Hemofilia_Projeto1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "52404f43-430c-476f-b39f-7c3fa9d7ec8e",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4bda2e9f-ad92-483c-a310-6823c3055503",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard Library Imports\n",
    "import re\n",
    "\n",
    "# Third-party Library Imports\n",
    "import pandas as pd\n",
    "from scipy.stats import ttest_rel as t_test, wilcoxon\n",
    "\n",
    "# Local Imports\n",
    "import MLE as mle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e05beb5-c1c6-4036-86a3-4df6ceeaebaf",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a12d6f08-7604-4300-a512-7c30b3424d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linhas x Colunas: (415, 19)\n",
      "Qnt. linhas nulas: 0\n",
      "Qnt. linhas duplicadas: 0\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 415 entries, 0 to 414\n",
      "Data columns (total 19 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   AA_HGVS              415 non-null    int64  \n",
      " 1   AA_Legacy            415 non-null    int64  \n",
      " 2   aa1                  415 non-null    object \n",
      " 3   AA_dist              415 non-null    float64\n",
      " 4   psi                  415 non-null    float64\n",
      " 5   phi                  415 non-null    float64\n",
      " 6   bfactor              415 non-null    float64\n",
      " 7   areaSAS              415 non-null    float64\n",
      " 8   areaSES              415 non-null    float64\n",
      " 9   kdHydrophobicity     415 non-null    float64\n",
      " 10  ConsurfDB            415 non-null    float64\n",
      " 11  degree               415 non-null    float64\n",
      " 12  betweenness          415 non-null    float64\n",
      " 13  closeness            415 non-null    float64\n",
      " 14  burts                415 non-null    float64\n",
      " 15  pr                   415 non-null    float64\n",
      " 16  auth                 415 non-null    float64\n",
      " 17  kcore                415 non-null    float64\n",
      " 18  Calculated_Severity  415 non-null    object \n",
      "dtypes: float64(15), int64(2), object(2)\n",
      "memory usage: 61.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('hemofilia.csv')\n",
    "\n",
    "shape = df.shape\n",
    "nan = df.isna().sum().sum()\n",
    "dup = df.duplicated().sum()\n",
    "cols = df.columns.tolist()\n",
    "\n",
    "print(f'Linhas x Colunas: {shape}')\n",
    "print(f'Qnt. linhas nulas: {nan}')\n",
    "print(f'Qnt. linhas duplicadas: {dup}\\n')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7bcfe1c3-fa24-4eb1-ad5a-7e37b8c9a697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Severe\n",
       "1      Moderate\n",
       "2        Severe\n",
       "3      Moderate\n",
       "4          Mild\n",
       "         ...   \n",
       "410    Moderate\n",
       "411        Mild\n",
       "412    Moderate\n",
       "413      Severe\n",
       "414        Mild\n",
       "Name: Calculated_Severity, Length: 415, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = df['Calculated_Severity']\n",
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef4723d1-d88a-49af-8f7c-3906b774d356",
   "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>AA_HGVS</th>\n",
       "      <th>AA_Legacy</th>\n",
       "      <th>aa1</th>\n",
       "      <th>AA_dist</th>\n",
       "      <th>psi</th>\n",
       "      <th>phi</th>\n",
       "      <th>bfactor</th>\n",
       "      <th>areaSAS</th>\n",
       "      <th>areaSES</th>\n",
       "      <th>kdHydrophobicity</th>\n",
       "      <th>ConsurfDB</th>\n",
       "      <th>degree</th>\n",
       "      <th>betweenness</th>\n",
       "      <th>closeness</th>\n",
       "      <th>burts</th>\n",
       "      <th>pr</th>\n",
       "      <th>auth</th>\n",
       "      <th>kcore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>Leu</td>\n",
       "      <td>1.622514</td>\n",
       "      <td>150.866429</td>\n",
       "      <td>-107.867773</td>\n",
       "      <td>146.53</td>\n",
       "      <td>4.380023</td>\n",
       "      <td>20.015846</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-793.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.010313</td>\n",
       "      <td>0.115715</td>\n",
       "      <td>0.225146</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.074260</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>Ala</td>\n",
       "      <td>1.523528</td>\n",
       "      <td>18.239183</td>\n",
       "      <td>168.251651</td>\n",
       "      <td>139452.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-1019.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.004677</td>\n",
       "      <td>0.107229</td>\n",
       "      <td>0.179909</td>\n",
       "      <td>0.001077</td>\n",
       "      <td>0.081676</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>Val</td>\n",
       "      <td>1.568551</td>\n",
       "      <td>116.506984</td>\n",
       "      <td>-35.703606</td>\n",
       "      <td>193867.00</td>\n",
       "      <td>10.603600</td>\n",
       "      <td>41.534812</td>\n",
       "      <td>4.2</td>\n",
       "      <td>-746.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.002008</td>\n",
       "      <td>0.101614</td>\n",
       "      <td>0.239194</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>0.043540</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>Leu</td>\n",
       "      <td>1.641493</td>\n",
       "      <td>177.755055</td>\n",
       "      <td>-104.656175</td>\n",
       "      <td>154735.00</td>\n",
       "      <td>45.264135</td>\n",
       "      <td>75.880436</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-134.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>0.097581</td>\n",
       "      <td>0.263617</td>\n",
       "      <td>0.000879</td>\n",
       "      <td>0.034803</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>Asp</td>\n",
       "      <td>1.602138</td>\n",
       "      <td>-66.129605</td>\n",
       "      <td>-99.559307</td>\n",
       "      <td>195175.00</td>\n",
       "      <td>27.504607</td>\n",
       "      <td>44.515711</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>-925.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.098256</td>\n",
       "      <td>0.373760</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>0.004828</td>\n",
       "      <td>4.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2322</td>\n",
       "      <td>2303</td>\n",
       "      <td>Thr</td>\n",
       "      <td>1.486620</td>\n",
       "      <td>156.667100</td>\n",
       "      <td>-145.542531</td>\n",
       "      <td>137589.00</td>\n",
       "      <td>9.031416</td>\n",
       "      <td>14.374980</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-608.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.009058</td>\n",
       "      <td>0.096300</td>\n",
       "      <td>0.195930</td>\n",
       "      <td>0.001149</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>2324</td>\n",
       "      <td>2305</td>\n",
       "      <td>Tyr</td>\n",
       "      <td>1.540734</td>\n",
       "      <td>136.859439</td>\n",
       "      <td>-146.304906</td>\n",
       "      <td>186622.00</td>\n",
       "      <td>39.680483</td>\n",
       "      <td>36.738038</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-243.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.027182</td>\n",
       "      <td>0.093559</td>\n",
       "      <td>0.188421</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.000591</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2327</td>\n",
       "      <td>2308</td>\n",
       "      <td>Ile</td>\n",
       "      <td>1.531856</td>\n",
       "      <td>138.332710</td>\n",
       "      <td>-148.494916</td>\n",
       "      <td>169267.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-703.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000740</td>\n",
       "      <td>0.080816</td>\n",
       "      <td>0.255136</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2330</td>\n",
       "      <td>2311</td>\n",
       "      <td>Gln</td>\n",
       "      <td>1.445797</td>\n",
       "      <td>-63.539770</td>\n",
       "      <td>-138.920341</td>\n",
       "      <td>176917.00</td>\n",
       "      <td>52.479163</td>\n",
       "      <td>63.099512</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>666.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.005663</td>\n",
       "      <td>0.077490</td>\n",
       "      <td>0.230786</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>2334</td>\n",
       "      <td>2315</td>\n",
       "      <td>His</td>\n",
       "      <td>1.436106</td>\n",
       "      <td>-55.755947</td>\n",
       "      <td>63.847554</td>\n",
       "      <td>161833.00</td>\n",
       "      <td>135.099596</td>\n",
       "      <td>100.928181</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>623.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.066560</td>\n",
       "      <td>0.415753</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>415 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     AA_HGVS  AA_Legacy  aa1   AA_dist         psi         phi    bfactor  \\\n",
       "0         26          7  Leu  1.622514  150.866429 -107.867773     146.53   \n",
       "1         28          9  Ala  1.523528   18.239183  168.251651  139452.00   \n",
       "2         29         10  Val  1.568551  116.506984  -35.703606  193867.00   \n",
       "3         31         12  Leu  1.641493  177.755055 -104.656175  154735.00   \n",
       "4         34         15  Asp  1.602138  -66.129605  -99.559307  195175.00   \n",
       "..       ...        ...  ...       ...         ...         ...        ...   \n",
       "410     2322       2303  Thr  1.486620  156.667100 -145.542531  137589.00   \n",
       "411     2324       2305  Tyr  1.540734  136.859439 -146.304906  186622.00   \n",
       "412     2327       2308  Ile  1.531856  138.332710 -148.494916  169267.00   \n",
       "413     2330       2311  Gln  1.445797  -63.539770 -138.920341  176917.00   \n",
       "414     2334       2315  His  1.436106  -55.755947   63.847554  161833.00   \n",
       "\n",
       "        areaSAS     areaSES  kdHydrophobicity  ConsurfDB  degree  betweenness  \\\n",
       "0      4.380023   20.015846               3.8     -793.0     8.0     0.010313   \n",
       "1      0.000000    0.000000               1.8    -1019.0    10.0     0.004677   \n",
       "2     10.603600   41.534812               4.2     -746.0     7.0     0.002008   \n",
       "3     45.264135   75.880436               3.8     -134.0     7.0     0.002169   \n",
       "4     27.504607   44.515711              -3.5     -925.0     4.0     0.001263   \n",
       "..          ...         ...               ...        ...     ...          ...   \n",
       "410    9.031416   14.374980              -0.7     -608.0    10.0     0.009058   \n",
       "411   39.680483   36.738038              -1.3     -243.0     9.0     0.027182   \n",
       "412    0.000000    0.000000               4.5     -703.0     7.0     0.000740   \n",
       "413   52.479163   63.099512              -3.5      666.0     7.0     0.005663   \n",
       "414  135.099596  100.928181              -3.2      623.0     4.0     0.000016   \n",
       "\n",
       "     closeness     burts        pr      auth  kcore  \n",
       "0     0.115715  0.225146  0.000859  0.074260    4.0  \n",
       "1     0.107229  0.179909  0.001077  0.081676    4.0  \n",
       "2     0.101614  0.239194  0.000837  0.043540    4.0  \n",
       "3     0.097581  0.263617  0.000879  0.034803    4.0  \n",
       "4     0.098256  0.373760  0.000559  0.004828    4.0  \n",
       "..         ...       ...       ...       ...    ...  \n",
       "410   0.096300  0.195930  0.001149  0.001092    4.0  \n",
       "411   0.093559  0.188421  0.001049  0.000591    4.0  \n",
       "412   0.080816  0.255136  0.000778  0.000115    4.0  \n",
       "413   0.077490  0.230786  0.000863  0.000043    4.0  \n",
       "414   0.066560  0.415753  0.000534  0.000008    4.0  \n",
       "\n",
       "[415 rows x 18 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.drop(columns='Calculated_Severity')\n",
    "features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6651311f-01b2-45b2-be3b-fc9a6b872221",
   "metadata": {},
   "source": [
    "# Get Top 3 Classifiers Without Tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "326cdf10-555c-4ee0-89e3-de338692df77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     AA_HGVS  AA_Legacy   AA_dist         psi         phi    bfactor  \\\n",
      "0         26          7  1.622514  150.866429 -107.867773     146.53   \n",
      "1         28          9  1.523528   18.239183  168.251651  139452.00   \n",
      "2         29         10  1.568551  116.506984  -35.703606  193867.00   \n",
      "3         31         12  1.641493  177.755055 -104.656175  154735.00   \n",
      "4         34         15  1.602138  -66.129605  -99.559307  195175.00   \n",
      "..       ...        ...       ...         ...         ...        ...   \n",
      "410     2322       2303  1.486620  156.667100 -145.542531  137589.00   \n",
      "411     2324       2305  1.540734  136.859439 -146.304906  186622.00   \n",
      "412     2327       2308  1.531856  138.332710 -148.494916  169267.00   \n",
      "413     2330       2311  1.445797  -63.539770 -138.920341  176917.00   \n",
      "414     2334       2315  1.436106  -55.755947   63.847554  161833.00   \n",
      "\n",
      "        areaSAS     areaSES  kdHydrophobicity  ConsurfDB  ...  aa1_Leu  \\\n",
      "0      4.380023   20.015846               3.8     -793.0  ...     True   \n",
      "1      0.000000    0.000000               1.8    -1019.0  ...    False   \n",
      "2     10.603600   41.534812               4.2     -746.0  ...    False   \n",
      "3     45.264135   75.880436               3.8     -134.0  ...     True   \n",
      "4     27.504607   44.515711              -3.5     -925.0  ...    False   \n",
      "..          ...         ...               ...        ...  ...      ...   \n",
      "410    9.031416   14.374980              -0.7     -608.0  ...    False   \n",
      "411   39.680483   36.738038              -1.3     -243.0  ...    False   \n",
      "412    0.000000    0.000000               4.5     -703.0  ...    False   \n",
      "413   52.479163   63.099512              -3.5      666.0  ...    False   \n",
      "414  135.099596  100.928181              -3.2      623.0  ...    False   \n",
      "\n",
      "     aa1_Lys  aa1_Met  aa1_Phe  aa1_Pro  aa1_Ser  aa1_Thr  aa1_Trp  aa1_Tyr  \\\n",
      "0      False    False    False    False    False    False    False    False   \n",
      "1      False    False    False    False    False    False    False    False   \n",
      "2      False    False    False    False    False    False    False    False   \n",
      "3      False    False    False    False    False    False    False    False   \n",
      "4      False    False    False    False    False    False    False    False   \n",
      "..       ...      ...      ...      ...      ...      ...      ...      ...   \n",
      "410    False    False    False    False    False     True    False    False   \n",
      "411    False    False    False    False    False    False    False     True   \n",
      "412    False    False    False    False    False    False    False    False   \n",
      "413    False    False    False    False    False    False    False    False   \n",
      "414    False    False    False    False    False    False    False    False   \n",
      "\n",
      "     aa1_Val  \n",
      "0      False  \n",
      "1      False  \n",
      "2       True  \n",
      "3      False  \n",
      "4      False  \n",
      "..       ...  \n",
      "410    False  \n",
      "411    False  \n",
      "412    False  \n",
      "413    False  \n",
      "414    False  \n",
      "\n",
      "[415 rows x 37 columns]\n",
      "[2 1 2 1 0 2 0 0 2 2 0 0 0 1 2 2 2 2 0 0 2 2 2 0 2 0 2 2 0 1 2 2 2 2 2 0 2\n",
      " 0 0 2 1 0 1 0 0 0 0 0 0 0 2 2 2 2 0 2 2 1 0 2 2 0 2 0 0 1 2 0 0 1 2 2 0 0\n",
      " 0 0 0 2 2 1 1 0 0 2 2 0 0 0 1 2 2 2 2 2 0 0 0 0 2 0 0 2 2 0 1 0 0 0 0 0 0\n",
      " 0 1 2 0 1 0 0 0 2 0 2 2 2 2 1 2 2 2 2 2 2 2 0 2 1 2 0 1 1 1 1 2 0 0 1 1 1\n",
      " 1 0 2 2 2 2 2 0 0 2 1 0 0 2 2 2 2 2 2 0 2 0 0 0 1 1 0 0 0 0 2 2 2 1 2 2 2\n",
      " 0 2 2 0 0 0 2 2 0 1 0 0 0 1 1 0 0 0 1 1 0 2 2 0 1 0 0 2 0 1 0 1 0 0 0 0 0\n",
      " 0 0 0 0 2 2 2 0 0 0 0 1 0 2 2 0 0 2 1 2 2 2 0 0 0 2 0 0 2 2 2 0 0 2 0 0 0\n",
      " 0 2 0 0 2 1 1 1 1 0 0 0 0 2 2 2 1 0 0 0 1 2 2 2 2 2 2 0 2 2 2 2 0 1 0 2 0\n",
      " 0 0 2 2 2 2 0 0 0 0 1 2 2 2 0 2 0 0 1 0 0 0 0 0 2 0 0 0 1 2 2 2 0 1 1 0 0\n",
      " 2 0 2 1 2 2 2 2 2 2 0 1 0 0 0 0 0 0 0 0 2 1 2 2 0 1 2 0 0 0 2 0 0 1 2 1 1\n",
      " 1 0 0 0 0 0 0 1 1 2 2 2 1 0 0 2 0 0 1 2 0 2 0 1 0 0 0 1 0 2 0 0 1 1 1 0 0\n",
      " 0 0 1 1 0 1 2 0]\n"
     ]
    }
   ],
   "source": [
    "worker = mle.MLE(features,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb22afd8-3642-464c-9f8a-ae4a760c5634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<MLE.MLE at 0x77cd1c126050>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94be95a7-89a3-4916-9cad-513b9558bab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None Dummy {}\n",
      "Standard Dummy {}\n",
      "None GaussianNB {}\n",
      "Standard GaussianNB {}\n",
      "None KNeighbors {}\n",
      "Standard KNeighbors {}\n",
      "None DecisionTree {}\n",
      "Standard DecisionTree {}\n",
      "None RandomForest {}\n",
      "Standard RandomForest {}\n",
      "None AdaBoost {}\n",
      "Standard AdaBoost {}\n",
      "None Bagging {}\n",
      "Standard Bagging {}\n",
      "None GradientBoosting {}\n",
      "Standard GradientBoosting {}\n",
      "None XGBoost {}\n",
      "Standard XGBoost {}\n",
      "None LightGBM {}\n",
      "Standard LightGBM {}\n",
      "None SVM {}\n",
      "Standard SVM {}\n",
      "None MLP {}\n",
      "Standard MLP {}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/home/vbbonella/.local/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "list_all_default_results = []\n",
    "for classifier in worker.classifiers.keys():\n",
    "    for scaler in worker.scalers.keys():\n",
    "        results = worker.estimator_validate_without_param_search(scaler, classifier)\n",
    "        list_all_default_results.append(results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6891c2d2-a648-4198-a600-3b740e23751f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaler</th>\n",
       "      <th>classifier</th>\n",
       "      <th>params</th>\n",
       "      <th>accuracy std</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>roc_auc_ovr</th>\n",
       "      <th>f1_macro</th>\n",
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       "      <th>accuracy_scores</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>RandomForest</td>\n",
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       "      <td>0.756103</td>\n",
       "      <td>0.540879</td>\n",
       "      <td>0.543627</td>\n",
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       "      <th>2</th>\n",
       "      <td>Standard</td>\n",
       "      <td>GradientBoosting</td>\n",
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       "      <td>0.068989</td>\n",
       "      <td>0.623790</td>\n",
       "      <td>0.735612</td>\n",
       "      <td>0.551431</td>\n",
       "      <td>0.548359</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.071785</td>\n",
       "      <td>0.622300</td>\n",
       "      <td>0.724569</td>\n",
       "      <td>0.534466</td>\n",
       "      <td>0.537961</td>\n",
       "      <td>[0.6904761904761905, 0.6904761904761905, 0.619...</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.622280</td>\n",
       "      <td>0.725418</td>\n",
       "      <td>0.534433</td>\n",
       "      <td>0.537961</td>\n",
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       "      <th>5</th>\n",
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       "      <td>GradientBoosting</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.067598</td>\n",
       "      <td>0.619899</td>\n",
       "      <td>0.735177</td>\n",
       "      <td>0.546627</td>\n",
       "      <td>0.544167</td>\n",
       "      <td>[0.6904761904761905, 0.5476190476190477, 0.642...</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>LightGBM</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.070947</td>\n",
       "      <td>0.615873</td>\n",
       "      <td>0.746411</td>\n",
       "      <td>0.528852</td>\n",
       "      <td>0.532365</td>\n",
       "      <td>[0.6666666666666666, 0.5476190476190477, 0.642...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.080690</td>\n",
       "      <td>0.611963</td>\n",
       "      <td>0.755442</td>\n",
       "      <td>0.540175</td>\n",
       "      <td>0.537943</td>\n",
       "      <td>[0.6904761904761905, 0.5238095238095238, 0.571...</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Standard</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.080690</td>\n",
       "      <td>0.611963</td>\n",
       "      <td>0.755442</td>\n",
       "      <td>0.540175</td>\n",
       "      <td>0.537943</td>\n",
       "      <td>[0.6904761904761905, 0.5238095238095238, 0.571...</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Standard</td>\n",
       "      <td>LightGBM</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.078620</td>\n",
       "      <td>0.604530</td>\n",
       "      <td>0.742532</td>\n",
       "      <td>0.521414</td>\n",
       "      <td>0.523356</td>\n",
       "      <td>[0.6666666666666666, 0.5714285714285714, 0.619...</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Standard</td>\n",
       "      <td>MLP</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.063810</td>\n",
       "      <td>0.601413</td>\n",
       "      <td>0.677539</td>\n",
       "      <td>0.494554</td>\n",
       "      <td>0.508636</td>\n",
       "      <td>[0.6190476190476191, 0.6428571428571429, 0.642...</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Standard</td>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.067890</td>\n",
       "      <td>0.593419</td>\n",
       "      <td>0.669237</td>\n",
       "      <td>0.544831</td>\n",
       "      <td>0.552560</td>\n",
       "      <td>[0.5714285714285714, 0.5476190476190477, 0.642...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>None</td>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.068995</td>\n",
       "      <td>0.592605</td>\n",
       "      <td>0.668767</td>\n",
       "      <td>0.544001</td>\n",
       "      <td>0.551975</td>\n",
       "      <td>[0.5714285714285714, 0.5476190476190477, 0.642...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Standard</td>\n",
       "      <td>SVM</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.065609</td>\n",
       "      <td>0.577274</td>\n",
       "      <td>0.644253</td>\n",
       "      <td>0.417032</td>\n",
       "      <td>0.459635</td>\n",
       "      <td>[0.6190476190476191, 0.6428571428571429, 0.595...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>None</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.074609</td>\n",
       "      <td>0.557375</td>\n",
       "      <td>0.641285</td>\n",
       "      <td>0.477084</td>\n",
       "      <td>0.480195</td>\n",
       "      <td>[0.47619047619047616, 0.5714285714285714, 0.52...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Standard</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.074609</td>\n",
       "      <td>0.557375</td>\n",
       "      <td>0.641285</td>\n",
       "      <td>0.477084</td>\n",
       "      <td>0.480195</td>\n",
       "      <td>[0.47619047619047616, 0.5714285714285714, 0.52...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>None</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.078622</td>\n",
       "      <td>0.532404</td>\n",
       "      <td>0.633191</td>\n",
       "      <td>0.389826</td>\n",
       "      <td>0.436034</td>\n",
       "      <td>[0.5476190476190477, 0.6428571428571429, 0.571...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>None</td>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.067622</td>\n",
       "      <td>0.487282</td>\n",
       "      <td>0.621080</td>\n",
       "      <td>0.403550</td>\n",
       "      <td>0.410360</td>\n",
       "      <td>[0.5476190476190477, 0.5, 0.5238095238095238, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Standard</td>\n",
       "      <td>KNeighbors</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.061810</td>\n",
       "      <td>0.486605</td>\n",
       "      <td>0.567844</td>\n",
       "      <td>0.383259</td>\n",
       "      <td>0.397845</td>\n",
       "      <td>[0.47619047619047616, 0.42857142857142855, 0.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>None</td>\n",
       "      <td>SVM</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.008282</td>\n",
       "      <td>0.462660</td>\n",
       "      <td>0.553735</td>\n",
       "      <td>0.210861</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>[0.4523809523809524, 0.4523809523809524, 0.452...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>None</td>\n",
       "      <td>Dummy</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.008282</td>\n",
       "      <td>0.462660</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.210861</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>[0.4523809523809524, 0.4523809523809524, 0.452...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Standard</td>\n",
       "      <td>Dummy</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.008282</td>\n",
       "      <td>0.462660</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.210861</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>[0.4523809523809524, 0.4523809523809524, 0.452...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Standard</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.087069</td>\n",
       "      <td>0.461808</td>\n",
       "      <td>0.613913</td>\n",
       "      <td>0.429252</td>\n",
       "      <td>0.443781</td>\n",
       "      <td>[0.4523809523809524, 0.5476190476190477, 0.547...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>None</td>\n",
       "      <td>MLP</td>\n",
       "      <td>{}</td>\n",
       "      <td>0.090791</td>\n",
       "      <td>0.414402</td>\n",
       "      <td>0.540977</td>\n",
       "      <td>0.268121</td>\n",
       "      <td>0.348035</td>\n",
       "      <td>[0.5, 0.5238095238095238, 0.47619047619047616,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      scaler        classifier params  accuracy std  accuracy  roc_auc_ovr  \\\n",
       "0       None      RandomForest     {}      0.074447  0.642509     0.755590   \n",
       "1   Standard      RandomForest     {}      0.074401  0.641696     0.756103   \n",
       "2   Standard  GradientBoosting     {}      0.068989  0.623790     0.735612   \n",
       "3       None           Bagging     {}      0.071785  0.622300     0.724569   \n",
       "4   Standard           Bagging     {}      0.071305  0.622280     0.725418   \n",
       "5       None  GradientBoosting     {}      0.067598  0.619899     0.735177   \n",
       "6       None          LightGBM     {}      0.070947  0.615873     0.746411   \n",
       "7       None           XGBoost     {}      0.080690  0.611963     0.755442   \n",
       "8   Standard           XGBoost     {}      0.080690  0.611963     0.755442   \n",
       "9   Standard          LightGBM     {}      0.078620  0.604530     0.742532   \n",
       "10  Standard               MLP     {}      0.063810  0.601413     0.677539   \n",
       "11  Standard      DecisionTree     {}      0.067890  0.593419     0.669237   \n",
       "12      None      DecisionTree     {}      0.068995  0.592605     0.668767   \n",
       "13  Standard               SVM     {}      0.065609  0.577274     0.644253   \n",
       "14      None          AdaBoost     {}      0.074609  0.557375     0.641285   \n",
       "15  Standard          AdaBoost     {}      0.074609  0.557375     0.641285   \n",
       "16      None        GaussianNB     {}      0.078622  0.532404     0.633191   \n",
       "17      None        KNeighbors     {}      0.067622  0.487282     0.621080   \n",
       "18  Standard        KNeighbors     {}      0.061810  0.486605     0.567844   \n",
       "19      None               SVM     {}      0.008282  0.462660     0.553735   \n",
       "20      None             Dummy     {}      0.008282  0.462660     0.500000   \n",
       "21  Standard             Dummy     {}      0.008282  0.462660     0.500000   \n",
       "22  Standard        GaussianNB     {}      0.087069  0.461808     0.613913   \n",
       "23      None               MLP     {}      0.090791  0.414402     0.540977   \n",
       "\n",
       "    f1_macro  recall_macro                                    accuracy_scores  \n",
       "0   0.541448      0.544212  [0.6666666666666666, 0.6428571428571429, 0.619...  \n",
       "1   0.540879      0.543627  [0.6666666666666666, 0.6428571428571429, 0.619...  \n",
       "2   0.551431      0.548359  [0.6904761904761905, 0.6190476190476191, 0.642...  \n",
       "3   0.534466      0.537961  [0.6904761904761905, 0.6904761904761905, 0.619...  \n",
       "4   0.534433      0.537961  [0.6904761904761905, 0.6904761904761905, 0.619...  \n",
       "5   0.546627      0.544167  [0.6904761904761905, 0.5476190476190477, 0.642...  \n",
       "6   0.528852      0.532365  [0.6666666666666666, 0.5476190476190477, 0.642...  \n",
       "7   0.540175      0.537943  [0.6904761904761905, 0.5238095238095238, 0.571...  \n",
       "8   0.540175      0.537943  [0.6904761904761905, 0.5238095238095238, 0.571...  \n",
       "9   0.521414      0.523356  [0.6666666666666666, 0.5714285714285714, 0.619...  \n",
       "10  0.494554      0.508636  [0.6190476190476191, 0.6428571428571429, 0.642...  \n",
       "11  0.544831      0.552560  [0.5714285714285714, 0.5476190476190477, 0.642...  \n",
       "12  0.544001      0.551975  [0.5714285714285714, 0.5476190476190477, 0.642...  \n",
       "13  0.417032      0.459635  [0.6190476190476191, 0.6428571428571429, 0.595...  \n",
       "14  0.477084      0.480195  [0.47619047619047616, 0.5714285714285714, 0.52...  \n",
       "15  0.477084      0.480195  [0.47619047619047616, 0.5714285714285714, 0.52...  \n",
       "16  0.389826      0.436034  [0.5476190476190477, 0.6428571428571429, 0.571...  \n",
       "17  0.403550      0.410360  [0.5476190476190477, 0.5, 0.5238095238095238, ...  \n",
       "18  0.383259      0.397845  [0.47619047619047616, 0.42857142857142855, 0.4...  \n",
       "19  0.210861      0.333333  [0.4523809523809524, 0.4523809523809524, 0.452...  \n",
       "20  0.210861      0.333333  [0.4523809523809524, 0.4523809523809524, 0.452...  \n",
       "21  0.210861      0.333333  [0.4523809523809524, 0.4523809523809524, 0.452...  \n",
       "22  0.429252      0.443781  [0.4523809523809524, 0.5476190476190477, 0.547...  \n",
       "23  0.268121      0.348035  [0.5, 0.5238095238095238, 0.47619047619047616,...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_default_results = pd.DataFrame(list_all_default_results)\n",
    "df_all_default_results = df_all_default_results.sort_values(by=worker.scoring, ascending=False).reset_index(drop=True)\n",
    "df_all_default_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56f7c824-0b8c-4e23-a8a7-6b7fddf85cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_default_results.to_csv('df_all_default_results.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3206960-4f7d-4af6-a542-1e221f716564",
   "metadata": {},
   "source": [
    "# Adjust Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f36993b5-a76e-4f12-bdad-eb314ea55ecf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Dummy {'estimator__strategy': ['stratified', 'most_frequent', 'prior', 'uniform']}\n",
      "Time elapsed: 4.05671238899231s\n",
      "Standard GaussianNB {'estimator__var_smoothing': [1e-09, 1e-08, 1e-07, 1e-06, 1e-05]}\n",
      "Time elapsed: 4.006795883178711s\n",
      "Standard KNeighbors {'estimator__n_neighbors': [3, 5, 7, 9, 11], 'estimator__weights': ['uniform', 'distance'], 'estimator__metric': ['euclidean', 'manhattan', 'minkowski']}\n",
      "Time elapsed: 59.607866525650024s\n",
      "Standard DecisionTree {'estimator__criterion': ['gini', 'entropy'], 'estimator__max_depth': [None, 10, 20, 30, 40, 50], 'estimator__min_samples_split': [2, 5, 10], 'estimator__min_samples_leaf': [1, 2, 4]}\n",
      "Time elapsed: 86.77610802650452s\n",
      "Standard RandomForest {'estimator__n_estimators': [100, 200, 300], 'estimator__criterion': ['gini', 'entropy'], 'estimator__max_depth': [None, 10, 20, 30, 40, 50], 'estimator__min_samples_split': [2, 5, 10], 'estimator__min_samples_leaf': [1, 2, 4], 'estimator__bootstrap': [True, False]}\n",
      "Time elapsed: 13147.449079751968s\n",
      "Standard AdaBoost {'estimator__n_estimators': [50, 100, 150], 'estimator__learning_rate': [0.01, 0.1, 1, 10]}\n",
      "Time elapsed: 157.22772645950317s\n",
      "Standard Bagging {'estimator__n_estimators': [10, 50, 100], 'estimator__max_samples': [0.5, 0.7, 1.0], 'estimator__max_features': [0.5, 0.7, 1.0]}\n",
      "Time elapsed: 256.314288854599s\n",
      "Standard GradientBoosting {'estimator__n_estimators': [100, 200, 300], 'estimator__learning_rate': [0.01, 0.1, 0.2], 'estimator__max_depth': [3, 5, 7], 'estimator__min_samples_split': [2, 5, 10], 'estimator__min_samples_leaf': [1, 2, 4], 'estimator__subsample': [0.8, 0.9, 1.0]}\n"
     ]
    }
   ],
   "source": [
    "list_all_search_results = []\n",
    "for classifier in worker.classifiers.keys():\n",
    "    results = worker.estimator_validate('Standard', classifier)\n",
    "    list_all_search_results.append(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "440ddcca-37fc-4960-9eb1-f112f7f09b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_search_results = pd.DataFrame(list_all_search_results)\n",
    "df_all_search_results = df_all_search_results.sort_values(by=worker.scoring, ascending=False).reset_index(drop=True)\n",
    "df_all_search_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e592eba-fb51-4a00-b0b1-8ad27f569048",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_search_results.to_csv('df_all_search_results.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ae15673-ef6b-47f3-b595-e5984c7afe52",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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