "metadata": {
        "id": "yk9YevzJ4prq"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "# Best Classifications based on log-loss from previous experiments\n",
        "#\n",
        "classifiers = {'log_reg':LogisticRegression(random_state=33), #log_reg_1\n",
        "                'd_tree': tree.DecisionTreeClassifier(class_weight={0: 0.9184, 1: 0.0815}, random_state=33), #d_tree_2a\n",
        "                'n_bayes': GaussianNB(),\n",
        "                'r_forest': RandomForestClassifier(class_weight={0: 0.9184, 1: 0.0815}, criterion='entropy',random_state=33) #r_forest_2a\n",
        "                }\n"
      ],
      "metadata": {
        "id": "DKkjiXJf4pvB"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_metrics_dict ={}\n",
        "final_confusion_matrix_dict = {}\n",
        "\n",
        "for key,value in classifiers.items():\n",
        "  clf = value\n",
        "  clf.fit(X_train,y_train)\n",
        "  y_prob = clf.predict_proba(X_test)[:,1]\n",
        "  y_pred = clf.predict(X_test)\n",
        "  metrics,matrix = metrics_summary(y_test,y_pred,y_prob)\n",
        "\n",
        "  final_metrics_dict[key] = metrics\n",
        "  final_confusion_matrix_dict[key] = matrix"
      ],
      "metadata": {
        "id": "KvbK6b8I4py8"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Experiment 3:  Results"
      ],
      "metadata": {
        "id": "dpsBIjNrBNtf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#print('Results: Experiment 3')\n",
        "#final_metrics_dict"
      ],
      "metadata": {
        "id": "RxmefOzCBWsL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exp_3_results = pd.DataFrame(final_metrics_dict)\n",
        "print('Experiment 3: Best Overall Classifier')\n",
        "print(exp_3_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5IwMtk59C1OQ",
        "outputId": "b644dd0d-1b73-4ca6-952b-a5ae104ef5c8"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment 3: Best Overall Classifier\n",
            "            log_reg        d_tree       n_bayes  r_forest\n",
            "Accuracy   0.975904  1.000000e+00  1.000000e+00  0.987952\n",
            "Recall     0.714286  1.000000e+00  1.000000e+00  0.857143\n",
            "Precision  1.000000  1.000000e+00  1.000000e+00  1.000000\n",
            "F1         0.833333  1.000000e+00  1.000000e+00  0.923077\n",
            "Log-loss   0.060990  2.220446e-16  5.603559e-08  0.044188\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tree.plot_tree(classifiers['d_tree'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "UkeSKzDsM4mY",
        "outputId": "a97f1eef-c941-4758-b2df-6a8c4fcac9b0"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "importances = classifiers['d_tree'].feature_importances_\n",
        "feat_names = X.columns\n",
        "sorted(zip(importances, feat_names), reverse= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Yy2aRt5Dw1L",
        "outputId": "2d399a02-3dc1-4eb4-8710-aa21264e7904"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.6209074813192169, 'OUT'),\n",
              " (0.37909251868078303, 'avg_rank'),\n",
              " (0.0, 'total_mcwins'),\n",
              " (0.0, 'total_mc'),\n",
              " (0.0, 'top_safe_ratio'),\n",
              " (0.0, 'mcwins_pct'),\n",
              " (0.0, 'age'),\n",
              " (0.0, 'WIN'),\n",
              " (0.0, 'SAFE'),\n",
              " (0.0, 'LOW'),\n",
              " (0.0, 'HIGH'),\n",
              " (0.0, 'BTM')]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Final Result: **Decision Tree** is the best model for the dataset."
      ],
      "metadata": {
        "id": "7_QoMLH2EiQG"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wc-ZfhyXEt5o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}