infilteration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset, get the desired values only.\n",
    "# Make the points into categorical data\n",
    "# USe smoot\n",
    "# train \n",
    "# evaluate.\n",
    "\n",
    "# 'protocol'\n",
    "# 'flow_duration'\n",
    "# 'tot_fwd_pkts'\n",
    "# 'tot_bwd_pkts'\n",
    "# 'totlen_fwd_pkts'\n",
    "# 'totlen_bwd_pkts'\n",
    "# 'fwd_pkt_len_mean'\n",
    "# 'fwd_pkt_len_std'\n",
    "# 'bwd_pkt_len_mean'\n",
    "# 'flow_byts_s'\n",
    "# 'flow_pkts_s'\n",
    "# 'flow_iat_std'\n",
    "# 'flow_iat_min'\n",
    "# 'fwd_iat_tot'\n",
    "# 'fwd_iat_min'\n",
    "# 'bwd_iat_tot'\n",
    "# 'bwd_iat_min'\n",
    "# 'fwd_psh_flags'\n",
    "# 'fwd_urg_flags'\n",
    "# 'bwd_pkts_s'\n",
    "# 'fin_flag_cnt'\n",
    "# 'rst_flag_cnt'\n",
    "# 'psh_flag_cnt'\n",
    "# 'ack_flag_cnt'\n",
    "# 'urg_flag_cnt'\n",
    "# 'down_up_ratio'\n",
    "# 'init_fwd_win_byts'\n",
    "# 'init_bwd_win_byts'\n",
    "# 'fwd_seg_size_min'\n",
    "# 'active_mean'\n",
    "# 'idle_mean'\n",
    "\n",
    "\n",
    "# label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dst_port</th>\n",
       "      <th>protocol</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>flow_duration</th>\n",
       "      <th>tot_fwd_pkts</th>\n",
       "      <th>tot_bwd_pkts</th>\n",
       "      <th>totlen_fwd_pkts</th>\n",
       "      <th>totlen_bwd_pkts</th>\n",
       "      <th>fwd_pkt_len_max</th>\n",
       "      <th>fwd_pkt_len_min</th>\n",
       "      <th>...</th>\n",
       "      <th>fwd_seg_size_min</th>\n",
       "      <th>active_mean</th>\n",
       "      <th>active_std</th>\n",
       "      <th>active_max</th>\n",
       "      <th>active_min</th>\n",
       "      <th>idle_mean</th>\n",
       "      <th>idle_std</th>\n",
       "      <th>idle_max</th>\n",
       "      <th>idle_min</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
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       "      <td>61000000</td>\n",
       "      <td>52500000</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>01/03/2018 08:20:07</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>17</td>\n",
       "      <td>01/03/2018 08:17:18</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>3530939.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3530939</td>\n",
       "      <td>3530939</td>\n",
       "      <td>19200000.0</td>\n",
       "      <td>1.250000e+07</td>\n",
       "      <td>32600000</td>\n",
       "      <td>7999725</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>01/03/2018 08:22:09</td>\n",
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       "      <td>2</td>\n",
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       "      <td>61000000</td>\n",
       "      <td>61000000</td>\n",
       "      <td>Benign</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>61000000</td>\n",
       "      <td>61000000</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   dst_port  protocol            timestamp  flow_duration  tot_fwd_pkts  \\\n",
       "0         0         0  01/03/2018 08:17:11      115307855             5   \n",
       "1         0         0  01/03/2018 08:20:07       60997457             2   \n",
       "2        67        17  01/03/2018 08:17:18       61149019             5   \n",
       "3         0         0  01/03/2018 08:22:09       60997555             2   \n",
       "4         0         0  01/03/2018 08:24:11       61997503             3   \n",
       "\n",
       "   tot_bwd_pkts  totlen_fwd_pkts  totlen_bwd_pkts  fwd_pkt_len_max  \\\n",
       "0             0                0                0                0   \n",
       "1             0                0                0                0   \n",
       "2             0             1500                0              300   \n",
       "3             0                0                0                0   \n",
       "4             0                0                0                0   \n",
       "\n",
       "   fwd_pkt_len_min  ...  fwd_seg_size_min  active_mean  active_std  \\\n",
       "0                0  ...                 0    1812348.0         0.0   \n",
       "1                0  ...                 0          0.0         0.0   \n",
       "2              300  ...                 8    3530939.0         0.0   \n",
       "3                0  ...                 0          0.0         0.0   \n",
       "4                0  ...                 0     999909.0         0.0   \n",
       "\n",
       "   active_max  active_min   idle_mean      idle_std  idle_max  idle_min  \\\n",
       "0     1812348     1812348  56700000.0  6.010058e+06  61000000  52500000   \n",
       "1           0           0  61000000.0  0.000000e+00  61000000  61000000   \n",
       "2     3530939     3530939  19200000.0  1.250000e+07  32600000   7999725   \n",
       "3           0           0  61000000.0  0.000000e+00  61000000  61000000   \n",
       "4      999909      999909  61000000.0  0.000000e+00  61000000  61000000   \n",
       "\n",
       "    label  \n",
       "0  Benign  \n",
       "1  Benign  \n",
       "2  Benign  \n",
       "3  Benign  \n",
       "4  Benign  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('infiltration_01-03-2018.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "Benign           238037\n",
       "Infilteration     93063\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\n",
    "    'protocol',\n",
    "'flow_duration',\n",
    "'tot_fwd_pkts',\n",
    "'tot_bwd_pkts',\n",
    "'totlen_fwd_pkts',\n",
    "'totlen_bwd_pkts',\n",
    "'fwd_pkt_len_mean',\n",
    "'fwd_pkt_len_std',\n",
    "'bwd_pkt_len_mean',\n",
    "'flow_byts_s',\n",
    "'flow_pkts_s',\n",
    "'flow_iat_std',\n",
    "'flow_iat_min',\n",
    "'fwd_iat_tot',\n",
    "'fwd_iat_min',\n",
    "'bwd_iat_tot',\n",
    "'bwd_iat_min',\n",
    "'fwd_psh_flags',\n",
    "'fwd_urg_flags',\n",
    "'bwd_pkts_s',\n",
    "'fin_flag_cnt',\n",
    "'rst_flag_cnt',\n",
    "'psh_flag_cnt',\n",
    "'ack_flag_cnt',\n",
    "'urg_flag_cnt',\n",
    "'down_up_ratio',\n",
    "'init_fwd_win_byts',\n",
    "'init_bwd_win_byts',\n",
    "'fwd_seg_size_min',\n",
    "'active_mean',\n",
    "'idle_mean',\n",
    "'label'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331097</th>\n",
       "      <td>6</td>\n",
       "      <td>1404866</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1132</td>\n",
       "      <td>1581</td>\n",
       "      <td>141.5</td>\n",
       "      <td>222.792023</td>\n",
       "      <td>225.857143</td>\n",
       "      <td>1931.145034</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8192</td>\n",
       "      <td>62868</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331098</th>\n",
       "      <td>6</td>\n",
       "      <td>3966663</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>1386</td>\n",
       "      <td>392</td>\n",
       "      <td>99.0</td>\n",
       "      <td>189.204814</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>448.235708</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8192</td>\n",
       "      <td>62652</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331099</th>\n",
       "      <td>6</td>\n",
       "      <td>86637706</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62608</td>\n",
       "      <td>-1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86600000.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>331100 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        protocol  flow_duration  tot_fwd_pkts  tot_bwd_pkts  totlen_fwd_pkts  \\\n",
       "0              0      115307855             5             0                0   \n",
       "1              0       60997457             2             0                0   \n",
       "2             17       61149019             5             0             1500   \n",
       "3              0       60997555             2             0                0   \n",
       "4              0       61997503             3             0                0   \n",
       "...          ...            ...           ...           ...              ...   \n",
       "331095         0      118494070            31             0                0   \n",
       "331096         6         114340             2             1                0   \n",
       "331097         6        1404866             8             7             1132   \n",
       "331098         6        3966663            14             7             1386   \n",
       "331099         6       86637706             2             0                0   \n",
       "\n",
       "        totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
       "0                     0               0.0         0.000000          0.000000   \n",
       "1                     0               0.0         0.000000          0.000000   \n",
       "2                     0             300.0         0.000000          0.000000   \n",
       "3                     0               0.0         0.000000          0.000000   \n",
       "4                     0               0.0         0.000000          0.000000   \n",
       "...                 ...               ...              ...               ...   \n",
       "331095                0               0.0         0.000000          0.000000   \n",
       "331096                0               0.0         0.000000          0.000000   \n",
       "331097             1581             141.5       222.792023        225.857143   \n",
       "331098              392              99.0       189.204814         56.000000   \n",
       "331099                0               0.0         0.000000          0.000000   \n",
       "\n",
       "        flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
       "0          0.000000  ...             0             0             0   \n",
       "1          0.000000  ...             0             0             0   \n",
       "2         24.530238  ...             0             0             0   \n",
       "3          0.000000  ...             0             0             0   \n",
       "4          0.000000  ...             0             0             0   \n",
       "...             ...  ...           ...           ...           ...   \n",
       "331095     0.000000  ...             0             0             0   \n",
       "331096     0.000000  ...             1             0             0   \n",
       "331097  1931.145034  ...             1             0             0   \n",
       "331098   448.235708  ...             1             0             0   \n",
       "331099     0.000000  ...             0             1             0   \n",
       "\n",
       "        down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  fwd_seg_size_min  \\\n",
       "0                   0                 -1                 -1                 0   \n",
       "1                   0                 -1                 -1                 0   \n",
       "2                   0                 -1                 -1                 8   \n",
       "3                   0                 -1                 -1                 0   \n",
       "4                   0                 -1                 -1                 0   \n",
       "...               ...                ...                ...               ...   \n",
       "331095              0                 -1                 -1                 0   \n",
       "331096              0               1024                  0                20   \n",
       "331097              0               8192              62868                20   \n",
       "331098              0               8192              62652                20   \n",
       "331099              0              62608                 -1                20   \n",
       "\n",
       "        active_mean   idle_mean   label  \n",
       "0         1812348.0  56700000.0  Benign  \n",
       "1               0.0  61000000.0  Benign  \n",
       "2         3530939.0  19200000.0  Benign  \n",
       "3               0.0  61000000.0  Benign  \n",
       "4          999909.0  61000000.0  Benign  \n",
       "...             ...         ...     ...  \n",
       "331095          0.0         0.0  Benign  \n",
       "331096          0.0         0.0  Benign  \n",
       "331097          0.0         0.0  Benign  \n",
       "331098          0.0         0.0  Benign  \n",
       "331099          0.0  86600000.0  Benign  \n",
       "\n",
       "[331100 rows x 32 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = data[cols]\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "LE = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ld/bwffsrf52mnfq4_gy_k9p9480000gn/T/ipykernel_47556/3086081652.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['protocol'] = LE.fit_transform(df_new['protocol'])\n",
      "/var/folders/ld/bwffsrf52mnfq4_gy_k9p9480000gn/T/ipykernel_47556/3086081652.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['label'] = LE.fit_transform(df_new['label'])\n"
     ]
    }
   ],
   "source": [
    "df_new['protocol'] = LE.fit_transform(df_new['protocol'])\n",
    "df_new['label'] = LE.fit_transform(df_new['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "protocol\n",
       "1    212899\n",
       "2    111052\n",
       "0      7149\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['protocol'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame after removing rows with any value greater than float64:\n",
      "        protocol  flow_duration  tot_fwd_pkts  tot_bwd_pkts  totlen_fwd_pkts  \\\n",
      "0              0      115307855             5             0                0   \n",
      "1              0       60997457             2             0                0   \n",
      "2              2       61149019             5             0             1500   \n",
      "3              0       60997555             2             0                0   \n",
      "4              0       61997503             3             0                0   \n",
      "...          ...            ...           ...           ...              ...   \n",
      "331095         0      118494070            31             0                0   \n",
      "331096         1         114340             2             1                0   \n",
      "331097         1        1404866             8             7             1132   \n",
      "331098         1        3966663            14             7             1386   \n",
      "331099         1       86637706             2             0                0   \n",
      "\n",
      "        totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
      "0                     0               0.0         0.000000          0.000000   \n",
      "1                     0               0.0         0.000000          0.000000   \n",
      "2                     0             300.0         0.000000          0.000000   \n",
      "3                     0               0.0         0.000000          0.000000   \n",
      "4                     0               0.0         0.000000          0.000000   \n",
      "...                 ...               ...              ...               ...   \n",
      "331095                0               0.0         0.000000          0.000000   \n",
      "331096                0               0.0         0.000000          0.000000   \n",
      "331097             1581             141.5       222.792023        225.857143   \n",
      "331098              392              99.0       189.204814         56.000000   \n",
      "331099                0               0.0         0.000000          0.000000   \n",
      "\n",
      "        flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
      "0          0.000000  ...             0             0             0   \n",
      "1          0.000000  ...             0             0             0   \n",
      "2         24.530238  ...             0             0             0   \n",
      "3          0.000000  ...             0             0             0   \n",
      "4          0.000000  ...             0             0             0   \n",
      "...             ...  ...           ...           ...           ...   \n",
      "331095     0.000000  ...             0             0             0   \n",
      "331096     0.000000  ...             1             0             0   \n",
      "331097  1931.145034  ...             1             0             0   \n",
      "331098   448.235708  ...             1             0             0   \n",
      "331099     0.000000  ...             0             1             0   \n",
      "\n",
      "        down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  fwd_seg_size_min  \\\n",
      "0                   0                 -1                 -1                 0   \n",
      "1                   0                 -1                 -1                 0   \n",
      "2                   0                 -1                 -1                 8   \n",
      "3                   0                 -1                 -1                 0   \n",
      "4                   0                 -1                 -1                 0   \n",
      "...               ...                ...                ...               ...   \n",
      "331095              0                 -1                 -1                 0   \n",
      "331096              0               1024                  0                20   \n",
      "331097              0               8192              62868                20   \n",
      "331098              0               8192              62652                20   \n",
      "331099              0              62608                 -1                20   \n",
      "\n",
      "        active_mean   idle_mean  label  \n",
      "0         1812348.0  56700000.0      0  \n",
      "1               0.0  61000000.0      0  \n",
      "2         3530939.0  19200000.0      0  \n",
      "3               0.0  61000000.0      0  \n",
      "4          999909.0  61000000.0      0  \n",
      "...             ...         ...    ...  \n",
      "331095          0.0         0.0      0  \n",
      "331096          0.0         0.0      0  \n",
      "331097          0.0         0.0      0  \n",
      "331098          0.0         0.0      0  \n",
      "331099          0.0  86600000.0      0  \n",
      "\n",
      "[328181 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "threshold = pd.to_numeric(np.finfo(np.float64).max)\n",
    "\n",
    "# Remove rows with any value greater than float64\n",
    "df_new = df_new[(df_new <= threshold).all(axis=1)]\n",
    "\n",
    "print(\"DataFrame after removing rows with any value greater than float64:\")\n",
    "print(df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with NaN values: []\n"
     ]
    }
   ],
   "source": [
    "columns_with_na = df_new.columns[df_new.isna().any()].tolist()\n",
    "print(\"Columns with NaN values:\", columns_with_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df_new.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with NaN values: []\n"
     ]
    }
   ],
   "source": [
    "columns_with_na = df_new.columns[df_new.isna().any()].tolist()\n",
    "print(\"Columns with NaN values:\", columns_with_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ld/bwffsrf52mnfq4_gy_k9p9480000gn/T/ipykernel_47556/2687928747.py:1: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  pd.value_counts(df_new['label']).plot.bar()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    235778\n",
       "1     92403\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 62,
     "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": [
    "pd.value_counts(df_new['label']).plot.bar()\n",
    "plt.title('Fraud class histogram')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')\n",
    "df_new['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_x = [\n",
    "    'protocol',\n",
    "'flow_duration',\n",
    "'tot_fwd_pkts',\n",
    "'tot_bwd_pkts',\n",
    "'totlen_fwd_pkts',\n",
    "'totlen_bwd_pkts',\n",
    "'fwd_pkt_len_mean',\n",
    "'fwd_pkt_len_std',\n",
    "'bwd_pkt_len_mean',\n",
    "'flow_byts_s',\n",
    "'flow_pkts_s',\n",
    "'flow_iat_std',\n",
    "'flow_iat_min',\n",
    "'fwd_iat_tot',\n",
    "'fwd_iat_min',\n",
    "'bwd_iat_tot',\n",
    "'bwd_iat_min',\n",
    "'fwd_psh_flags',\n",
    "'fwd_urg_flags',\n",
    "'bwd_pkts_s',\n",
    "'fin_flag_cnt',\n",
    "'rst_flag_cnt',\n",
    "'psh_flag_cnt',\n",
    "'ack_flag_cnt',\n",
    "'urg_flag_cnt',\n",
    "'down_up_ratio',\n",
    "'init_fwd_win_byts',\n",
    "'init_bwd_win_byts',\n",
    "'fwd_seg_size_min',\n",
    "'active_mean',\n",
    "'idle_mean'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X: (328181, 31)\n",
      "Shape of y: (328181,)\n"
     ]
    }
   ],
   "source": [
    "X = np.array(df_new[cols_x])\n",
    "y = np.array(df_new['label'])\n",
    "print('Shape of X: {}'.format(X.shape))\n",
    "print('Shape of y: {}'.format(y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>protocol</th>\n",
       "      <th>flow_duration</th>\n",
       "      <th>tot_fwd_pkts</th>\n",
       "      <th>tot_bwd_pkts</th>\n",
       "      <th>totlen_fwd_pkts</th>\n",
       "      <th>totlen_bwd_pkts</th>\n",
       "      <th>fwd_pkt_len_mean</th>\n",
       "      <th>fwd_pkt_len_std</th>\n",
       "      <th>bwd_pkt_len_mean</th>\n",
       "      <th>flow_byts_s</th>\n",
       "      <th>...</th>\n",
       "      <th>psh_flag_cnt</th>\n",
       "      <th>ack_flag_cnt</th>\n",
       "      <th>urg_flag_cnt</th>\n",
       "      <th>down_up_ratio</th>\n",
       "      <th>init_fwd_win_byts</th>\n",
       "      <th>init_bwd_win_byts</th>\n",
       "      <th>fwd_seg_size_min</th>\n",
       "      <th>active_mean</th>\n",
       "      <th>idle_mean</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>115307855</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1812348.0</td>\n",
       "      <td>56700000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>60997457</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61000000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>61149019</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.530238</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>3530939.0</td>\n",
       "      <td>19200000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>60997555</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61000000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>61997503</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>999909.0</td>\n",
       "      <td>61000000.0</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>331095</th>\n",
       "      <td>0</td>\n",
       "      <td>118494070</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331096</th>\n",
       "      <td>1</td>\n",
       "      <td>114340</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1024</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331097</th>\n",
       "      <td>1</td>\n",
       "      <td>1404866</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>1132</td>\n",
       "      <td>1581</td>\n",
       "      <td>141.5</td>\n",
       "      <td>222.792023</td>\n",
       "      <td>225.857143</td>\n",
       "      <td>1931.145034</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8192</td>\n",
       "      <td>62868</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331098</th>\n",
       "      <td>1</td>\n",
       "      <td>3966663</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>1386</td>\n",
       "      <td>392</td>\n",
       "      <td>99.0</td>\n",
       "      <td>189.204814</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>448.235708</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8192</td>\n",
       "      <td>62652</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331099</th>\n",
       "      <td>1</td>\n",
       "      <td>86637706</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62608</td>\n",
       "      <td>-1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86600000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328181 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        protocol  flow_duration  tot_fwd_pkts  tot_bwd_pkts  totlen_fwd_pkts  \\\n",
       "0              0      115307855             5             0                0   \n",
       "1              0       60997457             2             0                0   \n",
       "2              2       61149019             5             0             1500   \n",
       "3              0       60997555             2             0                0   \n",
       "4              0       61997503             3             0                0   \n",
       "...          ...            ...           ...           ...              ...   \n",
       "331095         0      118494070            31             0                0   \n",
       "331096         1         114340             2             1                0   \n",
       "331097         1        1404866             8             7             1132   \n",
       "331098         1        3966663            14             7             1386   \n",
       "331099         1       86637706             2             0                0   \n",
       "\n",
       "        totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
       "0                     0               0.0         0.000000          0.000000   \n",
       "1                     0               0.0         0.000000          0.000000   \n",
       "2                     0             300.0         0.000000          0.000000   \n",
       "3                     0               0.0         0.000000          0.000000   \n",
       "4                     0               0.0         0.000000          0.000000   \n",
       "...                 ...               ...              ...               ...   \n",
       "331095                0               0.0         0.000000          0.000000   \n",
       "331096                0               0.0         0.000000          0.000000   \n",
       "331097             1581             141.5       222.792023        225.857143   \n",
       "331098              392              99.0       189.204814         56.000000   \n",
       "331099                0               0.0         0.000000          0.000000   \n",
       "\n",
       "        flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
       "0          0.000000  ...             0             0             0   \n",
       "1          0.000000  ...             0             0             0   \n",
       "2         24.530238  ...             0             0             0   \n",
       "3          0.000000  ...             0             0             0   \n",
       "4          0.000000  ...             0             0             0   \n",
       "...             ...  ...           ...           ...           ...   \n",
       "331095     0.000000  ...             0             0             0   \n",
       "331096     0.000000  ...             1             0             0   \n",
       "331097  1931.145034  ...             1             0             0   \n",
       "331098   448.235708  ...             1             0             0   \n",
       "331099     0.000000  ...             0             1             0   \n",
       "\n",
       "        down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  fwd_seg_size_min  \\\n",
       "0                   0                 -1                 -1                 0   \n",
       "1                   0                 -1                 -1                 0   \n",
       "2                   0                 -1                 -1                 8   \n",
       "3                   0                 -1                 -1                 0   \n",
       "4                   0                 -1                 -1                 0   \n",
       "...               ...                ...                ...               ...   \n",
       "331095              0                 -1                 -1                 0   \n",
       "331096              0               1024                  0                20   \n",
       "331097              0               8192              62868                20   \n",
       "331098              0               8192              62652                20   \n",
       "331099              0              62608                 -1                20   \n",
       "\n",
       "        active_mean   idle_mean  label  \n",
       "0         1812348.0  56700000.0      0  \n",
       "1               0.0  61000000.0      0  \n",
       "2         3530939.0  19200000.0      0  \n",
       "3               0.0  61000000.0      0  \n",
       "4          999909.0  61000000.0      0  \n",
       "...             ...         ...    ...  \n",
       "331095          0.0         0.0      0  \n",
       "331096          0.0         0.0      0  \n",
       "331097          0.0         0.0      0  \n",
       "331098          0.0         0.0      0  \n",
       "331099          0.0  86600000.0      0  \n",
       "\n",
       "[328181 rows x 32 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    235778\n",
       "1     92403\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions X_train dataset:  (262544, 31)\n",
      "Number transactions y_train dataset:  (262544,)\n",
      "Number transactions X_test dataset:  (65637, 31)\n",
      "Number transactions y_test dataset:  (65637,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "X_t, X_v, y_t, y_v = train_test_split(X_train, y_train, test_size=0.2)\n",
    "print(\"Number transactions X_train dataset: \", X_train.shape)\n",
    "print(\"Number transactions y_train dataset: \", y_train.shape)\n",
    "print(\"Number transactions X_test dataset: \", X_test.shape)\n",
    "print(\"Number transactions y_test dataset: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '0': 188720\n",
      "Before OverSampling, counts of label '1': 73824\n",
      "After OverSampling, the shape of train_X: (301944, 31)\n",
      "After OverSampling, the shape of train_y: (301944,) \n",
      "\n",
      "After OverSampling, counts of label '0': 150972\n",
      "After OverSampling, counts of label '1': 150972\n"
     ]
    }
   ],
   "source": [
    "print(\"Before OverSampling, counts of label '0': {}\".format(sum(y_train==0)))\n",
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train==1)))\n",
    "\n",
    "\n",
    "sm = SMOTE(random_state=2)\n",
    "X_train_res, y_train_res = sm.fit_resample(X_t, y_t.ravel())\n",
    "\n",
    "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))\n",
    "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape))\n",
    "\n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res==0)))\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res==1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'learning_rate': [0.01, 0.1, 0.2],\n",
    "    'max_depth': [3, 5, 7],\n",
    "    'n_estimators': [50, 100, 200]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n",
      "[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.616 total time=   1.1s\n",
      "[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.616 total time=   1.1s\n",
      "[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.618 total time=   1.2s\n",
      "[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.618 total time=   1.6s\n",
      "[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.617 total time=   1.6s\n",
      "[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.618 total time=   2.6s\n",
      "[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.616 total time=   2.9s\n",
      "[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.616 total time=   2.9s\n",
      "[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.618 total time=   2.9s\n",
      "[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.617 total time=   2.6s\n",
      "[CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.618 total time=   5.2s\n",
      "[CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.617 total time=   5.1s\n",
      "[CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.631 total time=   5.0s\n",
      "[CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.619 total time=   2.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karthiknamboori/Desktop/Hackathons/Vajra/.venv/lib/python3.11/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.633 total time=   4.9s\n",
      "[CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.631 total time=   5.1s\n",
      "[CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.619 total time=   1.7s\n",
      "[CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.641 total time=   1.8s\n",
      "[CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.643 total time=   1.7s\n",
      "[CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.637 total time=   2.0s\n",
      "[CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.621 total time=   4.8s\n",
      "[CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.621 total time=   4.9s\n",
      "[CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.648 total time=   4.9s\n",
      "[CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.651 total time=   4.2s\n",
      "[CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.648 total time=   4.0s\n",
      "[CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.635 total time=   7.0s\n",
      "[CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.638 total time=   7.7s\n",
      "[CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.661 total time=   7.7s\n",
      "[CV 1/5] END learning_rate=0.01, max_depth=7, n_estimators=50;, score=0.627 total time=   2.8s\n",
      "[CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.668 total time=   8.3s\n",
      "[CV 2/5] END learning_rate=0.01, max_depth=7, n_estimators=50;, score=0.628 total time=   3.5s\n",
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      "[CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.632 total time=   1.6s\n",
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      "[CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.777 total time=   5.7s\n",
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      "[CV 1/5] END learning_rate=0.1, max_depth=7, n_estimators=50;, score=0.645 total time=   2.5s\n",
      "[CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.785 total time=   5.7s\n",
      "[CV 2/5] END learning_rate=0.1, max_depth=7, n_estimators=50;, score=0.649 total time=   2.4s\n",
      "[CV 3/5] END learning_rate=0.1, max_depth=7, n_estimators=50;, score=0.737 total time=   2.4s\n",
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      "[CV 3/5] END learning_rate=0.1, max_depth=7, n_estimators=200;, score=0.799 total time=   8.2s\n",
      "[CV 1/5] END learning_rate=0.2, max_depth=3, n_estimators=50;, score=0.638 total time=   1.6s\n",
      "[CV 2/5] END learning_rate=0.2, max_depth=3, n_estimators=50;, score=0.638 total time=   1.7s\n",
      "[CV 4/5] END learning_rate=0.1, max_depth=7, n_estimators=200;, score=0.816 total time=   8.4s\n",
      "[CV 3/5] END learning_rate=0.2, max_depth=3, n_estimators=50;, score=0.695 total time=   1.7s\n",
      "[CV 5/5] END learning_rate=0.1, max_depth=7, n_estimators=200;, score=0.814 total time=   7.7s\n",
      "[CV 5/5] END learning_rate=0.2, max_depth=3, n_estimators=50;, score=0.701 total time=   1.6s\n",
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      "[CV 1/5] END learning_rate=0.2, max_depth=3, n_estimators=100;, score=0.641 total time=   2.4s\n",
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      "[CV 3/5] END learning_rate=0.2, max_depth=3, n_estimators=100;, score=0.732 total time=   2.5s\n",
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      "[CV 1/5] END learning_rate=0.2, max_depth=3, n_estimators=200;, score=0.642 total time=   4.8s\n",
      "[CV 2/5] END learning_rate=0.2, max_depth=3, n_estimators=200;, score=0.648 total time=   5.0s\n",
      "[CV 3/5] END learning_rate=0.2, max_depth=3, n_estimators=200;, score=0.760 total time=   5.1s\n",
      "[CV 1/5] END learning_rate=0.2, max_depth=5, n_estimators=50;, score=0.644 total time=   1.8s\n",
      "[CV 4/5] END learning_rate=0.2, max_depth=3, n_estimators=200;, score=0.780 total time=   4.2s\n",
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      "[CV 2/5] END learning_rate=0.2, max_depth=5, n_estimators=50;, score=0.646 total time=   1.8s\n",
      "[CV 3/5] END learning_rate=0.2, max_depth=5, n_estimators=50;, score=0.741 total time=   1.7s\n",
      "[CV 4/5] END learning_rate=0.2, max_depth=5, n_estimators=50;, score=0.763 total time=   1.6s\n",
      "[CV 5/5] END learning_rate=0.2, max_depth=5, n_estimators=50;, score=0.752 total time=   1.8s\n",
      "[CV 1/5] END learning_rate=0.2, max_depth=5, n_estimators=100;, score=0.645 total time=   3.0s\n",
      "[CV 2/5] END learning_rate=0.2, max_depth=5, n_estimators=100;, score=0.649 total time=   2.9s\n",
      "[CV 3/5] END learning_rate=0.2, max_depth=5, n_estimators=100;, score=0.778 total time=   2.7s\n",
      "[CV 4/5] END learning_rate=0.2, max_depth=5, n_estimators=100;, score=0.792 total time=   2.7s\n",
      "[CV 5/5] END learning_rate=0.2, max_depth=5, n_estimators=100;, score=0.787 total time=   2.8s\n",
      "[CV 1/5] END learning_rate=0.2, max_depth=5, n_estimators=200;, score=0.647 total time=   5.0s\n",
      "[CV 2/5] END learning_rate=0.2, max_depth=5, n_estimators=200;, score=0.653 total time=   5.3s\n",
      "[CV 3/5] END learning_rate=0.2, max_depth=5, n_estimators=200;, score=0.799 total time=   5.9s\n",
      "[CV 4/5] END learning_rate=0.2, max_depth=5, n_estimators=200;, score=0.813 total time=   5.9s\n",
      "[CV 1/5] END learning_rate=0.2, max_depth=7, n_estimators=50;, score=0.648 total time=   2.3s\n",
      "[CV 5/5] END learning_rate=0.2, max_depth=5, n_estimators=200;, score=0.810 total time=   5.5s\n",
      "[CV 2/5] END learning_rate=0.2, max_depth=7, n_estimators=50;, score=0.653 total time=   2.5s\n",
      "[CV 3/5] END learning_rate=0.2, max_depth=7, n_estimators=50;, score=0.778 total time=   2.5s\n",
      "[CV 4/5] END learning_rate=0.2, max_depth=7, n_estimators=50;, score=0.797 total time=   2.2s\n",
      "[CV 5/5] END learning_rate=0.2, max_depth=7, n_estimators=50;, score=0.787 total time=   2.1s\n",
      "[CV 1/5] END learning_rate=0.2, max_depth=7, n_estimators=100;, score=0.650 total time=   3.4s\n",
      "[CV 2/5] END learning_rate=0.2, max_depth=7, n_estimators=100;, score=0.655 total time=   3.5s\n",
      "[CV 3/5] END learning_rate=0.2, max_depth=7, n_estimators=100;, score=0.804 total time=   3.9s\n",
      "[CV 4/5] END learning_rate=0.2, max_depth=7, n_estimators=100;, score=0.813 total time=   3.9s\n",
      "[CV 5/5] END learning_rate=0.2, max_depth=7, n_estimators=100;, score=0.812 total time=   3.6s\n",
      "[CV 1/5] END learning_rate=0.2, max_depth=7, n_estimators=200;, score=0.653 total time=   7.8s\n",
      "[CV 3/5] END learning_rate=0.2, max_depth=7, n_estimators=200;, score=0.820 total time=   7.6s\n",
      "[CV 2/5] END learning_rate=0.2, max_depth=7, n_estimators=200;, score=0.658 total time=   7.7s\n",
      "[CV 4/5] END learning_rate=0.2, max_depth=7, n_estimators=200;, score=0.825 total time=   4.9s\n",
      "[CV 5/5] END learning_rate=0.2, max_depth=7, n_estimators=200;, score=0.826 total time=   4.4s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\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",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
       "             verbose=5)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\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",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
       "             verbose=5)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><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=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><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=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\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",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={'learning_rate': [0.01, 0.1, 0.2],\n",
       "                         'max_depth': [3, 5, 7],\n",
       "                         'n_estimators': [50, 100, 200]},\n",
       "             verbose=5)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GridSearchCV(xgb_model, parameters, cv=5, verbose=5, n_jobs=3)\n",
    "clf.fit(X_train_res, y_train_res.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.2, 'max_depth': 7, 'n_estimators': 200}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_v)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_labels = LE.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75611799881925"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "acc = accuracy_score(y_pred, y_v)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7506589271295153"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred = clf.predict(X_test)\n",
    "acc = accuracy_score(y_test_pred, y_test)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[42019,  5039],\n",
       "       [11327,  7252]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_test_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210035"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_t.size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52509"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_v.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65637"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\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",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
       "             verbose=5)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\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",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1, 0.2],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n",
       "             verbose=5)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><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=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><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=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\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",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={'learning_rate': [0.01, 0.1, 0.2],\n",
       "                         'max_depth': [3, 5, 7],\n",
       "                         'n_estimators': [50, 100, 200]},\n",
       "             verbose=5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['infiltration_01-03-2018.joblib']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(clf, \"infiltration_01-03-2018.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_clf = load(\"bruteforce-web-xss_sql-injection_23-02-2018.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_row = X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_row = current_row.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "prediction = loaded_clf.predict(current_row)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "65347 [0] 1\n",
      "65349 [0] 1\n",
      "65350 [1] 0\n",
      "65351 [0] 1\n",
      "65357 [0] 1\n",
      "65358 [0] 1\n",
      "65364 [0] 1\n",
      "65369 [0] 1\n",
      "65371 [0] 1\n",
      "65376 [0] 1\n",
      "65378 [3] 0\n",
      "65379 [0] 1\n",
      "65382 [0] 1\n",
      "65389 [0] 1\n",
      "65403 [0] 1\n",
      "65406 [2] 0\n",
      "65408 [0] 1\n",
      "65410 [0] 1\n",
      "65412 [0] 1\n",
      "65413 [0] 1\n",
      "65419 [0] 1\n",
      "65422 [0] 1\n",
      "65424 [0] 1\n",
      "65426 [0] 1\n",
      "65438 [0] 1\n",
      "65442 [0] 1\n",
      "65443 [1] 0\n",
      "65447 [0] 1\n",
      "65453 [0] 1\n",
      "65456 [0] 1\n",
      "65465 [0] 1\n",
      "65468 [0] 1\n",
      "65470 [0] 1\n",
      "65471 [0] 1\n",
      "65482 [0] 1\n",
      "65483 [1] 0\n",
      "65485 [0] 1\n",
      "65489 [0] 1\n",
      "65490 [0] 1\n",
      "65498 [0] 1\n",
      "65501 [0] 1\n",
      "65505 [0] 1\n",
      "65510 [0] 1\n",
      "65517 [0] 1\n",
      "65521 [0] 1\n",
      "65523 [1] 0\n",
      "65524 [0] 1\n",
      "65529 [0] 1\n",
      "65530 [3] 0\n",
      "65533 [0] 1\n",
      "65535 [0] 1\n",
      "65536 [0] 1\n",
      "65538 [3] 1\n",
      "65544 [0] 1\n",
      "65554 [0] 1\n",
      "65556 [0] 1\n",
      "65561 [0] 1\n",
      "65563 [0] 1\n",
      "65564 [0] 1\n",
      "65567 [0] 1\n",
      "65568 [0] 1\n",
      "65571 [0] 1\n",
      "65573 [0] 1\n",
      "65575 [0] 1\n",
      "65580 [0] 1\n",
      "65581 [0] 1\n",
      "65583 [0] 1\n",
      "65589 [0] 1\n",
      "65594 [0] 1\n",
      "65595 [0] 1\n",
      "65596 [0] 1\n",
      "65597 [0] 1\n",
      "65598 [0] 1\n",
      "65601 [1] 0\n",
      "65608 [0] 1\n",
      "65617 [0] 1\n",
      "65618 [0] 1\n",
      "65620 [0] 1\n",
      "65622 [0] 1\n",
      "65626 [0] 1\n",
      "65628 [0] 1\n",
      "65636 [0] 1\n",
      "19464\n"
     ]
    }
   ],
   "source": [
    "mis = 0\n",
    "for i in range(len(X_test)):\n",
    "\n",
    "    current_row = X_test[i]\n",
    "    current_row = current_row.reshape(1, -1)\n",
    "    prediction = loaded_clf.predict(current_row)\n",
    "    actual = y_test[i]\n",
    "    if prediction[0] != actual :\n",
    "        mis+=1\n",
    "        print(i, prediction, actual)\n",
    "print(mis)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19464\n"
     ]
    }
   ],
   "source": [
    "print(mis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Encodings:\n",
      "Benign: 0\n",
      "Infilteration: 1\n"
     ]
    }
   ],
   "source": [
    "label_encodings = LE.classes_\n",
    "print(\"Label Encodings:\")\n",
    "for label, encoding in zip(label_encodings, range(len(label_encodings))):\n",
    "    print(f\"{label}: {encoding}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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