Milestone1.ipynb

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    "colab": {
      "name": "Milestone1.ipynb",
      "provenance": [],
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      "machine_shape": "hm"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7B8Xm3CVqTj"
      },
      "source": [
        "import tarfile\n",
        "import codecs\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "from io import BytesIO\n",
        "import json\n",
        "import re\n",
        "import altair as alt\n",
        "import itertools\n",
        "from mlxtend.preprocessing import TransactionEncoder\n",
        "from mlxtend.frequent_patterns import apriori\n",
        "from mlxtend.frequent_patterns import association_rules\n",
        "import matplotlib.pyplot as plt\n",
        "import string\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction import text\n",
        "from collections import Counter\n",
        "from wordcloud import WordCloud\n",
        "import gensim\n",
        "import pickle\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.pipeline import make_union\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import SelectFromModel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xw_3JHHGVwLS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0afc6004-76a1-4e4a-e624-f2f880cc6cdc"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/data586_Project')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/data586_Project\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YT28PlYIV1Fc"
      },
      "source": [
        "**Describe Dataset**\n",
        "\n",
        "Response variable: Label - Either the log is normal or anomaly"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yj68pzj7Vy2M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5d165b13-41e5-4f29-fe33-d80196c989c1"
      },
      "source": [
        "df = pd.read_csv('/content/data586_Project/MyDrive/DATA586_Project/processed_log.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Pid</th>\n",
              "      <th>Level</th>\n",
              "      <th>Component</th>\n",
              "      <th>Content</th>\n",
              "      <th>BlockId</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81109</td>\n",
              "      <td>203518</td>\n",
              "      <td>143</td>\n",
              "      <td>INFO</td>\n",
              "      <td>dfs.DataNode$DataXceiver</td>\n",
              "      <td>Receiving block blk_-1608999687919862906 src: ...</td>\n",
              "      <td>1608999687919862906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>81109</td>\n",
              "      <td>203518</td>\n",
              "      <td>35</td>\n",
              "      <td>INFO</td>\n",
              "      <td>dfs.FSNamesystem</td>\n",
              "      <td>BLOCK* NameSystem.allocateBlock: /mnt/hadoop/m...</td>\n",
              "      <td>1608999687919862906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>81109</td>\n",
              "      <td>203519</td>\n",
              "      <td>143</td>\n",
              "      <td>INFO</td>\n",
              "      <td>dfs.DataNode$DataXceiver</td>\n",
              "      <td>Receiving block blk_-1608999687919862906 src: ...</td>\n",
              "      <td>1608999687919862906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>81109</td>\n",
              "      <td>203519</td>\n",
              "      <td>145</td>\n",
              "      <td>INFO</td>\n",
              "      <td>dfs.DataNode$DataXceiver</td>\n",
              "      <td>Receiving block blk_-1608999687919862906 src: ...</td>\n",
              "      <td>1608999687919862906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>81109</td>\n",
              "      <td>203519</td>\n",
              "      <td>145</td>\n",
              "      <td>INFO</td>\n",
              "      <td>dfs.DataNode$PacketResponder</td>\n",
              "      <td>PacketResponder 1 for block blk_-1608999687919...</td>\n",
              "      <td>1608999687919862906</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "    Date  ...              BlockId\n",
              "0  81109  ...  1608999687919862906\n",
              "1  81109  ...  1608999687919862906\n",
              "2  81109  ...  1608999687919862906\n",
              "3  81109  ...  1608999687919862906\n",
              "4  81109  ...  1608999687919862906\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcfDSdKR73u8",
        "outputId": "7cdef0fb-99c1-40e0-98c0-13a220ef3c68"
      },
      "source": [
        "df.BlockId.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "575061"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 5
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    {
      "cell_type": "code",
      "metadata": {
        "id": "aclExQ4g8CcE"
      },
      "source": [
        "event_description=[]\n",
        "for line in df.Content:\n",
        "  # extract only character add description of the event \n",
        "  match=re.findall('[a-zA-Z]+',str(line))\n",
        "  event_description.append(match[0:6])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItHvNdsP9uwJ"
      },
      "source": [
        "df['Event_Description'] = event_description"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eync0-S4_SJR"
      },
      "source": [
        "df.to_csv('event_desc.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4M1rbktx_aAY"
      },
      "source": [
        "df = pd.read_csv('/content/event_desc.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QqYwOnM-VFc"
      },
      "source": [
        "df['Event_id'] = df.groupby('Event_Description').ngroup()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "vgyHRYiR_peV",
        "outputId": "f0afaffe-9762-454a-b824-8d524b9a1597"
      },
      "source": [
        "df_event = df.iloc[:, 6:8].copy()\n",
        "df_event.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
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              "      <th>Event_Description</th>\n",
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              "      <th>1</th>\n",
              "      <td>1608999687919862906</td>\n",
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              "      <th>2</th>\n",
              "      <td>1608999687919862906</td>\n",
              "      <td>['Receiving', 'block', 'blk', 'src', 'dest']</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1608999687919862906</td>\n",
              "      <td>['Receiving', 'block', 'blk', 'src', 'dest']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1608999687919862906</td>\n",
              "      <td>['PacketResponder', 'for', 'block', 'blk', 'te...</td>\n",
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            "text/plain": [
              "               BlockId                                  Event_Description\n",
              "0  1608999687919862906       ['Receiving', 'block', 'blk', 'src', 'dest']\n",
              "1  1608999687919862906  ['BLOCK', 'NameSystem', 'allocateBlock', 'mnt'...\n",
              "2  1608999687919862906       ['Receiving', 'block', 'blk', 'src', 'dest']\n",
              "3  1608999687919862906       ['Receiving', 'block', 'blk', 'src', 'dest']\n",
              "4  1608999687919862906  ['PacketResponder', 'for', 'block', 'blk', 'te..."
            ]
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          "execution_count": 11
        }
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          "base_uri": "https://localhost:8080/",
          "height": 204
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        "id": "myxSAQif95Th",
        "outputId": "8ab48ac2-e7d0-4c1b-f8b6-6fb993069bdd"
      },
      "source": [
        "df_event = df.groupby(['BlockId'])['Event_Description'].apply(','.join).reset_index()\n",
        "df_event.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Event_Description</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24807389905763</td>\n",
              "      <td>['BLOCK', 'NameSystem', 'allocateBlock', 'user...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27644297472372</td>\n",
              "      <td>['Receiving', 'block', 'blk', 'src', 'dest'],[...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33380551139732</td>\n",
              "      <td>['BLOCK', 'NameSystem', 'allocateBlock', 'user...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35641716531335</td>\n",
              "      <td>['BLOCK', 'NameSystem', 'allocateBlock', 'user...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40083153975957</td>\n",
              "      <td>['Receiving', 'block', 'blk', 'src', 'dest'],[...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "          BlockId                                  Event_Description\n",
              "0  24807389905763  ['BLOCK', 'NameSystem', 'allocateBlock', 'user...\n",
              "1  27644297472372  ['Receiving', 'block', 'blk', 'src', 'dest'],[...\n",
              "2  33380551139732  ['BLOCK', 'NameSystem', 'allocateBlock', 'user...\n",
              "3  35641716531335  ['BLOCK', 'NameSystem', 'allocateBlock', 'user...\n",
              "4  40083153975957  ['Receiving', 'block', 'blk', 'src', 'dest'],[..."
            ]
          },
          "metadata": {
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          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uh2c35u4WA4B"
      },
      "source": [
        "#label = pd.read_csv('/content/data586_Project/MyDrive/DATA586_Project/anomaly_label.csv')\n",
        "\n",
        "# for yx\n",
        "label = pd.read_csv('/content/data586_Project/MyDrive/project 585/anomaly_label.csv')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "QZD9Cy8dNMXW",
        "outputId": "dba36cc2-ce46-43ee-c2c0-fe729481a9b8"
      },
      "source": [
        "label.groupby('Label').count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BlockId</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Anomaly</th>\n",
              "      <td>16838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Normal</th>\n",
              "      <td>558223</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "         BlockId\n",
              "Label           \n",
              "Anomaly    16838\n",
              "Normal    558223"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "czRtepMyWVDW",
        "outputId": "fe6aa8f9-6040-4316-ffaa-6bc94cb35f5b"
      },
      "source": [
        "len(label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "575061"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 6
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SjmdHguWErR"
      },
      "source": [
        "label['BlockId2'] = label.BlockId.str.extract('(\\d+)') \n",
        "label.drop('BlockId', axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZnIbd8PWGaA"
      },
      "source": [
        "label.BlockId2 = pd.to_numeric(label.BlockId2, errors='coerce').fillna(0).astype(np.int64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7el8Iz6WJAN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6c7e79c1-4293-45fa-bfab-459877af1fd5"
      },
      "source": [
        "log = pd.merge(df_event, label, left_on='BlockId', right_on='BlockId2', how='left').drop('BlockId2', axis=1)\n",
        "log.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>BlockId</th>\n",
              "      <th>Event_Description</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1608999687919862906</td>\n",
              "      <td>['Receiving', 'block', 'blk', 'src', 'dest']</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1608999687919862906</td>\n",
              "      <td>['BLOCK', 'NameSystem', 'allocateBlock', 'mnt'...</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1608999687919862906</td>\n",
              "      <td>['Receiving', 'block', 'blk', 'src', 'dest']</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1608999687919862906</td>\n",
              "      <td>['Receiving', 'block', 'blk', 'src', 'dest']</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1608999687919862906</td>\n",
              "      <td>['PacketResponder', 'for', 'block', 'blk', 'te...</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               BlockId  ...   Label\n",
              "0  1608999687919862906  ...  Normal\n",
              "1  1608999687919862906  ...  Normal\n",
              "2  1608999687919862906  ...  Normal\n",
              "3  1608999687919862906  ...  Normal\n",
              "4  1608999687919862906  ...  Normal\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NsfzDNTMWMqm"
      },
      "source": [
        "normal = log[log['Label'] == 'Normal']\n",
        "anomaly = log[log['Label'] == 'Anomaly']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfEuVvv8TNTy"
      },
      "source": [
        "items = (log['Event_Description'].unique())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HKOPTLj1GM7"
      },
      "source": [
        "data_dtm=data_dtm.transpose()\n",
        "\n",
        "top_dict = {}\n",
        "for i in data_dtm.columns:\n",
        "  top = data_dtm[i].sort_values(ascending=False).head(20)\n",
        "  top_dict[i]=list(zip(top.index,top.values))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkhqtKUV0_Bf"
      },
      "source": [
        "words =[]\n",
        "\n",
        "for x in data_dtm.columns:\n",
        "  top = [word for (word, count) in top_dict[x]]\n",
        "  for t in top:\n",
        "    words.append(t)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2vonVh-1Wlq"
      },
      "source": [
        "stop_words = [word for word,count in Counter(words).most_common() if count>575061*0.7]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meLBDn7USUZC"
      },
      "source": [
        "# One hot encoding the data\n",
        "\n",
        "itemset = set(items)\n",
        "encoded_vals = []\n",
        "for index, row in log.iterrows():\n",
        "    rowset = set(row) \n",
        "    labels = {}\n",
        "    uncommons = list(itemset - rowset)\n",
        "    commons = list(itemset.intersection(rowset))\n",
        "    for uc in uncommons:\n",
        "        labels[uc] = 0\n",
        "    for com in commons:\n",
        "        labels[com] = 1\n",
        "    encoded_vals.append(labels)\n",
        "encoded_vals[0]\n",
        "ohe_df = pd.DataFrame(encoded_vals)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJpYRABRd8yx"
      },
      "source": [
        "# **Random Forest - Using Bag of Words**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fyfo0_z6pq6j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "outputId": "093e3b2d-5aa5-469b-b4f5-d874626d2974"
      },
      "source": [
        "data_dtm = pd.read_pickle(\"/content/data586_Project/MyDrive/DATA586_Project/dtm.pkl\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-b3419aa2b311>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata_dtm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/data586_Project/MyDrive/DATA586_Project/dtm.pkl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "3l5iaqpQeZbd",
        "outputId": "2b02186d-f78c-40a6-ae43-e981e18d7e9a"
      },
      "source": [
        "data_dtm.reset_index(inplace=True)\n",
        "data_dtm.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
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              "      <th>3</th>\n",
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              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
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              "      <td>6</td>\n",
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              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>40083153975957</td>\n",
              "      <td>Normal</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>72472557232301</td>\n",
              "      <td>Normal</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>72859758335309</td>\n",
              "      <td>Normal</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>78222097481044</td>\n",
              "      <td>Normal</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>97457323857639</td>\n",
              "      <td>Normal</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>99286907018543</td>\n",
              "      <td>Normal</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 133 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   index         BlockId   Label  added  ...  writer  writing  written  xml\n",
              "0      0  24807389905763  Normal      6  ...       0        0        0    0\n",
              "1      1  27644297472372  Normal      6  ...       0        0        0    0\n",
              "2      2  33380551139732  Normal      6  ...       0        0        0    0\n",
              "3      3  35641716531335  Normal      6  ...       0        0        0    0\n",
              "4      4  40083153975957  Normal      6  ...       0        0        0    0\n",
              "5      5  72472557232301  Normal      6  ...       0        0        0    0\n",
              "6      6  72859758335309  Normal      6  ...       0        0        0    0\n",
              "7      7  78222097481044  Normal      3  ...       0        0        0    0\n",
              "8      8  97457323857639  Normal      3  ...       0        0        0    0\n",
              "9      9  99286907018543  Normal      3  ...       0        0        0    0\n",
              "\n",
              "[10 rows x 133 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPYwd5vrkhzJ"
      },
      "source": [
        "label_col = {'Normal': 1, 'Anomaly': 0}\n",
        "\n",
        "data_dtm.Label = [label_col[item] for item in data_dtm.Label]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7poL3l-uCwZ"
      },
      "source": [
        "y = data_dtm.iloc[:, 2]\n",
        "X = data_dtm.iloc[:, 3:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZInklFP33X9E"
      },
      "source": [
        "**TFIDF Bag of Words**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EoBJQNol3Iec"
      },
      "source": [
        "\n",
        "tfidfconverter = TfidfTransformer()\n",
        "X = tfidfconverter.fit_transform(X).toarray()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I32LEEGqgDU1"
      },
      "source": [
        "**Split into training and test data sets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgGbNsbEf5F9"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIa8oo-NlQ7k"
      },
      "source": [
        "# Train the model\n",
        "classifier = RandomForestClassifier(n_estimators=1000, random_state=0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Prediction\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2BgUz36ob_B",
        "outputId": "4febc1fe-3522-43e7-d209-f8df3f95a3e7"
      },
      "source": [
        "# Evaluate model\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  3311      4]\n",
            " [     5 111693]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      3315\n",
            "           1       1.00      1.00      1.00    111698\n",
            "\n",
            "    accuracy                           1.00    115013\n",
            "   macro avg       1.00      1.00      1.00    115013\n",
            "weighted avg       1.00      1.00      1.00    115013\n",
            "\n",
            "0.9999217479763157\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1sroKvIo9Sk"
      },
      "source": [
        "# Save model\n",
        "\n",
        "with open('RF_BW_classifier', 'wb') as picklefile:\n",
        "    pickle.dump(classifier,picklefile)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T01iGd4Mp653"
      },
      "source": [
        "# Load model\n",
        "\n",
        "with open('RF_BW_classifier', 'rb') as training_model:\n",
        "    model = pickle.load(training_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8n3PMCvHrijg",
        "outputId": "edfddba3-d7d3-4b56-f375-bee7171c972b"
      },
      "source": [
        "# Feature selection\n",
        "sel = SelectFromModel(RandomForestClassifier(n_estimators = 100))\n",
        "sel.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SelectFromModel(estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                                 class_weight=None,\n",
              "                                                 criterion='gini',\n",
              "                                                 max_depth=None,\n",
              "                                                 max_features='auto',\n",
              "                                                 max_leaf_nodes=None,\n",
              "                                                 max_samples=None,\n",
              "                                                 min_impurity_decrease=0.0,\n",
              "                                                 min_impurity_split=None,\n",
              "                                                 min_samples_leaf=1,\n",
              "                                                 min_samples_split=2,\n",
              "                                                 min_weight_fraction_leaf=0.0,\n",
              "                                                 n_estimators=100, n_jobs=None,\n",
              "                                                 oob_score=False,\n",
              "                                                 random_state=None, verbose=0,\n",
              "                                                 warm_start=False),\n",
              "                max_features=None, norm_order=1, prefit=False, threshold=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "99r16KvKry0j",
        "outputId": "73b3f88d-4174-43da-d5d7-a66b001b29cf"
      },
      "source": [
        "sel.get_support()\n",
        "selected_feat= X_train.columns[(sel.get_support())]\n",
        "len(selected_feat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIF7jMczr7DR",
        "outputId": "e3a8ace5-3d3d-47b7-c130-70fc2cd2854d"
      },
      "source": [
        "print(selected_feat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['added', 'addstoredblock', 'blk', 'block', 'blockinfo', 'blockmap',\n",
            "       'current', 'delete', 'deleting', 'dest', 'error', 'file', 'hadoop',\n",
            "       'namesystem', 'packet', 'packetresponder', 'received', 'receiving',\n",
            "       'redundant', 'request', 'size', 'src', 'terminating', 'trying',\n",
            "       'unexpected', 'updated', 'volumemap'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4GPPh5zqIdw"
      },
      "source": [
        "# **Logistic Regression - Using Bag of Word**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FX7xGMGqqOax",
        "outputId": "6fc0f895-2010-441c-8275-3dd41dd689b9"
      },
      "source": [
        "# Train the model\n",
        "classifier = LogisticRegression(verbose=1, solver='liblinear',random_state=0, C=5, penalty='l2',max_iter=1000)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Prediction\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[LibLinear]"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zV7KskQuuPT2",
        "outputId": "948e0c10-744e-4945-db28-f0160c833594"
      },
      "source": [
        "# Evaluate model\n",
        "\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  3231     84]\n",
            " [   127 111571]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.97      0.97      3315\n",
            "           1       1.00      1.00      1.00    111698\n",
            "\n",
            "    accuracy                           1.00    115013\n",
            "   macro avg       0.98      0.99      0.98    115013\n",
            "weighted avg       1.00      1.00      1.00    115013\n",
            "\n",
            "0.9981654247780686\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pq3WQwmTuYpF"
      },
      "source": [
        "# Save model\n",
        "\n",
        "with open('LR_BW_classifier', 'wb') as picklefile:\n",
        "    pickle.dump(classifier,picklefile)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVWMaYi-ucn0"
      },
      "source": [
        "# **Logistic Regression - Using one hot encoding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XRCsl9ysvAZb"
      },
      "source": [
        "one_hot = data_dtm.iloc[:, 3:]\n",
        "one_hot = one_hot.astype(bool).astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0WK7Enz8j91"
      },
      "source": [
        "# Find TFIDF\n",
        "\n",
        "tfidfconverter = TfidfTransformer()\n",
        "one_hot = tfidfconverter.fit_transform(one_hot).toarray()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19GM_q83x6PP"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(one_hot, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCENqBoayGSd",
        "outputId": "9e3bdab0-8e75-4e19-d9d0-edc073adcf19"
      },
      "source": [
        "# Train the model\n",
        "log_reg = LogisticRegression(verbose=1, solver='liblinear',random_state=0, C=5, penalty='l2',max_iter=1000)\n",
        "log_reg.fit(X_train,y_train)\n",
        "\n",
        "# Prediction\n",
        "y_pred = log_reg.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[LibLinear]"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SnzWc54Myus8",
        "outputId": "d29d5239-593a-4671-9053-f5fa4fd04395"
      },
      "source": [
        "# Evaluate model\n",
        "\n",
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  3314      1]\n",
            " [    68 111630]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      3315\n",
            "           1       1.00      1.00      1.00    111698\n",
            "\n",
            "    accuracy                           1.00    115013\n",
            "   macro avg       0.99      1.00      0.99    115013\n",
            "weighted avg       1.00      1.00      1.00    115013\n",
            "\n",
            "0.9994000678184205\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRLw1hPP0Qd5"
      },
      "source": [
        "# **Random Forest - Using one hot encoding**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guGrpdh80VK_"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(one_hot, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvgJgDjr0i2s"
      },
      "source": [
        "# Train the model\n",
        "rf = RandomForestClassifier(n_estimators=1000, random_state=0)\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "# Prediction\n",
        "y_pred = rf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_btUomWI0pXl",
        "outputId": "5b7d2c50-2d9c-4c0b-aa85-2e21d66c718f"
      },
      "source": [
        "print(confusion_matrix(y_test,y_pred))\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  3312      3]\n",
            " [    60 111638]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      3315\n",
            "           1       1.00      1.00      1.00    111698\n",
            "\n",
            "    accuracy                           1.00    115013\n",
            "   macro avg       0.99      1.00      1.00    115013\n",
            "weighted avg       1.00      1.00      1.00    115013\n",
            "\n",
            "0.9994522358342101\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5m46-Thm0fyF"
      },
      "source": [
        "# **Random Forest - Using word2vec**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyA_2LAc2LpG"
      },
      "source": [
        "def content_to_wordlist(content, remove_stopwords=False):\n",
        "    # Convert a document to a sequence of words\n",
        "\n",
        "    content = re.sub(\"[^a-zA-Z]\",\" \", content)\n",
        "\n",
        "    # Convert words to lower case and split them\n",
        "    words = content.lower().split()\n",
        "\n",
        "    # Optionally remove stop words (false by default)\n",
        "    if remove_stopwords:\n",
        "        stops = set(stopwords.words(\"english\"))\n",
        "        words = [w for w in words if not w in stops]\n",
        "\n",
        "    # Return a list of words\n",
        "    return(words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRrs_33dYJRb"
      },
      "source": [
        "**Normal - Association Rules**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMPXqR4IWNOJ"
      },
      "source": [
        "# Normal event list\n",
        "\n",
        "normal_events = []\n",
        "for line in normal.Content:\n",
        "  # extract only character add description of the event \n",
        "  match = re.findall('[a-zA-Z]+', str(line))\n",
        "  normal_events.append(match[0:6]) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LV4Ei_SZWPEl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "44d63ad9-b9b7-4b0d-a1ff-2a1f1a7a5ab7"
      },
      "source": [
        "te = TransactionEncoder()\n",
        "te_array = te.fit(normal_events).transform(normal_events)\n",
        "df = pd.DataFrame(te_array, columns=te.columns_)\n",
        "df.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>BLOCK</th>\n",
              "      <th>Deleting</th>\n",
              "      <th>Got</th>\n",
              "      <th>NameSystem</th>\n",
              "      <th>PacketResponder</th>\n",
              "      <th>Received</th>\n",
              "      <th>Receiving</th>\n",
              "      <th>Redundant</th>\n",
              "      <th>Served</th>\n",
              "      <th>Starting</th>\n",
              "      <th>Transmitted</th>\n",
              "      <th>Unexpected</th>\n",
              "      <th>Verification</th>\n",
              "      <th>addStoredBlock</th>\n",
              "      <th>added</th>\n",
              "      <th>allocateBlock</th>\n",
              "      <th>ask</th>\n",
              "      <th>blk</th>\n",
              "      <th>block</th>\n",
              "      <th>blockMap</th>\n",
              "      <th>delete</th>\n",
              "      <th>dest</th>\n",
              "      <th>error</th>\n",
              "      <th>exception</th>\n",
              "      <th>file</th>\n",
              "      <th>for</th>\n",
              "      <th>from</th>\n",
              "      <th>grep</th>\n",
              "      <th>grepa</th>\n",
              "      <th>grepb</th>\n",
              "      <th>hadoop</th>\n",
              "      <th>is</th>\n",
              "      <th>mapred</th>\n",
              "      <th>mnt</th>\n",
              "      <th>of</th>\n",
              "      <th>rand</th>\n",
              "      <th>randtxt</th>\n",
              "      <th>received</th>\n",
              "      <th>replicate</th>\n",
              "      <th>request</th>\n",
              "      <th>root</th>\n",
              "      <th>serving</th>\n",
              "      <th>size</th>\n",
              "      <th>sortrand</th>\n",
              "      <th>src</th>\n",
              "      <th>succeeded</th>\n",
              "      <th>terminating</th>\n",
              "      <th>thread</th>\n",
              "      <th>to</th>\n",
              "      <th>transfer</th>\n",
              "      <th>trying</th>\n",
              "      <th>updated</th>\n",
              "      <th>user</th>\n",
              "      <th>wcnt</th>\n",
              "      <th>while</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   BLOCK  Deleting    Got  NameSystem  ...  updated   user   wcnt  while\n",
              "0  False     False  False       False  ...    False  False  False  False\n",
              "1   True     False  False        True  ...    False  False  False  False\n",
              "2  False     False  False       False  ...    False  False  False  False\n",
              "3  False     False  False       False  ...    False  False  False  False\n",
              "4  False     False  False       False  ...    False  False  False  False\n",
              "\n",
              "[5 rows x 55 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0P34HcdWQQT"
      },
      "source": [
        "df.to_csv('normal_rules.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPUUXVsRYYrX"
      },
      "source": [
        "df_normal = pd.read_csv('/content/normal_rules.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cT09Tf5Yblc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "07757250-499b-49b9-c3a0-2c993cef771b"
      },
      "source": [
        "frequent_normal = apriori(df_normal, min_support=0.01, use_colnames=True)\n",
        "frequent_normal.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.125519</td>\n",
              "      <td>(Deleting)</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.031959</td>\n",
              "      <td>(Got)</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.330839</td>\n",
              "      <td>(NameSystem)</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.153817</td>\n",
              "      <td>(PacketResponder)</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.154058</td>\n",
              "      <td>(Received)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.154058</td>\n",
              "      <td>(Receiving)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.038291</td>\n",
              "      <td>(Served)</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.010738</td>\n",
              "      <td>(Verification)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.154261</td>\n",
              "      <td>(addStoredBlock)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    support           itemsets\n",
              "0  0.331073            (BLOCK)\n",
              "1  0.125519         (Deleting)\n",
              "2  0.031959              (Got)\n",
              "3  0.330839       (NameSystem)\n",
              "4  0.153817  (PacketResponder)\n",
              "5  0.154058         (Received)\n",
              "6  0.154058        (Receiving)\n",
              "7  0.038291           (Served)\n",
              "8  0.010738     (Verification)\n",
              "9  0.154261   (addStoredBlock)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hl61P9S-Yc-Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "outputId": "21981d7a-d637-46af-8c64-f13ec45df52f"
      },
      "source": [
        "rules_normal = association_rules(frequent_normal, metric=\"confidence\", min_threshold=0.8)\n",
        "rules_normal = rules_normal.sort_values(['confidence', 'lift'], ascending =[False, False])\n",
        "rules_normal.head(30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
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              "      <th>conviction</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>67</th>\n",
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              "      <td>(succeeded)</td>\n",
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              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>(Verification, blk)</td>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>(blk, succeeded)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
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              "    <tr>\n",
              "      <th>569</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(blk, succeeded)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>(Verification, blk)</td>\n",
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              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>571</th>\n",
              "      <td>(for, Verification)</td>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>572</th>\n",
              "      <td>(for, succeeded)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(for, succeeded)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>(for, Verification)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1758</th>\n",
              "      <td>(for, Verification, blk)</td>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1761</th>\n",
              "      <td>(for, blk, succeeded)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1762</th>\n",
              "      <td>(for, Verification)</td>\n",
              "      <td>(blk, succeeded)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1763</th>\n",
              "      <td>(Verification, blk)</td>\n",
              "      <td>(for, succeeded)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1765</th>\n",
              "      <td>(for, succeeded)</td>\n",
              "      <td>(Verification, blk)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1766</th>\n",
              "      <td>(blk, succeeded)</td>\n",
              "      <td>(for, Verification)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1767</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(for, blk, succeeded)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1768</th>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>(for, Verification, blk)</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>0.010738</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.128547</td>\n",
              "      <td>0.010623</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>(exception)</td>\n",
              "      <td>(Got)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>(Got)</td>\n",
              "      <td>(exception)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>(serving)</td>\n",
              "      <td>(Got)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>(Got)</td>\n",
              "      <td>(serving)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>(Got)</td>\n",
              "      <td>(while)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>(while)</td>\n",
              "      <td>(Got)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>(exception)</td>\n",
              "      <td>(serving)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>(serving)</td>\n",
              "      <td>(exception)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>(exception)</td>\n",
              "      <td>(while)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>(while)</td>\n",
              "      <td>(exception)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>(serving)</td>\n",
              "      <td>(while)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>(while)</td>\n",
              "      <td>(serving)</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>0.031959</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.290247</td>\n",
              "      <td>0.030937</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   antecedents               consequents  ...  leverage  conviction\n",
              "67              (Verification)               (succeeded)  ...  0.010623         inf\n",
              "68                 (succeeded)            (Verification)  ...  0.010623         inf\n",
              "566        (Verification, blk)               (succeeded)  ...  0.010623         inf\n",
              "568           (blk, succeeded)            (Verification)  ...  0.010623         inf\n",
              "569             (Verification)          (blk, succeeded)  ...  0.010623         inf\n",
              "570                (succeeded)       (Verification, blk)  ...  0.010623         inf\n",
              "571        (for, Verification)               (succeeded)  ...  0.010623         inf\n",
              "572           (for, succeeded)            (Verification)  ...  0.010623         inf\n",
              "574             (Verification)          (for, succeeded)  ...  0.010623         inf\n",
              "575                (succeeded)       (for, Verification)  ...  0.010623         inf\n",
              "1758  (for, Verification, blk)               (succeeded)  ...  0.010623         inf\n",
              "1761     (for, blk, succeeded)            (Verification)  ...  0.010623         inf\n",
              "1762       (for, Verification)          (blk, succeeded)  ...  0.010623         inf\n",
              "1763       (Verification, blk)          (for, succeeded)  ...  0.010623         inf\n",
              "1765          (for, succeeded)       (Verification, blk)  ...  0.010623         inf\n",
              "1766          (blk, succeeded)       (for, Verification)  ...  0.010623         inf\n",
              "1767            (Verification)     (for, blk, succeeded)  ...  0.010623         inf\n",
              "1768               (succeeded)  (for, Verification, blk)  ...  0.010623         inf\n",
              "23                 (exception)                     (Got)  ...  0.030937         inf\n",
              "24                       (Got)               (exception)  ...  0.030937         inf\n",
              "25                   (serving)                     (Got)  ...  0.030937         inf\n",
              "26                       (Got)                 (serving)  ...  0.030937         inf\n",
              "28                       (Got)                   (while)  ...  0.030937         inf\n",
              "29                     (while)                     (Got)  ...  0.030937         inf\n",
              "117                (exception)                 (serving)  ...  0.030937         inf\n",
              "118                  (serving)               (exception)  ...  0.030937         inf\n",
              "120                (exception)                   (while)  ...  0.030937         inf\n",
              "121                    (while)               (exception)  ...  0.030937         inf\n",
              "145                  (serving)                   (while)  ...  0.030937         inf\n",
              "146                    (while)                 (serving)  ...  0.030937         inf\n",
              "\n",
              "[30 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yoPiEOYpYjPB"
      },
      "source": [
        "**Anomaly - Association Rules**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANoFjUIJYlGI"
      },
      "source": [
        "# Anomaly event list\n",
        "\n",
        "anomaly_events = []\n",
        "for line in anomaly.Content:\n",
        "  # extract only character add description of the event \n",
        "  match = re.findall('[a-zA-Z]+', str(line))\n",
        "  anomaly_events.append(match[0:6]) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgBIKAKCYoHe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "0a6b9b1a-ffd1-449b-a5ec-c6b5be85fb49"
      },
      "source": [
        "te = TransactionEncoder()\n",
        "te_array = te.fit(anomaly_events).transform(anomaly_events)\n",
        "df_anomaly = pd.DataFrame(te_array, columns=te.columns_)\n",
        "df_anomaly.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 86 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Adding  BLOCK  Block  Changing  ...   wcnt  while  writeBlock  writing\n",
              "0   False  False  False     False  ...  False  False       False    False\n",
              "1   False   True  False     False  ...  False  False       False    False\n",
              "2   False  False  False     False  ...  False  False       False    False\n",
              "3   False  False  False     False  ...  False  False       False    False\n",
              "4   False  False  False     False  ...  False  False       False    False\n",
              "\n",
              "[5 rows x 86 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--9FZismYupM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "2be50153-4d89-45a1-a9be-cf5f9eef42c8"
      },
      "source": [
        "frequent_anomaly = apriori(df_anomaly, min_support=0.01, use_colnames=True)\n",
        "frequent_anomaly.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.123063</td>\n",
              "      <td>(Deleting)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.028652</td>\n",
              "      <td>(Got)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.316618</td>\n",
              "      <td>(NameSystem)</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.111594</td>\n",
              "      <td>(PacketResponder)</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.126019</td>\n",
              "      <td>(Received)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.164472</td>\n",
              "      <td>(Receiving)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.041082</td>\n",
              "      <td>(Served)</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.015469</td>\n",
              "      <td>(Starting)</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.015247</td>\n",
              "      <td>(Transmitted)</td>\n",
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            ],
            "text/plain": [
              "    support           itemsets\n",
              "0  0.332101            (BLOCK)\n",
              "1  0.123063         (Deleting)\n",
              "2  0.028652              (Got)\n",
              "3  0.316618       (NameSystem)\n",
              "4  0.111594  (PacketResponder)\n",
              "5  0.126019         (Received)\n",
              "6  0.164472        (Receiving)\n",
              "7  0.041082           (Served)\n",
              "8  0.015469         (Starting)\n",
              "9  0.015247      (Transmitted)"
            ]
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          "metadata": {
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          },
          "execution_count": 14
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "cgJojqaTYwH3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "outputId": "de7cb8f9-088c-4870-e190-1d7930ece4e6"
      },
      "source": [
        "rules_anomaly = association_rules(frequent_anomaly, metric=\"confidence\", min_threshold=0.8)\n",
        "rules_anomaly = rules_anomaly.sort_values(['confidence', 'lift'], ascending =[False, False])\n",
        "rules_anomaly.head(30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
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              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
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              "      <th>conviction</th>\n",
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              "  </thead>\n",
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              "      <th>83</th>\n",
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              "      <td>(Verification, blk)</td>\n",
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              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
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              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>(blk, succeeded)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
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              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(blk, succeeded)</td>\n",
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              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
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              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
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              "    <tr>\n",
              "      <th>742</th>\n",
              "      <td>(succeeded)</td>\n",
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              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
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              "    <tr>\n",
              "      <th>743</th>\n",
              "      <td>(for, Verification)</td>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>744</th>\n",
              "      <td>(for, succeeded)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(for, succeeded)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>747</th>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>(for, Verification)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2412</th>\n",
              "      <td>(for, Verification, blk)</td>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2415</th>\n",
              "      <td>(for, blk, succeeded)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2416</th>\n",
              "      <td>(for, Verification)</td>\n",
              "      <td>(blk, succeeded)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2417</th>\n",
              "      <td>(Verification, blk)</td>\n",
              "      <td>(for, succeeded)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2419</th>\n",
              "      <td>(for, succeeded)</td>\n",
              "      <td>(Verification, blk)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2420</th>\n",
              "      <td>(blk, succeeded)</td>\n",
              "      <td>(for, Verification)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2421</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(for, blk, succeeded)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2422</th>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>(for, Verification, blk)</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>0.010855</td>\n",
              "      <td>1.0</td>\n",
              "      <td>92.122084</td>\n",
              "      <td>0.010737</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>936</th>\n",
              "      <td>(blk, received)</td>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011710</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>938</th>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>(blk, received)</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011710</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1074</th>\n",
              "      <td>(exception, io)</td>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>0.011726</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011726</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011587</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1083</th>\n",
              "      <td>(exception, java)</td>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011710</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1084</th>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>(exception, java)</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011710</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1087</th>\n",
              "      <td>(exception, received)</td>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011710</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>(exception, received)</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011710</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>(io, received)</td>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>0.011726</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011726</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011587</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1133</th>\n",
              "      <td>(java, received)</td>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011710</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1134</th>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>(java, received)</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011710</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2612</th>\n",
              "      <td>(exception, io)</td>\n",
              "      <td>(blk, received)</td>\n",
              "      <td>0.011726</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011726</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011587</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2621</th>\n",
              "      <td>(exception, blk, io)</td>\n",
              "      <td>(writeBlock)</td>\n",
              "      <td>0.011726</td>\n",
              "      <td>0.011851</td>\n",
              "      <td>0.011726</td>\n",
              "      <td>1.0</td>\n",
              "      <td>84.382319</td>\n",
              "      <td>0.011587</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   antecedents               consequents  ...  leverage  conviction\n",
              "83              (Verification)               (succeeded)  ...  0.010737         inf\n",
              "84                 (succeeded)            (Verification)  ...  0.010737         inf\n",
              "738        (Verification, blk)               (succeeded)  ...  0.010737         inf\n",
              "740           (blk, succeeded)            (Verification)  ...  0.010737         inf\n",
              "741             (Verification)          (blk, succeeded)  ...  0.010737         inf\n",
              "742                (succeeded)       (Verification, blk)  ...  0.010737         inf\n",
              "743        (for, Verification)               (succeeded)  ...  0.010737         inf\n",
              "744           (for, succeeded)            (Verification)  ...  0.010737         inf\n",
              "746             (Verification)          (for, succeeded)  ...  0.010737         inf\n",
              "747                (succeeded)       (for, Verification)  ...  0.010737         inf\n",
              "2412  (for, Verification, blk)               (succeeded)  ...  0.010737         inf\n",
              "2415     (for, blk, succeeded)            (Verification)  ...  0.010737         inf\n",
              "2416       (for, Verification)          (blk, succeeded)  ...  0.010737         inf\n",
              "2417       (Verification, blk)          (for, succeeded)  ...  0.010737         inf\n",
              "2419          (for, succeeded)       (Verification, blk)  ...  0.010737         inf\n",
              "2420          (blk, succeeded)       (for, Verification)  ...  0.010737         inf\n",
              "2421            (Verification)     (for, blk, succeeded)  ...  0.010737         inf\n",
              "2422               (succeeded)  (for, Verification, blk)  ...  0.010737         inf\n",
              "936            (blk, received)              (writeBlock)  ...  0.011710         inf\n",
              "938               (writeBlock)           (blk, received)  ...  0.011710         inf\n",
              "1074           (exception, io)              (writeBlock)  ...  0.011587         inf\n",
              "1083         (exception, java)              (writeBlock)  ...  0.011710         inf\n",
              "1084              (writeBlock)         (exception, java)  ...  0.011710         inf\n",
              "1087     (exception, received)              (writeBlock)  ...  0.011710         inf\n",
              "1089              (writeBlock)     (exception, received)  ...  0.011710         inf\n",
              "1128            (io, received)              (writeBlock)  ...  0.011587         inf\n",
              "1133          (java, received)              (writeBlock)  ...  0.011710         inf\n",
              "1134              (writeBlock)          (java, received)  ...  0.011710         inf\n",
              "2612           (exception, io)           (blk, received)  ...  0.011587         inf\n",
              "2621      (exception, blk, io)              (writeBlock)  ...  0.011587         inf\n",
              "\n",
              "[30 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hcmx0t-SY37G"
      },
      "source": [
        "**Association Rules for entire set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1yf1Hw6Yx7S"
      },
      "source": [
        "# total events\n",
        "\n",
        "event_description=[]\n",
        "for line in log.Content:\n",
        "  # extract only character add description of the event \n",
        "  match=re.findall('[a-zA-Z]+',str(line))\n",
        "  event_description.append(match[0:6]) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CLH0I3sY7NF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "7b8b7fe9-b3d7-46de-b23e-2dc69daaad7b"
      },
      "source": [
        "te = TransactionEncoder()\n",
        "te_array = te.fit(event_description).transform(event_description)\n",
        "df = pd.DataFrame(te_array, columns=te.columns_)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adding</th>\n",
              "      <th>BLOCK</th>\n",
              "      <th>Block</th>\n",
              "      <th>Changing</th>\n",
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              "      <th>SocketTimeoutException</th>\n",
              "      <th>Starting</th>\n",
              "      <th>Transmitted</th>\n",
              "      <th>Unexpected</th>\n",
              "      <th>Verification</th>\n",
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              "      <th>mapred</th>\n",
              "      <th>mirror</th>\n",
              "      <th>mnt</th>\n",
              "      <th>neededReplications</th>\n",
              "      <th>net</th>\n",
              "      <th>nio</th>\n",
              "      <th>of</th>\n",
              "      <th>offset</th>\n",
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              "      <th>rand</th>\n",
              "      <th>randtxt</th>\n",
              "      <th>receiveBlock</th>\n",
              "      <th>received</th>\n",
              "      <th>replicate</th>\n",
              "      <th>request</th>\n",
              "      <th>root</th>\n",
              "      <th>serving</th>\n",
              "      <th>size</th>\n",
              "      <th>sortrand</th>\n",
              "      <th>src</th>\n",
              "      <th>succeeded</th>\n",
              "      <th>terminating</th>\n",
              "      <th>thread</th>\n",
              "      <th>timed</th>\n",
              "      <th>to</th>\n",
              "      <th>transfer</th>\n",
              "      <th>trying</th>\n",
              "      <th>updated</th>\n",
              "      <th>user</th>\n",
              "      <th>wcnt</th>\n",
              "      <th>while</th>\n",
              "      <th>writeBlock</th>\n",
              "      <th>writing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
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              "      <td>True</td>\n",
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              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 88 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Adding  BLOCK  Block  Changing  ...   wcnt  while  writeBlock  writing\n",
              "0   False  False  False     False  ...  False  False       False    False\n",
              "1   False   True  False     False  ...  False  False       False    False\n",
              "2   False  False  False     False  ...  False  False       False    False\n",
              "3   False  False  False     False  ...  False  False       False    False\n",
              "4   False  False  False     False  ...  False  False       False    False\n",
              "\n",
              "[5 rows x 88 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j598EGOjY8-K"
      },
      "source": [
        "df.to_csv('Association_rule.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bf-dTHYBY-bE"
      },
      "source": [
        "df_ap = pd.read_csv('/content/Association_rule.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "np33x921ZAKY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "6e41461c-4f22-4ad8-d0c7-825a9ece80ad"
      },
      "source": [
        "frequent_itemsets_ap = apriori(df_ap, min_support=0.01, use_colnames=True)\n",
        "frequent_itemsets_ap.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        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>support</th>\n",
              "      <th>itemsets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.331099</td>\n",
              "      <td>(BLOCK)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.125456</td>\n",
              "      <td>(Deleting)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.031874</td>\n",
              "      <td>(Got)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.330473</td>\n",
              "      <td>(NameSystem)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.152728</td>\n",
              "      <td>(PacketResponder)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.153335</td>\n",
              "      <td>(Received)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.154327</td>\n",
              "      <td>(Receiving)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.038363</td>\n",
              "      <td>(Served)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.010741</td>\n",
              "      <td>(Verification)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.154086</td>\n",
              "      <td>(addStoredBlock)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    support           itemsets\n",
              "0  0.331099            (BLOCK)\n",
              "1  0.125456         (Deleting)\n",
              "2  0.031874              (Got)\n",
              "3  0.330473       (NameSystem)\n",
              "4  0.152728  (PacketResponder)\n",
              "5  0.153335         (Received)\n",
              "6  0.154327        (Receiving)\n",
              "7  0.038363           (Served)\n",
              "8  0.010741     (Verification)\n",
              "9  0.154086   (addStoredBlock)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjMsfpzmZBmm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "d9679169-ef1d-4dd1-c541-e4e4e4be0248"
      },
      "source": [
        "rules_ap = association_rules(frequent_itemsets_ap, metric=\"confidence\", min_threshold=0.8)\n",
        "rules_ap = rules_ap.sort_values(['confidence', 'lift'], ascending =[False, False])\n",
        "rules_ap.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\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>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>(Verification, blk)</td>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>(succeeded, blk)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>569</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(succeeded, blk)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>(Verification, blk)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>571</th>\n",
              "      <td>(Verification, for)</td>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573</th>\n",
              "      <td>(succeeded, for)</td>\n",
              "      <td>(Verification)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>574</th>\n",
              "      <td>(Verification)</td>\n",
              "      <td>(succeeded, for)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575</th>\n",
              "      <td>(succeeded)</td>\n",
              "      <td>(Verification, for)</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>0.010741</td>\n",
              "      <td>1.0</td>\n",
              "      <td>93.102311</td>\n",
              "      <td>0.010626</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             antecedents          consequents  ...  leverage  conviction\n",
              "67        (Verification)          (succeeded)  ...  0.010626         inf\n",
              "68           (succeeded)       (Verification)  ...  0.010626         inf\n",
              "567  (Verification, blk)          (succeeded)  ...  0.010626         inf\n",
              "568     (succeeded, blk)       (Verification)  ...  0.010626         inf\n",
              "569       (Verification)     (succeeded, blk)  ...  0.010626         inf\n",
              "570          (succeeded)  (Verification, blk)  ...  0.010626         inf\n",
              "571  (Verification, for)          (succeeded)  ...  0.010626         inf\n",
              "573     (succeeded, for)       (Verification)  ...  0.010626         inf\n",
              "574       (Verification)     (succeeded, for)  ...  0.010626         inf\n",
              "575          (succeeded)  (Verification, for)  ...  0.010626         inf\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a66z3-yUjcdr"
      },
      "source": [
        "**Add Event Description**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s49MY0v-ZP3A"
      },
      "source": [
        "log['Event_Description'] = event_description"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRxR_MdnZR5x"
      },
      "source": [
        "log.to_csv('log_with_event_desc.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHK7p8l0ZTmK"
      },
      "source": [
        "log = pd.read_csv('/content/log_with_event_desc.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LQkRb6sZWKa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75f3c763-9c52-491b-e107-6c823676ef9b"
      },
      "source": [
        "# number of unqiue Event\n",
        "log['Event_Description'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7OGlUwgjiex"
      },
      "source": [
        "**Count of Events**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SK0SmHn8HE5E"
      },
      "source": [
        "normal = log[log['Label'] == 'Normal'].groupby(['Event_id']).count().reset_index().set_index('Event_id')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odyrgy9YnPmL",
        "outputId": "49a1abc7-7d9c-4dd3-9ada-98ed5d052a51"
      },
      "source": [
        "# Event_id only in normal logs\n",
        "normal.index.difference(anomaly.index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([6, 7], dtype='int64', name='Event_id')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1thGO8MSqDUh",
        "outputId": "a62a010e-5945-4aa0-ae28-9222c82dabb5"
      },
      "source": [
        "normal_list = normal.index.difference(anomaly.index).tolist()\n",
        "normal_list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6, 7]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kp5Io5vLHIMI"
      },
      "source": [
        "anomaly = log[log['Label'] == 'Anomaly'].groupby(['Event_id']).count().reset_index().set_index('Event_id')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxuLQAreniEP",
        "outputId": "c0909094-3792-42b7-88f6-c3249f9c9c9b"
      },
      "source": [
        "# Event_id only in anomaly logs\n",
        "anomaly_list = anomaly.index.difference(normal.index).tolist()\n",
        "anomaly_list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 13, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 32, 33, 39, 40, 41]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sr32qsJjZtYg"
      },
      "source": [
        "**Check the Anomoly label within each event_id group**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4jB7Kyjksn4"
      },
      "source": [
        "log['Event_id'] = log.groupby('Event_Description').ngroup()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8_Jdz_tG-s9"
      },
      "source": [
        "events = pd.DataFrame(event_description)\n",
        "events.drop_duplicates(inplace=True)\n",
        "events = events.values.tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gU4q9xnP5IDC",
        "outputId": "21a7369b-35ac-4bd3-b2c3-455bfb3494e2"
      },
      "source": [
        "only_normal = log[log['Event_id'].isin(normal_list)]\n",
        "only_normal['Event_Description'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([\"['BLOCK', 'NameSystem', 'allocateBlock', 'user', 'root', 'grepa']\",\n",
              "       \"['BLOCK', 'NameSystem', 'allocateBlock', 'user', 'root', 'grepb']\"],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4g_MghtLBPnl",
        "outputId": "12385668-c41b-4fb4-ee3c-c4f5487b67f2"
      },
      "source": [
        "only_anomaly = log[log['Event_id'].isin(anomaly_list)]\n",
        "only_anomaly['Event_Description'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([\"['writeBlock', 'blk', 'received', 'exception', 'java', 'net']\",\n",
              "       \"['PacketResponder', 'blk', 'Exception', 'java', 'io', 'EOFException']\",\n",
              "       \"['writeBlock', 'blk', 'received', 'exception', 'java', 'io']\",\n",
              "       \"['Receiving', 'empty', 'packet', 'for', 'block', 'blk']\",\n",
              "       \"['Exception', 'in', 'receiveBlock', 'for', 'block', 'blk']\",\n",
              "       \"['PacketResponder', 'blk', 'Exception', 'java', 'net', 'SocketTimeoutException']\",\n",
              "       \"['PacketResponder', 'blk', 'Exception', 'java', 'io', 'InterruptedIOException']\",\n",
              "       \"['PacketResponder', 'for', 'block', 'blk', 'Interrupted']\",\n",
              "       \"['Changing', 'block', 'file', 'offset', 'of', 'block']\",\n",
              "       \"['Exception', 'writing', 'block', 'blk', 'to', 'mirror']\",\n",
              "       \"['PacketResponder', 'blk', 'Exception', 'java', 'io', 'IOException']\",\n",
              "       \"['Failed', 'to', 'transfer', 'blk', 'to', 'got']\",\n",
              "       \"['writeBlock', 'blk', 'received', 'exception', 'java', 'nio']\",\n",
              "       \"['Reopen', 'Block', 'blk']\",\n",
              "       \"['PacketResponder', 'blk', 'Exception', 'java', 'nio', 'channels']\",\n",
              "       \"['BLOCK', 'Removing', 'block', 'blk', 'from', 'neededReplications']\",\n",
              "       \"['PendingReplicationMonitor', 'timed', 'out', 'block', 'blk']\",\n",
              "       \"['Adding', 'an', 'already', 'existing', 'block', 'blk']\"],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ncv17Bj1Za-7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "747901da-9190-4fe7-a986-60c2e9caf3ac"
      },
      "source": [
        "log.groupby(['Event_id','Label'])['Label'].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Event_id  Label  \n",
              "0         Anomaly        10\n",
              "1         Anomaly       972\n",
              "          Normal          3\n",
              "2         Anomaly      1276\n",
              "          Normal         12\n",
              "                      ...  \n",
              "38        Anomaly      3129\n",
              "          Normal     116907\n",
              "39        Anomaly      3380\n",
              "40        Anomaly        29\n",
              "41        Anomaly         7\n",
              "Name: Label, Length: 64, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6KrhBfdZwVr"
      },
      "source": [
        "**Component and Event Description**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ec6X8h5fHNpU"
      },
      "source": [
        "log.groupby(['Component','Event_Description'])['Event_Description'].count()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9FQvskvZx3S"
      },
      "source": [
        "**Unique level**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfeIHTohZm1y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d99d6585-0205-4ede-942c-76e8049fa808"
      },
      "source": [
        "log['Level'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['INFO', 'WARN'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkNvApYI8EPb"
      },
      "source": [
        "**NPL processsing with log content**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AER5MKcR7_ch"
      },
      "source": [
        "log_label=pd.read_pickle(\"/content/drive/MyDrive/586_project_NPL/log_label_clean.pkl\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Xjab41Y8HPU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "29a7a0d2-1fca-4e06-d490-a83a58368d57"
      },
      "source": [
        "cv=CountVectorizer(stop_words='english')\n",
        "data_cv=cv.fit_transform(log_label.Content_npl)\n",
        "data=pd.DataFrame(data_cv.toarray(),columns=cv.get_feature_names())\n",
        "data.index=log_label.index\n",
        "data"
      ],
      "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>added</th>\n",
              "      <th>adding</th>\n",
              "      <th>addstoredblock</th>\n",
              "      <th>allocateblock</th>\n",
              "      <th>ask</th>\n",
              "      <th>belong</th>\n",
              "      <th>blk</th>\n",
              "      <th>block</th>\n",
              "      <th>blockinfo</th>\n",
              "      <th>blockmap</th>\n",
              "      <th>broken</th>\n",
              "      <th>ch</th>\n",
              "      <th>changing</th>\n",
              "      <th>channel</th>\n",
              "      <th>channels</th>\n",
              "      <th>closed</th>\n",
              "      <th>closedbyinterruptexception</th>\n",
              "      <th>conf</th>\n",
              "      <th>connected</th>\n",
              "      <th>connection</th>\n",
              "      <th>current</th>\n",
              "      <th>data</th>\n",
              "      <th>datanode</th>\n",
              "      <th>delete</th>\n",
              "      <th>deleting</th>\n",
              "      <th>dest</th>\n",
              "      <th>dfs</th>\n",
              "      <th>does</th>\n",
              "      <th>ec</th>\n",
              "      <th>eofexception</th>\n",
              "      <th>error</th>\n",
              "      <th>exception</th>\n",
              "      <th>existing</th>\n",
              "      <th>failed</th>\n",
              "      <th>file</th>\n",
              "      <th>got</th>\n",
              "      <th>grep</th>\n",
              "      <th>grepa</th>\n",
              "      <th>grepb</th>\n",
              "      <th>hadoop</th>\n",
              "      <th>...</th>\n",
              "      <th>served</th>\n",
              "      <th>serving</th>\n",
              "      <th>size</th>\n",
              "      <th>socketchannel</th>\n",
              "      <th>sockettimeoutexception</th>\n",
              "      <th>sort</th>\n",
              "      <th>sorter</th>\n",
              "      <th>sortrand</th>\n",
              "      <th>split</th>\n",
              "      <th>src</th>\n",
              "      <th>starting</th>\n",
              "      <th>stream</th>\n",
              "      <th>subdir</th>\n",
              "      <th>succeeded</th>\n",
              "      <th>task</th>\n",
              "      <th>temp</th>\n",
              "      <th>temporary</th>\n",
              "      <th>terminating</th>\n",
              "      <th>text</th>\n",
              "      <th>thread</th>\n",
              "      <th>timed</th>\n",
              "      <th>timeout</th>\n",
              "      <th>transfer</th>\n",
              "      <th>transmitted</th>\n",
              "      <th>trying</th>\n",
              "      <th>unexpected</th>\n",
              "      <th>updated</th>\n",
              "      <th>user</th>\n",
              "      <th>valid</th>\n",
              "      <th>verification</th>\n",
              "      <th>volumemap</th>\n",
              "      <th>waiting</th>\n",
              "      <th>wcnt</th>\n",
              "      <th>wordcount</th>\n",
              "      <th>write</th>\n",
              "      <th>writeblock</th>\n",
              "      <th>writer</th>\n",
              "      <th>writing</th>\n",
              "      <th>written</th>\n",
              "      <th>xml</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Label</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Anomaly</th>\n",
              "      <td>72179</td>\n",
              "      <td>10</td>\n",
              "      <td>44752</td>\n",
              "      <td>16838</td>\n",
              "      <td>4459</td>\n",
              "      <td>1280</td>\n",
              "      <td>323732</td>\n",
              "      <td>273407</td>\n",
              "      <td>5323</td>\n",
              "      <td>40256</td>\n",
              "      <td>14</td>\n",
              "      <td>42</td>\n",
              "      <td>65</td>\n",
              "      <td>103</td>\n",
              "      <td>128</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>98</td>\n",
              "      <td>170</td>\n",
              "      <td>35473</td>\n",
              "      <td>35473</td>\n",
              "      <td>4459</td>\n",
              "      <td>37246</td>\n",
              "      <td>35473</td>\n",
              "      <td>50425</td>\n",
              "      <td>35473</td>\n",
              "      <td>1280</td>\n",
              "      <td>5</td>\n",
              "      <td>114</td>\n",
              "      <td>5323</td>\n",
              "      <td>11972</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>36883</td>\n",
              "      <td>8268</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35480</td>\n",
              "      <td>...</td>\n",
              "      <td>11842</td>\n",
              "      <td>8259</td>\n",
              "      <td>78829</td>\n",
              "      <td>103</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1982</td>\n",
              "      <td>2</td>\n",
              "      <td>50425</td>\n",
              "      <td>4459</td>\n",
              "      <td>3228</td>\n",
              "      <td>32766</td>\n",
              "      <td>3129</td>\n",
              "      <td>16826</td>\n",
              "      <td>1</td>\n",
              "      <td>16826</td>\n",
              "      <td>32010</td>\n",
              "      <td>0</td>\n",
              "      <td>4459</td>\n",
              "      <td>47</td>\n",
              "      <td>103</td>\n",
              "      <td>4468</td>\n",
              "      <td>4395</td>\n",
              "      <td>5323</td>\n",
              "      <td>5323</td>\n",
              "      <td>40256</td>\n",
              "      <td>16831</td>\n",
              "      <td>9</td>\n",
              "      <td>3129</td>\n",
              "      <td>5323</td>\n",
              "      <td>103</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3416</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Normal</th>\n",
              "      <td>3043736</td>\n",
              "      <td>0</td>\n",
              "      <td>1679515</td>\n",
              "      <td>558223</td>\n",
              "      <td>2543</td>\n",
              "      <td>12</td>\n",
              "      <td>12253953</td>\n",
              "      <td>10422524</td>\n",
              "      <td>222</td>\n",
              "      <td>1679485</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1366574</td>\n",
              "      <td>1366574</td>\n",
              "      <td>2543</td>\n",
              "      <td>1364473</td>\n",
              "      <td>1366574</td>\n",
              "      <td>1679904</td>\n",
              "      <td>1366574</td>\n",
              "      <td>12</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "      <td>347948</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1366586</td>\n",
              "      <td>347948</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1366640</td>\n",
              "      <td>...</td>\n",
              "      <td>416884</td>\n",
              "      <td>347948</td>\n",
              "      <td>3356786</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>64936</td>\n",
              "      <td>22</td>\n",
              "      <td>1679904</td>\n",
              "      <td>2543</td>\n",
              "      <td>0</td>\n",
              "      <td>1304257</td>\n",
              "      <td>116907</td>\n",
              "      <td>558114</td>\n",
              "      <td>7</td>\n",
              "      <td>558114</td>\n",
              "      <td>1674669</td>\n",
              "      <td>5</td>\n",
              "      <td>2543</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2543</td>\n",
              "      <td>2542</td>\n",
              "      <td>222</td>\n",
              "      <td>222</td>\n",
              "      <td>1679485</td>\n",
              "      <td>558157</td>\n",
              "      <td>0</td>\n",
              "      <td>116907</td>\n",
              "      <td>222</td>\n",
              "      <td>0</td>\n",
              "      <td>1247</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 130 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           added  adding  addstoredblock  ...  writing  written  xml\n",
              "Label                                     ...                       \n",
              "Anomaly    72179      10           44752  ...       34        9    5\n",
              "Normal   3043736       0         1679515  ...        0        0   43\n",
              "\n",
              "[2 rows x 130 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkByKYqr8JbJ"
      },
      "source": [
        "# add some stop words\n",
        "\n",
        "data_t=data.transpose()\n",
        "# find the top words by blockid\n",
        "top_dict = {}\n",
        "for i in data_t.columns:\n",
        "  top=data_t[i].sort_values(ascending=False).head(45)\n",
        "  top_dict[i]=list(zip(top.index,top.values))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-GhvaL28L4I"
      },
      "source": [
        "words2 =[]\n",
        "\n",
        "for x in data_t.columns:\n",
        "  top = [ word for (word,count) in top_dict[x] ]\n",
        "  for t in top:\n",
        "    words2.append(t)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxqB_MvQ8OGI"
      },
      "source": [
        "# if more than half of the blockid have it as the most frequent word, exclude it form the oist\n",
        "\n",
        "add_stop_words2 = [word for word,count in Counter(words2).most_common() if count>1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qt4tEBE8RRj"
      },
      "source": [
        "**recreate document-term matrix**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjWNSdpS8SeY"
      },
      "source": [
        "# add new stop words\n",
        "stop_words=text.ENGLISH_STOP_WORDS.union(add_stop_words2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvKH1y028V4G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "39df6dae-7a8a-4910-bbcb-9c7e306dc2f5"
      },
      "source": [
        "cv=CountVectorizer(stop_words=stop_words)\n",
        "data_cv=cv.fit_transform(log_label.Content_npl)\n",
        "data_stop=pd.DataFrame(data_cv.toarray(),columns=cv.get_feature_names())\n",
        "data_stop.index=log_label.index\n",
        "data_stop"
      ],
      "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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>adding</th>\n",
              "      <th>belong</th>\n",
              "      <th>blockinfo</th>\n",
              "      <th>broken</th>\n",
              "      <th>ch</th>\n",
              "      <th>changing</th>\n",
              "      <th>channel</th>\n",
              "      <th>channels</th>\n",
              "      <th>closed</th>\n",
              "      <th>closedbyinterruptexception</th>\n",
              "      <th>conf</th>\n",
              "      <th>connected</th>\n",
              "      <th>connection</th>\n",
              "      <th>does</th>\n",
              "      <th>ec</th>\n",
              "      <th>eofexception</th>\n",
              "      <th>error</th>\n",
              "      <th>existing</th>\n",
              "      <th>failed</th>\n",
              "      <th>grep</th>\n",
              "      <th>grepa</th>\n",
              "      <th>grepb</th>\n",
              "      <th>history</th>\n",
              "      <th>host</th>\n",
              "      <th>internal</th>\n",
              "      <th>interruped</th>\n",
              "      <th>interrupted</th>\n",
              "      <th>interruptedioexception</th>\n",
              "      <th>io</th>\n",
              "      <th>ioexception</th>\n",
              "      <th>ip</th>\n",
              "      <th>jar</th>\n",
              "      <th>java</th>\n",
              "      <th>job</th>\n",
              "      <th>left</th>\n",
              "      <th>local</th>\n",
              "      <th>logs</th>\n",
              "      <th>mapred</th>\n",
              "      <th>meta</th>\n",
              "      <th>millis</th>\n",
              "      <th>...</th>\n",
              "      <th>random</th>\n",
              "      <th>read</th>\n",
              "      <th>ready</th>\n",
              "      <th>receiveblock</th>\n",
              "      <th>redundant</th>\n",
              "      <th>remote</th>\n",
              "      <th>removing</th>\n",
              "      <th>reopen</th>\n",
              "      <th>replicate</th>\n",
              "      <th>request</th>\n",
              "      <th>reset</th>\n",
              "      <th>route</th>\n",
              "      <th>search</th>\n",
              "      <th>socketchannel</th>\n",
              "      <th>sockettimeoutexception</th>\n",
              "      <th>sort</th>\n",
              "      <th>sorter</th>\n",
              "      <th>sortrand</th>\n",
              "      <th>split</th>\n",
              "      <th>stream</th>\n",
              "      <th>succeeded</th>\n",
              "      <th>temp</th>\n",
              "      <th>text</th>\n",
              "      <th>timed</th>\n",
              "      <th>timeout</th>\n",
              "      <th>transmitted</th>\n",
              "      <th>trying</th>\n",
              "      <th>unexpected</th>\n",
              "      <th>valid</th>\n",
              "      <th>verification</th>\n",
              "      <th>volumemap</th>\n",
              "      <th>waiting</th>\n",
              "      <th>wcnt</th>\n",
              "      <th>wordcount</th>\n",
              "      <th>write</th>\n",
              "      <th>writeblock</th>\n",
              "      <th>writer</th>\n",
              "      <th>writing</th>\n",
              "      <th>written</th>\n",
              "      <th>xml</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Label</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Anomaly</th>\n",
              "      <td>10</td>\n",
              "      <td>1280</td>\n",
              "      <td>5323</td>\n",
              "      <td>14</td>\n",
              "      <td>42</td>\n",
              "      <td>65</td>\n",
              "      <td>103</td>\n",
              "      <td>128</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>98</td>\n",
              "      <td>170</td>\n",
              "      <td>1280</td>\n",
              "      <td>5</td>\n",
              "      <td>114</td>\n",
              "      <td>5323</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>61</td>\n",
              "      <td>60</td>\n",
              "      <td>61</td>\n",
              "      <td>3668</td>\n",
              "      <td>3432</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3791</td>\n",
              "      <td>19</td>\n",
              "      <td>61</td>\n",
              "      <td>98</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>65</td>\n",
              "      <td>103</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>3258</td>\n",
              "      <td>42</td>\n",
              "      <td>166</td>\n",
              "      <td>972</td>\n",
              "      <td>98</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4459</td>\n",
              "      <td>2248</td>\n",
              "      <td>170</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>103</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1982</td>\n",
              "      <td>2</td>\n",
              "      <td>3228</td>\n",
              "      <td>3129</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>47</td>\n",
              "      <td>103</td>\n",
              "      <td>4395</td>\n",
              "      <td>5323</td>\n",
              "      <td>5323</td>\n",
              "      <td>9</td>\n",
              "      <td>3129</td>\n",
              "      <td>5323</td>\n",
              "      <td>103</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3416</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Normal</th>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>222</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2543</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>64936</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>116907</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2542</td>\n",
              "      <td>222</td>\n",
              "      <td>222</td>\n",
              "      <td>0</td>\n",
              "      <td>116907</td>\n",
              "      <td>222</td>\n",
              "      <td>0</td>\n",
              "      <td>1247</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 90 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         adding  belong  blockinfo  broken  ...  writer  writing  written  xml\n",
              "Label                                       ...                               \n",
              "Anomaly      10    1280       5323      14  ...       0       34        9    5\n",
              "Normal        0      12        222       0  ...      14        0        0   43\n",
              "\n",
              "[2 rows x 90 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSE77vKt8Y4W"
      },
      "source": [
        "**Compute word clouds**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_uX3l0l8YJN"
      },
      "source": [
        "wc = WordCloud(stopwords=stop_words,background_color=\"white\",colormap=\"Dark2\",max_font_size=150,random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVBTvJMa8iI1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "c646d411-764a-4a8d-f377-b2be6cf70b09"
      },
      "source": [
        "# create subplot for Anomaly and normal\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [16,6]\n",
        "Label=[\"Anomaly\",\"Normal\"]\n",
        "\n",
        "for index, label in enumerate(data_t.columns):\n",
        "  print(label)\n",
        "  wc.generate(log_label.Content_npl[label])\n",
        "  plt.subplot(1,2,index+1)\n",
        "  plt.imshow(wc,interpolation='bilinear')\n",
        "  plt.axis(\"off\")\n",
        "  plt.title(Label[index])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Anomaly\n",
            "Normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4i_3QboSARrX"
      },
      "source": [
        "**Plot**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqKZ5mWkAOhc"
      },
      "source": [
        "import altair as alt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlY9p4TOAVTX"
      },
      "source": [
        "df=data_t.sort_values(by=['Anomaly'], ascending=False)\n",
        "df=df.reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "3ivxJ_WxAZBk",
        "outputId": "2003a8d1-4e47-4da1-a68d-5c11229b9b73"
      },
      "source": [
        "alt.Chart(df).mark_bar().encode(\n",
        "    x=alt.X('index:O', sort='-y',axis=alt.Axis(title='Words')),\n",
        "    y=alt.Y('Anomaly:Q'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alt.Chart(...)"
            ],
            "text/html": [
              "\n",
              "<div id=\"altair-viz-32c0af471f044f7ea0b9bd84c30771fe\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "  (function(spec, embedOpt){\n",
              "    let outputDiv = document.currentScript.previousElementSibling;\n",
              "    if (outputDiv.id !== \"altair-viz-32c0af471f044f7ea0b9bd84c30771fe\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-32c0af471f044f7ea0b9bd84c30771fe\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
              "    };\n",
              "\n",
              "    function loadScript(lib) {\n",
              "      return new Promise(function(resolve, reject) {\n",
              "        var s = document.createElement('script');\n",
              "        s.src = paths[lib];\n",
              "        s.async = true;\n",
              "        s.onload = () => resolve(paths[lib]);\n",
              "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "      });\n",
              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else if (typeof vegaEmbed === \"function\") {\n",
              "      displayChart(vegaEmbed);\n",
              "    } else {\n",
              "      loadScript(\"vega\")\n",
              "        .then(() => loadScript(\"vega-lite\"))\n",
              "        .then(() => loadScript(\"vega-embed\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-1e83cf1485771198dfd8155ea5a55b02\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"ordinal\", \"axis\": {\"title\": \"Words\"}, \"field\": \"index\", \"sort\": \"-y\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"Anomaly\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-1e83cf1485771198dfd8155ea5a55b02\": [{\"index\": \"blk\", \"Anomaly\": 323732, \"Normal\": 12253953}, {\"index\": \"block\", \"Anomaly\": 273407, \"Normal\": 10422524}, {\"index\": \"namesystem\", \"Anomaly\": 91265, \"Normal\": 3601974}, {\"index\": \"size\", \"Anomaly\": 78829, \"Normal\": 3356786}, {\"index\": \"added\", \"Anomaly\": 72179, \"Normal\": 3043736}, {\"index\": \"dest\", \"Anomaly\": 50425, \"Normal\": 1679904}, {\"index\": \"src\", \"Anomaly\": 50425, \"Normal\": 1679904}, {\"index\": \"receiving\", \"Anomaly\": 47409, \"Normal\": 1677287}, {\"index\": \"addstoredblock\", \"Anomaly\": 44752, \"Normal\": 1679515}, {\"index\": \"received\", \"Anomaly\": 41989, \"Normal\": 1677301}, {\"index\": \"updated\", \"Anomaly\": 40256, \"Normal\": 1679485}, {\"index\": \"blockmap\", \"Anomaly\": 40256, \"Normal\": 1679485}, {\"index\": \"delete\", \"Anomaly\": 37246, \"Normal\": 1364473}, {\"index\": \"file\", \"Anomaly\": 36883, \"Normal\": 1366586}, {\"index\": \"hadoop\", \"Anomaly\": 35480, \"Normal\": 1366640}, {\"index\": \"mnt\", \"Anomaly\": 35480, \"Normal\": 1366640}, {\"index\": \"current\", \"Anomaly\": 35473, \"Normal\": 1366574}, {\"index\": \"deleting\", \"Anomaly\": 35473, \"Normal\": 1366574}, {\"index\": \"data\", \"Anomaly\": 35473, \"Normal\": 1366574}, {\"index\": \"dfs\", \"Anomaly\": 35473, \"Normal\": 1366574}, {\"index\": \"subdir\", \"Anomaly\": 32766, \"Normal\": 1304257}, {\"index\": \"packetresponder\", \"Anomaly\": 32167, \"Normal\": 1674669}, {\"index\": \"terminating\", \"Anomaly\": 32010, \"Normal\": 1674669}, {\"index\": \"invalidset\", \"Anomaly\": 31923, \"Normal\": 1364251}, {\"index\": \"allocateblock\", \"Anomaly\": 16838, \"Normal\": 558223}, {\"index\": \"root\", \"Anomaly\": 16833, \"Normal\": 558179}, {\"index\": \"user\", \"Anomaly\": 16831, \"Normal\": 558157}, {\"index\": \"task\", \"Anomaly\": 16826, \"Normal\": 558114}, {\"index\": \"temporary\", \"Anomaly\": 16826, \"Normal\": 558114}, {\"index\": \"exception\", \"Anomaly\": 11972, \"Normal\": 347948}, {\"index\": \"served\", \"Anomaly\": 11842, \"Normal\": 416884}, {\"index\": \"rand\", \"Anomaly\": 8868, \"Normal\": 280158}, {\"index\": \"got\", \"Anomaly\": 8268, \"Normal\": 347948}, {\"index\": \"serving\", \"Anomaly\": 8259, \"Normal\": 347948}, {\"index\": \"randtxt\", \"Anomaly\": 5961, \"Normal\": 211805}, {\"index\": \"trying\", \"Anomaly\": 5323, \"Normal\": 222}, {\"index\": \"volumemap\", \"Anomaly\": 5323, \"Normal\": 222}, {\"index\": \"error\", \"Anomaly\": 5323, \"Normal\": 222}, {\"index\": \"unexpected\", \"Anomaly\": 5323, \"Normal\": 222}, {\"index\": \"blockinfo\", \"Anomaly\": 5323, \"Normal\": 222}, {\"index\": \"transfer\", \"Anomaly\": 4468, \"Normal\": 2543}, {\"index\": \"replicate\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"ask\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"thread\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"starting\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"datanode\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"transmitted\", \"Anomaly\": 4395, \"Normal\": 2542}, {\"index\": \"java\", \"Anomaly\": 3791, \"Normal\": 0}, {\"index\": \"io\", \"Anomaly\": 3668, \"Normal\": 0}, {\"index\": \"ioexception\", \"Anomaly\": 3432, \"Normal\": 0}, {\"index\": \"writeblock\", \"Anomaly\": 3416, \"Normal\": 0}, {\"index\": \"read\", \"Anomaly\": 3258, \"Normal\": 0}, {\"index\": \"stream\", \"Anomaly\": 3228, \"Normal\": 0}, {\"index\": \"succeeded\", \"Anomaly\": 3129, \"Normal\": 116907}, {\"index\": \"verification\", \"Anomaly\": 3129, \"Normal\": 116907}, {\"index\": \"request\", \"Anomaly\": 2248, \"Normal\": 15}, {\"index\": \"sortrand\", \"Anomaly\": 1982, \"Normal\": 64936}, {\"index\": \"packet\", \"Anomaly\": 1464, \"Normal\": 0}, {\"index\": \"belong\", \"Anomaly\": 1280, \"Normal\": 12}, {\"index\": \"does\", \"Anomaly\": 1280, \"Normal\": 12}, {\"index\": \"redundant\", \"Anomaly\": 972, \"Normal\": 3}, {\"index\": \"peer\", \"Anomaly\": 170, \"Normal\": 0}, {\"index\": \"connection\", \"Anomaly\": 170, \"Normal\": 0}, {\"index\": \"reset\", \"Anomaly\": 170, \"Normal\": 0}, {\"index\": \"receiveblock\", \"Anomaly\": 166, \"Normal\": 0}, {\"index\": \"offset\", \"Anomaly\": 130, \"Normal\": 0}, {\"index\": \"channels\", \"Anomaly\": 128, \"Normal\": 0}, {\"index\": \"nio\", \"Anomaly\": 128, \"Normal\": 0}, {\"index\": \"eofexception\", \"Anomaly\": 114, \"Normal\": 0}, {\"index\": \"timeout\", \"Anomaly\": 103, \"Normal\": 0}, {\"index\": \"waiting\", \"Anomaly\": 103, \"Normal\": 0}, {\"index\": \"socketchannel\", \"Anomaly\": 103, \"Normal\": 0}, {\"index\": \"channel\", \"Anomaly\": 103, \"Normal\": 0}, {\"index\": \"millis\", \"Anomaly\": 103, \"Normal\": 0}, {\"index\": \"remote\", \"Anomaly\": 98, \"Normal\": 0}, {\"index\": \"connected\", \"Anomaly\": 98, \"Normal\": 0}, {\"index\": \"local\", \"Anomaly\": 98, \"Normal\": 0}, {\"index\": \"changing\", \"Anomaly\": 65, \"Normal\": 0}, {\"index\": \"meta\", \"Anomaly\": 65, \"Normal\": 0}, {\"index\": \"left\", \"Anomaly\": 61, \"Normal\": 0}, {\"index\": \"interruptedioexception\", \"Anomaly\": 61, \"Normal\": 0}, {\"index\": \"interruped\", \"Anomaly\": 61, \"Normal\": 0}, {\"index\": \"interrupted\", \"Anomaly\": 60, \"Normal\": 0}, {\"index\": \"net\", \"Anomaly\": 56, \"Normal\": 0}, {\"index\": \"sockettimeoutexception\", \"Anomaly\": 55, \"Normal\": 0}, {\"index\": \"timed\", \"Anomaly\": 47, \"Normal\": 0}, {\"index\": \"pendingreplicationmonitor\", \"Anomaly\": 47, \"Normal\": 0}, {\"index\": \"ready\", \"Anomaly\": 42, \"Normal\": 0}, {\"index\": \"ch\", \"Anomaly\": 42, \"Normal\": 0}, {\"index\": \"writing\", \"Anomaly\": 34, \"Normal\": 0}, {\"index\": \"mirror\", \"Anomaly\": 34, \"Normal\": 0}, {\"index\": \"closedbyinterruptexception\", \"Anomaly\": 25, \"Normal\": 0}, {\"index\": \"job\", \"Anomaly\": 19, \"Normal\": 175}, {\"index\": \"wcnt\", \"Anomaly\": 19, \"Normal\": 1247}, {\"index\": \"pipe\", \"Anomaly\": 14, \"Normal\": 0}, {\"index\": \"broken\", \"Anomaly\": 14, \"Normal\": 0}, {\"index\": \"write\", \"Anomaly\": 10, \"Normal\": 0}, {\"index\": \"adding\", \"Anomaly\": 10, \"Normal\": 0}, {\"index\": \"existing\", \"Anomaly\": 10, \"Normal\": 0}, {\"index\": \"written\", \"Anomaly\": 9, \"Normal\": 0}, {\"index\": \"failed\", \"Anomaly\": 9, \"Normal\": 0}, {\"index\": \"valid\", \"Anomaly\": 9, \"Normal\": 0}, {\"index\": \"closed\", \"Anomaly\": 7, \"Normal\": 0}, {\"index\": \"mapred\", \"Anomaly\": 7, \"Normal\": 66}, {\"index\": \"xml\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"internal\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"ec\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"history\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"reopen\", \"Anomaly\": 5, \"Normal\": 0}, {\"index\": \"logs\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"ip\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"removing\", \"Anomaly\": 4, \"Normal\": 0}, {\"index\": \"neededreplications\", \"Anomaly\": 4, \"Normal\": 0}, {\"index\": \"jar\", \"Anomaly\": 3, \"Normal\": 22}, {\"index\": \"conf\", \"Anomaly\": 3, \"Normal\": 21}, {\"index\": \"grep\", \"Anomaly\": 2, \"Normal\": 11}, {\"index\": \"split\", \"Anomaly\": 2, \"Normal\": 22}, {\"index\": \"wordcount\", \"Anomaly\": 1, \"Normal\": 2}, {\"index\": \"host\", \"Anomaly\": 1, \"Normal\": 0}, {\"index\": \"temp\", \"Anomaly\": 1, \"Normal\": 7}, {\"index\": \"route\", \"Anomaly\": 1, \"Normal\": 0}, {\"index\": \"search\", \"Anomaly\": 1, \"Normal\": 2}, {\"index\": \"noroutetohostexception\", \"Anomaly\": 1, \"Normal\": 0}, {\"index\": \"random\", \"Anomaly\": 0, \"Normal\": 14}, {\"index\": \"grepb\", \"Anomaly\": 0, \"Normal\": 2}, {\"index\": \"grepa\", \"Anomaly\": 0, \"Normal\": 2}, {\"index\": \"sort\", \"Anomaly\": 0, \"Normal\": 2}, {\"index\": \"writer\", \"Anomaly\": 0, \"Normal\": 14}, {\"index\": \"text\", \"Anomaly\": 0, \"Normal\": 5}, {\"index\": \"sorter\", \"Anomaly\": 0, \"Normal\": 2}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQgLyLBsAfqi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "2ca31a38-bf99-43a3-cf82-7613cd614823"
      },
      "source": [
        "alt.Chart(df).mark_bar().encode(\n",
        "    x=alt.X('index:O', sort='-y',axis=alt.Axis(title='Words')),\n",
        "    y=alt.Y('Normal:Q'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alt.Chart(...)"
            ],
            "text/html": [
              "\n",
              "<div id=\"altair-viz-3db1491f6fb642eb8ade03297b584866\"></div>\n",
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              "      outputDiv = document.getElementById(\"altair-viz-3db1491f6fb642eb8ade03297b584866\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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              "        s.src = paths[lib];\n",
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              "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else if (typeof vegaEmbed === \"function\") {\n",
              "      displayChart(vegaEmbed);\n",
              "    } else {\n",
              "      loadScript(\"vega\")\n",
              "        .then(() => loadScript(\"vega-lite\"))\n",
              "        .then(() => loadScript(\"vega-embed\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
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\"Normal\": 222}, {\"index\": \"unexpected\", \"Anomaly\": 5323, \"Normal\": 222}, {\"index\": \"blockinfo\", \"Anomaly\": 5323, \"Normal\": 222}, {\"index\": \"transfer\", \"Anomaly\": 4468, \"Normal\": 2543}, {\"index\": \"replicate\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"ask\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"thread\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"starting\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"datanode\", \"Anomaly\": 4459, \"Normal\": 2543}, {\"index\": \"transmitted\", \"Anomaly\": 4395, \"Normal\": 2542}, {\"index\": \"java\", \"Anomaly\": 3791, \"Normal\": 0}, {\"index\": \"io\", \"Anomaly\": 3668, \"Normal\": 0}, {\"index\": \"ioexception\", \"Anomaly\": 3432, \"Normal\": 0}, {\"index\": \"writeblock\", \"Anomaly\": 3416, \"Normal\": 0}, {\"index\": \"read\", \"Anomaly\": 3258, \"Normal\": 0}, {\"index\": \"stream\", \"Anomaly\": 3228, \"Normal\": 0}, {\"index\": \"succeeded\", \"Anomaly\": 3129, 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\"mapred\", \"Anomaly\": 7, \"Normal\": 66}, {\"index\": \"xml\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"internal\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"ec\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"history\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"reopen\", \"Anomaly\": 5, \"Normal\": 0}, {\"index\": \"logs\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"ip\", \"Anomaly\": 5, \"Normal\": 43}, {\"index\": \"removing\", \"Anomaly\": 4, \"Normal\": 0}, {\"index\": \"neededreplications\", \"Anomaly\": 4, \"Normal\": 0}, {\"index\": \"jar\", \"Anomaly\": 3, \"Normal\": 22}, {\"index\": \"conf\", \"Anomaly\": 3, \"Normal\": 21}, {\"index\": \"grep\", \"Anomaly\": 2, \"Normal\": 11}, {\"index\": \"split\", \"Anomaly\": 2, \"Normal\": 22}, {\"index\": \"wordcount\", \"Anomaly\": 1, \"Normal\": 2}, {\"index\": \"host\", \"Anomaly\": 1, \"Normal\": 0}, {\"index\": \"temp\", \"Anomaly\": 1, \"Normal\": 7}, {\"index\": \"route\", \"Anomaly\": 1, \"Normal\": 0}, {\"index\": \"search\", \"Anomaly\": 1, \"Normal\": 2}, {\"index\": \"noroutetohostexception\", \"Anomaly\": 1, \"Normal\": 0}, {\"index\": \"random\", \"Anomaly\": 0, \"Normal\": 14}, {\"index\": \"grepb\", \"Anomaly\": 0, \"Normal\": 2}, {\"index\": \"grepa\", \"Anomaly\": 0, \"Normal\": 2}, {\"index\": \"sort\", \"Anomaly\": 0, \"Normal\": 2}, {\"index\": \"writer\", \"Anomaly\": 0, \"Normal\": 14}, {\"index\": \"text\", \"Anomaly\": 0, \"Normal\": 5}, {\"index\": \"sorter\", \"Anomaly\": 0, \"Normal\": 2}]}}, {\"mode\": \"vega-lite\"});\n",
              "</script>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-4ax0Z68Yal"
      },
      "source": [
        "# **Tentative Model Choices**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJ_YtDKdIk_I"
      },
      "source": [
        "Supervised\n",
        "\n",
        "- Random Forest\n",
        "- Logistic Regression\n",
        "- Nerual Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NWmgv7U6LrHm"
      },
      "source": [
        "Unsupervised\n",
        "- PCA"
      ]
    }
  ]
}