EDA & Models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            1\n",
       "1            2\n",
       "2            3\n",
       "3            4\n",
       "4            5\n",
       "         ...  \n",
       "59995    59996\n",
       "59996    59997\n",
       "59997    59998\n",
       "59998    59999\n",
       "59999    60000\n",
       "Name: id, Length: 60000, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('data/aps_failure_training_set.csv')\n",
    "train_df.pop('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>aa_000</th>\n",
       "      <th>ab_000</th>\n",
       "      <th>ac_000</th>\n",
       "      <th>ad_000</th>\n",
       "      <th>ae_000</th>\n",
       "      <th>af_000</th>\n",
       "      <th>ag_000</th>\n",
       "      <th>ag_001</th>\n",
       "      <th>ag_002</th>\n",
       "      <th>...</th>\n",
       "      <th>ee_002</th>\n",
       "      <th>ee_003</th>\n",
       "      <th>ee_004</th>\n",
       "      <th>ee_005</th>\n",
       "      <th>ee_006</th>\n",
       "      <th>ee_007</th>\n",
       "      <th>ee_008</th>\n",
       "      <th>ee_009</th>\n",
       "      <th>ef_000</th>\n",
       "      <th>eg_000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neg</td>\n",
       "      <td>76698</td>\n",
       "      <td>na</td>\n",
       "      <td>2130706438</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1240520</td>\n",
       "      <td>493384</td>\n",
       "      <td>721044</td>\n",
       "      <td>469792</td>\n",
       "      <td>339156</td>\n",
       "      <td>157956</td>\n",
       "      <td>73224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neg</td>\n",
       "      <td>33058</td>\n",
       "      <td>na</td>\n",
       "      <td>0</td>\n",
       "      <td>na</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>421400</td>\n",
       "      <td>178064</td>\n",
       "      <td>293306</td>\n",
       "      <td>245416</td>\n",
       "      <td>133654</td>\n",
       "      <td>81140</td>\n",
       "      <td>97576</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neg</td>\n",
       "      <td>41040</td>\n",
       "      <td>na</td>\n",
       "      <td>228</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>277378</td>\n",
       "      <td>159812</td>\n",
       "      <td>423992</td>\n",
       "      <td>409564</td>\n",
       "      <td>320746</td>\n",
       "      <td>158022</td>\n",
       "      <td>95128</td>\n",
       "      <td>514</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>neg</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>240</td>\n",
       "      <td>46</td>\n",
       "      <td>58</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>neg</td>\n",
       "      <td>60874</td>\n",
       "      <td>na</td>\n",
       "      <td>1368</td>\n",
       "      <td>458</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>622012</td>\n",
       "      <td>229790</td>\n",
       "      <td>405298</td>\n",
       "      <td>347188</td>\n",
       "      <td>286954</td>\n",
       "      <td>311560</td>\n",
       "      <td>433954</td>\n",
       "      <td>1218</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",
       "      <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>59995</th>\n",
       "      <td>neg</td>\n",
       "      <td>153002</td>\n",
       "      <td>na</td>\n",
       "      <td>664</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>998500</td>\n",
       "      <td>566884</td>\n",
       "      <td>1290398</td>\n",
       "      <td>1218244</td>\n",
       "      <td>1019768</td>\n",
       "      <td>717762</td>\n",
       "      <td>898642</td>\n",
       "      <td>28588</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>neg</td>\n",
       "      <td>2286</td>\n",
       "      <td>na</td>\n",
       "      <td>2130706538</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10578</td>\n",
       "      <td>6760</td>\n",
       "      <td>21126</td>\n",
       "      <td>68424</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>neg</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>2130706432</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>792</td>\n",
       "      <td>386</td>\n",
       "      <td>452</td>\n",
       "      <td>144</td>\n",
       "      <td>146</td>\n",
       "      <td>2622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>neg</td>\n",
       "      <td>80292</td>\n",
       "      <td>na</td>\n",
       "      <td>2130706432</td>\n",
       "      <td>494</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>699352</td>\n",
       "      <td>222654</td>\n",
       "      <td>347378</td>\n",
       "      <td>225724</td>\n",
       "      <td>194440</td>\n",
       "      <td>165070</td>\n",
       "      <td>802280</td>\n",
       "      <td>388422</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>neg</td>\n",
       "      <td>40222</td>\n",
       "      <td>na</td>\n",
       "      <td>698</td>\n",
       "      <td>628</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>440066</td>\n",
       "      <td>183200</td>\n",
       "      <td>344546</td>\n",
       "      <td>254068</td>\n",
       "      <td>225148</td>\n",
       "      <td>158304</td>\n",
       "      <td>170384</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 171 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      class  aa_000 ab_000      ac_000 ad_000 ae_000 af_000 ag_000 ag_001  \\\n",
       "0       neg   76698     na  2130706438    280      0      0      0      0   \n",
       "1       neg   33058     na           0     na      0      0      0      0   \n",
       "2       neg   41040     na         228    100      0      0      0      0   \n",
       "3       neg      12      0          70     66      0     10      0      0   \n",
       "4       neg   60874     na        1368    458      0      0      0      0   \n",
       "...     ...     ...    ...         ...    ...    ...    ...    ...    ...   \n",
       "59995   neg  153002     na         664    186      0      0      0      0   \n",
       "59996   neg    2286     na  2130706538    224      0      0      0      0   \n",
       "59997   neg     112      0  2130706432     18      0      0      0      0   \n",
       "59998   neg   80292     na  2130706432    494      0      0      0      0   \n",
       "59999   neg   40222     na         698    628      0      0      0      0   \n",
       "\n",
       "      ag_002  ...   ee_002  ee_003   ee_004   ee_005   ee_006  ee_007  ee_008  \\\n",
       "0          0  ...  1240520  493384   721044   469792   339156  157956   73224   \n",
       "1          0  ...   421400  178064   293306   245416   133654   81140   97576   \n",
       "2          0  ...   277378  159812   423992   409564   320746  158022   95128   \n",
       "3          0  ...      240      46       58       44       10       0       0   \n",
       "4          0  ...   622012  229790   405298   347188   286954  311560  433954   \n",
       "...      ...  ...      ...     ...      ...      ...      ...     ...     ...   \n",
       "59995      0  ...   998500  566884  1290398  1218244  1019768  717762  898642   \n",
       "59996      0  ...    10578    6760    21126    68424      136       0       0   \n",
       "59997      0  ...      792     386      452      144      146    2622       0   \n",
       "59998      0  ...   699352  222654   347378   225724   194440  165070  802280   \n",
       "59999      0  ...   440066  183200   344546   254068   225148  158304  170384   \n",
       "\n",
       "       ee_009 ef_000 eg_000  \n",
       "0           0      0      0  \n",
       "1        1500      0      0  \n",
       "2         514      0      0  \n",
       "3           0      4     32  \n",
       "4        1218      0      0  \n",
       "...       ...    ...    ...  \n",
       "59995   28588      0      0  \n",
       "59996       0      0      0  \n",
       "59997       0      0      0  \n",
       "59998  388422      0      0  \n",
       "59999     158      0      0  \n",
       "\n",
       "[60000 rows x 171 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7      ag_000\n",
       "8      ag_001\n",
       "9      ag_002\n",
       "10     ag_003\n",
       "11     ag_004\n",
       "        ...  \n",
       "164    ee_005\n",
       "165    ee_006\n",
       "166    ee_007\n",
       "167    ee_008\n",
       "168    ee_009\n",
       "Length: 70, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = pd.Series(train_df.columns)\n",
    "\n",
    "counts = cols.apply(lambda x: x.split(\"_\")[0]).value_counts().to_dict()\n",
    "categorical_cols_idx = cols.apply(\n",
    "    lambda x: True if counts[x.split(\"_\")[0]] > 1 else False)\n",
    "\n",
    "#get the columns that are categorical\n",
    "categorical_cols = cols[categorical_cols_idx]\n",
    "\n",
    "categorical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ag': 10, 'ay': 10, 'az': 10, 'ba': 10, 'cn': 10, 'cs': 10, 'ee': 10}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols.apply(lambda x: x.split(\"_\")[0]).value_counts().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#given this dataset plot the means train_df.filter(regex='cs_[0-9][0-9][0-9]')\n",
    "train_df.filter(regex='cs_[0-9][0-9][0-9]').replace('na', 0).astype(int).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.filter(regex='ee_[0-9][0-9][0-9]').replace('na', 0).astype(int).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAHRCAYAAAC4mLk+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAur0lEQVR4nO3de1SVdb7H8c8GdINjkEqgEIaXvI2JJMWQNWlRjHkom9XJlRUuKluazjixrJF0JE8X7KLZWVoey0s2llaTzpQdtGjIU9HyiFFW3i/BMUHNkygWKPt3/mjNbjiCsVX4bX68X2s9f+znwvP9ZMmnZz/P3h5jjBEAAIAlIbYHAAAAbRtlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFjVqsrI+vXrlZmZqbi4OHk8Hq1evTrgn2GM0dNPP60+ffrI6/UqPj5ejz322LkfFgAANEmY7QECUV1draSkJN1111367W9/e0Y/Y/LkyVq3bp2efvppXXLJJTp8+LAOHz58jicFAABN5WmtX5Tn8Xi0atUqjRo1yr+upqZG06ZN06uvvqrvvvtOAwcO1BNPPKFhw4ZJkrZs2aJBgwbpiy++UN++fe0MDgAA6mlVb9P8nEmTJqm4uFgrVqzQ559/rn/913/Vb37zG+3YsUOS9NZbb6lnz556++231aNHDyUmJuqee+7hyggAABY5U0bKysq0ZMkSvf7667rqqqvUq1cvTZkyRVdeeaWWLFkiSdq9e7e+/vprvf7661q2bJmWLl2qkpIS3XLLLZanBwCg7WpV94yczubNm1VXV6c+ffrUW19TU6MuXbpIknw+n2pqarRs2TL/fosWLdKQIUO0bds23roBAMACZ8rIsWPHFBoaqpKSEoWGhtbb1rFjR0lSt27dFBYWVq+w9O/fX9KPV1YoIwAAtDxnykhycrLq6up04MABXXXVVQ3uM3ToUJ08eVK7du1Sr169JEnbt2+XJF100UUtNisAAPhJq3qa5tixY9q5c6ekH8vHnDlzNHz4cHXu3Fndu3fXHXfcoY8++kizZ89WcnKyDh48qMLCQg0aNEgjR46Uz+fTZZddpo4dO2ru3Lny+XyaOHGiIiMjtW7dOsvpAABom1pVGSkqKtLw4cNPWT927FgtXbpUJ06c0KOPPqply5Zp3759io6O1q9+9SvNnDlTl1xyiSTpm2++0e9+9zutW7dOv/jFLzRixAjNnj1bnTt3buk4AABArayMAAAA9zjzaC8AAGidKCMAAMCqVvE0jc/n0zfffKPzzjtPHo/H9jgAAKAJjDE6evSo4uLiFBLS+PWPVlFGvvnmGyUkJNgeAwAAnIHy8nJdeOGFjW5vFWXkvPPOk/RjmMjISMvTAACApqiqqlJCQoL/93hjWkUZ+cdbM5GRkZQRAABamZ+7xYIbWAEAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYFWY7QEAoK1KnLqm2c+xd9bIZj8HcLa4MgIAAKyijAAAAKsoIwAAwCrKCAAAsCrgMrJ+/XplZmYqLi5OHo9Hq1ev/tljampqNG3aNF100UXyer1KTEzU4sWLz2ReAADgmICfpqmurlZSUpLuuusu/fa3v23SMbfeeqsqKyu1aNEi9e7dW/v375fP5wt4WAAA4J6Ay8iIESM0YsSIJu9fUFCgDz74QLt371bnzp0lSYmJiYGeFgAAOKrZ7xn529/+ppSUFD355JOKj49Xnz59NGXKFH3//feNHlNTU6Oqqqp6CwAAcFOzf+jZ7t279eGHHyo8PFyrVq3SoUOHdN999+nbb7/VkiVLGjwmPz9fM2fObO7RAABAEGj2KyM+n08ej0fLly/X5ZdfrhtuuEFz5szRSy+91OjVkdzcXB05csS/lJeXN/eYAADAkma/MtKtWzfFx8crKirKv65///4yxuh//ud/dPHFF59yjNfrldfrbe7RAABAEGj2KyNDhw7VN998o2PHjvnXbd++XSEhIbrwwgub+/QAACDIBVxGjh07ptLSUpWWlkqS9uzZo9LSUpWVlUn68S2WrKws//5jxoxRly5dlJ2dra+++krr16/XAw88oLvuuksRERHnJgUAAGi1Ai4jGzduVHJyspKTkyVJOTk5Sk5O1owZMyRJ+/fv9xcTSerYsaPeffddfffdd0pJSdHtt9+uzMxM/fu///s5igAAAFozjzHG2B7i51RVVSkqKkpHjhxRZGSk7XEA4JxInLqm2c+xd9bIZj8H0Jim/v7mu2kAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVgVcRtavX6/MzEzFxcXJ4/Fo9erVTT72o48+UlhYmAYPHhzoaQEAgKMCLiPV1dVKSkrS/PnzAzruu+++U1ZWlq699tpATwkAABwWFugBI0aM0IgRIwI+0fjx4zVmzBiFhoYGdDUFAAC4rUXuGVmyZIl2796tvLy8Ju1fU1OjqqqqegsAAHBTs5eRHTt2aOrUqfrzn/+ssLCmXYjJz89XVFSUf0lISGjmKQEAgC3NWkbq6uo0ZswYzZw5U3369Gnycbm5uTpy5Ih/KS8vb8YpAQCATQHfMxKIo0ePauPGjfr00081adIkSZLP55MxRmFhYVq3bp2uueaaU47zer3yer3NORoAAAgSzVpGIiMjtXnz5nrrnnvuOb3//vt644031KNHj+Y8PQAAaAUCLiPHjh3Tzp07/a/37Nmj0tJSde7cWd27d1dubq727dunZcuWKSQkRAMHDqx3fExMjMLDw09ZDwAA2qaAy8jGjRs1fPhw/+ucnBxJ0tixY7V06VLt379fZWVl525CAADgNI8xxtge4udUVVUpKipKR44cUWRkpO1xAOCcSJy6ptnPsXfWyGY/B9CYpv7+5rtpAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFYFXEbWr1+vzMxMxcXFyePxaPXq1afd/80339R1112nCy64QJGRkUpLS9PatWvPdF4AAOCYgMtIdXW1kpKSNH/+/Cbtv379el133XV65513VFJSouHDhyszM1OffvppwMMCAAD3hAV6wIgRIzRixIgm7z937tx6rx9//HH99a9/1VtvvaXk5ORATw8AABwTcBk5Wz6fT0ePHlXnzp0b3aempkY1NTX+11VVVS0xGgAAsKDFb2B9+umndezYMd16662N7pOfn6+oqCj/kpCQ0IITAgCAltSiZeSVV17RzJkz9dprrykmJqbR/XJzc3XkyBH/Ul5e3oJTAgCAltRib9OsWLFC99xzj15//XWlp6efdl+v1yuv19tCkwEAAJta5MrIq6++quzsbL366qsaOXJkS5wSAAC0EgFfGTl27Jh27tzpf71nzx6Vlpaqc+fO6t69u3Jzc7Vv3z4tW7ZM0o9vzYwdO1bPPvusUlNTVVFRIUmKiIhQVFTUOYoBAABaq4CvjGzcuFHJycn+x3JzcnKUnJysGTNmSJL279+vsrIy//4LFy7UyZMnNXHiRHXr1s2/TJ48+RxFAAAArVnAV0aGDRsmY0yj25cuXVrvdVFRUaCnAAAAbQjfTQMAAKyijAAAAKsoIwAAwCrKCAAAsIoyAgAArKKMAAAAqygjAADAKsoIAACwijICAACsoowAAACrKCMAAMAqyggAALCKMgIAAKyijAAAAKsoIwAAwKow2wMAQKASp65p9nPsnTWy2c8B4EdcGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWBVwGVm/fr0yMzMVFxcnj8ej1atX/+wxRUVFuvTSS+X1etW7d28tXbr0DEYFAAAuCriMVFdXKykpSfPnz2/S/nv27NHIkSM1fPhwlZaW6g9/+IPuuecerV27NuBhAQCAe8ICPWDEiBEaMWJEk/dfsGCBevToodmzZ0uS+vfvrw8//FDPPPOMMjIyAj09AABwTLPfM1JcXKz09PR66zIyMlRcXNzoMTU1Naqqqqq3AAAANzV7GamoqFBsbGy9dbGxsaqqqtL333/f4DH5+fmKioryLwkJCc09JgAAsCQon6bJzc3VkSNH/Et5ebntkQAAQDMJ+J6RQHXt2lWVlZX11lVWVioyMlIRERENHuP1euX1ept7NAAAEASa/cpIWlqaCgsL66179913lZaW1tynBgAArUDAZeTYsWMqLS1VaWmppB8f3S0tLVVZWZmkH99iycrK8u8/fvx47d69Ww8++KC2bt2q5557Tq+99pruv//+c5MAAAC0agGXkY0bNyo5OVnJycmSpJycHCUnJ2vGjBmSpP379/uLiST16NFDa9as0bvvvqukpCTNnj1bL774Io/1AgAASWdwz8iwYcNkjGl0e0Ofrjps2DB9+umngZ4KAAC0AUH5NA0AAGg7KCMAAMAqyggAALCKMgIAAKyijAAAAKsoIwAAwCrKCAAAsIoyAgAArKKMAAAAqygjAADAKsoIAACwijICAACsoowAAACrKCMAAMAqyggAALCKMgIAAKyijAAAAKsoIwAAwCrKCAAAsIoyAgAArKKMAAAAqygjAADAKsoIAACwijICAACsoowAAACrKCMAAMAqyggAALCKMgIAAKyijAAAAKsoIwAAwCrKCAAAsOqMysj8+fOVmJio8PBwpaamasOGDafdf+7cuerbt68iIiKUkJCg+++/Xz/88MMZDQwAANwScBlZuXKlcnJylJeXp02bNikpKUkZGRk6cOBAg/u/8sormjp1qvLy8rRlyxYtWrRIK1eu1EMPPXTWwwMAgNYv4DIyZ84cjRs3TtnZ2RowYIAWLFigDh06aPHixQ3u//HHH2vo0KEaM2aMEhMTdf311+u222772aspAACgbQiojNTW1qqkpETp6ek//YCQEKWnp6u4uLjBY6644gqVlJT4y8fu3bv1zjvv6IYbbjiLsQEAgCvCAtn50KFDqqurU2xsbL31sbGx2rp1a4PHjBkzRocOHdKVV14pY4xOnjyp8ePHn/ZtmpqaGtXU1PhfV1VVBTImAABoRZr9aZqioiI9/vjjeu6557Rp0ya9+eabWrNmjR555JFGj8nPz1dUVJR/SUhIaO4xAQCAJQFdGYmOjlZoaKgqKyvrra+srFTXrl0bPOZPf/qT7rzzTt1zzz2SpEsuuUTV1dW69957NW3aNIWEnNqHcnNzlZOT439dVVVFIQEAwFEBXRlp3769hgwZosLCQv86n8+nwsJCpaWlNXjM8ePHTykcoaGhkiRjTIPHeL1eRUZG1lsAAICbAroyIkk5OTkaO3asUlJSdPnll2vu3Lmqrq5Wdna2JCkrK0vx8fHKz8+XJGVmZmrOnDlKTk5Wamqqdu7cqT/96U/KzMz0lxIAANB2BVxGRo8erYMHD2rGjBmqqKjQ4MGDVVBQ4L+ptaysrN6VkOnTp8vj8Wj69Onat2+fLrjgAmVmZuqxxx47dykAAECr5TGNvVcSRKqqqhQVFaUjR47wlg0AJU5d0+zn2DtrZLOfw5UcQGOa+vub76YBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWHVGZWT+/PlKTExUeHi4UlNTtWHDhtPu/91332nixInq1q2bvF6v+vTpo3feeeeMBgYAAG4JC/SAlStXKicnRwsWLFBqaqrmzp2rjIwMbdu2TTExMafsX1tbq+uuu04xMTF64403FB8fr6+//lrnn3/+uZgfAAC0cgGXkTlz5mjcuHHKzs6WJC1YsEBr1qzR4sWLNXXq1FP2X7x4sQ4fPqyPP/5Y7dq1kyQlJiae3dQAAMAZAb1NU1tbq5KSEqWnp//0A0JClJ6eruLi4gaP+dvf/qa0tDRNnDhRsbGxGjhwoB5//HHV1dWd3eQAAMAJAV0ZOXTokOrq6hQbG1tvfWxsrLZu3drgMbt379b777+v22+/Xe+884527typ++67TydOnFBeXl6Dx9TU1Kimpsb/uqqqKpAxAQBAK9LsT9P4fD7FxMRo4cKFGjJkiEaPHq1p06ZpwYIFjR6Tn5+vqKgo/5KQkNDcYwIAAEsCKiPR0dEKDQ1VZWVlvfWVlZXq2rVrg8d069ZNffr0UWhoqH9d//79VVFRodra2gaPyc3N1ZEjR/xLeXl5IGMCAIBWJKAy0r59ew0ZMkSFhYX+dT6fT4WFhUpLS2vwmKFDh2rnzp3y+Xz+ddu3b1e3bt3Uvn37Bo/xer2KjIystwAAADcF/DZNTk6OXnjhBb300kvasmWLJkyYoOrqav/TNVlZWcrNzfXvP2HCBB0+fFiTJ0/W9u3btWbNGj3++OOaOHHiuUsBAABarYAf7R09erQOHjyoGTNmqKKiQoMHD1ZBQYH/ptaysjKFhPzUcRISErR27Vrdf//9GjRokOLj4zV58mT98Y9/PHcpAABAqxVwGZGkSZMmadKkSQ1uKyoqOmVdWlqaPvnkkzM5FQAAcBzfTQMAAKyijAAAAKsoIwAAwCrKCAAAsIoyAgAArKKMAAAAqygjAADAKsoIAACwijICAACsoowAAACrKCMAAMAqyggAALCKMgIAAKyijAAAAKsoIwAAwCrKCAAAsCrM9gAAgNYrceqaZj/H3lkjm/0csIsrIwAAwCrKCAAAsIoyAgAArKKMAAAAqygjAADAKsoIAACwijICAACsoowAAACrKCMAAMAqyggAALCKMgIAAKyijAAAAKsoIwAAwCq+tRdoY5r7W1b5hlUAgeLKCAAAsOqMysj8+fOVmJio8PBwpaamasOGDU06bsWKFfJ4PBo1atSZnBYAADgo4DKycuVK5eTkKC8vT5s2bVJSUpIyMjJ04MCB0x63d+9eTZkyRVddddUZDwsAANwTcBmZM2eOxo0bp+zsbA0YMEALFixQhw4dtHjx4kaPqaur0+23366ZM2eqZ8+eZzUwAABwS0BlpLa2ViUlJUpPT//pB4SEKD09XcXFxY0e92//9m+KiYnR3XfffeaTAgAAJwX0NM2hQ4dUV1en2NjYeutjY2O1devWBo/58MMPtWjRIpWWljb5PDU1NaqpqfG/rqqqCmRMAADQijTr0zRHjx7VnXfeqRdeeEHR0dFNPi4/P19RUVH+JSEhoRmnBAAANgV0ZSQ6OlqhoaGqrKyst76yslJdu3Y9Zf9du3Zp7969yszM9K/z+Xw/njgsTNu2bVOvXr1OOS43N1c5OTn+11VVVRQSAAAcFVAZad++vYYMGaLCwkL/47k+n0+FhYWaNGnSKfv369dPmzdvrrdu+vTpOnr0qJ599tlGC4bX65XX6w1kNAAA0EoF/AmsOTk5Gjt2rFJSUnT55Zdr7ty5qq6uVnZ2tiQpKytL8fHxys/PV3h4uAYOHFjv+PPPP1+STlkPAADapoDLyOjRo3Xw4EHNmDFDFRUVGjx4sAoKCvw3tZaVlSkkhA92BQAATXNG300zadKkBt+WkaSioqLTHrt06dIzOSUAAHAUlzAAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVp1RGZk/f74SExMVHh6u1NRUbdiwodF9X3jhBV111VXq1KmTOnXqpPT09NPuDwAA2paAy8jKlSuVk5OjvLw8bdq0SUlJScrIyNCBAwca3L+oqEi33Xab/v73v6u4uFgJCQm6/vrrtW/fvrMeHgAAtH4Bl5E5c+Zo3Lhxys7O1oABA7RgwQJ16NBBixcvbnD/5cuX67777tPgwYPVr18/vfjii/L5fCosLDzr4QEAQOsXUBmpra1VSUmJ0tPTf/oBISFKT09XcXFxk37G8ePHdeLECXXu3LnRfWpqalRVVVVvAQAAbgqojBw6dEh1dXWKjY2ttz42NlYVFRVN+hl//OMfFRcXV6/Q/H/5+fmKioryLwkJCYGMCQAAWpEWfZpm1qxZWrFihVatWqXw8PBG98vNzdWRI0f8S3l5eQtOCQAAWlJYIDtHR0crNDRUlZWV9dZXVlaqa9eupz326aef1qxZs/Tee+9p0KBBp93X6/XK6/UGMhoAAGilAroy0r59ew0ZMqTezaf/uBk1LS2t0eOefPJJPfLIIyooKFBKSsqZTwsAAJwT0JURScrJydHYsWOVkpKiyy+/XHPnzlV1dbWys7MlSVlZWYqPj1d+fr4k6YknntCMGTP0yiuvKDEx0X9vSceOHdWxY8dzGAUAALRGAZeR0aNH6+DBg5oxY4YqKio0ePBgFRQU+G9qLSsrU0jITxdcnn/+edXW1uqWW26p93Py8vL08MMPn930AACg1Qu4jEjSpEmTNGnSpAa3FRUV1Xu9d+/eMzkFAABoI/huGgAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBAABWUUYAAIBVlBEAAGAVZQQAAFhFGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYFWZ7AKC1SJy6ptnPsXfWyGY/BwAEG66MAAAAqygjAADAKsoIAACwijICAACsoowAAACrKCMAAMAqyggAALCKMgIAAKyijAAAAKsoIwAAwCo+Dh4A0ObxdQ92cWUEAABYRRkBAABWnVEZmT9/vhITExUeHq7U1FRt2LDhtPu//vrr6tevn8LDw3XJJZfonXfeOaNhAQCAewIuIytXrlROTo7y8vK0adMmJSUlKSMjQwcOHGhw/48//li33Xab7r77bn366acaNWqURo0apS+++OKshwcAAK1fwGVkzpw5GjdunLKzszVgwAAtWLBAHTp00OLFixvc/9lnn9VvfvMbPfDAA+rfv78eeeQRXXrppZo3b95ZDw8AAFq/gJ6mqa2tVUlJiXJzc/3rQkJClJ6eruLi4gaPKS4uVk5OTr11GRkZWr16daPnqampUU1Njf/1kSNHJElVVVWBjIsgMTBvbbOf44uZGc1+Dl/N8WY/R0v8O97cOVzIIJGjqVzIILmTo7n/vg3079p/ZDbGnHa/gMrIoUOHVFdXp9jY2HrrY2NjtXXr1gaPqaioaHD/ioqKRs+Tn5+vmTNnnrI+ISEhkHHRhkTNtT3BueFCDhcySOQIJi5kkNzIcaYZjh49qqioqEa3B+XnjOTm5ta7muLz+XT48GF16dJFHo+nWc5ZVVWlhIQElZeXKzIyslnO0dxcyCC5kcOFDBI5gokLGSQ3criQQWqZHMYYHT16VHFxcafdL6AyEh0drdDQUFVWVtZbX1lZqa5duzZ4TNeuXQPaX5K8Xq+8Xm+9deeff34go56xyMjIVv0vl+RGBsmNHC5kkMgRTFzIILmRw4UMUvPnON0VkX8I6AbW9u3ba8iQISosLPSv8/l8KiwsVFpaWoPHpKWl1dtfkt59991G9wcAAG1LwG/T5OTkaOzYsUpJSdHll1+uuXPnqrq6WtnZ2ZKkrKwsxcfHKz8/X5I0efJkXX311Zo9e7ZGjhypFStWaOPGjVq4cOG5TQIAAFqlgMvI6NGjdfDgQc2YMUMVFRUaPHiwCgoK/DeplpWVKSTkpwsuV1xxhV555RVNnz5dDz30kC6++GKtXr1aAwcOPHcpzgGv16u8vLxT3h5qTVzIILmRw4UMEjmCiQsZJDdyuJBBCq4cHvNzz9sAAAA0I76bBgAAWEUZAQAAVlFGAACAVZQRAABgFWUEAABYRRkBcFo8cAegubXZMnLo0CE9+eSTuvnmm5WWlqa0tDTdfPPNeuqpp3Tw4EHb45218vJy3XXXXbbHaJLvv/9eH374ob766qtTtv3www9atmyZhakCs2XLFi1ZssT/hZFbt27VhAkTdNddd+n999+3PN3Z8Xq92rJli+0xzlh1dbWWLFmiadOmad68efr2229tj/SzNm3apD179vhfv/zyyxo6dKgSEhJ05ZVXasWKFRana7rf/e53+q//+i/bY5y1efPmKSsry//P/eWXX9aAAQPUr18/PfTQQzp58qTlCZtm//79mjFjhq655hr1799fv/zlL5WZmalFixaprq7O6mxt8nNG/vu//1sZGRnq0KGD0tPT/R/YVllZqcLCQh0/flxr165VSkqK5UnP3GeffaZLL73U+r9gP2f79u26/vrrVVZWJo/H4/+Ltlu3bpJ+/DOJi4sL6hwFBQW66aab1LFjRx0/flyrVq1SVlaWkpKS5PP59MEHH2jdunW65pprbI96Wv/85ZT/7Nlnn9Udd9yhLl26SJLmzJnTkmMFbMCAAfrwww/VuXNnlZeX69e//rX+93//V3369NGuXbsUFhamTz75RD169LA9aqOSkpI0e/Zspaen68UXX9Tvf/97jRs3Tv3799e2bdv04osv6tlnnw36/+EICQmRx+NRr169dPfdd2vs2LGn/V6yYPToo4/qySef1PXXX6+PPvpIf/jDH/TUU0/p/vvvV0hIiJ555hlNmDChwW+aDyYbN25Uenq6evfurYiICBUXF2vMmDGqra3V2rVrNWDAABUUFOi8886zM6Bpg1JTU829995rfD7fKdt8Pp+59957za9+9SsLkzXdX//619MuzzzzjAkJCbE95s8aNWqUGTlypDl48KDZsWOHGTlypOnRo4f5+uuvjTHGVFRUBH2OtLQ0M23aNGOMMa+++qrp1KmTeeihh/zbp06daq677jpb4zWZx+MxgwcPNsOGDau3eDwec9lll5lhw4aZ4cOH2x7zZ3k8HlNZWWmMMeb22283V1xxhfnuu++MMcYcPXrUpKenm9tuu83miD8rIiLC7N271xhjTHJyslm4cGG97cuXLzcDBgywMVpAPB6Pee+998zkyZNNdHS0adeunbnxxhvNW2+9Zerq6myP1yS9evUyf/nLX4wxxpSWlprQ0FDz5z//2b/9zTffNL1797Y1XpMNHTrUPPzww/7XL7/8sklNTTXGGHP48GEzePBg8/vf/97WeKZNlpHw8HCzZcuWRrdv2bLFhIeHt+BEgfN4PCYkJMR4PJ5Gl2D/JW6MMTExMebzzz/3v/b5fGb8+PGme/fuZteuXa2ijERGRpodO3YYY4ypq6szYWFhZtOmTf7tmzdvNrGxsbbGa7L8/HzTo0cPU1hYWG99WFiY+fLLLy1NFbh/LiM9e/Y069atq7f9o48+MgkJCTZGa7IuXbqYjRs3GmN+/G+ktLS03vadO3eaiIgIG6MF5J//LGpra83KlStNRkaGCQ0NNXFxceahhx7y/7cTrCIiIvz/c2SMMe3atTNffPGF//XevXtNhw4dbIwWkIiICLNr1y7/67q6OtOuXTtTUVFhjDFm3bp1Ji4uztZ4pk3eM9K1a1dt2LCh0e0bNmzwv3UTrLp166Y333xTPp+vwWXTpk22R2yS77//XmFhP31Fksfj0fPPP6/MzExdffXV2r59u8Xpms7j8Uj68bJ0eHh4va/MPu+883TkyBFbozXZ1KlTtXLlSk2YMEFTpkzRiRMnbI90xv7x5/HDDz/43/L7h/j4+KC/L2zEiBF6/vnnJUlXX3213njjjXrbX3vtNfXu3dvGaGesXbt2uvXWW1VQUKDdu3dr3LhxWr58ufr27Wt7tNPq2rWr/362HTt2qK6urt79bV9++aViYmJsjddkMTEx2r9/v/91ZWWlTp48qcjISEnSxRdfrMOHD9saL/AvynPBlClTdO+996qkpETXXnvtKfeMvPDCC3r66actT3l6Q4YMUUlJiW666aYGt3s8nlbxFES/fv20ceNG9e/fv976efPmSZJuvPFGG2MFJDExUTt27FCvXr0kScXFxerevbt/e1lZ2Sm/EIPVZZddppKSEk2cOFEpKSlavny5/xd7a3LttdcqLCxMVVVV2rZtW70v5vz666/9978EqyeeeEJDhw7V1VdfrZSUFM2ePVtFRUX+e0Y++eQTrVq1yvaYZ6x79+56+OGHlZeXp/fee8/2OKd1++23KysrSzfddJMKCwv14IMPasqUKfr222/l8Xj02GOP6ZZbbrE95s8aNWqUxo8fr6eeekper1ePPPKIrr76akVEREiStm3bpvj4eGvztckyMnHiREVHR+uZZ57Rc8895785MjQ0VEOGDNHSpUt16623Wp7y9B544AFVV1c3ur137976+9//3oITnZmbb75Zr776qu68885Tts2bN08+n08LFiywMFnTTZgwod4Ntv//G6n/8z//M+hvXv1nHTt21EsvvaQVK1YoPT09qG8ebkheXl691x07dqz3+q233tJVV13VkiMFLC4uTp9++qlmzZqlt956S8YYbdiwQeXl5Ro6dKg++uijVnGD/UUXXaTQ0NBGt3s8Hl133XUtOFHgZs6c6b/hc9y4cZo6daqSkpL04IMP6vjx48rMzNQjjzxie8yf9eijj2r//v3KzMxUXV2d0tLS9PLLL/u3ezwe5efnW5uvTT5N889OnDihQ4cOSZKio6PVrl07yxMBwaO8vFybNm1Senq6fvGLX9geB8BZ+uGHH3Ty5MlTSrptbb6MSFJNTY2kHz9PobVyIYPkRg4XMkjkCCYuZJDcyOFCBin4crTJG1gl6d1339UNN9ygTp06qUOHDurQoYM6deqkG264Iejfw/wHFzJIbuRwIYNEjmDiQgbJjRwuZJCCO0ebvDLy0ksv6Z577tEtt9yijIyMejewrlu3Tm+88YYWLVrU4H0MwcKFDJIbOVzIIJEjmLiQQXIjhwsZpFaQw9pDxRZdfPHFZt68eY1unz9/ftB/iI0LGYxxI4cLGYwhRzBxIYMxbuRwIYMxwZ+jTZYRr9drtm7d2uj2rVu3Bv2HnrmQwRg3criQwRhyBBMXMhjjRg4XMhgT/Dna5D0jv/zlL7Vo0aJGty9evFgDBgxowYkC50IGyY0cLmSQyBFMXMgguZHDhQxS8Odok/eMFBUV6V/+5V/Us2fPBr8ob/fu3VqzZo1+/etfW560cS5kkNzI4UIGiRzBxIUMkhs5XMggBX+ONllGJGnv3r16/vnn9cknn6iiokLSjx/7m5aWpvHjxysxMdHugE3gQgbJjRwuZJDIEUxcyCC5kcOFDFJw52izZQQAAASHNvlx8P9w8uRJffnll/6G2K1bN/Xv379VfQqrCxkkN3K4kEEiRzBxIYPkRg4XMkhBnMParbMW1dXVmWnTppnzzz/feDyeesv5559vpk+fburq6myPeVouZDDGjRwuZDCGHMHEhQzGuJHDhQzGBH+ONllGHnjgAXPBBReYBQsWmD179pjjx4+b48ePmz179pj/+I//MDExMebBBx+0PeZpuZDBGDdyuJDBGHIEExcyGONGDhcyGBP8OdpkGYmNjTUFBQWNbi8oKDAxMTEtOFHgXMhgjBs5XMhgDDmCiQsZjHEjhwsZjAn+HG3yc0aOHj2quLi4Rrd369ZN1dXVLThR4FzIILmRw4UMEjmCiQsZJDdyuJBBCv4cbfJpmpEjR+rkyZNavny5oqOj6207dOiQ7rzzToWGhurtt9+2NOHPcyGD5EYOFzJI5AgmLmSQ3MjhQgYp+HO0yTJSXl6uG264QVu3btUll1xS78NfNm/erAEDBujtt99WQkKC5Ukb50IGyY0cLmSQyBFMXMgguZHDhQxS8Odok2VEknw+n9auXdvgh79cf/31CgkJ/newXMgguZHDhQwSOYKJCxkkN3K4kEEK8hzW7lZpRSZMmGAOHjxoe4yz4kIGY9zI4UIGY8gRTFzIYIwbOVzIYEzL52izV0YCERkZqdLSUvXs2dP2KGfMhQySGzlcyCCRI5i4kEFyI4cLGaSWz9E6ri1Z5kJfcyGD5EYOFzJI5AgmLmSQ3MjhQgap5XNQRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGmuCOO+5QZGSk7THOigsZJDdyuJBBIkcwcSGD5EYOFzJILZ+jTT/a+/nnnze43uPxKDw8XN27d5fX623hqQLjQgbJjRwuZJDIEUxcyCC5kcOFDFIQ52ixTzQJQh6Px4SEhDS6eL1ek5WVZb7//nvbozbKhQzGuJHDhQzGkCOYuJDBGDdyuJDBmODN0abfplm1apUuvvhiLVy4UKWlpSotLdXChQvVt29fvfLKK1q0aJHef/99TZ8+3faojXIhg+RGDhcySOQIJi5kkNzI4UIGKYhztGj1CTKXXXaZKSgoOGV9QUGBueyyy4wxxqxatcr07NmzpUdrMhcyGONGDhcyGEOOYOJCBmPcyOFCBmOCN0ebLiPh4eFmy5Ytp6zfsmWLCQ8PN8YYs2fPHhMREdHSozWZCxmMcSOHCxmMIUcwcSGDMW7kcCGDMcGbo02/TdOvXz/NmjVLtbW1/nUnTpzQrFmz1K9fP0nSvn37/F+1HIxcyCC5kcOFDBI5gokLGSQ3criQQQreHGEterYgM3/+fN1444268MILNWjQIEnS5s2bVVdXp7fffluStHv3bt133302xzwtFzJIbuRwIYNEjmDiQgbJjRwuZJCCN0ebfrRXko4eParly5dr+/btkqS+fftqzJgxOu+88yxP1nQuZJDcyOFCBokcwcSFDJIbOVzIIAVnjjZfRiTpq6++UllZWb3LVpJ04403WpoocC5kkNzI4UIGiRzBxIUMkhs5XMggBWGOFr1DJcjs2rXLDBo0yP/c9f9//ro1cCGDMW7kcCGDMeQIJi5kMMaNHC5kMCZ4c7TpG1gnT56sHj166MCBA+rQoYO++OILffDBB0pJSVFRUZHt8ZrEhQySGzlcyCCRI5i4kEFyI4cLGaQgzmGtBgWBLl26mM8++8wYY0xkZKTZunWrMcaYwsJCM3jwYJujNZkLGYxxI4cLGYwhRzBxIYMxbuRwIYMxwZujTV8Zqaur89+wEx0drW+++UaSdNFFF2nbtm02R2syFzJIbuRwIYNEjmDiQgbJjRwuZJCCN0ebfrR34MCB+uyzz9SjRw+lpqbqySefVPv27bVw4UL17NnT9nhN4kIGyY0cLmSQyBFMXMgguZHDhQxSEOewdk0mCBQUFJi//OUvxhhjduzYYfr27Ws8Ho+Jjo42hYWFlqdrGhcyGONGDhcyGEOOYOJCBmPcyOFCBmOCNweP9v4/hw8fVqdOneTxeGyPcsZcyCC5kcOFDBI5gokLGSQ3criQQQqOHJQRAABgVZu+gRUAANhHGQEAAFZRRgAAgFWUEQAAYBVlBAAAWEUZAQAAVlFGAACAVZQRAABg1f8BH+tCua8j7K0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.filter(regex='ag_[0-9][0-9][0-9]').replace('na', 0).astype(int).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.filter(regex='ay_[0-9][0-9][0-9]').replace('na', 0).astype(int).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.filter(regex='az_[0-9][0-9][0-9]').replace('na', 0).astype(int).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.filter(regex='ba_[0-9][0-9][0-9]').replace('na', 0).astype(int).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df.filter(regex='cn_[0-9][0-9][0-9]').replace('na', 0).astype(int).mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_for_corr = train_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#look at the correlation between the columns against the targe\n",
    "train_df_for_corr[\"class\"] = train_df_for_corr[\"class\"].replace(['neg', 'pos'], [0, 1])\n",
    "train_df_for_corr = train_df_for_corr.replace('na', 0).astype(float).corr()\n",
    "labels = train_df.pop('class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get non categorical columns\n",
    "numerical_columns = cols[~categorical_cols_idx]\n",
    "numerical_columns = numerical_columns[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      aa_000\n",
       "2      ab_000\n",
       "3      ac_000\n",
       "4      ad_000\n",
       "5      ae_000\n",
       "        ...  \n",
       "156    eb_000\n",
       "157     ec_00\n",
       "158    ed_000\n",
       "169    ef_000\n",
       "170    eg_000\n",
       "Length: 100, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df_for_corr[\"class\"].abs()[numerical_columns].sort_values(ascending=False).plot(kind='bar')\n",
    "relevant_col_bools = train_df_for_corr[\"class\"].abs()[numerical_columns].sort_values(ascending=False) > 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the columns that are relevant\n",
    "relevant_numerical_cols = relevant_col_bools[relevant_col_bools].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add two column types together\n",
    "num_cat_cols = pd.Index(categorical_cols).append(pd.Index(relevant_numerical_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Y = labels.apply(lambda x: 1 if x == 'pos' else 0).values\n",
    "X = train_df[categorical_cols].replace('na', 0).astype(int).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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>LogisticRegression(multi_class=&#x27;multinomial&#x27;, random_state=0)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(multi_class=&#x27;multinomial&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(multi_class='multinomial', random_state=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed, Y, test_size=0.33, random_state=42, stratify=Y)\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99     19470\n",
      "           1       0.76      0.54      0.63       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.87      0.77      0.81     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19413    57]\n",
      " [  152   178]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('data/aps_failure_test_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = test_df[categorical_cols].replace('na', 0).astype(int).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "y_pred_ = clf.predict(X_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def labelize(x):\n",
    "    if x == 1:\n",
    "        return 'pos'\n",
    "    else:\n",
    "        return 'neg'\n",
    "labelize = np.vectorize(labelize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_)+1),\"class\": labelize(y_pred_)})\n",
    "output.to_csv('categorical values standardized logistic regression, output.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99     19470\n",
      "           1       0.74      0.51      0.61       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.87      0.75      0.80     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19412    58]\n",
      " [  161   169]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from preprocessing import FactorAnalysis\n",
    "\n",
    "FA = FactorAnalysis(50)\n",
    "X = train_df[categorical_cols].replace('na', 0).astype(int).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "\n",
    "FA.fit(X_transformed)\n",
    "X_transformed_ = FA.transform(X_transformed)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed_, Y, test_size=0.33, random_state=42, stratify=Y)\n",
    "clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9934842737430886"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FA.explained_variance_ratio.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00     19470\n",
      "           1       0.88      0.64      0.74       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.93      0.82      0.87     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19440    30]\n",
      " [  118   212]]\n"
     ]
    }
   ],
   "source": [
    "#xgboost without factor analysis\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "clf = XGBClassifier()\n",
    "X = train_df[categorical_cols].replace('na', 0).astype(int).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed, Y, test_size=0.33, random_state=42, stratify=Y)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use test set to predict\n",
    "X = test_df[categorical_cols].replace('na', 0).astype(int).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "y_pred_ = clf.predict(X_transformed)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_)+1),\"class\": labelize(y_pred_)})\n",
    "output.to_csv('categorical values standardized xgboost, output.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00     19470\n",
      "           1       0.82      0.58      0.68       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.91      0.79      0.84     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19429    41]\n",
      " [  137   193]]\n"
     ]
    }
   ],
   "source": [
    "#run the same experiment with XGBoost\n",
    "\n",
    "FA = FactorAnalysis(50)\n",
    "X = train_df[categorical_cols].replace('na', 0).astype(int).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "\n",
    "FA.fit(X_transformed)\n",
    "X_transformed_ = FA.transform(X_transformed)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed_, Y, test_size=0.33, random_state=42, stratify=Y)\n",
    "\n",
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Factor analysis fails."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     19470\n",
      "           1       0.86      0.75      0.80       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.93      0.87      0.90     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19431    39]\n",
      " [   83   247]]\n"
     ]
    }
   ],
   "source": [
    "X = train_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed, Y, test_size=0.33, random_state=42, stratify=Y)\n",
    "\n",
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use test set to predict\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "y_pred_ = clf.predict(X_transformed)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_)+1),\"class\": labelize(y_pred_)})\n",
    "output.to_csv('categorical+relevant numerical values standardized xgboost, output.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets apply factor analysis to the numerical columns\n",
    "FA = FactorAnalysis(50)\n",
    "\n",
    "numericals = train_df[numerical_columns].replace('na', 0).astype(float).values\n",
    "numericals_transformed = StandardScaler().fit_transform(numericals)\n",
    "\n",
    "FA.fit(numericals_transformed)\n",
    "numericals_transformed_ = FA.transform(numericals_transformed)\n",
    "X = train_df[categorical_cols].replace('na', 0).astype(int).values\n",
    "X = np.concatenate((X, numericals_transformed_), axis=1)\n",
    "\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     19470\n",
      "           1       0.86      0.72      0.79       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.93      0.86      0.89     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19432    38]\n",
      " [   91   239]]\n"
     ]
    }
   ],
   "source": [
    "clf = XGBClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FA Hurts performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "from preprocessing import LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 97) (97, 70)\n"
     ]
    }
   ],
   "source": [
    "X = train_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "lda = LDA(70)\n",
    "lda.fit(X_transformed, Y)\n",
    "X_transformed_ = lda.transform(X_transformed)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed_, Y, test_size=0.33, random_state=42, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/xgboost/core.py:160: UserWarning: [23:00:01] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:742: \n",
      "Parameters: { \"silent\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00     19470\n",
      "           1       0.83      0.65      0.72       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.91      0.82      0.86     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19425    45]\n",
      " [  117   213]]\n"
     ]
    }
   ],
   "source": [
    "clf = XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective='binary:logistic', booster='gbtree')\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use keras to create a neural network\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "#use momentum optimizer\n",
    "from keras.optimizers.legacy import Adam\n",
    "from keras import regularizers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(input_size= X_transformed.shape[1]):\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(64, input_dim=input_size,\n",
    "                    activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(0.01),\n",
    "                    activity_regularizer=regularizers.l1(0.01)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(0.01),\n",
    "                    activity_regularizer=regularizers.l1(0.01)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(0.01),\n",
    "                    activity_regularizer=regularizers.l1(0.01)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(0.01),\n",
    "                    activity_regularizer=regularizers.l1(0.01)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(0.01),\n",
    "                    activity_regularizer=regularizers.l1(0.01)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(0.01),\n",
    "                    activity_regularizer=regularizers.l1(0.01)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(0.01),\n",
    "                    activity_regularizer=regularizers.l1(0.01)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_8 (Dense)             (None, 64)                6272      \n",
      "                                                                 \n",
      " dropout_7 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout_8 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 32)                2080      \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dropout_10 (Dropout)        (None, 32)                0         \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dropout_11 (Dropout)        (None, 32)                0         \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dropout_12 (Dropout)        (None, 32)                0         \n",
      "                                                                 \n",
      " dense_14 (Dense)            (None, 32)                1056      \n",
      "                                                                 \n",
      " dropout_13 (Dropout)        (None, 32)                0         \n",
      "                                                                 \n",
      " dense_15 (Dense)            (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 16769 (65.50 KB)\n",
      "Trainable params: 16769 (65.50 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/keras/src/optimizers/legacy/adam.py:118: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "print(get_model().summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import class_weight\n",
    "class_weights = class_weight.compute_class_weight(classes=np.unique(Y), y=Y, class_weight='balanced')\n",
    "\n",
    "cw = {0: float(class_weights[0]), 1: float(class_weights[1])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/keras/src/optimizers/legacy/adam.py:118: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "315/315 [==============================] - 2s 3ms/step - loss: 3.9300 - accuracy: 0.5466 - val_loss: 3.6754 - val_accuracy: 0.8529\n",
      "Epoch 2/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 3.5024 - accuracy: 0.8353 - val_loss: 3.4318 - val_accuracy: 0.9173\n",
      "Epoch 3/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 3.2293 - accuracy: 0.8883 - val_loss: 3.2130 - val_accuracy: 0.9381\n",
      "Epoch 4/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.9847 - accuracy: 0.9132 - val_loss: 3.0027 - val_accuracy: 0.9451\n",
      "Epoch 5/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.7634 - accuracy: 0.9266 - val_loss: 2.7983 - val_accuracy: 0.9472\n",
      "Epoch 6/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.5474 - accuracy: 0.9350 - val_loss: 2.5884 - val_accuracy: 0.9517\n",
      "Epoch 7/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.3339 - accuracy: 0.9423 - val_loss: 2.3857 - val_accuracy: 0.9540\n",
      "Epoch 8/50\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 2.1292 - accuracy: 0.9478 - val_loss: 2.1838 - val_accuracy: 0.9581\n",
      "Epoch 9/50\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 1.9451 - accuracy: 0.9535 - val_loss: 1.9908 - val_accuracy: 0.9615\n",
      "Epoch 10/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 1.7692 - accuracy: 0.9570 - val_loss: 1.8103 - val_accuracy: 0.9636\n",
      "Epoch 11/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 1.6025 - accuracy: 0.9588 - val_loss: 1.6537 - val_accuracy: 0.9620\n",
      "Epoch 12/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 1.4489 - accuracy: 0.9627 - val_loss: 1.4878 - val_accuracy: 0.9664\n",
      "Epoch 13/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 1.3059 - accuracy: 0.9644 - val_loss: 1.3593 - val_accuracy: 0.9631\n",
      "Epoch 14/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 1.1730 - accuracy: 0.9651 - val_loss: 1.2141 - val_accuracy: 0.9691\n",
      "Epoch 15/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 1.0534 - accuracy: 0.9667 - val_loss: 1.0983 - val_accuracy: 0.9693\n",
      "Epoch 16/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.9572 - accuracy: 0.9683 - val_loss: 0.9954 - val_accuracy: 0.9697\n",
      "Epoch 17/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.8567 - accuracy: 0.9704 - val_loss: 0.9168 - val_accuracy: 0.9670\n",
      "Epoch 18/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.7833 - accuracy: 0.9693 - val_loss: 0.8241 - val_accuracy: 0.9703\n",
      "Epoch 19/50\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.7177 - accuracy: 0.9698 - val_loss: 0.7547 - val_accuracy: 0.9703\n",
      "Epoch 20/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.6542 - accuracy: 0.9703 - val_loss: 0.6974 - val_accuracy: 0.9699\n",
      "Epoch 21/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.6040 - accuracy: 0.9719 - val_loss: 0.6547 - val_accuracy: 0.9688\n",
      "Epoch 22/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.5709 - accuracy: 0.9711 - val_loss: 0.6148 - val_accuracy: 0.9686\n",
      "Epoch 23/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.5318 - accuracy: 0.9712 - val_loss: 0.5607 - val_accuracy: 0.9734\n",
      "Epoch 24/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.5011 - accuracy: 0.9727 - val_loss: 0.5351 - val_accuracy: 0.9721\n",
      "Epoch 25/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.4765 - accuracy: 0.9712 - val_loss: 0.5139 - val_accuracy: 0.9714\n",
      "Epoch 26/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.4618 - accuracy: 0.9732 - val_loss: 0.5157 - val_accuracy: 0.9661\n",
      "Epoch 27/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.4451 - accuracy: 0.9711 - val_loss: 0.4829 - val_accuracy: 0.9694\n",
      "Epoch 28/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.4236 - accuracy: 0.9725 - val_loss: 0.4484 - val_accuracy: 0.9744\n",
      "Epoch 29/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.4132 - accuracy: 0.9725 - val_loss: 0.4461 - val_accuracy: 0.9713\n",
      "Epoch 30/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.4005 - accuracy: 0.9737 - val_loss: 0.4266 - val_accuracy: 0.9731\n",
      "Epoch 31/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3992 - accuracy: 0.9739 - val_loss: 0.4293 - val_accuracy: 0.9709\n",
      "Epoch 32/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3877 - accuracy: 0.9740 - val_loss: 0.4105 - val_accuracy: 0.9727\n",
      "Epoch 33/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3795 - accuracy: 0.9730 - val_loss: 0.3967 - val_accuracy: 0.9738\n",
      "Epoch 34/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3786 - accuracy: 0.9737 - val_loss: 0.3859 - val_accuracy: 0.9748\n",
      "Epoch 35/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3665 - accuracy: 0.9755 - val_loss: 0.4150 - val_accuracy: 0.9667\n",
      "Epoch 36/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3692 - accuracy: 0.9739 - val_loss: 0.4053 - val_accuracy: 0.9679\n",
      "Epoch 37/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3620 - accuracy: 0.9746 - val_loss: 0.3691 - val_accuracy: 0.9749\n",
      "Epoch 38/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3553 - accuracy: 0.9733 - val_loss: 0.3604 - val_accuracy: 0.9753\n",
      "Epoch 39/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3520 - accuracy: 0.9751 - val_loss: 0.3820 - val_accuracy: 0.9692\n",
      "Epoch 40/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3452 - accuracy: 0.9747 - val_loss: 0.3850 - val_accuracy: 0.9684\n",
      "Epoch 41/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3493 - accuracy: 0.9754 - val_loss: 0.3530 - val_accuracy: 0.9739\n",
      "Epoch 42/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3448 - accuracy: 0.9754 - val_loss: 0.3735 - val_accuracy: 0.9688\n",
      "Epoch 43/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3441 - accuracy: 0.9751 - val_loss: 0.3378 - val_accuracy: 0.9762\n",
      "Epoch 44/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3471 - accuracy: 0.9761 - val_loss: 0.3526 - val_accuracy: 0.9720\n",
      "Epoch 45/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3453 - accuracy: 0.9750 - val_loss: 0.3545 - val_accuracy: 0.9708\n",
      "Epoch 46/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3347 - accuracy: 0.9758 - val_loss: 0.3416 - val_accuracy: 0.9737\n",
      "Epoch 47/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3376 - accuracy: 0.9744 - val_loss: 0.3360 - val_accuracy: 0.9739\n",
      "Epoch 48/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3337 - accuracy: 0.9763 - val_loss: 0.3828 - val_accuracy: 0.9653\n",
      "Epoch 49/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3276 - accuracy: 0.9749 - val_loss: 0.3413 - val_accuracy: 0.9723\n",
      "Epoch 50/50\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.3277 - accuracy: 0.9754 - val_loss: 0.3366 - val_accuracy: 0.9731\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x28b35f490>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed, Y, test_size=0.33, random_state=42, stratify=Y)\n",
    "model_small = get_model(X_train.shape[1])\n",
    "model_small.fit(X_train, y_train, epochs=100, batch_size=128, validation_data=(X_test, y_test), class_weight=cw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "619/619 [==============================] - 0s 501us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99     19470\n",
      "           1       0.38      0.94      0.54       330\n",
      "\n",
      "    accuracy                           0.97     19800\n",
      "   macro avg       0.69      0.96      0.76     19800\n",
      "weighted avg       0.99      0.97      0.98     19800\n",
      "\n",
      "[[18958   512]\n",
      " [   21   309]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_small.predict(X_test)\n",
    "y_pred = np.round(y_pred)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500/500 [==============================] - 0s 497us/step\n"
     ]
    }
   ],
   "source": [
    "#use test set to predict\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "y_pred_ = model_small.predict(X_transformed)\n",
    "y_pred_ = np.round(y_pred_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "output.to_csv('categorical+relevant numerical values standardized neural network bigger model (1 extra 32 neuron layer), output.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed, Y, test_size=0.33, random_state=42, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40200, 97)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7869 - val_loss: 0.6979\n",
      "Epoch 2/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7628 - val_loss: 0.6923\n",
      "Epoch 3/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7585 - val_loss: 0.6888\n",
      "Epoch 4/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7569 - val_loss: 0.6881\n",
      "Epoch 5/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7554 - val_loss: 0.6863\n",
      "Epoch 6/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7541 - val_loss: 0.6854\n",
      "Epoch 7/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7534 - val_loss: 0.6849\n",
      "Epoch 8/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7528 - val_loss: 0.6844\n",
      "Epoch 9/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7524 - val_loss: 0.6841\n",
      "Epoch 10/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7525 - val_loss: 0.6836\n",
      "Epoch 11/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7518 - val_loss: 0.6833\n",
      "Epoch 12/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7513 - val_loss: 0.6829\n",
      "Epoch 13/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7512 - val_loss: 0.6829\n",
      "Epoch 14/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7510 - val_loss: 0.6827\n",
      "Epoch 15/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7508 - val_loss: 0.6825\n",
      "Epoch 16/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7506 - val_loss: 0.6823\n",
      "Epoch 17/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7506 - val_loss: 0.6820\n",
      "Epoch 18/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7503 - val_loss: 0.6845\n",
      "Epoch 19/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7503 - val_loss: 0.6820\n",
      "Epoch 20/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7499 - val_loss: 0.6817\n",
      "Epoch 21/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7500 - val_loss: 0.6816\n",
      "Epoch 22/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7499 - val_loss: 0.6819\n",
      "Epoch 23/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7497 - val_loss: 0.6813\n",
      "Epoch 24/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7502 - val_loss: 0.6813\n",
      "Epoch 25/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7496 - val_loss: 0.6811\n",
      "Epoch 26/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7495 - val_loss: 0.6810\n",
      "Epoch 27/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7494 - val_loss: 0.6812\n",
      "Epoch 28/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7494 - val_loss: 0.6825\n",
      "Epoch 29/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7493 - val_loss: 0.6807\n",
      "Epoch 30/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7491 - val_loss: 0.6808\n",
      "Epoch 31/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7491 - val_loss: 0.6809\n",
      "Epoch 32/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7492 - val_loss: 0.6807\n",
      "Epoch 33/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7490 - val_loss: 0.6808\n",
      "Epoch 34/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7490 - val_loss: 0.6806\n",
      "Epoch 35/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7490 - val_loss: 0.6812\n",
      "Epoch 36/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7489 - val_loss: 0.6807\n",
      "Epoch 37/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7489 - val_loss: 0.6806\n",
      "Epoch 38/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7488 - val_loss: 0.6806\n",
      "Epoch 39/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7488 - val_loss: 0.6803\n",
      "Epoch 40/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7488 - val_loss: 0.6803\n",
      "Epoch 41/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7488 - val_loss: 0.6809\n",
      "Epoch 42/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7488 - val_loss: 0.6809\n",
      "Epoch 43/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7488 - val_loss: 0.6805\n",
      "Epoch 44/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7486 - val_loss: 0.6806\n",
      "Epoch 45/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7486 - val_loss: 0.6801\n",
      "Epoch 46/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7487 - val_loss: 0.6802\n",
      "Epoch 47/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7485 - val_loss: 0.6811\n",
      "Epoch 48/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7486 - val_loss: 0.6805\n",
      "Epoch 49/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7485 - val_loss: 0.6802\n",
      "Epoch 50/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7485 - val_loss: 0.6806\n",
      "Epoch 51/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7485 - val_loss: 0.6804\n",
      "Epoch 52/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7486 - val_loss: 0.6800\n",
      "Epoch 53/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7484 - val_loss: 0.6806\n",
      "Epoch 54/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7485 - val_loss: 0.6806\n",
      "Epoch 55/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7484 - val_loss: 0.6807\n",
      "Epoch 56/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7484 - val_loss: 0.6800\n",
      "Epoch 57/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7484 - val_loss: 0.6807\n",
      "Epoch 58/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7485 - val_loss: 0.6805\n",
      "Epoch 59/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6800\n",
      "Epoch 60/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7484 - val_loss: 0.6797\n",
      "Epoch 61/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7484 - val_loss: 0.6800\n",
      "Epoch 62/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7485 - val_loss: 0.6798\n",
      "Epoch 63/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6800\n",
      "Epoch 64/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7484 - val_loss: 0.6803\n",
      "Epoch 65/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6800\n",
      "Epoch 66/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7482 - val_loss: 0.6799\n",
      "Epoch 67/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7482 - val_loss: 0.6802\n",
      "Epoch 68/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6801\n",
      "Epoch 69/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7482 - val_loss: 0.6801\n",
      "Epoch 70/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6800\n",
      "Epoch 71/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7481 - val_loss: 0.6799\n",
      "Epoch 72/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7481 - val_loss: 0.6799\n",
      "Epoch 73/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7482 - val_loss: 0.6797\n",
      "Epoch 74/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7482 - val_loss: 0.6816\n",
      "Epoch 75/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7482 - val_loss: 0.6801\n",
      "Epoch 76/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6796\n",
      "Epoch 77/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7481 - val_loss: 0.6798\n",
      "Epoch 78/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7481 - val_loss: 0.6798\n",
      "Epoch 79/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6798\n",
      "Epoch 80/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7481 - val_loss: 0.6795\n",
      "Epoch 81/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7480 - val_loss: 0.6804\n",
      "Epoch 82/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7480 - val_loss: 0.6798\n",
      "Epoch 83/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7481 - val_loss: 0.6802\n",
      "Epoch 84/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7480 - val_loss: 0.6802\n",
      "Epoch 85/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7481 - val_loss: 0.6800\n",
      "Epoch 86/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7482 - val_loss: 0.6804\n",
      "Epoch 87/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7481 - val_loss: 0.6797\n",
      "Epoch 88/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.7479 - val_loss: 0.6796\n",
      "Epoch 89/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6798\n",
      "Epoch 90/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7480 - val_loss: 0.6796\n",
      "Epoch 91/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7480 - val_loss: 0.6799\n",
      "Epoch 92/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7479 - val_loss: 0.6799\n",
      "Epoch 93/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7480 - val_loss: 0.6798\n",
      "Epoch 94/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7479 - val_loss: 0.6796\n",
      "Epoch 95/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7480 - val_loss: 0.6797\n",
      "Epoch 96/100\n",
      "469/469 [==============================] - 782s 2s/step - loss: 0.7479 - val_loss: 0.6795\n",
      "Epoch 97/100\n",
      "469/469 [==============================] - 3s 7ms/step - loss: 0.7479 - val_loss: 0.6797\n",
      "Epoch 98/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7483 - val_loss: 0.6798\n",
      "Epoch 99/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7479 - val_loss: 0.6794\n",
      "Epoch 100/100\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7480 - val_loss: 0.6809\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x2b2902c90>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create encoder decoder model for the data 6 layers deep\n",
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "from keras import regularizers\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "def custom_sigmoid(x):\n",
    "    return 2 * (1 / (1 + tf.exp(-x))) - 1\n",
    "\n",
    "input_df = Input(shape=(X_transformed.shape[1],))\n",
    "encoded = Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-6),\n",
    "                activity_regularizer=regularizers.l1(1e-6))(input_df)\n",
    "\n",
    "encoded = Dense(48, activation='relu', kernel_regularizer=regularizers.l2(1e-6),\n",
    "                activity_regularizer=regularizers.l1(1e-6))(encoded)\n",
    "\n",
    "decoded = Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-6),\n",
    "                activity_regularizer=regularizers.l1(1e-6))(encoded)\n",
    "decoded = Dense(X_transformed.shape[1], activation=custom_sigmoid)(decoded)\n",
    "\n",
    "autoencoder = Model(input_df, decoded)\n",
    "encoder = Model(input_df, encoded)\n",
    "adam = Adam(learning_rate=1e-3, clipvalue=0.5)\n",
    "autoencoder.compile(optimizer=adam, loss='mean_squared_error')\n",
    "autoencoder.fit(X_transformed, X_transformed, epochs=100, batch_size=128, validation_data=(X_test, X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1257/1257 [==============================] - 1s 425us/step\n",
      "619/619 [==============================] - 0s 520us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00     19470\n",
      "           1       0.77      0.68      0.72       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.88      0.84      0.86     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19404    66]\n",
      " [  107   223]]\n"
     ]
    }
   ],
   "source": [
    "#create XGBoost model\n",
    "import xgboost as xgb\n",
    "clf = xgb.XGBClassifier()\n",
    "clf.fit(encoder.predict(X_train), y_train)\n",
    "y_pred = clf.predict(encoder.predict(X_test))\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a predictor deep neural network\n",
    "\n",
    "def get_predictor_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(32, input_dim=48,\n",
    "                    activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    \n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    \n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    \n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    \n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dense(32, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dense(16, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    \n",
    "    model.add(Dense(8, activation='relu',\n",
    "                    kernel_regularizer=regularizers.l2(1e-5),\n",
    "                    activity_regularizer=regularizers.l1(1e-5)))\n",
    "    \n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    \n",
    "\n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "preditctor = get_predictor_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1257/1257 [==============================] - 1s 533us/step\n",
      "619/619 [==============================] - 0s 417us/step\n",
      "Epoch 1/100\n",
      "315/315 [==============================] - 2s 3ms/step - loss: 0.4915 - accuracy: 0.9833 - val_loss: 0.1743 - val_accuracy: 0.9833\n",
      "Epoch 2/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0780 - accuracy: 0.9833 - val_loss: 0.0557 - val_accuracy: 0.9833\n",
      "Epoch 3/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.9833 - val_loss: 0.0491 - val_accuracy: 0.9833\n",
      "Epoch 4/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0450 - accuracy: 0.9833 - val_loss: 0.0447 - val_accuracy: 0.9833\n",
      "Epoch 5/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0416 - accuracy: 0.9833 - val_loss: 0.0423 - val_accuracy: 0.9833\n",
      "Epoch 6/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0398 - accuracy: 0.9833 - val_loss: 0.0402 - val_accuracy: 0.9833\n",
      "Epoch 7/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0386 - accuracy: 0.9833 - val_loss: 0.0393 - val_accuracy: 0.9833\n",
      "Epoch 8/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0377 - accuracy: 0.9833 - val_loss: 0.0385 - val_accuracy: 0.9833\n",
      "Epoch 9/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0370 - accuracy: 0.9833 - val_loss: 0.0376 - val_accuracy: 0.9833\n",
      "Epoch 10/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0365 - accuracy: 0.9833 - val_loss: 0.0377 - val_accuracy: 0.9833\n",
      "Epoch 11/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0362 - accuracy: 0.9833 - val_loss: 0.0368 - val_accuracy: 0.9833\n",
      "Epoch 12/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0355 - accuracy: 0.9833 - val_loss: 0.0366 - val_accuracy: 0.9833\n",
      "Epoch 13/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0352 - accuracy: 0.9833 - val_loss: 0.0365 - val_accuracy: 0.9833\n",
      "Epoch 14/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0349 - accuracy: 0.9833 - val_loss: 0.0362 - val_accuracy: 0.9833\n",
      "Epoch 15/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0342 - accuracy: 0.9833 - val_loss: 0.0358 - val_accuracy: 0.9833\n",
      "Epoch 16/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0342 - accuracy: 0.9849 - val_loss: 0.0361 - val_accuracy: 0.9891\n",
      "Epoch 17/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0337 - accuracy: 0.9902 - val_loss: 0.0356 - val_accuracy: 0.9890\n",
      "Epoch 18/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0331 - accuracy: 0.9903 - val_loss: 0.0360 - val_accuracy: 0.9890\n",
      "Epoch 19/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0329 - accuracy: 0.9904 - val_loss: 0.0358 - val_accuracy: 0.9890\n",
      "Epoch 20/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0330 - accuracy: 0.9903 - val_loss: 0.0355 - val_accuracy: 0.9896\n",
      "Epoch 21/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0322 - accuracy: 0.9910 - val_loss: 0.0357 - val_accuracy: 0.9896\n",
      "Epoch 22/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0318 - accuracy: 0.9913 - val_loss: 0.0357 - val_accuracy: 0.9894\n",
      "Epoch 23/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0316 - accuracy: 0.9912 - val_loss: 0.0359 - val_accuracy: 0.9893\n",
      "Epoch 24/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0313 - accuracy: 0.9916 - val_loss: 0.0345 - val_accuracy: 0.9889\n",
      "Epoch 25/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0310 - accuracy: 0.9912 - val_loss: 0.0349 - val_accuracy: 0.9893\n",
      "Epoch 26/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0307 - accuracy: 0.9918 - val_loss: 0.0348 - val_accuracy: 0.9890\n",
      "Epoch 27/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0304 - accuracy: 0.9915 - val_loss: 0.0356 - val_accuracy: 0.9898\n",
      "Epoch 28/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0300 - accuracy: 0.9920 - val_loss: 0.0351 - val_accuracy: 0.9887\n",
      "Epoch 29/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0305 - accuracy: 0.9916 - val_loss: 0.0350 - val_accuracy: 0.9897\n",
      "Epoch 30/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0297 - accuracy: 0.9921 - val_loss: 0.0349 - val_accuracy: 0.9892\n",
      "Epoch 31/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0292 - accuracy: 0.9921 - val_loss: 0.0345 - val_accuracy: 0.9887\n",
      "Epoch 32/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0295 - accuracy: 0.9921 - val_loss: 0.0346 - val_accuracy: 0.9888\n",
      "Epoch 33/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0288 - accuracy: 0.9923 - val_loss: 0.0352 - val_accuracy: 0.9872\n",
      "Epoch 34/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0288 - accuracy: 0.9923 - val_loss: 0.0349 - val_accuracy: 0.9895\n",
      "Epoch 35/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0284 - accuracy: 0.9924 - val_loss: 0.0354 - val_accuracy: 0.9895\n",
      "Epoch 36/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0280 - accuracy: 0.9928 - val_loss: 0.0345 - val_accuracy: 0.9884\n",
      "Epoch 37/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0277 - accuracy: 0.9928 - val_loss: 0.0348 - val_accuracy: 0.9883\n",
      "Epoch 38/100\n",
      "315/315 [==============================] - 1s 4ms/step - loss: 0.0277 - accuracy: 0.9926 - val_loss: 0.0351 - val_accuracy: 0.9885\n",
      "Epoch 39/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0275 - accuracy: 0.9926 - val_loss: 0.0355 - val_accuracy: 0.9894\n",
      "Epoch 40/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0270 - accuracy: 0.9929 - val_loss: 0.0382 - val_accuracy: 0.9898\n",
      "Epoch 41/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0273 - accuracy: 0.9929 - val_loss: 0.0353 - val_accuracy: 0.9895\n",
      "Epoch 42/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9929 - val_loss: 0.0390 - val_accuracy: 0.9899\n",
      "Epoch 43/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0266 - accuracy: 0.9930 - val_loss: 0.0349 - val_accuracy: 0.9885\n",
      "Epoch 44/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0262 - accuracy: 0.9933 - val_loss: 0.0355 - val_accuracy: 0.9893\n",
      "Epoch 45/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0267 - accuracy: 0.9928 - val_loss: 0.0358 - val_accuracy: 0.9893\n",
      "Epoch 46/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0260 - accuracy: 0.9932 - val_loss: 0.0372 - val_accuracy: 0.9897\n",
      "Epoch 47/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0257 - accuracy: 0.9933 - val_loss: 0.0363 - val_accuracy: 0.9896\n",
      "Epoch 48/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0255 - accuracy: 0.9931 - val_loss: 0.0357 - val_accuracy: 0.9892\n",
      "Epoch 49/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0253 - accuracy: 0.9935 - val_loss: 0.0364 - val_accuracy: 0.9898\n",
      "Epoch 50/100\n",
      "315/315 [==============================] - 1s 4ms/step - loss: 0.0255 - accuracy: 0.9933 - val_loss: 0.0359 - val_accuracy: 0.9896\n",
      "Epoch 51/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0247 - accuracy: 0.9935 - val_loss: 0.0372 - val_accuracy: 0.9902\n",
      "Epoch 52/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0245 - accuracy: 0.9937 - val_loss: 0.0357 - val_accuracy: 0.9889\n",
      "Epoch 53/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0241 - accuracy: 0.9937 - val_loss: 0.0369 - val_accuracy: 0.9875\n",
      "Epoch 54/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0242 - accuracy: 0.9937 - val_loss: 0.0370 - val_accuracy: 0.9887\n",
      "Epoch 55/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0238 - accuracy: 0.9939 - val_loss: 0.0374 - val_accuracy: 0.9886\n",
      "Epoch 56/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0239 - accuracy: 0.9940 - val_loss: 0.0372 - val_accuracy: 0.9882\n",
      "Epoch 57/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0235 - accuracy: 0.9940 - val_loss: 0.0379 - val_accuracy: 0.9893\n",
      "Epoch 58/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0232 - accuracy: 0.9943 - val_loss: 0.0374 - val_accuracy: 0.9892\n",
      "Epoch 59/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0237 - accuracy: 0.9938 - val_loss: 0.0393 - val_accuracy: 0.9900\n",
      "Epoch 60/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0230 - accuracy: 0.9941 - val_loss: 0.0375 - val_accuracy: 0.9894\n",
      "Epoch 61/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0227 - accuracy: 0.9943 - val_loss: 0.0379 - val_accuracy: 0.9894\n",
      "Epoch 62/100\n",
      "315/315 [==============================] - 138s 440ms/step - loss: 0.0230 - accuracy: 0.9943 - val_loss: 0.0364 - val_accuracy: 0.9877\n",
      "Epoch 63/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0225 - accuracy: 0.9943 - val_loss: 0.0374 - val_accuracy: 0.9892\n",
      "Epoch 64/100\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 0.0225 - accuracy: 0.9943 - val_loss: 0.0384 - val_accuracy: 0.9894\n",
      "Epoch 65/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0219 - accuracy: 0.9945 - val_loss: 0.0376 - val_accuracy: 0.9881\n",
      "Epoch 66/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0218 - accuracy: 0.9946 - val_loss: 0.0410 - val_accuracy: 0.9898\n",
      "Epoch 67/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0215 - accuracy: 0.9947 - val_loss: 0.0386 - val_accuracy: 0.9892\n",
      "Epoch 68/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0219 - accuracy: 0.9942 - val_loss: 0.0413 - val_accuracy: 0.9898\n",
      "Epoch 69/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0216 - accuracy: 0.9949 - val_loss: 0.0377 - val_accuracy: 0.9889\n",
      "Epoch 70/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0212 - accuracy: 0.9948 - val_loss: 0.0423 - val_accuracy: 0.9900\n",
      "Epoch 71/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0210 - accuracy: 0.9949 - val_loss: 0.0406 - val_accuracy: 0.9897\n",
      "Epoch 72/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0211 - accuracy: 0.9950 - val_loss: 0.0400 - val_accuracy: 0.9899\n",
      "Epoch 73/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0205 - accuracy: 0.9950 - val_loss: 0.0394 - val_accuracy: 0.9894\n",
      "Epoch 74/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0200 - accuracy: 0.9952 - val_loss: 0.0421 - val_accuracy: 0.9892\n",
      "Epoch 75/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0205 - accuracy: 0.9949 - val_loss: 0.0435 - val_accuracy: 0.9896\n",
      "Epoch 76/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0201 - accuracy: 0.9952 - val_loss: 0.0438 - val_accuracy: 0.9899\n",
      "Epoch 77/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0202 - accuracy: 0.9948 - val_loss: 0.0410 - val_accuracy: 0.9881\n",
      "Epoch 78/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0215 - accuracy: 0.9946 - val_loss: 0.0420 - val_accuracy: 0.9889\n",
      "Epoch 79/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0197 - accuracy: 0.9951 - val_loss: 0.0402 - val_accuracy: 0.9886\n",
      "Epoch 80/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0197 - accuracy: 0.9951 - val_loss: 0.0388 - val_accuracy: 0.9873\n",
      "Epoch 81/100\n",
      "315/315 [==============================] - 6s 18ms/step - loss: 0.0194 - accuracy: 0.9955 - val_loss: 0.0394 - val_accuracy: 0.9863\n",
      "Epoch 82/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0196 - accuracy: 0.9954 - val_loss: 0.0449 - val_accuracy: 0.9898\n",
      "Epoch 83/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0192 - accuracy: 0.9954 - val_loss: 0.0463 - val_accuracy: 0.9904\n",
      "Epoch 84/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0187 - accuracy: 0.9957 - val_loss: 0.0417 - val_accuracy: 0.9878\n",
      "Epoch 85/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0185 - accuracy: 0.9958 - val_loss: 0.0422 - val_accuracy: 0.9886\n",
      "Epoch 86/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0186 - accuracy: 0.9956 - val_loss: 0.0426 - val_accuracy: 0.9881\n",
      "Epoch 87/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0188 - accuracy: 0.9955 - val_loss: 0.0462 - val_accuracy: 0.9895\n",
      "Epoch 88/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0191 - accuracy: 0.9953 - val_loss: 0.0442 - val_accuracy: 0.9893\n",
      "Epoch 89/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0184 - accuracy: 0.9955 - val_loss: 0.0444 - val_accuracy: 0.9884\n",
      "Epoch 90/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0191 - accuracy: 0.9953 - val_loss: 0.0455 - val_accuracy: 0.9889\n",
      "Epoch 91/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0194 - accuracy: 0.9950 - val_loss: 0.0472 - val_accuracy: 0.9898\n",
      "Epoch 92/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0179 - accuracy: 0.9959 - val_loss: 0.0436 - val_accuracy: 0.9889\n",
      "Epoch 93/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0181 - accuracy: 0.9956 - val_loss: 0.0445 - val_accuracy: 0.9885\n",
      "Epoch 94/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0173 - accuracy: 0.9960 - val_loss: 0.0489 - val_accuracy: 0.9895\n",
      "Epoch 95/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0184 - accuracy: 0.9953 - val_loss: 0.0454 - val_accuracy: 0.9885\n",
      "Epoch 96/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0173 - accuracy: 0.9960 - val_loss: 0.0482 - val_accuracy: 0.9895\n",
      "Epoch 97/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0182 - accuracy: 0.9955 - val_loss: 0.0469 - val_accuracy: 0.9887\n",
      "Epoch 98/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0168 - accuracy: 0.9963 - val_loss: 0.0482 - val_accuracy: 0.9895\n",
      "Epoch 99/100\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 0.0169 - accuracy: 0.9960 - val_loss: 0.0524 - val_accuracy: 0.9894\n",
      "Epoch 100/100\n",
      "315/315 [==============================] - 50s 161ms/step - loss: 0.0171 - accuracy: 0.9958 - val_loss: 0.0511 - val_accuracy: 0.9893\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x2876687d0>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preditctor.fit(encoder.predict(X_train), y_train, epochs=100, batch_size=128, validation_data=(encoder.predict(X_test), y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "619/619 [==============================] - 0s 432us/step\n",
      "619/619 [==============================] - 0s 503us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99     19470\n",
      "           1       0.66      0.73      0.69       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.83      0.86      0.84     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19347   123]\n",
      " [   89   241]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = preditctor.predict(encoder.predict(X_test))\n",
    "y_pred = np.round(y_pred)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 97)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add ln and exp of numerical columns and look at the correlation\n",
    "log_df = train_df[numerical_columns].replace('na', 0).astype(float).apply(np.log)\n",
    "log_df = log_df.replace(-np.inf, 0)\n",
    "log_df = log_df.replace(np.inf, 0)\n",
    "log_df = log_df.replace(np.nan, 0)\n",
    "\n",
    "exp_df = train_df[numerical_columns].replace('na', 0).astype(float).apply(np.exp)\n",
    "exp_df = exp_df.replace(-np.inf, 0)\n",
    "exp_df = exp_df.replace(np.inf, 0)\n",
    "exp_df = exp_df.replace(np.nan, 0)\n",
    "\n",
    "#create a new pandas df with the log and exp columns and name them accordingly\n",
    "log_df.columns = log_df.columns.map(lambda x: 'log_' + x)\n",
    "exp_df.columns = exp_df.columns.map(lambda x: 'exp_' + x)\n",
    "\n",
    "#concatenate the log and exp columns with the original df\n",
    "train_df = pd.concat([train_df, log_df, exp_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#corr matrix\n",
    "\n",
    "train_df_for_corr = train_df.copy()\n",
    "\n",
    "#look at the correlation between the columns against the targe\n",
    "train_df_for_corr[\"label\"] = labels.apply(lambda x: 1 if x == 'pos' else 0)\n",
    "train_df_for_corr = train_df_for_corr.replace('na', 0).astype(float).corr()\n",
    "\n",
    "#plot the correlation matrix against label\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "train_df_for_corr[\"label\"].abs()[numerical_columns].sort_values(ascending=False).plot(kind='bar')\n",
    "relevant_col_bools = train_df_for_corr[\"label\"].abs()[numerical_columns].sort_values(ascending=False) > 0.3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ci_000    0.550049\n",
       "bt_000    0.533964\n",
       "bb_000    0.529501\n",
       "bv_000    0.528056\n",
       "bu_000    0.528056\n",
       "            ...   \n",
       "dm_000    0.002839\n",
       "dj_000    0.001098\n",
       "ad_000    0.000530\n",
       "cf_000    0.000530\n",
       "co_000    0.000527\n",
       "Name: label, Length: 99, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_for_corr[\"label\"].abs()[numerical_columns].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df_for_corr['label'].filter(regex='^exp_').abs().sort_values(ascending=False).plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df_for_corr['label'].filter(regex='^log_').abs().sort_values(ascending=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the columns that are relevant >.2\n",
    "relevant_col_bools = train_df_for_corr[\"label\"].abs()[numerical_columns].sort_values(ascending=False) > 0.20\n",
    "relevant_numerical_cols = relevant_col_bools[relevant_col_bools].index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add two column types together\n",
    "num_cat_cols = pd.Index(categorical_cols).append(pd.Index(relevant_numerical_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create neural network\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "\n",
    "def get_model_for_aug_data(input_size= len(num_cat_cols)):\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Dense(64, input_dim=input_size, activation='relu'))\n",
    "    model.add(Dropout(0.3))\n",
    "    \n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-5), activity_regularizer=regularizers.l1(1e-5)))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/keras/src/optimizers/legacy/adam.py:118: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "aug_model = get_model_for_aug_data()\n",
    "X = train_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed, Y, test_size=0.33, random_state=42, stratify=Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aug_model.fit(X_train, y_train, epochs=100, batch_size=128, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy model for fine tuning\n",
    "from keras.models import clone_model\n",
    "aug_model_2 = clone_model(aug_model)\n",
    "aug_model_2.set_weights(aug_model.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "619/619 [==============================] - 0s 449us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     19470\n",
      "           1       0.33      0.93      0.49       330\n",
      "\n",
      "    accuracy                           0.97     19800\n",
      "   macro avg       0.66      0.95      0.74     19800\n",
      "weighted avg       0.99      0.97      0.97     19800\n",
      "\n",
      "[[18844   626]\n",
      " [   22   308]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = aug_model.predict(X_test)\n",
    "y_pred = np.round(y_pred)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/keras/src/optimizers/legacy/adam.py:118: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "#finetune with a diffent objective function\n",
    "\n",
    "\n",
    "import keras.backend as K\n",
    "\n",
    "def weighted_binary_crossentropy(y_true, y_pred):\n",
    "    # Cast y_true to float32\n",
    "    y_true = K.cast(y_true, 'float32')\n",
    "\n",
    "    # Calculate the binary cross entropy\n",
    "    bce = K.binary_crossentropy(y_true, y_pred)\n",
    "\n",
    "    # Define the weights\n",
    "    weight_for_false_negatives = K.constant(500.0)  # Use keras constant for float\n",
    "    weight_for_false_positives = K.constant(10.0)   # Use keras constant for float\n",
    "\n",
    "    # Apply the weights\n",
    "    weight_vector = y_true * weight_for_false_negatives + (1 - y_true) * weight_for_false_positives\n",
    "    weighted_bce = weight_vector * bce\n",
    "\n",
    "    return K.mean(weighted_bce)\n",
    "\n",
    "adam = Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-8)\n",
    "\n",
    "aug_model.compile(optimizer='adam', loss=weighted_binary_crossentropy, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "315/315 [==============================] - 1s 3ms/step - loss: 4.5242 - accuracy: 0.9624 - val_loss: 2.7047 - val_accuracy: 0.9420\n",
      "Epoch 2/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 3.2109 - accuracy: 0.9471 - val_loss: 3.1082 - val_accuracy: 0.9543\n",
      "Epoch 3/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.9003 - accuracy: 0.9556 - val_loss: 3.0364 - val_accuracy: 0.9583\n",
      "Epoch 4/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.8530 - accuracy: 0.9570 - val_loss: 2.8492 - val_accuracy: 0.9703\n",
      "Epoch 5/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.9514 - accuracy: 0.9570 - val_loss: 3.0863 - val_accuracy: 0.9686\n",
      "Epoch 6/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.5472 - accuracy: 0.9584 - val_loss: 3.0928 - val_accuracy: 0.9702\n",
      "Epoch 7/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.5023 - accuracy: 0.9636 - val_loss: 3.1843 - val_accuracy: 0.9519\n",
      "Epoch 8/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.3097 - accuracy: 0.9565 - val_loss: 3.4467 - val_accuracy: 0.9729\n",
      "Epoch 9/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.3817 - accuracy: 0.9616 - val_loss: 3.0046 - val_accuracy: 0.9668\n",
      "Epoch 10/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.2133 - accuracy: 0.9627 - val_loss: 3.2053 - val_accuracy: 0.9744\n",
      "Epoch 11/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.1505 - accuracy: 0.9617 - val_loss: 2.9370 - val_accuracy: 0.9683\n",
      "Epoch 12/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.2240 - accuracy: 0.9637 - val_loss: 3.3130 - val_accuracy: 0.9652\n",
      "Epoch 13/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.2202 - accuracy: 0.9647 - val_loss: 3.3560 - val_accuracy: 0.9736\n",
      "Epoch 14/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.2215 - accuracy: 0.9561 - val_loss: 3.4584 - val_accuracy: 0.9709\n",
      "Epoch 15/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.1783 - accuracy: 0.9610 - val_loss: 3.6591 - val_accuracy: 0.9745\n",
      "Epoch 16/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.3518 - accuracy: 0.9606 - val_loss: 2.8514 - val_accuracy: 0.9718\n",
      "Epoch 17/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.0633 - accuracy: 0.9641 - val_loss: 3.4324 - val_accuracy: 0.9705\n",
      "Epoch 18/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.1675 - accuracy: 0.9629 - val_loss: 2.9394 - val_accuracy: 0.9682\n",
      "Epoch 19/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.1185 - accuracy: 0.9638 - val_loss: 3.5722 - val_accuracy: 0.9761\n",
      "Epoch 20/20\n",
      "315/315 [==============================] - 1s 2ms/step - loss: 2.0595 - accuracy: 0.9640 - val_loss: 3.3281 - val_accuracy: 0.9673\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x336444190>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aug_model.fit(X_train, y_train, epochs=20, batch_size=128, validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "619/619 [==============================] - 0s 342us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     19470\n",
      "           1       0.33      0.93      0.49       330\n",
      "\n",
      "    accuracy                           0.97     19800\n",
      "   macro avg       0.66      0.95      0.74     19800\n",
      "weighted avg       0.99      0.97      0.97     19800\n",
      "\n",
      "[[18844   626]\n",
      " [   22   308]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = aug_model.predict(X_test)\n",
    "\n",
    "y_pred = np.round(y_pred)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>id</th>\n",
       "      <th>aa_000</th>\n",
       "      <th>ab_000</th>\n",
       "      <th>ac_000</th>\n",
       "      <th>ad_000</th>\n",
       "      <th>ae_000</th>\n",
       "      <th>af_000</th>\n",
       "      <th>ag_000</th>\n",
       "      <th>ag_001</th>\n",
       "      <th>ag_002</th>\n",
       "      <th>...</th>\n",
       "      <th>exp_dv_000</th>\n",
       "      <th>exp_dx_000</th>\n",
       "      <th>exp_dy_000</th>\n",
       "      <th>exp_dz_000</th>\n",
       "      <th>exp_ea_000</th>\n",
       "      <th>exp_eb_000</th>\n",
       "      <th>exp_ec_00</th>\n",
       "      <th>exp_ed_000</th>\n",
       "      <th>exp_ef_000</th>\n",
       "      <th>exp_eg_000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.459815e+01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.459087e+202</td>\n",
       "      <td>1.220403e+39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.311232e+15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.746153e+01</td>\n",
       "      <td>1.986265e+44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>66002</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>59816</td>\n",
       "      <td>na</td>\n",
       "      <td>1010</td>\n",
       "      <td>936</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1814</td>\n",
       "      <td>na</td>\n",
       "      <td>156</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.294273e+171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.920972e+47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.810410e+49</td>\n",
       "      <td>3.831008e+22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15995</th>\n",
       "      <td>15996</td>\n",
       "      <td>81852</td>\n",
       "      <td>na</td>\n",
       "      <td>2130706432</td>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15996</th>\n",
       "      <td>15997</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.572186e+147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.980958e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.664456e+00</td>\n",
       "      <td>5.184706e+21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15997</th>\n",
       "      <td>15998</td>\n",
       "      <td>79636</td>\n",
       "      <td>na</td>\n",
       "      <td>1670</td>\n",
       "      <td>1518</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15998</th>\n",
       "      <td>15999</td>\n",
       "      <td>110</td>\n",
       "      <td>na</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.688117e+43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.336514e+58</td>\n",
       "      <td>2.872650e+56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15999</th>\n",
       "      <td>16000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.423977e+138</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.459815e+01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.264519e+33</td>\n",
       "      <td>1.068647e+13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16000 rows × 765 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  aa_000 ab_000      ac_000 ad_000 ae_000 af_000 ag_000 ag_001  \\\n",
       "0          1      60      0          20     12      0      0      0      0   \n",
       "1          2      82      0          68     40      0      0      0      0   \n",
       "2          3   66002      2         212    112      0      0      0      0   \n",
       "3          4   59816     na        1010    936      0      0      0      0   \n",
       "4          5    1814     na         156    140      0      0      0      0   \n",
       "...      ...     ...    ...         ...    ...    ...    ...    ...    ...   \n",
       "15995  15996   81852     na  2130706432    892      0      0      0      0   \n",
       "15996  15997      18      0          52     46      8     26      0      0   \n",
       "15997  15998   79636     na        1670   1518      0      0      0      0   \n",
       "15998  15999     110     na          36     32      0      0      0      0   \n",
       "15999  16000       8      0           6      4      2      2      0      0   \n",
       "\n",
       "      ag_002  ...     exp_dv_000 exp_dx_000    exp_dy_000 exp_dz_000  \\\n",
       "0          0  ...   0.000000e+00        1.0  5.459815e+01        1.0   \n",
       "1          0  ...   0.000000e+00        0.0  4.311232e+15        1.0   \n",
       "2          0  ...   0.000000e+00        0.0  0.000000e+00        1.0   \n",
       "3          0  ...   0.000000e+00        1.0  1.000000e+00        1.0   \n",
       "4          0  ...  1.294273e+171        0.0  5.920972e+47        1.0   \n",
       "...      ...  ...            ...        ...           ...        ...   \n",
       "15995      0  ...   0.000000e+00        0.0  0.000000e+00        1.0   \n",
       "15996      0  ...  4.572186e+147        0.0  2.980958e+03        1.0   \n",
       "15997      0  ...   0.000000e+00        0.0  0.000000e+00        1.0   \n",
       "15998      0  ...   1.000000e+00        0.0  2.688117e+43        1.0   \n",
       "15999      0  ...  9.423977e+138        1.0  5.459815e+01        1.0   \n",
       "\n",
       "      exp_ea_000 exp_eb_000      exp_ec_00    exp_ed_000 exp_ef_000 exp_eg_000  \n",
       "0            1.0        1.0  1.459087e+202  1.220403e+39        1.0        1.0  \n",
       "1            1.0        1.0   1.746153e+01  1.986265e+44        1.0        1.0  \n",
       "2            1.0        0.0   0.000000e+00  0.000000e+00        1.0        1.0  \n",
       "3            1.0        0.0   0.000000e+00  0.000000e+00        1.0        1.0  \n",
       "4            1.0        0.0   2.810410e+49  3.831008e+22        1.0        1.0  \n",
       "...          ...        ...            ...           ...        ...        ...  \n",
       "15995        1.0        0.0   0.000000e+00  0.000000e+00        1.0        1.0  \n",
       "15996        1.0        1.0   2.664456e+00  5.184706e+21        1.0        1.0  \n",
       "15997        1.0        0.0   0.000000e+00  0.000000e+00        1.0        1.0  \n",
       "15998        1.0        1.0   1.336514e+58  2.872650e+56        1.0        1.0  \n",
       "15999        1.0        1.0   1.264519e+33  1.068647e+13        1.0        1.0  \n",
       "\n",
       "[16000 rows x 765 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use test set to predict\n",
    "log_df = test_df[numerical_columns].replace('na', 0).astype(float).apply(np.log)\n",
    "log_df = log_df.replace(-np.inf, 0)\n",
    "log_df = log_df.replace(np.inf, 0)\n",
    "log_df = log_df.replace(np.nan, 0)\n",
    "\n",
    "exp_df = test_df[numerical_columns].replace('na', 0).astype(float).apply(np.exp)\n",
    "exp_df = exp_df.replace(-np.inf, 0)\n",
    "exp_df = exp_df.replace(np.inf, 0)\n",
    "exp_df = exp_df.replace(np.nan, 0)\n",
    "\n",
    "#create a new pandas df with the log and exp columns and name them accordingly\n",
    "log_df.columns = log_df.columns.map(lambda x: 'log_' + x)\n",
    "exp_df.columns = exp_df.columns.map(lambda x: 'exp_' + x)\n",
    "\n",
    "#concatenate the log and exp columns with the original df\n",
    "test_df = pd.concat([test_df, log_df, exp_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500/500 [==============================] - 0s 348us/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "\n",
    "\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "y_pred_ = aug_model.predict(X_transformed)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "output.to_csv('categorical+relevant numerical values standardized neural network with custom obj func finetuned 20 epochs', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      1.00     19470\n",
      "           1       0.73      0.86      0.79       330\n",
      "\n",
      "    accuracy                           0.99     19800\n",
      "   macro avg       0.86      0.93      0.89     19800\n",
      "weighted avg       0.99      0.99      0.99     19800\n",
      "\n",
      "[[19364   106]\n",
      " [   45   285]]\n"
     ]
    }
   ],
   "source": [
    "#lets do xgboost with the augmented data\n",
    "import xgboost as xgb\n",
    "\n",
    "clf = xgb.XGBClassifier(\n",
    "    n_estimators=1000,\n",
    "    max_depth=9,\n",
    "    learning_rate=0.1,\n",
    "    subsample=0.5,\n",
    "    colsample_bytree=0.5,\n",
    "    random_state=42,\n",
    "    scale_pos_weight=1000,\n",
    ")\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define custom scoring for CV\n",
    "from sklearn.metrics import make_scorer\n",
    "def custom_cost_score(y_true, y_pred):\n",
    "    # Define your costs\n",
    "    Cost_1 = 10  # Cost for false positives\n",
    "    Cost_2 = 500  # Cost for false negatives\n",
    "\n",
    "    # Calculate confusion matrix\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n",
    "\n",
    "    # Calculate the total cost\n",
    "    total_cost = (Cost_1 * fp + Cost_2 * fn)\n",
    "    return total_cost\n",
    "\n",
    "# Create a scorer object\n",
    "custom_scorer = make_scorer(custom_cost_score, greater_is_better=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 40 candidates, totalling 120 fits\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=18002.000 total time=   0.8s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=17996.000 total time=   0.8s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=18002.000 total time=   0.8s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=18002.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=18002.000 total time=   0.8s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=17996.000 total time=   0.9s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=17996.000 total time=   0.9s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=18002.000 total time=   1.0s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=18002.000 total time=   1.0s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=17996.000 total time=   1.0s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   1.0s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   1.0s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=17996.000 total time=   1.0s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.8s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=18002.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=18002.000 total time=   0.7s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=17996.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.8s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=17996.000 total time=   0.8s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=17996.000 total time=   0.7s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.7s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=17996.000 total time=   0.7s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=17996.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=17996.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=17996.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=18002.000 total time=   0.7s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=17996.000 total time=   0.7s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=18002.000 total time=   0.9s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=17996.000 total time=   0.9s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.9s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   1.0s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   1.0s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   1.0s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=17996.000 total time=   1.1s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=17996.000 total time=   1.2s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=18002.000 total time=   0.9s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=18002.000 total time=   0.8s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=18002.000 total time=   0.8s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=17996.000 total time=   0.8s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=17996.000 total time=   0.7s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=18002.000 total time=   0.9s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.7s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=17996.000 total time=   0.6s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 1/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.7s\n",
      "[CV 3/3] END colsample_bytree=0.5, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=17996.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=18002.000 total time=   0.7s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=17996.000 total time=   0.7s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=17996.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=17996.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=17996.000 total time=   0.6s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=18002.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.01, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=5, subsample=1.0;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=18002.000 total time=   0.5s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=50, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.4s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=17996.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.6s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=0.5;, score=18002.000 total time=   0.6s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=500, subsample=1.0;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=17996.000 total time=   0.5s\n",
      "[CV 1/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=0.5;, score=18002.000 total time=   0.5s\n",
      "[CV 2/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=18002.000 total time=   0.5s\n",
      "[CV 3/3] END colsample_bytree=1.0, learning_rate=0.1, max_depth=3, scale_pos_weight=100, subsample=1.0;, score=17996.000 total time=   0.5s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=-1,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=42, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;colsample_bytree&#x27;: [0.5, 1.0],\n",
       "                         &#x27;learning_rate&#x27;: [0.01, 0.1], &#x27;max_depth&#x27;: [3],\n",
       "                         &#x27;scale_pos_weight&#x27;: [5, 50, 100, 500, 100],\n",
       "                         &#x27;subsample&#x27;: [0.5, 1.0]},\n",
       "             scoring=make_scorer(custom_cost_score, greater_is_better=False),\n",
       "             verbose=3)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=-1,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=42, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;colsample_bytree&#x27;: [0.5, 1.0],\n",
       "                         &#x27;learning_rate&#x27;: [0.01, 0.1], &#x27;max_depth&#x27;: [3],\n",
       "                         &#x27;scale_pos_weight&#x27;: [5, 50, 100, 500, 100],\n",
       "                         &#x27;subsample&#x27;: [0.5, 1.0]},\n",
       "             scoring=make_scorer(custom_cost_score, greater_is_better=False),\n",
       "             verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=-1, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=-1, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=-1,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=42, ...),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'colsample_bytree': [0.5, 1.0],\n",
       "                         'learning_rate': [0.01, 0.1], 'max_depth': [3],\n",
       "                         'scale_pos_weight': [5, 50, 100, 500, 100],\n",
       "                         'subsample': [0.5, 1.0]},\n",
       "             scoring=make_scorer(custom_cost_score, greater_is_better=False),\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, make_scorer\n",
    "\n",
    "X = train_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "\n",
    "# Define the parameter grid to search\n",
    "param_grid = {\n",
    "    'max_depth': [3, ],\n",
    "    'learning_rate': [0.01, 0.1],\n",
    "    'subsample': [0.5, 1.0],\n",
    "    'colsample_bytree': [0.5, 1.0],\n",
    "    'scale_pos_weight': [5, 50 ,100, 500, 100]\n",
    "}\n",
    "\n",
    "# Initialize the XGBClassifier\n",
    "clf = xgb.XGBClassifier(\n",
    "    random_state=42,\n",
    "    n_estimators=-1,\n",
    "  )\n",
    "\n",
    "\n",
    "# Create a GridSearchCV object\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=clf,\n",
    "    param_grid=param_grid,\n",
    "    scoring=custom_scorer,\n",
    "    cv=3,  # Number of cross-validation folds (3-fold cross-validation)\n",
    "    verbose=3,\n",
    "    n_jobs=-1  # Use all available cores\n",
    ")\n",
    "\n",
    "grid_search.fit(X_transformed, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18000.0"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_transformed, Y, test_size=0.33, random_state=42, stratify=Y)\n",
    "\n",
    "clf = xgb.XGBClassifier(\n",
    "    n_estimators=-1,\n",
    "    random_state=42,\n",
    "\n",
    "    **grid_search.best_params_\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=-1, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</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-25\" type=\"checkbox\" checked><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=-1, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=3, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=-1, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99     19470\n",
      "           1       0.00      0.00      0.00       330\n",
      "\n",
      "    accuracy                           0.98     19800\n",
      "   macro avg       0.49      0.50      0.50     19800\n",
      "weighted avg       0.97      0.98      0.98     19800\n",
      "\n",
      "[[19470     0]\n",
      " [  330     0]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=9, n_estimators=1000,\n",
       "                       random_state=42)</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-26\" type=\"checkbox\" checked><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=9, n_estimators=1000,\n",
       "                       random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', max_depth=9, n_estimators=1000,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#random forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=1000, max_depth=9, random_state=42, class_weight='balanced')\n",
    "clf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99     19470\n",
      "           1       0.41      0.90      0.56       330\n",
      "\n",
      "    accuracy                           0.98     19800\n",
      "   macro avg       0.70      0.94      0.77     19800\n",
      "weighted avg       0.99      0.98      0.98     19800\n",
      "\n",
      "[[19040   430]\n",
      " [   33   297]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.94      0.97     19470\n",
      "           1       0.21      0.97      0.34       330\n",
      "\n",
      "    accuracy                           0.94     19800\n",
      "   macro avg       0.60      0.95      0.65     19800\n",
      "weighted avg       0.99      0.94      0.96     19800\n",
      "\n",
      "[[18237  1233]\n",
      " [   11   319]]\n"
     ]
    }
   ],
   "source": [
    "#random forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=1000, max_depth=6, random_state=42, class_weight={0: 1, 1: 500})\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use test set to predict\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "y_pred_ = clf.predict(X_transformed)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "output.to_csv('categorical+relevant numerical values ln e transformations standardized random forest witjh 1:500 biasing.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.94      0.97     19470\n",
      "           1       0.23      0.96      0.37       330\n",
      "\n",
      "    accuracy                           0.94     19800\n",
      "   macro avg       0.61      0.95      0.67     19800\n",
      "weighted avg       0.99      0.94      0.96     19800\n",
      "\n",
      "[[18389  1081]\n",
      " [   14   316]]\n"
     ]
    }
   ],
   "source": [
    "#random forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=1000, max_depth=6, random_state=42, class_weight={0: 1, 1: 250})\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "\n",
    "#use test set to predict\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "y_pred_ = clf.predict(X_transformed)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "output.to_csv('categorical+relevant numerical values ln e transformations standardized random forest witjh 1:250 biasing.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "output.to_csv('categorical+relevant numerical values ln e transformations standardized random forest witjh 1:250 biasing.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use test set to predict\n",
    "\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "\n",
    "y_pred_ = clf.predict(X_transformed)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "output.to_csv('categorical+relevant numerical values ln e transformations standardized random forest with 1:500 biasing entire dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99     19470\n",
      "           1       0.33      0.60      0.43       330\n",
      "\n",
      "    accuracy                           0.97     19800\n",
      "   macro avg       0.66      0.79      0.71     19800\n",
      "weighted avg       0.98      0.97      0.98     19800\n",
      "\n",
      "[[19075   395]\n",
      " [  133   197]]\n"
     ]
    }
   ],
   "source": [
    "#train an svm\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "clf = SVC(gamma='auto', class_weight={0: 1, 1: 500}, degree=4)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bad performance by svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 45 candidates, totalling 135 fits\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/3] END class_weight={0: 1, 1: 1}, max_depth=3, random_state=42;, score=-111160.000 total time=   7.6s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1}, max_depth=3, random_state=42;, score=-112680.000 total time=   8.0s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1}, max_depth=3, random_state=42;, score=-119090.000 total time=   8.3s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1}, max_depth=6, random_state=42;, score=-77690.000 total time=  16.3s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1}, max_depth=6, random_state=42;, score=-76730.000 total time=  16.6s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1}, max_depth=6, random_state=42;, score=-72230.000 total time=  16.9s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1}, max_depth=9, random_state=42;, score=-60760.000 total time=  24.6s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1}, max_depth=9, random_state=42;, score=-63210.000 total time=  24.8s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1}, max_depth=9, random_state=42;, score=-64810.000 total time=  25.9s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 5}, max_depth=3, random_state=42;, score=-38160.000 total time=   9.0s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1}, max_depth=12, random_state=42;, score=-56300.000 total time=  33.7s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1}, max_depth=12, random_state=42;, score=-51230.000 total time=  34.1s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 5}, max_depth=3, random_state=42;, score=-30270.000 total time=   8.9s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 5}, max_depth=3, random_state=42;, score=-31790.000 total time=   9.4s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1}, max_depth=12, random_state=42;, score=-56270.000 total time=  32.8s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1}, max_depth=15, random_state=42;, score=-55780.000 total time=  38.6s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1}, max_depth=15, random_state=42;, score=-51270.000 total time=  39.2s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 5}, max_depth=6, random_state=42;, score=-31800.000 total time=  15.2s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 5}, max_depth=6, random_state=42;, score=-33760.000 total time=  15.2s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 5}, max_depth=6, random_state=42;, score=-36120.000 total time=  15.1s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1}, max_depth=15, random_state=42;, score=-54800.000 total time=  37.7s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 5}, max_depth=9, random_state=42;, score=-41440.000 total time=  21.8s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 5}, max_depth=9, random_state=42;, score=-43750.000 total time=  24.1s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 5}, max_depth=9, random_state=42;, score=-48820.000 total time=  26.3s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 10}, max_depth=3, random_state=42;, score=-29270.000 total time=  10.1s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 5}, max_depth=12, random_state=42;, score=-50530.000 total time=  33.0s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 5}, max_depth=12, random_state=42;, score=-55880.000 total time=  33.2s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 5}, max_depth=12, random_state=42;, score=-50380.000 total time=  34.1s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 10}, max_depth=3, random_state=42;, score=-26820.000 total time=   9.3s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 10}, max_depth=3, random_state=42;, score=-23130.000 total time=   8.9s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 5}, max_depth=15, random_state=42;, score=-52860.000 total time=  39.5s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 5}, max_depth=15, random_state=42;, score=-56270.000 total time=  40.3s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 5}, max_depth=15, random_state=42;, score=-62280.000 total time=  40.7s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 10}, max_depth=6, random_state=42;, score=-28680.000 total time=  17.9s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 10}, max_depth=6, random_state=42;, score=-26220.000 total time=  18.5s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 10}, max_depth=6, random_state=42;, score=-27140.000 total time=  18.4s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 10}, max_depth=9, random_state=42;, score=-35370.000 total time=  26.1s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 10}, max_depth=9, random_state=42;, score=-35380.000 total time=  27.3s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 10}, max_depth=9, random_state=42;, score=-41260.000 total time=  26.0s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 25}, max_depth=3, random_state=42;, score=-21970.000 total time=   8.5s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 25}, max_depth=3, random_state=42;, score=-23170.000 total time=   9.2s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 10}, max_depth=12, random_state=42;, score=-45410.000 total time=  31.7s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 25}, max_depth=3, random_state=42;, score=-20620.000 total time=   8.5s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 10}, max_depth=12, random_state=42;, score=-51770.000 total time=  30.3s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 10}, max_depth=12, random_state=42;, score=-46700.000 total time=  31.5s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 10}, max_depth=15, random_state=42;, score=-51070.000 total time=  33.9s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 10}, max_depth=15, random_state=42;, score=-52970.000 total time=  33.8s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 25}, max_depth=6, random_state=42;, score=-26870.000 total time=  16.2s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 10}, max_depth=15, random_state=42;, score=-57510.000 total time=  33.2s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 25}, max_depth=6, random_state=42;, score=-24350.000 total time=  16.5s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 25}, max_depth=6, random_state=42;, score=-19250.000 total time=  16.3s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 25}, max_depth=9, random_state=42;, score=-28210.000 total time=  23.4s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 25}, max_depth=9, random_state=42;, score=-32620.000 total time=  24.4s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 25}, max_depth=9, random_state=42;, score=-24960.000 total time=  24.0s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 50}, max_depth=3, random_state=42;, score=-18690.000 total time=   8.6s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 25}, max_depth=12, random_state=42;, score=-42560.000 total time=  28.9s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 50}, max_depth=3, random_state=42;, score=-21100.000 total time=   9.1s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 25}, max_depth=12, random_state=42;, score=-35160.000 total time=  27.9s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 50}, max_depth=3, random_state=42;, score=-19740.000 total time=   8.3s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 25}, max_depth=12, random_state=42;, score=-38440.000 total time=  27.9s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 25}, max_depth=15, random_state=42;, score=-47580.000 total time=  29.3s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 25}, max_depth=15, random_state=42;, score=-43580.000 total time=  28.0s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 50}, max_depth=6, random_state=42;, score=-21060.000 total time=  15.2s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 25}, max_depth=15, random_state=42;, score=-46480.000 total time=  27.5s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 50}, max_depth=6, random_state=42;, score=-22460.000 total time=  14.6s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 50}, max_depth=6, random_state=42;, score=-16810.000 total time=  14.4s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 50}, max_depth=9, random_state=42;, score=-25300.000 total time=  19.8s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 50}, max_depth=9, random_state=42;, score=-32110.000 total time=  20.6s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 50}, max_depth=9, random_state=42;, score=-20060.000 total time=  19.7s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 50}, max_depth=12, random_state=42;, score=-38360.000 total time=  22.5s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 100}, max_depth=3, random_state=42;, score=-19220.000 total time=   7.7s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 100}, max_depth=3, random_state=42;, score=-20060.000 total time=   7.6s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 50}, max_depth=12, random_state=42;, score=-31540.000 total time=  22.4s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 50}, max_depth=12, random_state=42;, score=-31410.000 total time=  23.0s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 100}, max_depth=3, random_state=42;, score=-20610.000 total time=   7.9s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 50}, max_depth=15, random_state=42;, score=-43950.000 total time=  23.8s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 50}, max_depth=15, random_state=42;, score=-35490.000 total time=  23.6s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 100}, max_depth=6, random_state=42;, score=-19140.000 total time=  16.8s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 50}, max_depth=15, random_state=42;, score=-39240.000 total time=  26.2s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 100}, max_depth=6, random_state=42;, score=-22360.000 total time=  17.1s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 100}, max_depth=6, random_state=42;, score=-16260.000 total time=  17.5s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 100}, max_depth=9, random_state=42;, score=-23520.000 total time=  21.3s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 100}, max_depth=9, random_state=42;, score=-27280.000 total time=  21.7s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 100}, max_depth=9, random_state=42;, score=-20240.000 total time=  21.9s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 100}, max_depth=12, random_state=42;, score=-36490.000 total time=  22.5s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 500}, max_depth=3, random_state=42;, score=-24280.000 total time=   8.4s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 100}, max_depth=12, random_state=42;, score=-27290.000 total time=  21.2s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 500}, max_depth=3, random_state=42;, score=-27690.000 total time=   8.6s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 500}, max_depth=3, random_state=42;, score=-23940.000 total time=   9.1s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 100}, max_depth=15, random_state=42;, score=-38000.000 total time=  21.7s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 100}, max_depth=12, random_state=42;, score=-23790.000 total time=  22.4s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 100}, max_depth=15, random_state=42;, score=-31560.000 total time=  20.9s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 100}, max_depth=15, random_state=42;, score=-30380.000 total time=  22.4s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 500}, max_depth=6, random_state=42;, score=-17590.000 total time=  13.7s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 500}, max_depth=6, random_state=42;, score=-19110.000 total time=  13.0s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 500}, max_depth=6, random_state=42;, score=-19500.000 total time=  13.4s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 500}, max_depth=9, random_state=42;, score=-23170.000 total time=  14.2s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 500}, max_depth=9, random_state=42;, score=-22320.000 total time=  14.7s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 500}, max_depth=9, random_state=42;, score=-19550.000 total time=  15.3s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 500}, max_depth=12, random_state=42;, score=-25840.000 total time=  14.7s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1000}, max_depth=3, random_state=42;, score=-29630.000 total time=   7.5s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1000}, max_depth=3, random_state=42;, score=-31590.000 total time=   7.3s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 500}, max_depth=12, random_state=42;, score=-26110.000 total time=  14.7s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1000}, max_depth=3, random_state=42;, score=-30530.000 total time=   7.3s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 500}, max_depth=12, random_state=42;, score=-20590.000 total time=  15.3s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 500}, max_depth=15, random_state=42;, score=-32250.000 total time=  15.0s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 500}, max_depth=15, random_state=42;, score=-26150.000 total time=  15.1s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 500}, max_depth=15, random_state=42;, score=-24010.000 total time=  15.6s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1000}, max_depth=6, random_state=42;, score=-18020.000 total time=  12.6s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1000}, max_depth=6, random_state=42;, score=-20490.000 total time=  12.4s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1000}, max_depth=6, random_state=42;, score=-18490.000 total time=  12.3s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1000}, max_depth=9, random_state=42;, score=-22680.000 total time=  13.3s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1000}, max_depth=9, random_state=42;, score=-21430.000 total time=  13.5s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1000}, max_depth=9, random_state=42;, score=-21440.000 total time=  14.0s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1000}, max_depth=12, random_state=42;, score=-23160.000 total time=  14.1s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1000}, max_depth=12, random_state=42;, score=-24070.000 total time=  14.2s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 2000}, max_depth=3, random_state=42;, score=-38860.000 total time=   8.1s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 2000}, max_depth=3, random_state=42;, score=-39360.000 total time=   7.9s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 2000}, max_depth=3, random_state=42;, score=-35710.000 total time=   8.1s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1000}, max_depth=12, random_state=42;, score=-22430.000 total time=  15.1s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 1000}, max_depth=15, random_state=42;, score=-27380.000 total time=  15.0s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 1000}, max_depth=15, random_state=42;, score=-25610.000 total time=  15.4s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 1000}, max_depth=15, random_state=42;, score=-22290.000 total time=  15.8s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 2000}, max_depth=6, random_state=42;, score=-18920.000 total time=  12.3s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 2000}, max_depth=6, random_state=42;, score=-21260.000 total time=  12.8s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 2000}, max_depth=6, random_state=42;, score=-19300.000 total time=  12.8s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 2000}, max_depth=9, random_state=42;, score=-22390.000 total time=  13.2s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 2000}, max_depth=9, random_state=42;, score=-19990.000 total time=  13.5s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 2000}, max_depth=9, random_state=42;, score=-22040.000 total time=  13.1s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 2000}, max_depth=12, random_state=42;, score=-23450.000 total time=  13.4s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 2000}, max_depth=12, random_state=42;, score=-24120.000 total time=  12.9s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 2000}, max_depth=12, random_state=42;, score=-22290.000 total time=  12.4s\n",
      "[CV 1/3] END class_weight={0: 1, 1: 2000}, max_depth=15, random_state=42;, score=-26540.000 total time=  11.7s\n",
      "[CV 2/3] END class_weight={0: 1, 1: 2000}, max_depth=15, random_state=42;, score=-24850.000 total time=  11.0s\n",
      "[CV 3/3] END class_weight={0: 1, 1: 2000}, max_depth=15, random_state=42;, score=-23160.000 total time=  10.8s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'class_weight': {0: 1, 1: 500}, 'max_depth': 6, 'random_state': 42}"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets hyperparameter tune the Random Forest\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'max_depth': [3, 6, 9, 12, 15],\n",
    "    'class_weight': [ {0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 10},{0: 1, 1: 25},{0: 1, 1: 50}, {0: 1, 1: 100} ,{0: 1, 1: 500}, {0: 1, 1: 1000}, {0: 1, 1: 2000}],\n",
    "    'random_state': [42]\n",
    "}\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=clf,\n",
    "    param_grid=param_grid,\n",
    "    scoring=custom_scorer,\n",
    "    cv=3,  # Number of cross-validation folds (3-fold cross-validation)\n",
    "    verbose=3,\n",
    "    n_jobs=-1  # Use all available cores\n",
    ")\n",
    "\n",
    "grid_search.fit(X_transformed, Y)\n",
    "\n",
    "grid_search.best_score_\n",
    "\n",
    "grid_search.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA & Models.ipynb Cell 92\u001b[0m line \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y230sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# train a random forest with the best params\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y230sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m clf \u001b[39m=\u001b[39m RandomForestClassifier(\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y230sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mgrid_search\u001b[39m.\u001b[39mbest_params_,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y230sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     n_estimators\u001b[39m=\u001b[39m\u001b[39m1000\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y230sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m )\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y230sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m clf\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y230sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m y_pred \u001b[39m=\u001b[39m clf\u001b[39m.\u001b[39mpredict(X_test)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y230sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mprint\u001b[39m(classification_report(y_test, y_pred))\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m   1147\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[1;32m   1148\u001b[0m     skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[1;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1150\u001b[0m     )\n\u001b[1;32m   1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m     \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/ensemble/_forest.py:456\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    445\u001b[0m trees \u001b[39m=\u001b[39m [\n\u001b[1;32m    446\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_estimator(append\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, random_state\u001b[39m=\u001b[39mrandom_state)\n\u001b[1;32m    447\u001b[0m     \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(n_more_estimators)\n\u001b[1;32m    448\u001b[0m ]\n\u001b[1;32m    450\u001b[0m \u001b[39m# Parallel loop: we prefer the threading backend as the Cython code\u001b[39;00m\n\u001b[1;32m    451\u001b[0m \u001b[39m# for fitting the trees is internally releasing the Python GIL\u001b[39;00m\n\u001b[1;32m    452\u001b[0m \u001b[39m# making threading more efficient than multiprocessing in\u001b[39;00m\n\u001b[1;32m    453\u001b[0m \u001b[39m# that case. However, for joblib 0.12+ we respect any\u001b[39;00m\n\u001b[1;32m    454\u001b[0m \u001b[39m# parallel_backend contexts set at a higher level,\u001b[39;00m\n\u001b[1;32m    455\u001b[0m \u001b[39m# since correctness does not rely on using threads.\u001b[39;00m\n\u001b[0;32m--> 456\u001b[0m trees \u001b[39m=\u001b[39m Parallel(\n\u001b[1;32m    457\u001b[0m     n_jobs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mn_jobs,\n\u001b[1;32m    458\u001b[0m     verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m    459\u001b[0m     prefer\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mthreads\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    460\u001b[0m )(\n\u001b[1;32m    461\u001b[0m     delayed(_parallel_build_trees)(\n\u001b[1;32m    462\u001b[0m         t,\n\u001b[1;32m    463\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbootstrap,\n\u001b[1;32m    464\u001b[0m         X,\n\u001b[1;32m    465\u001b[0m         y,\n\u001b[1;32m    466\u001b[0m         sample_weight,\n\u001b[1;32m    467\u001b[0m         i,\n\u001b[1;32m    468\u001b[0m         \u001b[39mlen\u001b[39;49m(trees),\n\u001b[1;32m    469\u001b[0m         verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m    470\u001b[0m         class_weight\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mclass_weight,\n\u001b[1;32m    471\u001b[0m         n_samples_bootstrap\u001b[39m=\u001b[39;49mn_samples_bootstrap,\n\u001b[1;32m    472\u001b[0m     )\n\u001b[1;32m    473\u001b[0m     \u001b[39mfor\u001b[39;49;00m i, t \u001b[39min\u001b[39;49;00m \u001b[39menumerate\u001b[39;49m(trees)\n\u001b[1;32m    474\u001b[0m )\n\u001b[1;32m    476\u001b[0m \u001b[39m# Collect newly grown trees\u001b[39;00m\n\u001b[1;32m    477\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mestimators_\u001b[39m.\u001b[39mextend(trees)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/utils/parallel.py:65\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     60\u001b[0m config \u001b[39m=\u001b[39m get_config()\n\u001b[1;32m     61\u001b[0m iterable_with_config \u001b[39m=\u001b[39m (\n\u001b[1;32m     62\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     63\u001b[0m     \u001b[39mfor\u001b[39;00m delayed_func, args, kwargs \u001b[39min\u001b[39;00m iterable\n\u001b[1;32m     64\u001b[0m )\n\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(iterable_with_config)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/joblib/parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m     output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m   1862\u001b[0m     \u001b[39mnext\u001b[39m(output)\n\u001b[0;32m-> 1863\u001b[0m     \u001b[39mreturn\u001b[39;00m output \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturn_generator \u001b[39melse\u001b[39;00m \u001b[39mlist\u001b[39m(output)\n\u001b[1;32m   1865\u001b[0m \u001b[39m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m   1866\u001b[0m \u001b[39m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m   1867\u001b[0m \u001b[39m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m   1868\u001b[0m \u001b[39m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m   1869\u001b[0m \u001b[39m# callback.\u001b[39;00m\n\u001b[1;32m   1870\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/joblib/parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_batches \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m   1791\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m-> 1792\u001b[0m res \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1793\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_completed_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m   1794\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_progress()\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/utils/parallel.py:127\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m     config \u001b[39m=\u001b[39m {}\n\u001b[1;32m    126\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mconfig):\n\u001b[0;32m--> 127\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunction(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/ensemble/_forest.py:188\u001b[0m, in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[39melif\u001b[39;00m class_weight \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mbalanced_subsample\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    186\u001b[0m         curr_sample_weight \u001b[39m*\u001b[39m\u001b[39m=\u001b[39m compute_sample_weight(\u001b[39m\"\u001b[39m\u001b[39mbalanced\u001b[39m\u001b[39m\"\u001b[39m, y, indices\u001b[39m=\u001b[39mindices)\n\u001b[0;32m--> 188\u001b[0m     tree\u001b[39m.\u001b[39;49mfit(X, y, sample_weight\u001b[39m=\u001b[39;49mcurr_sample_weight, check_input\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m    189\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    190\u001b[0m     tree\u001b[39m.\u001b[39mfit(X, y, sample_weight\u001b[39m=\u001b[39msample_weight, check_input\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m   1147\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[1;32m   1148\u001b[0m     skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[1;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1150\u001b[0m     )\n\u001b[1;32m   1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m     \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/tree/_classes.py:959\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    928\u001b[0m \u001b[39m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m    929\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, X, y, sample_weight\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, check_input\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m    930\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[1;32m    931\u001b[0m \n\u001b[1;32m    932\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    956\u001b[0m \u001b[39m        Fitted estimator.\u001b[39;00m\n\u001b[1;32m    957\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 959\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m_fit(\n\u001b[1;32m    960\u001b[0m         X,\n\u001b[1;32m    961\u001b[0m         y,\n\u001b[1;32m    962\u001b[0m         sample_weight\u001b[39m=\u001b[39;49msample_weight,\n\u001b[1;32m    963\u001b[0m         check_input\u001b[39m=\u001b[39;49mcheck_input,\n\u001b[1;32m    964\u001b[0m     )\n\u001b[1;32m    965\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/tree/_classes.py:443\u001b[0m, in \u001b[0;36mBaseDecisionTree._fit\u001b[0;34m(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    433\u001b[0m     builder \u001b[39m=\u001b[39m BestFirstTreeBuilder(\n\u001b[1;32m    434\u001b[0m         splitter,\n\u001b[1;32m    435\u001b[0m         min_samples_split,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmin_impurity_decrease,\n\u001b[1;32m    441\u001b[0m     )\n\u001b[0;32m--> 443\u001b[0m builder\u001b[39m.\u001b[39;49mbuild(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtree_, X, y, sample_weight, missing_values_in_feature_mask)\n\u001b[1;32m    445\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_outputs_ \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39mand\u001b[39;00m is_classifier(\u001b[39mself\u001b[39m):\n\u001b[1;32m    446\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_classes_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_classes_[\u001b[39m0\u001b[39m]\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# train a random forest with the best params\n",
    "\n",
    "clf = RandomForestClassifier(**grid_search.best_params_)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found the same hyperparameters :D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting catboost\n",
      "  Downloading catboost-1.2.2-cp311-cp311-macosx_11_0_universal2.whl (25.7 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m25.7/25.7 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hCollecting graphviz (from catboost)\n",
      "  Using cached graphviz-0.20.1-py3-none-any.whl (47 kB)\n",
      "Requirement already satisfied: matplotlib in ./.venv/lib/python3.11/site-packages (from catboost) (3.8.2)\n",
      "Requirement already satisfied: numpy>=1.16.0 in ./.venv/lib/python3.11/site-packages (from catboost) (1.26.2)\n",
      "Requirement already satisfied: pandas>=0.24 in ./.venv/lib/python3.11/site-packages (from catboost) (2.1.3)\n",
      "Requirement already satisfied: scipy in ./.venv/lib/python3.11/site-packages (from catboost) (1.11.4)\n",
      "Collecting plotly (from catboost)\n",
      "  Downloading plotly-5.18.0-py3-none-any.whl (15.6 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: six in ./.venv/lib/python3.11/site-packages (from catboost) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in ./.venv/lib/python3.11/site-packages (from pandas>=0.24->catboost) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in ./.venv/lib/python3.11/site-packages (from pandas>=0.24->catboost) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in ./.venv/lib/python3.11/site-packages (from pandas>=0.24->catboost) (2023.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in ./.venv/lib/python3.11/site-packages (from matplotlib->catboost) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in ./.venv/lib/python3.11/site-packages (from matplotlib->catboost) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in ./.venv/lib/python3.11/site-packages (from matplotlib->catboost) (4.46.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in ./.venv/lib/python3.11/site-packages (from matplotlib->catboost) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.11/site-packages (from matplotlib->catboost) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in ./.venv/lib/python3.11/site-packages (from matplotlib->catboost) (10.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in ./.venv/lib/python3.11/site-packages (from matplotlib->catboost) (3.1.1)\n",
      "Collecting tenacity>=6.2.0 (from plotly->catboost)\n",
      "  Using cached tenacity-8.2.3-py3-none-any.whl (24 kB)\n",
      "Installing collected packages: tenacity, graphviz, plotly, catboost\n",
      "Successfully installed catboost-1.2.2 graphviz-0.20.1 plotly-5.18.0 tenacity-8.2.3\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = labels.apply(lambda x: 1 if x == 'pos' else 0).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.98     19470\n",
      "           1       0.26      0.97      0.41       330\n",
      "\n",
      "    accuracy                           0.95     19800\n",
      "   macro avg       0.63      0.96      0.69     19800\n",
      "weighted avg       0.99      0.95      0.97     19800\n",
      "\n",
      "[[18558   912]\n",
      " [   11   319]]\n"
     ]
    }
   ],
   "source": [
    "# train a catboost model\n",
    "\n",
    "\n",
    "clf = CatBoostClassifier(iterations=1000, learning_rate=0.01, random_seed=42,  depth=10, loss_function='Logloss', class_weights={0:1, 1:1000}, logging_level='Silent')\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23740"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "912 * 20 + 11 * 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>id</th>\n",
       "      <th>aa_000</th>\n",
       "      <th>ab_000</th>\n",
       "      <th>ac_000</th>\n",
       "      <th>ad_000</th>\n",
       "      <th>ae_000</th>\n",
       "      <th>af_000</th>\n",
       "      <th>ag_000</th>\n",
       "      <th>ag_001</th>\n",
       "      <th>ag_002</th>\n",
       "      <th>...</th>\n",
       "      <th>exp_dv_000</th>\n",
       "      <th>exp_dx_000</th>\n",
       "      <th>exp_dy_000</th>\n",
       "      <th>exp_dz_000</th>\n",
       "      <th>exp_ea_000</th>\n",
       "      <th>exp_eb_000</th>\n",
       "      <th>exp_ec_00</th>\n",
       "      <th>exp_ed_000</th>\n",
       "      <th>exp_ef_000</th>\n",
       "      <th>exp_eg_000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.459815e+01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.459087e+202</td>\n",
       "      <td>1.220403e+39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.311232e+15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.746153e+01</td>\n",
       "      <td>1.986265e+44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>66002</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>59816</td>\n",
       "      <td>na</td>\n",
       "      <td>1010</td>\n",
       "      <td>936</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1814</td>\n",
       "      <td>na</td>\n",
       "      <td>156</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.294273e+171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.920972e+47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.810410e+49</td>\n",
       "      <td>3.831008e+22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15995</th>\n",
       "      <td>15996</td>\n",
       "      <td>81852</td>\n",
       "      <td>na</td>\n",
       "      <td>2130706432</td>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15996</th>\n",
       "      <td>15997</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>46</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.572186e+147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.980958e+03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.664456e+00</td>\n",
       "      <td>5.184706e+21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15997</th>\n",
       "      <td>15998</td>\n",
       "      <td>79636</td>\n",
       "      <td>na</td>\n",
       "      <td>1670</td>\n",
       "      <td>1518</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15998</th>\n",
       "      <td>15999</td>\n",
       "      <td>110</td>\n",
       "      <td>na</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.688117e+43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.336514e+58</td>\n",
       "      <td>2.872650e+56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15999</th>\n",
       "      <td>16000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.423977e+138</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.459815e+01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.264519e+33</td>\n",
       "      <td>1.068647e+13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16000 rows × 369 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  aa_000 ab_000      ac_000 ad_000 ae_000 af_000 ag_000 ag_001  \\\n",
       "0          1      60      0          20     12      0      0      0      0   \n",
       "1          2      82      0          68     40      0      0      0      0   \n",
       "2          3   66002      2         212    112      0      0      0      0   \n",
       "3          4   59816     na        1010    936      0      0      0      0   \n",
       "4          5    1814     na         156    140      0      0      0      0   \n",
       "...      ...     ...    ...         ...    ...    ...    ...    ...    ...   \n",
       "15995  15996   81852     na  2130706432    892      0      0      0      0   \n",
       "15996  15997      18      0          52     46      8     26      0      0   \n",
       "15997  15998   79636     na        1670   1518      0      0      0      0   \n",
       "15998  15999     110     na          36     32      0      0      0      0   \n",
       "15999  16000       8      0           6      4      2      2      0      0   \n",
       "\n",
       "      ag_002  ...     exp_dv_000 exp_dx_000    exp_dy_000 exp_dz_000  \\\n",
       "0          0  ...   0.000000e+00        1.0  5.459815e+01        1.0   \n",
       "1          0  ...   0.000000e+00        0.0  4.311232e+15        1.0   \n",
       "2          0  ...   0.000000e+00        0.0  0.000000e+00        1.0   \n",
       "3          0  ...   0.000000e+00        1.0  1.000000e+00        1.0   \n",
       "4          0  ...  1.294273e+171        0.0  5.920972e+47        1.0   \n",
       "...      ...  ...            ...        ...           ...        ...   \n",
       "15995      0  ...   0.000000e+00        0.0  0.000000e+00        1.0   \n",
       "15996      0  ...  4.572186e+147        0.0  2.980958e+03        1.0   \n",
       "15997      0  ...   0.000000e+00        0.0  0.000000e+00        1.0   \n",
       "15998      0  ...   1.000000e+00        0.0  2.688117e+43        1.0   \n",
       "15999      0  ...  9.423977e+138        1.0  5.459815e+01        1.0   \n",
       "\n",
       "      exp_ea_000 exp_eb_000      exp_ec_00    exp_ed_000 exp_ef_000 exp_eg_000  \n",
       "0            1.0        1.0  1.459087e+202  1.220403e+39        1.0        1.0  \n",
       "1            1.0        1.0   1.746153e+01  1.986265e+44        1.0        1.0  \n",
       "2            1.0        0.0   0.000000e+00  0.000000e+00        1.0        1.0  \n",
       "3            1.0        0.0   0.000000e+00  0.000000e+00        1.0        1.0  \n",
       "4            1.0        0.0   2.810410e+49  3.831008e+22        1.0        1.0  \n",
       "...          ...        ...            ...           ...        ...        ...  \n",
       "15995        1.0        0.0   0.000000e+00  0.000000e+00        1.0        1.0  \n",
       "15996        1.0        1.0   2.664456e+00  5.184706e+21        1.0        1.0  \n",
       "15997        1.0        0.0   0.000000e+00  0.000000e+00        1.0        1.0  \n",
       "15998        1.0        1.0   1.336514e+58  2.872650e+56        1.0        1.0  \n",
       "15999        1.0        1.0   1.264519e+33  1.068647e+13        1.0        1.0  \n",
       "\n",
       "[16000 rows x 369 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x117074c50>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train a catboost model\n",
    "\n",
    "clf = CatBoostClassifier(iterations=1000, learning_rate=0.01, random_seed=42,  depth=11, loss_function='Logloss', class_weights={0:1, 1:1000}, logging_level='Silent')\n",
    "clf.fit(train_df.values, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(test_df[train_df.columns].values)\n",
    "\n",
    "#save the results\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred.reshape(-1))+1),\"class\": labelize(y_pred.reshape(-1))})\n",
    "output.to_csv('all values ln e transformations standardized catboost depth 11 with 1:1000 biasing entire dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train a catboost model\n",
    "\n",
    "clf = CatBoostClassifier(iterations=1000, learning_rate=0.01, random_seed=42,  depth=12, loss_function='Logloss', class_weights={0:1, 1:1000}, logging_level='Silent')\n",
    "clf.fit(train_df.values, Y)\n",
    "\n",
    "y_pred = clf.predict(test_df[train_df.columns].values)\n",
    "\n",
    "#save the results\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred.reshape(-1))+1),\"class\": labelize(y_pred.reshape(-1))})\n",
    "output.to_csv('all values ln e transformations standardized catboost depth 12 with 1:1000 biasing entire dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'custom_cost_score' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA & Models.ipynb Cell 106\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y206sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m custom_cost_score(y_test, y_pred)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'custom_cost_score' is not defined"
     ]
    }
   ],
   "source": [
    "custom_cost_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test set\n",
    "\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "y_pred_ = clf.predict(X)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "\n",
    "output.to_csv('categorical+relevant numerical values ln e transformations catboost with 1:250 biasing.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14620"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "custom_cost_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use test set to predict\n",
    "\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "\n",
    "X_transformed = StandardScaler().fit_transform(X)\n",
    "\n",
    "y_pred_ = clf.predict(X_transformed)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "\n",
    "output.to_csv('categorical+relevant numerical values ln e transformations standardized catboost with 1:1000 biasing.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train test split with stratify\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = train_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42, stratify=Y)\n",
    "#use bagging to create an ensemble of models CatBoost, Random Forest, XGBoost\n",
    "\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from catboost import CatBoostClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "clf1 = CatBoostClassifier(iterations=1000, learning_rate=0.01, random_seed=42,  depth=10, loss_function='Logloss', class_weights={0:1, 1:1000}, logging_level='Silent')\n",
    "clf2 = RandomForestClassifier(n_estimators=1000, max_depth=6, random_state=42, class_weight={0: 1, 1: 500})\n",
    "clf3 = XGBClassifier(n_estimators=1000,\n",
    "    max_depth=9,\n",
    "    learning_rate=0.1,\n",
    "    subsample=0.5,\n",
    "    colsample_bytree=0.5,\n",
    "    random_state=42,\n",
    "    scale_pos_weight=1000,)\n",
    "\n",
    "\n",
    "ensemble = VotingClassifier(estimators=[\n",
    "    ('catboost', clf1),\n",
    "    ('random_forest', clf2),\n",
    "    ('xgboost', clf3)\n",
    "], voting='soft')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensemble.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98     19470\n",
      "           1       0.32      0.95      0.48       330\n",
      "\n",
      "    accuracy                           0.97     19800\n",
      "   macro avg       0.66      0.96      0.73     19800\n",
      "weighted avg       0.99      0.97      0.97     19800\n",
      "\n",
      "[[18809   661]\n",
      " [   17   313]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_pred = ensemble.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test set to predict\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "\n",
    "#predict and save to csv \n",
    "y_pred_ = ensemble.predict(X)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "\n",
    "output.to_csv('categorical+relevant numerical values ln e transformations standardized ensemble of catboost, random forest, xgboost.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#STACKING CLASSIFIER\n",
    "\n",
    "clf1 = CatBoostClassifier(iterations=1000, learning_rate=0.01, random_seed=42,  depth=10, loss_function='Logloss', class_weights={0:1, 1:1000}, logging_level='Silent')\n",
    "clf2 = RandomForestClassifier(n_estimators=1000, max_depth=6, random_state=42, class_weight={0: 1, 1: 500}) \n",
    "clf3 = XGBClassifier(n_estimators=1000,\n",
    "    max_depth=9,\n",
    "    learning_rate=0.1,\n",
    "    subsample=0.5,\n",
    "    colsample_bytree=0.5,\n",
    "    random_state=42,\n",
    "    scale_pos_weight=1000,)\n",
    "\n",
    "\n",
    "ensemble = StackingClassifier(estimators=[\n",
    "    ('catboost', clf1),\n",
    "    ('random_forest', clf2),\n",
    "    ('xgboost', clf3)\n",
    "], final_estimator=RandomForestClassifier(n_estimators=1000, max_depth=3, random_state=42, class_weight={0: 1, 1: 1000}))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;catboost&#x27;,\n",
       "                                &lt;catboost.core.CatBoostClassifier object at 0x296aa6750&gt;),\n",
       "                               (&#x27;random_forest&#x27;,\n",
       "                                RandomForestClassifier(class_weight={0: 1,\n",
       "                                                                     1: 500},\n",
       "                                                       max_depth=6,\n",
       "                                                       n_estimators=1000,\n",
       "                                                       random_state=42)),\n",
       "                               (&#x27;xgboost&#x27;,\n",
       "                                XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=0.5, devi...\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None, max_depth=9,\n",
       "                                              max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=1000, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=42, ...))],\n",
       "                   final_estimator=RandomForestClassifier(class_weight={0: 1,\n",
       "                                                                        1: 1000},\n",
       "                                                          max_depth=3,\n",
       "                                                          n_estimators=1000,\n",
       "                                                          random_state=42))</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;catboost&#x27;,\n",
       "                                &lt;catboost.core.CatBoostClassifier object at 0x296aa6750&gt;),\n",
       "                               (&#x27;random_forest&#x27;,\n",
       "                                RandomForestClassifier(class_weight={0: 1,\n",
       "                                                                     1: 500},\n",
       "                                                       max_depth=6,\n",
       "                                                       n_estimators=1000,\n",
       "                                                       random_state=42)),\n",
       "                               (&#x27;xgboost&#x27;,\n",
       "                                XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=0.5, devi...\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None, max_depth=9,\n",
       "                                              max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=1000, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=42, ...))],\n",
       "                   final_estimator=RandomForestClassifier(class_weight={0: 1,\n",
       "                                                                        1: 1000},\n",
       "                                                          max_depth=3,\n",
       "                                                          n_estimators=1000,\n",
       "                                                          random_state=42))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>catboost</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x296aa6750&gt;</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>random_forest</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight={0: 1, 1: 500}, max_depth=6,\n",
       "                       n_estimators=1000, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>xgboost</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=9, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=1000, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight={0: 1, 1: 1000}, max_depth=3,\n",
       "                       n_estimators=1000, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "StackingClassifier(estimators=[('catboost',\n",
       "                                <catboost.core.CatBoostClassifier object at 0x296aa6750>),\n",
       "                               ('random_forest',\n",
       "                                RandomForestClassifier(class_weight={0: 1,\n",
       "                                                                     1: 500},\n",
       "                                                       max_depth=6,\n",
       "                                                       n_estimators=1000,\n",
       "                                                       random_state=42)),\n",
       "                               ('xgboost',\n",
       "                                XGBClassifier(base_score=None, booster=None,\n",
       "                                              callbacks=None,\n",
       "                                              colsample_bylevel=None,\n",
       "                                              colsample_bynode=None,\n",
       "                                              colsample_bytree=0.5, devi...\n",
       "                                              max_cat_threshold=None,\n",
       "                                              max_cat_to_onehot=None,\n",
       "                                              max_delta_step=None, max_depth=9,\n",
       "                                              max_leaves=None,\n",
       "                                              min_child_weight=None,\n",
       "                                              missing=nan,\n",
       "                                              monotone_constraints=None,\n",
       "                                              multi_strategy=None,\n",
       "                                              n_estimators=1000, n_jobs=None,\n",
       "                                              num_parallel_tree=None,\n",
       "                                              random_state=42, ...))],\n",
       "                   final_estimator=RandomForestClassifier(class_weight={0: 1,\n",
       "                                                                        1: 1000},\n",
       "                                                          max_depth=3,\n",
       "                                                          n_estimators=1000,\n",
       "                                                          random_state=42))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.92      0.96     19470\n",
      "           1       0.18      0.97      0.30       330\n",
      "\n",
      "    accuracy                           0.92     19800\n",
      "   macro avg       0.59      0.95      0.63     19800\n",
      "weighted avg       0.99      0.92      0.95     19800\n",
      "\n",
      "[[17982  1488]\n",
      " [    9   321]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = ensemble.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test set to predict\n",
    "X = test_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "\n",
    "#predict and save to csv\n",
    "y_pred_ = ensemble.predict(X)\n",
    "\n",
    "output = pd.DataFrame({'id': np.arange(1,len(y_pred_.reshape(-1))+1),\"class\": labelize(y_pred_.reshape(-1))})\n",
    "\n",
    "output.to_csv('categorical+relevant numerical values ln e transformations standardized stacking of catboost, random forest, xgboost.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA & Models.ipynb Cell 109\u001b[0m line \u001b[0;36m9\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# keep the clf1, clf2, clf3 but lets retrain the last layer of the stacking classifier\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m STACKING_CLASSIFIER \u001b[39m=\u001b[39m StackingClassifier(estimators\u001b[39m=\u001b[39m[\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mcatboost\u001b[39m\u001b[39m'\u001b[39m, clf1),\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mrandom_forest\u001b[39m\u001b[39m'\u001b[39m, clf2),\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mxgboost\u001b[39m\u001b[39m'\u001b[39m, clf3)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m ], final_estimator\u001b[39m=\u001b[39mRandomForestClassifier(n_estimators\u001b[39m=\u001b[39m\u001b[39m1000\u001b[39m, max_depth\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m42\u001b[39m, class_weight\u001b[39m=\u001b[39m{\u001b[39m0\u001b[39m: \u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m: \u001b[39m500\u001b[39m}))\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m STACKING_CLASSIFIER\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m y_pred \u001b[39m=\u001b[39m STACKING_CLASSIFIER\u001b[39m.\u001b[39mpredict(X_test)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/firatkizilboga/Projects/LFD/APS-Failure-Prediction/EDA%20%26%20Models.ipynb#Y215sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mprint\u001b[39m(classification_report(y_test, y_pred))\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/ensemble/_stacking.py:658\u001b[0m, in \u001b[0;36mStackingClassifier.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    656\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclasses_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_label_encoder\u001b[39m.\u001b[39mclasses_\n\u001b[1;32m    657\u001b[0m     y_encoded \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_label_encoder\u001b[39m.\u001b[39mtransform(y)\n\u001b[0;32m--> 658\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mfit(X, y_encoded, sample_weight)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m   1147\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[1;32m   1148\u001b[0m     skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[1;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1150\u001b[0m     )\n\u001b[1;32m   1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m     \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/ensemble/_stacking.py:206\u001b[0m, in \u001b[0;36m_BaseStacking.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    201\u001b[0m             \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mestimators_\u001b[39m.\u001b[39mappend(estimator)\n\u001b[1;32m    202\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    203\u001b[0m     \u001b[39m# Fit the base estimators on the whole training data. Those\u001b[39;00m\n\u001b[1;32m    204\u001b[0m     \u001b[39m# base estimators will be used in transform, predict, and\u001b[39;00m\n\u001b[1;32m    205\u001b[0m     \u001b[39m# predict_proba. They are exposed publicly.\u001b[39;00m\n\u001b[0;32m--> 206\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mestimators_ \u001b[39m=\u001b[39m Parallel(n_jobs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mn_jobs)(\n\u001b[1;32m    207\u001b[0m         delayed(_fit_single_estimator)(clone(est), X, y, sample_weight)\n\u001b[1;32m    208\u001b[0m         \u001b[39mfor\u001b[39;49;00m est \u001b[39min\u001b[39;49;00m all_estimators\n\u001b[1;32m    209\u001b[0m         \u001b[39mif\u001b[39;49;00m est \u001b[39m!=\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mdrop\u001b[39;49m\u001b[39m\"\u001b[39;49m\n\u001b[1;32m    210\u001b[0m     )\n\u001b[1;32m    212\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnamed_estimators_ \u001b[39m=\u001b[39m Bunch()\n\u001b[1;32m    213\u001b[0m est_fitted_idx \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/utils/parallel.py:65\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     60\u001b[0m config \u001b[39m=\u001b[39m get_config()\n\u001b[1;32m     61\u001b[0m iterable_with_config \u001b[39m=\u001b[39m (\n\u001b[1;32m     62\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     63\u001b[0m     \u001b[39mfor\u001b[39;00m delayed_func, args, kwargs \u001b[39min\u001b[39;00m iterable\n\u001b[1;32m     64\u001b[0m )\n\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(iterable_with_config)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/joblib/parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m     output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m   1862\u001b[0m     \u001b[39mnext\u001b[39m(output)\n\u001b[0;32m-> 1863\u001b[0m     \u001b[39mreturn\u001b[39;00m output \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturn_generator \u001b[39melse\u001b[39;00m \u001b[39mlist\u001b[39m(output)\n\u001b[1;32m   1865\u001b[0m \u001b[39m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m   1866\u001b[0m \u001b[39m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m   1867\u001b[0m \u001b[39m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m   1868\u001b[0m \u001b[39m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m   1869\u001b[0m \u001b[39m# callback.\u001b[39;00m\n\u001b[1;32m   1870\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/joblib/parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_batches \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m   1791\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m-> 1792\u001b[0m res \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1793\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_completed_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m   1794\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_progress()\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/utils/parallel.py:127\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m     config \u001b[39m=\u001b[39m {}\n\u001b[1;32m    126\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mconfig):\n\u001b[0;32m--> 127\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunction(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/ensemble/_base.py:36\u001b[0m, in \u001b[0;36m_fit_single_estimator\u001b[0;34m(estimator, X, y, sample_weight, message_clsname, message)\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     35\u001b[0m     \u001b[39mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[0;32m---> 36\u001b[0m         estimator\u001b[39m.\u001b[39;49mfit(X, y)\n\u001b[1;32m     37\u001b[0m \u001b[39mreturn\u001b[39;00m estimator\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m   1147\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[1;32m   1148\u001b[0m     skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[1;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1150\u001b[0m     )\n\u001b[1;32m   1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m     \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/ensemble/_forest.py:456\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    445\u001b[0m trees \u001b[39m=\u001b[39m [\n\u001b[1;32m    446\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_estimator(append\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, random_state\u001b[39m=\u001b[39mrandom_state)\n\u001b[1;32m    447\u001b[0m     \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(n_more_estimators)\n\u001b[1;32m    448\u001b[0m ]\n\u001b[1;32m    450\u001b[0m \u001b[39m# Parallel loop: we prefer the threading backend as the Cython code\u001b[39;00m\n\u001b[1;32m    451\u001b[0m \u001b[39m# for fitting the trees is internally releasing the Python GIL\u001b[39;00m\n\u001b[1;32m    452\u001b[0m \u001b[39m# making threading more efficient than multiprocessing in\u001b[39;00m\n\u001b[1;32m    453\u001b[0m \u001b[39m# that case. However, for joblib 0.12+ we respect any\u001b[39;00m\n\u001b[1;32m    454\u001b[0m \u001b[39m# parallel_backend contexts set at a higher level,\u001b[39;00m\n\u001b[1;32m    455\u001b[0m \u001b[39m# since correctness does not rely on using threads.\u001b[39;00m\n\u001b[0;32m--> 456\u001b[0m trees \u001b[39m=\u001b[39m Parallel(\n\u001b[1;32m    457\u001b[0m     n_jobs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mn_jobs,\n\u001b[1;32m    458\u001b[0m     verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m    459\u001b[0m     prefer\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mthreads\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    460\u001b[0m )(\n\u001b[1;32m    461\u001b[0m     delayed(_parallel_build_trees)(\n\u001b[1;32m    462\u001b[0m         t,\n\u001b[1;32m    463\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbootstrap,\n\u001b[1;32m    464\u001b[0m         X,\n\u001b[1;32m    465\u001b[0m         y,\n\u001b[1;32m    466\u001b[0m         sample_weight,\n\u001b[1;32m    467\u001b[0m         i,\n\u001b[1;32m    468\u001b[0m         \u001b[39mlen\u001b[39;49m(trees),\n\u001b[1;32m    469\u001b[0m         verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m    470\u001b[0m         class_weight\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mclass_weight,\n\u001b[1;32m    471\u001b[0m         n_samples_bootstrap\u001b[39m=\u001b[39;49mn_samples_bootstrap,\n\u001b[1;32m    472\u001b[0m     )\n\u001b[1;32m    473\u001b[0m     \u001b[39mfor\u001b[39;49;00m i, t \u001b[39min\u001b[39;49;00m \u001b[39menumerate\u001b[39;49m(trees)\n\u001b[1;32m    474\u001b[0m )\n\u001b[1;32m    476\u001b[0m \u001b[39m# Collect newly grown trees\u001b[39;00m\n\u001b[1;32m    477\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mestimators_\u001b[39m.\u001b[39mextend(trees)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/utils/parallel.py:65\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     60\u001b[0m config \u001b[39m=\u001b[39m get_config()\n\u001b[1;32m     61\u001b[0m iterable_with_config \u001b[39m=\u001b[39m (\n\u001b[1;32m     62\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[1;32m     63\u001b[0m     \u001b[39mfor\u001b[39;00m delayed_func, args, kwargs \u001b[39min\u001b[39;00m iterable\n\u001b[1;32m     64\u001b[0m )\n\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(iterable_with_config)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/joblib/parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m     output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m   1862\u001b[0m     \u001b[39mnext\u001b[39m(output)\n\u001b[0;32m-> 1863\u001b[0m     \u001b[39mreturn\u001b[39;00m output \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturn_generator \u001b[39melse\u001b[39;00m \u001b[39mlist\u001b[39m(output)\n\u001b[1;32m   1865\u001b[0m \u001b[39m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m   1866\u001b[0m \u001b[39m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m   1867\u001b[0m \u001b[39m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m   1868\u001b[0m \u001b[39m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m   1869\u001b[0m \u001b[39m# callback.\u001b[39;00m\n\u001b[1;32m   1870\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/joblib/parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_batches \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m   1791\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_dispatched_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m-> 1792\u001b[0m res \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1793\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_completed_tasks \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m   1794\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_progress()\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/utils/parallel.py:127\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m     config \u001b[39m=\u001b[39m {}\n\u001b[1;32m    126\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mconfig):\n\u001b[0;32m--> 127\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunction(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/ensemble/_forest.py:188\u001b[0m, in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[39melif\u001b[39;00m class_weight \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mbalanced_subsample\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    186\u001b[0m         curr_sample_weight \u001b[39m*\u001b[39m\u001b[39m=\u001b[39m compute_sample_weight(\u001b[39m\"\u001b[39m\u001b[39mbalanced\u001b[39m\u001b[39m\"\u001b[39m, y, indices\u001b[39m=\u001b[39mindices)\n\u001b[0;32m--> 188\u001b[0m     tree\u001b[39m.\u001b[39;49mfit(X, y, sample_weight\u001b[39m=\u001b[39;49mcurr_sample_weight, check_input\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m    189\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    190\u001b[0m     tree\u001b[39m.\u001b[39mfit(X, y, sample_weight\u001b[39m=\u001b[39msample_weight, check_input\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1145\u001b[0m     estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m   1147\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[1;32m   1148\u001b[0m     skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[1;32m   1149\u001b[0m         prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[1;32m   1150\u001b[0m     )\n\u001b[1;32m   1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m     \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/tree/_classes.py:959\u001b[0m, in \u001b[0;36mDecisionTreeClassifier.fit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    928\u001b[0m \u001b[39m@_fit_context\u001b[39m(prefer_skip_nested_validation\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m    929\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, X, y, sample_weight\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, check_input\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m):\n\u001b[1;32m    930\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Build a decision tree classifier from the training set (X, y).\u001b[39;00m\n\u001b[1;32m    931\u001b[0m \n\u001b[1;32m    932\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    956\u001b[0m \u001b[39m        Fitted estimator.\u001b[39;00m\n\u001b[1;32m    957\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 959\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m_fit(\n\u001b[1;32m    960\u001b[0m         X,\n\u001b[1;32m    961\u001b[0m         y,\n\u001b[1;32m    962\u001b[0m         sample_weight\u001b[39m=\u001b[39;49msample_weight,\n\u001b[1;32m    963\u001b[0m         check_input\u001b[39m=\u001b[39;49mcheck_input,\n\u001b[1;32m    964\u001b[0m     )\n\u001b[1;32m    965\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n",
      "File \u001b[0;32m~/Projects/LFD/APS-Failure-Prediction/.venv/lib/python3.11/site-packages/sklearn/tree/_classes.py:443\u001b[0m, in \u001b[0;36mBaseDecisionTree._fit\u001b[0;34m(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    433\u001b[0m     builder \u001b[39m=\u001b[39m BestFirstTreeBuilder(\n\u001b[1;32m    434\u001b[0m         splitter,\n\u001b[1;32m    435\u001b[0m         min_samples_split,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmin_impurity_decrease,\n\u001b[1;32m    441\u001b[0m     )\n\u001b[0;32m--> 443\u001b[0m builder\u001b[39m.\u001b[39;49mbuild(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtree_, X, y, sample_weight, missing_values_in_feature_mask)\n\u001b[1;32m    445\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_outputs_ \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39mand\u001b[39;00m is_classifier(\u001b[39mself\u001b[39m):\n\u001b[1;32m    446\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_classes_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_classes_[\u001b[39m0\u001b[39m]\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# keep the clf1, clf2, clf3 but lets retrain the last layer of the stacking classifier\n",
    "\n",
    "STACKING_CLASSIFIER = StackingClassifier(estimators=[\n",
    "    ('catboost', clf1),\n",
    "    ('random_forest', clf2),\n",
    "    ('xgboost', clf3)\n",
    "], final_estimator=RandomForestClassifier(n_estimators=1000, max_depth=4, random_state=42, class_weight={0: 1, 1: 500}))\n",
    "\n",
    "STACKING_CLASSIFIER.fit(X_train, y_train)\n",
    "\n",
    "y_pred = STACKING_CLASSIFIER.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hyperparameter tune the a catboost model\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "\n",
    "    'iterations': [1000],\n",
    "    'learning_rate': [0.01, 0.1],\n",
    "    'depth': [9, 12, 15],\n",
    "    'loss_function': ['Logloss', 'CrossEntropy'],\n",
    "    'class_weight': [{0: 1, 1: 500},{0: 1, 1: 1000}, ],\n",
    "    'task_type' : ['GPU']\n",
    "}\n",
    "\n",
    "clf = CatBoostClassifier()\n",
    "\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=clf,\n",
    "    param_grid=param_grid,\n",
    "    scoring=custom_scorer,\n",
    "    cv=3,  # Number of cross-validation folds (3-fold cross-validation)\n",
    "    verbose=3,\n",
    "    n_jobs=-1  # Use all available cores\n",
    ")\n",
    "\n",
    "\n",
    "X = train_df[num_cat_cols].replace('na', 0).astype(float).values\n",
    "grid_search.fit(X,Y)\n",
    "grid_search.best_score_"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}