{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HY_fM8pgHYm0"
   },
   "source": [
    "__Association Rules__ is an unsupervised technique to extract pattern or relation between items. The rule defines association between A and B as A => B i.e if A is purchased B is likely to be purchased.\n",
    "\n",
    "An association rule consists of an antecedent and a consequent.\n",
    "\n",
    "$${\\{Pen, Pencil\\}} \\to \\{Paper\\}$$\n",
    "$$     {antecedent} \\to consequent$$\n",
    "\n",
    "For a given rule, `itemset` is the list of all the items in the antecedent and the consequent.\n",
    "\n",
    "$${itemset} \\to \\{Pen, Pencil, Paper\\}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OphIUDXpHfWC"
   },
   "source": [
    "### Measuring the strength of a rule\n",
    "\n",
    "**Support**\n",
    "\n",
    "Support is the fraction of the total number of transactions in which the itemset occurs.\n",
    "\n",
    "$$\n",
    "{Support(\\{A\\} \\to \\{B\\}) = \\frac{Transactions\\ containing\\ both\\ A\\ and\\ B\"}{Total\\ number\\ of\\ transactions}}\n",
    "$$\n",
    "\n",
    "**Confidence**\n",
    "\n",
    "Confidence is the conditional probability of occurrence of consequent given the antecedent.\n",
    "\n",
    "$$\n",
    "{Confidence(\\{A\\} \\to \\{B\\}) = \\frac{Transactions\\ containing\\ both\\ A\\ and\\ B\"}{Transactions\\ containing\\ A}}\n",
    "$$\n",
    "\n",
    "**Lift**\n",
    "\n",
    "Lift is a very literal term given to this measure. Think of it as the **`lift`** that {A} provides to our confidence for having {B} on the cart. To rephrase, lift is the rise in probability of having {B} on the cart with the knowledge of {A} being present over the probability of having {B} on the cart without any knowledge about presence of {A}. Mathematically,\n",
    "\n",
    "$$\n",
    "{Lift(\\{A\\} \\to \\{B\\}) = ( \\frac{Transactions\\ containing\\ both\\ A\\ and\\ B}{Transactions\\ containing\\ A}} )/{(Fractions\\ of\\ transactions\\ containing\\ B )}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "8e4IigyLtz62"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from mlxtend.frequent_patterns import apriori, association_rules\n",
    "from mlxtend.preprocessing import TransactionEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QaFFufO5uS3i",
    "outputId": "c7155ad9-47c9-4a92-dd2c-2736d7301b31"
   },
   "outputs": [],
   "source": [
    "# Step 1: Read dataset\n",
    "weblog_data = pd.read_csv('weblog.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WtBCTiFA1TY8"
   },
   "source": [
    "Error code 500, also known as the Internal Server Error, is a common HTTP status code that indicates an issue on the web server’s side. It occurs when the server encounters an unexpected condition or configuration problem that prevents it from fulfilling the request made by the browser or client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nJugxIVLvPqk",
    "outputId": "36a21d24-de95-4a6a-9bed-631044e2790d"
   },
   "outputs": [
    {
     "data": {
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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>IP</th>\n",
       "      <th>Time</th>\n",
       "      <th>Method</th>\n",
       "      <th>Path</th>\n",
       "      <th>Status</th>\n",
       "      <th>Is_Downtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192.168.1.103</td>\n",
       "      <td>01-01-2023 00:00</td>\n",
       "      <td>GET</td>\n",
       "      <td>/error</td>\n",
       "      <td>404</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>192.168.1.180</td>\n",
       "      <td>01-01-2023 00:01</td>\n",
       "      <td>GET</td>\n",
       "      <td>/logout</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>192.168.1.93</td>\n",
       "      <td>01-01-2023 00:02</td>\n",
       "      <td>POST</td>\n",
       "      <td>/logout</td>\n",
       "      <td>500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192.168.1.15</td>\n",
       "      <td>01-01-2023 00:03</td>\n",
       "      <td>PUT</td>\n",
       "      <td>/home</td>\n",
       "      <td>404</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.168.1.107</td>\n",
       "      <td>01-01-2023 00:04</td>\n",
       "      <td>GET</td>\n",
       "      <td>/login</td>\n",
       "      <td>401</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              IP              Time Method     Path  Status  Is_Downtime\n",
       "0  192.168.1.103  01-01-2023 00:00    GET   /error     404            0\n",
       "1  192.168.1.180  01-01-2023 00:01    GET  /logout     400            0\n",
       "2   192.168.1.93  01-01-2023 00:02   POST  /logout     500            1\n",
       "3   192.168.1.15  01-01-2023 00:03    PUT    /home     404            0\n",
       "4  192.168.1.107  01-01-2023 00:04    GET   /login     401            0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 2: Data Preprocessing\n",
    "\n",
    "# Create a new column to identify whether the event was a system breakdown (e.g., Status code 500 or other server errors)\n",
    "weblog_data['Is_Downtime'] = weblog_data['Status'].apply(lambda x: 1 if x == 500 else 0)\n",
    "weblog_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4cnFs7BA8Oxs",
    "outputId": "cd86c529-f9a2-45dc-eca6-3431278d3180"
   },
   "outputs": [],
   "source": [
    "# Step 3: Unsupervised Analysis - Finding events that occured around downtime\n",
    "\n",
    "# Filter only relevant columns for association rule mining\n",
    "filtered_data = weblog_data[['Path', 'Is_Downtime']]\n",
    "\n",
    "# Convert the data into a list of transactions (each transaction is a list of events)\n",
    "transactions = filtered_data.groupby(weblog_data['IP'])['Path'].apply(list).tolist()\n",
    "\n",
    "for i, (idx, downtime) in enumerate(weblog_data.groupby(weblog_data['IP'])['Is_Downtime'].max().items()):\n",
    "    if downtime == 1:\n",
    "        # Access the transactions list using the integer index 'i'\n",
    "        transactions[i].append('Is_Downtime')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Path</th>\n",
       "      <th>Is_Downtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/error</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/logout</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/logout</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/login</td>\n",
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      "text/plain": [
       "      Path  Is_Downtime\n",
       "0   /error            0\n",
       "1  /logout            0\n",
       "2  /logout            1\n",
       "3    /home            0\n",
       "4   /login            0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RHOFSVow3d-8",
    "outputId": "b94e6276-3765-4aad-8d69-b9ff57561e73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['/dashboard', '/logout', '/login', '/settings', '/logout', 'Is_Downtime'],\n",
       " ['/logout'],\n",
       " ['/logout', '/login', '/home', '/error'],\n",
       " ['/login',\n",
       "  '/dashboard',\n",
       "  '/error',\n",
       "  '/settings',\n",
       "  '/dashboard',\n",
       "  '/dashboard',\n",
       "  '/error'],\n",
       " ['/logout']]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[transactions[0],transactions[1],transactions[2],transactions[3],transactions[4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lB4KiyyP2OEa",
    "outputId": "aed57f08-9422-45ae-cb21-ef4667aa1e3f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(transactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Lj3Z-k-O0-hc",
    "outputId": "43539082-2f75-4740-9ba3-4d60af90bc31"
   },
   "outputs": [],
   "source": [
    "# Apply TransactionEncoder to prepare the dataset for Apriori\n",
    "te = TransactionEncoder()\n",
    "transaction_data = te.fit_transform(transactions)\n",
    "transaction_df = pd.DataFrame(transaction_data, columns=te.columns_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False, False, ...,  True,  True,  True],\n",
       "       [False, False, False, ...,  True, False, False],\n",
       "       [False,  True,  True, ...,  True, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ...,  True, False,  True],\n",
       "       [False, False, False, ...,  True,  True, False],\n",
       "       [ True, False,  True, ...,  True,  True,  True]])"
      ]
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "transaction_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "0uXGSozp1kby",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>/dashboard</th>\n",
       "      <th>/error</th>\n",
       "      <th>/home</th>\n",
       "      <th>/login</th>\n",
       "      <th>/logout</th>\n",
       "      <th>/settings</th>\n",
       "      <th>Is_Downtime</th>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\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>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>246</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>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     /dashboard  /error  /home  /login  /logout  /settings  Is_Downtime\n",
       "0          True   False  False    True     True       True         True\n",
       "1         False   False  False   False     True      False        False\n",
       "2         False    True   True    True     True      False        False\n",
       "3          True    True  False    True    False       True        False\n",
       "4         False   False  False   False     True      False        False\n",
       "..          ...     ...    ...     ...      ...        ...          ...\n",
       "244        True    True  False   False    False       True         True\n",
       "245        True   False   True   False     True       True         True\n",
       "246       False   False  False   False     True      False         True\n",
       "247       False   False  False   False     True       True        False\n",
       "248        True   False   True    True     True       True         True\n",
       "\n",
       "[249 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transaction_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mwxcei4e1iW3",
    "outputId": "debbd864-2710-4e9b-a7f7-77bbf7580c2d"
   },
   "outputs": [],
   "source": [
    "# Apply the Apriori algorithm to find frequent itemsets with a lower support threshold\n",
    "frequent_itemsets_unsupervised = apriori(transaction_df, min_support=0.01, use_colnames=True)\n",
    "\n",
    "# Generate association rules with a focus on confidence\n",
    "rules = association_rules(frequent_itemsets_unsupervised, metric=\"confidence\", min_threshold=0.5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 619
    },
    "id": "eOUjU5sU4pr0",
    "outputId": "d5203a22-664f-41a9-9205-5a23aa02c90e"
   },
   "outputs": [
    {
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       "      <th>antecedents</th>\n",
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       "      <th>antecedent support</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(/login)</td>\n",
       "      <td>0.409639</td>\n",
       "      <td>0.493976</td>\n",
       "      <td>0.212851</td>\n",
       "      <td>0.519608</td>\n",
       "      <td>1.051889</td>\n",
       "      <td>0.010500</td>\n",
       "      <td>1.053356</td>\n",
       "      <td>0.083558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(/home)</td>\n",
       "      <td>(/error)</td>\n",
       "      <td>0.449799</td>\n",
       "      <td>0.493976</td>\n",
       "      <td>0.224900</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.012195</td>\n",
       "      <td>0.002710</td>\n",
       "      <td>1.012048</td>\n",
       "      <td>0.021898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(/error)</td>\n",
       "      <td>(/settings)</td>\n",
       "      <td>0.493976</td>\n",
       "      <td>0.514056</td>\n",
       "      <td>0.269076</td>\n",
       "      <td>0.544715</td>\n",
       "      <td>1.059642</td>\n",
       "      <td>0.015145</td>\n",
       "      <td>1.067341</td>\n",
       "      <td>0.111230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(/settings)</td>\n",
       "      <td>(/error)</td>\n",
       "      <td>0.514056</td>\n",
       "      <td>0.493976</td>\n",
       "      <td>0.269076</td>\n",
       "      <td>0.523438</td>\n",
       "      <td>1.059642</td>\n",
       "      <td>0.015145</td>\n",
       "      <td>1.061821</td>\n",
       "      <td>0.115826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>(/error)</td>\n",
       "      <td>0.409639</td>\n",
       "      <td>0.493976</td>\n",
       "      <td>0.212851</td>\n",
       "      <td>0.519608</td>\n",
       "      <td>1.051889</td>\n",
       "      <td>0.010500</td>\n",
       "      <td>1.053356</td>\n",
       "      <td>0.083558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>(/error, /home, /logout, Is_Downtime, /dashboard)</td>\n",
       "      <td>(/settings, /login)</td>\n",
       "      <td>0.020080</td>\n",
       "      <td>0.269076</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.229851</td>\n",
       "      <td>0.006645</td>\n",
       "      <td>1.827309</td>\n",
       "      <td>0.562842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>(/error, /home, /logout, /settings, /dashboard)</td>\n",
       "      <td>(Is_Downtime, /login)</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>0.228916</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.184211</td>\n",
       "      <td>0.006532</td>\n",
       "      <td>1.542169</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>(/error, /home, /logout, /dashboard, /login)</td>\n",
       "      <td>(/settings, Is_Downtime)</td>\n",
       "      <td>0.020080</td>\n",
       "      <td>0.253012</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.371429</td>\n",
       "      <td>0.006968</td>\n",
       "      <td>1.867470</td>\n",
       "      <td>0.590164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>(/error, /home, Is_Downtime, /dashboard, /login)</td>\n",
       "      <td>(/settings, /logout)</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>0.261044</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.915385</td>\n",
       "      <td>0.005758</td>\n",
       "      <td>1.477912</td>\n",
       "      <td>0.489712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>(/error, /home, /settings, /dashboard, /login)</td>\n",
       "      <td>(Is_Downtime, /logout)</td>\n",
       "      <td>0.020080</td>\n",
       "      <td>0.208835</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.873077</td>\n",
       "      <td>0.007855</td>\n",
       "      <td>1.977912</td>\n",
       "      <td>0.665301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>286 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           antecedents  \\\n",
       "0                                         (/dashboard)   \n",
       "1                                              (/home)   \n",
       "2                                             (/error)   \n",
       "3                                          (/settings)   \n",
       "4                                        (Is_Downtime)   \n",
       "..                                                 ...   \n",
       "281  (/error, /home, /logout, Is_Downtime, /dashboard)   \n",
       "282    (/error, /home, /logout, /settings, /dashboard)   \n",
       "283       (/error, /home, /logout, /dashboard, /login)   \n",
       "284   (/error, /home, Is_Downtime, /dashboard, /login)   \n",
       "285     (/error, /home, /settings, /dashboard, /login)   \n",
       "\n",
       "                  consequents  antecedent support  consequent support  \\\n",
       "0                    (/login)            0.409639            0.493976   \n",
       "1                    (/error)            0.449799            0.493976   \n",
       "2                 (/settings)            0.493976            0.514056   \n",
       "3                    (/error)            0.514056            0.493976   \n",
       "4                    (/error)            0.409639            0.493976   \n",
       "..                        ...                 ...                 ...   \n",
       "281       (/settings, /login)            0.020080            0.269076   \n",
       "282     (Is_Downtime, /login)            0.024096            0.228916   \n",
       "283  (/settings, Is_Downtime)            0.020080            0.253012   \n",
       "284      (/settings, /logout)            0.024096            0.261044   \n",
       "285    (Is_Downtime, /logout)            0.020080            0.208835   \n",
       "\n",
       "      support  confidence      lift  leverage  conviction  zhangs_metric  \n",
       "0    0.212851    0.519608  1.051889  0.010500    1.053356       0.083558  \n",
       "1    0.224900    0.500000  1.012195  0.002710    1.012048       0.021898  \n",
       "2    0.269076    0.544715  1.059642  0.015145    1.067341       0.111230  \n",
       "3    0.269076    0.523438  1.059642  0.015145    1.061821       0.115826  \n",
       "4    0.212851    0.519608  1.051889  0.010500    1.053356       0.083558  \n",
       "..        ...         ...       ...       ...         ...            ...  \n",
       "281  0.012048    0.600000  2.229851  0.006645    1.827309       0.562842  \n",
       "282  0.012048    0.500000  2.184211  0.006532    1.542169       0.555556  \n",
       "283  0.012048    0.600000  2.371429  0.006968    1.867470       0.590164  \n",
       "284  0.012048    0.500000  1.915385  0.005758    1.477912       0.489712  \n",
       "285  0.012048    0.600000  2.873077  0.007855    1.977912       0.665301  \n",
       "\n",
       "[286 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oBfzx4sp4kU1",
    "outputId": "25d44b4a-5860-4cdb-e924-b3fb447ddc0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downtime Prediction Rules:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>(/error, /home, /settings, /dashboard, /login)</td>\n",
       "      <td>(Is_Downtime, /logout)</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.873077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>(/error, /home, /logout, /settings, /dashboard)</td>\n",
       "      <td>(Is_Downtime, /login)</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.184211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>(/error, /home, /logout, /settings, /dashboard...</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.830882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>(/error, /home, /logout, /dashboard, /login)</td>\n",
       "      <td>(/settings, Is_Downtime)</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.371429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>(/error, /dashboard, /home, /logout)</td>\n",
       "      <td>(/settings, Is_Downtime)</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.976190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>(/error, /home, /logout, /settings, /dashboard)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.627451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>(/error, /dashboard, /home, /login)</td>\n",
       "      <td>(/settings, Is_Downtime)</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>2.258503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>(/error, /home, /settings, /dashboard, /login)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.952941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>(/error, /dashboard, /home, /login)</td>\n",
       "      <td>(Is_Downtime, /logout)</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>2.736264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>(/error, /dashboard, /home, /logout)</td>\n",
       "      <td>(Is_Downtime, /login)</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.184211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           antecedents  \\\n",
       "285     (/error, /home, /settings, /dashboard, /login)   \n",
       "282    (/error, /home, /logout, /settings, /dashboard)   \n",
       "277  (/error, /home, /logout, /settings, /dashboard...   \n",
       "283       (/error, /home, /logout, /dashboard, /login)   \n",
       "256               (/error, /dashboard, /home, /logout)   \n",
       "252    (/error, /home, /logout, /settings, /dashboard)   \n",
       "250                (/error, /dashboard, /home, /login)   \n",
       "248     (/error, /home, /settings, /dashboard, /login)   \n",
       "244                (/error, /dashboard, /home, /login)   \n",
       "243               (/error, /dashboard, /home, /logout)   \n",
       "\n",
       "                  consequents   support  confidence      lift  \n",
       "285    (Is_Downtime, /logout)  0.012048    0.600000  2.873077  \n",
       "282     (Is_Downtime, /login)  0.012048    0.500000  2.184211  \n",
       "277             (Is_Downtime)  0.012048    0.750000  1.830882  \n",
       "283  (/settings, Is_Downtime)  0.012048    0.600000  2.371429  \n",
       "256  (/settings, Is_Downtime)  0.016064    0.500000  1.976190  \n",
       "252             (Is_Downtime)  0.016064    0.666667  1.627451  \n",
       "250  (/settings, Is_Downtime)  0.016064    0.571429  2.258503  \n",
       "248             (Is_Downtime)  0.016064    0.800000  1.952941  \n",
       "244    (Is_Downtime, /logout)  0.016064    0.571429  2.736264  \n",
       "243     (Is_Downtime, /login)  0.016064    0.500000  2.184211  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rules\n",
    "# Look for symptoms that indicate 'Is_Downtime'\n",
    "downtime_rules = rules[rules['consequents'].apply(lambda x: 'Is_Downtime' in x)]\n",
    "\n",
    "# Limit the number of downtime rules to avoid overwhelming memory usage\n",
    "downtime_rules = downtime_rules.sort_values(by='support').head(10)\n",
    "#downtime_rules = downtime_rules.head(10)\n",
    "\n",
    "# Display the first few downtime prediction rules\n",
    "downtime_rules = downtime_rules[['antecedents', 'consequents', 'support', 'confidence', 'lift']]\n",
    "print(\"Downtime Prediction Rules:\")\n",
    "downtime_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uEp50xsUAqQ9",
    "outputId": "325d8c9f-eba3-4fd1-9fab-5530820fcf2e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "</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>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>(/error, /home, /logout, /settings, /dashboard...</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.830882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>(/error, /home, /logout, /settings, /dashboard)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.627451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>(/error, /home, /settings, /dashboard, /login)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.016064</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.952941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           antecedents    consequents  \\\n",
       "277  (/error, /home, /logout, /settings, /dashboard...  (Is_Downtime)   \n",
       "252    (/error, /home, /logout, /settings, /dashboard)  (Is_Downtime)   \n",
       "248     (/error, /home, /settings, /dashboard, /login)  (Is_Downtime)   \n",
       "\n",
       "      support  confidence      lift  \n",
       "277  0.012048    0.750000  1.830882  \n",
       "252  0.016064    0.666667  1.627451  \n",
       "248  0.016064    0.800000  1.952941  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rules where the consequent has only one item\n",
    "rules_with_single_consequent = downtime_rules[downtime_rules['consequents'].apply(lambda x: len(x) == 1)]\n",
    "\n",
    "# Print the rules with a single consequent item\n",
    "rules_with_single_consequent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bx6RIYjPTERy"
   },
   "source": [
    "Key Takeaways:\n",
    "\n",
    "- High Lift and High Confidence: The rules with high lift and high confidence are very useful for targeted monitorin as these are strong indicators of potential system downtime, despite having low support.\n",
    "\n",
    "- Low Support and Practical Use: All the rules have low support, which means that the particular combinations of events happen infrequently. However, these rules can be valuable for alerting and monitoring in specific scenarios. They suggest that rare combinations of events can significantly increase the likelihood of a system failure.\n",
    "\n",
    "- Targeted Alerts: Given the low support but high lift and confidence, these rules could be used to set up targeted alerts for when the specific combinations of events occur. This allows IT teams to intervene proactively, potentially preventing downtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oWKxSIFNus4P",
    "outputId": "29f645b9-0a82-4f05-a334-88554760f0e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rules Showing Events That Occur Together:\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>(/error, /login)</td>\n",
       "      <td>(/dashboard, /logout)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.232143</td>\n",
       "      <td>1.204241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>(/settings)</td>\n",
       "      <td>(/error, /dashboard, /logout)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.101562</td>\n",
       "      <td>1.053711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(/error, /settings, /logout)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.127451</td>\n",
       "      <td>1.023719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>(/dashboard, /settings, /home)</td>\n",
       "      <td>(/logout)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>1.226136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>(/settings, /home, /logout)</td>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>1.057843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>(/settings, /dashboard, /logout)</td>\n",
       "      <td>(/home)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>1.156071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>(/dashboard, /home, /logout)</td>\n",
       "      <td>(/settings)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>1.331003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>(/error)</td>\n",
       "      <td>(/settings, /dashboard, /logout)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.105691</td>\n",
       "      <td>1.052683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>(/settings, /home)</td>\n",
       "      <td>(/dashboard, /logout)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.209677</td>\n",
       "      <td>1.087702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>(/settings, /logout)</td>\n",
       "      <td>(/dashboard, /home)</td>\n",
       "      <td>0.052209</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.276923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          antecedents                       consequents  \\\n",
       "93                   (/error, /login)             (/dashboard, /logout)   \n",
       "124                       (/settings)     (/error, /dashboard, /logout)   \n",
       "125                      (/dashboard)      (/error, /settings, /logout)   \n",
       "126    (/dashboard, /settings, /home)                         (/logout)   \n",
       "127       (/settings, /home, /logout)                      (/dashboard)   \n",
       "128  (/settings, /dashboard, /logout)                           (/home)   \n",
       "129      (/dashboard, /home, /logout)                       (/settings)   \n",
       "123                          (/error)  (/settings, /dashboard, /logout)   \n",
       "130                (/settings, /home)             (/dashboard, /logout)   \n",
       "132              (/settings, /logout)               (/dashboard, /home)   \n",
       "\n",
       "      support  confidence      lift  \n",
       "93   0.052209    0.232143  1.204241  \n",
       "124  0.052209    0.101562  1.053711  \n",
       "125  0.052209    0.127451  1.023719  \n",
       "126  0.052209    0.590909  1.226136  \n",
       "127  0.052209    0.433333  1.057843  \n",
       "128  0.052209    0.520000  1.156071  \n",
       "129  0.052209    0.684211  1.331003  \n",
       "123  0.052209    0.105691  1.052683  \n",
       "130  0.052209    0.209677  1.087702  \n",
       "132  0.052209    0.200000  1.276923  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 4: Supervised Analysis - Finding Events That Occur Together\n",
    "\n",
    "# Filter only relevant columns for association rule mining\n",
    "filtered_data = weblog_data[['Path']]\n",
    "\n",
    "# Convert the data into a list of transactions (each transaction is a list of events)\n",
    "transactions = filtered_data.groupby(weblog_data['IP'])['Path'].apply(list).tolist()\n",
    "\n",
    "# Apply TransactionEncoder to prepare the dataset for Apriori\n",
    "te = TransactionEncoder()\n",
    "transaction_data = te.fit_transform(transactions)\n",
    "transaction_df = pd.DataFrame(transaction_data, columns=te.columns_)\n",
    "\n",
    "# Apply the Apriori algorithm to find frequent itemsets with a lower support threshold\n",
    "frequent_itemsets_supervised = apriori(transaction_df, min_support=0.05, use_colnames=True)\n",
    "\n",
    "# Generate association rules to discover events that occur together\n",
    "supervised_rules = association_rules(frequent_itemsets_supervised, metric=\"lift\", min_threshold=1.0)\n",
    "\n",
    "# Limit the number of supervised rules to avoid overwhelming memory usage\n",
    "supervised_rules = supervised_rules.sort_values(by='support').head(10)\n",
    "#supervised_rules = supervised_rules.head(10)\n",
    "\n",
    "# Display the first few rules showing events that occur together\n",
    "supervised_rules = supervised_rules[['antecedents', 'consequents', 'support', 'confidence', 'lift']]\n",
    "print(\"Rules Showing Events That Occur Together:\")\n",
    "supervised_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "2LegLMC27MRs",
    "outputId": "a9fe59a6-9a7c-4657-f7bd-0098dc5846d3"
   },
   "outputs": [
    {
     "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(/login)</td>\n",
       "      <td>0.212851</td>\n",
       "      <td>0.519608</td>\n",
       "      <td>1.051889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(/login)</td>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>0.212851</td>\n",
       "      <td>0.430894</td>\n",
       "      <td>1.051889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>0.188755</td>\n",
       "      <td>0.460784</td>\n",
       "      <td>1.124856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.188755</td>\n",
       "      <td>0.460784</td>\n",
       "      <td>1.124856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(/error)</td>\n",
       "      <td>(/home)</td>\n",
       "      <td>0.224900</td>\n",
       "      <td>0.455285</td>\n",
       "      <td>1.012195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1643</th>\n",
       "      <td>(/logout)</td>\n",
       "      <td>(/error, /home, /settings, Is_Downtime, /dashb...</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>1.556250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>(/error, /home, /logout, /settings, /dashboard...</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>1.830882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1645</th>\n",
       "      <td>(/settings)</td>\n",
       "      <td>(/error, /home, /logout, Is_Downtime, /dashboa...</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>1.458984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1646</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(/error, /home, /settings, /logout, Is_Downtim...</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.029412</td>\n",
       "      <td>1.464706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>(/login)</td>\n",
       "      <td>(/error, /home, /settings, /logout, Is_Downtim...</td>\n",
       "      <td>0.012048</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>1.518293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1648 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        antecedents                                        consequents  \\\n",
       "0      (/dashboard)                                           (/login)   \n",
       "1          (/login)                                       (/dashboard)   \n",
       "2     (Is_Downtime)                                       (/dashboard)   \n",
       "3      (/dashboard)                                      (Is_Downtime)   \n",
       "4          (/error)                                            (/home)   \n",
       "...             ...                                                ...   \n",
       "1643      (/logout)  (/error, /home, /settings, Is_Downtime, /dashb...   \n",
       "1644  (Is_Downtime)  (/error, /home, /logout, /settings, /dashboard...   \n",
       "1645    (/settings)  (/error, /home, /logout, Is_Downtime, /dashboa...   \n",
       "1646   (/dashboard)  (/error, /home, /settings, /logout, Is_Downtim...   \n",
       "1647       (/login)  (/error, /home, /settings, /logout, Is_Downtim...   \n",
       "\n",
       "       support  confidence      lift  \n",
       "0     0.212851    0.519608  1.051889  \n",
       "1     0.212851    0.430894  1.051889  \n",
       "2     0.188755    0.460784  1.124856  \n",
       "3     0.188755    0.460784  1.124856  \n",
       "4     0.224900    0.455285  1.012195  \n",
       "...        ...         ...       ...  \n",
       "1643  0.012048    0.025000  1.556250  \n",
       "1644  0.012048    0.029412  1.830882  \n",
       "1645  0.012048    0.023438  1.458984  \n",
       "1646  0.012048    0.029412  1.464706  \n",
       "1647  0.012048    0.024390  1.518293  \n",
       "\n",
       "[1648 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = association_rules(frequent_itemsets_unsupervised, metric=\"lift\", min_threshold=1)\n",
    "\n",
    "rules = rules.loc[:,[\"antecedents\",\"consequents\",\"support\",\"confidence\",\"lift\"]]\n",
    "\n",
    "rules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z3aanHD1TOLu"
   },
   "source": [
    "Key Takeaways:\n",
    "\n",
    "- Moderate Lift and Confidence:\n",
    "  - The lift values for most rules are in the range of 1.01 to 1.33, indicating a positive but not very strong correlation between antecedents and consequents.\n",
    "  - The rules with the highest lift and highest confidence are useful for targeted actions, such as understanding patterns where specific events are likely to occur together.\n",
    "\n",
    "- Low Confidence and Its Implications:\n",
    "  - Rules with low confidence have limited practical use for prediction because the likelihood of the consequent occurring given the antecedent is relatively low. These rules are not as actionable for predicting behavior.\n",
    "\n",
    "- Frequent Patterns with Co-occurring Events:\n",
    "  - The support for all rules is 5.2%, meaning that these combinations are not exceptionally rare but also not dominant. This makes these rules useful for understanding common paths or behaviors, but not critical for high-frequency actions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uv8bgTFfi8Fh"
   },
   "source": [
    "Recommendations:\n",
    "\n",
    "1. Use Rules with High Lift and Confidence for Predictive Actions:\n",
    "\n",
    "- The rules with the highest lift and confidence values should be prioritized for monitoring. For instance, Rule 127 with a lift of 1.33 and confidence of 68.4% can be used to understand the likely occurrence of /settings after /dashboard, /logout, and /home.\n",
    "\n",
    "2. Consider Low-Confidence Rules for Further Analysis:\n",
    "\n",
    "- Low-confidence rules may indicate areas where additional factors influence the behavior. For instance, Rule 124 has a low confidence of 10.2%, suggesting that there are other influences on whether /dashboard, /logout, and /error occur after /settings.\n",
    "\n",
    "3. Investigate Event Sequences for System Understanding:\n",
    "\n",
    "- The analysis of these rules can provide insights into how users interact with the system, particularly for sequences of events. This can be valuable for improving user experience or preventing sequences that lead to undesired outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_cipwXpv8iYF",
    "outputId": "5f9132eb-4a68-4d85-bd69-a8f3b2148138"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Goodness of Downtime Prediction Rules:\n",
      "      support  confidence      lift\n",
      "285  0.012048    0.600000  2.873077\n",
      "282  0.012048    0.500000  2.184211\n",
      "277  0.012048    0.750000  1.830882\n",
      "283  0.012048    0.600000  2.371429\n",
      "256  0.016064    0.500000  1.976190\n",
      "252  0.016064    0.666667  1.627451\n",
      "250  0.016064    0.571429  2.258503\n",
      "248  0.016064    0.800000  1.952941\n",
      "244  0.016064    0.571429  2.736264\n",
      "243  0.016064    0.500000  2.184211\n",
      "\n",
      "Goodness of High lift Rules (Events Occurring Together):\n",
      "      support  confidence      lift\n",
      "93   0.052209    0.232143  1.204241\n",
      "124  0.052209    0.101562  1.053711\n",
      "125  0.052209    0.127451  1.023719\n",
      "126  0.052209    0.590909  1.226136\n",
      "127  0.052209    0.433333  1.057843\n",
      "128  0.052209    0.520000  1.156071\n",
      "129  0.052209    0.684211  1.331003\n",
      "123  0.052209    0.105691  1.052683\n",
      "130  0.052209    0.209677  1.087702\n",
      "132  0.052209    0.200000  1.276923\n"
     ]
    }
   ],
   "source": [
    "# Step 5: Goodness of Rules - Support, Confidence, and Lift\n",
    "\n",
    "# Evaluate the goodness of rules for both unsupervised and supervised rules\n",
    "print(\"\\nGoodness of Downtime Prediction Rules:\")\n",
    "print(downtime_rules[['support', 'confidence', 'lift']])\n",
    "\n",
    "print(\"\\nGoodness of High lift Rules (Events Occurring Together):\")\n",
    "print(supervised_rules[['support', 'confidence', 'lift']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fr9NDCgoWAo2"
   },
   "source": [
    "Analysis:\n",
    "\n",
    "- Confidence for downtime prediction rules vary from 0.500 to 0.733\n",
    "  - Rule 72 has the highest confidence of 0.733, meaning there is a 73.3% chance of a downtime if all the antecedent conditions (/error, /home, /dashboard) are present.\n",
    "\n",
    "- Lift values are all greater than 1, indicating a positive correlation between the antecedent and consequent.\n",
    "  - Rule 72 has a lift of 1.790, suggesting that the combination of /error, /home, and /dashboard is almost 1.79 times more likely to lead to downtime compared to random occurrence.\n",
    "\n",
    "Actionable Insight:\n",
    "\n",
    "- Confidence for Supervised rules vary from 0.430 and 0.544.\n",
    "  - Rule 4 (/settings leading to /error) has the highest confidence, meaning that 54.47% of the time, users who visit /settings end up on /error, indicationg that users are often encountering issues when modifying settings.\n",
    "  - An area which needs improvement\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bPuXMW6f-NxS",
    "outputId": "969e19be-263e-4ebf-ce2d-20db60cc7e84"
   },
   "outputs": [],
   "source": [
    "# Step 6: Save the Rules for Further Analysis\n",
    "# Save the downtime prediction rules to a CSV file\n",
    "downtime_rules.to_csv('weblog_downtime_prediction_rules.csv', index=False)\n",
    "\n",
    "# Save the supervised rules to a CSV file\n",
    "supervised_rules.to_csv('weblog_events_occurring_together_rules.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "4O-mNGcm2M1Q",
    "outputId": "8145d255-991a-4779-c1a9-4a46a8094465"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 7: Visualization\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Visualization of Supervised Rules - Items Bought Together\n",
    "plt.figure(figsize=(12, 8))\n",
    "G = nx.DiGraph()\n",
    "\n",
    "for _, row in supervised_rules.iterrows():\n",
    "    for ant in row['antecedents']:\n",
    "        for cons in row['consequents']:\n",
    "            G.add_edge(ant, cons, weight=row['lift'])\n",
    "\n",
    "pos = nx.spring_layout(G, k=0.5, seed=42)\n",
    "nx.draw(G, pos, with_labels=True, node_size=2000, font_size=10, font_weight='bold', node_color='skyblue', edge_color='gray')\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels={(u, v): f\"lift: {d['weight']:.2f}\" for u, v, d in G.edges(data=True)}, font_size=8)\n",
    "plt.title('Visualization of Items Bought Together (Supervised Rules)')\n",
    "plt.show()\n",
    "\n",
    "# Visualization of Unsupervised Rules - Items Bought Together\n",
    "plt.figure(figsize=(12, 8))\n",
    "G = nx.DiGraph()\n",
    "\n",
    "for _, row in downtime_rules.iterrows():\n",
    "    for ant in row['antecedents']:\n",
    "        for cons in row['consequents']:\n",
    "            G.add_edge(ant, cons, weight=row['lift'])\n",
    "\n",
    "pos = nx.spring_layout(G, k=0.5, seed=42)\n",
    "nx.draw(G, pos, with_labels=True, node_size=2000, font_size=10, font_weight='bold', node_color='lightcoral', edge_color='gray')\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels={(u, v): f\"lift: {d['weight']:.2f}\" for u, v, d in G.edges(data=True)}, font_size=8)\n",
    "plt.title('Visualization of Items Bought Together (Unsupervised Rules)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "71WwXFTmQTrl"
   },
   "source": [
    "Remove this plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mb405sCAxDho"
   },
   "source": [
    "# **Activity 1:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EfZiU9JKAKEC",
    "outputId": "d9b80566-a213-422b-96ab-ea553c8213bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rules Showing Events That Occur Together:\n"
     ]
    },
    {
     "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(/login)</td>\n",
       "      <td>0.212851</td>\n",
       "      <td>0.519608</td>\n",
       "      <td>1.051889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(/login)</td>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>0.212851</td>\n",
       "      <td>0.430894</td>\n",
       "      <td>1.051889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(DELETE)</td>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>0.273092</td>\n",
       "      <td>0.462585</td>\n",
       "      <td>1.129252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(DELETE)</td>\n",
       "      <td>0.273092</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.129252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(GET)</td>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>0.285141</td>\n",
       "      <td>0.446541</td>\n",
       "      <td>1.090085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(GET)</td>\n",
       "      <td>0.285141</td>\n",
       "      <td>0.696078</td>\n",
       "      <td>1.090085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(POST)</td>\n",
       "      <td>0.301205</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>1.076990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(POST)</td>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>0.301205</td>\n",
       "      <td>0.441176</td>\n",
       "      <td>1.076990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(PUT)</td>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>0.273092</td>\n",
       "      <td>0.427673</td>\n",
       "      <td>1.044025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(/dashboard)</td>\n",
       "      <td>(PUT)</td>\n",
       "      <td>0.273092</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.044025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    antecedents   consequents   support  confidence      lift\n",
       "0  (/dashboard)      (/login)  0.212851    0.519608  1.051889\n",
       "1      (/login)  (/dashboard)  0.212851    0.430894  1.051889\n",
       "2      (DELETE)  (/dashboard)  0.273092    0.462585  1.129252\n",
       "3  (/dashboard)      (DELETE)  0.273092    0.666667  1.129252\n",
       "4         (GET)  (/dashboard)  0.285141    0.446541  1.090085\n",
       "5  (/dashboard)         (GET)  0.285141    0.696078  1.090085\n",
       "6  (/dashboard)        (POST)  0.301205    0.735294  1.076990\n",
       "7        (POST)  (/dashboard)  0.301205    0.441176  1.076990\n",
       "8         (PUT)  (/dashboard)  0.273092    0.427673  1.044025\n",
       "9  (/dashboard)         (PUT)  0.273092    0.666667  1.044025"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#repeat step 3, 4, and 5 by adding Method\n",
    "\n",
    "# Filter only relevant columns for association rule mining\n",
    "filtered_data = weblog_data[['Path', 'Method']]\n",
    "\n",
    "# Convert the data into a list of transactions (each transaction is a list of events)\n",
    "transactions = filtered_data.groupby(weblog_data['IP'])[['Path', 'Method']].apply(lambda x: x.values.flatten().tolist()).tolist()\n",
    "\n",
    "# Apply TransactionEncoder to prepare the dataset for Apriori\n",
    "te = TransactionEncoder()\n",
    "transaction_data = te.fit_transform(transactions)\n",
    "transaction_df = pd.DataFrame(transaction_data, columns=te.columns_)\n",
    "\n",
    "# Apply the Apriori algorithm to find frequent itemsets with a lower support threshold\n",
    "frequent_itemsets_supervised = apriori(transaction_df, min_support=0.02, use_colnames=True)\n",
    "\n",
    "# Generate association rules to discover events that occur together\n",
    "supervised_rules = association_rules(frequent_itemsets_supervised, metric=\"lift\", min_threshold=1.0)\n",
    "\n",
    "# Limit the number of supervised rules to avoid overwhelming memory usage\n",
    "supervised_rules = supervised_rules.head(10)\n",
    "\n",
    "# Display the first few rules showing events that occur together\n",
    "supervised_rules = supervised_rules[['antecedents', 'consequents', 'support', 'confidence', 'lift']]\n",
    "print(\"Rules Showing Events That Occur Together:\")\n",
    "supervised_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M_ehbDW9AWWU",
    "outputId": "84cf2798-ca50-4c1e-b19c-52066858c74e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downtime Prediction Rules:\n"
     ]
    },
    {
     "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>(/home, /dashboard)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.092369</td>\n",
       "      <td>0.589744</td>\n",
       "      <td>1.439668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>(/dashboard, /login)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.112450</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>1.289678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>(/dashboard, /logout)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.096386</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.220588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>(/settings, /dashboard)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.112450</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.394958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>(/dashboard, DELETE)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.136546</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.220588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>(GET, /dashboard)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.152610</td>\n",
       "      <td>0.535211</td>\n",
       "      <td>1.306545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>(/dashboard, POST)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.160643</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1.301961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>(/error, /home)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.116466</td>\n",
       "      <td>0.517857</td>\n",
       "      <td>1.264181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>(/error, DELETE)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.156627</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.220588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>(/error, POST)</td>\n",
       "      <td>(Is_Downtime)</td>\n",
       "      <td>0.180723</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.220588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 antecedents    consequents   support  confidence      lift\n",
       "89       (/home, /dashboard)  (Is_Downtime)  0.092369    0.589744  1.439668\n",
       "101     (/dashboard, /login)  (Is_Downtime)  0.112450    0.528302  1.289678\n",
       "113    (/dashboard, /logout)  (Is_Downtime)  0.096386    0.500000  1.220588\n",
       "121  (/settings, /dashboard)  (Is_Downtime)  0.112450    0.571429  1.394958\n",
       "131     (/dashboard, DELETE)  (Is_Downtime)  0.136546    0.500000  1.220588\n",
       "138        (GET, /dashboard)  (Is_Downtime)  0.152610    0.535211  1.306545\n",
       "147       (/dashboard, POST)  (Is_Downtime)  0.160643    0.533333  1.301961\n",
       "160          (/error, /home)  (Is_Downtime)  0.116466    0.517857  1.264181\n",
       "208         (/error, DELETE)  (Is_Downtime)  0.156627    0.500000  1.220588\n",
       "226           (/error, POST)  (Is_Downtime)  0.180723    0.500000  1.220588"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 4: Unsupervised Analysis - Rules with Downtime Using Association Rules\n",
    "\n",
    "# Filter only relevant columns for association rule mining\n",
    "filtered_data = weblog_data[['Path', 'Method', 'Is_Downtime']]\n",
    "\n",
    "# Convert the data into a list of transactions (each transaction is a list of events)\n",
    "transactions = filtered_data.groupby(weblog_data['IP'])[['Path', 'Method']].apply(lambda x: x.values.flatten().tolist()).tolist()\n",
    "\n",
    "# Add 'Is_Downtime' to transactions where a downtime event occurred\n",
    "for i, (idx, downtime) in enumerate(weblog_data.groupby(weblog_data['IP'])['Is_Downtime'].max().items()):\n",
    "    if downtime == 1:\n",
    "        # Use 'i', the numerical index, to access the 'transactions' list\n",
    "        transactions[i].append('Is_Downtime')\n",
    "\n",
    "# Apply TransactionEncoder to prepare the dataset for Apriori\n",
    "transaction_data = te.fit_transform(transactions)\n",
    "transaction_df = pd.DataFrame(transaction_data, columns=te.columns_)\n",
    "\n",
    "# Apply the Apriori algorithm to find frequent itemsets with a lower support threshold\n",
    "frequent_itemsets_unsupervised = apriori(transaction_df, min_support=0.01, use_colnames=True)\n",
    "\n",
    "# Generate association rules with a focus on confidence\n",
    "rules = association_rules(frequent_itemsets_unsupervised, metric=\"confidence\", min_threshold=0.5)\n",
    "\n",
    "# Filter rules to predict downtime\n",
    "# Look for rules where 'Is_Downtime' might be indicated by other events\n",
    "downtime_rules = rules[rules['consequents'].apply(lambda x: 'Is_Downtime' in x)]\n",
    "\n",
    "# Limit the number of downtime rules to avoid overwhelming memory usage\n",
    "downtime_rules = downtime_rules.head(10)\n",
    "\n",
    "# Display the first few downtime prediction rules\n",
    "downtime_rules = downtime_rules[['antecedents', 'consequents', 'support', 'confidence', 'lift']]\n",
    "print(\"Downtime Prediction Rules:\")\n",
    "downtime_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MkS4NrKFBTzD",
    "outputId": "60633973-f51e-4342-8349-40a5eaadfbca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Goodness of Downtime Prediction Rules:\n",
      "      support  confidence      lift\n",
      "89   0.092369    0.589744  1.439668\n",
      "100  0.112450    0.528302  1.289678\n",
      "112  0.096386    0.500000  1.220588\n",
      "121  0.112450    0.571429  1.394958\n",
      "130  0.136546    0.500000  1.220588\n",
      "138  0.152610    0.535211  1.306545\n",
      "147  0.160643    0.533333  1.301961\n",
      "159  0.116466    0.517857  1.264181\n",
      "207  0.156627    0.500000  1.220588\n",
      "226  0.180723    0.500000  1.220588\n",
      "\n",
      "Goodness of Supervised Rules (Events Occurring Together):\n",
      "    support  confidence      lift\n",
      "0  0.212851    0.430894  1.051889\n",
      "1  0.212851    0.519608  1.051889\n",
      "2  0.273092    0.462585  1.129252\n",
      "3  0.273092    0.666667  1.129252\n",
      "4  0.285141    0.446541  1.090085\n",
      "5  0.285141    0.696078  1.090085\n",
      "6  0.301205    0.735294  1.076990\n",
      "7  0.301205    0.441176  1.076990\n",
      "8  0.273092    0.427673  1.044025\n",
      "9  0.273092    0.666667  1.044025\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# Step 5: Goodness of Rules - Support, Confidence, and Lift\n",
    "\n",
    "# Evaluate the goodness of rules for both unsupervised and supervised rules\n",
    "print(\"\\nGoodness of Downtime Prediction Rules:\")\n",
    "print(downtime_rules[['support', 'confidence', 'lift']])\n",
    "\n",
    "print(\"\\nGoodness of Supervised Rules (Events Occurring Together):\")\n",
    "print(supervised_rules[['support', 'confidence', 'lift']])\n",
    "\n",
    "# Step 6: Save the Rules for Further Analysis\n",
    "# Save the downtime prediction rules to a CSV file\n",
    "downtime_rules.to_csv('downtime_prediction_rules.csv', index=False)\n",
    "\n",
    "# Save the supervised rules to a CSV file\n",
    "supervised_rules.to_csv('events_occurring_together_rules.csv', index=False)"
   ]
  }
 ],
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