FYP_final Code_V3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.metrics import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline \n",
    "from sklearn.compose import ColumnTransformer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, TensorDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Size</th>\n",
       "      <th>Dose</th>\n",
       "      <th>P_density</th>\n",
       "      <th>CSKKKK</th>\n",
       "      <th>QHREDGS</th>\n",
       "      <th>RKDVY</th>\n",
       "      <th>WKYMVM</th>\n",
       "      <th>CLPFFD</th>\n",
       "      <th>QEINSSY</th>\n",
       "      <th>fold_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.699131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.749064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.761759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.690541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.174875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.151180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.554630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.048382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Size  Dose  P_density  CSKKKK  QHREDGS  RKDVY  WKYMVM  CLPFFD  QEINSSY  \\\n",
       "0    HNP  1000       1.71       1        0      0       0       0        0   \n",
       "1    HNP  1000       1.71       0        1      0       0       0        0   \n",
       "2    HNP  1000       1.71       0        0      1       0       0        0   \n",
       "3    HNP  1000       1.71       0        0      0       1       0        0   \n",
       "4    HNP  1000       1.71       0        0      0       0       1        0   \n",
       "..   ...   ...        ...     ...      ...    ...     ...     ...      ...   \n",
       "149  HMP   100       4.99       0        0      1       0       0        0   \n",
       "150  HMP   100       4.99       1        1      0       0       0        0   \n",
       "151  HMP   100       4.99       1        0      1       0       0        0   \n",
       "152  HMP   100       4.99       0        1      1       0       0        0   \n",
       "153  HMP   100       4.99       1        1      1       0       0        0   \n",
       "\n",
       "     fold_change  \n",
       "0      -0.713886  \n",
       "1      -0.699131  \n",
       "2      -0.749064  \n",
       "3      -0.761759  \n",
       "4      -0.690541  \n",
       "..           ...  \n",
       "149     0.069006  \n",
       "150     1.174875  \n",
       "151    -0.151180  \n",
       "152     0.554630  \n",
       "153    -0.048382  \n",
       "\n",
       "[154 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_in = pd.read_csv('finaldatabase_HNP_v2.csv')  # this one contains different sizes\n",
    "df_in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.9.7\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['WKYMVM']\n",
      "['CLPFFD']\n",
      "['QEINSSY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['CSKKKK', 'WKYMVM']\n",
      "['CSKKKK', 'CLPFFD']\n",
      "['CSKKKK', 'QEINSSY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['QHREDGS', 'WKYMVM']\n",
      "['QHREDGS', 'CLPFFD']\n",
      "['QHREDGS', 'QEINSSY']\n",
      "['RKDVY', 'WKYMVM']\n",
      "['RKDVY', 'CLPFFD']\n",
      "['RKDVY', 'QEINSSY']\n",
      "['WKYMVM', 'CLPFFD']\n",
      "['WKYMVM', 'QEINSSY']\n",
      "['CLPFFD', 'QEINSSY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['WKYMVM']\n",
      "['CLPFFD']\n",
      "['QEINSSY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['CSKKKK', 'WKYMVM']\n",
      "['CSKKKK', 'CLPFFD']\n",
      "['CSKKKK', 'QEINSSY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['QHREDGS', 'WKYMVM']\n",
      "['QHREDGS', 'CLPFFD']\n",
      "['QHREDGS', 'QEINSSY']\n",
      "['RKDVY', 'WKYMVM']\n",
      "['RKDVY', 'CLPFFD']\n",
      "['RKDVY', 'QEINSSY']\n",
      "['WKYMVM', 'CLPFFD']\n",
      "['WKYMVM', 'QEINSSY']\n",
      "['CLPFFD', 'QEINSSY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['WKYMVM']\n",
      "['CLPFFD']\n",
      "['QEINSSY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['CSKKKK', 'WKYMVM']\n",
      "['CSKKKK', 'CLPFFD']\n",
      "['CSKKKK', 'QEINSSY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['QHREDGS', 'WKYMVM']\n",
      "['QHREDGS', 'CLPFFD']\n",
      "['QHREDGS', 'QEINSSY']\n",
      "['RKDVY', 'WKYMVM']\n",
      "['RKDVY', 'CLPFFD']\n",
      "['RKDVY', 'QEINSSY']\n",
      "['WKYMVM', 'CLPFFD']\n",
      "['WKYMVM', 'QEINSSY']\n",
      "['CLPFFD', 'QEINSSY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['WKYMVM']\n",
      "['CLPFFD']\n",
      "['QEINSSY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['CSKKKK', 'WKYMVM']\n",
      "['CSKKKK', 'CLPFFD']\n",
      "['CSKKKK', 'QEINSSY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['QHREDGS', 'WKYMVM']\n",
      "['QHREDGS', 'CLPFFD']\n",
      "['QHREDGS', 'QEINSSY']\n",
      "['RKDVY', 'WKYMVM']\n",
      "['RKDVY', 'CLPFFD']\n",
      "['RKDVY', 'QEINSSY']\n",
      "['WKYMVM', 'CLPFFD']\n",
      "['WKYMVM', 'QEINSSY']\n",
      "['CLPFFD', 'QEINSSY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['WKYMVM']\n",
      "['CLPFFD']\n",
      "['QEINSSY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['CSKKKK', 'WKYMVM']\n",
      "['CSKKKK', 'CLPFFD']\n",
      "['CSKKKK', 'QEINSSY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['QHREDGS', 'WKYMVM']\n",
      "['QHREDGS', 'CLPFFD']\n",
      "['QHREDGS', 'QEINSSY']\n",
      "['RKDVY', 'WKYMVM']\n",
      "['RKDVY', 'CLPFFD']\n",
      "['RKDVY', 'QEINSSY']\n",
      "['WKYMVM', 'CLPFFD']\n",
      "['WKYMVM', 'QEINSSY']\n",
      "['CLPFFD', 'QEINSSY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['WKYMVM']\n",
      "['CLPFFD']\n",
      "['QEINSSY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['CSKKKK', 'WKYMVM']\n",
      "['CSKKKK', 'CLPFFD']\n",
      "['CSKKKK', 'QEINSSY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['QHREDGS', 'WKYMVM']\n",
      "['QHREDGS', 'CLPFFD']\n",
      "['QHREDGS', 'QEINSSY']\n",
      "['RKDVY', 'WKYMVM']\n",
      "['RKDVY', 'CLPFFD']\n",
      "['RKDVY', 'QEINSSY']\n",
      "['WKYMVM', 'CLPFFD']\n",
      "['WKYMVM', 'QEINSSY']\n",
      "['CLPFFD', 'QEINSSY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['CSKKKK', 'QHREDGS', 'RKDVY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['CSKKKK', 'QHREDGS', 'RKDVY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['CSKKKK', 'QHREDGS', 'RKDVY']\n",
      "['CSKKKK']\n",
      "['QHREDGS']\n",
      "['RKDVY']\n",
      "['CSKKKK', 'QHREDGS']\n",
      "['CSKKKK', 'RKDVY']\n",
      "['QHREDGS', 'RKDVY']\n",
      "['CSKKKK', 'QHREDGS', 'RKDVY']\n",
      "    Size  Dose  P_density  fold_change  Peptides\n",
      "0    HNP  1000       1.71    -0.713886       NaN\n",
      "1    HNP  1000       1.71    -0.699131       NaN\n",
      "2    HNP  1000       1.71    -0.749064       NaN\n",
      "3    HNP  1000       1.71    -0.761759       NaN\n",
      "4    HNP  1000       1.71    -0.690541       NaN\n",
      "..   ...   ...        ...          ...       ...\n",
      "149  HMP   100       4.99     0.069006       NaN\n",
      "150  HMP   100       4.99     1.174875       NaN\n",
      "151  HMP   100       4.99    -0.151180       NaN\n",
      "152  HMP   100       4.99     0.554630       NaN\n",
      "153  HMP   100       4.99    -0.048382       NaN\n",
      "\n",
      "[154 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kejahanmir\\Anaconda3\\envs\\my-rdkit-env\\lib\\site-packages\\ipykernel_launcher.py:5: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "### Make the peptides as category first\n",
    "peptide_list = [[] for _ in range(len(df_in.index))]\n",
    "df_new=df_in[['CSKKKK','QHREDGS','RKDVY','WKYMVM','CLPFFD','QEINSSY']]\n",
    "col_names=['CSKKKK','QHREDGS','RKDVY','WKYMVM','CLPFFD','QEINSSY']\n",
    "df_final = df_in.drop(col_names, axis=1)\n",
    "df_final['Peptides'] = pd.Series([])\n",
    "#print(df_final)\n",
    "for i in range(len(df_new)):\n",
    "    a=(df_new.loc[i,:]).to_numpy()\n",
    "    indices=np.where(a== 1)[0]\n",
    "    peptide_seqs = [col_names[j] for j in indices]\n",
    "    print(peptide_seqs)\n",
    "    peptide_list[i]=peptide_seqs\n",
    "    #df_final.at[i, 'Peptides'] = peptide_seqs\n",
    "print(df_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_in=df_final\n",
    "df_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  6.,  4., 11.,  8.,  5.,  1.,  1.,  0.,  2.,  6.,  1.,  0.,\n",
       "         2.,  2.,  5.,  2.,  6.,  2.,  2.,  2.,  3.,  4.,  6.,  1.,  2.,\n",
       "         1.,  4.,  3.,  1.,  5.,  2.,  1.,  1.,  1.,  2.,  2.,  2.,  2.,\n",
       "         1.,  1.,  0.,  0.,  1.,  0.,  2.,  1.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  1.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
       " array([-0.78266417, -0.76113787, -0.73961157, -0.71808528, -0.69655898,\n",
       "        -0.67503268, -0.65350638, -0.63198008, -0.61045378, -0.58892749,\n",
       "        -0.56740119, -0.54587489, -0.52434859, -0.50282229, -0.481296  ,\n",
       "        -0.4597697 , -0.4382434 , -0.4167171 , -0.3951908 , -0.3736645 ,\n",
       "        -0.35213821, -0.33061191, -0.30908561, -0.28755931, -0.26603301,\n",
       "        -0.24450671, -0.22298042, -0.20145412, -0.17992782, -0.15840152,\n",
       "        -0.13687522, -0.11534892, -0.09382263, -0.07229633, -0.05077003,\n",
       "        -0.02924373, -0.00771743,  0.01380887,  0.03533516,  0.05686146,\n",
       "         0.07838776,  0.09991406,  0.12144036,  0.14296666,  0.16449295,\n",
       "         0.18601925,  0.20754555,  0.22907185,  0.25059815,  0.27212444,\n",
       "         0.29365074,  0.31517704,  0.33670334,  0.35822964,  0.37975594,\n",
       "         0.40128223,  0.42280853,  0.44433483,  0.46586113,  0.48738743,\n",
       "         0.50891373,  0.53044002,  0.55196632,  0.57349262,  0.59501892,\n",
       "         0.61654522,  0.63807152,  0.65959781,  0.68112411,  0.70265041,\n",
       "         0.72417671,  0.74570301,  0.76722931,  0.7887556 ,  0.8102819 ,\n",
       "         0.8318082 ,  0.8533345 ,  0.8748608 ,  0.89638709,  0.91791339,\n",
       "         0.93943969,  0.96096599,  0.98249229,  1.00401859,  1.02554488,\n",
       "         1.04707118,  1.06859748,  1.09012378,  1.11165008,  1.13317638,\n",
       "         1.15470267,  1.17622897,  1.19775527,  1.21928157,  1.24080787,\n",
       "         1.26233417,  1.28386046,  1.30538676,  1.32691306,  1.34843936,\n",
       "         1.36996566]),\n",
       " <BarContainer object of 100 artists>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAqqqiKIquXsQntba2Rm1tbbS0tERNTU3Jjz+sflHJj/lZvH3j5E6P93Z92x7nsxyLyraj74Vtbfu9sbffP7szV1fa0TmU6uuzN3Pt7Xoq0f58rt3tPLOuxZ78++3OBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQqmzxsWDBghg2bFj06dMnxo8fHy+88EK5pgIAKkhZ4uOhhx6K2bNnx9y5c2P16tUxZsyYmDRpUmzcuLEc0wEAFaQs8XHrrbfGZZddFtOnT48TTjgh7rjjjjjwwAPj7rvvLsd0AEAF6VXqA3788cexatWqaGho6NjXo0ePmDhxYixbtmy78W1tbdHW1tbxuKWlJSIiWltbS720iIhob/uoLMfdW9ue596ub0dfr+52ruTYnffOtt8be/v9U673aans6BxK9fXZm7n2dj2VaH8+1+52nlnX4n/HLIri0wcXJfbee+8VEVE899xznfbPmTOnGDdu3Hbj586dW0SEzWaz2Wy2fWBramr61FYo+Z2PPdXQ0BCzZ8/ueNze3h4ffPBBHHLIIVFVVdWFK6tsra2tMXTo0GhqaoqampquXg474Bp1f65R9+b6dC9FUcSmTZuirq7uU8eWPD4OPfTQ6NmzZ2zYsKHT/g0bNsTgwYO3G19dXR3V1dWd9vXv37/Uy9pv1dTUeFN2c65R9+cadW+uT/dRW1u7W+NK/oHT3r17xymnnBJLlizp2Nfe3h5LliyJCRMmlHo6AKDClOXHLrNnz45p06bFqaeeGuPGjYv58+fHli1bYvr06eWYDgCoIGWJjwsvvDD+9re/xbXXXhvNzc1x0kknxeLFi2PQoEHlmI4dqK6ujrlz5273Iy26D9eo+3ONujfXp3JVFcXu/E4MAEBp+NsuAEAq8QEApBIfAEAq8QEApBIf+5APPvggpk6dGjU1NdG/f/+49NJLY/Pmzbt8zRe/+MWoqqrqtH3nO99JWvG+b8GCBTFs2LDo06dPjB8/Pl544YVdjv/Vr34VI0aMiD59+sTo0aPjt7/9bdJK9197co0WLly43fulT58+iavdvyxdujTOP//8qKuri6qqqnj00Uc/9TXPPPNMnHzyyVFdXR3HHHNMLFy4sOzrZM+Jj33I1KlTY+3atfHUU0/F448/HkuXLo0ZM2Z86usuu+yy+Otf/9qx3XzzzQmr3fc99NBDMXv27Jg7d26sXr06xowZE5MmTYqNGzfucPxzzz0XF198cVx66aWxZs2amDJlSkyZMiVefvnl5JXvP/b0GkX8//9N85Pvl3feeSdxxfuXLVu2xJgxY2LBggW7NX79+vUxefLkOPvss+PFF1+MWbNmxbe//e148skny7xS9lhp/pwcXe2VV14pIqJYsWJFx74nnniiqKqqKt57772dvu6ss84qrrzyyoQV7n/GjRtXzJw5s+Px1q1bi7q6uqKxsXGH47/2ta8VkydP7rRv/PjxxeWXX17Wde7P9vQa3XPPPUVtbW3S6vikiCgeeeSRXY656qqripEjR3bad+GFFxaTJk0q48rYG+587COWLVsW/fv3j1NPPbVj38SJE6NHjx6xfPnyXb72vvvui0MPPTRGjRoVDQ0N8dFHn/6n1Nm1jz/+OFatWhUTJ07s2NejR4+YOHFiLFu2bIevWbZsWafxERGTJk3a6Xg+m725RhERmzdvjiOPPDKGDh0aF1xwQaxduzZjuewG76HK0eV/1ZbSaG5ujoEDB3ba16tXrxgwYEA0Nzfv9HVf//rX48gjj4y6urr405/+FFdffXW89tpr8etf/7rcS96n/f3vf4+tW7du93/1HTRoUPz5z3/e4Wuam5t3OH5X14+9tzfX6Ljjjou77747TjzxxGhpaYlbbrklTj/99Fi7dm0cfvjhGctmF3b2HmptbY1//etf0bdv3y5aGdsSH91cfX193HTTTbsc8+qrr+718T/5mZDRo0fHkCFD4pxzzok333wzjj766L0+LuyLJkyY0OkPZJ5++ulx/PHHx5133hnXXXddF64MKov46OZ++MMfxje/+c1djjnqqKNi8ODB231I7r///W988MEHMXjw4N2eb/z48RERsW7dOvHxGRx66KHRs2fP2LBhQ6f9GzZs2On1GDx48B6N57PZm2u0rQMOOCDGjh0b69atK8cS2UM7ew/V1NS469HN+MxHN3fYYYfFiBEjdrn17t07JkyYEB9++GGsWrWq47W/+93vor29vSModseLL74YERFDhgwp9ansV3r37h2nnHJKLFmypGNfe3t7LFmypNN/OX/ShAkTOo2PiHjqqad2Op7PZm+u0ba2bt0aL730kvdLN+E9VEG6+hOvlM6Xv/zlYuzYscXy5cuLZ599tjj22GOLiy++uOP5v/zlL8Vxxx1XLF++vCiKoli3bl3xk5/8pFi5cmWxfv364rHHHiuOOuqo4swzz+yqU9inPPjgg0V1dXWxcOHC4pVXXilmzJhR9O/fv2hubi6KoiguueSSor6+vmP8H/7wh6JXr17FLbfcUrz66qvF3LlziwMOOKB46aWXuuoU9nl7eo3mzZtXPPnkk8Wbb75ZrFq1qrjooouKPn36FGvXru2qU9inbdq0qVizZk2xZs2aIiKKW2+9tVizZk3xzjvvFEVRFPX19cUll1zSMf6tt94qDjzwwGLOnDnFq6++WixYsKDo2bNnsXjx4q46BXZCfOxD/vGPfxQXX3xx8bnPfa6oqakppk+fXmzatKnj+fXr1xcRUTz99NNFURTFu+++W5x55pnFgAEDiurq6uKYY44p5syZU7S0tHTRGex7brvttuKII44oevfuXYwbN654/vnnO54766yzimnTpnUa//DDDxef//zni969excjR44sFi1alLzi/c+eXKNZs2Z1jB00aFDxla98pVi9enUXrHr/8PTTTxcRsd32v2sybdq04qyzztruNSeddFLRu3fv4qijjiruueee9HXz6aqKoii68MYLALCf8ZkPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUv0fi7773SXpVegAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_in.fold_change,  bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Size</th>\n",
       "      <th>Dose</th>\n",
       "      <th>P_density</th>\n",
       "      <th>CSKKKK</th>\n",
       "      <th>QHREDGS</th>\n",
       "      <th>RKDVY</th>\n",
       "      <th>WKYMVM</th>\n",
       "      <th>CLPFFD</th>\n",
       "      <th>QEINSSY</th>\n",
       "      <th>fold_change</th>\n",
       "      <th>Polarization_group</th>\n",
       "      <th>Size_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.713886</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.699131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.749064</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.761759</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HNP</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.690541</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.174875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.151180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.554630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>HMP</td>\n",
       "      <td>100</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.048382</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Size  Dose  P_density  CSKKKK  QHREDGS  RKDVY  WKYMVM  CLPFFD  QEINSSY  \\\n",
       "0    HNP  1000       1.71       1        0      0       0       0        0   \n",
       "1    HNP  1000       1.71       0        1      0       0       0        0   \n",
       "2    HNP  1000       1.71       0        0      1       0       0        0   \n",
       "3    HNP  1000       1.71       0        0      0       1       0        0   \n",
       "4    HNP  1000       1.71       0        0      0       0       1        0   \n",
       "..   ...   ...        ...     ...      ...    ...     ...     ...      ...   \n",
       "149  HMP   100       4.99       0        0      1       0       0        0   \n",
       "150  HMP   100       4.99       1        1      0       0       0        0   \n",
       "151  HMP   100       4.99       1        0      1       0       0        0   \n",
       "152  HMP   100       4.99       0        1      1       0       0        0   \n",
       "153  HMP   100       4.99       1        1      1       0       0        0   \n",
       "\n",
       "     fold_change  Polarization_group  Size_type  \n",
       "0      -0.713886                   0          0  \n",
       "1      -0.699131                   0          0  \n",
       "2      -0.749064                   0          0  \n",
       "3      -0.761759                   0          0  \n",
       "4      -0.690541                   0          0  \n",
       "..           ...                 ...        ...  \n",
       "149     0.069006                   0          0  \n",
       "150     1.174875                   0          0  \n",
       "151    -0.151180                   0          0  \n",
       "152     0.554630                   0          0  \n",
       "153    -0.048382                   0          0  \n",
       "\n",
       "[154 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_in = df_in[['Size','Dose','P_density','CSKKKK','QHREDGS','RKDVY','WKYMVM','CLPFFD','QEINSSY','fold_change']]\n",
    "df_in['Polarization_group'] = 0\n",
    "df_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Octavius Jessica\\AppData\\Local\\Temp\\ipykernel_21808\\1031293842.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_in.Size_type[i] = 1\n",
      "C:\\Users\\Octavius Jessica\\AppData\\Local\\Temp\\ipykernel_21808\\1031293842.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_in.Size_type[i] = 0\n",
      "C:\\Users\\Octavius Jessica\\AppData\\Local\\Temp\\ipykernel_21808\\1031293842.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_in.Polarization_group[i] = 1\n",
      "C:\\Users\\Octavius Jessica\\AppData\\Local\\Temp\\ipykernel_21808\\1031293842.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_in.Polarization_group[i] = 0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for i in range(len(df_in)):\n",
    "  if df_in.fold_change[i]<0:\n",
    "    df_in.Polarization_group[i] = 1\n",
    "  elif df_in.fold_change[i]>0:\n",
    "    df_in.Polarization_group[i] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Size_type</th>\n",
       "      <th>Dose</th>\n",
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       "      <td>4.99</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Size_type  Dose  P_density  CSKKKK  QHREDGS  RKDVY  WKYMVM  CLPFFD  \\\n",
       "0            1  1000       1.71       1        0      0       0       0   \n",
       "1            1  1000       1.71       0        1      0       0       0   \n",
       "2            1  1000       1.71       0        0      1       0       0   \n",
       "3            1  1000       1.71       0        0      0       1       0   \n",
       "4            1  1000       1.71       0        0      0       0       1   \n",
       "..         ...   ...        ...     ...      ...    ...     ...     ...   \n",
       "149          0   100       4.99       0        0      1       0       0   \n",
       "150          0   100       4.99       1        1      0       0       0   \n",
       "151          0   100       4.99       1        0      1       0       0   \n",
       "152          0   100       4.99       0        1      1       0       0   \n",
       "153          0   100       4.99       1        1      1       0       0   \n",
       "\n",
       "     QEINSSY  Polarization_group  \n",
       "0          0                   1  \n",
       "1          0                   1  \n",
       "2          0                   1  \n",
       "3          0                   1  \n",
       "4          0                   1  \n",
       "..       ...                 ...  \n",
       "149        0                   0  \n",
       "150        0                   0  \n",
       "151        0                   1  \n",
       "152        0                   0  \n",
       "153        0                   1  \n",
       "\n",
       "[154 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = df_in[['Size','Dose','P_density','CSKKKK','QHREDGS','RKDVY','WKYMVM','CLPFFD','QEINSSY','Polarization_group']]\n",
    "df=df_in.drop(['fold_change'], axis=1)\n",
    "df_in.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding and scaling \n",
    "#Simple way of encoding and scaling the object category and numeric columns without defining any piplines\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MultiLabelBinarizer, StandardScaler\n",
    "# perform one-hot encoding on the 'col' and 'size' columns\n",
    "mlb = MultiLabelBinarizer()\n",
    "encoded_col = pd.DataFrame(mlb.fit_transform(df['Peptides']), columns=mlb.classes_)\n",
    "encoded_size = pd.get_dummies(df['Size'])\n",
    "df = pd.concat([df.drop(['Peptides', 'Size'], axis=1), encoded_col, encoded_size], axis=1)\n",
    "\n",
    "# perform scaling on the 'Dose' and 'P_density' columns\n",
    "scaler = StandardScaler()\n",
    "scaled_Dose = scaler.fit_transform(df[['Dose']])\n",
    "scaled_P_value = scaler.fit_transform(df[['P_density']])\n",
    "df['Dose'] = scaled_Dose\n",
    "df['P_density'] = scaled_P_value\n",
    "\n",
    "# print the encoded and scaled dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 9)\n",
      "(26, 9)\n",
      "(128, 1)\n",
      "(26, 1)\n"
     ]
    },
    {
     "data": {
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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>Polarization_group</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Polarization_group\n",
       "26                    1\n",
       "72                    1\n",
       "79                    1\n",
       "136                   0\n",
       "28                    1\n",
       "..                  ...\n",
       "142                   0\n",
       "73                    1\n",
       "109                   1\n",
       "50                    0\n",
       "80                    1\n",
       "\n",
       "[128 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.DataFrame(df, columns=['Polarization_group']) \n",
    "X = df.drop(columns=['Polarization_group'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.1667, \n",
    "                                                    random_state=0, #We put random state to keep the same selection\n",
    "                                                    stratify=y) \n",
    "                             \n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Idid not use this one\n",
    "\n",
    "\"\"\"\n",
    "# Preprocessing\n",
    "\n",
    "\n",
    "## Identification of numerical and categorical columns\n",
    "numeric_cols = X_train.select_dtypes(exclude=['object', 'category']).columns.to_list()\n",
    "cat_cols = X_train.select_dtypes(include=['object', 'category']).columns.to_list()\n",
    "\n",
    "## Transformations for numerical variables\n",
    "numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])\n",
    "\n",
    "## Transformations for categorical variables\n",
    "categorical_transformer = Pipeline(steps=[('onehot', OneHotEncoder())])\n",
    "\n",
    "## Preprocessor\n",
    "ct = ColumnTransformer(\n",
    "                    transformers=[\n",
    "                        ('numeric', numeric_transformer, numeric_cols),\n",
    "                        ('cat', categorical_transformer, cat_cols)\n",
    "                    ],\n",
    "                    remainder='passthrough'\n",
    "                )\n",
    " \n",
    "ct.fit(X_train) # Fit all transformers using X training data. \n",
    "#We put the content inside the scaler.\n",
    "\n",
    "#Obtain a list with the names of the new columns after doing the one-hot encoding.\n",
    "colnames = pd.get_dummies(X_train).columns.to_list()\n",
    "\n",
    "scaledTrain_df = pd.DataFrame(ct.transform(X_train), # Transform X separately by \n",
    "                              #each transformer, concatenate results.\n",
    "                              index=X_train.index,\n",
    "                              columns=colnames) \n",
    "\n",
    "scaledValidation_df = pd.DataFrame(ct.transform(X_test), # Transform X separately \n",
    "                                   #by each transformer, concatenate results.\n",
    "                                   index=X_test.index,\n",
    "                                   columns=colnames)\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 9)\n",
      "(26, 9)\n",
      "(128, 1)\n",
      "(26, 1)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size_type</th>\n",
       "      <th>Dose</th>\n",
       "      <th>P_density</th>\n",
       "      <th>CSKKKK</th>\n",
       "      <th>QHREDGS</th>\n",
       "      <th>RKDVY</th>\n",
       "      <th>WKYMVM</th>\n",
       "      <th>CLPFFD</th>\n",
       "      <th>QEINSSY</th>\n",
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       "  <tbody>\n",
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       "      <th>26</th>\n",
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       "      <td>1.430950</td>\n",
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       "      <td>-0.529150</td>\n",
       "      <td>-0.565321</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.480384</td>\n",
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       "      <td>1.405967</td>\n",
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       "      <th>142</th>\n",
       "      <td>-2.081666</td>\n",
       "      <td>1.015749</td>\n",
       "      <td>-0.665726</td>\n",
       "      <td>-0.711254</td>\n",
       "      <td>-0.698836</td>\n",
       "      <td>1.381699</td>\n",
       "      <td>-0.565321</td>\n",
       "      <td>-0.529150</td>\n",
       "      <td>-0.565321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.480384</td>\n",
       "      <td>-0.984495</td>\n",
       "      <td>0.085797</td>\n",
       "      <td>1.405967</td>\n",
       "      <td>-0.698836</td>\n",
       "      <td>-0.723747</td>\n",
       "      <td>-0.565321</td>\n",
       "      <td>-0.529150</td>\n",
       "      <td>1.768907</td>\n",
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       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.480384</td>\n",
       "      <td>-0.984495</td>\n",
       "      <td>1.350859</td>\n",
       "      <td>-0.711254</td>\n",
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       "      <td>1.889822</td>\n",
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       "      <td>1.015749</td>\n",
       "      <td>0.085797</td>\n",
       "      <td>1.405967</td>\n",
       "      <td>-0.698836</td>\n",
       "      <td>-0.723747</td>\n",
       "      <td>1.768907</td>\n",
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       "      <td>-0.565321</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.480384</td>\n",
       "      <td>-0.984495</td>\n",
       "      <td>0.085797</td>\n",
       "      <td>-0.711254</td>\n",
       "      <td>-0.698836</td>\n",
       "      <td>1.381699</td>\n",
       "      <td>-0.565321</td>\n",
       "      <td>-0.529150</td>\n",
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       "<p>128 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Size_type      Dose  P_density    CSKKKK   QHREDGS     RKDVY    WKYMVM  \\\n",
       "26    0.480384 -0.984495  -1.122206 -0.711254 -0.698836 -0.723747 -0.565321   \n",
       "72    0.480384 -0.984495   0.085797  1.405967 -0.698836 -0.723747 -0.565321   \n",
       "79    0.480384 -0.984495   0.085797 -0.711254 -0.698836  1.381699 -0.565321   \n",
       "136  -2.081666 -0.984495  -1.209883  1.405967  1.430950 -0.723747 -0.565321   \n",
       "28    0.480384 -0.984495  -1.122206  1.405967 -0.698836  1.381699 -0.565321   \n",
       "..         ...       ...        ...       ...       ...       ...       ...   \n",
       "142  -2.081666  1.015749  -0.665726 -0.711254 -0.698836  1.381699 -0.565321   \n",
       "73    0.480384 -0.984495   0.085797  1.405967 -0.698836 -0.723747 -0.565321   \n",
       "109   0.480384 -0.984495   1.350859 -0.711254 -0.698836 -0.723747 -0.565321   \n",
       "50    0.480384  1.015749   0.085797  1.405967 -0.698836 -0.723747  1.768907   \n",
       "80    0.480384 -0.984495   0.085797 -0.711254 -0.698836  1.381699 -0.565321   \n",
       "\n",
       "       CLPFFD   QEINSSY  \n",
       "26  -0.529150  1.768907  \n",
       "72   1.889822 -0.565321  \n",
       "79   1.889822 -0.565321  \n",
       "136 -0.529150 -0.565321  \n",
       "28  -0.529150 -0.565321  \n",
       "..        ...       ...  \n",
       "142 -0.529150 -0.565321  \n",
       "73  -0.529150  1.768907  \n",
       "109  1.889822 -0.565321  \n",
       "50  -0.529150 -0.565321  \n",
       "80  -0.529150  1.768907  \n",
       "\n",
       "[128 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy in the training: 0.8359375\n",
      "[[ 4  3]\n",
      " [ 0 19]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.57      0.73         7\n",
      "           1       0.86      1.00      0.93        19\n",
      "\n",
      "    accuracy                           0.88        26\n",
      "   macro avg       0.93      0.79      0.83        26\n",
      "weighted avg       0.90      0.88      0.87        26\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# tune regularization for multinomial logistic regression\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "\n",
    "# Multinomial Logistic Regression: Modified version of logistic regression that predicts a multinomial probability \n",
    "#(i.e. more than two classes) for each input example.\n",
    "\n",
    "# The LogisticRegression class can be configured for multinomial logistic regression by setting the “multi_class” \n",
    "#argument to “multinomial” and the “solver” argument to a solver that supports multinomial logistic regression, \n",
    "#such as “lbfgs“.\n",
    "\n",
    "# define the multinomial logistic regression model\n",
    "clf = LogisticRegression(multi_class=\"multinomial\", solver='lbfgs')\n",
    "\n",
    "#The multinomial logistic regression model will be fit using cross-entropy \n",
    "#loss and will predict the integer value for each integer encoded class label.\n",
    "\n",
    "# fit the model on the whole dataset\n",
    "clf.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "print('Mean accuracy in the training:', clf.score(X_train, y_train.values.ravel())) #Apply transforms, and score with the final estimator\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred)) #Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: 0.41785\n",
      "Feature: 1, Score: -0.08591\n",
      "Feature: 2, Score: 0.05181\n",
      "Feature: 3, Score: -0.04416\n",
      "Feature: 4, Score: -0.01247\n",
      "Feature: 5, Score: -0.04821\n",
      "Feature: 6, Score: 0.15823\n",
      "Feature: 7, Score: 0.21178\n",
      "Feature: 8, Score: 0.06400\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "# Instantiate the logistic regression model\n",
    "log_reg = LogisticRegression()\n",
    "\n",
    "# Train the model using the training data\n",
    "log_reg.fit(X_train, np.ravel(y_train))\n",
    "print(y_train.shape)\n",
    "# Make predictions on the testing data\n",
    "y_pred =log_reg.predict(X_test)\n",
    "y_pred_tarin =log_reg.predict(X_train)\n",
    "# Evaluate the performance of the model\n",
    "#print(classification_report(y_test, y_pred))\n",
    "print(y_pred)\n",
    "print(y_test)\n",
    "print(y_pred_tarin)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance=np.asarray(log_reg.coef_[0])\n",
    "print(type(importance))\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "# plot feature importance\n",
    "plt.bar([x for x in range(len(importance))], importance)\n",
    "plt.xticks(ticks=range(len(importance)),labels=list(X_train.columns),rotation=90)\n",
    "plt.title(\"Importance of parameters\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install lazypredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import lazypredict\n",
    "from lazypredict.Supervised import LazyClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import matthews_corrcoef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defines and builds the lazyclassifier\n",
    "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=matthews_corrcoef)\n",
    "models_train,predictions_train = clf.fit(X_train, X_train, y_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prints the model performance of the train set\n",
    "models_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_test,predictions_test = clf.fit(X_train, X_test, y_train, y_test)\n",
    "models_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I used randome forest model and use GINI score as importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Octavius Jessica\\AppData\\Local\\Temp\\ipykernel_21808\\1385589263.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf.fit(scaledTrain_df, y_train)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=500)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build random forest model\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=500)\n",
    "\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = rf.predict(X_train)\n",
    "y_test_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8076923076923077"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### different ways of calculating  Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8076923076923077"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68796992481203"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import balanced_accuracy_score\n",
    "balanced_accuracy_score(y_test, y_test_pred)   #because the dataset is very imbalanced    balance accuracy is better to used as metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  4],\n",
       "       [ 1, 18]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classification report\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "model_report = classification_report(y_train, y_train_pred, target_names=['positive','negative'])\n",
    "\n",
    "f = open('model_report.txt','w')\n",
    "f.writelines(model_report) \n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kZGVloaSkBNbW1pqMi4iIiEgSSXNuFixYgGvXrgEALC0tmdgQERHRK0NScrNz5064uLigc+fOWL58Oe7cuaPpuIiIiIgkkZTcnD9/HufPn0ePHj0QEhKCBg0aoH///ti6dSvy8vI0HSMRERGRyiS/fqFFixZYvHgxrl+/jqNHj8LJyQn+/v6oX7++JuMjIiIiUotGXpxpYmICIyMjyOVyPHnyRBNNEhEREUkiOblJTk7GokWL0Lx5c7i7u+PcuXOYP38+MjMzNRkfERERkVok3QrepUsXnDlzBq1atYK3t7fiOTdERERENU1SctO9e3esX78eLVq00HQ8RERERJUiKblZvHixpuMgIiIi0giVk5vAwEB88cUXMDExQWBg4AvrhoSEVDowIiIiIilUTm7i4+MVd0LFx8dXWUBERERElaFycnP06NFy/01ERET0KpF0K/jEiRORm5tbpvzRo0eYOHFipYMiIiIikkpScvPjjz8iPz+/THl+fj42bdpU6aCIiIiIpFLrbqmcnBwIISCEQG5uLgwNDRXbiouLsX//fr4hnIiIiGqUWsmNhYUFZDIZZDIZXFxcymyXyWRYsGCBxoIjIiIiUpdaw1JHjx7FkSNHIITAjh078McffyiWmJgYpKamYt68eWoFsHLlSjg5OcHQ0BBubm6Ijo5+Yf2CggLMmzcPDg4OMDAwQOPGjbFhwwa1jklERETaS62em27dugF4+l6pRo0aQSaTVergERER8Pf3x8qVK+Hp6Yk1a9agX79+SExMRKNGjcrdZ8SIEfjnn38QFhaGJk2a4Pbt2ygqKqpUHERERKQ9ZEIIoUrF8+fPo2XLltDR0cH58+dfWLd169YqHbxTp05o3749Vq1apShzdXXFkCFDEBwcXKb+wYMHMXLkSFy/fh1169ZV6RgFBQUoKChQrOfk5MDe3h7Z2dkwMzNTqQ16ubzCIjT//BAAIHFhHxjLJT38moiIqFw5OTkwNzdX6ftb5W+gtm3bIjMzE9bW1mjbti1kMhnKy4tkMhmKi4tf2l5hYSHi4uIwZ84cpfLevXvj5MmT5e6zb98+uLu745tvvsHmzZthYmKCQYMG4YsvvoCRkVG5+wQHB3MeEBER0WtE5eQmOTkZVlZWin9XVlZWFoqLi2FjY6NUbmNjg8zMzHL3uX79OmJiYmBoaIjdu3cjKysLfn5+uHfvXoXzbubOnav0uojSnhsiIiLSTionNw4ODuX+u7Ken7cjhKhwLk9JSQlkMhm2bNkCc3NzAE/fYzVs2DCsWLGi3N4bAwMDGBgYaCxeIiIierVJfojfb7/9plifPXs2LCws4OHhgZSUFJXasLS0hK6ubplemtu3b5fpzSlla2uLBg0aKBIb4OkcHSEE0tPTJZwJERERaRtJyc3ixYsVvSSnTp3C8uXL8c0338DS0hIBAQEqtSGXy+Hm5obIyEil8sjISHh4eJS7j6enJ27duoWHDx8qyq5cuQIdHR00bNhQyqkQERGRlpGU3KSlpaFJkyYAgD179mDYsGH48MMPERwc/NLn1DwrMDAQ69evx4YNG5CUlISAgACkpqbC19cXwNP5MuPGjVPUHz16NOrVqwdvb28kJiYiKioKs2bNwsSJEyucUExERESvF0n369apUwd3795Fo0aNcPjwYUVvjaGhYbnvnKqIl5cX7t69i4ULFyIjIwMtW7bE/v37FXN6MjIykJqaqnTcyMhITJs2De7u7qhXrx5GjBiBL7/8UsppEBERkRaSlNz06tULPj4+aNeuHa5cuYIBAwYAAC5dugRHR0e12vLz84Ofn1+528LDw8uUNWvWrMxQFhEREVEpScNSK1asQJcuXXDnzh3s3LkT9erVAwDExcVh1KhRGg2QiIiISB2Sem4sLCywfPnyMuV8WB4RERHVNMnPyH/w4AHCwsKQlJQEmUwGV1dXTJo0Sek2bSIiIqLqJmlYKjY2Fo0bN8aSJUtw7949ZGVlYcmSJWjcuDHOnTun6RiJiIiIVCap5yYgIACDBg3CunXroKf3tImioiL4+PjA398fUVFRGg2SiIiISFWSkpvY2FilxAYA9PT0MHv2bLi7u2ssOCIiIiJ1SRqWMjMzU3r+TKm0tDSYmppWOigiIiIiqSQlN15eXpg0aRIiIiKQlpaG9PR0bN++HT4+PrwVnIiIiGqUpGGp7777DjKZDOPGjUNRUREAQF9fH1OmTMFXX32l0QCJiIiI1CEpuZHL5Vi6dCmCg4Nx7do1CCHQpEkTGBsbazo+IiIiIrWoNSyVl5eHqVOnokGDBrC2toaPjw9sbW3RunVrJjZERET0SlAruQkKCkJ4eDgGDBiAkSNHIjIyElOmTKmq2IiIiIjUptaw1K5duxAWFoaRI0cCAD744AN4enqiuLgYurq6VRIgERERkTrU6rlJS0tD165dFesdO3aEnp4ebt26pfHAiIiIiKRQK7kpLi6GXC5XKtPT01PcMUVERERU09QalhJCYMKECTAwMFCUPX78GL6+vjAxMVGU7dq1S3MREhEREalBreRm/PjxZco++OADjQVDREREVFlqJTcbN26sqjiIiIiINELS6xeIiIiIXlUqJze+vr5IS0tTqW5ERAS2bNkiOSgiIiIiqVQelrKyskLLli3h4eGBQYMGwd3dHXZ2djA0NMT9+/eRmJiImJgYbN++HQ0aNMDatWurMm4iIiKicqmc3HzxxReYNm0awsLCsHr1aly8eFFpu6mpKXr27In169ejd+/eGg+UiIiISBVqTSi2trbG3LlzMXfuXDx48AApKSnIz8+HpaUlGjduDJlMVlVxEhEREalE0lvBAcDCwgIWFhYaDIWIiIio8ni3FBEREWkVJjdERESkVZjcEBERkVZhckNERERaRXJyU1RUhN9//x1r1qxBbm4uAODWrVt4+PChxoIjIiIiUpeku6VSUlLQt29fpKamoqCgAL169YKpqSm++eYbPH78GKtXr9Z0nEREREQqkdRzM2PGDLi7u+P+/fswMjJSlA8dOhRHjhzRWHBERERE6pLUcxMTE4MTJ05ALpcrlTs4OODmzZsaCYyIiIhICkk9NyUlJSguLi5Tnp6eDlNT00oHRURERCSVpOSmV69eCA0NVazLZDI8fPgQQUFB6N+/v6ZiIyIiIlKbpGGpJUuWoHv37mjevDkeP36M0aNH4+rVq7C0tMS2bds0HSMRERGRyiQlN3Z2dkhISMD27dsRFxeHkpISTJo0CWPGjFGaYExERERU3SQlN1FRUfDw8IC3tze8vb0V5UVFRYiKisJbb72lsQCJiIiI1CFpzk337t1x7969MuXZ2dno3r17pYMiIiIikkpSciOEgEwmK1N+9+5dmJiYVDooIiIiIqnUGpZ67733ADy9O2rChAkwMDBQbCsuLsb58+fh4eGh2QiJiIiI1KBWcmNubg7gac+Nqamp0uRhuVyOzp07Y/LkyZqNkIiIiEgNaiU3GzduBAA4Ojpi5syZHIIiIiKiV46ku6WCgoI0HQcRERGRRkhKbgBgx44d+Pnnn5GamorCwkKlbefOnat0YERERERSSLpbatmyZfD29oa1tTXi4+PRsWNH1KtXD9evX0e/fv00HSMRERGRyiQlNytXrsTatWuxfPlyyOVyzJ49G5GRkZg+fTqys7M1HSMRERGRyiQlN6mpqYpbvo2MjJCbmwsAGDt2LN8tRURERDVKUnJTv3593L17FwDg4OCA06dPAwCSk5MhhNBcdERERERqkpTc9OjRA7/++isAYNKkSQgICECvXr3g5eWFoUOHajRAIiIiInVIultq7dq1KCkpAQD4+vqibt26iImJwcCBA+Hr66vRAImIiIjUISm50dHRgY7O/3X6jBgxAiNGjAAA3Lx5Ew0aNNBMdLWMEAL5T4prOowakVf4ep43ERG9eiQ/5+Z5mZmZWLRoEdavX4/8/HxNNVtrCCEwbPUpxKXcr+lQiIiIXmtqzbl58OABxowZAysrK9jZ2WHZsmUoKSnB559/DmdnZ5w+fRobNmyoqlhfaflPipnYAHB3eANG+ro1HQYREb3G1Oq5+c9//oOoqCiMHz8eBw8eREBAAA4ePIjHjx/jwIED6NatW1XFWavEftoTxvLX8wveSF8XMpmspsMgIqLXmFrJzW+//YaNGzeiZ8+e8PPzQ5MmTeDi4oLQ0NAqCq92MpbrwliusRE/IiIiUoNaw1K3bt1C8+bNAQDOzs4wNDSEj49PlQRGREREJIVayU1JSQn09fUV67q6ujAxMalUACtXroSTkxMMDQ3h5uaG6OholfY7ceIE9PT00LZt20odn4iIiLSLWmMnQghMmDABBgYGAIDHjx/D19e3TIKza9culdqLiIiAv78/Vq5cCU9PT6xZswb9+vVDYmIiGjVqVOF+2dnZGDduHN555x38888/6pwCERERaTm1em7Gjx8Pa2trmJubw9zcHB988AHs7OwU66WLqkJCQjBp0iT4+PjA1dUVoaGhsLe3x6pVq16430cffYTRo0ejS5cu6oRPRERErwG1em42btyosQMXFhYiLi4Oc+bMUSrv3bs3Tp48+cIYrl27hp9++glffvnlS49TUFCAgoICxXpOTo70oImIiOiVJ+ndUpqQlZWF4uJi2NjYKJXb2NggMzOz3H2uXr2KOXPmYMuWLdDTUy0vCw4OVupVsre3r3TsRERE9OqqseSm1PPPRBFClPuclOLiYowePRoLFiyAi4uLyu3PnTsX2dnZiiUtLa3SMRMREdGrq8YexmJpaQldXd0yvTS3b98u05sDALm5uYiNjUV8fDw+/vhjAE/v3hJCQE9PD4cPH0aPHj3K7GdgYKCYAE1ERETar8Z6buRyOdzc3BAZGalUHhkZCQ8PjzL1zczMcOHCBSQkJCgWX19fNG3aFAkJCejUqVN1hU5ERESvsBp9jG5gYCDGjh0Ld3d3dOnSBWvXrkVqaip8fX0BPB1SunnzJjZt2gQdHR20bNlSaX9ra2sYGhqWKSciIqLXl+Sem82bN8PT0xN2dnZISUkBAISGhmLv3r0qt+Hl5YXQ0FAsXLgQbdu2RVRUFPbv3w8HBwcAQEZGBlJTU6WGSERERK8hScnNqlWrEBgYiP79++PBgwcoLi4GAFhYWKj9nik/Pz/cuHEDBQUFiIuLw1tvvaXYFh4ejmPHjlW47/z585GQkCDhDIiIiEhbSUpufvjhB6xbtw7z5s2Dru7/vf3a3d0dFy5c0FhwREREROqSlNwkJyejXbt2ZcoNDAzw6NGjSgdFREREJJWk5MbJyanc4aADBw4o3hpOREREVBMk3S01a9YsTJ06FY8fP4YQAmfOnMG2bdsQHByM9evXazpGIiIiIpVJSm68vb1RVFSE2bNnIy8vD6NHj0aDBg2wdOlSjBw5UtMxEhEREalM8nNuJk+ejMmTJyMrKwslJSWwtrbWZFxEREREkkiac7NgwQJcu3YNwNPXKDCxISIioleFpORm586dcHFxQefOnbF8+XLcuXNH03ERERERSSIpuTl//jzOnz+PHj16ICQkBA0aNED//v2xdetW5OXlaTpGIiIiIpVJfv1CixYtsHjxYly/fh1Hjx6Fk5MT/P39Ub9+fU3GR0RERKQWjbwV3MTEBEZGRpDL5Xjy5IkmmiQiIiKSRHJyk5ycjEWLFqF58+Zwd3fHuXPnMH/+fGRmZmoyPiIiIiK1SLoVvEuXLjhz5gxatWoFb29vxXNuiIiIiGqapOSme/fuWL9+PVq0aKHpeIiIiIgqRVJys3jxYk3HQURERKQRKic3gYGB+OKLL2BiYoLAwMAX1g0JCal0YERERERSqJzcxMfHK+6Eio+Pr7KAiIiIiCpD5eTm6NGj5f6biIiI6FUi6VbwiRMnIjc3t0z5o0ePMHHixEoHRURERCSVpOTmxx9/RH5+fpny/Px8bNq0qdJBEREREUml1t1SOTk5EEJACIHc3FwYGhoqthUXF2P//v18QzgRERHVKLWSGwsLC8hkMshkMri4uJTZLpPJsGDBAo0FR0RERKQutZKbo0ePQgiBHj16YOfOnahbt65im1wuh4ODA+zs7DQeJBEREZGq1EpuunXrBuDpe6UaNWoEmUxWJUERERERSaVycnP+/Hm0bNkSOjo6yM7OxoULFyqs27p1a40ER0RERKQulZObtm3bIjMzE9bW1mjbti1kMhmEEGXqyWQyFBcXazRIIiIiIlWpnNwkJyfDyspK8W8iIiKiV5HKyY2Dg0O5/yYiIiJ6lUh+iN9vv/2mWJ89ezYsLCzg4eGBlJQUjQVHREREpC5Jyc3ixYthZGQEADh16hSWL1+Ob775BpaWlggICNBogERERETqUOtW8FJpaWlo0qQJAGDPnj0YNmwYPvzwQ3h6euLtt9/WZHxEREREapHUc1OnTh3cvXsXAHD48GH07NkTAGBoaFjuO6eIiIiIqouknptevXrBx8cH7dq1w5UrVzBgwAAAwKVLl+Do6KjJ+IiIiIjUIqnnZsWKFejSpQvu3LmDnTt3ol69egCAuLg4jBo1SqMBEhEREalDUs+NhYUFli9fXqacL80kIiKimiYpuQGABw8eICwsDElJSZDJZHB1dcWkSZNgbm6uyfiIiIiI1CJpWCo2NhaNGzfGkiVLcO/ePWRlZWHJkiVo3Lgxzp07p+kYiYiIiFQmqecmICAAgwYNwrp166Cn97SJoqIi+Pj4wN/fH1FRURoNkoiIiEhVkpKb2NhYpcQGAPT09DB79my4u7trLDgiIiIidUkaljIzM0NqamqZ8rS0NJiamlY6KCIiIiKpJCU3Xl5emDRpEiIiIpCWlob09HRs374dPj4+vBWciIiIapSkYanvvvsOMpkM48aNQ1FREQBAX18fU6ZMwVdffaXRAImIiIjUISm5kcvlWLp0KYKDg3Ht2jUIIdCkSRMYGxtrOj4iIiIitag1LJWXl4epU6eiQYMGsLa2ho+PD2xtbdG6dWsmNkRERPRKUCu5CQoKQnh4OAYMGICRI0ciMjISU6ZMqarYiIiIiNSm1rDUrl27EBYWhpEjRwIAPvjgA3h6eqK4uBi6urpVEiARERGROtTquUlLS0PXrl0V6x07doSenh5u3bql8cCIiIiIpFAruSkuLoZcLlcq09PTU9wxRURERFTT1BqWEkJgwoQJMDAwUJQ9fvwYvr6+MDExUZTt2rVLcxESERERqUGt5Gb8+PFlyj744AONBUNERERUWWolNxs3bqyqOIiIiIg0QtLrF4iIiIheVUxuiIiISKswuSEiIiKtwuSGiIiItAqTGyIiItIqkpObzZs3w9PTE3Z2dkhJSQEAhIaGYu/evWq1s3LlSjg5OcHQ0BBubm6Ijo6usO6uXbvQq1cvWFlZwczMDF26dMGhQ4ekngIRERFpIUnJzapVqxAYGIj+/fvjwYMHKC4uBgBYWFggNDRU5XYiIiLg7++PefPmIT4+Hl27dkW/fv2Qmppabv2oqCj06tUL+/fvR1xcHLp3746BAwciPj5eymkQERGRFpIJIYS6OzVv3hyLFy/GkCFDYGpqir/++gvOzs64ePEi3n77bWRlZanUTqdOndC+fXusWrVKUebq6oohQ4YgODhYpTZatGgBLy8vfP755yrVz8nJgbm5ObKzs2FmZqbSPqrIKyxC88+f9iIlLuwDY7lajxAiIiKiF1Dn+1tSz01ycjLatWtXptzAwACPHj1SqY3CwkLExcWhd+/eSuW9e/fGyZMnVWqjpKQEubm5qFu3boV1CgoKkJOTo7QQERGR9pKU3Dg5OSEhIaFM+YEDB9C8eXOV2sjKykJxcTFsbGyUym1sbJCZmalSG99//z0ePXqEESNGVFgnODgY5ubmisXe3l6ltomIiKh2kjR2MmvWLEydOhWPHz+GEAJnzpzBtm3bEBwcjPXr16vVlkwmU1oXQpQpK8+2bdswf/587N27F9bW1hXWmzt3LgIDAxXrOTk5THCIiIi0mKTkxtvbG0VFRZg9ezby8vIwevRoNGjQAEuXLsXIkSNVasPS0hK6urplemlu375dpjfneREREZg0aRJ++eUX9OzZ84V1DQwMlN5iTkRERNpN8q3gkydPRkpKCm7fvo3MzEykpaVh0qRJKu8vl8vh5uaGyMhIpfLIyEh4eHhUuN+2bdswYcIEbN26FQMGDJAaPhEREWmpSt/SY2lpKXnfwMBAjB07Fu7u7ujSpQvWrl2L1NRU+Pr6Ang6pHTz5k1s2rQJwNPEZty4cVi6dCk6d+6s6PUxMjKCubl5ZU+FiIiItICk5MbJyemF82KuX7+uUjteXl64e/cuFi5ciIyMDLRs2RL79++Hg4MDACAjI0PpmTdr1qxBUVERpk6diqlTpyrKx48fj/DwcCmnQkRERFpGUnLj7++vtP7kyRPEx8fj4MGDmDVrllpt+fn5wc/Pr9xtzycsx44dU6ttIiIiev1ISm5mzJhRbvmKFSsQGxtbqYCIiIiIKkOjL87s168fdu7cqckmiYiIiNSi0eRmx44dL3xaMBEREVFVkzQs1a5dO6UJxUIIZGZm4s6dO1i5cqXGgiMiIiJSl6TkZsiQIUrrOjo6sLKywttvv41mzZppIi4iIiIiSdROboqKiuDo6Ig+ffqgfv36VRETERERkWRqz7nR09PDlClTUFBQUBXxEBEREVWKpAnFnTp1Qnx8vKZjISIiIqo0SXNu/Pz88MknnyA9PR1ubm4wMTFR2t66dWuNBEdERESkLrWSm4kTJyI0NBReXl4AgOnTpyu2yWQyCCEgk8lQXFys2SiJiIiIVKRWcvPjjz/iq6++QnJyclXFQ0RERFQpaiU3QggAULzYkoiIiOhVo/aE4he9DZyIiIiopqk9odjFxeWlCc69e/ckB0RERERUGWonNwsWLIC5uXlVxEJERERUaWonNyNHjoS1tXVVxEJERERUaWrNueF8GyIiInrVqZXclN4tRURERPSqUmtYqqSkpKriICIiItIISe+WIiIiInpVMbkhIiIircLkhoiIiLQKkxsiIiLSKkxuiIiISKswuSEiIiKtwuSGiIiItAqTGyIiItIqTG6IiIhIqzC5ISIiIq3C5IaIiIi0CpMbIiIi0ipMboiIiEirMLkhIiIircLkhoiIiLQKkxsiIiLSKkxuiIiISKswuSEiIiKtwuSGiIiItAqTGyIiItIqTG6IiIhIqzC5ISIiIq3C5IaIiIi0CpMbIiIi0ipMboiIiEir6NV0AERUOUIIFBUVobi4uKZDISKqFH19fejq6la6HSY3RLVYYWEhMjIykJeXV9OhEBFVmkwmQ8OGDVGnTp1KtcPkhqiWKikpQXJyMnR1dWFnZwe5XA6ZTFbTYRERSSKEwJ07d5Ceno4333yzUj04TG6IaqnCwkKUlJTA3t4exsbGNR0OEVGlWVlZ4caNG3jy5EmlkhtOKCaq5XR0+N+YiLSDpnqf+VuRiIiItAqTGyIiItIqTG6IiIhIqzC5IaLXjqOjI0JDQ2s6jFpnwoQJGDJkSLUc6/nPKDMzE7169YKJiQksLCwAPJ2fsWfPniqL4a233sLWrVurrP3XTUFBARo1aoS4uLgqPxaTGyKqdhMmTIBMJoNMJoOenh4aNWqEKVOm4P79+zUdmkY5OjoqzrN0adiwYY3HVF5iJ4TA2rVr0alTJ9SpUwcWFhZwd3dHaGhojTxH6ezZs/jwww8V60uWLEFGRgYSEhJw5coVAEBGRgb69etXJcf/73//i8zMTIwcObLMtsWLF0NXVxdfffVVmW3z589H27Zty5Q/ePAAMpkMx44dUyrfuXMn3n77bZibm6NOnTpo3bo1Fi5ciHv37mnqVMooKCjAtGnTYGlpCRMTEwwaNAjp6ekv3KeoqAiffvopnJycYGRkBGdnZyxcuBAlJSWKOv/88w8mTJgAOzs7GBsbo2/fvrh69apiu4GBAWbOnIl///vfVXZupZjcEGkRIQTyCotqZBFCqBVr3759kZGRgRs3bmD9+vX49ddf4efnV0VXpuYsXLgQGRkZiiU+Pl5yW0+ePNFgZMrGjh0Lf39/DB48GEePHkVCQgI+++wz7N27F4cPH66y41bEyspK6REH165dg5ubG958801YW1sDAOrXrw8DAwPJxygsLKxw27Jly+Dt7V3u3YgbN27E7NmzsWHDBsnHBoB58+bBy8sLHTp0wIEDB3Dx4kV8//33+Ouvv7B58+ZKtf0i/v7+2L17N7Zv346YmBg8fPgQ77777gufcv71119j9erVWL58OZKSkvDNN9/g22+/xQ8//ADg6e+eIUOG4Pr169i7dy/i4+Ph4OCAnj174tGjR4p2xowZg+joaCQlJVXZ+ZUG9FrJzs4WAER2drZG231U8EQ4/Pu/wuHf/xWPCp5otG2i8uTn54vExESRn5+vKHv257C6F3V+7sePHy8GDx6sVBYYGCjq1q2rWC8qKhITJ04Ujo6OwtDQULi4uIjQ0NBy2/n2229F/fr1Rd26dYWfn58oLCxU1Pnnn3/Eu+++KwwNDYWjo6P46aefhIODg1iyZImiTkpKihg0aJAwMTERpqamYvjw4SIzM1OxPSgoSLRp00aEhYUJe3t7YWJiInx9fUVRUZH4+uuvhY2NjbCyshJffvmlUnzPH+d5K1euFM7OzkJfX1+4uLiITZs2KW0HIFatWiUGDRokjI2Nxeeffy6EEGLfvn2iffv2wsDAQDg5OYn58+eLJ0/+7/oHBQUJe3t7IZfLha2trZg2bZoQQohu3boJAEqLEEJEREQIAGLPnj1lYiwpKREPHjxQut6lDhw4IDw9PYW5ubmoW7euGDBggPj7778V2wsKCsTUqVNF/fr1hYGBgXBwcBCLFy9+aZzPXzsHBwelmMePH6+4Prt371bsk56eLkaMGCEsLCxE3bp1xaBBg0RycrJie2n8ixcvFra2tsLBwaHcz+XOnTtCJpOJixcvltl27Ngx0aBBA1FYWCjs7OzE8ePHlbaX/qw87/79+wKAOHr0qBBCiD///FMAKPMz/Wz9qvDgwQOhr68vtm/frii7efOm0NHREQcPHqxwvwEDBoiJEycqlb333nvigw8+EEIIcfnyZQFA6ZoVFRWJunXrinXr1int9/bbb4vPPvus3OOU93utlDrf3zXec7Ny5Uo4OTnB0NAQbm5uiI6OfmH948ePw83NDYaGhnB2dsbq1aurKVIiqirXr1/HwYMHoa+vrygrKSlBw4YN8fPPPyMxMRGff/45/vOf/+Dnn39W2vfo0aO4du0ajh49ih9//BHh4eEIDw9XbJ8wYQJu3LiBP/74Azt27MDKlStx+/ZtxXbx///ivHfvHo4fP47IyEhcu3YNXl5eSse5du0aDhw4gIMHD2Lbtm3YsGEDBgwYgPT0dBw/fhxff/01Pv30U5w+fVqlc969ezdmzJiBTz75BBcvXsRHH30Eb29vHD16VKleUFAQBg8ejAsXLmDixIk4dOgQPvjgA0yfPh2JiYlYs2YNwsPDsWjRIgDAjh07sGTJEqxZswZXr17Fnj170KpVKwDArl270LBhQ6XeJADYsmULmjZtisGDB5eJUyaTwdzcvNxzePToEQIDA3H27FkcOXIEOjo6GDp0qGKoYtmyZdi3bx9+/vlnXL58GT/99BMcHR1fGufzzp49i759+2LEiBHIyMjA0qVLy9TJy8tD9+7dUadOHURFRSEmJgZ16tRB3759lXpojhw5gqSkJERGRuK///1vuceLiYmBsbExXF1dy2wLCwvDqFGjoK+vj1GjRiEsLKzcNl5my5YtqFOnToW9laXzisrTokUL1KlTp8KlRYsWFe4bFxeHJ0+eoHfv3ooyOzs7tGzZEidPnqxwv3/96184cuSIYkjwr7/+QkxMDPr37w/g6VAXABgaGir20dXVhVwuR0xMjFJbHTt2fOl3faW9NP2pQtu3bxf6+vpi3bp1IjExUcyYMUOYmJiIlJSUcutfv35dGBsbixkzZojExESxbt06oa+vL3bs2KHyMdlzQ9qivL9wSkpKxKOCJzWylJSUqBz7+PHjha6urjAxMRGGhoaKv8hDQkJeuJ+fn594//33ldpxcHAQRUVFirLhw4cLLy8vIcT//TV5+vRpxfakpCQBQNErcPjwYaGrqytSU1MVdS5duiQAiDNnzgghnv41bmxsLHJychR1+vTpIxwdHUVxcbGirGnTpiI4OFix7uDgIORyuTAxMVEsS5cuFUII4eHhISZPnqx0fsOHDxf9+/dXrAMQ/v7+SnW6du2q1PshhBCbN28Wtra2Qgghvv/+e+Hi4qLUe/Ws8nqTXF1dxaBBg8qt/6zyetyedfv2bQFAXLhwQQghxLRp00SPHj3K/dlQN87BgwcremxK4Zmem7CwMNG0aVOlYxUUFAgjIyNx6NAhRfw2NjaioKDghee5ZMkS4ezsXKY8OztbGBsbi4SEBCGEEPHx8cLY2Fjp+0TVnpt+/fqJ1q1bvzCOity4cUNcvXq1wuXGjRsV7rtlyxYhl8vLlPfq1Ut8+OGHFe5XUlIi5syZI2QymdDT0xMymUzp57CwsFA4ODiI4cOHi3v37omCggIRHBwsAIjevXsrtbV06VLh6OhY7nE01XNTo69fCAkJwaRJk+Dj4wMACA0NxaFDh7Bq1SoEBweXqb969Wo0atRIMRnO1dUVsbGx+O677/D+++9XZ+hErySZTAZjee14q0r37t2xatUq5OXlYf369bhy5QqmTZumVGf16tVYv349UlJSkJ+fj8LCwjKTNVu0aKH0mHZbW1tcuHABAJCUlAQ9PT24u7srtjdr1kzpr+KkpCTY29vD3t5eUda8eXNYWFggKSkJHTp0APB0Iq6pqamijo2NDXR1dZXmZNjY2Cj1CgHArFmzMGHCBMW6paWl4rjPTpgFAE9PzzK9Es/GDjz9y/vs2bOKnhoAKC4uxuPHj5GXl4fhw4cjNDQUzs7O6Nu3L/r374+BAwdCT6/inwshhKQnw167dg2fffYZTp8+jaysLEWPTWpqKlq2bIkJEyagV69eaNq0Kfr27Yt3331X0WMgJc4XiYuLw99//630GQHA48ePce3aNcV6q1atIJfLX9hWfn6+Ug9Eqa1bt8LZ2Rlt2rQBALRt2xbOzs7Yvn17mc/yZaRecwBwcHCQtN+LvCyeiIgI/PTTT9i6dStatGiBhIQE+Pv7w87ODuPHj4e+vj527tyJSZMmoW7dutDV1UXPnj3LnfBtZGRU5ZPUa2xYqrCwEHFxcUpdYwDQu3fvCrvGTp06VaZ+nz59EBsbW+FEu4KCAuTk5CgtRFTzTExM0KRJE7Ru3RrLli1DQUEBFixYoNj+888/IyAgABMnTsThw4eRkJAAb2/vMpNAnx3KAp4meKVfsuL/T3J+0S/tin6pP19e3nFedOxSlpaWaNKkiWJ5NrF6/rjlxWJiYqK0XlJSggULFiAhIUGxXLhwAVevXoWhoSHs7e1x+fJlrFixAkZGRvDz88Nbb731wsnILi4ukiZ4Dhw4EHfv3sW6devw559/4s8//wTwfxN127dvj+TkZHzxxRfIz8/HiBEjMGzYMACQFOeLlJSUwM3NTem6lN5ZNXr0aEW9569neSwtLcu9c2/Dhg24dOkS9PT0FMulS5eUhqbMzMyQnZ1dZt8HDx4AgGKIz8XFBdeuXZN0vpUZlqpfvz4KCwvLnN/t27dhY2NT4X6zZs3CnDlzMHLkSLRq1Qpjx45FQECAUkdE6fV/8OABMjIycPDgQdy9exdOTk5Kbd27dw9WVlZqn7c6aiy5ycrKQnFxcZmLaWNjg8zMzHL3yczMLLd+UVERsrKyyt0nODgY5ubmiuXZv86I6NURFBSE7777Drdu3QIAREdHw8PDA35+fmjXrh2aNGmi9Be4KlxdXVFUVITY2FhF2eXLlxVfNMDTXprU1FSkpaUpyhITE5GdnV3unAtNcXV1LTMX4eTJky89Zvv27XH58mWlhKl0Ke1FMjIywqBBg7Bs2TIcO3YMp06dUvRmyeXyMnfFjB49GleuXMHevXvLHE8IUe6X9d27d5GUlIRPP/0U77zzDlxdXctNCMzMzODl5YV169YhIiICO3fuVNzm/KI41dW+fXtcvXoV1tbWZa5LRXOGKtKuXTtkZmYqnc+FCxcQGxuLY8eOKSVPUVFROHv2LC5evAjgac9genp6me+xs2fPQkdHB02aNAHw9Jo/fPgQK1euLDeGZ39Gn7d///4ySdyzy/79+yvc183NDfr6+oiMjFSUZWRk4OLFi/Dw8Khwv7y8vDJ3junq6pZJ5oGnCZyVlRWuXr2K2NjYMnO5Ll68iHbt2lV4LE2o8f5rVf5yeVn98spLzZ07F4GBgYr1nJycKklwjPR1kbiwj+LfRKSet99+Gy1atMDixYuxfPlyNGnSBJs2bcKhQ4fg5OSEzZs34+zZs2X+CnyR0uGQyZMnY+3atdDT04O/vz+MjIwUdXr27InWrVtjzJgxCA0NRVFREfz8/NCtW7cyQ0KaNGvWLIwYMQLt27fHO++8g19//RW7du3C77///sL9Pv/8c7z77ruwt7fH8OHDoaOjg/Pnz+PChQv48ssvER4ejuLiYnTq1AnGxsbYvHkzjIyMFEMZjo6OiIqKwsiRI2FgYABLS0uMGDECu3fvxqhRo/DZZ5+hV69esLKywoULF7BkyRJMmzatzMP73njjDdSrVw9r166Fra0tUlNTMWfOHKU6S5Ysga2tLdq2bQsdHR388ssvqF+/PiwsLF4ap7rGjBmDb7/9FoMHD8bChQvRsGFDpKamYteuXZg1a5Zazxdq164drKyscOLECbz77rsAnk4k7tixI956660y9bt06YKwsDAsWbIEvXv3hqurK0aOHIlFixbBzs4O58+fx8yZM+Hr66sYNuvUqRNmz56NTz75BDdv3sTQoUNhZ2eHv//+G6tXr8a//vUvzJgxo9z4KjMsZW5ujkmTJuGTTz5BvXr1ULduXcycOROtWrVCz549FfXeeecdDB06FB9//DGAp710ixYtQqNGjdCiRQvEx8cjJCQEEydOVOzzyy+/wMrKCo0aNcKFCxcwY8YMDBkypMyIS3R0NL744gvJ56CKGuu5sbS0hK6ubpns9kVdY/Xr1y+3vp6eHurVq1fuPgYGBjAzM1NaqkLpXAdjuZ7G3mpK9LoJDAzEunXrkJaWBl9fX7z33nvw8vJCp06dcPfuXUnPwdm4cSPs7e3RrVs3vPfee/jwww8Vz0kB/u8pt2+88Qbeeust9OzZE87OzoiIiNDkqZUxZMgQLF26FN9++y1atGiBNWvWYOPGjXj77bdfuF+fPn3w3//+F5GRkejQoQM6d+6MkJAQxReehYUF1q1bB09PT7Ru3RpHjhzBr7/+qvgduXDhQty4cQONGzdWDA3IZDJs3boVISEh2L17N7p164bWrVtj/vz5GDx4MPr06VMmDh0dHWzfvh1xcXFo2bIlAgIC8O233yrVqVOnDr7++mu4u7ujQ4cOuHHjBvbv3w8dHZ2XxqkuY2NjREVFoVGjRnjvvffg6uqKiRMnIj8/X+3f+7q6upg4cSK2bNkC4Okw208//VTh3M73338fP/30EwoLC6Gnp4fDhw/D2dkZY8aMQYsWLTBnzhz4+PggJCREab+vv/4aW7duxZ9//ok+ffqgRYsWCAwMROvWrTF+/HhJ10EVS5YswZAhQzBixAh4enrC2NgYv/76q9LctWvXrimNiPzwww8YNmwY/Pz84OrqipkzZ+Kjjz5SSlIyMjIwduxYNGvWDNOnT8fYsWOxbds2pWOfOnUK2dnZiuHJqiITQs0nb2lQp06d4ObmptQt17x5cwwePLjcCcX//ve/8euvvyIxMVFRNmXKFCQkJODUqVMqHTMnJwfm5ubIzs6uskSHqDo8fvwYycnJikcpEJHm/PPPP2jRogXi4uKqZALv62r48OFo164d/vOf/5S7/UW/19T5/q7R59wEBgZi/fr12LBhA5KSkhAQEIDU1FT4+voCeDqkNG7cOEV9X19fpKSkIDAwEElJSdiwYQPCwsIwc+bMmjoFIiLSQjY2NggLC0NqampNh6I1CgoK0KZNGwQEBFT5sWp0zo2Xlxfu3r2reKBUy5YtsX//fkWWnJGRofSD5eTkhP379yMgIAArVqyAnZ0dli1bxtvAiYhI48p7qCFJZ2BggE8//bRajlWjw1I1gcNSpC04LEVE2kYrhqWIqPJes79PiEiLaer3GZMbolqq9AFyVf2kTyKi6lL6AMhn79ySosafc0NE0ujq6sLCwkLxuH9jY2M+hoCIaq2SkhLcuXMHxsbGkl/DUYrJDVEtVr9+fQAo8z4jIqLaSEdHB40aNar0H2pMbohqMZlMBltbW1hbW0t+Jw8R0atCLpeXec2DFExuiLSArq5upceoiYi0BScUExERkVZhckNERERahckNERERaZXXbs5N6QOCcnJyajgSIiIiUlXp97YqD/p77ZKb3NxcAIC9vX0NR0JERETqys3Nhbm5+QvrvHbvliopKcGtW7dgamqq8Qee5eTkwN7eHmlpaXxvVRXida4evM7Vg9e5+vBaV4+qus5CCOTm5sLOzu6lt4u/dj03Ojo6aNiwYZUew8zMjP9xqgGvc/Xgda4evM7Vh9e6elTFdX5Zj00pTigmIiIircLkhoiIiLQKkxsNMjAwQFBQEAwMDGo6FK3G61w9eJ2rB69z9eG1rh6vwnV+7SYUExERkXZjzw0RERFpFSY3REREpFWY3BAREZFWYXJDREREWoXJjZpWrlwJJycnGBoaws3NDdHR0S+sf/z4cbi5ucHQ0BDOzs5YvXp1NUVau6lznXft2oVevXrBysoKZmZm6NKlCw4dOlSN0dZe6v48lzpx4gT09PTQtm3bqg1QS6h7nQsKCjBv3jw4ODjAwMAAjRs3xoYNG6op2tpL3eu8ZcsWtGnTBsbGxrC1tYW3tzfu3r1bTdHWTlFRURg4cCDs7Owgk8mwZ8+el+5TI9+DglS2fft2oa+vL9atWycSExPFjBkzhImJiUhJSSm3/vXr14WxsbGYMWOGSExMFOvWrRP6+vpix44d1Rx57aLudZ4xY4b4+uuvxZkzZ8SVK1fE3Llzhb6+vjh37lw1R167qHudSz148EA4OzuL3r17izZt2lRPsLWYlOs8aNAg0alTJxEZGSmSk5PFn3/+KU6cOFGNUdc+6l7n6OhooaOjI5YuXSquX78uoqOjRYsWLcSQIUOqOfLaZf/+/WLevHli586dAoDYvXv3C+vX1Pcgkxs1dOzYUfj6+iqVNWvWTMyZM6fc+rNnzxbNmjVTKvvoo49E586dqyxGbaDudS5P8+bNxYIFCzQdmlaRep29vLzEp59+KoKCgpjcqEDd63zgwAFhbm4u7t69Wx3haQ11r/O3334rnJ2dlcqWLVsmGjZsWGUxahtVkpua+h7ksJSKCgsLERcXh969eyuV9+7dGydPnix3n1OnTpWp36dPH8TGxuLJkydVFmttJuU6P6+kpAS5ubmoW7duVYSoFaRe540bN+LatWsICgqq6hC1gpTrvG/fPri7u+Obb75BgwYN4OLigpkzZyI/P786Qq6VpFxnDw8PpKenY//+/RBC4J9//sGOHTswYMCA6gj5tVFT34Ov3YszpcrKykJxcTFsbGyUym1sbJCZmVnuPpmZmeXWLyoqQlZWFmxtbass3tpKynV+3vfff49Hjx5hxIgRVRGiVpByna9evYo5c+YgOjoaenr81aEKKdf5+vXriImJgaGhIXbv3o2srCz4+fnh3r17nHdTASnX2cPDA1u2bIGXlxceP36MoqIiDBo0CD/88EN1hPzaqKnvQfbcqEkmkymtCyHKlL2sfnnlpEzd61xq27ZtmD9/PiIiImBtbV1V4WkNVa9zcXExRo8ejQULFsDFxaW6wtMa6vw8l5SUQCaTYcuWLejYsSP69++PkJAQhIeHs/fmJdS5zomJiZg+fTo+//xzxMXF4eDBg0hOToavr291hPpaqYnvQf75pSJLS0vo6uqW+Svg9u3bZbLSUvXr1y+3vp6eHurVq1dlsdZmUq5zqYiICEyaNAm//PILevbsWZVh1nrqXufc3FzExsYiPj4eH3/8MYCnX8JCCOjp6eHw4cPo0aNHtcRem0j5eba1tUWDBg1gbm6uKHN1dYUQAunp6XjzzTerNObaSMp1Dg4OhqenJ2bNmgUAaN26NUxMTNC1a1d8+eWX7FnXkJr6HmTPjYrkcjnc3NwQGRmpVB4ZGQkPD49y9+nSpUuZ+ocPH4a7uzv09fWrLNbaTMp1Bp722EyYMAFbt27lmLkK1L3OZmZmuHDhAhISEhSLr68vmjZtioSEBHTq1Km6Qq9VpPw8e3p64tatW3j48KGi7MqVK9DR0UHDhg2rNN7aSsp1zsvLg46O8legrq4ugP/rWaDKq7HvwSqdrqxlSm81DAsLE4mJicLf31+YmJiIGzduCCGEmDNnjhg7dqyifuktcAEBASIxMVGEhYXxVnAVqHudt27dKvT09MSKFStERkaGYnnw4EFNnUKtoO51fh7vllKNutc5NzdXNGzYUAwbNkxcunRJHD9+XLz55pvCx8enpk6hVlD3Om/cuFHo6emJlStXimvXromYmBjh7u4uOnbsWFOnUCvk5uaK+Ph4ER8fLwCIkJAQER8fr7jl/lX5HmRyo6YVK1YIBwcHIZfLRfv27cXx48cV28aPHy+6deumVP/YsWOiXbt2Qi6XC0dHR7Fq1apqjrh2Uuc6d+vWTQAos4wfP776A69l1P15fhaTG9Wpe52TkpJEz549hZGRkWjYsKEIDAwUeXl51Rx17aPudV62bJlo3ry5MDIyEra2tmLMmDEiPT29mqOuXY4ePfrC37evyvegTAj2vxEREZH24JwbIiIi0ipMboiIiEirMLkhIiIircLkhoiIiLQKkxsiIiLSKkxuiIiISKswuSEiIiKtwuSGiIiItAqTG6JyhIeHw8LCoqbDkMzR0RGhoaEvrDN//ny0bdu2WuJ51fzxxx9o1qwZSkpKquV4r8rnIeUYMpkMe/bsqdRxJ0yYgCFDhlSqjfJ06NABu3bt0ni7VPsxuSGtNWHCBMhksjLL33//XdOhITw8XCkmW1tbjBgxAsnJyRpp/+zZs/jwww8V6+V9Qc2cORNHjhzRyPEq8vx52tjYYODAgbh06ZLa7Wgy2Zw9ezbmzZuneHHi6/J51CZRUVEYOHAg7OzsKkywPvvsM8yZM6faklSqPZjckFbr27cvMjIylBYnJ6eaDgvA0zdtZ2Rk4NatW9i6dSsSEhIwaNAgFBcXV7ptKysrGBsbv7BOnTp1UK9evUof62WePc/ffvsNjx49woABA1BYWFjlxy7PyZMncfXqVQwfPrzCOLX586gtHj16hDZt2mD58uUV1hkwYACys7Nx6NChaoyMagMmN6TVDAwMUL9+faVFV1cXISEhaNWqFUxMTGBvbw8/Pz88fPiwwnb++usvdO/eHaampjAzM4ObmxtiY2MV20+ePIm33noLRkZGsLe3x/Tp0/Ho0aMXxiaTyVC/fn3Y2tqie/fuCAoKwsWLFxU9S6tWrULjxo0hl8vRtGlTbN68WWn/+fPno1GjRjAwMICdnR2mT5+u2PbsMIijoyMAYOjQoZDJZIr1Z4coDh06BENDQzx48EDpGNOnT0e3bt00dp7u7u4ICAhASkoKLl++rKjzos/j2LFj8Pb2RnZ2tqJnZf78+QCAwsJCzJ49Gw0aNICJiQk6deqEY8eOvTCe7du3o3fv3jA0NKwwTm3+PJ519uxZ9OrVC5aWljA3N0e3bt1w7ty5MvUyMjLQr18/GBkZwcnJCb/88ovS9ps3b8LLywtvvPEG6tWrh8GDB+PGjRsqx1Gefv364csvv8R7771XYR1dXV30798f27Ztq9SxSPswuaHXko6ODpYtW4aLFy/ixx9/xB9//IHZs2dXWH/MmDFo2LAhzp49i7i4OMyZMwf6+voAgAsXLqBPnz547733cP78eURERCAmJgYff/yxWjEZGRkBAJ48eYLdu3djxowZ+OSTT3Dx4kV89NFH8Pb2xtGjRwEAO3bswJIlS7BmzRpcvXoVe/bsQatWrcpt9+zZswCAjRs3IiMjQ7H+rJ49e8LCwgI7d+5UlBUXF+Pnn3/GmDFjNHaeDx48wNatWwFAcf2AF38eHh4eCA0NVfSsZGRkYObMmQAAb29vnDhxAtu3b8f58+cxfPhw9O3bF1evXq0whqioKLi7u7801tfh88jNzcX48eMRHR2N06dP480330T//v2Rm5urVO+zzz7D+++/j7/++gsffPABRo0ahaSkJABAXl4eunfvjjp16iAqKgoxMTGoU6cO+vbtW2HvXOkwoCZ07NgR0dHRGmmLtEiVv3ecqIaMHz9e6OrqChMTE8UybNiwcuv+/PPPol69eor1jRs3CnNzc8W6qampCA8PL3ffsWPHig8//FCpLDo6Wujo6Ij8/Pxy93m+/bS0NNG5c2fRsGFDUVBQIDw8PMTkyZOV9hk+fLjo37+/EEKI77//Xri4uIjCwsJy23dwcBBLlixRrAMQu3fvVqoTFBQk2rRpo1ifPn266NGjh2L90KFDQi6Xi3v37lXqPAEIExMTYWxsLAAIAGLQoEHl1i/1ss9DCCH+/vtvIZPJxM2bN5XK33nnHTF37twK2zY3NxebNm0qE+fr8Hk8f4znFRUVCVNTU/Hrr78qxerr66tUr1OnTmLKlClCCCHCwsJE06ZNRUlJiWJ7QUGBMDIyEocOHRJCPP2/OHjwYMX2Xbt2iaZNm1YYx/PKu16l9u7dK3R0dERxcbHK7ZH2Y88NabXu3bsjISFBsSxbtgwAcPToUfTq1QsNGjSAqakpxo0bh7t371bYpR8YGAgfHx/07NkTX331Fa5du6bYFhcXh/DwcNSpU0ex9OnTByUlJS+ckJqdnY06deoohmIKCwuxa9cuyOVyJCUlwdPTU6m+p6en4q/l4cOHIz8/H87Ozpg8eTJ2796NoqKiSl2rMWPG4NixY7h16xYAYMuWLejfvz/eeOONSp2nqakpEhISEBcXh9WrV6Nx48ZYvXq1Uh11Pw8AOHfuHIQQcHFxUYrp+PHjSp/P8/Lz88sMSQGvz+fxrNu3b8PX1xcuLi4wNzeHubk5Hj58iNTUVKV6Xbp0KbNeeu5xcXH4+++/YWpqqoijbt26ePz4cYWfw9ChQ/G///1PretRESMjI5SUlKCgoEAj7ZF20KvpAIiqkomJCZo0aaJUlpKSgv79+8PX1xdffPEF6tati5iYGEyaNAlPnjwpt5358+dj9OjR+O2333DgwAEEBQVh+/btGDp0KEpKSvDRRx8pzbEo1ahRowpjMzU1xblz56CjowMbGxuYmJgobX++214IoSizt7fH5cuXERkZid9//x1+fn749ttvcfz4caXhHnV07NgRjRs3xvbt2zFlyhTs3r0bGzduVGyXep46OjqKz6BZs2bIzMyEl5cXoqKiAEj7PErj0dXVRVxcHHR1dZW21alTp8L9LC0tcf/+/TLlr8vn8awJEybgzp07CA0NhYODAwwMDNClSxeVJnuXnntJSQnc3NywZcuWMnWsrKxUiqMy7t27B2NjY8UwIhHA5IZeQ7GxsSgqKsL333+vuBX4559/ful+Li4ucHFxQUBAAEaNGoWNGzdi6NChaN++PS5dulQmiXqZZ7/0n+fq6oqYmBiMGzdOUXby5Em4uroq1o2MjDBo0CAMGjQIU6dORbNmzXDhwgW0b9++THv6+voq3fUzevRobNmyBQ0bNoSOjg4GDBig2Cb1PJ8XEBCAkJAQ7N69G0OHDlXp85DL5WXib9euHYqLi3H79m107dpV5eO3a9cOiYmJZcpfx88jOjoaK1euRP/+/QEAaWlpyMrKKlPv9OnTSud++vRptGvXThFHREQErK2tYWZmJjkWqS5evFjuNabXG4el6LXTuHFjFBUV4YcffsD169exefPmMsMkz8rPz8fHH3+MY8eOISUlBSdOnMDZs2cVX2z//ve/cerUKUydOhUJCQm4evUq9u3bh2nTpkmOcdasWQgPD8fq1atx9epVhISEYNeuXYqJtOHh4QgLC8PFixcV52BkZAQHB4dy23N0dMSRI0eQmZlZbq9FqTFjxuDcuXNYtGgRhg0bpjR8o6nzNDMzg4+PD4KCgiCEUOnzcHR0xMOHD3HkyBFkZWUhLy8PLi4uGDNmDMaNG4ddu3YhOTkZZ8+exddff439+/dXePw+ffogJiZGrZi19fNo0qQJNm/ejKSkJPz5558YM2ZMuT0gv/zyCzZs2IArV64gKCgIZ86cUUxcHjNmDCwtLTF48GBER0cjOTkZx48fx4wZM5Cenl7ucXfv3o1mzZq9MLaHDx8qhpMBIDk5GQkJCWWGzKKjo9G7d2+Vz5leEzU75Yeo6jw/ifFZISEhwtbWVhgZGYk+ffqITZs2CQDi/v37QgjlCaYFBQVi5MiRwt7eXsjlcmFnZyc+/vhjpUmbZ86cEb169RJ16tQRJiYmonXr1mLRokUVxlbeBNnnrVy5Ujg7Owt9fX3h4uKiNAl29+7dolOnTsLMzEyYmJiIzp07i99//12x/fkJrPv27RNNmjQRenp6wsHBQQhR8eTSDh06CADijz/+KLNNU+eZkpIi9PT0REREhBDi5Z+HEEL4+vqKevXqCQAiKChICCFEYWGh+Pzzz4Wjo6PQ19cX9evXF0OHDhXnz5+vMKZ79+4JIyMj8b///e+lcT5LGz6P549x7tw54e7uLgwMDMSbb74pfvnll3InP69YsUL06tVLGBgYCAcHB7Ft2zaldjMyMsS4ceOEpaWlMDAwEM7OzmLy5MkiOztbCFH2/2LpRPMXOXr0qGIC+rPL+PHjFXXS09OFvr6+SEtLe2Fb9PqRCSFEzaRVREQ1Y/bs2cjOzsaaNWtqOhSqhFmzZiE7Oxtr166t6VDoFcNhKSJ67cybNw8ODg4aefow1Rxra2t88cUXNR0GvYLYc0NERERahT03REREpFWY3BAREZFWYXJDREREWoXJDREREWkVJjdERESkVZjcEBERkVZhckNERERahckNERERaRUmN0RERKRV/h+vmZoWvSLmFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve\n",
    "# ROC curve    for sklearn from version 1.2 or newer\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "rf_disp_test = RocCurveDisplay.from_estimator(rf, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature importance values\n",
    "importance = pd.Series(rf.feature_importances_, name = 'Gini')\n",
    "\n",
    "# feature names\n",
    "feature_names = pd.Series(X_train.columns, name = 'Feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Feature</th>\n",
       "      <th>Gini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Size_type</td>\n",
       "      <td>0.180339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dose</td>\n",
       "      <td>0.079734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P_density</td>\n",
       "      <td>0.356398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CSKKKK</td>\n",
       "      <td>0.075949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>QHREDGS</td>\n",
       "      <td>0.072500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RKDVY</td>\n",
       "      <td>0.088468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WKYMVM</td>\n",
       "      <td>0.039112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CLPFFD</td>\n",
       "      <td>0.058186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>QEINSSY</td>\n",
       "      <td>0.049314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature      Gini\n",
       "0  Size_type  0.180339\n",
       "1       Dose  0.079734\n",
       "2  P_density  0.356398\n",
       "3     CSKKKK  0.075949\n",
       "4    QHREDGS  0.072500\n",
       "5      RKDVY  0.088468\n",
       "6     WKYMVM  0.039112\n",
       "7     CLPFFD  0.058186\n",
       "8    QEINSSY  0.049314"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine feature names and importance values into a Dataframe\n",
    "df = pd.concat([feature_names, importance], axis=1, names=['Feature', 'Gini'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Feature Importance')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature importance plot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df_sorted = df.sort_values('Gini', ascending=False)[:] # Sort by Gini in descending order\n",
    "\n",
    "plt.figure(figsize=(5, 10))\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "ax = sns.barplot(x = 'Gini', y = 'Feature', data = df_sorted)\n",
    "plt.xlabel(\"Feature Importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}