Evaluation_of_algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline \n",
    "%config IPCompleter.greedy=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({-1: 149915, 1: 67713})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "### main dataset ###\n",
    "data = pd.read_csv(\"20151219.txt\", sep = \"\\t\", header = None)\n",
    "data.columns = [\"conn len\", \"service\", \"src bytes\", \"dst bytes\", \"conn numb\", \"Same srv rate\", \"Serror rate\", \"Srv serror rate\", \"Dst host count\", \"Dst host srv count\", \"Dst host same src port rate\", \"Dst host serror rate\", \"Dst host srv serror rate\", \"Conn state\", \"IDS detection\", \"Malware detection\", \"Ashula detection\", \"attack_flag\", \"src IP\", \"src port\", \"dst IP\", \"dst port\", \"start time\", \"proto\"]\n",
    "\n",
    "print(Counter(data[\"attack_flag\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform to binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 149915, 0: 67713})\n"
     ]
    }
   ],
   "source": [
    "data.loc[:, \"attack_flag\"] = data.loc[:,\"attack_flag\"].replace(-2, -1)\n",
    "data.loc[:, \"attack_flag\"] = data.loc[:, \"attack_flag\"] * -1\n",
    "data.loc[:, \"attack_flag\"] = data.loc[:,\"attack_flag\"].replace(-1, 0)\n",
    "\n",
    "print(Counter(data[\"attack_flag\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the attacks structure over time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67250054e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_red = data.groupby('start time')['attack_flag'].sum()\n",
    "ax = data_red.plot(x='start time',y='number of attacks')\n",
    "ax.set_ylabel(\"number of attacks\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(217628, 24)\n",
      "(217628, 21)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    149915\n",
       "0     67713\n",
       "Name: attack_flag, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice dataset, drop additional features\n",
    "print(data.shape)\n",
    "data = data.drop(['start time', 'dst IP', 'src IP'], axis=1)\n",
    "print(data.shape)\n",
    "\n",
    "# drop software markers\n",
    "data = data.drop([\"IDS detection\", \"Malware detection\", \"Ashula detection\"], axis=1)\n",
    "data[\"attack_flag\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217628 entries, 0 to 217627\n",
      "Data columns (total 18 columns):\n",
      "conn len                       217628 non-null float64\n",
      "service                        217628 non-null object\n",
      "src bytes                      217628 non-null int64\n",
      "dst bytes                      217628 non-null int64\n",
      "conn numb                      217628 non-null int64\n",
      "Same srv rate                  217628 non-null float64\n",
      "Serror rate                    217628 non-null float64\n",
      "Srv serror rate                217628 non-null float64\n",
      "Dst host count                 217628 non-null int64\n",
      "Dst host srv count             217628 non-null int64\n",
      "Dst host same src port rate    217628 non-null float64\n",
      "Dst host serror rate           217628 non-null float64\n",
      "Dst host srv serror rate       217628 non-null float64\n",
      "Conn state                     217628 non-null object\n",
      "attack_flag                    217628 non-null int64\n",
      "src port                       217628 non-null int64\n",
      "dst port                       217628 non-null int64\n",
      "proto                          217628 non-null object\n",
      "dtypes: float64(7), int64(8), object(3)\n",
      "memory usage: 29.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conn len</th>\n",
       "      <th>service</th>\n",
       "      <th>src bytes</th>\n",
       "      <th>dst bytes</th>\n",
       "      <th>conn numb</th>\n",
       "      <th>Same srv rate</th>\n",
       "      <th>Serror rate</th>\n",
       "      <th>Srv serror rate</th>\n",
       "      <th>Dst host count</th>\n",
       "      <th>Dst host srv count</th>\n",
       "      <th>Dst host same src port rate</th>\n",
       "      <th>Dst host serror rate</th>\n",
       "      <th>Dst host srv serror rate</th>\n",
       "      <th>Conn state</th>\n",
       "      <th>attack_flag</th>\n",
       "      <th>src port</th>\n",
       "      <th>dst port</th>\n",
       "      <th>proto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217618</th>\n",
       "      <td>0.410043</td>\n",
       "      <td>other</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>6338</td>\n",
       "      <td>17989</td>\n",
       "      <td>udp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217619</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OTH</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217620</th>\n",
       "      <td>1.215994</td>\n",
       "      <td>other</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>14015</td>\n",
       "      <td>17989</td>\n",
       "      <td>udp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217621</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>dns</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>44074</td>\n",
       "      <td>53</td>\n",
       "      <td>udp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217622</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>49263</td>\n",
       "      <td>25</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217623</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>58398</td>\n",
       "      <td>25</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217624</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>55373</td>\n",
       "      <td>23</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217625</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>33244</td>\n",
       "      <td>25</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217626</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>47476</td>\n",
       "      <td>25</td>\n",
       "      <td>tcp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217627</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S0</td>\n",
       "      <td>1</td>\n",
       "      <td>10106</td>\n",
       "      <td>17989</td>\n",
       "      <td>udp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        conn len service  src bytes  dst bytes  conn numb  Same srv rate  \\\n",
       "217618  0.410043   other        150          0          0            0.0   \n",
       "217619  0.000000   other          0          0          0            0.0   \n",
       "217620  1.215994   other         60          0          0            0.0   \n",
       "217621  0.000000     dns          0          0          0            0.0   \n",
       "217622  0.000000   other          0          0          0            0.0   \n",
       "217623  0.000000   other          0          0          1            1.0   \n",
       "217624  0.000000   other          0          0          0            0.0   \n",
       "217625  0.000000   other          0          0          0            0.0   \n",
       "217626  0.000000   other          0          0          1            1.0   \n",
       "217627  0.000000   other          0          0          0            0.0   \n",
       "\n",
       "        Serror rate  Srv serror rate  Dst host count  Dst host srv count  \\\n",
       "217618          0.0              1.0               0                   0   \n",
       "217619          0.0              1.0               0                   0   \n",
       "217620          0.0              0.0               0                   0   \n",
       "217621          0.0              0.0               0                   0   \n",
       "217622          0.0              1.0               0                   0   \n",
       "217623          1.0              1.0               0                   1   \n",
       "217624          0.0              1.0               2                   2   \n",
       "217625          0.0              1.0               0                   0   \n",
       "217626          1.0              1.0               0                   1   \n",
       "217627          0.0              1.0               0                   0   \n",
       "\n",
       "        Dst host same src port rate  Dst host serror rate  \\\n",
       "217618                          0.0                   0.0   \n",
       "217619                          0.0                   0.0   \n",
       "217620                          0.0                   0.0   \n",
       "217621                          0.0                   0.0   \n",
       "217622                          0.0                   0.0   \n",
       "217623                          0.0                   0.0   \n",
       "217624                          1.0                   1.0   \n",
       "217625                          0.0                   0.0   \n",
       "217626                          0.0                   0.0   \n",
       "217627                          0.0                   0.0   \n",
       "\n",
       "        Dst host srv serror rate Conn state  attack_flag  src port  dst port  \\\n",
       "217618                       0.0         S0            1      6338     17989   \n",
       "217619                       0.0        OTH            1        11         0   \n",
       "217620                       0.0         S0            1     14015     17989   \n",
       "217621                       0.0         S0            1     44074        53   \n",
       "217622                       0.0         S0            1     49263        25   \n",
       "217623                       1.0         S0            1     58398        25   \n",
       "217624                       1.0         S0            1     55373        23   \n",
       "217625                       0.0         S0            1     33244        25   \n",
       "217626                       1.0         S0            1     47476        25   \n",
       "217627                       0.0         S0            1     10106     17989   \n",
       "\n",
       "       proto  \n",
       "217618   udp  \n",
       "217619  icmp  \n",
       "217620   udp  \n",
       "217621   udp  \n",
       "217622   tcp  \n",
       "217623   tcp  \n",
       "217624   tcp  \n",
       "217625   tcp  \n",
       "217626   tcp  \n",
       "217627   udp  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>conn len</th>\n",
       "      <th>src bytes</th>\n",
       "      <th>dst bytes</th>\n",
       "      <th>conn numb</th>\n",
       "      <th>Same srv rate</th>\n",
       "      <th>Serror rate</th>\n",
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       "      <th>Dst host serror rate</th>\n",
       "      <th>Dst host srv serror rate</th>\n",
       "      <th>attack_flag</th>\n",
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       "      <th>dst port</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
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       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
       "      <td>217628.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.855259</td>\n",
       "      <td>56.899333</td>\n",
       "      <td>122.064555</td>\n",
       "      <td>2.952263</td>\n",
       "      <td>0.417481</td>\n",
       "      <td>0.089564</td>\n",
       "      <td>0.542215</td>\n",
       "      <td>29.491720</td>\n",
       "      <td>31.876730</td>\n",
       "      <td>0.071694</td>\n",
       "      <td>0.066464</td>\n",
       "      <td>0.146028</td>\n",
       "      <td>0.688859</td>\n",
       "      <td>34292.130006</td>\n",
       "      <td>3550.084736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.085410</td>\n",
       "      <td>519.846915</td>\n",
       "      <td>748.123612</td>\n",
       "      <td>5.519918</td>\n",
       "      <td>0.491905</td>\n",
       "      <td>0.284547</td>\n",
       "      <td>0.467622</td>\n",
       "      <td>44.351499</td>\n",
       "      <td>43.928101</td>\n",
       "      <td>0.256081</td>\n",
       "      <td>0.248495</td>\n",
       "      <td>0.350052</td>\n",
       "      <td>0.462961</td>\n",
       "      <td>19708.835701</td>\n",
       "      <td>10869.634486</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12375.000000</td>\n",
       "      <td>25.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39682.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.013333</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50473.000000</td>\n",
       "      <td>1433.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>563.012764</td>\n",
       "      <td>88066.000000</td>\n",
       "      <td>157723.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65535.000000</td>\n",
       "      <td>65498.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            conn len      src bytes      dst bytes      conn numb  \\\n",
       "count  217628.000000  217628.000000  217628.000000  217628.000000   \n",
       "mean        0.855259      56.899333     122.064555       2.952263   \n",
       "std         3.085410     519.846915     748.123612       5.519918   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000290       0.000000       0.000000       0.000000   \n",
       "75%         0.013333      45.000000      61.000000       5.000000   \n",
       "max       563.012764   88066.000000  157723.000000      84.000000   \n",
       "\n",
       "       Same srv rate    Serror rate  Srv serror rate  Dst host count  \\\n",
       "count  217628.000000  217628.000000    217628.000000   217628.000000   \n",
       "mean        0.417481       0.089564         0.542215       29.491720   \n",
       "std         0.491905       0.284547         0.467622       44.351499   \n",
       "min         0.000000       0.000000         0.000000        0.000000   \n",
       "25%         0.000000       0.000000         0.000000        0.000000   \n",
       "50%         0.000000       0.000000         0.750000        0.000000   \n",
       "75%         1.000000       0.000000         1.000000       96.000000   \n",
       "max         1.000000       1.000000         1.000000      100.000000   \n",
       "\n",
       "       Dst host srv count  Dst host same src port rate  Dst host serror rate  \\\n",
       "count       217628.000000                217628.000000         217628.000000   \n",
       "mean            31.876730                     0.071694              0.066464   \n",
       "std             43.928101                     0.256081              0.248495   \n",
       "min              0.000000                     0.000000              0.000000   \n",
       "25%              0.000000                     0.000000              0.000000   \n",
       "50%              1.000000                     0.000000              0.000000   \n",
       "75%             96.000000                     0.000000              0.000000   \n",
       "max            100.000000                     1.000000              1.000000   \n",
       "\n",
       "       Dst host srv serror rate    attack_flag       src port       dst port  \n",
       "count             217628.000000  217628.000000  217628.000000  217628.000000  \n",
       "mean                   0.146028       0.688859   34292.130006    3550.084736  \n",
       "std                    0.350052       0.462961   19708.835701   10869.634486  \n",
       "min                    0.000000       0.000000       0.000000       0.000000  \n",
       "25%                    0.000000       0.000000   12375.000000      25.000000  \n",
       "50%                    0.000000       1.000000   39682.000000      53.000000  \n",
       "75%                    0.000000       1.000000   50473.000000    1433.000000  \n",
       "max                    1.000000       1.000000   65535.000000   65498.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217628, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing NaNs\n",
    "data = data.dropna()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S0        127464\n",
       "SF         59944\n",
       "RSTO       11476\n",
       "OTH         8640\n",
       "REJ         7998\n",
       "RSTRH        733\n",
       "RSTOS0       634\n",
       "SHR          381\n",
       "RSTR         304\n",
       "S1            52\n",
       "SH             2\n",
       "Name: Conn state, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Conn state\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other    126080\n",
       "dns       61990\n",
       "sip       16519\n",
       "ssh       11518\n",
       "snmp        665\n",
       "smtp        495\n",
       "rdp         328\n",
       "dhcp         20\n",
       "http         11\n",
       "ssl           2\n",
       "Name: service, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"service\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tcp     119162\n",
       "udp      93548\n",
       "icmp      4918\n",
       "Name: proto, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"proto\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# a bit of feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## port numbers classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = 1023  # well-known port numbers\n",
    "t2 = 49151 # registered ports\n",
    "t3 = 65535 # client ports\n",
    "\n",
    "def wk(data_row):\n",
    "    if (data_row[\"src port\"] <= t1): \n",
    "        value = 1\n",
    "    elif ((data_row[\"src port\"] > t1) and (data_row[\"src port\"] <= t2)):\n",
    "        value = 0\n",
    "    elif ((data_row[\"src port\"] > t2) and (data_row[\"src port\"] <= t3)):\n",
    "        value = 0\n",
    "    return value\n",
    "\n",
    "def reg(data_row):\n",
    "    if (data_row[\"src port\"] <= t1): \n",
    "        value = 0\n",
    "    elif ((data_row[\"src port\"] > t1) and (data_row[\"src port\"] <= t2)):\n",
    "        value = 1\n",
    "    elif ((data_row[\"src port\"] > t2) and (data_row[\"src port\"] <= t3)):\n",
    "        value = 0\n",
    "    return value\n",
    "\n",
    "def cli(data_row):\n",
    "    if (data_row[\"src port\"] <= t1): \n",
    "        value = 0\n",
    "    elif ((data_row[\"src port\"] > t1) and (data_row[\"src port\"] <= t2)):\n",
    "        value = 0\n",
    "    elif ((data_row[\"src port\"] > t2) and (data_row[\"src port\"] <= t3)):\n",
    "        value = 1\n",
    "    return value\n",
    "\n",
    "data[\"well-known src pool\"] = data.apply(wk, axis=1)\n",
    "data[\"registered src pool\"] = data.apply(reg, axis=1)\n",
    "data[\"cli src pool\"] = data.apply(cli, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 211216, 1: 6412})\n",
      "Counter({1: 150534, 0: 67094})\n",
      "Counter({0: 156946, 1: 60682})\n"
     ]
    }
   ],
   "source": [
    "print(Counter(data[\"well-known src pool\"]))\n",
    "print(Counter(data[\"registered src pool\"]))\n",
    "print(Counter(data[\"cli src pool\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def wk(data_row):\n",
    "#     if (data_row[\"dst port\"] <= t1): \n",
    "#         value = 1\n",
    "#     elif ((data_row[\"dst port\"] > t1) and (data_row[\"dst port\"] <= t2)):\n",
    "#         value = 0\n",
    "#     elif ((data_row[\"dst port\"] > t2) and (data_row[\"dst port\"] <= t3)):\n",
    "#         value = 0\n",
    "#     return value\n",
    "\n",
    "# def reg(data_row):\n",
    "#     if (data_row[\"dst port\"] <= t1): \n",
    "#         value = 0\n",
    "#     elif ((data_row[\"dst port\"] > t1) and (data_row[\"dst port\"] <= t2)):\n",
    "#         value = 1\n",
    "#     elif ((data_row[\"dst port\"] > t2) and (data_row[\"dst port\"] <= t3)):\n",
    "#         value = 0\n",
    "#     return value\n",
    "\n",
    "# def cli(data_row):\n",
    "#     if (data_row[\"dst port\"] <= t1): \n",
    "#         value = 0\n",
    "#     elif ((data_row[\"dst port\"] > t1) and (data_row[\"dst port\"] <= t2)):\n",
    "#         value = 0\n",
    "#     elif ((data_row[\"dst port\"] > t2) and (data_row[\"dst port\"] <= t3)):\n",
    "#         value = 1\n",
    "#     return value\n",
    "\n",
    "# data[\"well-known dst pool\"] = data.apply(wk, axis=1)\n",
    "# data[\"registered dst pool\"] = data.apply(reg, axis=1)\n",
    "# data[\"cli dst pool\"] = data.apply(cli, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(Counter(data[\"well-known dst pool\"]))\n",
    "# print(Counter(data[\"registered dst pool\"]))\n",
    "# print(Counter(data[\"cli dst pool\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 217628 entries, 0 to 217627\n",
      "Data columns (total 21 columns):\n",
      "conn len                       217628 non-null float64\n",
      "service                        217628 non-null object\n",
      "src bytes                      217628 non-null int64\n",
      "dst bytes                      217628 non-null int64\n",
      "conn numb                      217628 non-null int64\n",
      "Same srv rate                  217628 non-null float64\n",
      "Serror rate                    217628 non-null float64\n",
      "Srv serror rate                217628 non-null float64\n",
      "Dst host count                 217628 non-null int64\n",
      "Dst host srv count             217628 non-null int64\n",
      "Dst host same src port rate    217628 non-null float64\n",
      "Dst host serror rate           217628 non-null float64\n",
      "Dst host srv serror rate       217628 non-null float64\n",
      "Conn state                     217628 non-null object\n",
      "attack_flag                    217628 non-null int64\n",
      "src port                       217628 non-null int64\n",
      "dst port                       217628 non-null int64\n",
      "proto                          217628 non-null object\n",
      "well-known src pool            217628 non-null int64\n",
      "registered src pool            217628 non-null int64\n",
      "cli src pool                   217628 non-null int64\n",
      "dtypes: float64(7), int64(11), object(3)\n",
      "memory usage: 36.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>src port</th>\n",
       "      <th>dst port</th>\n",
       "      <th>well-known src pool</th>\n",
       "      <th>registered src pool</th>\n",
       "      <th>cli src pool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40061</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53488</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35051</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1408</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55244</td>\n",
       "      <td>995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44544</td>\n",
       "      <td>995</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50678</td>\n",
       "      <td>5900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35051</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50678</td>\n",
       "      <td>5900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50678</td>\n",
       "      <td>5900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>59625</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>42626</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2194</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7432</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7206</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55133</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60169</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>63030</td>\n",
       "      <td>2222</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31905</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>59625</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>36939</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7432</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25385</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>36877</td>\n",
       "      <td>2332</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7206</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56346</td>\n",
       "      <td>8888</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>37854</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>56248</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>38273</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3026</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    src port  dst port  well-known src pool  registered src pool  cli src pool\n",
       "0      40061       111                    0                    1             0\n",
       "1      53488        80                    0                    0             1\n",
       "2      35051        23                    0                    1             0\n",
       "3       1408       445                    0                    1             0\n",
       "4      55244       995                    0                    0             1\n",
       "5      44544       995                    0                    1             0\n",
       "6      50678      5900                    0                    0             1\n",
       "7      35051        23                    0                    1             0\n",
       "8      50678      5900                    0                    0             1\n",
       "9      50678      5900                    0                    0             1\n",
       "10     59625        21                    0                    0             1\n",
       "11     42626       111                    0                    1             0\n",
       "12      2194       445                    0                    1             0\n",
       "13      7432        22                    0                    1             0\n",
       "14      7206        80                    0                    1             0\n",
       "15     55133        53                    0                    0             1\n",
       "16     60169        80                    0                    0             1\n",
       "17     63030      2222                    0                    0             1\n",
       "18     31905       445                    0                    1             0\n",
       "19     59625        21                    0                    0             1\n",
       "20     36939       111                    0                    1             0\n",
       "21      7432        22                    0                    1             0\n",
       "22     25385       445                    0                    1             0\n",
       "23     36877      2332                    0                    1             0\n",
       "24      7206        80                    0                    1             0\n",
       "25     56346      8888                    0                    0             1\n",
       "26     37854       445                    0                    1             0\n",
       "27     56248       445                    0                    0             1\n",
       "28     38273      1080                    0                    1             0\n",
       "29      3026       445                    0                    1             0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data.loc[:, [\"src port\", \"dst port\", \"well-known src pool\", \"registered src pool\", \"cli src pool\", \"well-known dst pool\", \"registered dst pool\", \"cli dst pool\"]].head(30)\n",
    "data.loc[:, [\"src port\", \"dst port\", \"well-known src pool\", \"registered src pool\", \"cli src pool\"]].head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change port number info to categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"src port\"] = data[\"src port\"].apply(str)\n",
    "data[\"dst port\"] = data[\"dst port\"].apply(str)\n",
    "data['src port'].nunique()\n",
    "data['dst port'].nunique()\n",
    "\n",
    "thresh1 = 125\n",
    "thresh2 = 125\n",
    "counts1 = data['src port'].value_counts()\n",
    "counts2 = data['dst port'].value_counts()\n",
    "\n",
    "repl1 = counts1[counts1 <= thresh1].index\n",
    "repl2 = counts2[counts2 <= thresh2].index\n",
    "\n",
    "#hot one encoding for src port numbers\n",
    "data = pd.concat([data, pd.get_dummies(data[\"src port\"].replace(repl1, 'other_src'), prefix=\"is_src\")], axis=1)\n",
    "\n",
    "#hot one encoding for dst port numbers\n",
    "data = pd.concat([data, pd.get_dummies(data[\"dst port\"].replace(repl2, 'other_dst'), prefix=\"is_dst\")], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217628, 214)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hot one encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>conn len</th>\n",
       "      <th>service</th>\n",
       "      <th>src bytes</th>\n",
       "      <th>dst bytes</th>\n",
       "      <th>conn numb</th>\n",
       "      <th>Same srv rate</th>\n",
       "      <th>Serror rate</th>\n",
       "      <th>Srv serror rate</th>\n",
       "      <th>Dst host count</th>\n",
       "      <th>Dst host srv count</th>\n",
       "      <th>...</th>\n",
       "      <th>state_REJ</th>\n",
       "      <th>state_RSTO</th>\n",
       "      <th>state_RSTOS0</th>\n",
       "      <th>state_RSTR</th>\n",
       "      <th>state_RSTRH</th>\n",
       "      <th>state_S0</th>\n",
       "      <th>state_S1</th>\n",
       "      <th>state_SF</th>\n",
       "      <th>state_SH</th>\n",
       "      <th>state_SHR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217618</th>\n",
       "      <td>0.410043</td>\n",
       "      <td>other</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217619</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217620</th>\n",
       "      <td>1.215994</td>\n",
       "      <td>other</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217621</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>dns</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217622</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217623</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217624</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217625</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217626</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217627</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 238 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        conn len service  src bytes  dst bytes  conn numb  Same srv rate  \\\n",
       "217618  0.410043   other        150          0          0            0.0   \n",
       "217619  0.000000   other          0          0          0            0.0   \n",
       "217620  1.215994   other         60          0          0            0.0   \n",
       "217621  0.000000     dns          0          0          0            0.0   \n",
       "217622  0.000000   other          0          0          0            0.0   \n",
       "217623  0.000000   other          0          0          1            1.0   \n",
       "217624  0.000000   other          0          0          0            0.0   \n",
       "217625  0.000000   other          0          0          0            0.0   \n",
       "217626  0.000000   other          0          0          1            1.0   \n",
       "217627  0.000000   other          0          0          0            0.0   \n",
       "\n",
       "        Serror rate  Srv serror rate  Dst host count  Dst host srv count  \\\n",
       "217618          0.0              1.0               0                   0   \n",
       "217619          0.0              1.0               0                   0   \n",
       "217620          0.0              0.0               0                   0   \n",
       "217621          0.0              0.0               0                   0   \n",
       "217622          0.0              1.0               0                   0   \n",
       "217623          1.0              1.0               0                   1   \n",
       "217624          0.0              1.0               2                   2   \n",
       "217625          0.0              1.0               0                   0   \n",
       "217626          1.0              1.0               0                   1   \n",
       "217627          0.0              1.0               0                   0   \n",
       "\n",
       "          ...      state_REJ  state_RSTO  state_RSTOS0 state_RSTR  \\\n",
       "217618    ...              0           0             0          0   \n",
       "217619    ...              0           0             0          0   \n",
       "217620    ...              0           0             0          0   \n",
       "217621    ...              0           0             0          0   \n",
       "217622    ...              0           0             0          0   \n",
       "217623    ...              0           0             0          0   \n",
       "217624    ...              0           0             0          0   \n",
       "217625    ...              0           0             0          0   \n",
       "217626    ...              0           0             0          0   \n",
       "217627    ...              0           0             0          0   \n",
       "\n",
       "        state_RSTRH state_S0 state_S1 state_SF  state_SH  state_SHR  \n",
       "217618            0        1        0        0         0          0  \n",
       "217619            0        0        0        0         0          0  \n",
       "217620            0        1        0        0         0          0  \n",
       "217621            0        1        0        0         0          0  \n",
       "217622            0        1        0        0         0          0  \n",
       "217623            0        1        0        0         0          0  \n",
       "217624            0        1        0        0         0          0  \n",
       "217625            0        1        0        0         0          0  \n",
       "217626            0        1        0        0         0          0  \n",
       "217627            0        1        0        0         0          0  \n",
       "\n",
       "[10 rows x 238 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### hot one encoding ###\n",
    "\n",
    "# hot one encoding for service\n",
    "data = pd.concat([data, pd.get_dummies(data[\"service\"], prefix=\"is\")], axis=1)\n",
    "data.tail(10)\n",
    "\n",
    "#hot one encoding for proto - commented due to reduced column number\n",
    "data = pd.concat([data, pd.get_dummies(data[\"proto\"], prefix=\"is\")], axis=1)\n",
    "data.tail(10)\n",
    "\n",
    "# hot one encoding for conn state\n",
    "data = pd.concat([data, pd.get_dummies(data[\"Conn state\"], prefix=\"state\")], axis=1)\n",
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 217628 entries, 0 to 217627\n",
      "Columns: 238 entries, conn len to state_SHR\n",
      "dtypes: float64(7), int64(9), object(5), uint8(217)\n",
      "memory usage: 81.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6725487240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67211c46a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6718f1d470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hisotgrams\n",
    "\n",
    "#print(max(data['conn len']))\n",
    "#ax = data['conn len'].plot.hist(bins=500, xlim=[0,20], title=\"Connection length - histogram\")\n",
    "ax = data['conn len'].plot.hist(bins=2000, xlim=[0,20])\n",
    "ax.set_xlabel(\"connection length [s]\")\n",
    "plt.show()\n",
    "\n",
    "#print(max(data['src bytes']))\n",
    "#ax = data['src bytes'].plot.hist(bins=500, xlim=[0,1500], title=\"Src bytes size - histogram\")\n",
    "ax = data['src bytes'].plot.hist(bins=2000, xlim=[0,1500])\n",
    "ax.set_xlabel(\"source bytes\")\n",
    "plt.show()\n",
    "\n",
    "#print(max(data['dst bytes']))\n",
    "# ax = data['dst bytes'].plot.hist(bins=500, xlim=[0,2000], title=\"Dst bytes size - histogram\")\n",
    "ax = data['dst bytes'].plot.hist(bins=2000, xlim=[0,2000])\n",
    "ax.set_xlabel(\"destination bytes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TaXh1KhKk+XsCz+/IeszMrD7KufponKSxaXg08A6gm2yP4b2p2UzgljS8KI2T5v8gVSkzMyu44QM3YX9gQTqvMAy4MSJulfQLYKGkzwP/BbSn9u3Av0taRbaH8P4a5DYzsxoYsChExIPAX/Yx/RHgLX1MXw+cUpV0ZmY2qPyNZjMzy7komJlZzkXBzMxyLgpmZpZzUTAzs5yLgpmZ5VwUzMws56JgZmY5FwUzM8u5KJiZWc5FwczMci4KZmaWc1EwM7Oci4KZmeVcFMzMLOeiYGZmORcFMzPLuSiYmVnORcHMzHIuCmZmlnNRMDOznIuCmZnlXBTMzCznomBmZjkXBaupjo4OJk2axLRp05g0aRIdHR31jmRm2+GiYDXT0dHBueeey7p164gI1q1bx7nnnuvCYFZgAxYFSQdJ6pTULWmlpHPT9L0l3SXp1+nvXmm6JH1N0ipJD0o6stYPworp/PPPp6Ghgfnz53PnnXcyf/58GhoaOP/88+sdzcz6Uc6ewibgExHRBBwDfEzSYcCFwNKIOBRYmsYBjgcOTbezgW9WPbXtElavXs0111xDc3Mzw4cPp7m5mWuuuYbVq1fXO5qZ9WPAohART0fET9PwGqAbOBA4CViQmi0ATk7DJwHXROZeYKyk/aue3MzMqk4RUX5jaSJwDzAJeCIixpbMeyEi9pJ0K3BZRCxL05cCF0TEA72WdTbZngTjx4+fvHDhwooeyNq1a2lsbKxoGdVSlCz1znHKKaewZcsWPv3pT/Oa17yGRx99lM9//vMMGzaM73znO3XLVe/tUrQcUJwsRckBxclSrRzNzc3LI+KoARtGRFk3oBFYDrwnjf+h1/wX0t/FwJSS6UuBydtb9uTJk6NSnZ2dFS+jWoqSpd45rr/++hg3blxMnDgxhg0bFhMnToxx48bF9ddfX9dc9d4uPYqSI6I4WYqSI6I4WaqVA3ggyvisL+vqI0kjgJuA6yLie2nyMz2HhdLfZ9P01cBBJXefADxVznrslWXGjBnMnTuXMWPGADBmzBjmzp3LjBkz6pzMzPpTztVHAtqB7oj4csmsRcDMNDwTuKVk+unpKqRjgBcj4ukqZjYzsxoZXkabvwY+BKyQ9LM07SLgMuBGSS3AE8Apad5twAnAKuAl4MyqJrZdRkdHB7Nnz6a9vZ3NmzfT0NBAS0sLgPcWzApqwKIQ2Qlj9TN7Wh/tA/hYhbnsFaCtrY329naam5vp6upi6tSptLe309ra6qJgVlD+RrPVTHd3N1OmTNlq2pQpU+ju7q5TIjMbiIuC1UxTUxPLli3batqyZctoamqqUyIzG4iLgtXM7NmzaWlpobOzk02bNtHZ2UlLSwuzZ8+udzQz60c5J5rNdkrPeYPW1la6u7tpamqira3N5xPMCsx7ClZTM2bM4KGHHmLp0qU89NBDLggF467NrTcXBbMhqrRrc8BdmxvgomA2ZJ1//vkMHz6c+fPnc8cddzB//nyGDx/urs2HOBcFsyFq9erVLFiwYKuuzRcsWOCuzYc4FwUzM8u5KJgNURMmTOD000/f6pLh008/nQkTJtQ7mtWRi4LVlK9uKa7LL7+czZs3M2vWLI499lhmzZrF5s2bufzyy+sdzerI31OwmnGHeMXW8xy0tbUhiTFjxvCFL3zBz80Q5z0Fq5nSDvF6TmS2t7fT1tZW72iW+Hsk1puLgtWMO8Qz2/W4KFjNuEM8s12PzylYzcyePZtTTz2VMWPG8Pjjj3PwwQezbt065s6dW+9oZtYP7ynYoMh+1dXMis5FwWqmra2NG264gUcffZSlS5fy6KOPcsMNN/hEs1mBuShYzfhEs9mux0WhilpbWxk1ahTNzc2MGjWK1tbWekeqK59oNtv1+ERzlbS2tnLllVcyZ84cDjvsMH7xi19wwQUXADBv3rw6p6uPnl9e6/nyWs8vr/nwkVlxuShUybe//W3mzJnDeeedR1dXF+eddx4AF1100ZAtCjNmzOBHP/oRxx9/PBs2bGDkyJGcddZZ/oKUWYH58FGVbNiwgXPOOWeraeeccw4bNmyoU6L66+joYPHixSxZsoS77rqLJUuWsHjxYvd/ZFZgLgpVMnLkSK688sqtpl155ZWMHDly0DJI2urW3Ny8zbTBvDTU3VyY7Xp8+KhKzjrrrPwcwmGHHcaXv/xlLrjggm32HmopIrYan3jhYh677F2Dtv7efPWR2a7HRaFKes4bXHTRRfnx83POOWfInk+A7Oqjz372s9x88810d3fT1NTEySef7KuPzApswMNHkuZLelbSQyXT9pZ0l6Rfp797pemS9DVJqyQ9KOnIWoYvmnnz5rF+/Xo6OztZv379kC4IAM3NzcyZM4dZs2axePFiZs2axZw5c2hubq53NDPrRzl7ClcDXweuKZl2IbA0Ii6TdGEavwA4Hjg03Y4Gvpn+2hDU2dnJXnvtxSc+8Yl82rhx4+js7KxjKjPbngGLQkTcI2lir8knAVPT8AKgi6wonARcE9nB7XsljZW0f0Q8Xa3AtutYuXLlNtOee+45nnvuuTqkMbNy7Ow5hfE9H/QR8bSkV6XpBwJPlrRbnaZtUxQknQ2cDTB+/Hi6urp2Mkpm7dq1FS+jUkuXLuXaa6/liSee4NWvfjUf/OAHmTZtWl0z1XubAAwbNoyIQBJbtmwB6purCK+VIuWA4mQpSg6oX5ZyD6/Wao+72iea+7reMfqYRkR8C/gWwFFHHRVTp06taMVdXV1UuoxKdHR0cN111zF//vytfnrysMMOq9+XtW5fXNdt0mPcuHE8++yzjBs3jmeeeQagrrnq/VopWg4oTpai5IDBy3L4gsO3Gp909aSy7tf6+Nbd6KyYuaIqeXa2KDzTc1hI0v7As2n6auCgknYTgKcqCbirKL0mv+fF1N7eTmtr65D+Bm9DQwOjR48GYPTo0TQ0NLB58+Y6pzIrjoE+zAe7UO7sl9cWATPT8EzglpLpp6erkI4BXhwq5xN8TX7fNm/ezBFHHMH3vvc9jjjiCBcEs4IbcE9BUgfZSeV9Ja0GLgYuA26U1AI8AZySmt8GnACsAl4CzqxB5kJqamrife97H0uWLMm/p3D88cf7mnxg0aJFLFq0qN4xzKwM5Vx91N+xj23OoKarjj5Waahd0YEHHsjNN9/MRz7yEU444QRuu+02vvnNb3LsscfWO1rd7L333jz//PN9TjezYnLfR1Vy9913c9ppp3HPPfdw0kkncc8993Daaadx99131zta3fTXGeBQ7iTQrOjczUWVbNiwgW9961vsvvvu+Ymhl156ieuuu65m63zjZ+/kxZc3brfNxAsXb3f+nqNH8POLa7M3s27duh2abmb156JQJT29pPb8jgLUvpfUF1/euN0O78q5amGgolENEZFnGcxeWs1sx7koVEkRekktqs7OzvyX18ys2FwUqmTevHncfffdW/Xzc/jhhw/5TvEg+wW2Z599lle96lUDNzazuvKJ5ippbW1lxYqtv4SyYsUKWltb+7nH0PHMM88QEfm3mc2suFwUquSKK64AYK+99trqb8/0oejjH//4Dk03s/pzUaiSiKCxsZGbbrqJu+66i5tuuonGxsZtfg1tKPn617++Q9PNrP58TqGKNm3axNvf/vZ8fNSoUXVMY2a247ynUEXr16/f7riZWdG5KNiQ0dHRwaRJk5g2bRqTJk2io6Oj3pHqQtI2t+bm5m2m2dDkomBDQkdHBzNnzmTlypVs2bKFlStXMnPmzCFZGCJim9vBF9y6zTQbmlwUbEg488wz2bhxI9OnT+f73/8+06dPZ+PGjZx55pDpyNesLC4KNiRs2LCBE088kVtuuYWxY8dyyy23cOKJJ7pzPrNeXBRsyDjkkEO2OqdwyCGH1DuSWeG4KNiQMXfuXGbNmsXixYuZNWsWc+fOrXckK6ChfkGCv6dgQ8LIkSPZsGEDF198MWvXrqWxsTGfbtajo6OD2bNn097ezubNm2loaKClpQVgyPzWuvcUbEjYuDH73Ym1a9du9bdnuhlAW1sb7e3tNDc3M3z4cJqbm2lvb6etra3e0QaNi4INCVu2bNmh6TY0dXd3M2XKlK2mTZkyhe7u7jolGnw+fGT2ClfOL/TB9n9wqZa/0FckTU1NLFu2jObm5nzasmXLaGpqqmOqweWiYENKY2Mj69atY8yYMfkhpFe6LRM/wR6VLgOAFQO02vXNnj2bd77znVsdVhwxYgQLFiyoY6rB5aJgQ8qaNWuG3E+Drum+bLs/2woD/3TrYPxsaxFceumlbNy4cZt/Hi699FKfaDZ7Jdpvv/147LHH2G+//eodxQpoxYoVTJ8+nTVr1vCDH/yANWvWMH369G1+QOuVzHsKNqQ888wz7trCtqu9vX2b8XHjxtUpzeBzUbBXtHIOEUlyB3CWG0oFoC81KQqSjgPmAg3AVRFxWS3WY9Zb7yttDr7g1nz4qfaPsfF3j+fjI/Y9mANasp9LLT1mPlSutDHrS9WLgqQG4Arg74DVwP2SFkXEL6q9Liuugf5Dr9V/59u70ub1X9oDmNRr6oXbLgMYClfamPWlFnsKbwFWRcQjAJIWAicBLgpDSERstzDU6nDNmu7Kd0r3HD2iCknMdk21KAoHAk+WjK8Gjq7Beqwgyv1yVKm+LnGsxmGb3pdelnvZqc8pDC2HLzi8z+mTru69J7n9+6yY+crbo1S13wySTgHeGREfTuMfAt4SEa292p0NnA0wfvz4yQsXLtzuclsfb93u/HLNO3heRfevVg4oTpai5IDKs5SjtEO8WirK83PG7eu2Gn98zoll3a/0fMyYEXDFtDEV5YDibJP+lH6T+fzzz+fyyy/Pxzs7O2uyzoFU6/Xa3Ny8PCKOGrBhXz/NV8kNeCtwR8n4p4BPbe8+kydPjkp1dnZWvIxKAP3e6qXe26SUs2yrKDkiipOl3jleye9j4IEo4zO8Fl9eux84VNJrJO0GvB9YVIP1mJlZlVX9nEJEbJL0ceAOsktS50fEymqvp2iinxOr4WPVZrsMv49r1M1FRNwWEa+LiEMiYsh0RN6z+9XZ2Vl6OM3MdiFD/X3svo/MzCznomBmZjkXBTMzy7komJlZzkXBzMxyVf9G806FkJ4DHh+w4fbtC/yuCnGqoShZipIDnKUvRckBxclSlBxQnCzVynFwRAzYL3ghikI1SHogyvkK9yAoSpai5ABnKXIOKE6WouSA4mQZ7Bw+fGRmZjkXBTMzy72SisK36h2gRFGyFCUHOEtfipIDipOlKDmgOFkGNccr5pyCmZlV7pW0p2BmZhXapYqCpEskfVJSl6S6XxUAf8pUh/VOlPRQH9PPkHRAyfg/Stp9EPKMlfTRkvGpkm7d3n3qIW2frxcgx1WSDqt3jt7S6+oD9c5RJJKulvTeeufoUevPnF2qKFhZzgAOKBn/R6DmRQEYC3x0wFZlklSVbt2VKdzrPCI+HBFF/N3yiYCLwhBWuDdLb5JmS/qVpP8AXl8y6xRJ90n6b0lvS20bJH1J0gpJD0pqTdMfkzQntb9P0murnSntvczpI9Mb0rSfpUyHVrLuXhokfVvSSkl3pp8+PQq4Lq3vXLIC0SmpM+VZK+lfJP1U0lJJA36ZpS+SzpP0ULr9I3AZcEha7xdTs0ZJ35X0S0nXKXVUL2mypLslLZd0h6T90/QuSV+QdDdw7s5ulPTfbrekbwA/Bf5vek7uBv66pN3Vkq6U9J9pfnm/U7njecZIWizp52l7nVq6t1ut56SMdT6Wtu+PJT0g6ci0/R+WdE6662XA29Lz+E9pz+oWSben1/zFVcjw2fRYV0j6i9T2EkkL0uv4MUnvkXR5anO7pBGpXdXey/3ku0zSL9J79Uslzf9G0o8kPaIa7TXsYJ7aKefn2ep1AyYDK8j+0/0zYBXwSaAL+JfU5gTgP9LwR4CbgOFpfO/09zFgdho+Hbh1EDPNA05Lw7sBo6u0bSYCm4A3pfEbgQ+mHEeVtHsM2LdkPEryfAb4egXbYAzQCKwE/hJ4qKTNVOBFYALZPx8/BqYAI4AfAeNSu1PJfoiJlP0bVdo2W4BjgP2BJ4Bxafv/sOcxA1cDt6d8hwKrgVE1eB3/L+DbJeN7lj5P1XhOylznY8BH0vhXgAfw+YFdAAAFoklEQVSBPdK2ebbkebu15H5nAE8D+wCjgYdKX187maE1jX8UuCoNXwIsS6+PNwIvAcened8HTi55PVfrvdw738HAr/jTBThjS14n30mvk8OAVdV+jexgnkuAT9YiQ0Rtfo6zmt4GfD8iXoqI/2Hrn/X8Xvq7nOxDAOAdwJURsQkgIp4vad9R8vetg5jpx8BFki4g+5r5yxWsu7dHI+Jnfaxze7YAN6Tha8k+qHfUFLJtsC4i1pI97rf10e6+iFgdEVuAn6V8rwcmAXdJ+hnwabLC0eOGbZaycx6PiHuBo4GuiHguIv7Yx/JvjIgtEfFr4BHgL6q0/lIrgHek/3DfFhEv9ppfjeek3HUuKpn/k4hYExHPAeslje1nWXdFxO/Ta/d7O5Cvvwx9vU8AlkTExnS/BrKC3bOc0nbVei9vlQ/4DbAeuErSe8gKU4+b0+vkF8D4CtZZrTw1U/SiANl/UX3ZkP5u5k8/K6rttI9+hmuaKSKuB6YDLwN3SHp7hevua31brXMH7cy22Pb3CvvWVz4BKyPiTel2eEQcW9Ju3U7k6Uvpcrb3GHvPq/o12hHx3/xp7+pSSZ8Z6C41XGfPc7KFrZ+fLfT/+tmpbVRGht6v2Q3pfluAjZH+Le4jW1Xey73zARcBbyE72nAyfypKpZmh/Nd/LfPUTNGLwj3A30saLWkP4N0DtL8TOEfpJKWkvUvmnVry98eDlUnSnwOPRMTXyP5LO6KCdZdjDdkhgf7GhwE9x0Q/QLbLvqPuAU6WtLukMcDfkx2W2WP7dwOy3eFxkt4KIGmEpDfsRIZy/QSYKmmfdFz6lF7zT5E0TNIhwJ+nfFWl7GqwlyLiWuBLwJG9mlTjOdnRdfan9+sF4O8k7S1pNNmH0w9rnGEgVXkv95Hvb4A9I+I2sgs03lRp0F0xT1Wu8KiViPippBvIDj08DvznAHe5Cngd8KCkjcC3gZ7LD0dK+gnZG3DGIGY6FfhgyvNb4HM7u+4yXQ1cKellsl3rbwFLJD0dEc1k/0G/QdJysmP+p/a7pH6kbXA1cF+adFVELJf0Q2WXyS4BFvdz3z+mE3Vfk7Qn2Wvwq2TnJaouIp6WdAnZh8fTZCeeG0qa/Aq4m+yQwDkRsb4GMQ4HvihpC7CR7NxX6UnDip+TMtf53TLu9yCwSdLPyV5LL5AVqX8HXgtcHxEP1DjDQKryXu4j33nArZJGke0N/FPFSXfBPEPiG82SHiM7OVaEbnDrStLaiGisd44iSIXt1oioxgdVJTkK+5xIOoPsvfPxemcBv5cHQ9EPH5mZ2SAaEnsKZmZWHu8pmJlZzkXBzMxyLgpmZpZzUTCrAkmfk/SOeucwq5RPNJuVSdLwni5UzF6pvKdgQ04/vVGW03Pr7NRL57A0b3dJT6ZvZed97kt6c+pR8+epJ889lPXg+0VJ96ceL/93HTeBWb8K/Y1msxo5DngqIt4FkL5ZvQQ4KSKek3Qq0AbMSu3HRsTfprZHAn8LdJJ1cXJHRGxU1is4knYj69zu1Ii4X9KfkfV71QK8GBFvljQS+KGkOyPi0UF6zGZlcVGwoWgF8CVJc4Bbybpy6Om5FbJuMJ4uaX9Dr+FTyYrC+4Fv9Fr264GnI+J+gNSTLpKOBY4o6Yt/T7Luul0UrFBcFGzIiYj/ljSZ7HcvLgXuIuu5tb9umEt7XF1E1uPn3mQ9Wv6gV9v+euoV2e8I3FFReLMa8zkFG3L66I3yaMrsuTX9fsR9wFyyfpM292ryS+AASW9Oy9oj9dp7B/AR/ekXxF6Xepg1KxTvKdhQ1FfvnZsov+fWG8h+iWtq7xmpF9hTgXmpq+mXyX786SqyH4r5qbJjVM+RdUNtVii+JNXMzHI+fGRmZjkXBTMzy7komJlZzkXBzMxyLgpmZpZzUTAzs5yLgpmZ5VwUzMws9/8B6pKII3k0xCAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6721a18f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6721936748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67210ad9b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67210a09b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplots\n",
    "data.boxplot(column='conn len', by='service')\n",
    "plt.title(\"Connection length - boxplot\")\n",
    "plt.suptitle(\"\")\n",
    "\n",
    "data.boxplot(column='src bytes', by='service')\n",
    "plt.title(\"Source bytes - boxplot\")\n",
    "plt.suptitle(\"\")\n",
    "\n",
    "data.boxplot(column='dst bytes', by='service')\n",
    "plt.title(\"Destination bytes - boxplot\")\n",
    "plt.suptitle(\"\")\n",
    "\n",
    "data.boxplot(column='conn numb', by='service')\n",
    "plt.title(\"Connections number - boxplot\")\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6720568898>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6720eb8358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67204d3518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67205c1588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plots\n",
    "data.plot.scatter(x='src bytes', y= 'dst bytes')\n",
    "data.plot.scatter(x='conn len', y= 'src bytes')\n",
    "data.plot.scatter(x='conn len', y= 'dst bytes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(217628, 238)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot function definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting method definition\n",
    "\n",
    "def plot_stats(stat, name, width, rot):\n",
    "    fig, ax = plt.subplots(figsize=(width, 4))    \n",
    "    ax.bar(range(len(stat)), list(stat.values()), align='center')\n",
    "    plt.xticks(range(len(stat)), list(stat.keys()), rotation=rot)\n",
    "    for a,b in enumerate(stat.items()):\n",
    "        ax.text(a, b[1] + max(stat.values())/60 , str(round(b[1],5)), horizontalalignment ='center', verticalalignment='baseline')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    plt.title(name + \" of tested algorithms\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={'Dst host count': 'Dst_host_count', \"Dst host srv count\" : \"Dst_host_srv_count\",\n",
    "                    \"Dst host same src port rate\" : \"Dst_host_same_src_port_rate\",\n",
    "                    \"Dst host serror rate\" : \"Dst_host_serror_rate\",\n",
    "                    \"Dst host srv serror rate\" : \"Dst_host_srv_serror_rate\",\n",
    "                    \"Same srv rate\" : \"Same_srv_rate\",\n",
    "                    \"Srv serror rate\" : \"Srv_serror_rate\",\n",
    "                    \"conn len\" : \"conn_len\",\n",
    "                    \"src bytes\" : \"src_bytes\",\n",
    "                    \"dst bytes\" : \"dst_bytes\",\n",
    "                    \"conn numb\" : \"conn_numb\",\n",
    "                    \"Serror rate\" : \"Serror_rate\",\n",
    "                    \"cli src pool\" : \"cli_src_pool\",\n",
    "                    \"registered src pool\" : \"registered_src_pool\",\n",
    "                    \"well-known src pool\" : \"well_known_src_pool\"\n",
    "                    }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 14952, 1: 7548})\n",
      "Counter({1: 14607, 0: 7893})\n",
      "Counter({1: 86179, 0: 18821})\n"
     ]
    }
   ],
   "source": [
    "sampled_data = data.loc[range(150000), :]\n",
    "train, validate, test = np.split(sampled_data, [int(.7*len(sampled_data)), int(.85*len(sampled_data))])\n",
    "\n",
    "train_ex = train\n",
    "validate_ex = validate\n",
    "test_ex = test\n",
    "\n",
    "train = train.drop([\"service\", \"Conn state\", \"proto\", \"src port\", \"dst port\"], axis=1)\n",
    "validate = validate.drop([\"service\", \"Conn state\", \"proto\", \"src port\", \"dst port\"], axis=1)\n",
    "test = test.drop([\"service\", \"Conn state\", \"proto\", \"src port\", \"dst port\"], axis=1)\n",
    "\n",
    "train = train.sample(frac=1)\n",
    "validate = validate.sample(frac=1)\n",
    "test = test.sample(frac=1)\n",
    "\n",
    "y_train_alg = train.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_train_alg = train.drop([\"attack_flag\"], axis=1)\n",
    "y_validate_alg = validate.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_validate_alg = validate.drop([\"attack_flag\"], axis=1)\n",
    "y_test_alg = test.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_test_alg = test.drop([\"attack_flag\"], axis=1)\n",
    "\n",
    "print(Counter(y_test_alg))\n",
    "print(Counter(y_validate_alg))\n",
    "print(Counter(y_train_alg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 30946, 0: 1699})\n",
      "Counter({0: 22937, 1: 9707})\n",
      "Counter({1: 109262, 0: 43077})\n"
     ]
    }
   ],
   "source": [
    "train, validate, test = np.split(data, [int(.7*len(data)), int(.85*len(data))])\n",
    "\n",
    "train_ex = train\n",
    "validate_ex = validate\n",
    "test_ex = test\n",
    "\n",
    "train = train.drop([\"service\", \"Conn state\", \"proto\", \"src port\", \"dst port\"], axis=1)\n",
    "validate = validate.drop([\"service\", \"Conn state\", \"proto\", \"src port\", \"dst port\"], axis=1)\n",
    "test = test.drop([\"service\", \"Conn state\", \"proto\", \"src port\", \"dst port\"], axis=1)\n",
    "\n",
    "train = train.sample(frac=1)\n",
    "validate = validate.sample(frac=1)\n",
    "test = test.sample(frac=1)\n",
    "\n",
    "y_train = train.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_train = train.drop([\"attack_flag\"], axis=1)\n",
    "y_validate = validate.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_validate = validate.drop([\"attack_flag\"], axis=1)\n",
    "y_test = test.loc[:, \"attack_flag\"].values.ravel()\n",
    "X_test = test.drop([\"attack_flag\"], axis=1)\n",
    "\n",
    "print(Counter(y_test))\n",
    "print(Counter(y_validate))\n",
    "print(Counter(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'class_weight': None,\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'max_iter': 100,\n",
       " 'multi_class': 'warn',\n",
       " 'n_jobs': None,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': None,\n",
       " 'solver': 'warn',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LogisticRegression().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparams = {'solver' : ['newton-cg', 'lbfgs', 'sag']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sorted(metrics.SCORERS.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clf = GridSearchCV(LogisticRegression(), hyperparams, cv=5, n_jobs=-1, scoring = \"f1\")\n",
    "# clf.fit(X_train, y_train)\n",
    "\n",
    "# print(clf.best_params_)\n",
    "\n",
    "# # {'solver': 'newton-cg'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: logistic Regression Accuracy: 0.99599, F1-score: 0.99329, Precision: 0.98748, Recall: 0.99918, FPR: 0.00536\n",
      "TN: 22814; FP: 123; FN:    8; TP: 9699\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "clf = LogisticRegression(solver = 'newton-cg')\n",
    "\n",
    "model = clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_validate)\n",
    "\n",
    "name = \"logistic Regression\"\n",
    "\n",
    "Acc = {}\n",
    "F1S = {}\n",
    "Prec = {}\n",
    "Rec = {}\n",
    "FPR = {}\n",
    "\n",
    "Acc[name] = metrics.accuracy_score(y_validate, y_pred)\n",
    "F1S[name] = metrics.f1_score(y_validate, y_pred)\n",
    "Prec[name] = metrics.precision_score(y_validate, y_pred)\n",
    "Rec[name] = metrics.recall_score(y_validate, y_pred)\n",
    "tn, fp, fn, tp = metrics.confusion_matrix(y_validate, y_pred).ravel()\n",
    "FPR[name] = fp/(fp+tn)\n",
    "\n",
    "print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name]))\n",
    "print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree parameters selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking tree depth impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'presort': False,\n",
       " 'random_state': 0,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using gridsearchcv to find the best parameters\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "DecisionTreeClassifier(random_state=0).get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparams = {'max_depth' : range(1,15)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 13}\n"
     ]
    }
   ],
   "source": [
    "clf = GridSearchCV(DecisionTreeClassifier(random_state=0), hyperparams, cv=5, n_jobs=-1, scoring = \"f1\")\n",
    "clf.fit(X_train_alg, y_train_alg)\n",
    "\n",
    "print(clf.best_params_)\n",
    "\n",
    "# max depth : 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: DecisionTree Accuracy: 0.99911, F1-score: 0.99932, Precision: 0.99952, Recall: 0.99911, FPR: 0.00089\n",
      "TN: 7886; FP:   7; FN:   13; TP: 14594\n",
      "\n",
      "Algorithm: DecisionTree_ent_13 Accuracy: 0.99924, F1-score: 0.99942, Precision: 0.99973, Recall: 0.99911, FPR: 0.00051\n",
      "TN: 7889; FP:   4; FN:   13; TP: 14594\n",
      "\n",
      "Algorithm: DecisionTree_13 Accuracy: 0.99907, F1-score: 0.99928, Precision: 0.99938, Recall: 0.99918, FPR: 0.00114\n",
      "TN: 7884; FP:   9; FN:   12; TP: 14595\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/base.py:465: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: DecisionTree_ent_13_scl Accuracy: 0.99924, F1-score: 0.99942, Precision: 0.99973, Recall: 0.99911, FPR: 0.00051\n",
      "TN: 7889; FP:   4; FN:   13; TP: 14594\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/pipeline.py:331: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "classifiers = {\n",
    "               \"DecisionTree\":DecisionTreeClassifier(random_state=0),\n",
    "               \"DecisionTree_ent_13\":DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13),\n",
    "               \"DecisionTree_13\":DecisionTreeClassifier(random_state=0, max_depth = 13),\n",
    "               \"DecisionTree_ent_13_scl\":make_pipeline(StandardScaler(),DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13)),\n",
    "}\n",
    "\n",
    "Acc = {}\n",
    "F1S = {}\n",
    "Prec = {}\n",
    "Rec = {}\n",
    "FPR = {}\n",
    "\n",
    "for name, clf in classifiers.items():\n",
    "    \n",
    "    model = clf.fit(X_train_alg, y_train_alg)\n",
    "    \n",
    "    y_pred = model.predict(X_validate_alg)\n",
    "    \n",
    "    Acc[name] = metrics.accuracy_score(y_validate_alg, y_pred)\n",
    "    F1S[name] = metrics.f1_score(y_validate_alg, y_pred)\n",
    "    Prec[name] = metrics.precision_score(y_validate_alg, y_pred)\n",
    "    Rec[name] = metrics.recall_score(y_validate_alg, y_pred)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y_validate_alg, y_pred).ravel()\n",
    "    FPR[name] = fp/(fp+tn)\n",
    "    \n",
    "    print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name]))\n",
    "    print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f135f1f4630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f135f1f4358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f136ea92ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f136ebb05c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sQA66ZmYFctA1MyuQg66ZWYEcdM3MCuSga2ZWIAddM7MCOeiamRXIQdfMrEAOumZmBaop6EqaIOkhSR2STqmyfaCk6/L2mZJGlG07Na9/SNL4svVXSHpa0l8q8hoq6XZJ8/K/Q9b+8MzMGku3QVdSEzAV+CgwGjhY0uiKZJOB5yKiBTgfODvvOxpoA8YAE4ALc34AP87rKp0C3BER2wF35NdmZr1CLTXdcUBHRDwSEcuBdmC/ijT7AVfm5WnAnpKU17dHxLKImA905PyIiN8Az1Z5v/K8rgQ+sQbHY2bW0GoJulsCj5W9XpjXVU0TESuApcBmNe5b6a0R8UTO6wlg8xrKaGbWI9QSdFVlXdSYppZ914qkYyTNljR78eLF6yNLM7MNrpaguxDYqux1M/B4Z2kk9QcGk5oOatm30lOStsh5bQE8XS1RRFwSEa0R0Tps2LAaDsPMrP5qCbr3AdtJGilpAOnG2IyKNDOAw/PyJODOiIi8vi33bhgJbAfM6ub9yvM6HJheQxnNzHqEboNubqM9HrgNeAC4PiLmSDpd0sSc7HJgM0kdwInkHgcRMQe4HpgL3AocFxErASRdC9wLjJK0UNLknNdZwN6S5gF759dmZr2CUoW0Z2ttbY3Zs2fXuxjWw4045Zf1LsIGseCsfetdhJ6s2n2pdeIRaWZmBXLQNTMrkIOumVmBHHTNzArkoNuJW2+9lVGjRtHS0sJZZ72+A8WyZcs46KCDaGlpYeedd2bBggWvbTvzzDNpaWlh1KhR3Hbbbd3meccdd/De976XnXbaiV133ZWOjg4AHn30Ufbcc0923HFHdt99dxYuXLjhDtjMCuGgW8XKlSs57rjjuOWWW5g7dy7XXnstc+fOXS3N5ZdfzpAhQ+jo6OCEE07g5JNPBmDu3Lm0t7czZ84cbr31Vj7/+c+zcuXKLvM89thjueaaa7j//vv59Kc/zbe+9S0AvvKVr3DYYYfxpz/9iSlTpnDqqacWeyLMbL1z0K1i1qxZtLS0sO222zJgwADa2tqYPn31MRrTp0/n8MPTGI5JkyZxxx13EBFMnz6dtrY2Bg4cyMiRI2lpaWHWrFld5imJF154AYClS5cyfPhwIAXwPffcE4APf/jDryuDmfU8DrpVLFq0iK22WjV6ubm5mUWLFnWapn///gwePJglS5Z0um9XeV522WXss88+NDc3c9VVV3HKKWk2y3e/+93ccMMNANx44428+OKLLFmyZMMctJkVwkG3imoDRtJMld2nWdP1AOeffz4333wzCxcu5Mgjj+TEE08E4Nxzz+Xuu+/mPe95D3fffTdbbrkl/fv3X6tjMrPG4G9wFc3NzTz22KoZKRcuXPjaJX9lmubmZlasWMHSpUsZOnRol/tWW7948WL++Mc/svPOOwNw0EEHMWFCmtt9+PDh/OxnPwPgpZde4oYbbmDw4MEb5qDNrBCu6VYxduxY5s2bx/z581m+fDnt7e1MnDhxtTQTJ07kyivTXOvTpk1jjz32QBITJ06kvb2dZcuWMX/+fObNm8e4ceM6zXPIkCEsXbqUhx9+GIDbb7+d7bffHoBnnnmGV199FUg9Io466qgCz4KZbQiu6VbRv39/LrjgAsaPH8/KlSs56qijGDNmDFOmTKG1tZWJEycyefJkDj30UFpaWhg6dCjt7e0AjBkzhgMPPJDRo0fTv39/pk6dSlNTekJRtTwBLr30Uvbff3/69evHkCFDuOKKKwC46667OPXUU5HEbrvtxtSpU+tzQsxsvfGEN2aZJ7yxKjzhjZlZT+aga2ZWoD7bptsbLyV9GWnW+FzTNTMrkIOumVmBHHTNzArkoGtmViAHXTOzAjnompkVyEHXzKxADrpmZgVy0DUzK5CDrplZgRx0zcwKVFPQlTRB0kOSOiSdUmX7QEnX5e0zJY0o23ZqXv+QpPHd5SlpT0l/kHS/pN9Kalm3QzQzaxzdBl1JTcBU4KPAaOBgSaMrkk0GnouIFuB84Oy872igDRgDTAAulNTUTZ4XAYdExE7AfwH/sW6HaGbWOGqp6Y4DOiLikYhYDrQD+1Wk2Q+4Mi9PA/ZUeurifkB7RCyLiPlAR86vqzwDeFNeHgw8vnaHZmbWeGqZ2nFL4LGy1wuBnTtLExErJC0FNsvrf1+x75Z5ubM8jwZulvQP4AXg/TWU0cysR6ilplvtcRWVz/jpLM2argc4AdgnIpqBHwHnVS2UdIyk2ZJmL168uGrBzcwaTS1BdyGwVdnrZl5/yf9aGkn9Sc0Cz3axb9X1koYB746ImXn9dcC/VCtURFwSEa0R0Tps2LAaDsPMrP5qCbr3AdtJGilpAOnG2IyKNDOAw/PyJODOSE+8nAG05d4NI4HtgFld5PkcMFjSO3JeewMPrP3hmZk1lm7bdHMb7fHAbUATcEVEzJF0OjA7ImYAlwNXSeog1XDb8r5zJF0PzAVWAMdFxEqAannm9Z8BbpD0KikIH7Vej9jMrI5qekZaRNwM3FyxbkrZ8ivAAZ3sewZwRi155vU3AjfWUi4zs57GI9LMzArkoGtmViAHXTOzAjnompkVyEHXzKxADrpmZgVy0DUzK5CDrplZgRx0zcwK5KBrZlYgB10zswI56JqZFchB18ysQA66ZmYFctA1MyuQg66ZWYEcdM3MCuSga2ZWIAddM7MCOeiamRXIQdfMrEAOumZmBXLQNTMrkIOumVmBHHTNzArkoGtmViAHXTOzAjnompkVqKagK2mCpIckdUg6pcr2gZKuy9tnShpRtu3UvP4hSeO7y1PJGZIelvSApC+s2yGamTWO/t0lkNQETAX2BhYC90maERFzy5JNBp6LiBZJbcDZwEGSRgNtwBhgOPDfkt6R9+kszyOArYB3RsSrkjZfHwdqZtYIaqnpjgM6IuKRiFgOtAP7VaTZD7gyL08D9pSkvL49IpZFxHygI+fXVZ7HAqdHxKsAEfH02h+emVljqSXobgk8VvZ6YV5XNU1ErACWApt1sW9Xeb6dVEueLekWSdvVdihmZo2vlqCrKuuixjRruh5gIPBKRLQClwJXVC2UdEwOzLMXL15cteBmZo2mlqC7kNTGWtIMPN5ZGkn9gcHAs13s21WeC4Eb8vKNwI7VChURl0REa0S0Dhs2rIbDMDOrv1qC7n3AdpJGShpAujE2oyLNDODwvDwJuDMiIq9vy70bRgLbAbO6yfMmYI+8/CHg4bU7NDOzxtNt74WIWCHpeOA2oAm4IiLmSDodmB0RM4DLgaskdZBquG153zmSrgfmAiuA4yJiJUC1PPNbngVcI+kE4CXg6PV3uGZm9dVt0AWIiJuBmyvWTSlbfgU4oJN9zwDOqCXPvP55YN9aymVm1tN4RJp16dZbb2XUqFG0tLRw1llnvW77smXLOOigg2hpaWHnnXdmwYIFr20788wzaWlpYdSoUdx2223d5nnEEUcwcuRIdtppJ3baaSfuv/9+AB588EF22WUXBg4cyLnnnrvhDtZq5s/F2quppmt908qVKznuuOO4/fbbaW5uZuzYsUycOJHRo0e/lubyyy9nyJAhdHR00N7ezsknn8x1113H3LlzaW9vZ86cOTz++OPstddePPxwap7vKs9zzjmHSZMmrVaOoUOH8oMf/ICbbrqpuIO3TvlzsW5c07VOzZo1i5aWFrbddlsGDBhAW1sb06dPXy3N9OnTOfzwdA910qRJ3HHHHUQE06dPp62tjYEDBzJy5EhaWlqYNWtWTXlW2nzzzRk7diwbbbTRBjtWq50/F+vGQdc6tWjRIrbaalXPvubmZhYtWtRpmv79+zN48GCWLFnS6b7d5fm1r32NHXfckRNOOIFly5ZtqEOzdeDPxbpx0LVOpV5/q0uju7tPs6brIbX1Pfjgg9x33308++yznH322WtbdNuA/LlYNw661qnm5mYee2zVaO2FCxcyfPjwTtOsWLGCpUuXMnTo0E737SrPLbbYAkkMHDiQI488klmzZm3Iw7O15M/FunHQtU6NHTuWefPmMX/+fJYvX057ezsTJ05cLc3EiRO58so019G0adPYY489kMTEiRNpb29n2bJlzJ8/n3nz5jFu3Lgu83ziiSeAVEu66aabeNe73lXsAVtN/LlYN+69YJ3q378/F1xwAePHj2flypUcddRRjBkzhilTptDa2srEiROZPHkyhx56KC0tLQwdOpT29nYAxowZw4EHHsjo0aPp378/U6dOpampCaBqngCHHHIIixcvJiLYaaeduPjiiwF48sknaW1t5YUXXqBfv35873vfY+7cubzpTW+qz4np4/y5WDeq1pbS07S2tsbs2bPXaJ8Rp/xyA5Wmfhac5TEl66I3fibAn4t1VG1yrnXi5gUzswI56JqZFchtun2cL6mtmt74uWiUz4RrumZmBXLQNTMrkIOumVmBHHTNzArkoGtmViAHXTOzAjnompkVyEHXzKxADrpmZgVy0DUzK5CDrplZgRx0zcwK5KBrZlYgB10zswI56JqZFaimoCtpgqSHJHVIOqXK9oGSrsvbZ0oaUbbt1Lz+IUnj1yDP/5T00todlplZY+o26EpqAqYCHwVGAwdLGl2RbDLwXES0AOcDZ+d9RwNtwBhgAnChpKbu8pTUCrx5HY/NzKzh1FLTHQd0RMQjEbEcaAf2q0izH3BlXp4G7ClJeX17RCyLiPlAR86v0zxzQD4HOGndDs3MrPHUEnS3BB4re70wr6uaJiJWAEuBzbrYt6s8jwdmRMQTtR2CmVnPUcsz0qo9grjyue2dpelsfbVgH5KGAwcAu3dbKOkY4BiArbfeurvkZmYNoZaa7kJgq7LXzcDjnaWR1B8YDDzbxb6drX8P0AJ0SFoAvEFSR7VCRcQlEdEaEa3Dhg2r4TDMzOqvlqB7H7CdpJGSBpBujM2oSDMDODwvTwLujIjI69ty74aRwHbArM7yjIhfRsTbImJERIwA/p5vzpmZ9QrdNi9ExApJxwO3AU3AFRExR9LpwOyImAFcDlyVa6XPkoIoOd31wFxgBXBcRKwEqJbn+j88M7PGUkubLhFxM3BzxbopZcuvkNpiq+17BnBGLXlWSbNpLeUzM+spPCLNzKxADrpmZgVy0DUzK5CDrplZgRx0zcwK5KBrZlYgB10zswI56JqZFchB18ysQA66ZmYFctA1MyuQg66ZWYEcdM3MCuSga2ZWIAddM7MCOeiamRXIQdfMrEAOumZmBXLQNTMrkIOumVmBHHTNzArkoGtmViAHXTOzAjnompkVyEHXzKxADrpmZgVy0DUzK1BNQVfSBEkPSeqQdEqV7QMlXZe3z5Q0omzbqXn9Q5LGd5enpGvy+r9IukLSRut2iGZmjaPboCupCZgKfBQYDRwsaXRFssnAcxHRApwPnJ33HQ20AWOACcCFkpq6yfMa4J3ADsDGwNHrdIRmZg2klpruOKAjIh6JiOVAO7BfRZr9gCvz8jRgT0nK69sjYllEzAc6cn6d5hkRN0cGzAKa1+0QzcwaRy1Bd0vgsbLXC/O6qmkiYgWwFNisi327zTM3KxwK3FpDGc3MeoRagq6qrIsa06zp+nIXAr+JiHuqFko6RtJsSbMXL15cLYmZWcOpJeguBLYqe90MPN5ZGkn9gcHAs13s22Wekk4DhgEndlaoiLgkIlojonXYsGE1HIaZWf3VEnTvA7aTNFLSANKNsRkVaWYAh+flScCduU12BtCWezeMBLYjtdN2mqeko4HxwMER8eq6HZ6ZWWPp312CiFgh6XjgNqAJuCIi5kg6HZgdETOAy4GrJHWQarhted85kq4H5gIrgOMiYiVAtTzzW14MPArcm+7F8bOIOH29HbGZWR11G3Qh9SgAbq5YN6Vs+RXggE72PQM4o5Y88/qaymRm1hN5RJqZWYEcdM3MCuSga2ZWIAddM7MCOeiamRXIQdfMrEAOumZmBXLQNTMrkIOumVmBHHTNzArkoGtmViAHXTOzAjnompkVyEHXzKxADrpmZgVy0DUzK5CDrplZgRx0zcwK5KBrZlYgB10zswI56JqZFchB18ysQA66ZmYFctA1MyuQg66ZWYEcdM3MCuSga2ZWIAddM7MC1RR0JU2Q9JCkDkmnVNk+UNJ1eftMSSPKtp2a1z8kaXyd15Z2AAAYwUlEQVR3eUoamfOYl/McsG6HaGbWOLoNupKagKnAR4HRwMGSRlckmww8FxEtwPnA2Xnf0UAbMAaYAFwoqambPM8Gzo+I7YDnct5mZr1CLTXdcUBHRDwSEcuBdmC/ijT7AVfm5WnAnpKU17dHxLKImA905Pyq5pn32SPnQc7zE2t/eGZmjaV/DWm2BB4re70Q2LmzNBGxQtJSYLO8/vcV+26Zl6vluRnwfESsqJJ+NZKOAY7JL1+S9FANx1IPbwGeKeKNdHYR77JOfC5W8blYpZBzsZbn4daImLA+y1FL0FWVdVFjms7WV6thd5X+9SsjLgEuqbatkUiaHRGt9S5HI/C5WMXnYpW+di5qaV5YCGxV9roZeLyzNJL6A4OBZ7vYt7P1zwBvznl09l5mZj1WLUH3PmC73KtgAOnG2IyKNDOAw/PyJODOiIi8vi33bhgJbAfM6izPvM+vcx7kPKev/eGZmTWWbpsXchvt8cBtQBNwRUTMkXQ6MDsiZgCXA1dJ6iDVcNvyvnMkXQ/MBVYAx0XESoBqeea3PBlol/Qt4P9y3j1ZwzeBFMjnYhWfi1X61LlQqlyamVkRPCLNzKxADrpmZgVy0DUzK5CDrvV5kvpL2igvV+srbrYaSS1ru6+Dbg8kqV/FaweKtSTpU8CNwAWStoleemdZ0lGS/l2Sh9WvI0lvAH4s6cS12d9BtweKiFclbSvpYElvobaRhVZB0gXAvwNnkEY+9vTuia8jaRNJPwU+DTwPnCxp/zoXq8eSpIj4OzCF6pN/dctBtweS9FnSoJH3AucBB9S3RD1Pbk64D9gzIn4PXAg8KGmb+pZsvdsPuD8i9oqIS4G7gQX1LVLPU7q6jIiQNCgi7iRdIX13TfNy0G1wVZoStgB2iIgdgLuADwBL6lC0HknSxgAR8U/gqoj4u6TdgNnAm4BpkiZJ2rSe5VyPboqIMwAkfR44EThC0oX1LVbPEhGvQmqmAa6XtDlwAfCKpK+vSV4Oug1MUlPZf/bgvPoVYGtJ00lfoIMi4jZJW5TNWWEVJLVIuhs4RNImsOqLBDwKvC8iDgPOBD4DvKE+JV03kj4o6T/LVv0jrx9OOqbtgVOB7SWdV4ci9hileyVl/34TOAw4PSKejogXgK8DH8s/3DVx0G1gEbFSycXAdyUdBryVNC9xRMSeETFbUitwEjCknuVtVJKGAd8ABpGaZHYq3x4Rj0bEn/Pyz0ht5G8tupzrStJ+pHbpo8tqX6XL4scj4tyI+GtEvAScBWxUeSVlSa7wlG6qliozQ4HP5+/cRgAR8UfgYuDiWp9y4xPeYMq/BLl2ey0wj9TmeAZp3uHZwBOSLpf0ReAy0qTwi+tQ5J7gZVKQ2QV4AdhX0turJZT07bzYE2e3exY4BBgBfEFSa/7hrnYF9BngsbLavmX5ZllpjphTgI/lTbsApSkoI29/E+lhCz8CNq6lJ5GDboMpa07YHfg78Dfgv0k12bsi4krgelIQmQsMB9oiYmpdCtwD5LvND+dzOxXYGviwpM0AJA2StIOkO0jTiU6MiB7XTh4R9wBzI+Ip4HTgmrx+Rb5ieoOkAyX9L/BIRHynnuVtVPlm2UhJvwFagJl50xTga7lr4QpJR5J7vkTEORGxtJYuh57wpgFI+gyppvrrfCl8JukH8SzSM+N2ILUj/SSn3yPfPbU1kGswIenjpJnwvh8Rs3LwXUpq152Z0zaVajs9laTbSLXZo8vWHZ7X3Zlf93Nt9/VyQB0XEcdK2pL0BJsFwKHARNLn5Y3AlyPiD2uUt4Nu/Uh6G6nZQMBngYHAKcA2EfGxnOYcYBnw44jokHQ56QNwUEQsrU/JG9OaBEpJx5Ke1bcz8NuIOKZsW48ORKXyS3oj8BdS7X4H4LsRcX9lunqVsxGV/TC3ApeSmmzmk5psBkXErvmm5PYRcUfeZ43Oo5sX6kRSG/Bn0g/fJyPiaVLb40PAcEnvyUmvBF4EfiTpHmAlsI8D7uokbU2+QSbpzaWuYVXSldrcNgYOBn5SHnBhtV4NDUfpSdpdjkIslT8iXgT+SGpq+J/ygFueri9SJ0O/S80DETEbOBL4MvBF0oMV5kkalm9KlgJu05qeR3cxKlj+Dz6D1L/2LGBXSTsAD0bEs5J+QXpA56ckzY2Iv0iaC1wFvDEiGvUBnPW2NTBBaXL8LUlPHflHZaJci3kL6XFRu+eBET2iOSHXqEpl3IJubvblc7ER8I6I+Ftep9461LlWSkO/jwQel3RmRCyolq70IyXp/aQfrr9R0Sd+bT4zbl4oWO4j2hoRd+fXZ5OC7NfyDRAk7Um6Y/pApAdwWhXlgTK3y/4GGAB8JiLu6izAlK/PNcdXe0ogkvRe0o/1IuB/gds7+yHOtbLFebnhf1SKoDT0+33ACcARQEtE7NVF+rcD3wF+ERE/Wh9lcPNCQUqXhRHxcingZv+P1Cf0wLJL4v8BHgTeK6nH9RctQkW3ns1zb4OvkIZHbytpSCcBt7z/JRGxsgcF3G2Ab5J6sswAjgfGVEmnfJylgNvPAbfq0O+L6H7o96PAIaWAW/oerwsH3QJUBIhjJe0paShARCwHvg18CmjNX5BXSMHj1FLt11Ypu9kxQGkyl58qDQb4H+AWYCywa067qcr6qeZ+q4MlfaGRf9BUfdDCG4HHSDd1vkrqffEzpVmvSvs1RbJSUrOkkX257RY6Hfr9QdJDcktDvw9QxdDvfC5XRMQrZZWmdf7xctAtQA4QQyX9iNR5/SDgJ2Xb7wV+Tnoo51Z53ZMR8Vw9ytvo8vncFfgCcD/wJVJAOivf4JgH7C7pJ8B/Am8u7StpH9KcFX9u1B+0/KNS6q+9n9K0jMNJAztagP8A9o+Ii3JAmaDU17i8ueUI4A6gzwZcdT30+2+kZr7S0O+jyUO/85VCv1h9gMT711e5HHQ3EK0+sqwV+C/gqYjYFfgcMEhlE2VExHnAI/ThL0lnSl+CitWHkZoTfhYR/0e6VHyrpEOA7wO/I/X0OC0insn5/ACYTLqB9uvCDqBGpbvo+UelSWn49+eAbUjDe99Hare+G9hU0jtIgXVXYHlZLf4KUne4HSPi0XocS71pzYd+NwFvy68jd7kbKekmYFPg3vVWuIjw33r8A/qVLQ/I/wr4KXA18Ka87l2k7mEfqHeZe8ofqf3yUGBIfn0P8MW8vBGwN2mI9Hsq9msi9WZoq/cxdHFs/cuWNyYF2u/m16eRLoU3IzUt/BupGeX3wJFl+22Sg8OEeh9Pvf9ItdYdWDXI6NvAtp2kPQO4E9isbN3xpOaqHdd32dx7YQOR9EnS+PaZpNrJQ6R5FL4O/CZSreQk4GMRUfMMRX1VPleTgFuBUaT5JhaT2r7HR8SDSnNVfAr4deRuQKVL7ka9ey9pFLAo0iQ0SDqdVOP6PilQbEUKuP8WEf+UtGVELFIa8x+R+uIiaaO8fUCk+wR9nqSBEbFM0lakJoS7gBsjYomkQcB2wPdIPUGOjYiX8379SN/TMyJi2foul5sX1gNJo5RmeCq9nkSqnVwEPEkayrs5afTZ8aRaDJHGvu9TeIF7GKV+tcMjYhzwV1KNt1+kfpSXkZpuiDT2/UdR1u+yFGgbNOB+FbgC2EbSZpK+AWwVabDGEuCfwPSI+FwOqF8E/iMH1hci4kWtmlz7n/lfB9wsB1xFxGPAdcCHgdJER5sADwBfjYjDIuLl3KTTFBGvRsSUDRFwwYMj1pcWYJbSjPKvkC4Bvx8RPweQ9DzwrYj4mKQDgaMkfSMi/lmq4ViXBgD/IulnpDa6AyNibq71fVPSeyS1RERHnctZE0kjSMF2BfCJiFis1P/2naQfZyLiSUn/Teo2eA2pfXoE8KXywBrumdDlFUzkS/mI+LmkZuBYST9m1dDv0lwbhXWrc013HZS6IkXEL0kjhL6VG/BfJs2lUPK/QKknwheAi0s1E6vJk8D/AZtExD454L4fOEnSGyLiUz0o4B5NeszLZqQv/BKASJOmXAbMl1R6/NJlpPbIW4B7I2K3iPhD6YZbX6ceOvTbQXctlF3SrZC0Zf7PX0aqhe1PqsU8J+msvEsrMEzSGyNiUUQsrEvBG5S6mU8gfyFuAF6UdH6+LP8h8IdI0zZ2ORdBo8jdvrYG9ouId5O6If2rVk1+PSv/fUjSmEh9RB+JiKsj4qKcx2qDO/q4rYFP5q6Y11PWNbBcxOuGfp8F62egw9rwjbQ1UHmTQtIJpF/OMyPiRkkHAR8EbiLNdfsz0nRw7wCOi9Qf18qobIYmScMjotP5BCRtC3ycNLfCBZHnE2h06mQWKkkTSW38JwF/zMHhHaR5ATYmTRu4siy9503oBUO/HXRrJOldwEeAKyLieaXHWB8NTCq769lEmpFoOKlN9zGlIYaeob8LqmE+gWpfpkbtkVCu4sv+dmBxpGdrlbafCbyFNPfG03ndHsBLETGrHmVuVBXncvOIeFrSR4E9SZWcG6PKgKJG+5y4eaF2j5KaDQbl7iYrSJ2m95Z0qqQbgeNITwgdTJolbECkTtgOuJ1QjfMJ5Fpg+YATNdIXqZqKIHE28CvSs7QOLUt2GjAM+LTycN6IuNMBd3Wlc6leMPTbQbcLyuC1uUkHkm5ufIwUIB4G9iBNsXclqZa7Ealr2KXuvrM6rcN8ApDadrVqPoGGvUQr+8yEpG1yG3R/4D2kATJTclNJqYvXd4EDSDfXrIp8LnvH0O9ogNEjjf5HerrAlLz8b6SZ+LevSPMJ0vwJm9a7vI34R27Kysv7AUeRmmG2Jj0Dbjawdd6+MWmQwyCgqWy/I0iDTEbU+3i6OM6mitd7kZ7e/PWydd8kNaGUp9u83mVvpD/SKM5+FesuIfVk2T6/biHdYD2ENOpwf9IDIrcu2+cHOc3geh9T6c813W7km2U/JnWkhnTX/FXSXdNtJQ2R9EtS08LJ4X63q6mo9a2v+QQW1OFQapLL+1ZJJ0h6H+lYvgO8U9LmOdkUYHBucijt93RP6IFRlEhelTRG0qFKU3UeQ6rRfiQne5T0+PMTSJ+LGyLiyIj4W/6sHQ78LiL2jwZ60opvpJWpvMusNP3ipcDxEfGEpP6Ruom9m/QffRepz+WukfrqWpnS+crLG5M6/n8hIr4s6TRgX+CjpMvEj5NG5w0Bfhir5i/dhFQT/kZE3FqHw+iSpIGkmtW8/Hof4BzSvAgbAUTEYZLaSTcJp0aaWvCdwFtj9bmVrYx66dBv13Sziq5Lw/LqUt/bd5anjYg/kgLuu0hdTxxwyygNi960LOCeTppLYFOgRdIfSINJPhBp8vF/RsR/kqa83Lss4G4UqWfIhxo44E4mzcla0kpqSphMauMfJOkLpBuFk0h9cBURDzrgdk69dOg3eBjwa/KlzBDSPLdLJD1BalZ4kPSgyGGRhmvuRnrm1GXuN/l6+abRvsAxkp4k3fjYKiKOVHr6cWk+gW/k9F8kXXp/MXJXqtIPYDTwfAK5FrVMqWP+myQdFBHXkZpLVuRkS0nzb3wmIn6gNJz3FX9matKrhn6Xc9DNcleS75OmYLwJeAr4A2n6wL1JEyH/iXQZfAKsGtdtfWc+gdzuqrLa1D+Unuy8m6QHSV3AbpB0XUT8NffC+HtO+4O6FbznKQ39HhER4wGUhn4fLOnUiPhUXUu3Dvpk0FXqfL4bqX1oQW4eeBmYQ3qC7PXAJRHx05x+JrA7aSTU7pGfPWWJ0nwCx5Gaq2ZQNp+ApMtIX5QD8vm8jNRj4V9ITzc+LOfRI64achlD0k6kJ31cRLo7Phw4kDRV4EWkwDuN1BVsKvScYyxCWbtr1XOSrzxvIF0xnU/6rh4EnBdlQ7974vnsczfScvviJ0lfjPcAHyL1SPgJqUvKNsD/K7XTSvrXiLi6TsVteErzCXwOuCzfNb6d9Lj49ohYnm90HExq/74oIuZUyaMhb3iU5Db+9wF355rtx0k9Er4REe05zQ6k+ZPnRsTFkj5EOuaZETG7XmVvROoDQ7+70meCbu5g/xfgTxFxUNn6VuBmUhPCh0mXw1eTmhZ+RLpJ0ga80BN/VTeUyp4eZet73XwCknYnPZ/s//LxnEZqMvguaa6NUaRmqR1J/Y+nR3oETGn/fuReUEWXvVGpFw/97k6fCbqQaq2kuRI+kYNwv0iTQ38T2Csidsk3dnYlDc28NyJOrWeZG1H5l0G9eD6Bii5vW5La8n9I6oXxK1Lf4ieB7YFBEfEBSceRAsjDdSp2w1Ma+n0h8DVgJOmpDl8t/6EqS1teK274H+ha9KmgCyDpFuC2iPie8qxh+ebIH4BPR8QDSlPtvaWry56+qiLgnk3qBjUTuCUirsrrB5Daxe8itY3/vU7FXWul48zH8l7SVdLFwH2kUU7bAE/kHgxbk0aZHdEbgsL6VO2KSGnyqONJ/W+/RppE6iKluZFL7bXls4k1AxtFxPyCi79B9MV+uvuTHnkysuyO+TbAs6QeC0TEcgfc1eUfptLIsl4/n0A+zr1JE6rsEWmk4Q9JE6tMBJ4Glis9Yudm4FcOuKvLP1zr61Hyvebc9rmgm39JP0ma67bkKNKQwldKwcVW0esnzt6OdM5ejPSsrpuBdlJQAiAi7gE+Gen5VD1Orr2eCPx7RHwbXjumWaRJjt5F6oWxNbBvRFxTr7I2moof6F4/9HtN9bmgC699eX4v6ce5v+gOwEkR8XfXVl4vevl8Alp9ysjS8gDSTFV/yOsH5fUXkYYt70Nqyz4yIh5V9RnU+pzcDl5qftoYaAZejoiPkuYsKU08/hPSBP/fzcuXRsSJuavYJqSmh59GxOdjAz0gsl76XJtuidJ8mwtJT334fr3L00jUR+cTkHQ86fh+Q3qm3YnAf0XE7/L2t5GCcQD9S22MveUGz7qQHyVfsz7765zvSm/lgLs69aH5BEq1cEmbKA3n3ZnUCX8a6VHdLwIH5LbIXUjdwvaIiMciYn75ZXR9jqAxyI+SXyN9NujCqv9gS3Lb7TJS/+SFSs98g9QGt11eLs0n8P5IHdV71HwCOcB+QGnY8pC8upnUX/RoYDTpCuge4FzSjbS9gK8D342IH5fy6inHvKFIGiHpTtJozU9EGviyDamv+whIQ79Js8RtKekapUnG96dikv/KHg69WZ8cBmyryzW2Xj+fQG4m+Q5puPeWwKtKgzn6kQLFH4AZEfHBnH54RExTmi95RelH2s0JfWvo9/rWp2u6lkTyqqSdJF2rNJPaDaQnHhxIqgWW5hP4D1Ib3SxYdYne6PKd8C+R5vM9iDS09BHgWuAZUgXk6siDYZRGnZ2Xf2BeyZfFpUvgPhcoysmPkl8nffZGWl+nPjafgKTHSD1UrpU0sHRHXNIDwHmkG4JfJDU59CMF4c9GxKP1KnOjUR8a+r0hOej2Uepj8wnkH5k/A++MiOclDYqIV5Qedng56TgHkybLfnNE3Jj3qxpo+pryQKlePPS7CA66fYz68HwCkg4mDdUdn380ROp8fzpwQEQ8X5G+x0+usj5UBNxeO/S7KG7T7UPyl2eFpAFKE0IvJfWl3If0WOuxwCERcTzp0rAj7zO1pwdcgIi4lnTz7EuRnkxRmkT9wcqAm9P36YBb3iVOfWDod1EcdPuQ/OXp6/MJ7A+cJKlZ0imk8f/X17lMDafKja5eP/S7KA66fYg8n0Bp7o024G/AO0j9je+pb6kaT/Tyod/15DbdXkqrz0PaL3cJayE91WF8RLxQdjNpI1KN5W/Ad2LV9Hq99iaSpHGlGzxuu+27Q7/rwUG3l5PnE+iSA+5qQ79nRsT/5nVTgAci4qdKT8n+IfBb0kNbf0oaoXdrX/iMrG9uXuhlSpd28nwCNXHA7f1DvxuNg24v4PkEbE0p6RdlQ79JvVgmSHo3aej3ZElvz01Mqw39johf16vsPZ3nXujhPJ+ArY38f+1HydeBa7o9mOcTsDUhaZikCUqTi5OHfl9LGm34mzzCbAZpkvb9I+Jc0tDo54DJEXEJ+LOyrnwjrQfzfAK2Jvra0O9G5aDbg3k+AatFXx763YjcptuDRcRiSScA1wHjSaPJmoCVpD63G+fJR54u7eMuUn1L+dBvVj1KvjT0+wek0YirPUq+NPS7fqXu3dym28N5PgHriod+Nx7XdHuH/UmT00wD/hU4lPTIa+vjKoZ+3wVp6Lek95CGfj8BPMWqod9u79/AXNPtBTyfgIEfJd9T+EZaL+L5BAw89LvR+VetF3HA7Zs89LtncU3XrAeStAmwE2mo9wsR8aykUaT2/G+ShvF+ENgb2AT4MPAp0gTjF0XETXUpuPlGmllP46HfPZubF8x6EA/97vncvGDWg3jod8/noGvWg3jod8/nNl2zHsRDv3s+t+ma9TAe+t2zuXnBrAfKN8Y6gHGUDf32SMTG55quWQ/kod89l2u6Zj2Yh373PA66Zr2AA27P4aBrZlYgt+mamRXIQdfMrEAOumZmBXLQNTMrkIOumVmBHHTNzAr0/wGLWG9FaMV+xgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f136eba6438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stats(Acc, \"Accuracy\", 5, 35)\n",
    "plot_stats(F1S, \"F1-score\", 5, 35)\n",
    "plot_stats(Prec, \"Precision\", 5, 35)\n",
    "plot_stats(Rec, \"Recall\", 5, 35)\n",
    "plot_stats(FPR, \"False Positive Rate\", 5, 35)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost parameters selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'SAMME.R',\n",
       " 'base_estimator__class_weight': None,\n",
       " 'base_estimator__criterion': 'gini',\n",
       " 'base_estimator__max_depth': None,\n",
       " 'base_estimator__max_features': None,\n",
       " 'base_estimator__max_leaf_nodes': None,\n",
       " 'base_estimator__min_impurity_decrease': 0.0,\n",
       " 'base_estimator__min_impurity_split': None,\n",
       " 'base_estimator__min_samples_leaf': 1,\n",
       " 'base_estimator__min_samples_split': 2,\n",
       " 'base_estimator__min_weight_fraction_leaf': 0.0,\n",
       " 'base_estimator__presort': False,\n",
       " 'base_estimator__random_state': 0,\n",
       " 'base_estimator__splitter': 'best',\n",
       " 'base_estimator': DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "             max_features=None, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "             splitter='best'),\n",
       " 'learning_rate': 1.0,\n",
       " 'n_estimators': 50,\n",
       " 'random_state': None}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "AdaBoostClassifier(DecisionTreeClassifier(random_state=0)).get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparams = {\"n_estimators\" : range (1, 50)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 45}\n"
     ]
    }
   ],
   "source": [
    "clf = GridSearchCV(AdaBoostClassifier(DecisionTreeClassifier(random_state=0)), hyperparams, cv=5, scoring = \"f1\", n_jobs=-1)\n",
    "clf.fit(X_train_alg, y_train_alg)\n",
    "\n",
    "print(clf.best_params_)\n",
    "\n",
    "# Result: {'n_estimators': 45}, range tested: 1-50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: AdaBoost_n_est_dct_rs Accuracy: 0.99956, F1-score: 0.99966, Precision: 0.99959, Recall: 0.99973, FPR: 0.00076\n",
      "TN: 7887; FP:   6; FN:    4; TP: 14603\n",
      "\n",
      "Algorithm: AdaBoost_n_est_dct_0 Accuracy: 0.99947, F1-score: 0.99959, Precision: 0.99952, Recall: 0.99966, FPR: 0.00089\n",
      "TN: 7886; FP:   7; FN:    5; TP: 14602\n",
      "\n",
      "Algorithm: AdaBoost_n_est_dct_def Accuracy: 0.99969, F1-score: 0.99976, Precision: 0.99973, Recall: 0.99979, FPR: 0.00051\n",
      "TN: 7889; FP:   4; FN:    3; TP: 14604\n",
      "\n",
      "Algorithm: AdaBoost_n_est Accuracy: 0.99827, F1-score: 0.99866, Precision: 0.99890, Recall: 0.99843, FPR: 0.00203\n",
      "TN: 7877; FP:  16; FN:   23; TP: 14584\n",
      "\n",
      "Algorithm: AdaBoost_n_est_0 Accuracy: 0.99827, F1-score: 0.99866, Precision: 0.99890, Recall: 0.99843, FPR: 0.00203\n",
      "TN: 7877; FP:  16; FN:   23; TP: 14584\n",
      "\n",
      "Algorithm: AdaBoost_orig Accuracy: 0.99853, F1-score: 0.99887, Precision: 0.99931, Recall: 0.99843, FPR: 0.00127\n",
      "TN: 7883; FP:  10; FN:   23; TP: 14584\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/base.py:465: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n",
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/pipeline.py:331: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: AdaBoost_n_est_dct_def_scl Accuracy: 0.99938, F1-score: 0.99952, Precision: 0.99952, Recall: 0.99952, FPR: 0.00089\n",
      "TN: 7886; FP:   7; FN:    7; TP: 14600\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/base.py:465: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: AdaBoost_n_est_dct_rs_scl Accuracy: 0.99956, F1-score: 0.99966, Precision: 0.99959, Recall: 0.99973, FPR: 0.00076\n",
      "TN: 7887; FP:   6; FN:    4; TP: 14603\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/pipeline.py:331: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "num_trees = 45\n",
    "seed = 12\n",
    "\n",
    "classifiers = {\n",
    "               \"AdaBoost_n_est_dct_rs\":AdaBoostClassifier(DecisionTreeClassifier(max_depth = 13), n_estimators=num_trees, random_state = seed),\n",
    "               \"AdaBoost_n_est_dct_0\":AdaBoostClassifier(DecisionTreeClassifier(max_depth = 13), n_estimators=num_trees, random_state = 0),\n",
    "               \"AdaBoost_n_est_dct_def\":AdaBoostClassifier(DecisionTreeClassifier(max_depth = 13), n_estimators=num_trees),\n",
    "               \"AdaBoost_n_est\":AdaBoostClassifier(n_estimators=num_trees, random_state = seed),\n",
    "               \"AdaBoost_n_est_0\":AdaBoostClassifier(n_estimators=num_trees, random_state = 0), \n",
    "               \"AdaBoost_orig\":AdaBoostClassifier(),\n",
    "               \"AdaBoost_n_est_dct_def_scl\":make_pipeline(StandardScaler(), AdaBoostClassifier(DecisionTreeClassifier(max_depth = 13), n_estimators=num_trees)),\n",
    "               \"AdaBoost_n_est_dct_rs_scl\":make_pipeline(StandardScaler(), AdaBoostClassifier(DecisionTreeClassifier(max_depth = 13), n_estimators=num_trees, random_state = seed))\n",
    "}\n",
    "\n",
    "Acc = {}\n",
    "F1S = {}\n",
    "Prec = {}\n",
    "Rec = {}\n",
    "FPR = {}\n",
    "\n",
    "for name, clf in classifiers.items():\n",
    "    \n",
    "    model = clf.fit(X_train_alg, y_train_alg)\n",
    "    \n",
    "    y_pred = model.predict(X_validate_alg)\n",
    "    \n",
    "    Acc[name] = metrics.accuracy_score(y_validate_alg, y_pred)\n",
    "    F1S[name] = metrics.f1_score(y_validate_alg, y_pred)\n",
    "    Prec[name] = metrics.precision_score(y_validate_alg, y_pred)\n",
    "    Rec[name] = metrics.recall_score(y_validate_alg, y_pred)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y_validate_alg, y_pred).ravel()\n",
    "    FPR[name] = fp/(fp+tn)\n",
    "    \n",
    "    print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name]))\n",
    "    print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f136eb13eb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1354144be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1354144e10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f136eb13080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f136eb337b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stats(Acc, \"Accuracy\",7,  55)\n",
    "plot_stats(F1S, \"F1-score\", 7, 55)\n",
    "plot_stats(Prec, \"Precision\", 7, 55)\n",
    "plot_stats(Rec, \"Recall\", 7, 55)\n",
    "plot_stats(FPR, \"False Positive Rate\", 7, 55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random tree parameters selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'base_estimator__class_weight': None,\n",
       " 'base_estimator__criterion': 'entropy',\n",
       " 'base_estimator__max_depth': 13,\n",
       " 'base_estimator__max_features': None,\n",
       " 'base_estimator__max_leaf_nodes': None,\n",
       " 'base_estimator__min_impurity_decrease': 0.0,\n",
       " 'base_estimator__min_impurity_split': None,\n",
       " 'base_estimator__min_samples_leaf': 1,\n",
       " 'base_estimator__min_samples_split': 2,\n",
       " 'base_estimator__min_weight_fraction_leaf': 0.0,\n",
       " 'base_estimator__presort': False,\n",
       " 'base_estimator__random_state': 0,\n",
       " 'base_estimator__splitter': 'best',\n",
       " 'base_estimator': DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=13,\n",
       "             max_features=None, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "             splitter='best'),\n",
       " 'bootstrap': True,\n",
       " 'bootstrap_features': False,\n",
       " 'max_features': 1.0,\n",
       " 'max_samples': 1.0,\n",
       " 'n_estimators': 10,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "BaggingClassifier(DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13)).get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparams = {'n_estimators' : range(1,120)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 93}\n"
     ]
    }
   ],
   "source": [
    "clf = GridSearchCV(BaggingClassifier(DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13)), hyperparams, cv=5, scoring = \"f1\", n_jobs=-1)\n",
    "clf.fit(X_train_alg, y_train_alg)\n",
    "\n",
    "print(clf.best_params_) # --> 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: RandomTree_def Accuracy: 0.99920, F1-score: 0.99938, Precision: 0.99952, Recall: 0.99925, FPR: 0.00089\n",
      "TN: 7886; FP:   7; FN:   11; TP: 14596\n",
      "\n",
      "Algorithm: RandomTree_est_def Accuracy: 0.99951, F1-score: 0.99962, Precision: 0.99966, Recall: 0.99959, FPR: 0.00063\n",
      "TN: 7888; FP:   5; FN:    6; TP: 14601\n",
      "\n",
      "Algorithm: RandomTree   Accuracy: 0.99956, F1-score: 0.99966, Precision: 0.99966, Recall: 0.99966, FPR: 0.00063\n",
      "TN: 7888; FP:   5; FN:    5; TP: 14602\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/base.py:465: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n",
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/pipeline.py:331: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: RandomTree_13_scl Accuracy: 0.99938, F1-score: 0.99952, Precision: 0.99945, Recall: 0.99959, FPR: 0.00101\n",
      "TN: 7885; FP:   8; FN:    6; TP: 14601\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics\n",
    "\n",
    "classifiers = { \"RandomTree_def\":BaggingClassifier(DecisionTreeClassifier()),\n",
    "                \"RandomTree_est_def\":BaggingClassifier(DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13)),\n",
    "                \"RandomTree\":BaggingClassifier(DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13), n_estimators=93),\n",
    "                \"RandomTree_13_scl\":make_pipeline(StandardScaler(), BaggingClassifier(DecisionTreeClassifier(random_state=0, criterion = \"entropy\", max_depth = 13), n_estimators=93)), \n",
    "}\n",
    "\n",
    "Acc = {}\n",
    "F1S = {}\n",
    "Prec = {}\n",
    "Rec = {}\n",
    "FPR = {}\n",
    "\n",
    "for name, clf in classifiers.items():\n",
    "    \n",
    "    model = clf.fit(X_train_alg, y_train_alg)\n",
    "    \n",
    "    y_pred = model.predict(X_validate_alg)\n",
    "    \n",
    "    Acc[name] = metrics.accuracy_score(y_validate_alg, y_pred)\n",
    "    F1S[name] = metrics.f1_score(y_validate_alg, y_pred)\n",
    "    Prec[name] = metrics.precision_score(y_validate_alg, y_pred)\n",
    "    Rec[name] = metrics.recall_score(y_validate_alg, y_pred)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y_validate_alg, y_pred).ravel()\n",
    "    FPR[name] = fp/(fp+tn)\n",
    "    \n",
    "    print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name]))\n",
    "    print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67204b8b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6720eb8a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6720568d30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6719519be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67210a0cc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stats(Acc, \"Accuracy\",4, 35)\n",
    "plot_stats(F1S, \"F1-score\", 4, 35)\n",
    "plot_stats(Prec, \"Precision\", 4, 35)\n",
    "plot_stats(Rec, \"Recall\", 4, 35)\n",
    "plot_stats(FPR, \"False Positive Rate\", 4, 35)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest parameters selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 'warn',\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': 0,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "\n",
    "RandomForestClassifier(random_state=0).get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparams = {'n_estimators' : range(1, 250)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 178}\n"
     ]
    }
   ],
   "source": [
    "clf = GridSearchCV(RandomForestClassifier(random_state=0), hyperparams, cv=5, scoring = \"f1\", n_jobs=-1)\n",
    "clf.fit(X_train_alg, y_train_alg)\n",
    "\n",
    "n_estimators_calc = clf.best_params_['n_estimators']\n",
    "print(clf.best_params_)\n",
    "\n",
    "# {'n_estimators': 178}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparams = {'max_features' : [None, 'auto', 'sqrt', 'log2']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = GridSearchCV(RandomForestClassifier(random_state=0, n_estimators=41), hyperparams, cv=5 ,scoring = \"f1\", n_jobs=-1)\n",
    "# clf.fit(X_train_alg, y_train_alg)\n",
    "\n",
    "# print(clf.best_params_)\n",
    "# {'max_features': 'auto'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparams = {'max_depth' : range(1,50)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 31}\n"
     ]
    }
   ],
   "source": [
    "clf = GridSearchCV(RandomForestClassifier(random_state=0, n_estimators=n_estimators_calc, max_features='auto'), hyperparams, cv=5, scoring = \"f1\", n_jobs=-1)\n",
    "clf.fit(X_train_alg, y_train_alg)\n",
    "\n",
    "print(clf.best_params_)\n",
    "# {'max_depth': 31}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: RandomForest_new Accuracy: 0.99964, F1-score: 0.99973, Precision: 0.99966, Recall: 0.99979, FPR: 0.00063\n",
      "TN: 7888; FP:   5; FN:    3; TP: 14604\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/base.py:465: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n",
      "/home/matz/.local/lib/python3.6/site-packages/sklearn/pipeline.py:331: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm: RandomForest_new_scl Accuracy: 0.99964, F1-score: 0.99973, Precision: 0.99966, Recall: 0.99979, FPR: 0.00063\n",
      "TN: 7888; FP:   5; FN:    3; TP: 14604\n",
      "\n",
      "Algorithm: RandomForest_orig Accuracy: 0.99960, F1-score: 0.99969, Precision: 0.99973, Recall: 0.99966, FPR: 0.00051\n",
      "TN: 7889; FP:   4; FN:    5; TP: 14602\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# # zmiana n_estimators daje poprawe\n",
    "# # kfold tylko dla crossvalidation\n",
    "# # learning rate [1 - default] najlepsze wyniki\n",
    "from sklearn import metrics\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "classifiers = {\n",
    "                \"RandomForest_new\":RandomForestClassifier(n_estimators=178, max_depth=31, random_state=0),\n",
    "                \"RandomForest_new_scl\":make_pipeline(StandardScaler(),RandomForestClassifier(n_estimators=178, max_depth=31, random_state=0)),\n",
    "                \"RandomForest_orig\":RandomForestClassifier(n_estimators=100),\n",
    "}\n",
    "\n",
    "Acc = {}\n",
    "F1S = {}\n",
    "Prec = {}\n",
    "Rec = {}\n",
    "FPR = {}\n",
    "\n",
    "for name, clf in classifiers.items():\n",
    "    \n",
    "    model = clf.fit(X_train_alg, y_train_alg)\n",
    "    \n",
    "    y_pred = model.predict(X_validate_alg)\n",
    "    \n",
    "    Acc[name] = metrics.accuracy_score(y_validate_alg, y_pred)\n",
    "    F1S[name] = metrics.f1_score(y_validate_alg, y_pred)\n",
    "    Prec[name] = metrics.precision_score(y_validate_alg, y_pred)\n",
    "    Rec[name] = metrics.recall_score(y_validate_alg, y_pred)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y_validate_alg, y_pred).ravel()\n",
    "    FPR[name] = fp/(fp+tn)\n",
    "    \n",
    "    print(\"Algorithm: {0:12} Accuracy: {1:.5f}, F1-score: {2:.5f}, Precision: {3:.5f}, Recall: {4:.5f}, FPR: {5:.5f}\".format(name, Acc[name], F1S[name], Prec[name], Rec[name], FPR[name]))\n",
    "    print(\"TN: {0:3}; FP: {1:3}; FN: {2:4}; TP: {3:3}\\n\".format(tn, fp, fn, tp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1354110940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1364174588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1364085748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f135413d160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1364032080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_stats(Acc, \"Accuracy\", 4, 35)\n",
    "plot_stats(F1S, \"F1-score\", 4, 35)\n",
    "plot_stats(Prec, \"Precision\", 4, 35)\n",
    "plot_stats(Rec, \"Recall\", 4, 35)\n",
    "plot_stats(FPR, \"False Positive Rate\", 4, 35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}