05_3_machine_learning_implementation_with_7_feature.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File              ML algorithm       accuracy        Precision       Recall          F1-score        Time           \n",
      "all_data          Naive Bayes        0.82            0.8             0.82            0.81            1.6258         \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          QDA                0.38            0.83            0.38            0.41            1.9025         \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          Random Forest      0.94            0.95            0.94            0.94            20.512         \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          ID3                0.95            0.95            0.95            0.95            11.5528        \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          AdaBoost           0.94            0.94            0.94            0.94            144.1661       \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          MLP                0.84            0.82            0.84            0.79            51.7993        \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEICAYAAAB1f3LfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFpJJREFUeJzt3X+0XlV95/H3x/wAW42gRKcSfqQDItSFiFdE7BCliwqdVrDTpcnU5Y/aoUuLs4aZcaTTWWrxV2UxpYNSO+nU8cdyiJhqDVWMM4JW2zByoyCGAAaWQgbEIFII2krwO3+cc+Ex3Jv7JPs+uTzJ+7XWWXnOOfvss09Wcj937/2cc1JVSJK0p54w3w2QJI03g0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEINE+KcmHk7yr//ySJFvnu03Svsog0VhL8qUkP0xyQEMdleTBJNuT/CDJF5O8ajeO3ytBNRfnSfK6/nr/ZKftZ/fbP9yvH9mvL5ymjnckeaj/+7ovyd8neVFLuzTeDBKNrSRHAv8CKODljdU9t6qeBBwDfBj4QJK3N9b5eHUr8KqdQuI1wC27Uccn+r+vpcBXgU8lyRy2UWPEINE4ew1wDd0P/tfORYVVdU9VfQx4I/AHSZ4GkOT1STYneSDJbUl+r9/+88CVwDP739C3J3lmkpOSbOh/Y78ryQeSLO6PSZKLk3w/yT8k+WaS5/T7DkhyUZLbk9yd5M+TPHGm8+zhZX4PuAF4WX/OpwKnAOv24O/rIeAjwD8DnraH7dGYM0g0zl4DfLxfXpbkGXNY92eAhcBJ/fr3gV8HlgCvBy5OcmJVPQicCdxZVU/qlzuBh4HzgEOAFwG/Arypr+tXgVOBZwEHAa8CftDve1+//QTgKOBQ4G27OM+e+ijd3x/Ayv56/2l3K+mHFF8HbK2qexraozFmkGgsJfll4Ajg8qraSDdc86/nqv7+N+17gKf265+tqlur82XgC3TDajMdv7GqrqmqHVX1HeC/Ayv63Q8BTwaeDaSqNlfVXf3Q0L8Bzquqe6vqAeA9dD/o59qngZckeQpdoHx0N49/ZZL7gDuA5wNnz3H7NEYMEo2r1wJfGPgt+H8xR8NbAEkW0Y3/39uvn5nkmiT39j9Af42utzHT8c9K8jdJvpfkfrpAOASgqq4CPgBcCtydZHWSJf35fg7Y2A+J3Qd8vt8+TJsPHxj22r6rslX1Y+CzwH8BDqmqvxvmHAMur6qDqurpVXVaH+baTxkkGjtJngi8EljR/6D+Ht0w0nOTPHeOTnMWsAP4Wj9881fARcAzquog4HPA1OTydI/Q/iBwE3B0VS0B/vNAearqkqp6PvBLdENZb6HrAf0Y+KX+h/RBVfWUflJ7pvM8oqpuHxj2etKuyvY+CvwH4GNDlJVmZJBoHJ1NNwdxHN1cwgnAscBXeHTcf48keWqS36brLbyvqn4ALAYOALYBO5KcSTfPMeVu4Gn9MNGUJwP3A9uTPJtu8n7qHC9I8sK+1/Mg8I/Aw1X1U+Av6OZfnt6XPTTJy3ZxnhZfBk4H3r+LMgckOXBg8WeGHsN/FBpHrwX+Z/8b+PemFrrhot+e7t6HIVzfDwdtAX6Xbp7ibQD9XMW/BS4Hfkg3F/PIN5yq6ibgMuC2fkjqmcB/7Ms9QBcOnxg415J+2w+B79JNtF/U73tr34Zr+iGx/0P3leSZzrPH+vmeL1bVvbsotp2ulzS1nNZyTu2b4outJEkt7JFIkpoYJJKkJgaJJKmJQSJJarIn324ZO4ccckgdeeSR890MSRorGzduvKeqZr0hdr8IkiOPPJLJycn5boYkjZUk3x2mnENbkqQmBokkqYlBIklqYpBIkpoYJJKkJiMNkiRnJLk5yZYk50+z/+Ik1/XLLf37F6b2vS/Jt/rlVQPblyf5v0m+neQTU68vlSTNj5EFSZIFdI/iPpPucd+rkhw3WKaqzquqE6rqBLpHWX+qP/ZfAifSPR78hcBb+hf/QPcq0our6mi6p6e+YVTXIEma3Sh7JCcBW6rqtqr6CbCG7mVBM1lF94hs6ILny/1rSh8ErgfO6F9Fehqwti/3EXzFpyTNq1HekHgo3fucp2yl6108RpIjgOXAVf2m64G3J/kTulePvhS4EXgacF9V7Rio89AZ6jwHOAfg8MMPb7oQ7afeMVfvj3oceMc/zHcLtA8bZZBkmm0zvfxkJbC2qh4GqKovJHkB8Pd0b6XbQPfa06HrrKrVwGqAiYkJX7qi3ZY/up994X09Sah3zHcrtC8b5dDWVuCwgfVlwJ0zlF3Jo8NaAFTVu/v5k9PpAuTbdO+0PmjgDXi7qlOStBeMMkiuBY7uv2W1mC4s1u1cKMkxwMF0vY6pbQuSPK3/fDxwPPCF6n49vBr4rb7oa4HPjPAaJEmzGNnQVlXtSHIusB5YAHyoqjYluQCYrKqpUFkFrKmfHUNYBHylm1vnfuDVA/MibwXWJHkX8A3gL0d1DZKk2e0X72yfmJgon/6r3ZVk35kj2QeuQ3tfko1VNTFbOe9slyQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUpORBkmSM5LcnGRLkvOn2X9xkuv65ZYk9w3suzDJpiSbk1ySJP32VUluSPLNJJ9Pcsgor0GStGsjC5IkC4BLgTOB44BVSY4bLFNV51XVCVV1AvB+4FP9sacALwaOB54DvABYkWQh8N+Al1bV8cA3gXNHdQ2SpNmNskdyErClqm6rqp8Aa4CzdlF+FXBZ/7mAA4HFwAHAIuBuIP3y830PZQlw52iaL0kaxiiD5FDgjoH1rf22x0hyBLAcuAqgqjYAVwN39cv6qtpcVQ8BbwRuoAuQ44C/nKHOc5JMJpnctm3b3FyRJOkxRhkkmWZbzVB2JbC2qh4GSHIUcCywjC58TktyapJFdEHyPOCZdENbfzBdhVW1uqomqmpi6dKlbVciSZrRKINkK3DYwPoyZh6GWsmjw1oArwCuqartVbUduBI4GTgBoKpuraoCLgdOmeuGS5KGN8oguRY4OsnyJIvpwmLdzoWSHAMcDGwY2Hw7/eR63wtZAWwG/h9wXJKpLsbp/XZJ0jxZOKqKq2pHknOB9cAC4ENVtSnJBcBkVU2FyipgTd/DmLIWOI1uLqSAz1fVFQBJ/gj42yQPAd8FXjeqa5AkzS4/+/N73zQxMVGTk5Pz3QyNmSTsC/8/9pXr0N6XZGNVTcxWzjvbJUlNDBJJUhODRJLUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDUxSCRJTQwSSVITg0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElNDBJJUhODRJLUxCCRJDUxSCRJTYYKknReneRt/frhSU4abdMkSeNg2B7JnwEvAlb16w8Al852UJIzktycZEuS86fZf3GS6/rlliT3Dey7MMmmJJuTXJIk/fbFSVb35W9K8q+GvAZJ0ggsHLLcC6vqxCTfAKiqHyZZvKsDkiygC5vTga3AtUnWVdWNU2Wq6ryB8m8Gntd/PgV4MXB8v/urwArgS8AfAt+vqmcleQLw1CGvQZI0AsMGyUN9MBRAkqXAT2c55iRgS1Xd1h+zBjgLuHGG8quAt/efCzgQWAwEWATc3e/7HeDZAFX1U+CeIa9BkjQCww5tXQJ8Gnh6knfT9RDeM8sxhwJ3DKxv7bc9RpIjgOXAVQBVtQG4GrirX9ZX1eYkB/WHvDPJ15N8MskzZqjznCSTSSa3bds21EVKknbfUEFSVR8H/hPwXrof7GdX1SdnOSzTVTVD2ZXA2qp6GCDJUcCxwDK68Dktyal0PahlwN9V1YnABuCiGdq8uqomqmpi6dKlszRVkrSnZh3a6uchvllVzwFu2o26twKHDawvA+6coexK4PcH1l8BXFNV2/s2XAmcDHwF+BFd7wjgk8AbdqNNkqQ5NmuPpJ+HuD7J4btZ97XA0UmW9xPzK4F1OxdKcgxwMF3vYsrtwIokC5Msopto31xVBVwBvKQv9yvMPOciSdoLhp1s/wVgU5KvAQ9Obayql890QFXtSHIusB5YAHyoqjYluQCYrKqpUFkFrOlDYspa4DTgBrrhsM9X1RX9vrcCH0vyp8A24PVDXoMkaQTysz+/ZyiUrJhue1V9ec5bNAITExM1OTk5383QmEnCMP8/Hu/2levQ3pdkY1VNzFZuqB7JuASGJGnvGypIkjzAo9+4Wkx3X8eDVbVkVA2TJI2HYXskTx5cT3I23Q2HkqT93B49/beq/ppuMlyStJ8bdmjrNwdWnwBMMPPNhZKk/ciwX//9jYHPO4Dv0D03S5K0nxt2jsR7NSRJ0xr2xVYXJlmSZFGSLya5J8mrR904SdLj37CT7b9aVfcDv073DK1nAW8ZWaskSWNj2CBZ1P/5a8BlVXXviNojSRozw062X5HkJuDHwJv6F1v94+iaJUkaF8O+j+R8une2T1TVQ3QPbvRbW5KkoXsk0L1g6vQkBw5s++gct0eSNGaGvSHx7XTvADkO+BxwJt3rdg0SSdrPDTvZ/lt0L5H6Xn9PyXOBA0bWKknS2Bg2SH7cvylxR5IlwPeBXxxdsyRJ42LYOZLJJAcBfwFsBLYDXxtZqyRJY2PYR6S8qf/450k+Dyypqm+OrlmSpHEx7CNSkuTVSd5WVd8B7kvi+0gkSUPPkfwZ3X0kq/r1B4BLR9IiSdJYGXaO5IVVdWKSbwBU1Q+TLB5huyRJY2LYHslDSRbQv8yqf0TKT0fWKknS2Bg2SC4BPg08Pcm76W5GfM/IWiVJGhvDfmvr40k20t2UGODsqto80pZJksbC7jxr627gK/0xT0xyYlV9fTTNkiSNi2GftfVO4HXArfTzJP2fp42mWZKkcTFsj+SVwD+vqp+MsjGSpPEz7GT7t4CDRtkQSdJ4GrZH8l7gG0m+BfzT1MaqevlIWiVJGhvDBslHgPcBN+D9I5KkAcMGyT1VdclIWyJJGkvDzpFsTPLeJC9KcuLUMttBSc5IcnOSLUnOn2b/xUmu65dbktw3sO/CJJuSbE5ySZLsdOy6fqhNkjSPhu2RPK//8+SBbbv8+m//SJVLgdOBrcC1SdZV1Y2PVFB13kD5N0+dJ8kpwIuB4/vdXwVWAF/q9/8m3TtRJEnzbNg721+6B3WfBGypqtsAkqwBzgJunKH8KuDtU6cEDgQW091Jv4juhkiSPAn498A5wOV70C5J0hwadmjrEUn+ZsiihwJ3DKxv7bdNV+cRwHLgKoCq2gBcDdzVL+sHHsnyTuC/Aj+apZ3nJJlMMrlt27YhmyxJ2l27HSTMEAbTyDTbapptACuBtVX1MECSo4BjgWX9+U5LcmqSE4CjqurTs528qlZX1URVTSxdunTIJkuSdtfuPGtryjeGLLcVOGxgfRlw5wxlVwK/P7D+CuCaqtoOkORKuvmZB4DnJ/kOXdufnuRLVfWSoVsvSZpTu+yRJDl8521V9TtD1n0tcHSS5f1LsFYC66Y5xzHAwcCGgc23AyuSLEyyiG6ifXNVfbCqnllVRwK/DNxiiEjS/JptaOuvpz4k+avdqbiqdgDnAuuBzcDlVbUpyQVJBu+IXwWsqarBYa+1dA+IvAG4Hri+qq7YnfNLkvaO2Ya2Buc5fnF3K6+qzwGf22nb23Zaf8c0xz0M/N4sdX8HeM7utkmSNLdm65HUDJ8lSQJm75E8N8n9dD2TJ/af6derqpaMtHWSpMe9XQZJVS3YWw2RJI2nPbmPRJKkRxgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKmJQSJJamKQSJKaGCSSpCYGiSSpiUEiSWpikEiSmhgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKmJQSJJarJwvhsgPZ4lme8mNDv44IPnuwnaxxkk0gyqauTnSLJXziONkkNbkqQmBokkqYlBIklqYpBIkpoYJJKkJiMNkiRnJLk5yZYk50+z/+Ik1/XLLUnuG9h3YZJNSTYnuSSdn0vy2SQ39fv+eJTtlyTNbmRf/02yALgUOB3YClybZF1V3ThVpqrOGyj/ZuB5/edTgBcDx/e7vwqsAL4GXFRVVydZDHwxyZlVdeWorkOStGuj7JGcBGypqtuq6ifAGuCsXZRfBVzWfy7gQGAxcACwCLi7qn5UVVcD9HV+HVg2ovZLkoYwyiA5FLhjYH1rv+0xkhwBLAeuAqiqDcDVwF39sr6qNu90zEHAbwBfnKHOc5JMJpnctm1b46VIkmYyyiCZ7tkSM93CuxJYW1UPAyQ5CjiWrrdxKHBaklMfqThZSNd7uaSqbpuuwqpaXVUTVTWxdOnShsuQJO3KKINkK3DYwPoy4M4Zyq7k0WEtgFcA11TV9qraDlwJnDywfzXw7ar60zlsryRpD4wySK4Fjk6yvJ8YXwms27lQkmOAg4ENA5tvB1YkWZhkEd1E++a+/LuApwD/boRtlyQNaWRBUlU7gHOB9XQhcHlVbUpyQZKXDxRdBaypn31y3VrgVuAG4Hrg+qq6Isky4A+B44Cv918b/t1RXYMkaXbZH548OjExUZOTk/PdDOkxfPqvHs+SbKyqidnKeWe7JKmJQSJJamKQSJKaGCSSpCYGiSSpiUEiSWpikEiSmhgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKmJQSJJamKQSJKaGCSSpCYGiSSpiUEiSWpikEiSmhgkkqQmBokkqYlBIklqYpBIkpoYJJKkJgaJJKmJQSJJamKQSJKaGCSSpCYGiSSpiUEiSWpikEiSmow0SJKckeTmJFuSnD/N/ouTXNcvtyS5b2DfhUk2Jdmc5JIk6bc/P8kNfZ2PbJckzY+RBUmSBcClwJnAccCqJMcNlqmq86rqhKo6AXg/8Kn+2FOAFwPHA88BXgCs6A/7IHAOcHS/nDGqa5AkzW6UPZKTgC1VdVtV/QRYA5y1i/KrgMv6zwUcCCwGDgAWAXcn+QVgSVVtqKoCPgqcPaoLkCTNbpRBcihwx8D61n7bYyQ5AlgOXAVQVRuAq4G7+mV9VW3uj986ZJ3nJJlMMrlt27bGS5GGk2S3lj05xtFcPd4sHGHd0/1rrxnKrgTWVtXDAEmOAo4FlvX7/3eSU4EfD1tnVa0GVgNMTEzMdF5pTnUdZWn/MsoeyVbgsIH1ZcCdM5RdyaPDWgCvAK6pqu1VtR24Eji5r3PZQLld1SlJ2gtGGSTXAkcnWZ5kMV1YrNu5UJJjgIOBDQObbwdWJFmYZBHdRPvmqroLeCDJyf23tV4DfGaE1yBJmsXIgqSqdgDnAuuBzcDlVbUpyQVJXj5QdBWwpn52TGAtcCtwA3A9cH1VXdHveyPwP4AtfZkrR3UNkqTZZX8Y052YmKjJycn5boYkjZUkG6tqYrZy3tkuSWpikEiSmhgkkqQmBokkqcl+MdmeZBvw3fluhzSNQ4B75rsR0gyOqKqlsxXaL4JEerxKMjnMt2KkxzOHtiRJTQwSSVITg0SaX6vnuwFSK+dIJElN7JFIkpoYJJKkJgaJNA+SfCjJ95N8a77bIrUySKT58WHgjPluhDQXDBJpHlTV3wL3znc7pLlgkEiSmhgkkqQmBokkqYlBIklqYpBI8yDJZcAG4JgkW5O8Yb7bJO0pH5EiSWpij0SS1MQgkSQ1MUgkSU0MEklSE4NEktTEIJEkNTFIJElN/j9/HGN24CX12QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all_data          Nearest Neighbors  0.97            0.97            0.97            0.97            1038.2531      \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mission accomplished!\n",
      "Total operation time: =  12714.621414899826 seconds\n"
     ]
    }
   ],
   "source": [
    "##  \"all_data.csv\" file is required for the operation of the program.\n",
    "##  \"all_data.csv\" file must be located in the same directory as the program.\n",
    "\n",
    "##  the purpose of this program is to apply machine learning algorithms to the dataset and observe the performance of algorithms.\n",
    "##  the algorithms used are:Naive Bayes, QDA, Random Forest, ID3, AdaBoost, MLP, Nearest Neighbors\n",
    "##  As the program display output data include: file name, machine learning algorithm name, accuracy,Precision, Recall, F1-score,Time\n",
    "##  the program will create a CSV file that prints the results and a folder containing graphics.\n",
    "\n",
    "##  the some codes parts used for calculation and graphing are taken from the following site.\n",
    "##  http://scikit-learn.org\n",
    "\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import os\n",
    "import pandas as pd\n",
    "import csv\n",
    "import time\n",
    "import warnings\n",
    "import math\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "result=\"./results/results_3.csv\" #a CSV file is named in which the results are saved.\n",
    "csv_files=[\"all_data.csv\"]# CSV files names: #The names of the dataset files (csv_files).\n",
    "path=\"\"\n",
    "repetition=10\n",
    "\n",
    "\n",
    "def folder(f_name): #this function creates a folder named \"results\" and \"result_graph_1\" in the program directory.\n",
    "    try:\n",
    "        if not os.path.exists(f_name):\n",
    "            os.makedirs(f_name)\n",
    "    except OSError:\n",
    "        print (\"The folder could not be created!\")\n",
    "\n",
    "folder_name=\"./results/\"\n",
    "folder(folder_name)\n",
    "folder_name=\"./results/result_graph_3/\"\n",
    "folder(folder_name)\n",
    "\n",
    "\n",
    "#The machine learning algorithms to be used are defined in a dictionary (ml_list).\n",
    "ml_list={\n",
    "\"Naive Bayes\":GaussianNB(),\n",
    "\"QDA\":QDA(),\n",
    "\"Random Forest\":RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "\"ID3\" :DecisionTreeClassifier(max_depth=5,criterion=\"entropy\"),\n",
    "\"AdaBoost\":AdaBoostClassifier(),\n",
    "\"MLP\":MLPClassifier(hidden_layer_sizes=(13,13,13),max_iter=500),\n",
    "\"Nearest Neighbors\":KNeighborsClassifier(3)}\n",
    "\n",
    "\n",
    "\n",
    "#list of all columns to be imported\n",
    "# the 7 features with the highest importance weight selected by the file \"04_2_feature_selection_for_attack_files.py\" are used here. (+ Label Feature)\n",
    "\n",
    "features={\"all_data\":[\"Bwd Packet Length Std\", \"Flow Bytes/s\", \"Total Length of Fwd Packets\", \"Fwd Packet Length Std\",\n",
    "     \"Flow IAT Std\", \"Flow IAT Min\", \"Fwd IAT Total\",\"Label\"]}\n",
    "\n",
    "seconds=time.time()#time stamp for all processing time\n",
    "\n",
    "\n",
    "\n",
    "def f1_score(raw_f1): #this function, separates the \"classification report\" sent to itself and makes the precision, f-measure, and recall values into float values.\n",
    "    raw_f1=raw_f1[raw_f1.index(\"total\"):-2]\n",
    "    raw_f1=raw_f1.split(\"      \")\n",
    "    precision=(raw_f1[1])\n",
    "    recall=(raw_f1[2])\n",
    "    f1_score=(raw_f1[3])\n",
    "    precision=precision[0:6]\n",
    "    recall=recall[0:4]\n",
    "    f1_score=f1_score[0:4]   \n",
    "    return precision,recall,f1_score\n",
    "\n",
    "\n",
    "with open(result, \"w\", newline=\"\",encoding=\"utf-8\") as f:#a CSV file is created to save the results obtained.\n",
    "    wrt = csv.writer(f)\n",
    "    wrt.writerow([\"File\",\"ML algorithm\",\"accuracy\",\"Precision\", \"Recall\" , \"F1-score\",\"Time\"])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for j in csv_files: #this loop runs on the list containing the filenames.Operations are repeated for all attack files\n",
    "    print ('%-17s %-17s  %-15s %-15s %-15s %-15s %-15s' % (\"File\",\"ML algorithm\",\"accuracy\",\"Precision\", \"Recall\" , \"F1-score\",\"Time\"))# print output header   \n",
    "    feature_list=list(features[j[0:-4]])\n",
    "    df=pd.read_csv(path+j,usecols=feature_list)#read an attack file.\n",
    "    df=df.fillna(0)\n",
    "    attack_or_not=[]\n",
    "    for i in df[\"Label\"]: #it changes the normal label to \"1\" and the attack tag to \"0\" for use in the machine learning algorithm\n",
    "        if i ==\"BENIGN\":\n",
    "            attack_or_not.append(1)\n",
    "        else:\n",
    "            attack_or_not.append(0)           \n",
    "    df[\"Label\"]=attack_or_not\n",
    "\n",
    "    \n",
    "    y = df[\"Label\"] #this section separates the label and the data into two separate pieces, as Label=y Data=X \n",
    "    del df[\"Label\"]\n",
    "    feature_list.remove('Label')\n",
    "    X = df[feature_list]\n",
    "\n",
    "    \n",
    "    for ii in ml_list: #this loop runs on the list containing the machine learning algorithm names. Operations are repeated for all the 7 algorithm\n",
    "        precision=[]\n",
    "        recall=[]\n",
    "        f1=[]\n",
    "        accuracy=[]\n",
    "        t_time=[]\n",
    "        for i in range(repetition): # This loop allows cross-validation and machine learning algorithm to be repeated 10 times\n",
    "            second=time.time()#time stamp for processing time\n",
    "\n",
    "            # cross-validation\n",
    "            X_train, X_test, y_train, y_test = train_test_split(X, y,#  data (X) and labels (y) are divided into 2 parts to be sent to the machine learning algorithm (80% train,%20 test). \n",
    "                test_size = 0.20, random_state = repetition)#  So, in total there are 4 tracks: training data(X_train), training tag (y_train), test data(X_test) and test tag(y_test).\n",
    "\n",
    "            #machine learning algorithm is applied in this section\n",
    "            clf = ml_list[ii]#choose algorithm from ml_list dictionary                                                                          \n",
    "            clf.fit(X_train, y_train)\n",
    "            predict =clf.predict(X_test)\n",
    "        \n",
    "            #makes \"classification report\" and assigns the precision, f-measure, and recall values.s.    \n",
    "            pr,rc,f_1=f1_score(metrics.classification_report(y_test, predict))\n",
    "            precision.append(float(pr))\n",
    "            recall.append(float(rc))\n",
    "            f1.append(float(f_1))\n",
    "            accuracy.append(clf.score(X_test, y_test))\n",
    "            t_time.append(float((time.time()-second)) )\n",
    "\n",
    "\n",
    "            \n",
    "        print ('%-17s %-17s  %-15s %-15s %-15s %-15s %-15s' % (j[0:-4],ii,str(round(np.mean(accuracy),2)),str(round(np.mean(precision),2)), \n",
    "            str(round(np.mean(recall),2)),str(round(np.mean(f1),2)),str(round(np.mean(t_time),4))))#the result of the ten repetitions is printed on the screen.\n",
    "\n",
    "        with open(result, \"a\", newline=\"\",encoding=\"utf-8\") as f: # all the values found are saved in the opened file.\n",
    "            wrt = csv.writer(f)\n",
    "            for i in range(0,len(t_time)):\n",
    "                wrt.writerow([j[0:-4],ii,accuracy[i],precision[i],recall[i],f1[i],t_time[i]])#file name, algorithm name, precision, recall and f-measure are writed in CSV file\n",
    "   \n",
    "     # In this section, Box graphics are created for the results of machine learning algorithms and saved in the feaure_graph folder.\n",
    "        plt.boxplot(f1)\n",
    "        plt.title(\"All Dataset - \" +str(ii))\n",
    "        plt.ylabel('F-measure')\n",
    "        plt.savefig(folder_name+j[0:-4]+str(ii)+\".pdf\",bbox_inches='tight', papertype = 'a4', orientation = 'portrait', format = 'pdf')\n",
    "        plt.show()# you can remove the # sign if you want to see the graphics simultaneously\n",
    "\n",
    "print(\"mission accomplished!\")\n",
    "print(\"Total operation time: = \",time.time()- seconds ,\"seconds\")\n"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}