Project_ML_main_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sys\n",
    "\n",
    "# ignore warnigns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_df = pd.read_csv('log2.csv')\n",
    "#log_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "allow         37640\n",
       "deny          14987\n",
       "drop          12851\n",
       "reset-both       54\n",
       "Name: Action, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df.head()\n",
    "log_df.shape\n",
    "log_df['Action'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'reset_both' and ' drop' change to 'deny'\n",
    "#log_df.loc[log_df['Action'] !='allow', 'Action'] = 'not allow'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Action', ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\": (5, 5)})\n",
    "sns.countplot(log_df['Action'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13728,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = log_df[log_df['Action'] !='allow']\n",
    "test['Source Port'].unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Source Port', 'Destination Port', 'NAT Source Port',\n",
       "       'NAT Destination Port', 'Action', 'Bytes', 'Bytes Sent',\n",
       "       'Bytes Received', 'Packets', 'Elapsed Time (sec)', 'pkts_sent',\n",
       "       'pkts_received'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 53277 entries, 0 to 65530\n",
      "Data columns (total 5 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   Source Port           53277 non-null  int64 \n",
      " 1   Destination Port      53277 non-null  int64 \n",
      " 2   NAT Source Port       53277 non-null  int64 \n",
      " 3   NAT Destination Port  53277 non-null  int64 \n",
      " 4   Action                53277 non-null  object\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "log_df = log_df[['Source Port','Destination Port', 'NAT Source Port', 'NAT Destination Port', 'Action']]\n",
    "log_df = log_df.drop_duplicates()\n",
    "log_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical features:  ['Source Port', 'Destination Port', 'NAT Source Port', 'NAT Destination Port']\n"
     ]
    }
   ],
   "source": [
    "categorical= log_df[['Source Port','Destination Port', 'NAT Source Port', 'NAT Destination Port']]\n",
    "\n",
    "print('Categorical features: ',list(categorical.columns)) # categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Action', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DYeLj4yPr8PlVcUdHBzfeeCMDAwMEg0FSUlJGsh0AOjuDDA5eHPOJEKL29guxHkFk3ImLsw17ETeiS8E//elPkV9NTU388pe/5MMPPxz2MWfPnuWxxx6jurqa/Px8AGbPnk1LSwunT58mHA6zb98+srKymDZtGklJSTQ2NgLg8XjIysoiISGBzMxMvF7vkHX4/N0NHo8H+PzdEpmZmSQkJIxkOyIixtmsS/1b/W/o6upi7dq1/OxnP7vsfX70ox+xd+9e/v7v/z6ytmTJEr72ta9F3vqVnZ3NunXrsNlsfPjhh5SXl9PT08PMmTPZtGkTiYmJtLa2UlpaSmdnJ+np6WzdupWvfvWrfPrpp5SWlvLnP/+ZyZMnU11dzU033TTiPQx3ZVu49tXRvSDXkV1binRlKxIFf+vK9opiCzBv3rzIe2ivR4qtiFxLfyu2oz6ztSyLpqamId9NJiIiwxtRbP/0pz8NuZ2ens7atWujMpCIyHg04g+iAWhtbWVgYICMjIyoDiUiMt6MKLanT5/m+9//PoFAgMHBQaZMmcJPfvITZsyYEe35RETGhRG99euZZ57hkUce4ciRIzQ2NrJ69WqqqqqiPZuIyLgxoth2dnZy3333RW4vXryY7u7uqA0lIjLejCi24XCYTz/9NHK7q6srWvOIiIxLIzqz/e53v8t3vvMd5s2bB8D+/ft58MEHozqYiMh4MqIr2+zsbABCoRAnT57E7/eTk5MT1cFERMaTEV3ZlpaWUlRURHFxMX19fbz22muUlZXx05/+NNrziYiMCyO6su3u7qa4uBiApKQkli9fTnt7e1QHExEZT0b8P8i++NBugI6Ojkt+1qyIiFzaiI4Rli9fzsKFC/nmN7+JzWajoaFB364rIjIKI4rt/fffz6xZs/jggw+Ij4/ne9/7Hrfccku0ZxMRGTdGFFuAW2+9lVtvvTWas4iIjFsj/6FdIiJyxRRbEREDFFsREQMUWxERAxRbEREDFFsREQMUWxERAxRbEREDFFsREQMUWxERAxRbEREDoh7bYDBIQUEBZ86cAWDdunW4XC4WLFjAggULOHDgAAANDQ243W5cLhfbtm2LPL65uZnFixeTm5vL+vXrGRgYAKCtrY2ioiLy8vJYvXo1PT090d6KiMgVi2psjx49ytKlSzl16lRkrampiVdeeYW6ujrq6urIycmht7eXsrIyampq8Hq9NDU1cfDgQQBKSkqoqKigvr4ey7Kora0FoKqqisLCQnw+H7NmzaKmpiaaWxERuSpRjW1tbS2VlZU4nU4APvvsM9ra2qioqMDtdrN9+3YGBwc5duwYGRkZTJ8+Hbvdjtvtxufz0draSm9vL3PmzAFg0aJF+Hw+QqEQR44cITc3d8i6iMhYNeKPWLwSGzZsGHK7s7OTu+66i2eeeYZJkyaxcuVK9uzZw6RJk3A4HJH7OZ1O/H4/gUBgyLrD4cDv99Pd3U1ycjJ2u33I+mhMnZp8FTu7vjkck2M9gsiEE9XY/rXp06fzwgsvRG4vW7YMj8dDXl7eRfe12WyX/NE7w62PRmdnkMHBi59nIoSovf1CrEcQGXfi4mzDXsQZfTfCRx99RH19feS2ZVnY7XbS0tLo6OiIrAcCAZxO50Xr7e3tOJ1OUlNTCQaDhMPhIesiImOV0dhalsXGjRs5d+4coVCI3bt3k5OTw+zZs2lpaeH06dOEw2H27dtHVlYW06ZNIykpicbGRgA8Hg9ZWVkkJCSQmZmJ1+sdsi4iMlYZPUa49dZbefTRR1m6dCkDAwO4XC4KCgoA2Lx5M2vWrKGvr4/s7OzI0UJ1dTXl5eX09PQwc+bMyI9Ur6yspLS0lB07dpCens7WrVtNbkVEZFRs1gT9meTDndkWrn01BhOZsWtLkc5sRaJgTJ3ZiohMVIqtiIgBiq2IiAGKrYiIAYqtiIgBiq2IiAGKrYiIAYqtiIgBiq2IiAGKrYiIAYqtiIgBiq2IiAGKrYiIAYqtiIgBiq2IiAGKrYiIAYqtiIgBiq2IiAGKrYiIAYqtiIgBiq2IiAGKrYiIAYqtiIgBiq2IiAGKrYiIAYqtiIgBUY1tMBikoKCAM2fOANDQ0IDb7cblcrFt27bI/Zqbm1m8eDG5ubmsX7+egYEBANra2igqKiIvL4/Vq1fT09MDwPnz53n00UeZN28eRUVFtLe3R3MbIiJXLWqxPXr0KEuXLuXUqVMA9Pb2UlZWRk1NDV6vl6amJg4ePAhASUkJFRUV1NfXY1kWtbW1AFRVVVFYWIjP52PWrFnU1NQA8Oyzz5KZmcn+/ft54IEH2LBhQ7S2ISJyTUQttrW1tVRWVuJ0OgE4duwYGRkZTJ8+Hbvdjtvtxufz0draSm9vL3PmzAFg0aJF+Hw+QqEQR44cITc3d8g6wLvvvovb7QagoKCA9957j1AoFK2tiIhcNXu0nvivrzYDgQAOhyNy2+l04vf7L1p3OBz4/X66u7tJTk7GbrcPWf/r57Lb7SQnJ9PV1UVaWtqI55s6NfmK93a9czgmx3oEkQknarH9a5ZlXbRms9lGvX45cXGju0jv7AwyOHjxnzERQtTefiHWI4iMO3FxtmEv4ozFNi0tjY6OjsjtQCCA0+m8aL29vR2n00lqairBYJBwOEx8fHxkHT6/Ku7o6ODGG29kYGCAYDBISkqKqa2IRHzlq0kkJSbGeoyo6evv5/y5vliPMS4Yi+3s2bNpaWnh9OnT3HTTTezbt4/Fixczbdo0kpKSaGxs5Pbbb8fj8ZCVlUVCQgKZmZl4vV7cbndkHSA7OxuPx8OqVavwer1kZmaSkJBgaisiEUmJiSzf+USsx4iaXzz0HKDYXgvGYpuUlMTmzZtZs2YNfX19ZGdnk5eXB0B1dTXl5eX09PQwc+ZMiouLAaisrKS0tJQdO3aQnp7O1q1bAXjiiScoLS0lPz+fyZMnU11dbWobIiJXxGZd6nB0AhjuzLZw7asxmMiMXVuKdGZ7DTkck8f9la3+exmZv3Vmq+8gExExQLEVETFAsRURMUCxFRExQLEVETFAsRURMUCxFRExQLEVETFAsRURMUCxFRExQLEVETFAsRURMUCxFRExQLEVETFAsRURMUCxFRExQLEVETFAsRURMUCxFRExQLEVETFAsRURMcDYjzKX69uUryZiT0yK9RhRM9DfR/e5/liPIeOYYisjYk9MonHLI7EeI2puX/szQLGV6NExgoiIAYqtiIgBMTlGKC4uprOzE7v98z/+mWee4X/+53/YsWMHoVCI5cuXU1RUBEBDQwObNm2ir6+PefPm8eSTTwLQ3NxMeXk5wWCQzMxMqqqqIs8nIjLWGL+ytSyLkydPUldXF/l14403sm3bNnbt2kVdXR27d+/mk08+obe3l7KyMmpqavB6vTQ1NXHw4EEASkpKqKiooL6+HsuyqK2tNb0VEZERMx7bkydPYrPZWLFiBd/+9rd55ZVXaGho4K677iIlJYVJkyaRm5uLz+fj2LFjZGRkMH36dOx2O263G5/PR2trK729vcyZMweARYsW4fP5TG9FRGTEjMf2/PnzzJ07lxdeeIFf/OIXvP7667S1teFwOCL3cTqd+P1+AoHAiNYdDgd+v9/oPkRERsP4Iec3vvENvvGNbwAwadIk7r//fjZt2sSqVauG3M9ms2FZ1kWPH259NKZOTR7V/ccTh2NyrEcYk/S6XJpel2vDeGz/8Ic/EAqFmDt3LvD5Ge60adPo6OiI3CcQCOB0OklLSxvRent7O06nc1RzdHYGGRy8ONoT4T+s9vYLo36MXpdL0+siX4iLsw17EWf8GOHChQts2bKFvr4+gsEgb7zxBj/+8Y85dOgQXV1d/OUvf+Gtt94iKyuL2bNn09LSwunTpwmHw+zbt4+srCymTZtGUlISjY2NAHg8HrKyskxvRURkxIxf2X7rW9/i6NGjLFy4kMHBQQoLC7n99tt58sknKS4uJhQKcf/993PbbbcBsHnzZtasWUNfXx/Z2dnk5eUBUF1dTXl5OT09PcycOZPi4mLTWxERGbGYvDH1hz/8IT/84Q+HrLndbtxu90X3nTt3Lm+++eZF67feeit79uyJ1ogiIteUvoNMRMQAxVZExADFVkTEAMVWRMQAxVZExADFVkTEAMVWRMQAxVZExADFVkTEAMVWRMQAxVZExADFVkTEAMVWRMQAxVZExADFVkTEAMVWRMQAxVZExADFVkTEAMVWRMQAxVZExADFVkTEAMVWRMQAxVZExADFVkTEAMVWRMQAxVZExIDrOra//vWvmT9/Pjk5Obz66quxHkdE5LLssR7gSvn9frZt28avfvUrEhMTWbJkCXfeeSf/+I//GOvRREQuct3GtqGhgbvuuouUlBQAcnNz8fl8/OAHPxjR4+PibJf92t9N+fK1GHHMGm7vw0n8ytRrPMnYcqWvy98lp17jScaWK31dJpq/9Tpdt7ENBAI4HI7IbafTybFjx0b8+CnDBHX7uoVXM9qYN3Vq8hU97v+t+q9rPMnYcqWvS/UDldd4krHlSl8XGeq6PbO1LOuiNZtNfwOLyNh03cY2LS2Njo6OyO1AIIDT6YzhRCIil3fdxvbf/u3fOHToEF1dXfzlL3/hrbfeIisrK9ZjiYhc0nV7ZpuWlsaTTz5JcXExoVCI+++/n9tuuy3WY4mIXJLNutThp4iIXFPX7TGCiMj1RLEVETFAsRURMUCxFRExQLEVETFAsY0hfWrZpQWDQQoKCjhz5kysRxkznn/+efLz88nPz2fLli2xHmfMeO6555g/fz75+fns3Lkz1uMMS7GNkS8+tWzXrl3U1dWxe/duPvnkk1iPFXNHjx5l6dKlnDp1KtajjBkNDQ28//77vPHGG3g8Ho4fP86BAwdiPVbMHT58mA8++IA333yTvXv38vLLL3Py5MlYj3VZim2M/N9PLZs0aVLkU8smutraWiorK/Wt1/+Hw+GgtLSUxMREEhISmDFjBm1tbbEeK+buuOMOXnrpJex2O52dnYTDYSZNmhTrsS7ruv0Osuvd1X5q2Xi1YcOGWI8w5tx8882R3586dQqv18vrr78ew4nGjoSEBLZv387Pf/5z8vLySEtLi/VIl6Ur2xjRp5bJaH388cc8/PDDPP3003zta1+L9ThjxuOPP86hQ4c4e/YstbW1sR7nshTbGNGnlsloNDY2snz5cp566inuu+++WI8zJpw4cYLm5mYAbrjhBlwuFx999FGMp7o8xTZG9KllMlJnz57lscceo7q6mvz8/FiPM2acOXOG8vJy+vv76e/v5+233+b222+P9ViXpTPbGNGnlslIvfjii/T19bF58+bI2pIlS1i6dGkMp4q97Oxsjh49ysKFC4mPj8flco3pv4z0qV8iIgboGEFExADFVkTEAMVWRMQAxVZExADFVkTEAMVWJoxQKMTdd9/N9773vRHd/+GHH6arqwuAFStW6IOC5KrofbYyYRw4cIB/+qd/4vjx45w4cYIZM2YMe//f/e53kd//9Kc/jfZ4Ms7pylYmjNdee417772X+fPn88tf/jKyvmfPHvLz83G73RQXF3P27FnWrVsHwIMPPsjZs2e55557+OMf/wjA7t27KSgo4Nvf/jYPP/wwLS0tAJSWlvKjH/2IZcuWkZOTw8qVK+np6TG/URmbLJEJ4OOPP7ZmzZpldXd3W0ePHrVuu+02q6ury2pubrbuvPNOq62tzbIsy9q5c6dVUVFhWZZl3XLLLVZnZ6dlWZb1rW99yzp27JjV0NBg3XvvvZH1vXv3WvPmzbMGBwetp59+2vrOd75j9fX1Wf39/dbChQutPXv2xGbDMuboGEEmhNdee43/+I//ICUlhZSUFG666SZ2795NUlISd999N+np6QAsX7582Of57W9/y/z580lNTQVg0aJFbNiwIfJTJb75zW+SmJgIwC233MK5c+eitym5rugYQca9zz77DI/HQ2NjI/fccw/33HMP7e3tvPrqq8TFxQ35aMve3l5OnDhx2eeyLvHd7ZZlMTAwAMCXvvSlyLrNZrvk/WViUmxl3Pv1r3/NlClT+O1vf8s777zDO++8w3//93/z2WefceHCBQ4dOkQgEADg9ddf58c//jEA8fHxkYh+4e6778br9UbepbB3715SUlLIyMgwuym57ugYQca91157jYceeoj4+PjI2le+8hWWLVvGb37zG0pKSnjkkUeAz38EzcaNGwHIycmhsLCQmpqayOP+/d//neXLl/Pggw8yODhIamoqP/nJT4iL03WLDE+f+iUiYoD+OhYRMUCxFRExQLEVETFAsRURMUCxFRExQLEVETFAsRURMeD/AwQbNIihB10aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# transforming categorical variables\n",
    "le = preprocessing.LabelEncoder()\n",
    "log_df['Action'] = le.fit_transform(log_df['Action'])\n",
    "log_df['Action'].unique()\n",
    "sns.set(rc={\"figure.figsize\": (5, 5)})\n",
    "sns.countplot(log_df['Action'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x = categorical\n",
    "y = log_df[['Action']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 300 ms, sys: 12.3 ms, total: 313 ms\n",
      "Wall time: 120 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Action', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "from imblearn.over_sampling import SMOTE, ADASYN\n",
    "x_resampled, y_resampled = SMOTE().fit_resample(x, y)\n",
    "sns.set(rc={\"figure.figsize\": (5, 5)})\n",
    "sns.countplot(y_resampled['Action'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_resampled, y_resampled,test_size=0.33,\n",
    "                                                   random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 574 ms, sys: 26.5 ms, total: 601 ms\n",
      "Wall time: 180 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_features = list(categorical.columns)\n",
    "categorical_transformer = Pipeline(steps=[('onehot', OneHotEncoder(sparse = True))])\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', categorical_transformer, cat_features)])\n",
    "\n",
    "preprocessor.fit(x_resampled)\n",
    "\n",
    "# transform train and test data\n",
    "X_train_transformed = preprocessor.transform(X_train)\n",
    "X_test_transformed = preprocessor.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dalyapraz/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for SVM 0.9176258043562106\n",
      "CPU times: user 5min 36s, sys: 3.09 s, total: 5min 39s\n",
      "Wall time: 5min 38s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# SVM\n",
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel='linear')\n",
    "svm.fit(X_train_transformed, y_train)\n",
    "svm_scores = svm.score(X_test_transformed, y_test)\n",
    "y_pred_svm = svm.predict(X_test_transformed)\n",
    "print('Score for SVM {}'.format(svm_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dalyapraz/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for KNeighborsClassifier 0.856342488277206\n",
      "CPU times: user 5min 1s, sys: 1min 14s, total: 6min 16s\n",
      "Wall time: 4min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# KNC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knc = KNeighborsClassifier()\n",
    "knc.fit(X_train_transformed, y_train)\n",
    "knc_scores = knc.score(X_test_transformed, y_test)\n",
    "y_pred_knc = knc.predict(X_test_transformed)\n",
    "print('Score for KNeighborsClassifier {}'.format(knc_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dalyapraz/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for RandomForestClassifier 0.989099324036295\n",
      "CPU times: user 39.3 s, sys: 74.8 ms, total: 39.4 s\n",
      "Wall time: 39.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc=RandomForestClassifier(n_estimators=10)\n",
    "rfc.fit(X_test_transformed, y_test)\n",
    "y_pred_rfc = rfc.predict(X_test_transformed)\n",
    "rfc_scores = rfc.score(X_test_transformed, y_test)\n",
    "print('Score for RandomForestClassifier {}'.format(rfc_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall SVM: 0.918\n",
      "Precision SVM: 0.922\n",
      "F1 score SVM: 0.917\n",
      "Recall KNeighborsClassifier: 0.855\n",
      "Precision KNeighborsClassifier: 0.876\n",
      "F1 score KNeighborsClassifier: 0.848\n",
      "Recall RandomForestClassifier: 0.989\n",
      "Precision RandomForestClassifier: 0.989\n",
      "F1 score RandomForestClassifier: 0.989\n",
      "CPU times: user 131 ms, sys: 2.94 ms, total: 134 ms\n",
      "Wall time: 132 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "#SVM\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_svm = recall_score(y_test, y_pred_svm, average='macro')\n",
    "print('Recall SVM: %.3f' % recall_svm)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_svm = precision_score(y_test, y_pred_svm, average='macro')\n",
    "print('Precision SVM: %.3f' % precision_svm)\n",
    "\n",
    "f1_svm = f1_score(y_test, y_pred_svm, average='macro')\n",
    "print('F1 score SVM: %.3f' % f1_svm)\n",
    "\n",
    "#KNN\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_knc = recall_score(y_test, y_pred_knc, average='macro')\n",
    "print('Recall KNeighborsClassifier: %.3f' % recall_knc)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_knc = precision_score(y_test, y_pred_knc, average='macro')\n",
    "print('Precision KNeighborsClassifier: %.3f' % precision_knc)\n",
    "\n",
    "f1_knc = f1_score(y_test, y_pred_knc, average='macro')\n",
    "print('F1 score KNeighborsClassifier: %.3f' % f1_knc)\n",
    "\n",
    "#RandomForest\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_rfc = recall_score(y_test, y_pred_rfc, average='macro')\n",
    "print('Recall RandomForestClassifier: %.3f' % recall_rfc)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_rfc = precision_score(y_test, y_pred_rfc, average='macro')\n",
    "print('Precision RandomForestClassifier: %.3f' % precision_rfc)\n",
    "\n",
    "f1_rfc = f1_score(y_test, y_pred_rfc, average='macro')\n",
    "print('F1 score RandomForestClassifier: %.3f' % f1_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 7s, sys: 29.8 s, total: 3min 37s\n",
      "Wall time: 2min 32s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "labels = ['allow', 'deny', 'drop', 'reset']\n",
    "plot_confusion_matrix(svm, X_test_transformed, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plot_confusion_matrix(knc, X_test_transformed, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plot_confusion_matrix(rfc, X_test_transformed, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def plot_cm(cm,\n",
    "                          target_names,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=None,\n",
    "                          normalize=True):\n",
    "    \"\"\"\n",
    "    given a sklearn confusion matrix (cm), make a nice plot\n",
    "\n",
    "    Arguments\n",
    "    ---------\n",
    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
    "\n",
    "    target_names: given classification classes such as [0, 1, 2]\n",
    "                  the class names, for example: ['high', 'medium', 'low']\n",
    "\n",
    "    title:        the text to display at the top of the matrix\n",
    "\n",
    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
    "\n",
    "    normalize:    If False, plot the raw numbers\n",
    "                  If True, plot the proportions\n",
    "\n",
    "    Usage\n",
    "    -----\n",
    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
    "                                                              # sklearn.metrics.confusion_matrix\n",
    "                          normalize    = True,                # show proportions\n",
    "                          target_names = y_labels_vals,       # list of names of the classes\n",
    "                          title        = best_estimator_name) # title of graph\n",
    "\n",
    "    Citiation\n",
    "    ---------\n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    import numpy as np\n",
    "    import itertools\n",
    "\n",
    "    accuracy = np.trace(cm) / np.sum(cm).astype('float')\n",
    "    misclass = 1 - accuracy\n",
    "\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SVM and KNC, RF\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "cm_knc = confusion_matrix(y_test, y_pred_knc)\n",
    "cm_rfc = confusion_matrix(y_test, y_pred_rfc)\n",
    "labels = ['allow', 'deny', 'drop', 'reset']\n",
    "plot_cm(cm_svm, labels, title = 'Confusion Matrix SVM')\n",
    "plot_cm(cm_knc, labels, title = 'Confusion Matrix KNC')\n",
    "plot_cm(cm_rfc, labels, title = 'Confusion Matrix Random Forest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### without SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y,test_size=0.33,\n",
    "                                                   random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 66.2 ms, sys: 4.99 ms, total: 71.2 ms\n",
      "Wall time: 70.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_features = list(categorical.columns)\n",
    "categorical_transformer = Pipeline(steps=[('onehot', OneHotEncoder(sparse = True))])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', categorical_transformer, cat_features)])\n",
    "\n",
    "preprocessor.fit(x)\n",
    "\n",
    "# transform train and test data\n",
    "X_train_transformed = preprocessor.transform(X_train)\n",
    "X_test_transformed = preprocessor.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dalyapraz/opt/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for SVM 0.9973268115117734\n",
      "CPU times: user 14.7 s, sys: 406 ms, total: 15.1 s\n",
      "Wall time: 14.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# SVM\n",
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel='linear')\n",
    "svm.fit(X_train_transformed, y_train)\n",
    "svm_scores = svm.score(X_test_transformed, y_test)\n",
    "y_pred_svm = svm.predict(X_test_transformed)\n",
    "print('Score for SVM {}'.format(svm_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dalyapraz/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for KNeighborsClassifier 0.9899328859060402\n",
      "CPU times: user 31.8 s, sys: 6.81 s, total: 38.6 s\n",
      "Wall time: 25.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# KNC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knc = KNeighborsClassifier()\n",
    "knc.fit(X_train_transformed, y_train)\n",
    "knc_scores = knc.score(X_test_transformed, y_test)\n",
    "y_pred_knc = knc.predict(X_test_transformed)\n",
    "print('Score for KNeighborsClassifier {}'.format(knc_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dalyapraz/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for RandomForestClassifier 0.9994312364918667\n",
      "CPU times: user 3.24 s, sys: 11.8 ms, total: 3.25 s\n",
      "Wall time: 3.26 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc=RandomForestClassifier(n_estimators=10)\n",
    "rfc.fit(X_test_transformed, y_test)\n",
    "y_pred_rfc = rfc.predict(X_test_transformed)\n",
    "rfc_scores = rfc.score(X_test_transformed, y_test)\n",
    "print('Score for RandomForestClassifier {}'.format(rfc_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall SVM: 0.859\n",
      "Precision SVM: 0.934\n",
      "F1 score SVM: 0.887\n",
      "Recall KNeighborsClassifier: 0.821\n",
      "Precision KNeighborsClassifier: 0.931\n",
      "F1 score KNeighborsClassifier: 0.856\n",
      "Recall RandomForestClassifier: 0.950\n",
      "Precision RandomForestClassifier: 0.999\n",
      "F1 score RandomForestClassifier: 0.972\n",
      "CPU times: user 41.3 ms, sys: 2.34 ms, total: 43.6 ms\n",
      "Wall time: 42.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "#SVM\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_svm = recall_score(y_test, y_pred_svm, average='macro')\n",
    "print('Recall SVM: %.3f' % recall_svm)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_svm = precision_score(y_test, y_pred_svm, average='macro')\n",
    "print('Precision SVM: %.3f' % precision_svm)\n",
    "\n",
    "f1_svm = f1_score(y_test, y_pred_svm, average='macro')\n",
    "print('F1 score SVM: %.3f' % f1_svm)\n",
    "\n",
    "#KNN\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_knc = recall_score(y_test, y_pred_knc, average='macro')\n",
    "print('Recall KNeighborsClassifier: %.3f' % recall_knc)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_knc = precision_score(y_test, y_pred_knc, average='macro')\n",
    "print('Precision KNeighborsClassifier: %.3f' % precision_knc)\n",
    "\n",
    "f1_knc = f1_score(y_test, y_pred_knc, average='macro')\n",
    "print('F1 score KNeighborsClassifier: %.3f' % f1_knc)\n",
    "\n",
    "#RandomForest\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_rfc = recall_score(y_test, y_pred_rfc, average='macro')\n",
    "print('Recall RandomForestClassifier: %.3f' % recall_rfc)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_rfc = precision_score(y_test, y_pred_rfc, average='macro')\n",
    "print('Precision RandomForestClassifier: %.3f' % precision_rfc)\n",
    "\n",
    "f1_rfc = f1_score(y_test, y_pred_rfc, average='macro')\n",
    "print('F1 score RandomForestClassifier: %.3f' % f1_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SVM and KNC, RF\n",
    "#%%time\n",
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "cm_knc = confusion_matrix(y_test, y_pred_knc)\n",
    "cm_rfc = confusion_matrix(y_test, y_pred_rfc)\n",
    "labels = ['allow', 'deny', 'drop', 'reset']\n",
    "plot_cm(cm_svm, labels, title = 'Confusion Matrix SVM')\n",
    "plot_cm(cm_knc, labels, title = 'Confusion Matrix KNC')\n",
    "plot_cm(cm_rfc, labels, title = 'Confusion Matrix Random Forest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.2 s, sys: 3.43 s, total: 22.6 s\n",
      "Wall time: 16 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "labels = ['allow', 'deny', 'drop', 'reset']\n",
    "plot_confusion_matrix(svm, X_test_transformed, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plot_confusion_matrix(knc, X_test_transformed, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plot_confusion_matrix(rfc, X_test_transformed, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NO Smote and NO OneHotCoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and test split\n",
    "x = categorical\n",
    "y = log_df['Action']\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y,test_size=0.33,\n",
    "                                                   random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for SVM 0.9819133204413605\n",
      "CPU times: user 6.4 s, sys: 190 ms, total: 6.59 s\n",
      "Wall time: 5.66 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# SVM\n",
    "from sklearn.svm import SVC\n",
    "svm = SVC(kernel='rbf')\n",
    "svm.fit(X_train, y_train)\n",
    "svm_scores = svm.score(X_test, y_test)\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "print('Score for SVM {}'.format(svm_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for KNeighborsClassifier 0.9912410419747469\n",
      "CPU times: user 2.92 s, sys: 27.8 ms, total: 2.95 s\n",
      "Wall time: 1.09 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# KNC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knc = KNeighborsClassifier()\n",
    "knc.fit(X_train, y_train)\n",
    "knc_scores = knc.score(X_test, y_test)\n",
    "y_pred_knc = knc.predict(X_test)\n",
    "print('Score for KNeighborsClassifier {}'.format(knc_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for RandomForestClassifier 0.9990899783869867\n",
      "CPU times: user 134 ms, sys: 8.15 ms, total: 142 ms\n",
      "Wall time: 151 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc=RandomForestClassifier(n_estimators=10)\n",
    "rfc.fit(X_test, y_test)\n",
    "y_pred_rfc = rfc.predict(X_test)\n",
    "rfc_scores = rfc.score(X_test, y_test)\n",
    "print('Score for RandomForestClassifier {}'.format(rfc_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall SVM: 0.732\n",
      "Precision SVM: 0.724\n",
      "F1 score SVM: 0.728\n",
      "Recall KNeighborsClassifier: 0.742\n",
      "Precision KNeighborsClassifier: 0.736\n",
      "F1 score KNeighborsClassifier: 0.739\n",
      "Recall RandomForestClassifier: 0.937\n",
      "Precision RandomForestClassifier: 0.999\n",
      "F1 score RandomForestClassifier: 0.963\n",
      "CPU times: user 47.7 ms, sys: 2.7 ms, total: 50.4 ms\n",
      "Wall time: 48.8 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dalyapraz/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "#SVM\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_svm = recall_score(y_test, y_pred_svm, average='macro')\n",
    "print('Recall SVM: %.3f' % recall_svm)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_svm = precision_score(y_test, y_pred_svm, average='macro')\n",
    "print('Precision SVM: %.3f' % precision_svm)\n",
    "\n",
    "f1_svm = f1_score(y_test, y_pred_svm, average='macro')\n",
    "print('F1 score SVM: %.3f' % f1_svm)\n",
    "\n",
    "#KNN\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_knc = recall_score(y_test, y_pred_knc, average='macro')\n",
    "print('Recall KNeighborsClassifier: %.3f' % recall_knc)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_knc = precision_score(y_test, y_pred_knc, average='macro')\n",
    "print('Precision KNeighborsClassifier: %.3f' % precision_knc)\n",
    "\n",
    "f1_knc = f1_score(y_test, y_pred_knc, average='macro')\n",
    "print('F1 score KNeighborsClassifier: %.3f' % f1_knc)\n",
    "\n",
    "#RandomForest\n",
    "# calculate recall (Recall = TruePositives / (TruePositives + FalseNegatives))\n",
    "recall_rfc = recall_score(y_test, y_pred_rfc, average='macro')\n",
    "print('Recall RandomForestClassifier: %.3f' % recall_rfc)\n",
    "\n",
    "# calculate precision (Precision = TruePositives / (TruePositives + FalsePositives))\n",
    "precision_rfc = precision_score(y_test, y_pred_rfc, average='macro')\n",
    "print('Precision RandomForestClassifier: %.3f' % precision_rfc)\n",
    "\n",
    "f1_rfc = f1_score(y_test, y_pred_rfc, average='macro')\n",
    "print('F1 score RandomForestClassifier: %.3f' % f1_rfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.65 s, sys: 36.2 ms, total: 2.68 s\n",
      "Wall time: 1.74 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "labels = ['allow', 'deny', 'drop', 'reset']\n",
    "plot_confusion_matrix(svm, X_test, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plot_confusion_matrix(knc, X_test, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plot_confusion_matrix(rfc, X_test, y_test, cmap = 'Blues', display_labels = labels)\n",
    "plt.show() "
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}