models/failure_prediction/jnotebooks/FeatureCreation.ipynb

   1 {
   2   "nbformat": 4,
   3   "nbformat_minor": 0,
   4   "metadata": {
   5     "colab": {
   6       "name": "FeatureCreation.ipynb",
   7       "provenance": []
   8     },
   9     "kernelspec": {
  10       "name": "python3",
  11       "display_name": "Python 3"
  12     },
  13     "language_info": {
  14       "name": "python"
  15     }
  16   },
  17   "cells": [
  18     {
  19       "cell_type": "markdown",
  20       "metadata": {
  21         "id": "zyycU3DFlecK"
  22       },
  23       "source": [
  24         "Contributors: **Rohit Singh Rathaur, Girish L.** \n",
  25         "\n",
  26         "Copyright [2021](2021) [*Rohit Singh Rathaur, BIT Mesra and Girish L., CIT GUBBI, Karnataka*]\n",
  27         "\n",
  28         "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
  29         "you may not use this file except in compliance with the License.\n",
  30         "You may obtain a copy of the License at\n",
  31         "\n",
  32         "    http://www.apache.org/licenses/LICENSE-2.0\n",
  33         "\n",
  34         "Unless required by applicable law or agreed to in writing, software\n",
  35         "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
  36         "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
  37         "See the License for the specific language governing permissions and\n",
  38         "limitations under the License."
  39       ]
  40     },
  41     {
  42       "cell_type": "code",
  43       "metadata": {
  44         "id": "gehKp2rySVf8"
  45       },
  46       "source": [
  47         "# Import libraries use for visualization and analysis\n",
  48         "import pandas as pd\n",
  49         "import numpy as np\n",
  50         "\n",
  51         "%matplotlib inline\n",
  52         "import matplotlib\n",
  53         "import matplotlib.pyplot as plt\n",
  54         "\n",
  55         "from pandas import Series,DataFrame\n",
  56         "import matplotlib.pyplot as plt\n",
  57         "import seaborn as sns\n",
  58         "from sklearn.preprocessing import scale\n",
  59         "from sklearn.decomposition import PCA\n",
  60         "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
  61         "from scipy import stats\n",
  62         "from IPython.display import display, HTML"
  63       ],
  64       "execution_count": null,
  65       "outputs": []
  66     },
  67     {
  68       "cell_type": "code",
  69       "metadata": {
  70         "colab": {
  71           "base_uri": "https://localhost:8080/"
  72         },
  73         "id": "tkuBlbCXSsdP",
  74         "outputId": "2b3ef633-a851-4c53-80eb-6b1bf4ffcc1c"
  75       },
  76       "source": [
  77         "from google.colab import drive\n",
  78         "drive.mount('/gdrive')"
  79       ],
  80       "execution_count": null,
  81       "outputs": [
  82         {
  83           "output_type": "stream",
  84           "text": [
  85             "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"
  86           ],
  87           "name": "stdout"
  88         }
  89       ]
  90     },
  91     {
  92       "cell_type": "markdown",
  93       "metadata": {
  94         "id": "wZXe8D88S-6R"
  95       },
  96       "source": [
  97         "# **Loading the Data**"
  98       ]
  99     },
 100     {
 101       "cell_type": "code",
 102       "metadata": {
 103         "id": "KiDSpl37Sy39"
 104       },
 105       "source": [
 106         "df_Ellis  = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/Final.csv\")\n",
 107         "#df_Bono  = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Bono.csv\", error_bad_lines=False)\n",
 108         "#df_Sprout  = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Sprout.csv\", error_bad_lines=False)\n",
 109         "#df_Homer  = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Homer.csv\", error_bad_lines=False)\n",
 110         "#df_Homestead  = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Homestead.csv\", error_bad_lines=False)\n",
 111         "#df_Ralf  = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Ralf.csv\", error_bad_lines=False)"
 112       ],
 113       "execution_count": null,
 114       "outputs": []
 115     },
 116     {
 117       "cell_type": "code",
 118       "metadata": {
 119         "colab": {
 120           "base_uri": "https://localhost:8080/",
 121           "height": 204
 122         },
 123         "id": "dpy8jAm-TsCs",
 124         "outputId": "d8ad2072-1fa3-4b3c-fb55-b5128767b349"
 125       },
 126       "source": [
 127         "df_Ellis.head()"
 128       ],
 129       "execution_count": null,
 130       "outputs": [
 131         {
 132           "output_type": "execute_result",
 133           "data": {
 134             "text/html": [
 135               "<div>\n",
 136               "<style scoped>\n",
 137               "    .dataframe tbody tr th:only-of-type {\n",
 138               "        vertical-align: middle;\n",
 139               "    }\n",
 140               "\n",
 141               "    .dataframe tbody tr th {\n",
 142               "        vertical-align: top;\n",
 143               "    }\n",
 144               "\n",
 145               "    .dataframe thead th {\n",
 146               "        text-align: right;\n",
 147               "    }\n",
 148               "</style>\n",
 149               "<table border=\"1\" class=\"dataframe\">\n",
 150               "  <thead>\n",
 151               "    <tr style=\"text-align: right;\">\n",
 152               "      <th></th>\n",
 153               "      <th>Timestamp</th>\n",
 154               "      <th>ellis-cpu.system_perc</th>\n",
 155               "      <th>ellis-cpu.wait_perc</th>\n",
 156               "      <th>ellis-load.avg_1_min</th>\n",
 157               "      <th>ellis-mem.free_mb</th>\n",
 158               "      <th>ellis-net.in_bytes_sec</th>\n",
 159               "      <th>ellis-net.out_packets_sec</th>\n",
 160               "    </tr>\n",
 161               "  </thead>\n",
 162               "  <tbody>\n",
 163               "    <tr>\n",
 164               "      <th>0</th>\n",
 165               "      <td>14/09/2016 0:00</td>\n",
 166               "      <td>0.5</td>\n",
 167               "      <td>12.9</td>\n",
 168               "      <td>1.73</td>\n",
 169               "      <td>3949</td>\n",
 170               "      <td>5413.200</td>\n",
 171               "      <td>62.067</td>\n",
 172               "    </tr>\n",
 173               "    <tr>\n",
 174               "      <th>1</th>\n",
 175               "      <td>14/09/2016 0:00</td>\n",
 176               "      <td>0.4</td>\n",
 177               "      <td>10.3</td>\n",
 178               "      <td>1.79</td>\n",
 179               "      <td>3950</td>\n",
 180               "      <td>5201.667</td>\n",
 181               "      <td>59.567</td>\n",
 182               "    </tr>\n",
 183               "    <tr>\n",
 184               "      <th>2</th>\n",
 185               "      <td>14/09/2016 0:01</td>\n",
 186               "      <td>0.4</td>\n",
 187               "      <td>11.8</td>\n",
 188               "      <td>1.52</td>\n",
 189               "      <td>3950</td>\n",
 190               "      <td>5370.733</td>\n",
 191               "      <td>61.200</td>\n",
 192               "    </tr>\n",
 193               "    <tr>\n",
 194               "      <th>3</th>\n",
 195               "      <td>14/09/2016 0:01</td>\n",
 196               "      <td>0.4</td>\n",
 197               "      <td>12.9</td>\n",
 198               "      <td>1.43</td>\n",
 199               "      <td>3949</td>\n",
 200               "      <td>5292.467</td>\n",
 201               "      <td>60.400</td>\n",
 202               "    </tr>\n",
 203               "    <tr>\n",
 204               "      <th>4</th>\n",
 205               "      <td>14/09/2016 0:02</td>\n",
 206               "      <td>0.5</td>\n",
 207               "      <td>12.1</td>\n",
 208               "      <td>1.44</td>\n",
 209               "      <td>3950</td>\n",
 210               "      <td>5318.167</td>\n",
 211               "      <td>61.700</td>\n",
 212               "    </tr>\n",
 213               "  </tbody>\n",
 214               "</table>\n",
 215               "</div>"
 216             ],
 217             "text/plain": [
 218               "         Timestamp  ...  ellis-net.out_packets_sec\n",
 219               "0  14/09/2016 0:00  ...                     62.067\n",
 220               "1  14/09/2016 0:00  ...                     59.567\n",
 221               "2  14/09/2016 0:01  ...                     61.200\n",
 222               "3  14/09/2016 0:01  ...                     60.400\n",
 223               "4  14/09/2016 0:02  ...                     61.700\n",
 224               "\n",
 225               "[5 rows x 7 columns]"
 226             ]
 227           },
 228           "metadata": {
 229             "tags": []
 230           },
 231           "execution_count": 264
 232         }
 233       ]
 234     },
 235     {
 236       "cell_type": "code",
 237       "metadata": {
 238         "colab": {
 239           "base_uri": "https://localhost:8080/",
 240           "height": 297
 241         },
 242         "id": "dJa9FgJNgqpI",
 243         "outputId": "54d6c43d-489f-4347-93e5-12e4a4da2066"
 244       },
 245       "source": [
 246         "df_Ellis.describe()"
 247       ],
 248       "execution_count": null,
 249       "outputs": [
 250         {
 251           "output_type": "execute_result",
 252           "data": {
 253             "text/html": [
 254               "<div>\n",
 255               "<style scoped>\n",
 256               "    .dataframe tbody tr th:only-of-type {\n",
 257               "        vertical-align: middle;\n",
 258               "    }\n",
 259               "\n",
 260               "    .dataframe tbody tr th {\n",
 261               "        vertical-align: top;\n",
 262               "    }\n",
 263               "\n",
 264               "    .dataframe thead th {\n",
 265               "        text-align: right;\n",
 266               "    }\n",
 267               "</style>\n",
 268               "<table border=\"1\" class=\"dataframe\">\n",
 269               "  <thead>\n",
 270               "    <tr style=\"text-align: right;\">\n",
 271               "      <th></th>\n",
 272               "      <th>ellis-cpu.system_perc</th>\n",
 273               "      <th>ellis-cpu.wait_perc</th>\n",
 274               "      <th>ellis-load.avg_1_min</th>\n",
 275               "      <th>ellis-mem.free_mb</th>\n",
 276               "      <th>ellis-net.in_bytes_sec</th>\n",
 277               "      <th>ellis-net.out_packets_sec</th>\n",
 278               "    </tr>\n",
 279               "  </thead>\n",
 280               "  <tbody>\n",
 281               "    <tr>\n",
 282               "      <th>count</th>\n",
 283               "      <td>177000.000000</td>\n",
 284               "      <td>177000.000000</td>\n",
 285               "      <td>177000.000000</td>\n",
 286               "      <td>177000.000000</td>\n",
 287               "      <td>1.770000e+05</td>\n",
 288               "      <td>177000.000000</td>\n",
 289               "    </tr>\n",
 290               "    <tr>\n",
 291               "      <th>mean</th>\n",
 292               "      <td>2.315540</td>\n",
 293               "      <td>1.024163</td>\n",
 294               "      <td>0.198842</td>\n",
 295               "      <td>4206.847232</td>\n",
 296               "      <td>1.855987e+07</td>\n",
 297               "      <td>1336.694851</td>\n",
 298               "    </tr>\n",
 299               "    <tr>\n",
 300               "      <th>std</th>\n",
 301               "      <td>1.170977</td>\n",
 302               "      <td>3.127178</td>\n",
 303               "      <td>0.262227</td>\n",
 304               "      <td>173.364297</td>\n",
 305               "      <td>5.612164e+06</td>\n",
 306               "      <td>2220.146124</td>\n",
 307               "    </tr>\n",
 308               "    <tr>\n",
 309               "      <th>min</th>\n",
 310               "      <td>0.100000</td>\n",
 311               "      <td>0.000000</td>\n",
 312               "      <td>0.000000</td>\n",
 313               "      <td>2320.000000</td>\n",
 314               "      <td>0.000000e+00</td>\n",
 315               "      <td>0.000000</td>\n",
 316               "    </tr>\n",
 317               "    <tr>\n",
 318               "      <th>25%</th>\n",
 319               "      <td>1.500000</td>\n",
 320               "      <td>0.200000</td>\n",
 321               "      <td>0.095000</td>\n",
 322               "      <td>4095.000000</td>\n",
 323               "      <td>1.797602e+07</td>\n",
 324               "      <td>182.033000</td>\n",
 325               "    </tr>\n",
 326               "    <tr>\n",
 327               "      <th>50%</th>\n",
 328               "      <td>1.700000</td>\n",
 329               "      <td>0.200000</td>\n",
 330               "      <td>0.140000</td>\n",
 331               "      <td>4214.000000</td>\n",
 332               "      <td>2.087674e+07</td>\n",
 333               "      <td>200.067000</td>\n",
 334               "    </tr>\n",
 335               "    <tr>\n",
 336               "      <th>75%</th>\n",
 337               "      <td>3.500000</td>\n",
 338               "      <td>0.400000</td>\n",
 339               "      <td>0.198000</td>\n",
 340               "      <td>4331.000000</td>\n",
 341               "      <td>2.160859e+07</td>\n",
 342               "      <td>1069.667000</td>\n",
 343               "    </tr>\n",
 344               "    <tr>\n",
 345               "      <th>max</th>\n",
 346               "      <td>16.700000</td>\n",
 347               "      <td>22.400000</td>\n",
 348               "      <td>2.580000</td>\n",
 349               "      <td>4633.000000</td>\n",
 350               "      <td>2.339041e+07</td>\n",
 351               "      <td>7887.552000</td>\n",
 352               "    </tr>\n",
 353               "  </tbody>\n",
 354               "</table>\n",
 355               "</div>"
 356             ],
 357             "text/plain": [
 358               "       ellis-cpu.system_perc  ...  ellis-net.out_packets_sec\n",
 359               "count          177000.000000  ...              177000.000000\n",
 360               "mean                2.315540  ...                1336.694851\n",
 361               "std                 1.170977  ...                2220.146124\n",
 362               "min                 0.100000  ...                   0.000000\n",
 363               "25%                 1.500000  ...                 182.033000\n",
 364               "50%                 1.700000  ...                 200.067000\n",
 365               "75%                 3.500000  ...                1069.667000\n",
 366               "max                16.700000  ...                7887.552000\n",
 367               "\n",
 368               "[8 rows x 6 columns]"
 369             ]
 370           },
 371           "metadata": {
 372             "tags": []
 373           },
 374           "execution_count": 265
 375         }
 376       ]
 377     },
 378     {
 379       "cell_type": "code",
 380       "metadata": {
 381         "id": "xGVleQbnhRm6"
 382       },
 383       "source": [
 384         "#df_Ellis['SLO1'] = 0\n",
 385         "#print('Column names are: ',list(df_Ellis.columns))"
 386       ],
 387       "execution_count": null,
 388       "outputs": []
 389     },
 390     {
 391       "cell_type": "code",
 392       "metadata": {
 393         "colab": {
 394           "base_uri": "https://localhost:8080/"
 395         },
 396         "id": "b-F_gA61xowR",
 397         "outputId": "f9bd6232-2603-40ad-ccff-18887839e2da"
 398       },
 399       "source": [
 400         "df4 = df_Ellis[\"ellis-load.avg_1_min\"] > 2.45\n",
 401         "df4\n",
 402         "df4.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/EllisLoadAvgLabel_lessthan0198.csv')\n",
 403         "df4.head(50)"
 404       ],
 405       "execution_count": null,
 406       "outputs": [
 407         {
 408           "output_type": "execute_result",
 409           "data": {
 410             "text/plain": [
 411               "0     False\n",
 412               "1     False\n",
 413               "2     False\n",
 414               "3     False\n",
 415               "4     False\n",
 416               "5     False\n",
 417               "6     False\n",
 418               "7     False\n",
 419               "8     False\n",
 420               "9     False\n",
 421               "10    False\n",
 422               "11    False\n",
 423               "12    False\n",
 424               "13    False\n",
 425               "14    False\n",
 426               "15    False\n",
 427               "16    False\n",
 428               "17    False\n",
 429               "18    False\n",
 430               "19    False\n",
 431               "20    False\n",
 432               "21    False\n",
 433               "22    False\n",
 434               "23    False\n",
 435               "24    False\n",
 436               "25    False\n",
 437               "26    False\n",
 438               "27    False\n",
 439               "28    False\n",
 440               "29    False\n",
 441               "30    False\n",
 442               "31    False\n",
 443               "32    False\n",
 444               "33    False\n",
 445               "34    False\n",
 446               "35    False\n",
 447               "36    False\n",
 448               "37    False\n",
 449               "38    False\n",
 450               "39    False\n",
 451               "40    False\n",
 452               "41    False\n",
 453               "42    False\n",
 454               "43    False\n",
 455               "44    False\n",
 456               "45    False\n",
 457               "46    False\n",
 458               "47    False\n",
 459               "48    False\n",
 460               "49    False\n",
 461               "Name: ellis-load.avg_1_min, dtype: bool"
 462             ]
 463           },
 464           "metadata": {
 465             "tags": []
 466           },
 467           "execution_count": 267
 468         }
 469       ]
 470     },
 471     {
 472       "cell_type": "code",
 473       "metadata": {
 474         "colab": {
 475           "base_uri": "https://localhost:8080/"
 476         },
 477         "id": "8xcPRerCz8nA",
 478         "outputId": "fb66f20e-7365-40ec-857a-9dd9a8072401"
 479       },
 480       "source": [
 481         "df3 = df_Ellis[\"ellis-cpu.wait_perc\"] > 5\n",
 482         "df3\n",
 483         "df3.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/ellis-cpu>5.csv')\n",
 484         "df3.head(50)"
 485       ],
 486       "execution_count": null,
 487       "outputs": [
 488         {
 489           "output_type": "execute_result",
 490           "data": {
 491             "text/plain": [
 492               "0     True\n",
 493               "1     True\n",
 494               "2     True\n",
 495               "3     True\n",
 496               "4     True\n",
 497               "5     True\n",
 498               "6     True\n",
 499               "7     True\n",
 500               "8     True\n",
 501               "9     True\n",
 502               "10    True\n",
 503               "11    True\n",
 504               "12    True\n",
 505               "13    True\n",
 506               "14    True\n",
 507               "15    True\n",
 508               "16    True\n",
 509               "17    True\n",
 510               "18    True\n",
 511               "19    True\n",
 512               "20    True\n",
 513               "21    True\n",
 514               "22    True\n",
 515               "23    True\n",
 516               "24    True\n",
 517               "25    True\n",
 518               "26    True\n",
 519               "27    True\n",
 520               "28    True\n",
 521               "29    True\n",
 522               "30    True\n",
 523               "31    True\n",
 524               "32    True\n",
 525               "33    True\n",
 526               "34    True\n",
 527               "35    True\n",
 528               "36    True\n",
 529               "37    True\n",
 530               "38    True\n",
 531               "39    True\n",
 532               "40    True\n",
 533               "41    True\n",
 534               "42    True\n",
 535               "43    True\n",
 536               "44    True\n",
 537               "45    True\n",
 538               "46    True\n",
 539               "47    True\n",
 540               "48    True\n",
 541               "49    True\n",
 542               "Name: ellis-cpu.wait_perc, dtype: bool"
 543             ]
 544           },
 545           "metadata": {
 546             "tags": []
 547           },
 548           "execution_count": 268
 549         }
 550       ]
 551     },
 552     {
 553       "cell_type": "code",
 554       "metadata": {
 555         "id": "EED56Wiq_NjM",
 556         "colab": {
 557           "base_uri": "https://localhost:8080/"
 558         },
 559         "outputId": "20b06258-c5ba-457b-a022-cf5823217cbf"
 560       },
 561       "source": [
 562         "df5 = df_Ellis[\"ellis-net.out_packets_sec\"] > 1000\n",
 563         "df5\n",
 564         "df5.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/ellis-net.in_bytes_sec21139.csv')\n",
 565         "df5.head(50)"
 566       ],
 567       "execution_count": null,
 568       "outputs": [
 569         {
 570           "output_type": "execute_result",
 571           "data": {
 572             "text/plain": [
 573               "0     False\n",
 574               "1     False\n",
 575               "2     False\n",
 576               "3     False\n",
 577               "4     False\n",
 578               "5     False\n",
 579               "6     False\n",
 580               "7     False\n",
 581               "8     False\n",
 582               "9     False\n",
 583               "10    False\n",
 584               "11    False\n",
 585               "12    False\n",
 586               "13    False\n",
 587               "14    False\n",
 588               "15    False\n",
 589               "16    False\n",
 590               "17    False\n",
 591               "18    False\n",
 592               "19    False\n",
 593               "20    False\n",
 594               "21    False\n",
 595               "22    False\n",
 596               "23    False\n",
 597               "24    False\n",
 598               "25    False\n",
 599               "26    False\n",
 600               "27    False\n",
 601               "28    False\n",
 602               "29    False\n",
 603               "30    False\n",
 604               "31    False\n",
 605               "32    False\n",
 606               "33    False\n",
 607               "34    False\n",
 608               "35    False\n",
 609               "36    False\n",
 610               "37    False\n",
 611               "38    False\n",
 612               "39    False\n",
 613               "40    False\n",
 614               "41    False\n",
 615               "42    False\n",
 616               "43    False\n",
 617               "44    False\n",
 618               "45    False\n",
 619               "46    False\n",
 620               "47    False\n",
 621               "48    False\n",
 622               "49    False\n",
 623               "Name: ellis-net.out_packets_sec, dtype: bool"
 624             ]
 625           },
 626           "metadata": {
 627             "tags": []
 628           },
 629           "execution_count": 269
 630         }
 631       ]
 632     },
 633     {
 634       "cell_type": "code",
 635       "metadata": {
 636         "colab": {
 637           "base_uri": "https://localhost:8080/"
 638         },
 639         "id": "phlI40_y0mug",
 640         "outputId": "7fa177b9-bf9a-4b96-db65-7402f7f6cf32"
 641       },
 642       "source": [
 643         "# We are applying Logical OR Operator between df4 and df3\n",
 644         "df6 = (df4[0:176999]) | (df3[0:176999])\n",
 645         "df6.head(50)"
 646       ],
 647       "execution_count": null,
 648       "outputs": [
 649         {
 650           "output_type": "execute_result",
 651           "data": {
 652             "text/plain": [
 653               "0     True\n",
 654               "1     True\n",
 655               "2     True\n",
 656               "3     True\n",
 657               "4     True\n",
 658               "5     True\n",
 659               "6     True\n",
 660               "7     True\n",
 661               "8     True\n",
 662               "9     True\n",
 663               "10    True\n",
 664               "11    True\n",
 665               "12    True\n",
 666               "13    True\n",
 667               "14    True\n",
 668               "15    True\n",
 669               "16    True\n",
 670               "17    True\n",
 671               "18    True\n",
 672               "19    True\n",
 673               "20    True\n",
 674               "21    True\n",
 675               "22    True\n",
 676               "23    True\n",
 677               "24    True\n",
 678               "25    True\n",
 679               "26    True\n",
 680               "27    True\n",
 681               "28    True\n",
 682               "29    True\n",
 683               "30    True\n",
 684               "31    True\n",
 685               "32    True\n",
 686               "33    True\n",
 687               "34    True\n",
 688               "35    True\n",
 689               "36    True\n",
 690               "37    True\n",
 691               "38    True\n",
 692               "39    True\n",
 693               "40    True\n",
 694               "41    True\n",
 695               "42    True\n",
 696               "43    True\n",
 697               "44    True\n",
 698               "45    True\n",
 699               "46    True\n",
 700               "47    True\n",
 701               "48    True\n",
 702               "49    True\n",
 703               "dtype: bool"
 704             ]
 705           },
 706           "metadata": {
 707             "tags": []
 708           },
 709           "execution_count": 270
 710         }
 711       ]
 712     },
 713     {
 714       "cell_type": "code",
 715       "metadata": {
 716         "colab": {
 717           "base_uri": "https://localhost:8080/"
 718         },
 719         "id": "9xKYzZcLAZGy",
 720         "outputId": "bc15e547-c791-4104-8bb2-8ed4d3288ac1"
 721       },
 722       "source": [
 723         "df6.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/OR_TwoCondition(2).csv')\n",
 724         "df6.head(50)"
 725       ],
 726       "execution_count": null,
 727       "outputs": [
 728         {
 729           "output_type": "execute_result",
 730           "data": {
 731             "text/plain": [
 732               "0     True\n",
 733               "1     True\n",
 734               "2     True\n",
 735               "3     True\n",
 736               "4     True\n",
 737               "5     True\n",
 738               "6     True\n",
 739               "7     True\n",
 740               "8     True\n",
 741               "9     True\n",
 742               "10    True\n",
 743               "11    True\n",
 744               "12    True\n",
 745               "13    True\n",
 746               "14    True\n",
 747               "15    True\n",
 748               "16    True\n",
 749               "17    True\n",
 750               "18    True\n",
 751               "19    True\n",
 752               "20    True\n",
 753               "21    True\n",
 754               "22    True\n",
 755               "23    True\n",
 756               "24    True\n",
 757               "25    True\n",
 758               "26    True\n",
 759               "27    True\n",
 760               "28    True\n",
 761               "29    True\n",
 762               "30    True\n",
 763               "31    True\n",
 764               "32    True\n",
 765               "33    True\n",
 766               "34    True\n",
 767               "35    True\n",
 768               "36    True\n",
 769               "37    True\n",
 770               "38    True\n",
 771               "39    True\n",
 772               "40    True\n",
 773               "41    True\n",
 774               "42    True\n",
 775               "43    True\n",
 776               "44    True\n",
 777               "45    True\n",
 778               "46    True\n",
 779               "47    True\n",
 780               "48    True\n",
 781               "49    True\n",
 782               "dtype: bool"
 783             ]
 784           },
 785           "metadata": {
 786             "tags": []
 787           },
 788           "execution_count": 271
 789         }
 790       ]
 791     },
 792     {
 793       "cell_type": "code",
 794       "metadata": {
 795         "id": "wRADpDibBZo5",
 796         "colab": {
 797           "base_uri": "https://localhost:8080/"
 798         },
 799         "outputId": "dfc6dc79-3d9f-4979-8210-e62e77b1aa6e"
 800       },
 801       "source": [
 802         "df7 = (df6[0:176999]) | (df5[0:176999])\n",
 803         "df7.head(50)"
 804       ],
 805       "execution_count": null,
 806       "outputs": [
 807         {
 808           "output_type": "execute_result",
 809           "data": {
 810             "text/plain": [
 811               "0     True\n",
 812               "1     True\n",
 813               "2     True\n",
 814               "3     True\n",
 815               "4     True\n",
 816               "5     True\n",
 817               "6     True\n",
 818               "7     True\n",
 819               "8     True\n",
 820               "9     True\n",
 821               "10    True\n",
 822               "11    True\n",
 823               "12    True\n",
 824               "13    True\n",
 825               "14    True\n",
 826               "15    True\n",
 827               "16    True\n",
 828               "17    True\n",
 829               "18    True\n",
 830               "19    True\n",
 831               "20    True\n",
 832               "21    True\n",
 833               "22    True\n",
 834               "23    True\n",
 835               "24    True\n",
 836               "25    True\n",
 837               "26    True\n",
 838               "27    True\n",
 839               "28    True\n",
 840               "29    True\n",
 841               "30    True\n",
 842               "31    True\n",
 843               "32    True\n",
 844               "33    True\n",
 845               "34    True\n",
 846               "35    True\n",
 847               "36    True\n",
 848               "37    True\n",
 849               "38    True\n",
 850               "39    True\n",
 851               "40    True\n",
 852               "41    True\n",
 853               "42    True\n",
 854               "43    True\n",
 855               "44    True\n",
 856               "45    True\n",
 857               "46    True\n",
 858               "47    True\n",
 859               "48    True\n",
 860               "49    True\n",
 861               "dtype: bool"
 862             ]
 863           },
 864           "metadata": {
 865             "tags": []
 866           },
 867           "execution_count": 272
 868         }
 869       ]
 870     },
 871     {
 872       "cell_type": "code",
 873       "metadata": {
 874         "id": "w6BrDjX4CODn",
 875         "colab": {
 876           "base_uri": "https://localhost:8080/"
 877         },
 878         "outputId": "a6c956e7-6aed-4bdd-f37f-505a994de51a"
 879       },
 880       "source": [
 881         "df7.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/FinalORLabel8.5.csv')\n",
 882         "df7.head(50)"
 883       ],
 884       "execution_count": null,
 885       "outputs": [
 886         {
 887           "output_type": "execute_result",
 888           "data": {
 889             "text/plain": [
 890               "0     True\n",
 891               "1     True\n",
 892               "2     True\n",
 893               "3     True\n",
 894               "4     True\n",
 895               "5     True\n",
 896               "6     True\n",
 897               "7     True\n",
 898               "8     True\n",
 899               "9     True\n",
 900               "10    True\n",
 901               "11    True\n",
 902               "12    True\n",
 903               "13    True\n",
 904               "14    True\n",
 905               "15    True\n",
 906               "16    True\n",
 907               "17    True\n",
 908               "18    True\n",
 909               "19    True\n",
 910               "20    True\n",
 911               "21    True\n",
 912               "22    True\n",
 913               "23    True\n",
 914               "24    True\n",
 915               "25    True\n",
 916               "26    True\n",
 917               "27    True\n",
 918               "28    True\n",
 919               "29    True\n",
 920               "30    True\n",
 921               "31    True\n",
 922               "32    True\n",
 923               "33    True\n",
 924               "34    True\n",
 925               "35    True\n",
 926               "36    True\n",
 927               "37    True\n",
 928               "38    True\n",
 929               "39    True\n",
 930               "40    True\n",
 931               "41    True\n",
 932               "42    True\n",
 933               "43    True\n",
 934               "44    True\n",
 935               "45    True\n",
 936               "46    True\n",
 937               "47    True\n",
 938               "48    True\n",
 939               "49    True\n",
 940               "dtype: bool"
 941             ]
 942           },
 943           "metadata": {
 944             "tags": []
 945           },
 946           "execution_count": 273
 947         }
 948       ]
 949     },
 950     {
 951       "cell_type": "code",
 952       "metadata": {
 953         "id": "wwv2cjFAIFHL"
 954       },
 955       "source": [
 956         "df_Ellis.insert (7, \"Label\", df7)"
 957       ],
 958       "execution_count": null,
 959       "outputs": []
 960     },
 961     {
 962       "cell_type": "code",
 963       "metadata": {
 964         "id": "hrPqpjd96I1x"
 965       },
 966       "source": [
 967         "#df_Ellis.insert (8, \"Label\", df7)"
 968       ],
 969       "execution_count": null,
 970       "outputs": []
 971     },
 972     {
 973       "cell_type": "code",
 974       "metadata": {
 975         "id": "_zKkQLOz6qPY"
 976       },
 977       "source": [
 978         "# We applied Logical OR operator in two features only known as  and df3 and df4 and stored result in df6 which is known as Final Label after applying OR condition\n",
 979         "df_Ellis\n",
 980         "df_Ellis.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/Ellis_FinalTwoConditionwithOR.csv')"
 981       ],
 982       "execution_count": null,
 983       "outputs": []
 984     },
 985     {
 986       "cell_type": "code",
 987       "metadata": {
 988         "id": "3rEy1vtp67M9",
 989         "colab": {
 990           "base_uri": "https://localhost:8080/",
 991           "height": 606
 992         },
 993         "outputId": "4e2175cc-dccb-4aaf-a152-e2452de241b0"
 994       },
 995       "source": [
 996         "df_Ellis.head(100)"
 997       ],
 998       "execution_count": null,
 999       "outputs": [
1000         {
1001           "output_type": "execute_result",
1002           "data": {
1003             "text/html": [
1004               "<div>\n",
1005               "<style scoped>\n",
1006               "    .dataframe tbody tr th:only-of-type {\n",
1007               "        vertical-align: middle;\n",
1008               "    }\n",
1009               "\n",
1010               "    .dataframe tbody tr th {\n",
1011               "        vertical-align: top;\n",
1012               "    }\n",
1013               "\n",
1014               "    .dataframe thead th {\n",
1015               "        text-align: right;\n",
1016               "    }\n",
1017               "</style>\n",
1018               "<table border=\"1\" class=\"dataframe\">\n",
1019               "  <thead>\n",
1020               "    <tr style=\"text-align: right;\">\n",
1021               "      <th></th>\n",
1022               "      <th>Timestamp</th>\n",
1023               "      <th>ellis-cpu.system_perc</th>\n",
1024               "      <th>ellis-cpu.wait_perc</th>\n",
1025               "      <th>ellis-load.avg_1_min</th>\n",
1026               "      <th>ellis-mem.free_mb</th>\n",
1027               "      <th>ellis-net.in_bytes_sec</th>\n",
1028               "      <th>ellis-net.out_packets_sec</th>\n",
1029               "      <th>Label</th>\n",
1030               "    </tr>\n",
1031               "  </thead>\n",
1032               "  <tbody>\n",
1033               "    <tr>\n",
1034               "      <th>0</th>\n",
1035               "      <td>14/09/2016 0:00</td>\n",
1036               "      <td>0.5</td>\n",
1037               "      <td>12.9</td>\n",
1038               "      <td>1.73</td>\n",
1039               "      <td>3949</td>\n",
1040               "      <td>5413.200</td>\n",
1041               "      <td>62.067</td>\n",
1042               "      <td>True</td>\n",
1043               "    </tr>\n",
1044               "    <tr>\n",
1045               "      <th>1</th>\n",
1046               "      <td>14/09/2016 0:00</td>\n",
1047               "      <td>0.4</td>\n",
1048               "      <td>10.3</td>\n",
1049               "      <td>1.79</td>\n",
1050               "      <td>3950</td>\n",
1051               "      <td>5201.667</td>\n",
1052               "      <td>59.567</td>\n",
1053               "      <td>True</td>\n",
1054               "    </tr>\n",
1055               "    <tr>\n",
1056               "      <th>2</th>\n",
1057               "      <td>14/09/2016 0:01</td>\n",
1058               "      <td>0.4</td>\n",
1059               "      <td>11.8</td>\n",
1060               "      <td>1.52</td>\n",
1061               "      <td>3950</td>\n",
1062               "      <td>5370.733</td>\n",
1063               "      <td>61.200</td>\n",
1064               "      <td>True</td>\n",
1065               "    </tr>\n",
1066               "    <tr>\n",
1067               "      <th>3</th>\n",
1068               "      <td>14/09/2016 0:01</td>\n",
1069               "      <td>0.4</td>\n",
1070               "      <td>12.9</td>\n",
1071               "      <td>1.43</td>\n",
1072               "      <td>3949</td>\n",
1073               "      <td>5292.467</td>\n",
1074               "      <td>60.400</td>\n",
1075               "      <td>True</td>\n",
1076               "    </tr>\n",
1077               "    <tr>\n",
1078               "      <th>4</th>\n",
1079               "      <td>14/09/2016 0:02</td>\n",
1080               "      <td>0.5</td>\n",
1081               "      <td>12.1</td>\n",
1082               "      <td>1.44</td>\n",
1083               "      <td>3950</td>\n",
1084               "      <td>5318.167</td>\n",
1085               "      <td>61.700</td>\n",
1086               "      <td>True</td>\n",
1087               "    </tr>\n",
1088               "    <tr>\n",
1089               "      <th>...</th>\n",
1090               "      <td>...</td>\n",
1091               "      <td>...</td>\n",
1092               "      <td>...</td>\n",
1093               "      <td>...</td>\n",
1094               "      <td>...</td>\n",
1095               "      <td>...</td>\n",
1096               "      <td>...</td>\n",
1097               "      <td>...</td>\n",
1098               "    </tr>\n",
1099               "    <tr>\n",
1100               "      <th>95</th>\n",
1101               "      <td>14/09/2016 0:47</td>\n",
1102               "      <td>0.5</td>\n",
1103               "      <td>10.8</td>\n",
1104               "      <td>0.45</td>\n",
1105               "      <td>3948</td>\n",
1106               "      <td>5187.133</td>\n",
1107               "      <td>60.100</td>\n",
1108               "      <td>True</td>\n",
1109               "    </tr>\n",
1110               "    <tr>\n",
1111               "      <th>96</th>\n",
1112               "      <td>14/09/2016 0:48</td>\n",
1113               "      <td>0.5</td>\n",
1114               "      <td>10.4</td>\n",
1115               "      <td>0.42</td>\n",
1116               "      <td>3949</td>\n",
1117               "      <td>5223.100</td>\n",
1118               "      <td>60.233</td>\n",
1119               "      <td>True</td>\n",
1120               "    </tr>\n",
1121               "    <tr>\n",
1122               "      <th>97</th>\n",
1123               "      <td>14/09/2016 0:48</td>\n",
1124               "      <td>0.6</td>\n",
1125               "      <td>13.0</td>\n",
1126               "      <td>0.56</td>\n",
1127               "      <td>3947</td>\n",
1128               "      <td>5335.200</td>\n",
1129               "      <td>60.667</td>\n",
1130               "      <td>True</td>\n",
1131               "    </tr>\n",
1132               "    <tr>\n",
1133               "      <th>98</th>\n",
1134               "      <td>14/09/2016 0:49</td>\n",
1135               "      <td>0.6</td>\n",
1136               "      <td>10.1</td>\n",
1137               "      <td>0.47</td>\n",
1138               "      <td>3948</td>\n",
1139               "      <td>5185.733</td>\n",
1140               "      <td>60.367</td>\n",
1141               "      <td>True</td>\n",
1142               "    </tr>\n",
1143               "    <tr>\n",
1144               "      <th>99</th>\n",
1145               "      <td>14/09/2016 0:49</td>\n",
1146               "      <td>0.6</td>\n",
1147               "      <td>10.8</td>\n",
1148               "      <td>0.28</td>\n",
1149               "      <td>3948</td>\n",
1150               "      <td>5204.233</td>\n",
1151               "      <td>59.600</td>\n",
1152               "      <td>True</td>\n",
1153               "    </tr>\n",
1154               "  </tbody>\n",
1155               "</table>\n",
1156               "<p>100 rows × 8 columns</p>\n",
1157               "</div>"
1158             ],
1159             "text/plain": [
1160               "          Timestamp  ellis-cpu.system_perc  ...  ellis-net.out_packets_sec  Label\n",
1161               "0   14/09/2016 0:00                    0.5  ...                     62.067   True\n",
1162               "1   14/09/2016 0:00                    0.4  ...                     59.567   True\n",
1163               "2   14/09/2016 0:01                    0.4  ...                     61.200   True\n",
1164               "3   14/09/2016 0:01                    0.4  ...                     60.400   True\n",
1165               "4   14/09/2016 0:02                    0.5  ...                     61.700   True\n",
1166               "..              ...                    ...  ...                        ...    ...\n",
1167               "95  14/09/2016 0:47                    0.5  ...                     60.100   True\n",
1168               "96  14/09/2016 0:48                    0.5  ...                     60.233   True\n",
1169               "97  14/09/2016 0:48                    0.6  ...                     60.667   True\n",
1170               "98  14/09/2016 0:49                    0.6  ...                     60.367   True\n",
1171               "99  14/09/2016 0:49                    0.6  ...                     59.600   True\n",
1172               "\n",
1173               "[100 rows x 8 columns]"
1174             ]
1175           },
1176           "metadata": {
1177             "tags": []
1178           },
1179           "execution_count": 277
1180         }
1181       ]
1182     },
1183     {
1184       "cell_type": "code",
1185       "metadata": {
1186         "colab": {
1187           "base_uri": "https://localhost:8080/"
1188         },
1189         "id": "11Qu45RY0HNG",
1190         "outputId": "305c5dd5-ec61-48a8-abb6-e29bbc4b9e42"
1191       },
1192       "source": [
1193         "# pandas count distinct values in column\n",
1194         "df_Ellis['Label'].value_counts()"
1195       ],
1196       "execution_count": null,
1197       "outputs": [
1198         {
1199           "output_type": "execute_result",
1200           "data": {
1201             "text/plain": [
1202               "False    112145\n",
1203               "True      64854\n",
1204               "Name: Label, dtype: int64"
1205             ]
1206           },
1207           "metadata": {
1208             "tags": []
1209           },
1210           "execution_count": 278
1211         }
1212       ]
1213     },
1214     {
1215       "cell_type": "code",
1216       "metadata": {
1217         "id": "0sB-W_Ny4eHk"
1218       },
1219       "source": [
1220         "#final.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/New/FinalLabel.csv')"
1221       ],
1222       "execution_count": null,
1223       "outputs": []
1224     },
1225     {
1226       "cell_type": "code",
1227       "metadata": {
1228         "id": "ERsufys7wcSg"
1229       },
1230       "source": [
1231         "#df_Ellis.loc[(df_Ellis[\"ellis-cpu.wait_perc\"] > 5) & (df_Ellis[\"ellis-load.avg_1_min\"] > 2)]"
1232       ],
1233       "execution_count": null,
1234       "outputs": []
1235     },
1236     {
1237       "cell_type": "markdown",
1238       "metadata": {
1239         "id": "9le7MwnDhlnH"
1240       },
1241       "source": [
1242         "# **Creating New Features**"
1243       ]
1244     },
1245     {
1246       "cell_type": "code",
1247       "metadata": {
1248         "id": "090QXGpPlEF6"
1249       },
1250       "source": [
1251         ""
1252       ],
1253       "execution_count": null,
1254       "outputs": []
1255     }
1256   ]
1257 }