GP-Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from os import path\n",
    "from deap import creator, base, tools, gp\n",
    "import random\n",
    "import csv\n",
    "import operator\n",
    "import math\n",
    "random.seed(117)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PATH = 'grape/'\n",
    "\n",
    "# # check if 'grape' already exists\n",
    "# if os.path.exists(PATH):\n",
    "#     print('grape directory already exists')\n",
    "\n",
    "# # change directory to 'grape'\n",
    "# %cd grape/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</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>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</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>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</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>24.0</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>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</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>24.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "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",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_file = 'train.csv'\n",
    "# load train set\n",
    "df_train = pd.read_csv(train_file)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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",
       "      <td>...</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",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </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>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>"
      ],
      "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]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train.copy()\n",
    "# warning: cannot drop it more than once\n",
    "X_train.drop(['output'], axis=1, inplace=True)\n",
    "\n",
    "y_train = df_train['output'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(y_train[0:5]) #print head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
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       "      <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",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>4.0</td>\n",
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       "    <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",
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       "      <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",
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       "    <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",
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       "      <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>3.0</td>\n",
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       "      <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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <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",
       "</div>"
      ],
      "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]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_file = 'test.csv'\n",
    "# load test set\n",
    "df_test = pd.read_csv(test_file)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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",
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       "      <td>95804.000000</td>\n",
       "      <td>...</td>\n",
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       "      <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>"
      ],
      "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]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Binary_data = ['HighBP', 'HighChol', 'CholCheck', 'Smoker', 'Stroke', 'HeartDiseaseorAttack', 'PhysActivity', 'Fruits', 'Veggies', 'HvyAlcoholConsump', 'AnyHealthcare', 'NoDocbcCost', 'DiffWalk', 'Sex']\n",
    "Categorical_data = ['GenHlth', 'Age', 'Education', 'Income']\n",
    "Numerical_data = ['BMI', 'MentHlth', 'PhysHlth','MentHlth', 'PhysHlth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GenHlth</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",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5037</th>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5038</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5039</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5040</th>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5042 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      GenHlth   Age  Education  Income\n",
       "0         1.0   7.0        6.0     8.0\n",
       "1         2.0   9.0        6.0     8.0\n",
       "2         2.0  13.0        5.0     4.0\n",
       "3         2.0   7.0        4.0     6.0\n",
       "4         3.0   8.0        5.0     7.0\n",
       "...       ...   ...        ...     ...\n",
       "5037      3.0  12.0        4.0     3.0\n",
       "5038      2.0   8.0        5.0     8.0\n",
       "5039      3.0  11.0        4.0     2.0\n",
       "5040      4.0  13.0        5.0     4.0\n",
       "5041      3.0  13.0        6.0     3.0\n",
       "\n",
       "[5042 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[Categorical_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder # type: ignore\n",
    "drop_enc = OneHotEncoder()\n",
    "drop_enc.fit(df_train[Categorical_data])\n",
    "X_train_cat = drop_enc.transform(df_train[Categorical_data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_cat = X_train_cat.toarray()\n",
    "X_test_cat = drop_enc.transform(df_test[Categorical_data])\n",
    "X_test_cat = X_test_cat.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[Binary_data].join(pd.DataFrame(X_train_cat)) \n",
    "X_test = X_test[Binary_data].join(pd.DataFrame(X_test_cat))\n",
    "X_train = X_train.to_numpy()\n",
    "X_test = X_test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5042, 46)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function set\n",
    "def and_int(x1, x2):\n",
    "    if x1 == x2:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "def or_int(x1, x2):\n",
    "    if x1 == 1 or x2 == 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/achyut/micromamba/envs/UL/lib/python3.10/site-packages/deap/gp.py:254: RuntimeWarning: Ephemeral rand101 function cannot be pickled because its generating function is a lambda function. Use functools.partial instead.\n",
      "  warnings.warn(\"Ephemeral {name} function cannot be \"\n"
     ]
    }
   ],
   "source": [
    "# Create a primitive set for GP\n",
    "pset = gp.PrimitiveSet(\"MAIN\", X_train.shape[1])  # Each feature in X is a variable\n",
    "pset.addPrimitive(operator.add, 2)\n",
    "pset.addPrimitive(operator.sub, 2)\n",
    "pset.addPrimitive(operator.mul, 2)\n",
    "pset.addPrimitive(operator.neg, 1)\n",
    "pset.addPrimitive(and_int, 2)\n",
    "pset.addPrimitive(or_int, 2)\n",
    "pset.addEphemeralConstant(\"rand101\", lambda: random.randint(-1, 1))\n",
    "pset.addTerminal(1)\n",
    "pset.addTerminal(0)\n",
    "\n",
    "# Define Fitness and Individual\n",
    "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,))\n",
    "creator.create(\"Individual\", gp.PrimitiveTree, fitness=creator.FitnessMax)\n",
    "\n",
    "toolbox = base.Toolbox()\n",
    "toolbox.register(\"expr\", gp.genHalfAndHalf, pset=pset, min_=1, max_=3)\n",
    "toolbox.register(\"individual\", tools.initIterate, creator.Individual, toolbox.expr)\n",
    "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n",
    "toolbox.register(\"compile\", gp.compile, pset=pset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Define the evaluation function\n",
    "def eval_individual(individual):\n",
    "    func = toolbox.compile(expr=individual)\n",
    "    predictions = [func(*x) > 0 for x in X_train]  # Binary classification: >0 as class 1\n",
    "    size_penalty = len(individual) * 0.0000001\n",
    "    return accuracy_score(y_train, predictions) - size_penalty,\n",
    "    # return accuracy_score(y_train, predictions),\n",
    "\n",
    "toolbox.register(\"evaluate\", eval_individual)\n",
    "toolbox.register(\"select\", tools.selTournament, tournsize=5)\n",
    "toolbox.register(\"mate\", gp.cxOnePoint)\n",
    "toolbox.register(\"mutate\", gp.mutUniform, expr=toolbox.expr, pset=pset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 0: {'avg': 0.559159259999524, 'std': 0.10441899030991038, 'min': 0.3343908146370488, 'max': 0.6943670145577152}\n",
      "Generation 1: {'avg': 0.6283659458217374, 'std': 0.061340740858417016, 'min': 0.317929093097977, 'max': 0.6943670145577152}\n",
      "Generation 2: {'avg': 0.6435487973646172, 'std': 0.05976103899313912, 'min': 0.32149780499801667, 'max': 0.6943670145577152}\n",
      "Generation 3: {'avg': 0.6543382136625149, 'std': 0.05846376572289439, 'min': 0.32645695485918286, 'max': 0.6955570185243951}\n",
      "Generation 4: {'avg': 0.6586639670813963, 'std': 0.06313706715237219, 'min': 0.34787722625942086, 'max': 0.6955570185243951}\n",
      "Generation 5: {'avg': 0.6629329481778659, 'std': 0.0677471138547519, 'min': 0.350059100198334, 'max': 0.6955570185243951}\n",
      "Generation 6: {'avg': 0.6703610029378817, 'std': 0.060625898913213766, 'min': 0.3343896146370488, 'max': 0.6955570185243951}\n",
      "Generation 7: {'avg': 0.6691801195349465, 'std': 0.06517175599242418, 'min': 0.3391506305037683, 'max': 0.6955570185243951}\n",
      "Generation 8: {'avg': 0.6726156535867513, 'std': 0.053858534889620516, 'min': 0.3736603455374851, 'max': 0.6955570185243951}\n",
      "Generation 9: {'avg': 0.665326895490837, 'std': 0.07162821592421015, 'min': 0.34252190840936136, 'max': 0.6955570185243951}\n",
      "Generation 10: {'avg': 0.6651447984839352, 'std': 0.06331408455619711, 'min': 0.36314904383181273, 'max': 0.6955570185243951}\n",
      "Generation 11: {'avg': 0.6648746693834987, 'std': 0.06806484857793643, 'min': 0.33974563248710826, 'max': 0.6955570185243951}\n",
      "Generation 12: {'avg': 0.6647727229103532, 'std': 0.06911890861982269, 'min': 0.33260560868702893, 'max': 0.6955570185243951}\n"
     ]
    }
   ],
   "source": [
    "# Parameters\n",
    "population = toolbox.population(n=500)\n",
    "num_generations = 200\n",
    "cxpb, mutpb = 0.5, 0.2  # Crossover and mutation probabilities\n",
    "elite_size = 5  # Number of best individuals to carry over to the next generation\n",
    "\n",
    "# Statistics to track progress\n",
    "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
    "stats.register(\"avg\", np.mean)\n",
    "stats.register(\"std\", np.std)\n",
    "stats.register(\"min\", np.min)\n",
    "stats.register(\"max\", np.max)\n",
    "\n",
    "# Run the GP evolution\n",
    "for gen in range(num_generations):\n",
    "    # Adaptive crossover and mutation\n",
    "    # cxpb = max(0.3, 0.8 - 0.02 * gen)  # Adaptive crossover rate\n",
    "    # mutpb = max(0.1, 0.5 - 0.01 * gen)  # Adaptive mutation rate\n",
    "\n",
    "    offspring = toolbox.select(population, len(population))\n",
    "    offspring = list(map(toolbox.clone, offspring))\n",
    "\n",
    "    # Apply crossover and mutation\n",
    "    for child1, child2 in zip(offspring[::2], offspring[1::2]):\n",
    "        if random.random() < cxpb:\n",
    "            toolbox.mate(child1, child2)\n",
    "            del child1.fitness.values\n",
    "            del child2.fitness.values\n",
    "\n",
    "    for mutant in offspring:\n",
    "        if random.random() < mutpb:\n",
    "            toolbox.mutate(mutant)\n",
    "            del mutant.fitness.values\n",
    "\n",
    "    # Evaluate fitness of individuals with invalid fitness\n",
    "    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n",
    "    fitnesses = map(toolbox.evaluate, invalid_ind)\n",
    "    for ind, fit in zip(invalid_ind, fitnesses):\n",
    "        ind.fitness.values = fit\n",
    "\n",
    "    elite = tools.selBest(population, elite_size)\n",
    "\n",
    "    # Replace old population with offspring\n",
    "    population[:] = offspring\n",
    "    # population[:] = elite + offspring[:500 - elite_size]\n",
    "\n",
    "    # Gather and print statistics for the generation\n",
    "    record = stats.compile(population)\n",
    "    print(f\"Generation {gen}: {record}\")\n",
    "\n",
    "# Find the best individual\n",
    "best_individual = tools.selBest(population, 1)[0]\n",
    "print(\"Best individual:\", best_individual)\n",
    "print(\"Best individual fitness:\", best_individual.fitness.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'best_individual' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m best_func \u001b[38;5;241m=\u001b[39m toolbox\u001b[38;5;241m.\u001b[39mcompile(expr\u001b[38;5;241m=\u001b[39m\u001b[43mbest_individual\u001b[49m)\n\u001b[1;32m      2\u001b[0m predictions \u001b[38;5;241m=\u001b[39m [best_func(\u001b[38;5;241m*\u001b[39mx) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m X_test] \u001b[38;5;66;03m# Binary classification: >0 as class 1\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'best_individual' is not defined"
     ]
    }
   ],
   "source": [
    "best_func = toolbox.compile(expr=best_individual)\n",
    "predictions = [best_func(*x) > 0 for x in X_test] # Binary classification: >0 as class 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 0, 0, 1, 0, 0, 0, 1, 0]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = [int(x) for x in predictions]\n",
    "predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('output.csv', 'w') as f:\n",
    "    f.write('index,output\\n')\n",
    "    for i in enumerate(predictions):\n",
    "        f.write(f\"{i[0]},{i[1]}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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