2_24128937_pushpak_maximization.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XOdi_QPFAhs"
      },
      "source": [
        "<div>\n",
        "<img src=\"https://www.ul.ie/themes/custom/ul/logo.jpg\" />\n",
        "</div>\n",
        "\n",
        "#**MSc in Artificial Intelligence and Machine Learning**\n",
        "##CS6271 - Evolutionary Algorithms and Humanoid Robotics 2024\n",
        "### Kaggle Competition\n",
        "\n",
        "\n",
        "Module Leader: Conor Ryan\n",
        "\n",
        "Developer: Allan De Lima\n",
        "\n",
        "Link to access the competition: https://www.kaggle.com/competitions/cs-6271-2024-5-final-project\n",
        "\n",
        "Link to join the competition: https://www.kaggle.com/t/017d7291cce043fcb79fb087b6a78437\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bTCSqsLKpNUm"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lix_hvw6pPfW"
      },
      "source": [
        "Predict whether a pacient has diabetes. This is a shorter version of the also known as \"Diabetes\" dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIEqtVydu7fj"
      },
      "source": [
        "# Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vQB8et9rEpq"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QscZZ7V1p-hE"
      },
      "source": [
        "**Class:**\n",
        "\n",
        "output: 1, 0.\n",
        "\n",
        "\n",
        "**Listing of features:**\n",
        "\n",
        "HighBP\n",
        "\n",
        "HighChol\n",
        "\n",
        "CholCheck\n",
        "\n",
        "BMI\n",
        "\n",
        "Smoker\n",
        "\n",
        "Stroke\n",
        "\n",
        "HeartDiseaseorAttack\n",
        "\n",
        "PhysActivity\n",
        "\n",
        "Fruits\n",
        "\n",
        "Veggies\n",
        "\n",
        "HvyAlcoholConsump\n",
        "\n",
        "AnyHealthcare\n",
        "\n",
        "NoDocbcCost\n",
        "\n",
        "GenHlth\n",
        "\n",
        "MentHlth\n",
        "\n",
        "PhysHlth\n",
        "\n",
        "DiffWalk\n",
        "\n",
        "Sex\n",
        "\n",
        "Age\n",
        "\n",
        "Education\n",
        "\n",
        "Income"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIGPTYpVreAH"
      },
      "source": [
        "### Load the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5YbcZQU7xfz"
      },
      "source": [
        "# Suppressing Warnings:\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkRwXuzOZ_ci",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "412419fb-7257-4d46-fb74-87af217c6431"
      },
      "source": [
        "## mount your Google drive\n",
        "# 1) run this cell\n",
        "# 2) sign in\n",
        "# 3) verify your drive is mounted\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrYC0mfKFkh7"
      },
      "source": [
        "Clone the GRAPE repository at first because the dataset to be used is already there."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSDPe5O9FOt0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b77e168d-6321-48f5-f3bb-c94b3d24e1ad"
      },
      "source": [
        "import os\n",
        "# Get the library from our BDS research Group\n",
        "# copy the path from your drive\n",
        "PATH = '/content/drive/MyDrive/grape/'\n",
        "\n",
        "# check if 'grape' already exists\n",
        "if os.path.exists(PATH):\n",
        "    print('grape directory already exists')\n",
        "else:\n",
        "    %cd /content/drive/MyDrive/\n",
        "    !git clone https://github.com/bdsul/grape.git\n",
        "    print('Cloning grape in your Drive')\n",
        "\n",
        "# change directory to 'grape'\n",
        "%cd /content/drive/MyDrive/grape/"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "grape directory already exists\n",
            "/content/drive/MyDrive/grape\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now you have a grape folder in your Drive account.\n",
        "\n",
        "Upload the files training.csv and test.csv from the competition to the folder grape/datasets in your Drive before running the next cells."
      ],
      "metadata": {
        "id": "8Qj3VEGNxsew"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CpHKEV4avk8j"
      },
      "source": [
        "### Train set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NjzisImfFrNS"
      },
      "source": [
        "train_file = 'datasets/training.csv'"
      ],
      "execution_count": 5,
      "outputs": []
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      "source": [
        "# load train set\n",
        "df_train = pd.read_csv(PATH+train_file)\n",
        "df_train.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  HeartDiseaseorAttack  \\\n",
              "0     0.0       0.0        1.0  27.0     0.0     0.0                   0.0   \n",
              "1     1.0       1.0        1.0  35.0     0.0     0.0                   1.0   \n",
              "2     1.0       1.0        1.0  24.0     0.0     0.0                   0.0   \n",
              "3     0.0       0.0        1.0  24.0     1.0     0.0                   0.0   \n",
              "4     1.0       0.0        1.0  41.0     0.0     0.0                   0.0   \n",
              "\n",
              "   PhysActivity  Fruits  Veggies  ...  NoDocbcCost  GenHlth  MentHlth  \\\n",
              "0           1.0     1.0      1.0  ...          0.0      1.0       1.0   \n",
              "1           1.0     1.0      1.0  ...          0.0      2.0       0.0   \n",
              "2           1.0     1.0      1.0  ...          0.0      2.0       0.0   \n",
              "3           1.0     0.0      1.0  ...          0.0      2.0       0.0   \n",
              "4           0.0     0.0      0.0  ...          0.0      3.0       3.0   \n",
              "\n",
              "   PhysHlth  DiffWalk  Sex   Age  Education  Income  output  \n",
              "0       0.0       0.0  0.0   7.0        6.0     8.0     0.0  \n",
              "1       0.0       0.0  1.0   9.0        6.0     8.0     0.0  \n",
              "2       0.0       1.0  0.0  13.0        5.0     4.0     0.0  \n",
              "3       0.0       0.0  1.0   7.0        4.0     6.0     0.0  \n",
              "4       0.0       0.0  0.0   8.0        5.0     7.0     0.0  \n",
              "\n",
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              "      <th>HighBP</th>\n",
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              "    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-908ce196-4190-4bc1-9d48-e76a3d4171c6 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": "df_train"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBQDVfs4vQNP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "c9237716-e12b-4051-a885-05ac32760337"
      },
      "source": [
        "df_train.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            HighBP     HighChol    CholCheck          BMI       Smoker  \\\n",
              "count  5042.000000  5042.000000  5042.000000  5042.000000  5042.000000   \n",
              "mean      0.513883     0.493653     0.972035    29.447441     0.473225   \n",
              "std       0.499857     0.500009     0.164889     7.080019     0.499332   \n",
              "min       0.000000     0.000000     0.000000    14.000000     0.000000   \n",
              "25%       0.000000     0.000000     1.000000    25.000000     0.000000   \n",
              "50%       1.000000     0.000000     1.000000    28.000000     0.000000   \n",
              "75%       1.000000     1.000000     1.000000    33.000000     1.000000   \n",
              "max       1.000000     1.000000     1.000000    98.000000     1.000000   \n",
              "\n",
              "            Stroke  HeartDiseaseorAttack  PhysActivity       Fruits  \\\n",
              "count  5042.000000           5042.000000   5042.000000  5042.000000   \n",
              "mean      0.050377              0.133082      0.728877     0.616025   \n",
              "std       0.218743              0.339697      0.444583     0.486400   \n",
              "min       0.000000              0.000000      0.000000     0.000000   \n",
              "25%       0.000000              0.000000      0.000000     0.000000   \n",
              "50%       0.000000              0.000000      1.000000     1.000000   \n",
              "75%       0.000000              0.000000      1.000000     1.000000   \n",
              "max       1.000000              1.000000      1.000000     1.000000   \n",
              "\n",
              "           Veggies  ...  NoDocbcCost      GenHlth     MentHlth     PhysHlth  \\\n",
              "count  5042.000000  ...  5042.000000  5042.000000  5042.000000  5042.000000   \n",
              "mean      0.801666  ...     0.085879     2.702102     3.617017     5.195954   \n",
              "std       0.398785  ...     0.280213     1.106379     7.997166     9.538762   \n",
              "min       0.000000  ...     0.000000     1.000000     0.000000     0.000000   \n",
              "25%       1.000000  ...     0.000000     2.000000     0.000000     0.000000   \n",
              "50%       1.000000  ...     0.000000     3.000000     0.000000     0.000000   \n",
              "75%       1.000000  ...     0.000000     3.000000     2.000000     5.000000   \n",
              "max       1.000000  ...     1.000000     5.000000    30.000000    30.000000   \n",
              "\n",
              "          DiffWalk          Sex          Age    Education       Income  \\\n",
              "count  5042.000000  5042.000000  5042.000000  5042.000000  5042.000000   \n",
              "mean      0.207061     0.447441     8.376041     4.958548     5.852836   \n",
              "std       0.405240     0.497279     2.920253     1.018217     2.131317   \n",
              "min       0.000000     0.000000     1.000000     1.000000     1.000000   \n",
              "25%       0.000000     0.000000     7.000000     4.000000     4.000000   \n",
              "50%       0.000000     0.000000     9.000000     5.000000     6.000000   \n",
              "75%       0.000000     1.000000    10.000000     6.000000     8.000000   \n",
              "max       1.000000     1.000000    13.000000     6.000000     8.000000   \n",
              "\n",
              "            output  \n",
              "count  5042.000000  \n",
              "mean      0.356010  \n",
              "std       0.478866  \n",
              "min       0.000000  \n",
              "25%       0.000000  \n",
              "50%       0.000000  \n",
              "75%       1.000000  \n",
              "max       1.000000  \n",
              "\n",
              "[8 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e89cdd66-5131-462c-aa3a-715dcb3e28c6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>HighBP</th>\n",
              "      <th>HighChol</th>\n",
              "      <th>CholCheck</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoker</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <th>PhysActivity</th>\n",
              "      <th>Fruits</th>\n",
              "      <th>Veggies</th>\n",
              "      <th>...</th>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <th>GenHlth</th>\n",
              "      <th>MentHlth</th>\n",
              "      <th>PhysHlth</th>\n",
              "      <th>DiffWalk</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "      <td>5042.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.513883</td>\n",
              "      <td>0.493653</td>\n",
              "      <td>0.972035</td>\n",
              "      <td>29.447441</td>\n",
              "      <td>0.473225</td>\n",
              "      <td>0.050377</td>\n",
              "      <td>0.133082</td>\n",
              "      <td>0.728877</td>\n",
              "      <td>0.616025</td>\n",
              "      <td>0.801666</td>\n",
              "      <td>...</td>\n",
              "      <td>0.085879</td>\n",
              "      <td>2.702102</td>\n",
              "      <td>3.617017</td>\n",
              "      <td>5.195954</td>\n",
              "      <td>0.207061</td>\n",
              "      <td>0.447441</td>\n",
              "      <td>8.376041</td>\n",
              "      <td>4.958548</td>\n",
              "      <td>5.852836</td>\n",
              "      <td>0.356010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.499857</td>\n",
              "      <td>0.500009</td>\n",
              "      <td>0.164889</td>\n",
              "      <td>7.080019</td>\n",
              "      <td>0.499332</td>\n",
              "      <td>0.218743</td>\n",
              "      <td>0.339697</td>\n",
              "      <td>0.444583</td>\n",
              "      <td>0.486400</td>\n",
              "      <td>0.398785</td>\n",
              "      <td>...</td>\n",
              "      <td>0.280213</td>\n",
              "      <td>1.106379</td>\n",
              "      <td>7.997166</td>\n",
              "      <td>9.538762</td>\n",
              "      <td>0.405240</td>\n",
              "      <td>0.497279</td>\n",
              "      <td>2.920253</td>\n",
              "      <td>1.018217</td>\n",
              "      <td>2.131317</td>\n",
              "      <td>0.478866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "        document.querySelector('#df-e89cdd66-5131-462c-aa3a-715dcb3e28c6 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-e89cdd66-5131-462c-aa3a-715dcb3e28c6');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "      --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-a19f1c78-d36f-4814-a86e-daff0069b526 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"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
        "def scal(data):\n",
        "\n",
        "  X = data.copy()\n",
        "  # Columns for each scaling method\n",
        "  standard_cols = ['BMI', 'Age', 'MentHlth', 'PhysHlth']\n",
        "  minmax_cols = ['GenHlth', 'Education', 'Income']\n",
        "  binary_cols = [col for col in X.columns if col not in standard_cols + minmax_cols]\n",
        "\n",
        "  # Initialize scalers\n",
        "  standard_scaler = StandardScaler()\n",
        "  minmax_scaler = MinMaxScaler()\n",
        "  robust_scaler = RobustScaler()\n",
        "\n",
        "  # Apply scaling\n",
        "  X_standard_scaled = standard_scaler.fit_transform(X[standard_cols])\n",
        "  X_minmax_scaled = X[minmax_cols]\n",
        "  X_binary = X[binary_cols]  # No scaling for binary columns\n",
        "\n",
        "  # Combine scaled data\n",
        "  import numpy as np\n",
        "  X_scaled = np.hstack([X_standard_scaled, X_minmax_scaled, X_binary])\n",
        "\n",
        "  # Convert back to DataFrame for readability\n",
        "  scaled_columns = standard_cols + minmax_cols + binary_cols\n",
        "  X_scaled_df = pd.DataFrame(X_scaled, columns=scaled_columns)\n",
        "\n",
        "  print(X_scaled_df.head())\n",
        "\n",
        "  return X_scaled_df\n",
        "\n"
      ],
      "metadata": {
        "id": "37eR12is00dp"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NtyRPmvx4sq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cf52900-0358-415f-d9ac-8ec08d359010"
      },
      "source": [
        "X_train = df_train.copy()\n",
        "X_train.drop(['output'], axis=1, inplace=True)\n",
        "y_train = df_train['output'].to_numpy()\n",
        "X_train = scal(X_train)\n",
        "\n",
        "print(y_train)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        BMI       Age  MentHlth  PhysHlth  GenHlth  Education  Income  HighBP  \\\n",
            "0 -0.345717 -0.471253 -0.327276 -0.544774      1.0        6.0     8.0     0.0   \n",
            "1  0.784335  0.213687 -0.452332 -0.544774      2.0        6.0     8.0     1.0   \n",
            "2 -0.769487  1.583567 -0.452332 -0.544774      2.0        5.0     4.0     1.0   \n",
            "3 -0.769487 -0.471253 -0.452332 -0.544774      2.0        4.0     6.0     0.0   \n",
            "4  1.631875 -0.128783 -0.077162 -0.544774      3.0        5.0     7.0     1.0   \n",
            "\n",
            "   HighChol  CholCheck  ...  Stroke  HeartDiseaseorAttack  PhysActivity  \\\n",
            "0       0.0        1.0  ...     0.0                   0.0           1.0   \n",
            "1       1.0        1.0  ...     0.0                   1.0           1.0   \n",
            "2       1.0        1.0  ...     0.0                   0.0           1.0   \n",
            "3       0.0        1.0  ...     0.0                   0.0           1.0   \n",
            "4       0.0        1.0  ...     0.0                   0.0           0.0   \n",
            "\n",
            "   Fruits  Veggies  HvyAlcoholConsump  AnyHealthcare  NoDocbcCost  DiffWalk  \\\n",
            "0     1.0      1.0                0.0            1.0          0.0       0.0   \n",
            "1     1.0      1.0                0.0            1.0          0.0       0.0   \n",
            "2     1.0      1.0                0.0            1.0          0.0       1.0   \n",
            "3     0.0      1.0                0.0            1.0          0.0       0.0   \n",
            "4     0.0      0.0                0.0            1.0          0.0       0.0   \n",
            "\n",
            "   Sex  \n",
            "0  0.0  \n",
            "1  1.0  \n",
            "2  0.0  \n",
            "3  1.0  \n",
            "4  0.0  \n",
            "\n",
            "[5 rows x 21 columns]\n",
            "[0. 0. 0. ... 1. 1. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "te26rlaBjLXd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8e9ec1f-ea36-49fd-a884-c8808dd09ca2"
      },
      "source": [
        "print(y_train[0:5]) #print head"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CwAxKV7nvpZ8"
      },
      "source": [
        "### Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IzzBvGUvtkI"
      },
      "source": [
        "test_file = 'datasets/test.csv'"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbAn0FpFvPhO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "e4d10d5f-bff8-434e-f057-601a1c79bd9e"
      },
      "source": [
        "# load test set\n",
        "df_test = pd.read_csv(PATH+test_file)\n",
        "df_test.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  HeartDiseaseorAttack  \\\n",
              "0     1.0       1.0        1.0  28.0     1.0     0.0                   1.0   \n",
              "1     1.0       0.0        1.0  27.0     1.0     0.0                   1.0   \n",
              "2     1.0       1.0        1.0  27.0     0.0     0.0                   0.0   \n",
              "3     0.0       0.0        1.0  27.0     0.0     0.0                   0.0   \n",
              "4     1.0       1.0        1.0  32.0     1.0     0.0                   1.0   \n",
              "\n",
              "   PhysActivity  Fruits  Veggies  ...  AnyHealthcare  NoDocbcCost  GenHlth  \\\n",
              "0           1.0     0.0      0.0  ...            0.0          0.0      4.0   \n",
              "1           0.0     1.0      1.0  ...            1.0          0.0      3.0   \n",
              "2           1.0     1.0      1.0  ...            1.0          0.0      1.0   \n",
              "3           1.0     0.0      1.0  ...            1.0          0.0      3.0   \n",
              "4           0.0     0.0      0.0  ...            1.0          0.0      5.0   \n",
              "\n",
              "   MentHlth  PhysHlth  DiffWalk  Sex   Age  Education  Income  \n",
              "0       2.0       2.0       1.0  1.0   8.0        4.0     1.0  \n",
              "1       0.0      14.0       0.0  0.0  11.0        6.0     6.0  \n",
              "2       3.0       0.0       0.0  1.0  12.0        6.0     6.0  \n",
              "3       0.0       0.0       0.0  0.0   9.0        6.0     3.0  \n",
              "4       3.0       6.0       1.0  0.0  10.0        5.0     6.0  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1b5681d-7c14-4050-96e5-ce259cc6a128\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HighBP</th>\n",
              "      <th>HighChol</th>\n",
              "      <th>CholCheck</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoker</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <th>PhysActivity</th>\n",
              "      <th>Fruits</th>\n",
              "      <th>Veggies</th>\n",
              "      <th>...</th>\n",
              "      <th>AnyHealthcare</th>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <th>GenHlth</th>\n",
              "      <th>MentHlth</th>\n",
              "      <th>PhysHlth</th>\n",
              "      <th>DiffWalk</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "\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",
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              "\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",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-a1b5681d-7c14-4050-96e5-ce259cc6a128 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a1b5681d-7c14-4050-96e5-ce259cc6a128');\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-eaefc4d5-5ecd-4308-80bd-825705fc3f25\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eaefc4d5-5ecd-4308-80bd-825705fc3f25')\"\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-eaefc4d5-5ecd-4308-80bd-825705fc3f25 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": "df_test"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Q0uvjTzvfLU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "78b3936f-2946-4958-853d-c56dd0bb6d17"
      },
      "source": [
        "df_test.describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             HighBP      HighChol     CholCheck           BMI        Smoker  \\\n",
              "count  95804.000000  95804.000000  95804.000000  95804.000000  95804.000000   \n",
              "mean       0.508330      0.484239      0.970628     29.262797      0.461442   \n",
              "std        0.499933      0.499754      0.168849      6.972092      0.498514   \n",
              "min        0.000000      0.000000      0.000000     12.000000      0.000000   \n",
              "25%        0.000000      0.000000      1.000000     25.000000      0.000000   \n",
              "50%        1.000000      0.000000      1.000000     28.000000      0.000000   \n",
              "75%        1.000000      1.000000      1.000000     32.000000      1.000000   \n",
              "max        1.000000      1.000000      1.000000     98.000000      1.000000   \n",
              "\n",
              "             Stroke  HeartDiseaseorAttack  PhysActivity        Fruits  \\\n",
              "count  95804.000000          95804.000000  95804.000000  95804.000000   \n",
              "mean       0.053839              0.125329      0.724427      0.621352   \n",
              "std        0.225701              0.331093      0.446805      0.485053   \n",
              "min        0.000000              0.000000      0.000000      0.000000   \n",
              "25%        0.000000              0.000000      0.000000      0.000000   \n",
              "50%        0.000000              0.000000      1.000000      1.000000   \n",
              "75%        0.000000              0.000000      1.000000      1.000000   \n",
              "max        1.000000              1.000000      1.000000      1.000000   \n",
              "\n",
              "            Veggies  ...  AnyHealthcare   NoDocbcCost       GenHlth  \\\n",
              "count  95804.000000  ...   95804.000000  95804.000000  95804.000000   \n",
              "mean       0.798161  ...       0.953353      0.088973      2.701891   \n",
              "std        0.401375  ...       0.210883      0.284707      1.106275   \n",
              "min        0.000000  ...       0.000000      0.000000      1.000000   \n",
              "25%        1.000000  ...       1.000000      0.000000      2.000000   \n",
              "50%        1.000000  ...       1.000000      0.000000      3.000000   \n",
              "75%        1.000000  ...       1.000000      0.000000      3.000000   \n",
              "max        1.000000  ...       1.000000      1.000000      5.000000   \n",
              "\n",
              "           MentHlth      PhysHlth      DiffWalk           Sex           Age  \\\n",
              "count  95804.000000  95804.000000  95804.000000  95804.000000  95804.000000   \n",
              "mean       3.488654      5.155662      0.219573      0.447841      8.362761   \n",
              "std        7.819002      9.550624      0.413960      0.497275      2.959191   \n",
              "min        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      7.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      9.000000   \n",
              "75%        2.000000      5.000000      0.000000      1.000000     11.000000   \n",
              "max       30.000000     30.000000      1.000000      1.000000     13.000000   \n",
              "\n",
              "          Education        Income  \n",
              "count  95804.000000  95804.000000  \n",
              "mean       4.977642      5.846739  \n",
              "std        1.012794      2.136366  \n",
              "min        1.000000      1.000000  \n",
              "25%        4.000000      4.000000  \n",
              "50%        5.000000      6.000000  \n",
              "75%        6.000000      8.000000  \n",
              "max        6.000000      8.000000  \n",
              "\n",
              "[8 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a1664d8-fbae-46c6-b564-488ae41b412e\" 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",
              "    }\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>HighBP</th>\n",
              "      <th>HighChol</th>\n",
              "      <th>CholCheck</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Smoker</th>\n",
              "      <th>Stroke</th>\n",
              "      <th>HeartDiseaseorAttack</th>\n",
              "      <th>PhysActivity</th>\n",
              "      <th>Fruits</th>\n",
              "      <th>Veggies</th>\n",
              "      <th>...</th>\n",
              "      <th>AnyHealthcare</th>\n",
              "      <th>NoDocbcCost</th>\n",
              "      <th>GenHlth</th>\n",
              "      <th>MentHlth</th>\n",
              "      <th>PhysHlth</th>\n",
              "      <th>DiffWalk</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "      <td>95804.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.508330</td>\n",
              "      <td>0.484239</td>\n",
              "      <td>0.970628</td>\n",
              "      <td>29.262797</td>\n",
              "      <td>0.461442</td>\n",
              "      <td>0.053839</td>\n",
              "      <td>0.125329</td>\n",
              "      <td>0.724427</td>\n",
              "      <td>0.621352</td>\n",
              "      <td>0.798161</td>\n",
              "      <td>...</td>\n",
              "      <td>0.953353</td>\n",
              "      <td>0.088973</td>\n",
              "      <td>2.701891</td>\n",
              "      <td>3.488654</td>\n",
              "      <td>5.155662</td>\n",
              "      <td>0.219573</td>\n",
              "      <td>0.447841</td>\n",
              "      <td>8.362761</td>\n",
              "      <td>4.977642</td>\n",
              "      <td>5.846739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.499933</td>\n",
              "      <td>0.499754</td>\n",
              "      <td>0.168849</td>\n",
              "      <td>6.972092</td>\n",
              "      <td>0.498514</td>\n",
              "      <td>0.225701</td>\n",
              "      <td>0.331093</td>\n",
              "      <td>0.446805</td>\n",
              "      <td>0.485053</td>\n",
              "      <td>0.401375</td>\n",
              "      <td>...</td>\n",
              "      <td>0.210883</td>\n",
              "      <td>0.284707</td>\n",
              "      <td>1.106275</td>\n",
              "      <td>7.819002</td>\n",
              "      <td>9.550624</td>\n",
              "      <td>0.413960</td>\n",
              "      <td>0.497275</td>\n",
              "      <td>2.959191</td>\n",
              "      <td>1.012794</td>\n",
              "      <td>2.136366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 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-5a1664d8-fbae-46c6-b564-488ae41b412e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
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              "\n",
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              "        document.querySelector('#df-5a1664d8-fbae-46c6-b564-488ae41b412e button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-5a1664d8-fbae-46c6-b564-488ae41b412e');\n",
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              "                                                    [key], {});\n",
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              "\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",
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              "      --bg-color: #E8F0FE;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Vu3FBV70F-M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2314aa18-33a2-46cc-fdee-dc0a2428fe29"
      },
      "source": [
        "X1_test = df_test.copy()\n",
        "X1_test = scal(X1_test)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        BMI       Age  MentHlth  PhysHlth  GenHlth  Education  Income  HighBP  \\\n",
            "0 -0.181123 -0.122589 -0.190390 -0.330416      4.0        4.0     1.0     1.0   \n",
            "1 -0.324552  0.891207 -0.446179  0.926053      3.0        6.0     6.0     1.0   \n",
            "2 -0.324552  1.229139 -0.062496 -0.539827      1.0        6.0     6.0     1.0   \n",
            "3 -0.324552  0.215343 -0.446179 -0.539827      3.0        6.0     3.0     0.0   \n",
            "4  0.392596  0.553275 -0.062496  0.088407      5.0        5.0     6.0     1.0   \n",
            "\n",
            "   HighChol  CholCheck  ...  Stroke  HeartDiseaseorAttack  PhysActivity  \\\n",
            "0       1.0        1.0  ...     0.0                   1.0           1.0   \n",
            "1       0.0        1.0  ...     0.0                   1.0           0.0   \n",
            "2       1.0        1.0  ...     0.0                   0.0           1.0   \n",
            "3       0.0        1.0  ...     0.0                   0.0           1.0   \n",
            "4       1.0        1.0  ...     0.0                   1.0           0.0   \n",
            "\n",
            "   Fruits  Veggies  HvyAlcoholConsump  AnyHealthcare  NoDocbcCost  DiffWalk  \\\n",
            "0     0.0      0.0                0.0            0.0          0.0       1.0   \n",
            "1     1.0      1.0                0.0            1.0          0.0       0.0   \n",
            "2     1.0      1.0                0.0            1.0          0.0       0.0   \n",
            "3     0.0      1.0                0.0            1.0          0.0       0.0   \n",
            "4     0.0      0.0                0.0            1.0          0.0       1.0   \n",
            "\n",
            "   Sex  \n",
            "0  1.0  \n",
            "1  0.0  \n",
            "2  1.0  \n",
            "3  0.0  \n",
            "4  0.0  \n",
            "\n",
            "[5 rows x 21 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xogSlXT5v6GQ"
      },
      "source": [
        "You will need to prepare both training and test datasets before working with a Machine Learning method.\n",
        "\n",
        "Consider you need to use some encoding method with categorical data.\n",
        "\n",
        "You are free to use any other pre-processing ideas."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Include your code here\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "3gWHFwiDZLNH"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2OqYdgmy_GDa"
      },
      "source": [
        "Convert the datasets to NumPy to easily use them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lW9tL2ydv9kl"
      },
      "source": [
        "# data features\n",
        "X_train = X_train.to_numpy()\n",
        "X_test = X_test.to_numpy()\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f_test = X1_test"
      ],
      "metadata": {
        "id": "DaUrjaQzp8FN"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8b5xrjIrqD_"
      },
      "source": [
        "## GRAPE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJ05U2S409Dl"
      },
      "source": [
        "<div>\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1hw43Oi3lGTCkspQ0ged2bZB8q2EpcPhz\" width=\"150\"/>\n",
        "</div>\n",
        "\n",
        "GRammatical Algorithms in Python for Evolution (GRAPE)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "be7FcJqpWnJM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35c66e01-c805-498a-9f5e-6e69e82defaa"
      },
      "source": [
        "!pip install deap\n",
        "\n",
        "import grape\n",
        "import algorithms\n",
        "\n",
        "from os import path\n",
        "from deap import creator, base, tools\n",
        "import random\n",
        "import csv"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: deap in /usr/local/lib/python3.10/dist-packages (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from deap) (1.26.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HKB9j6fmlFEr"
      },
      "source": [
        "You can import functions to be used with your grammar from [functions.py](https://github.com/UL-BDS/grape/blob/main/functions.py) on GRAPE repository and / or you can define your own functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMu0U8j-loXC"
      },
      "source": [
        "from functions import add, sub, mul, pdiv, psqrt, plog, and_, or_, not_, less_than_or_equal, greater_than_or_equal"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7sMFCXtoCyb"
      },
      "source": [
        "'heartDisease.bnf' is a grammar used for another problem just to check if everything is working well.\n",
        "\n",
        "Write your own grammar in a text file and save it in your Drive account.\n",
        "\n",
        "Put the whole address on GRAMMAR_FILE and print to check it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Xc60pTemiIb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54aa4389-dbbb-4f82-d2ca-5a3e70c64fc5"
      },
      "source": [
        "#GRAMMAR_FILE = '/content/drive/MyDrive/data/example.bnf' #put the whole address of your own grammar and remove the # in the beginning of this line\n",
        "GRAMMAR_FILE = 'heartDisease.bnf' #remove this line when you are using your own grammar\n",
        "\n",
        "#f = open(GRAMMAR_FILE, \"r\") #remove the # in the beginning of this line when you are using your own grammar\n",
        "f = open(\"grammars/\" + GRAMMAR_FILE, \"r\") #remove this line when you are using your own grammar\n",
        "print(f.read())\n",
        "f.close()\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<log_op> ::= <conditional_branches> | and_(<log_op>,<log_op>) | or_(<log_op>,<log_op>) | not_(<log_op>) | <boolean_feature>\n",
            "<conditional_branches> ::= less_than_or_equal(<num_op>,<num_op>) | greater_than_or_equal(<num_op>, <num_op>)\n",
            "<num_op>   ::= add(<num_op>,<num_op>) | sub(<num_op>,<num_op>) | mul(<num_op>,<num_op>) | pdiv(<num_op>,<num_op>) | <nonboolean_feature>\n",
            "<boolean_feature> ::= x[0]|x[1]|x[2]|x[4]|x[5]|x[6]|x[7]|x[8]|x[9]|x[10]|x[11]|x[12]|x[13]|x[14]|x[15]|x[16]|x[17]\n",
            "<nonboolean_feature> ::= x[3]|x[18]|x[19]|x[20]|<c><c>.<c><c>\n",
            "<c>  ::= 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "082ga8rppORi"
      },
      "source": [
        "Run the following cell to put your grammar on the class Grammar."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEJcyIJKnUoz"
      },
      "source": [
        "#BNF_GRAMMAR = grape.Grammar(GRAMMAR_FILE) #remove the # in the beginning of this line when you are using your own grammar\n",
        "BNF_GRAMMAR = grape.Grammar(path.join(\"grammars\", GRAMMAR_FILE)) #remove this line when you are using your own grammar"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "L-LFeWnLK3K5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8216d22f-4ef0-4b82-efb3-8369cb98257b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOZl1P-spq3Y"
      },
      "source": [
        "The fitness function here is the percentage of outputs wrongly predicted.\n",
        "\n",
        "You can write your own fitness function if you prefer."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, log_loss\n",
        "import numpy as np\n",
        "\n",
        "def fitness_eval(individual, points):\n",
        "    high_variance_threshold = 0.1\n",
        "    x = points[0]\n",
        "    y = points[1]\n",
        "    penalty = 0\n",
        "\n",
        "    if individual.invalid:\n",
        "        return np.NaN,\n",
        "\n",
        "    try:\n",
        "        pred = eval(individual.phenotype)\n",
        "    except (FloatingPointError, ZeroDivisionError, OverflowError, MemoryError):\n",
        "        return np.NaN,\n",
        "\n",
        "    assert np.isrealobj(pred)\n",
        "\n",
        "    try:\n",
        "        Y_class = [1 if pred[i] > 0 else 0 for i in range(len(y))]\n",
        "        pred = [int(p) for p in pred]\n",
        "        Y_prob = [1 / (1 + np.exp(-p)) for p in pred]\n",
        "    except (IndexError, TypeError):\n",
        "        return np.NaN,\n",
        "\n",
        "    accuracy = accuracy_score(y, Y_class)\n",
        "    logloss_value = log_loss(y, Y_prob)\n",
        "\n",
        "    # Penalize for high log loss\n",
        "    max_logloss_threshold = 0.5\n",
        "    if logloss_value > max_logloss_threshold:\n",
        "        penalty += 1.0 * (logloss_value - max_logloss_threshold)\n",
        "\n",
        "    # Variance Penalty\n",
        "    y_class_variance = np.var(Y_class)\n",
        "    if y_class_variance > high_variance_threshold:\n",
        "        penalty += 0.5 * (y_class_variance - high_variance_threshold)\n",
        "\n",
        "    # Combine accuracy and penalties\n",
        "    fitness = accuracy - penalty / 5  # Adjusted scaling of penalty\n",
        "\n",
        "    # Ensure fitness is not negative\n",
        "    fitness = max(fitness, 0.1)  # Provide a small positive value\n",
        "\n",
        "    return (fitness,)\n"
      ],
      "metadata": {
        "id": "oePSwo6s4Y8_"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJ5ZUKRYWXRi"
      },
      "source": [
        "To use properly the fitness function above with GRAPE, the features must be in the lines, and the samples must be in the columns, so if your data is not like that, you need to transpose the matrix.\n",
        "\n",
        "Take a look at the print. If you run this cell two times, the matrix will be transposed again and will not work properly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPJQ-iGUVutk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5363b4b7-e4f3-4430-e7e8-20a95a66eb6e"
      },
      "source": [
        "X_train = np.transpose(X_train)\n",
        "X_test = np.transpose(X_test)\n",
        "\n",
        "print('Training (X,Y):\\t', X_train.shape, y_train.shape)\n",
        "print('Test (X):\\t', X_test.shape)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training (X,Y):\t (21, 3529) (3529,)\n",
            "Test (X):\t (21, 1513)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FZt9KkAqtUS"
      },
      "source": [
        "Set the Grammatical Evolution parameters.\n",
        "\n",
        "Make sure you set a random seed just in case we need to re-run your experiments."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHxhHk5uqwFa"
      },
      "source": [
        "POPULATION_SIZE = 1000\n",
        "MAX_GENERATIONS = 100\n",
        "P_CROSSOVER = 0.5\n",
        "P_MUTATION = 0.03\n",
        "ELITE_SIZE = 1\n",
        "HALL_OF_FAME_SIZE = 1\n",
        "\n",
        "TOURNAMENT_SIZE = 6\n",
        "RANDOM_SEED = 42\n",
        "random.seed(RANDOM_SEED)\n",
        "\n",
        "CODON_CONSUMPTION = 'lazy'\n",
        "GENOME_REPRESENTATION = 'list'\n",
        "MAX_GENOME_LENGTH = None\n",
        "\n",
        "MAX_INIT_TREE_DEPTH = 12\n",
        "MIN_INIT_TREE_DEPTH = 3\n",
        "MAX_TREE_DEPTH = 90\n",
        "MAX_WRAPS = 0\n",
        "CODON_SIZE = 255\n",
        "\n",
        "REPORT_ITEMS = ['gen', 'invalid', 'avg', 'std', 'min', 'max',\n",
        "                'best_ind_length', 'avg_length',\n",
        "                'best_ind_nodes', 'avg_nodes',\n",
        "                'best_ind_depth', 'avg_depth',\n",
        "                'avg_used_codons', 'best_ind_used_codons',\n",
        "                'structural_diversity', 'fitness_diversity',\n",
        "                'selection_time', 'generation_time']"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FfpeXPO_q9By"
      },
      "source": [
        "Create a toolbox."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mh3DJnexq-oy"
      },
      "source": [
        "toolbox = base.Toolbox()\n",
        "\n",
        "# define a single objective, minimising fitness strategy:\n",
        "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n",
        "\n",
        "creator.create('Individual', grape.Individual, fitness=creator.FitnessMax)\n",
        "\n",
        "\n",
        "toolbox.register(\"populationCreator\", grape.sensible_initialisation, creator.Individual)\n",
        "\n",
        "toolbox.register(\"evaluate\", fitness_eval)\n",
        "\n",
        "# Tournament selection:\n",
        "toolbox.register(\"select\", tools.selTournament, tournsize=TOURNAMENT_SIZE)\n",
        "\n",
        "# Single-point crossover:\n",
        "toolbox.register(\"mate\", grape.crossover_onepoint)\n",
        "\n",
        "# Flip-int mutation:\n",
        "toolbox.register(\"mutate\", grape.mutation_int_flip_per_codon)\n"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhCmqPRHrMHJ"
      },
      "source": [
        "# create initial population (generation 0):\n",
        "population = toolbox.populationCreator(pop_size=POPULATION_SIZE,\n",
        "                                           bnf_grammar=BNF_GRAMMAR,\n",
        "                                           min_init_depth=MIN_INIT_TREE_DEPTH,\n",
        "                                           max_init_depth=MAX_INIT_TREE_DEPTH,\n",
        "                                           codon_size=CODON_SIZE,\n",
        "                                           codon_consumption=CODON_CONSUMPTION,\n",
        "                                           genome_representation=GENOME_REPRESENTATION\n",
        "                                            )\n",
        "\n",
        "# define the hall-of-fame object:\n",
        "hof = tools.HallOfFame(HALL_OF_FAME_SIZE)\n",
        "\n",
        "# prepare the statistics object:\n",
        "stats = tools.Statistics(key=lambda ind: ind.fitness.values)\n",
        "stats.register(\"avg\", np.nanmean)\n",
        "stats.register(\"std\", np.nanstd)\n",
        "stats.register(\"min\", np.nanmin)\n",
        "stats.register(\"max\", np.nanmax)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for ind in population:\n",
        "  ind.fitness.values = toolbox.evaluate(ind, points=[X_train, y_train])\n",
        "  print(ind.fitness.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "plGchtgAUSV7",
        "outputId": "ed4f8a82-f029-4aa3-f9db-4693ac245a24"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "(0.45958859336096564,)\n",
            "(0.6477530612100547,)\n",
            "(0.6260066646753478,)\n",
            "(0.45760611502284504,)\n",
            "(0.45412352265712475,)\n",
            "(0.6536844902185901,)\n",
            "(0.31148811300564905,)\n",
            "(0.5277380482496836,)\n",
            "(0.32917256311629634,)\n",
            "(0.3246512890091117,)\n",
            "(0.34198513473629827,)\n",
            "(0.5068215252112138,)\n",
            "(0.6292685525538012,)\n",
            "(0.44629786663112814,)\n",
            "(0.5110839164002536,)\n",
            "(0.6466416531879386,)\n",
            "(0.638118214788948,)\n",
            "(0.638118214788948,)\n",
            "(0.4643150451105512,)\n",
            "(0.49424042973625365,)\n",
            "(0.4978581500268502,)\n",
            "(0.638118214788948,)\n",
            "(0.6134305306656925,)\n",
            "(0.6445431490813905,)\n",
            "(0.30473499458714826,)\n",
            "(0.32917256311629634,)\n",
            "(0.4775530411653791,)\n",
            "(0.5426694719856168,)\n",
            "(0.510874614048605,)\n",
            "(0.4833411090182353,)\n",
            "(0.46998784931458326,)\n",
            "(0.31298909254157103,)\n",
            "(0.6223073894247545,)\n",
            "(0.5974461656454801,)\n",
            "(0.6375752228053577,)\n",
            "(0.5758046297228183,)\n",
            "(0.6750148129480456,)\n",
            "(0.3315336075928205,)\n",
            "(0.5129082646811276,)\n",
            "(0.6240739182303287,)\n",
            "(0.5208910017653139,)\n",
            "(0.638118214788948,)\n",
            "(0.638118214788948,)\n",
            "(0.32917256311629634,)\n",
            "(0.46522329035435006,)\n",
            "(0.6295232648710848,)\n",
            "(0.6374807238772703,)\n",
            "(0.6529878043234046,)\n",
            "(0.6486881507751354,)\n",
            "(0.6210576533285238,)\n",
            "(0.3265801535738185,)\n",
            "(0.5912551879674824,)\n",
            "(0.31791533566032654,)\n",
            "(0.3088785692594658,)\n",
            "(0.4415750578257229,)\n",
            "(0.5131258062939114,)\n",
            "(0.513428491897647,)\n",
            "(0.47673599861615185,)\n",
            "(0.6390976575973016,)\n",
            "(0.31379904668933606,)\n",
            "(0.3403940415441988,)\n",
            "(0.3225505300997039,)\n",
            "(0.3475841286699808,)\n",
            "(0.6232087929641673,)\n",
            "(0.31871746480770646,)\n",
            "(0.42872272933797717,)\n",
            "(0.47103682041557354,)\n",
            "(0.49925527768661304,)\n",
            "(0.46382162453316156,)\n",
            "(0.5313926920709529,)\n",
            "(0.504814374098329,)\n",
            "(0.638118214788948,)\n",
            "(0.6410187558812719,)\n",
            "(0.326927242449253,)\n",
            "(0.30918520324236537,)\n",
            "(0.31730803049003553,)\n",
            "(0.638118214788948,)\n",
            "(0.638118214788948,)\n",
            "(0.6374386080816148,)\n",
            "(0.3205208728774236,)\n",
            "(0.5064830354417067,)\n",
            "(0.5029101082266649,)\n",
            "(0.3362226816169839,)\n",
            "(0.4590220596690173,)\n",
            "(0.638118214788948,)\n",
            "(0.638118214788948,)\n",
            "(0.6232400022948477,)\n",
            "(0.6391599244718468,)\n",
            "(0.3300966712165472,)\n",
            "(0.33766312173277546,)\n",
            "(0.3263599559099453,)\n",
            "(0.6505196741083784,)\n",
            "(0.4046733008743729,)\n",
            "(0.5219027439042563,)\n",
            "(0.3386065300980273,)\n",
            "(0.4630509497416363,)\n",
            "(0.6382537996552954,)\n",
            "(0.32567125986197704,)\n",
            "(0.3556654709402423,)\n",
            "(0.3231001720514009,)\n",
            "(0.3242784099918658,)\n",
            "(0.4803958217996072,)\n",
            "(0.6406624951121941,)\n",
            "(0.638366119823321,)\n",
            "(0.4913285144594378,)\n",
            "(0.3292031958893942,)\n",
            "(0.6300865869770718,)\n",
            "(0.3806025682986113,)\n",
            "(0.638118214788948,)\n",
            "(0.6194946000748218,)\n",
            "(0.640522945812526,)\n",
            "(0.638281976180412,)\n",
            "(0.6233165885613884,)\n",
            "(0.6287182842217702,)\n",
            "(0.638118214788948,)\n",
            "(0.3321103057255298,)\n",
            "(0.638118214788948,)\n",
            "(0.4871933461643798,)\n",
            "(0.500748023162324,)\n",
            "(0.4463267106497045,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByAiFpujrXmL"
      },
      "source": [
        "Run Grammatical Evolution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Si071Z_2raLB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a6d4685-76df-41fd-ddc3-280312ee974b"
      },
      "source": [
        "population, logbook = algorithms.ge_eaSimpleWithElitism(population, toolbox, cxpb=P_CROSSOVER, mutpb=P_MUTATION,\n",
        "                                              ngen=MAX_GENERATIONS, elite_size=ELITE_SIZE,\n",
        "                                              bnf_grammar=BNF_GRAMMAR,\n",
        "                                              codon_size=CODON_SIZE,\n",
        "                                              max_tree_depth=MAX_TREE_DEPTH,\n",
        "                                              max_genome_length=MAX_GENOME_LENGTH,\n",
        "                                              points_train=[X_train, y_train],\n",
        "                                              codon_consumption=CODON_CONSUMPTION,\n",
        "                                              report_items=REPORT_ITEMS,\n",
        "                                              genome_representation=GENOME_REPRESENTATION,\n",
        "                                              stats=stats, halloffame=hof, verbose=False)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gen = 0 , Best fitness = (0.6379978984946582,)\n",
            "gen = 1 , Best fitness = (0.6491831401891754,) , Number of invalids = 239\n",
            "gen = 2 , Best fitness = (0.6529677957953386,) , Number of invalids = 143\n",
            "gen = 3 , Best fitness = (0.6529677957953386,) , Number of invalids = 66\n",
            "gen = 4 , Best fitness = (0.6529677957953386,) , Number of invalids = 64\n",
            "gen = 5 , Best fitness = (0.6568683903292375,) , Number of invalids = 55\n",
            "gen = 6 , Best fitness = (0.6568683903292375,) , Number of invalids = 26\n",
            "gen = 7 , Best fitness = (0.6568683903292375,) , Number of invalids = 7\n",
            "gen = 8 , Best fitness = (0.6568683903292375,) , Number of invalids = 16\n",
            "gen = 9 , Best fitness = (0.6568683903292375,) , Number of invalids = 10\n",
            "gen = 10 , Best fitness = (0.6569724150454712,) , Number of invalids = 3\n",
            "gen = 11 , Best fitness = (0.6569724150454712,) , Number of invalids = 5\n",
            "gen = 12 , Best fitness = (0.6600412837347143,) , Number of invalids = 1\n",
            "gen = 13 , Best fitness = (0.6600412837347143,) , Number of invalids = 0\n",
            "gen = 14 , Best fitness = (0.6600412837347143,) , Number of invalids = 0\n",
            "gen = 15 , Best fitness = (0.6600412837347143,) , Number of invalids = 0\n",
            "gen = 16 , Best fitness = (0.6616219302577651,) , Number of invalids = 0\n",
            "gen = 17 , Best fitness = (0.6636503595075591,) , Number of invalids = 0\n",
            "gen = 18 , Best fitness = (0.6636503595075591,) , Number of invalids = 0\n",
            "gen = 19 , Best fitness = (0.6636503595075591,) , Number of invalids = 0\n",
            "gen = 20 , Best fitness = (0.6651365652125273,) , Number of invalids = 0\n",
            "gen = 21 , Best fitness = (0.6651365652125273,) , Number of invalids = 0\n",
            "gen = 22 , Best fitness = (0.6651365652125273,) , Number of invalids = 0\n",
            "gen = 23 , Best fitness = (0.6660063343734839,) , Number of invalids = 0\n",
            "gen = 24 , Best fitness = (0.6662024356848857,) , Number of invalids = 0\n",
            "gen = 25 , Best fitness = (0.6662024356848857,) , Number of invalids = 0\n",
            "gen = 26 , Best fitness = (0.6662024356848857,) , Number of invalids = 0\n",
            "gen = 27 , Best fitness = (0.6663008717638468,) , Number of invalids = 0\n",
            "gen = 28 , Best fitness = (0.6668750276698019,) , Number of invalids = 0\n",
            "gen = 29 , Best fitness = (0.6703163982662654,) , Number of invalids = 0\n",
            "gen = 30 , Best fitness = (0.6703163982662654,) , Number of invalids = 0\n",
            "gen = 31 , Best fitness = (0.6703163982662654,) , Number of invalids = 0\n",
            "gen = 32 , Best fitness = (0.6703163982662654,) , Number of invalids = 0\n",
            "gen = 33 , Best fitness = (0.670411686721935,) , Number of invalids = 0\n",
            "gen = 34 , Best fitness = (0.6709874164395361,) , Number of invalids = 0\n",
            "gen = 35 , Best fitness = (0.6709874164395361,) , Number of invalids = 0\n",
            "gen = 36 , Best fitness = (0.6709874164395361,) , Number of invalids = 0\n",
            "gen = 37 , Best fitness = (0.6709874164395361,) , Number of invalids = 0\n",
            "gen = 38 , Best fitness = (0.6709874164395361,) , Number of invalids = 0\n",
            "gen = 39 , Best fitness = (0.6709874164395361,) , Number of invalids = 0\n",
            "gen = 40 , Best fitness = (0.6709874164395361,) , Number of invalids = 0\n",
            "gen = 41 , Best fitness = (0.6709874164395361,) , Number of invalids = 0\n",
            "gen = 42 , Best fitness = (0.6723327126072272,) , Number of invalids = 0\n",
            "gen = 43 , Best fitness = (0.6723327126072272,) , Number of invalids = 0\n",
            "gen = 44 , Best fitness = (0.6723327126072272,) , Number of invalids = 0\n",
            "gen = 45 , Best fitness = (0.6723327126072272,) , Number of invalids = 0\n",
            "gen = 46 , Best fitness = (0.6723327126072272,) , Number of invalids = 0\n",
            "gen = 47 , Best fitness = (0.6723327126072272,) , Number of invalids = 0\n",
            "gen = 48 , Best fitness = (0.6723327126072272,) , Number of invalids = 0\n",
            "gen = 49 , Best fitness = (0.6725068171573054,) , Number of invalids = 0\n",
            "gen = 50 , Best fitness = (0.6725068171573054,) , Number of invalids = 0\n",
            "gen = 51 , Best fitness = (0.6725068171573054,) , Number of invalids = 0\n",
            "gen = 52 , Best fitness = (0.6725068171573054,) , Number of invalids = 0\n",
            "gen = 53 , Best fitness = (0.6728569760916261,) , Number of invalids = 0\n",
            "gen = 54 , Best fitness = (0.6728569760916261,) , Number of invalids = 0\n",
            "gen = 55 , Best fitness = (0.6728569760916261,) , Number of invalids = 0\n",
            "gen = 56 , Best fitness = (0.6728569760916261,) , Number of invalids = 0\n",
            "gen = 57 , Best fitness = (0.6728569760916261,) , Number of invalids = 0\n",
            "gen = 58 , Best fitness = (0.6728569760916261,) , Number of invalids = 0\n",
            "gen = 59 , Best fitness = (0.6728569760916261,) , Number of invalids = 0\n",
            "gen = 60 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 61 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 62 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 63 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 64 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 65 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 66 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 67 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 68 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 69 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 70 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 71 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 72 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 73 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 74 , Best fitness = (0.6732723088104116,) , Number of invalids = 0\n",
            "gen = 75 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 76 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 77 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 78 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 79 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 80 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 81 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 82 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 83 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 84 , Best fitness = (0.6735840138124141,) , Number of invalids = 0\n",
            "gen = 85 , Best fitness = (0.6744869214116311,) , Number of invalids = 0\n",
            "gen = 86 , Best fitness = (0.6744869214116311,) , Number of invalids = 0\n",
            "gen = 87 , Best fitness = (0.6749640266866804,) , Number of invalids = 0\n",
            "gen = 88 , Best fitness = (0.6749640266866804,) , Number of invalids = 0\n",
            "gen = 89 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 90 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 91 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 92 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 93 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 94 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 95 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 96 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 97 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 98 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 99 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n",
            "gen = 100 , Best fitness = (0.6765805662939834,) , Number of invalids = 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLXak5uTuVuh"
      },
      "source": [
        "Show the best individual as an expression."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "00RD1VvMtr5J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "916f251d-438b-47e4-b550-b674432cdd7c"
      },
      "source": [
        "# Best individual\n",
        "import textwrap\n",
        "best = hof.items[0].phenotype\n",
        "print(\"Best individual: \\n\",\"\\n\".join(textwrap.wrap(best,80)))\n",
        "print(\"\\nTraining Fitness: \", hof.items[0].fitness.values[0])"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best individual: \n",
            " and_(and_(not_(x[16]),or_(not_(x[8]),or_(not_(x[8]),or_(not_(x[14]),less_than_or\n",
            "_equal(x[18],x[19]))))),and_(less_than_or_equal(x[18],x[19]),and_(or_(not_(not_(\n",
            "x[12])),x[8]),and_(or_(not_(x[15]),or_(not_(x[14]),or_(not_(x[14]),and_(x[8],or_\n",
            "(not_(x[14]),or_(not_(x[14]),or_(not_(x[13]),greater_than_or_equal(x[3],\n",
            "mul(x[3],x[19]))))))))),x[7]))))\n",
            "\n",
            "Training Fitness:  0.6765805662939834\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnwyfFGmunWR"
      },
      "source": [
        "Define a function to predict values, without comparing to expected outputs."
      ]
    },
    {
      "source": [
        "def predict(individual, X ):\n",
        "    x = X\n",
        "\n",
        "    if individual.invalid == True:\n",
        "        return np.NaN, # Return NaN if the individual is invalid\n",
        "\n",
        "    # Evaluate the expression\n",
        "    try:\n",
        "      def evaluate_expression(x):\n",
        "          # Define local variables for the expression context\n",
        "          X = x\n",
        "          # Evaluate the expression and handle potential errors\n",
        "          try:\n",
        "              pred = eval(individual.phenotype)\n",
        "          except (TypeError, ValueError, NameError):  # Handle potential errors in expression\n",
        "              pred = np.nan  # Return NaN for invalid expressions\n",
        "          return pred  # Explicitly return the prediction\n",
        "\n",
        "      pred = np.apply_along_axis(evaluate_expression,0,x)\n",
        "    except (FloatingPointError, ZeroDivisionError, OverflowError,\n",
        "            MemoryError):\n",
        "        return np.NaN,\n",
        "\n",
        "    # Replace NaN values with a suitable default (e.g., -1)\n",
        "    pred = np.nan_to_num(pred, nan=-1).astype(int)\n",
        "\n",
        "    #y_test = y_test.astype(int)\n",
        "    pred = pred.astype(int)  # This line is now handled above\n",
        "    #acc = accuracy_score(y_test, pred)\n",
        "\n",
        "\n",
        "    return pred"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "DB3ogWjqirty"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "f_test = np.transpose(f_test)\n",
        "print(f_test.shape)\n"
      ],
      "metadata": {
        "collapsed": true,
        "id": "Evs7Cx-dCQHV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2f64e06-1642-4975-aa5c-0fba3c0a15fc"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(21, 95804)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U87HYdwKud_x"
      },
      "source": [
        "Predict the classes of the test set.\n",
        "\n",
        "Make sure you print here in the notebook you will submit to Brightspace the same predictions you used in your best submission to the Kaggle competition."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Um-DsUo4uefK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3f79ff4-1596-4aa3-9dc1-f8602f5baf0e"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(hof.items[0].phenotype)\n",
        "print(\"Training Fitness: \", hof.items[0].fitness.values[0])\n",
        "#f_test = f_test.to_numpy()\n",
        "#f_test = np.transpose(f_test)\n",
        "\n",
        "print(f_test.shape)\n",
        "print(y_test.shape)\n",
        "\n",
        "y_pred  = predict(hof.items[0], f_test)\n",
        "print(\"Predicted classes of the test set: \", y_pred )\n",
        "data = {\"output\" : y_pred}\n",
        "df1 = pd.DataFrame(data)\n",
        "print(df1.head(100))\n",
        "df1.to_csv(\"/content/sample_data/result.csv\", header=True)\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "and_(and_(not_(x[16]),or_(not_(x[8]),or_(not_(x[8]),or_(not_(x[14]),less_than_or_equal(x[18],x[19]))))),and_(less_than_or_equal(x[18],x[19]),and_(or_(not_(not_(x[12])),x[8]),and_(or_(not_(x[15]),or_(not_(x[14]),or_(not_(x[14]),and_(x[8],or_(not_(x[14]),or_(not_(x[14]),or_(not_(x[13]),greater_than_or_equal(x[3], mul(x[3],x[19]))))))))),x[7]))))\n",
            "Training Fitness:  0.6765805662939834\n",
            "(21, 95804)\n",
            "(1513,)\n",
            "Predicted classes of the test set:  [1 0 0 ... 0 1 0]\n",
            "    output\n",
            "0        1\n",
            "1        0\n",
            "2        0\n",
            "3        0\n",
            "4        1\n",
            "..     ...\n",
            "95       0\n",
            "96       0\n",
            "97       0\n",
            "98       0\n",
            "99       0\n",
            "\n",
            "[100 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cvilz5ApvV05"
      },
      "source": [
        "Write a code to create a .csv with the following format:\n",
        "1. First column is the index (from 0 to 95803);\n",
        "2. Second column is named `output` and contains the predictions (only 0's or 1's) you  got in the previous cell with y_pred.\n",
        "\n",
        "Example:\n",
        "\n",
        "    index,output\n",
        "\n",
        "    0,0\n",
        "\n",
        "    1,0\n",
        "\n",
        "    2,1\n",
        "\n",
        "    ...\n",
        "\n",
        "    95803,0\n",
        "\n",
        "\n",
        "Submit it to the competition and check your score there."
      ]
    }
  ]
}