diff options
Diffstat (limited to 'models/failure_prediction/jnotebooks/FeatureCreation.ipynb')
-rw-r--r-- | models/failure_prediction/jnotebooks/FeatureCreation.ipynb | 1257 |
1 files changed, 1257 insertions, 0 deletions
diff --git a/models/failure_prediction/jnotebooks/FeatureCreation.ipynb b/models/failure_prediction/jnotebooks/FeatureCreation.ipynb new file mode 100644 index 0000000..ae0ccec --- /dev/null +++ b/models/failure_prediction/jnotebooks/FeatureCreation.ipynb @@ -0,0 +1,1257 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "FeatureCreation.ipynb", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "zyycU3DFlecK" + }, + "source": [ + "Contributors: **Rohit Singh Rathaur, Girish L.** \n", + "\n", + "Copyright [2021](2021) [*Rohit Singh Rathaur, BIT Mesra and Girish L., CIT GUBBI, Karnataka*]\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "you may not use this file except in compliance with the License.\n", + "You may obtain a copy of the License at\n", + "\n", + " http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software\n", + "distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "See the License for the specific language governing permissions and\n", + "limitations under the License." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gehKp2rySVf8" + }, + "source": [ + "# Import libraries use for visualization and analysis\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pandas import Series,DataFrame\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import scale\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from scipy import stats\n", + "from IPython.display import display, HTML" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tkuBlbCXSsdP", + "outputId": "2b3ef633-a851-4c53-80eb-6b1bf4ffcc1c" + }, + "source": [ + "from google.colab import drive\n", + "drive.mount('/gdrive')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wZXe8D88S-6R" + }, + "source": [ + "# **Loading the Data**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KiDSpl37Sy39" + }, + "source": [ + "df_Ellis = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/Final.csv\")\n", + "#df_Bono = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Bono.csv\", error_bad_lines=False)\n", + "#df_Sprout = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Sprout.csv\", error_bad_lines=False)\n", + "#df_Homer = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Homer.csv\", error_bad_lines=False)\n", + "#df_Homestead = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Homestead.csv\", error_bad_lines=False)\n", + "#df_Ralf = pd.read_csv(\"/gdrive/MyDrive/LFN Anuket/Analysis/data/matrices/df_Ralf.csv\", error_bad_lines=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "dpy8jAm-TsCs", + "outputId": "d8ad2072-1fa3-4b3c-fb55-b5128767b349" + }, + "source": [ + "df_Ellis.head()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Timestamp</th>\n", + " <th>ellis-cpu.system_perc</th>\n", + " <th>ellis-cpu.wait_perc</th>\n", + " <th>ellis-load.avg_1_min</th>\n", + " <th>ellis-mem.free_mb</th>\n", + " <th>ellis-net.in_bytes_sec</th>\n", + " <th>ellis-net.out_packets_sec</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>14/09/2016 0:00</td>\n", + " <td>0.5</td>\n", + " <td>12.9</td>\n", + " <td>1.73</td>\n", + " <td>3949</td>\n", + " <td>5413.200</td>\n", + " <td>62.067</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>14/09/2016 0:00</td>\n", + " <td>0.4</td>\n", + " <td>10.3</td>\n", + " <td>1.79</td>\n", + " <td>3950</td>\n", + " <td>5201.667</td>\n", + " <td>59.567</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>14/09/2016 0:01</td>\n", + " <td>0.4</td>\n", + " <td>11.8</td>\n", + " <td>1.52</td>\n", + " <td>3950</td>\n", + " <td>5370.733</td>\n", + " <td>61.200</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>14/09/2016 0:01</td>\n", + " <td>0.4</td>\n", + " <td>12.9</td>\n", + " <td>1.43</td>\n", + " <td>3949</td>\n", + " <td>5292.467</td>\n", + " <td>60.400</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>14/09/2016 0:02</td>\n", + " <td>0.5</td>\n", + " <td>12.1</td>\n", + " <td>1.44</td>\n", + " <td>3950</td>\n", + " <td>5318.167</td>\n", + " <td>61.700</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " Timestamp ... ellis-net.out_packets_sec\n", + "0 14/09/2016 0:00 ... 62.067\n", + "1 14/09/2016 0:00 ... 59.567\n", + "2 14/09/2016 0:01 ... 61.200\n", + "3 14/09/2016 0:01 ... 60.400\n", + "4 14/09/2016 0:02 ... 61.700\n", + "\n", + "[5 rows x 7 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 264 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "dJa9FgJNgqpI", + "outputId": "54d6c43d-489f-4347-93e5-12e4a4da2066" + }, + "source": [ + "df_Ellis.describe()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>ellis-cpu.system_perc</th>\n", + " <th>ellis-cpu.wait_perc</th>\n", + " <th>ellis-load.avg_1_min</th>\n", + " <th>ellis-mem.free_mb</th>\n", + " <th>ellis-net.in_bytes_sec</th>\n", + " <th>ellis-net.out_packets_sec</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>177000.000000</td>\n", + " <td>177000.000000</td>\n", + " <td>177000.000000</td>\n", + " <td>177000.000000</td>\n", + " <td>1.770000e+05</td>\n", + " <td>177000.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>2.315540</td>\n", + " <td>1.024163</td>\n", + " <td>0.198842</td>\n", + " <td>4206.847232</td>\n", + " <td>1.855987e+07</td>\n", + " <td>1336.694851</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>1.170977</td>\n", + " <td>3.127178</td>\n", + " <td>0.262227</td>\n", + " <td>173.364297</td>\n", + " <td>5.612164e+06</td>\n", + " <td>2220.146124</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>0.100000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>2320.000000</td>\n", + " <td>0.000000e+00</td>\n", + " <td>0.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>1.500000</td>\n", + " <td>0.200000</td>\n", + " <td>0.095000</td>\n", + " <td>4095.000000</td>\n", + " <td>1.797602e+07</td>\n", + " <td>182.033000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>1.700000</td>\n", + " <td>0.200000</td>\n", + " <td>0.140000</td>\n", + " <td>4214.000000</td>\n", + " <td>2.087674e+07</td>\n", + " <td>200.067000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>3.500000</td>\n", + " <td>0.400000</td>\n", + " <td>0.198000</td>\n", + " <td>4331.000000</td>\n", + " <td>2.160859e+07</td>\n", + " <td>1069.667000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>16.700000</td>\n", + " <td>22.400000</td>\n", + " <td>2.580000</td>\n", + " <td>4633.000000</td>\n", + " <td>2.339041e+07</td>\n", + " <td>7887.552000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " ellis-cpu.system_perc ... ellis-net.out_packets_sec\n", + "count 177000.000000 ... 177000.000000\n", + "mean 2.315540 ... 1336.694851\n", + "std 1.170977 ... 2220.146124\n", + "min 0.100000 ... 0.000000\n", + "25% 1.500000 ... 182.033000\n", + "50% 1.700000 ... 200.067000\n", + "75% 3.500000 ... 1069.667000\n", + "max 16.700000 ... 7887.552000\n", + "\n", + "[8 rows x 6 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 265 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xGVleQbnhRm6" + }, + "source": [ + "#df_Ellis['SLO1'] = 0\n", + "#print('Column names are: ',list(df_Ellis.columns))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b-F_gA61xowR", + "outputId": "f9bd6232-2603-40ad-ccff-18887839e2da" + }, + "source": [ + "df4 = df_Ellis[\"ellis-load.avg_1_min\"] > 2.45\n", + "df4\n", + "df4.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/EllisLoadAvgLabel_lessthan0198.csv')\n", + "df4.head(50)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 False\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + "30 False\n", + "31 False\n", + "32 False\n", + "33 False\n", + "34 False\n", + "35 False\n", + "36 False\n", + "37 False\n", + "38 False\n", + "39 False\n", + "40 False\n", + "41 False\n", + "42 False\n", + "43 False\n", + "44 False\n", + "45 False\n", + "46 False\n", + "47 False\n", + "48 False\n", + "49 False\n", + "Name: ellis-load.avg_1_min, dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 267 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8xcPRerCz8nA", + "outputId": "fb66f20e-7365-40ec-857a-9dd9a8072401" + }, + "source": [ + "df3 = df_Ellis[\"ellis-cpu.wait_perc\"] > 5\n", + "df3\n", + "df3.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/ellis-cpu>5.csv')\n", + "df3.head(50)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 True\n", + "7 True\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 True\n", + "14 True\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 True\n", + "19 True\n", + "20 True\n", + "21 True\n", + "22 True\n", + "23 True\n", + "24 True\n", + "25 True\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 True\n", + "30 True\n", + "31 True\n", + "32 True\n", + "33 True\n", + "34 True\n", + "35 True\n", + "36 True\n", + "37 True\n", + "38 True\n", + "39 True\n", + "40 True\n", + "41 True\n", + "42 True\n", + "43 True\n", + "44 True\n", + "45 True\n", + "46 True\n", + "47 True\n", + "48 True\n", + "49 True\n", + "Name: ellis-cpu.wait_perc, dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 268 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EED56Wiq_NjM", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "20b06258-c5ba-457b-a022-cf5823217cbf" + }, + "source": [ + "df5 = df_Ellis[\"ellis-net.out_packets_sec\"] > 1000\n", + "df5\n", + "df5.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/ellis-net.in_bytes_sec21139.csv')\n", + "df5.head(50)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "5 False\n", + "6 False\n", + "7 False\n", + "8 False\n", + "9 False\n", + "10 False\n", + "11 False\n", + "12 False\n", + "13 False\n", + "14 False\n", + "15 False\n", + "16 False\n", + "17 False\n", + "18 False\n", + "19 False\n", + "20 False\n", + "21 False\n", + "22 False\n", + "23 False\n", + "24 False\n", + "25 False\n", + "26 False\n", + "27 False\n", + "28 False\n", + "29 False\n", + "30 False\n", + "31 False\n", + "32 False\n", + "33 False\n", + "34 False\n", + "35 False\n", + "36 False\n", + "37 False\n", + "38 False\n", + "39 False\n", + "40 False\n", + "41 False\n", + "42 False\n", + "43 False\n", + "44 False\n", + "45 False\n", + "46 False\n", + "47 False\n", + "48 False\n", + "49 False\n", + "Name: ellis-net.out_packets_sec, dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 269 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "phlI40_y0mug", + "outputId": "7fa177b9-bf9a-4b96-db65-7402f7f6cf32" + }, + "source": [ + "# We are applying Logical OR Operator between df4 and df3\n", + "df6 = (df4[0:176999]) | (df3[0:176999])\n", + "df6.head(50)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 True\n", + "7 True\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 True\n", + "14 True\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 True\n", + "19 True\n", + "20 True\n", + "21 True\n", + "22 True\n", + "23 True\n", + "24 True\n", + "25 True\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 True\n", + "30 True\n", + "31 True\n", + "32 True\n", + "33 True\n", + "34 True\n", + "35 True\n", + "36 True\n", + "37 True\n", + "38 True\n", + "39 True\n", + "40 True\n", + "41 True\n", + "42 True\n", + "43 True\n", + "44 True\n", + "45 True\n", + "46 True\n", + "47 True\n", + "48 True\n", + "49 True\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 270 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9xKYzZcLAZGy", + "outputId": "bc15e547-c791-4104-8bb2-8ed4d3288ac1" + }, + "source": [ + "df6.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/OR_TwoCondition(2).csv')\n", + "df6.head(50)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 True\n", + "7 True\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 True\n", + "14 True\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 True\n", + "19 True\n", + "20 True\n", + "21 True\n", + "22 True\n", + "23 True\n", + "24 True\n", + "25 True\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 True\n", + "30 True\n", + "31 True\n", + "32 True\n", + "33 True\n", + "34 True\n", + "35 True\n", + "36 True\n", + "37 True\n", + "38 True\n", + "39 True\n", + "40 True\n", + "41 True\n", + "42 True\n", + "43 True\n", + "44 True\n", + "45 True\n", + "46 True\n", + "47 True\n", + "48 True\n", + "49 True\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 271 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wRADpDibBZo5", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dfc6dc79-3d9f-4979-8210-e62e77b1aa6e" + }, + "source": [ + "df7 = (df6[0:176999]) | (df5[0:176999])\n", + "df7.head(50)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 True\n", + "7 True\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 True\n", + "14 True\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 True\n", + "19 True\n", + "20 True\n", + "21 True\n", + "22 True\n", + "23 True\n", + "24 True\n", + "25 True\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 True\n", + "30 True\n", + "31 True\n", + "32 True\n", + "33 True\n", + "34 True\n", + "35 True\n", + "36 True\n", + "37 True\n", + "38 True\n", + "39 True\n", + "40 True\n", + "41 True\n", + "42 True\n", + "43 True\n", + "44 True\n", + "45 True\n", + "46 True\n", + "47 True\n", + "48 True\n", + "49 True\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 272 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "w6BrDjX4CODn", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a6c956e7-6aed-4bdd-f37f-505a994de51a" + }, + "source": [ + "df7.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/FinalORLabel8.5.csv')\n", + "df7.head(50)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 True\n", + "7 True\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 True\n", + "14 True\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 True\n", + "19 True\n", + "20 True\n", + "21 True\n", + "22 True\n", + "23 True\n", + "24 True\n", + "25 True\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 True\n", + "30 True\n", + "31 True\n", + "32 True\n", + "33 True\n", + "34 True\n", + "35 True\n", + "36 True\n", + "37 True\n", + "38 True\n", + "39 True\n", + "40 True\n", + "41 True\n", + "42 True\n", + "43 True\n", + "44 True\n", + "45 True\n", + "46 True\n", + "47 True\n", + "48 True\n", + "49 True\n", + "dtype: bool" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 273 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wwv2cjFAIFHL" + }, + "source": [ + "df_Ellis.insert (7, \"Label\", df7)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "hrPqpjd96I1x" + }, + "source": [ + "#df_Ellis.insert (8, \"Label\", df7)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "_zKkQLOz6qPY" + }, + "source": [ + "# 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", + "df_Ellis\n", + "df_Ellis.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/Final/Ellis_FinalTwoConditionwithOR.csv')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3rEy1vtp67M9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "outputId": "4e2175cc-dccb-4aaf-a152-e2452de241b0" + }, + "source": [ + "df_Ellis.head(100)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>Timestamp</th>\n", + " <th>ellis-cpu.system_perc</th>\n", + " <th>ellis-cpu.wait_perc</th>\n", + " <th>ellis-load.avg_1_min</th>\n", + " <th>ellis-mem.free_mb</th>\n", + " <th>ellis-net.in_bytes_sec</th>\n", + " <th>ellis-net.out_packets_sec</th>\n", + " <th>Label</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>14/09/2016 0:00</td>\n", + " <td>0.5</td>\n", + " <td>12.9</td>\n", + " <td>1.73</td>\n", + " <td>3949</td>\n", + " <td>5413.200</td>\n", + " <td>62.067</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>14/09/2016 0:00</td>\n", + " <td>0.4</td>\n", + " <td>10.3</td>\n", + " <td>1.79</td>\n", + " <td>3950</td>\n", + " <td>5201.667</td>\n", + " <td>59.567</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>14/09/2016 0:01</td>\n", + " <td>0.4</td>\n", + " <td>11.8</td>\n", + " <td>1.52</td>\n", + " <td>3950</td>\n", + " <td>5370.733</td>\n", + " <td>61.200</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>14/09/2016 0:01</td>\n", + " <td>0.4</td>\n", + " <td>12.9</td>\n", + " <td>1.43</td>\n", + " <td>3949</td>\n", + " <td>5292.467</td>\n", + " <td>60.400</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>14/09/2016 0:02</td>\n", + " <td>0.5</td>\n", + " <td>12.1</td>\n", + " <td>1.44</td>\n", + " <td>3950</td>\n", + " <td>5318.167</td>\n", + " <td>61.700</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>95</th>\n", + " <td>14/09/2016 0:47</td>\n", + " <td>0.5</td>\n", + " <td>10.8</td>\n", + " <td>0.45</td>\n", + " <td>3948</td>\n", + " <td>5187.133</td>\n", + " <td>60.100</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>96</th>\n", + " <td>14/09/2016 0:48</td>\n", + " <td>0.5</td>\n", + " <td>10.4</td>\n", + " <td>0.42</td>\n", + " <td>3949</td>\n", + " <td>5223.100</td>\n", + " <td>60.233</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>97</th>\n", + " <td>14/09/2016 0:48</td>\n", + " <td>0.6</td>\n", + " <td>13.0</td>\n", + " <td>0.56</td>\n", + " <td>3947</td>\n", + " <td>5335.200</td>\n", + " <td>60.667</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>98</th>\n", + " <td>14/09/2016 0:49</td>\n", + " <td>0.6</td>\n", + " <td>10.1</td>\n", + " <td>0.47</td>\n", + " <td>3948</td>\n", + " <td>5185.733</td>\n", + " <td>60.367</td>\n", + " <td>True</td>\n", + " </tr>\n", + " <tr>\n", + " <th>99</th>\n", + " <td>14/09/2016 0:49</td>\n", + " <td>0.6</td>\n", + " <td>10.8</td>\n", + " <td>0.28</td>\n", + " <td>3948</td>\n", + " <td>5204.233</td>\n", + " <td>59.600</td>\n", + " <td>True</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>100 rows × 8 columns</p>\n", + "</div>" + ], + "text/plain": [ + " Timestamp ellis-cpu.system_perc ... ellis-net.out_packets_sec Label\n", + "0 14/09/2016 0:00 0.5 ... 62.067 True\n", + "1 14/09/2016 0:00 0.4 ... 59.567 True\n", + "2 14/09/2016 0:01 0.4 ... 61.200 True\n", + "3 14/09/2016 0:01 0.4 ... 60.400 True\n", + "4 14/09/2016 0:02 0.5 ... 61.700 True\n", + ".. ... ... ... ... ...\n", + "95 14/09/2016 0:47 0.5 ... 60.100 True\n", + "96 14/09/2016 0:48 0.5 ... 60.233 True\n", + "97 14/09/2016 0:48 0.6 ... 60.667 True\n", + "98 14/09/2016 0:49 0.6 ... 60.367 True\n", + "99 14/09/2016 0:49 0.6 ... 59.600 True\n", + "\n", + "[100 rows x 8 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 277 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "11Qu45RY0HNG", + "outputId": "305c5dd5-ec61-48a8-abb6-e29bbc4b9e42" + }, + "source": [ + "# pandas count distinct values in column\n", + "df_Ellis['Label'].value_counts()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "False 112145\n", + "True 64854\n", + "Name: Label, dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 278 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0sB-W_Ny4eHk" + }, + "source": [ + "#final.to_csv('/gdrive/MyDrive/LFN Anuket/Analysis/data/New/FinalLabel.csv')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ERsufys7wcSg" + }, + "source": [ + "#df_Ellis.loc[(df_Ellis[\"ellis-cpu.wait_perc\"] > 5) & (df_Ellis[\"ellis-load.avg_1_min\"] > 2)]" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9le7MwnDhlnH" + }, + "source": [ + "# **Creating New Features**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "090QXGpPlEF6" + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +}
\ No newline at end of file |