K_Mean_deep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
    "from sklearn.metrics.cluster import contingency_matrix\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import math\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"data_final_deep.csv\")\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = []\n",
    "for i in df['xAttack']:\n",
    "    if i==\"normal\":\n",
    "        Y.append(0)\n",
    "    elif i==\"dos\":\n",
    "        Y.append(1)\n",
    "    elif i==\"r2l\":\n",
    "        Y.append(2)\n",
    "    elif i==\"probe\":\n",
    "        Y.append(3)\n",
    "    elif i==\"u2r\":\n",
    "        Y.append(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.84902177, 0.8448608 , 0.84308496, ..., 0.85015785, 0.84556021,\n",
       "        0.84667182],\n",
       "       [0.80426793, 0.80019638, 0.79827975, ..., 0.80592719, 0.80110199,\n",
       "        0.8025164 ],\n",
       "       [0.78803915, 0.78406033, 0.78170094, ..., 0.78968705, 0.78495992,\n",
       "        0.78600073],\n",
       "       ...,\n",
       "       [0.83265752, 0.82842734, 0.82666033, ..., 0.83400816, 0.82922069,\n",
       "        0.83028288],\n",
       "       [0.79071004, 0.78668971, 0.7842709 , ..., 0.79223445, 0.78757703,\n",
       "        0.78860465],\n",
       "       [0.84998933, 0.84581503, 0.84409876, ..., 0.85112144, 0.84653713,\n",
       "        0.84767035]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,0:14].values\n",
    "kmeans = KMeans(n_clusters=5).fit(X)\n",
    "kmeans.labels_\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3676 33765     9  1429     1]\n",
      " [55984  2112   679  4327    30]\n",
      " [   49     0     0  2720     0]\n",
      " [ 7553  3165   258   884    21]\n",
      " [   81  6885    49  2296     0]]\n",
      "0.8486501075627317\n"
     ]
    }
   ],
   "source": [
    "kmeans.cluster_centers_\n",
    "cn = contingency_matrix(kmeans.labels_,Y)\n",
    "print(cn)\n",
    "ans = 0\n",
    "for i in cn:\n",
    "    ans = ans + max(i)\n",
    "purity = ans/len(df)\n",
    "print(purity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 ... 3 0 1]\n"
     ]
    }
   ],
   "source": [
    "ypred = kmeans.predict(X)\n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125973, 14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def euclid(a,b):\n",
    "    return np.sqrt(np.sum((a-b)**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = X.shape[0]\n",
    "d = X.shape[1]\n",
    "clusters = np.zeros(n)\n",
    "distances = np.zeros((n,k))\n",
    "mean = np.mean(X, axis = 0)\n",
    "std = np.std(X, axis = 0)\n",
    "centers_new = np.random.randn(k,d)*std + mean\n",
    "centers_old = np.zeros([k,d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 14)\n",
      "(5, 14)\n"
     ]
    }
   ],
   "source": [
    "print(centers_old.shape)\n",
    "print(centers_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.624605125174371\n"
     ]
    }
   ],
   "source": [
    "error = 0\n",
    "for i in range(5):\n",
    "    error = error + euclid(centers_new[i], centers_old[i])\n",
    "\n",
    "print(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "103\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.404556872502626\n",
      "104\n",
      "M= 58814\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.599927446842567\n",
      "105\n",
      "M= 771\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.272022545086994\n",
      "106\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.332626246065443\n",
      "107\n",
      "M= 9826\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.504004891920971\n",
      "108\n",
      "M= 55317\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.363518763716145\n",
      "109\n",
      "M= 63345\n",
      "M= 35291\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.547967452417847\n",
      "110\n",
      "M= 8279\n",
      "M= 21412\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.481407570378748\n",
      "111\n",
      "M= 1\n",
      "M= 68966\n",
      "M= 0\n",
      "FLAG =  True\n",
      "15.337190442745612\n",
      "112\n",
      "M= 10601\n",
      "M= 66362\n",
      "M= 1462\n",
      "M= 7288\n",
      "M= 40260\n",
      "FLAG =  False\n",
      "1.5111906079668493\n",
      "113\n",
      "M= 9583\n",
      "M= 64217\n",
      "M= 2427\n",
      "M= 11111\n",
      "M= 38635\n",
      "FLAG =  False\n",
      "0.05551988288944052\n",
      "114\n",
      "M= 9385\n",
      "M= 63882\n",
      "M= 2663\n",
      "M= 11556\n",
      "M= 38487\n",
      "FLAG =  False\n",
      "0.013462738883887375\n",
      "115\n",
      "M= 9340\n",
      "M= 63733\n",
      "M= 2728\n",
      "M= 11661\n",
      "M= 38511\n",
      "FLAG =  False\n",
      "0.004840250537738923\n",
      "116\n",
      "M= 9322\n",
      "M= 63625\n",
      "M= 2752\n",
      "M= 11716\n",
      "M= 38558\n",
      "FLAG =  False\n",
      "0.0024186378230322114\n",
      "117\n",
      "M= 9313\n",
      "M= 63547\n",
      "M= 2763\n",
      "M= 11740\n",
      "M= 38610\n",
      "FLAG =  False\n",
      "0.001536572500616603\n",
      "118\n",
      "M= 9311\n",
      "M= 63478\n",
      "M= 2767\n",
      "M= 11756\n",
      "M= 38661\n",
      "FLAG =  False\n",
      "0.0011247068986993227\n",
      "119\n",
      "M= 9310\n",
      "M= 63419\n",
      "M= 2768\n",
      "M= 11774\n",
      "M= 38702\n",
      "FLAG =  False\n",
      "0.000781565337912332\n",
      "120\n",
      "M= 9309\n",
      "M= 63360\n",
      "M= 2769\n",
      "M= 11797\n",
      "M= 38738\n",
      "FLAG =  False\n",
      "0.000736257417021198\n",
      "121\n",
      "M= 9310\n",
      "M= 63305\n",
      "M= 2769\n",
      "M= 11817\n",
      "M= 38772\n",
      "FLAG =  False\n",
      "0.0006650644066893365\n",
      "122\n",
      "M= 9311\n",
      "M= 63263\n",
      "M= 2769\n",
      "M= 11824\n",
      "M= 38806\n",
      "FLAG =  False\n",
      "0.0005830782573468536\n",
      "123\n",
      "M= 9311\n",
      "M= 63219\n",
      "M= 2769\n",
      "M= 11837\n",
      "M= 38837\n",
      "FLAG =  False\n",
      "0.0005464224186596558\n",
      "124\n",
      "M= 9311\n",
      "M= 63178\n",
      "M= 2769\n",
      "M= 11862\n",
      "M= 38853\n",
      "FLAG =  False\n",
      "0.0003949965979737291\n",
      "125\n",
      "M= 9311\n",
      "M= 63149\n",
      "M= 2769\n",
      "M= 11872\n",
      "M= 38872\n",
      "FLAG =  False\n",
      "0.00034654756290105227\n",
      "126\n",
      "M= 9311\n",
      "M= 63112\n",
      "M= 2769\n",
      "M= 11898\n",
      "M= 38883\n",
      "FLAG =  False\n",
      "0.00032491709417980304\n",
      "127\n",
      "M= 9311\n",
      "M= 63091\n",
      "M= 2769\n",
      "M= 11910\n",
      "M= 38892\n",
      "FLAG =  False\n",
      "0.00020834758103852518\n",
      "128\n",
      "M= 9311\n",
      "M= 63068\n",
      "M= 2769\n",
      "M= 11923\n",
      "M= 38902\n",
      "FLAG =  False\n",
      "0.0002291571487888636\n",
      "129\n",
      "M= 9311\n",
      "M= 63049\n",
      "M= 2769\n",
      "M= 11935\n",
      "M= 38909\n",
      "FLAG =  False\n",
      "0.00017798612540474257\n",
      "130\n",
      "M= 9311\n",
      "M= 63029\n",
      "M= 2769\n",
      "M= 11950\n",
      "M= 38914\n",
      "FLAG =  False\n",
      "0.0001662866141913599\n",
      "131\n",
      "M= 9311\n",
      "M= 63015\n",
      "M= 2769\n",
      "M= 11959\n",
      "M= 38919\n",
      "FLAG =  False\n",
      "0.00012945896040930693\n",
      "132\n",
      "M= 9311\n",
      "M= 63002\n",
      "M= 2769\n",
      "M= 11968\n",
      "M= 38923\n",
      "FLAG =  False\n",
      "0.00011446290736408944\n",
      "133\n",
      "M= 9311\n",
      "M= 62995\n",
      "M= 2769\n",
      "M= 11973\n",
      "M= 38925\n",
      "FLAG =  False\n",
      "6.024318405765273e-05\n",
      "134\n",
      "M= 9311\n",
      "M= 62990\n",
      "M= 2769\n",
      "M= 11975\n",
      "M= 38928\n",
      "FLAG =  False\n",
      "5.683734534238591e-05\n",
      "135\n",
      "M= 9311\n",
      "M= 62985\n",
      "M= 2769\n",
      "M= 11978\n",
      "M= 38930\n",
      "FLAG =  False\n",
      "4.803024071850428e-05\n",
      "136\n",
      "M= 9311\n",
      "M= 62983\n",
      "M= 2769\n",
      "M= 11979\n",
      "M= 38931\n",
      "FLAG =  False\n",
      "2.0968289288091925e-05\n",
      "137\n",
      "M= 9311\n",
      "M= 62981\n",
      "M= 2769\n",
      "M= 11980\n",
      "M= 38932\n",
      "FLAG =  False\n",
      "2.0965560029377175e-05\n",
      "138\n",
      "M= 9311\n",
      "M= 62980\n",
      "M= 2769\n",
      "M= 11980\n",
      "M= 38933\n",
      "FLAG =  False\n",
      "1.4879823507108628e-05\n",
      "139\n",
      "M= 9311\n",
      "M= 62980\n",
      "M= 2769\n",
      "M= 11980\n",
      "M= 38933\n",
      "FLAG =  False\n",
      "0.0\n",
      "140\n"
     ]
    }
   ],
   "source": [
    "iteration = 0\n",
    "force = False\n",
    "while (error != 0) or iteration<1000:\n",
    "    force = False\n",
    "    for i in range(k):\n",
    "        distances[:,i] = np.linalg.norm(X - centers_new[i], axis=1)\n",
    "    \n",
    "    clusters = np.argmin(distances, axis = 1)\n",
    "    \n",
    "    centers_old = copy.deepcopy(centers_new)\n",
    "    for i in range(k):\n",
    "        member = 0\n",
    "        new_center = [0] * d\n",
    "        \n",
    "        for c in range(len(clusters)):\n",
    "            if clusters[c] == i:\n",
    "                member = member + 1\n",
    "                for j in range(0,d):\n",
    "                    new_center[j] += X[c][j]\n",
    "        \n",
    "        print(\"M=\",member)\n",
    "        if member == 0:\n",
    "#             pass\n",
    "            force = True\n",
    "#             iteration = 0\n",
    "            centers_old = np.zeros([k,d])\n",
    "            centers_new = np.random.randn(k,d)*std + mean\n",
    "            break\n",
    "        else:\n",
    "            new_center = np.array(new_center)\n",
    "            centers_new[i] = new_center/member\n",
    "        \n",
    "#     print(\"d=\",d)\n",
    "    print(\"FLAG = \",force)\n",
    "    error = 0\n",
    "    for i in range(5):\n",
    "        error = error + euclid(centers_new[i], centers_old[i])\n",
    "    iteration = iteration + 1\n",
    "    print(error)\n",
    "    print(iteration)\n",
    "    if error == 0 and force == False:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(k):\n",
    "#     distances[:,i] = np.linalg.norm(X - centers_new[i], axis=1)\n",
    "    \n",
    "# clusters = np.argmin(distances, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 4, ..., 3, 4, 1])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters\n",
    "\n",
    "# centers_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   81  6885    49  2296     0]\n",
      " [55860  2093   672  4325    30]\n",
      " [   49     0     0  2720     0]\n",
      " [ 7644  3169   264   882    21]\n",
      " [ 3709 33780    10  1433     1]]\n"
     ]
    }
   ],
   "source": [
    "cont = contingency_matrix(clusters,Y)\n",
    "print(cont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8485072198010685\n"
     ]
    }
   ],
   "source": [
    "ans = 0\n",
    "for i in cont:\n",
    "    ans = ans + max(i)\n",
    "purity = ans/len(df)\n",
    "print(purity)"
   ]
  }
 ],
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