dmsMidTermProject.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install mlxtend"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e5mOJWs8dGe",
        "outputId": "81dabf43-d515-4008-e7fe-0b969a0c3a8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.10/dist-packages (0.23.1)\n",
            "Requirement already satisfied: scipy>=1.2.1 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.13.1)\n",
            "Requirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.26.4)\n",
            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (2.2.2)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.5.2)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (3.7.1)\n",
            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.4.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->mlxtend) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->mlxtend) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->mlxtend) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->mlxtend) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->mlxtend) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->mlxtend) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->mlxtend) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->mlxtend) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->mlxtend) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->mlxtend) (2024.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.2->mlxtend) (3.5.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Declare global variables\n",
        "bfTime = 0.0\n",
        "aTime = 0.0\n",
        "fpTime = 0.0\n",
        "choice = None\n",
        "minimumSup = None\n",
        "minimumConfi = None\n",
        "file_name = None"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wti2rnhKs5-2",
        "outputId": "2238cfc1-834c-4c32-9269-8d5c1188c61e"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "l0gz_Hs9gpik",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb79c82f-2470-4934-b89e-33ff75858433"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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": [
        "import sys\n",
        "import csv\n",
        "from itertools import combinations\n",
        "from typing import Dict, List, Tuple\n",
        "import time\n",
        "mini_conf=0\n",
        "\n",
        "#frequent item set is calculated anything below support is not considered\n",
        "def frequentset(list1: List[str], notconsider: List[set], item_set_list: List[set], n: int,\n",
        "                  min_supp: float, counttot: int, support_of_all_item_set: Dict[Tuple[str], int]) -> Tuple[Dict[Tuple[str], int], List[set]]:\n",
        "    comb = combinations(list1, n)\n",
        "    item_support_count = {}\n",
        "    for i in comb:\n",
        "        set_i = set(i)\n",
        "        i = tuple(sorted(i))\n",
        "        for j in item_set_list:\n",
        "            if set_i.issubset(j):\n",
        "                if notconsider:\n",
        "                    count = 0\n",
        "                    for k in notconsider:\n",
        "                        if k.issubset(set_i):\n",
        "                            count = 1\n",
        "                            break\n",
        "                    if not count:\n",
        "                        if i in item_support_count:\n",
        "                            item_support_count[i] += 1\n",
        "                        else:\n",
        "                            item_support_count[i] = 1\n",
        "                else:\n",
        "                    if i in item_support_count:\n",
        "                        item_support_count[i] += 1\n",
        "                    else:\n",
        "                        item_support_count[i] = 1\n",
        "    fsReturn = {}\n",
        "    rsReturn = []\n",
        "\n",
        "    if item_support_count:\n",
        "        print()\n",
        "        for i in item_support_count:\n",
        "            if (item_support_count[i] / counttot) * 100 >= min_supp:\n",
        "                fsReturn[i] = item_support_count[i]\n",
        "            else:\n",
        "                rsReturn.append(set(list(i)))\n",
        "        print()\n",
        "        if fsReturn:\n",
        "            itemprint(fsReturn, n, counttot)\n",
        "            support_of_all_item_set.update(item_support_count)\n",
        "            association_rules(fsReturn,support_of_all_item_set,mini_conf)\n",
        "            return fsReturn, rsReturn\n",
        "    return None, None\n",
        "def itemprint(frequent_set: Dict[Tuple[str], int], n: int, counttot: int):\n",
        "    print(\"Frequent itemsets\", n, \"iteration\")\n",
        "    for i in frequent_set:\n",
        "        print(i, round(frequent_set[i] * 100 / counttot, 2))\n",
        "    print()\n",
        "from itertools import combinations\n",
        "from typing import Dict, Tuple\n",
        "\n",
        "def association_rules(frequent_set: Dict[Tuple[str], int], support_of_all_item_set: Dict[Tuple[str], int], min_conf: float):\n",
        "    for items_set_tuple in frequent_set.keys():\n",
        "        print(\"Association Rule for itemset -\", items_set_tuple)\n",
        "        size_of_item_set = len(items_set_tuple)\n",
        "        itemset = set(items_set_tuple)\n",
        "        while size_of_item_set - 1 > 0:\n",
        "            comb = combinations(items_set_tuple, size_of_item_set - 1)\n",
        "            for i in comb:\n",
        "                left_side_items = i\n",
        "                right_side_items = tuple(itemset - set(i))\n",
        "                item_conf = round(support_of_all_item_set[items_set_tuple] * 100 / support_of_all_item_set[left_side_items], 2)\n",
        "                if item_conf >= min_conf:\n",
        "                    print(left_side_items, \"=>\", right_side_items, item_conf, \"Rule Selected\")\n",
        "                else:\n",
        "                    print(left_side_items, \"=>\", right_side_items, item_conf, \"Rule Rejected\")\n",
        "            size_of_item_set -= 1\n",
        "        print()\n",
        "\n",
        "\n",
        "#bruteforce function to extract the data from the file and find frequent itemsets and association rules\n",
        "def bruteforce(file_name: str, min_supp: int, min_conf: int):\n",
        "    global bfTime\n",
        "    global mini_conf\n",
        "    mini_conf=min_conf\n",
        "\n",
        "    start_time=time.time()\n",
        "    with open(file_name, \"r\") as file_object:\n",
        "        reader = csv.reader(file_object)\n",
        "        all_tx = []\n",
        "        counttot = 0\n",
        "        support_of_all_item_set = {}\n",
        "        c1 = {}  # type: Dict[str, int]\n",
        "        item_set_list = []\n",
        "\n",
        "#iterate through the contents of the folder\n",
        "        for row in reader:\n",
        "            transaction_id = row[0]\n",
        "            items = row[1].split(\", \")\n",
        "            all_tx.append(transaction_id)\n",
        "            seen = set()\n",
        "            for item in items:\n",
        "                c1[(item,)] = c1.get((item,), 0) + 1\n",
        "                seen.add(item)\n",
        "            item_set_list.append(seen)\n",
        "            counttot += 1\n",
        "\n",
        "        frequent_set = {}\n",
        "        rejected_set = []\n",
        "        print()\n",
        "        for i in c1:\n",
        "            if (c1[i] / counttot) * 100 >= min_supp:\n",
        "                frequent_set[i] = c1[i]\n",
        "            else:\n",
        "                rejected_set.append(set(i))\n",
        "        support_of_all_item_set.update(c1)\n",
        "\n",
        "        list1 = [item[0] for item in frequent_set.keys()]\n",
        "        print()\n",
        "        print(itemprint(frequent_set, 1, counttot))\n",
        "\n",
        "        item_set_size = 1\n",
        "        while len(list1) > item_set_size:\n",
        "            frequent_set1, rejected_set1 = frequentset(\n",
        "                list1, rejected_set, item_set_list, item_set_size + 1,\n",
        "                min_supp, counttot, support_of_all_item_set\n",
        "            )\n",
        "            if not frequent_set1:\n",
        "                break\n",
        "            item_list = [items for item_tuples in frequent_set1.keys() for items in item_tuples]\n",
        "            list1 = list(set(item_list))\n",
        "            rejected_set = rejected_set1\n",
        "            frequent_set = frequent_set1\n",
        "            item_set_size += 1\n",
        "\n",
        "        association_rules(frequent_set, support_of_all_item_set, min_conf)\n",
        "        end_time= time.time()\n",
        "        bfTime= end_time-start_time\n",
        "        print(f\"Time taken to complete the process using brute force method:{bfTime:.6f}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to prompt for dataset and parameters\n",
        "def get_user_input():\n",
        "    global choice, minimumSup, minimumConfi, file_name  # Declare as global to access and modify\n",
        "\n",
        "    print(\"Please select a dataset:\")\n",
        "    print(\"1. Juice Bar\")\n",
        "    print(\"2. Burlington\")\n",
        "    print(\"3. Costco\")\n",
        "    print(\"4. Walmart\")\n",
        "    print(\"5. ShopRite\")\n",
        "\n",
        "    choice = input(\"Enter the number corresponding to your choice: \")\n",
        "    minimumSup = int(input(\"Enter minimum support as %: \"))\n",
        "    minimumConfi = int(input(\"Enter minimum confidence as %: \"))\n",
        "\n",
        "    file_names = {\n",
        "        '1': 'juicebar.csv',\n",
        "        '2': 'burlington.csv',\n",
        "        '3': 'costco.csv',\n",
        "        '4': 'walmart.csv',\n",
        "        '5': 'shoprite.csv'\n",
        "    }\n",
        "\n",
        "    # Validate choice\n",
        "    if choice in file_names:\n",
        "        file_name = file_names[choice]\n",
        "        print(f\"You selected: {file_name}\")\n",
        "    else:\n",
        "        print(\"Invalid choice. Please try again.\")\n",
        "        get_user_input()  # Retry on invalid choice\n",
        "\n",
        "def run_analysis():\n",
        "    global file_name, minimumSup, minimumConfi  # Access global variables\n",
        "    bruteforce(file_name, minimumSup, minimumConfi)\n",
        "get_user_input()\n",
        "\n",
        "run_analysis()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCypxduTkh60",
        "outputId": "1a9bef5d-320d-4584-a306-1dd0fae8ad63"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please select a dataset:\n",
            "1. Juice Bar\n",
            "2. Burlington\n",
            "3. Costco\n",
            "4. Walmart\n",
            "5. ShopRite\n",
            "Enter the number corresponding to your choice: 1\n",
            "Enter minimum support as %: 20\n",
            "Enter minimum confidence as %: 40\n",
            "You selected: juicebar.csv\n",
            "\n",
            "\n",
            "Frequent itemsets 1 iteration\n",
            "('Water',) 55.0\n",
            "('Cocoa',) 35.0\n",
            "('Cider',) 20.0\n",
            "('Juice',) 35.0\n",
            "('Cocktail',) 40.0\n",
            "('Tea',) 30.0\n",
            "('Wine',) 25.0\n",
            "('Coffee',) 55.0\n",
            "('ProteinShake',) 45.0\n",
            "('Soda',) 30.0\n",
            "\n",
            "None\n",
            "\n",
            "\n",
            "Frequent itemsets 2 iteration\n",
            "('Cocoa', 'Water') 35.0\n",
            "('Cocktail', 'Water') 20.0\n",
            "('Tea', 'Water') 20.0\n",
            "('Water', 'Wine') 20.0\n",
            "('Coffee', 'Water') 30.0\n",
            "('ProteinShake', 'Water') 20.0\n",
            "('Coffee', 'Juice') 20.0\n",
            "('Juice', 'ProteinShake') 20.0\n",
            "('Cocktail', 'Coffee') 20.0\n",
            "('Cocktail', 'ProteinShake') 30.0\n",
            "('Coffee', 'Tea') 30.0\n",
            "('Coffee', 'Wine') 20.0\n",
            "('Coffee', 'ProteinShake') 30.0\n",
            "('Coffee', 'Soda') 20.0\n",
            "\n",
            "Association Rule for itemset - ('Cocoa', 'Water')\n",
            "('Cocoa',) => ('Water',) 100.0 Rule Selected\n",
            "('Water',) => ('Cocoa',) 63.64 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('Cocktail', 'Water')\n",
            "('Cocktail',) => ('Water',) 50.0 Rule Selected\n",
            "('Water',) => ('Cocktail',) 36.36 Rule Rejected\n",
            "\n",
            "Association Rule for itemset - ('Tea', 'Water')\n",
            "('Tea',) => ('Water',) 66.67 Rule Selected\n",
            "('Water',) => ('Tea',) 36.36 Rule Rejected\n",
            "\n",
            "Association Rule for itemset - ('Water', 'Wine')\n",
            "('Water',) => ('Wine',) 36.36 Rule Rejected\n",
            "('Wine',) => ('Water',) 80.0 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('Coffee', 'Water')\n",
            "('Coffee',) => ('Water',) 54.55 Rule Selected\n",
            "('Water',) => ('Coffee',) 54.55 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('ProteinShake', 'Water')\n",
            "('ProteinShake',) => ('Water',) 44.44 Rule Selected\n",
            "('Water',) => ('ProteinShake',) 36.36 Rule Rejected\n",
            "\n",
            "Association Rule for itemset - ('Coffee', 'Juice')\n",
            "('Coffee',) => ('Juice',) 36.36 Rule Rejected\n",
            "('Juice',) => ('Coffee',) 57.14 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('Juice', 'ProteinShake')\n",
            "('Juice',) => ('ProteinShake',) 57.14 Rule Selected\n",
            "('ProteinShake',) => ('Juice',) 44.44 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('Cocktail', 'Coffee')\n",
            "('Cocktail',) => ('Coffee',) 50.0 Rule Selected\n",
            "('Coffee',) => ('Cocktail',) 36.36 Rule Rejected\n",
            "\n",
            "Association Rule for itemset - ('Cocktail', 'ProteinShake')\n",
            "('Cocktail',) => ('ProteinShake',) 75.0 Rule Selected\n",
            "('ProteinShake',) => ('Cocktail',) 66.67 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('Coffee', 'Tea')\n",
            "('Coffee',) => ('Tea',) 54.55 Rule Selected\n",
            "('Tea',) => ('Coffee',) 100.0 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('Coffee', 'Wine')\n",
            "('Coffee',) => ('Wine',) 36.36 Rule Rejected\n",
            "('Wine',) => ('Coffee',) 80.0 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('Coffee', 'ProteinShake')\n",
            "('Coffee',) => ('ProteinShake',) 54.55 Rule Selected\n",
            "('ProteinShake',) => ('Coffee',) 66.67 Rule Selected\n",
            "\n",
            "Association Rule for itemset - ('Coffee', 'Soda')\n",
            "('Coffee',) => ('Soda',) 36.36 Rule Rejected\n",
            "('Soda',) => ('Coffee',) 66.67 Rule Selected\n",
            "\n",
            "\n",
            "\n",
            "Frequent itemsets 3 iteration\n",
            "('Coffee', 'Tea', 'Water') 20.0\n",
            "\n",
            "Association Rule for itemset - ('Coffee', 'Tea', 'Water')\n",
            "('Coffee', 'Tea') => ('Water',) 66.67 Rule Selected\n",
            "('Coffee', 'Water') => ('Tea',) 66.67 Rule Selected\n",
            "('Tea', 'Water') => ('Coffee',) 100.0 Rule Selected\n",
            "('Coffee',) => ('Water', 'Tea') 36.36 Rule Rejected\n",
            "('Tea',) => ('Water', 'Coffee') 66.67 Rule Selected\n",
            "('Water',) => ('Coffee', 'Tea') 36.36 Rule Rejected\n",
            "\n",
            "Association Rule for itemset - ('Coffee', 'Tea', 'Water')\n",
            "('Coffee', 'Tea') => ('Water',) 66.67 Rule Selected\n",
            "('Coffee', 'Water') => ('Tea',) 66.67 Rule Selected\n",
            "('Tea', 'Water') => ('Coffee',) 100.0 Rule Selected\n",
            "('Coffee',) => ('Water', 'Tea') 36.36 Rule Rejected\n",
            "('Tea',) => ('Water', 'Coffee') 66.67 Rule Selected\n",
            "('Water',) => ('Coffee', 'Tea') 36.36 Rule Rejected\n",
            "\n",
            "Time taken to complete the process using brute force method:0.068779\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "import time\n",
        "\n",
        "def apriori_from_csv(file_name: str, minimumSup: float, minimumConfi: float):\n",
        "    global aTime\n",
        "    start_time=time.time()\n",
        "    df = pd.read_csv(file_name, header=None)\n",
        "\n",
        "    # Preprocess the  read data into a one-hot encoded DataFrame\n",
        "    transactions = []\n",
        "    for row in df.itertuples(index=False):\n",
        "        transactions.append(row[1].split(\", \"))# the csv file is split according to the commas\n",
        "    from mlxtend.preprocessing import TransactionEncoder\n",
        "    encoder = TransactionEncoder()\n",
        "    encoded_data = encoder.fit(transactions).transform(transactions)\n",
        "    df = pd.DataFrame(encoded_data, columns=encoder.columns_)\n",
        "    print(df)\n",
        "\n",
        "    fi = apriori(df, min_support=minimumSup / 100, use_colnames=True)\n",
        "    rules = association_rules(fi, metric=\"confidence\", min_threshold=minimumConfi / 100)\n",
        "    print(\"Frequent Itemsets:\")\n",
        "    print(fi)\n",
        "    print(\"\\nAssociation Rules:\")\n",
        "    print(rules)\n",
        "    end_time= time.time()\n",
        "    aTime= end_time-start_time\n",
        "    print(f\"Time taken to complete the process using FP-Growth method:{fpTime:.6f}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHqH58w3eOmc",
        "outputId": "2a71a60f-cab3-4d10-f0dc-d34260fa95c7"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "global file_name, minimumSup, minimumConfi\n",
        "apriori_from_csv(file_name, minimumSup, minimumConfi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5r143GEeWAY",
        "outputId": "c6e8e6de-85ea-461d-bf01-6091b54e27f7"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Cider  Ciderss  Cocktail  Cocoa  Coffee  Juice  ProteinShake   Soda  \\\n",
            "0    True    False     False   True   False  False         False  False   \n",
            "1   False    False     False   True   False   True         False  False   \n",
            "2   False    False      True  False   False  False         False  False   \n",
            "3   False    False      True  False    True  False         False  False   \n",
            "4   False    False      True  False   False   True          True   True   \n",
            "5   False    False     False  False    True  False          True   True   \n",
            "6   False    False     False  False   False  False         False  False   \n",
            "7   False    False     False   True   False  False         False  False   \n",
            "8    True    False     False   True    True   True         False  False   \n",
            "9   False    False     False   True    True  False         False  False   \n",
            "10  False    False     False  False    True   True          True   True   \n",
            "11  False    False      True   True   False   True          True  False   \n",
            "12   True    False      True  False    True  False          True  False   \n",
            "13  False    False      True  False    True   True          True  False   \n",
            "14   True    False     False  False    True  False         False   True   \n",
            "15  False    False     False  False    True  False          True  False   \n",
            "16  False     True      True  False   False  False          True  False   \n",
            "17  False    False      True   True    True  False          True  False   \n",
            "18  False    False     False  False    True   True         False   True   \n",
            "19  False    False     False  False   False  False         False   True   \n",
            "\n",
            "      Tea  Water   Wine  \n",
            "0   False   True  False  \n",
            "1   False   True  False  \n",
            "2   False  False  False  \n",
            "3    True   True   True  \n",
            "4   False  False  False  \n",
            "5    True  False  False  \n",
            "6   False   True  False  \n",
            "7   False   True   True  \n",
            "8    True   True  False  \n",
            "9    True   True   True  \n",
            "10   True  False  False  \n",
            "11  False   True  False  \n",
            "12   True   True  False  \n",
            "13  False  False   True  \n",
            "14  False  False  False  \n",
            "15  False   True  False  \n",
            "16  False  False  False  \n",
            "17  False   True   True  \n",
            "18  False  False  False  \n",
            "19  False  False  False  \n",
            "Frequent Itemsets:\n",
            "    support                  itemsets\n",
            "0      0.20                   (Cider)\n",
            "1      0.40                (Cocktail)\n",
            "2      0.35                   (Cocoa)\n",
            "3      0.55                  (Coffee)\n",
            "4      0.35                   (Juice)\n",
            "5      0.45            (ProteinShake)\n",
            "6      0.30                    (Soda)\n",
            "7      0.30                     (Tea)\n",
            "8      0.55                   (Water)\n",
            "9      0.25                    (Wine)\n",
            "10     0.20        (Cocktail, Coffee)\n",
            "11     0.30  (Cocktail, ProteinShake)\n",
            "12     0.20         (Water, Cocktail)\n",
            "13     0.35            (Water, Cocoa)\n",
            "14     0.20           (Coffee, Juice)\n",
            "15     0.30    (ProteinShake, Coffee)\n",
            "16     0.20            (Soda, Coffee)\n",
            "17     0.30             (Coffee, Tea)\n",
            "18     0.30           (Water, Coffee)\n",
            "19     0.20            (Coffee, Wine)\n",
            "20     0.20     (ProteinShake, Juice)\n",
            "21     0.20     (Water, ProteinShake)\n",
            "22     0.20              (Water, Tea)\n",
            "23     0.20             (Water, Wine)\n",
            "24     0.20      (Water, Coffee, Tea)\n",
            "\n",
            "Association Rules:\n",
            "        antecedents      consequents  antecedent support  consequent support  \\\n",
            "0        (Cocktail)         (Coffee)                0.40                0.55   \n",
            "1        (Cocktail)   (ProteinShake)                0.40                0.45   \n",
            "2    (ProteinShake)       (Cocktail)                0.45                0.40   \n",
            "3        (Cocktail)          (Water)                0.40                0.55   \n",
            "4           (Water)          (Cocoa)                0.55                0.35   \n",
            "5           (Cocoa)          (Water)                0.35                0.55   \n",
            "6           (Juice)         (Coffee)                0.35                0.55   \n",
            "7    (ProteinShake)         (Coffee)                0.45                0.55   \n",
            "8          (Coffee)   (ProteinShake)                0.55                0.45   \n",
            "9            (Soda)         (Coffee)                0.30                0.55   \n",
            "10         (Coffee)            (Tea)                0.55                0.30   \n",
            "11            (Tea)         (Coffee)                0.30                0.55   \n",
            "12          (Water)         (Coffee)                0.55                0.55   \n",
            "13         (Coffee)          (Water)                0.55                0.55   \n",
            "14           (Wine)         (Coffee)                0.25                0.55   \n",
            "15   (ProteinShake)          (Juice)                0.45                0.35   \n",
            "16          (Juice)   (ProteinShake)                0.35                0.45   \n",
            "17   (ProteinShake)          (Water)                0.45                0.55   \n",
            "18            (Tea)          (Water)                0.30                0.55   \n",
            "19           (Wine)          (Water)                0.25                0.55   \n",
            "20  (Water, Coffee)            (Tea)                0.30                0.30   \n",
            "21     (Water, Tea)         (Coffee)                0.20                0.55   \n",
            "22    (Coffee, Tea)          (Water)                0.30                0.55   \n",
            "23            (Tea)  (Water, Coffee)                0.30                0.30   \n",
            "\n",
            "    support  confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0      0.20    0.500000  0.909091   -0.0200    0.900000      -0.142857  \n",
            "1      0.30    0.750000  1.666667    0.1200    2.200000       0.666667  \n",
            "2      0.30    0.666667  1.666667    0.1200    1.800000       0.727273  \n",
            "3      0.20    0.500000  0.909091   -0.0200    0.900000      -0.142857  \n",
            "4      0.35    0.636364  1.818182    0.1575    1.787500       1.000000  \n",
            "5      0.35    1.000000  1.818182    0.1575         inf       0.692308  \n",
            "6      0.20    0.571429  1.038961    0.0075    1.050000       0.057692  \n",
            "7      0.30    0.666667  1.212121    0.0525    1.350000       0.318182  \n",
            "8      0.30    0.545455  1.212121    0.0525    1.210000       0.388889  \n",
            "9      0.20    0.666667  1.212121    0.0350    1.350000       0.250000  \n",
            "10     0.30    0.545455  1.818182    0.1350    1.540000       1.000000  \n",
            "11     0.30    1.000000  1.818182    0.1350         inf       0.642857  \n",
            "12     0.30    0.545455  0.991736   -0.0025    0.990000      -0.018182  \n",
            "13     0.30    0.545455  0.991736   -0.0025    0.990000      -0.018182  \n",
            "14     0.20    0.800000  1.454545    0.0625    2.250000       0.416667  \n",
            "15     0.20    0.444444  1.269841    0.0425    1.170000       0.386364  \n",
            "16     0.20    0.571429  1.269841    0.0425    1.283333       0.326923  \n",
            "17     0.20    0.444444  0.808081   -0.0475    0.810000      -0.301587  \n",
            "18     0.20    0.666667  1.212121    0.0350    1.350000       0.250000  \n",
            "19     0.20    0.800000  1.454545    0.0625    2.250000       0.416667  \n",
            "20     0.20    0.666667  2.222222    0.1100    2.100000       0.785714  \n",
            "21     0.20    1.000000  1.818182    0.0900         inf       0.562500  \n",
            "22     0.20    0.666667  1.212121    0.0350    1.350000       0.250000  \n",
            "23     0.20    0.666667  2.222222    0.1100    2.100000       0.785714  \n",
            "Time taken to complete the process using FP-Growth method:0.027581\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import fpgrowth, association_rules\n",
        "import time\n",
        "\n",
        "def fpgrowth_from_csv(file_name: str, minimumSup: float, minimumConfi: float):\n",
        "    global fpTime\n",
        "    start_time=time.time()\n",
        "    # Read the dataset\n",
        "    df = pd.read_csv(file_name, header=None)\n",
        "\n",
        "    # Preprocess the data into a list of transactions\n",
        "    transactions = []\n",
        "    for row in df.itertuples(index=False):\n",
        "        transactions.append(row[1].split(\", \"))\n",
        "\n",
        "    from mlxtend.preprocessing import TransactionEncoder\n",
        "    encoder = TransactionEncoder()\n",
        "    encoded_data = encoder.fit(transactions).transform(transactions)\n",
        "    df = pd.DataFrame(encoded_data, columns=encoder.columns_)\n",
        "\n",
        "    # Generate frequent itemsets using FP-Growth\n",
        "    fi2 = fpgrowth(df, min_support=minimumSup / 100, use_colnames=True)\n",
        "\n",
        "    rules = association_rules(fi2, metric=\"confidence\", min_threshold=minimumConfi / 100)\n",
        "    print(\"Frequent Itemsets:\")\n",
        "    print(fi2)\n",
        "    print(\"\\nAssociation Rules:\")\n",
        "    print(rules)\n",
        "    end_time= time.time()\n",
        "    fpTime= end_time-start_time\n",
        "    print(f\"Time taken to complete the process using FP-Growth method:{fpTime:.6f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQhQdPKMfWFS",
        "outputId": "408bb32c-2104-4858-d463-8e72d76be933"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "global file_name, minimumSup, minimumConfi\n",
        "fpgrowth_from_csv(file_name, minimumSup, minimumConfi)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48-qOQSEhImr",
        "outputId": "b1c2653e-7965-4033-bb5e-0a13090132f1"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frequent Itemsets:\n",
            "    support                  itemsets\n",
            "0      0.55                   (Water)\n",
            "1      0.35                   (Cocoa)\n",
            "2      0.20                   (Cider)\n",
            "3      0.35                   (Juice)\n",
            "4      0.40                (Cocktail)\n",
            "5      0.55                  (Coffee)\n",
            "6      0.30                     (Tea)\n",
            "7      0.25                    (Wine)\n",
            "8      0.45            (ProteinShake)\n",
            "9      0.30                    (Soda)\n",
            "10     0.35            (Water, Cocoa)\n",
            "11     0.20     (ProteinShake, Juice)\n",
            "12     0.20           (Coffee, Juice)\n",
            "13     0.20        (Cocktail, Coffee)\n",
            "14     0.20         (Water, Cocktail)\n",
            "15     0.30  (Cocktail, ProteinShake)\n",
            "16     0.30           (Water, Coffee)\n",
            "17     0.30             (Coffee, Tea)\n",
            "18     0.20              (Water, Tea)\n",
            "19     0.20      (Water, Coffee, Tea)\n",
            "20     0.20            (Coffee, Wine)\n",
            "21     0.20             (Water, Wine)\n",
            "22     0.30    (ProteinShake, Coffee)\n",
            "23     0.20     (Water, ProteinShake)\n",
            "24     0.20            (Soda, Coffee)\n",
            "\n",
            "Association Rules:\n",
            "        antecedents      consequents  antecedent support  consequent support  \\\n",
            "0           (Water)          (Cocoa)                0.55                0.35   \n",
            "1           (Cocoa)          (Water)                0.35                0.55   \n",
            "2    (ProteinShake)          (Juice)                0.45                0.35   \n",
            "3           (Juice)   (ProteinShake)                0.35                0.45   \n",
            "4           (Juice)         (Coffee)                0.35                0.55   \n",
            "5        (Cocktail)         (Coffee)                0.40                0.55   \n",
            "6        (Cocktail)          (Water)                0.40                0.55   \n",
            "7        (Cocktail)   (ProteinShake)                0.40                0.45   \n",
            "8    (ProteinShake)       (Cocktail)                0.45                0.40   \n",
            "9           (Water)         (Coffee)                0.55                0.55   \n",
            "10         (Coffee)          (Water)                0.55                0.55   \n",
            "11         (Coffee)            (Tea)                0.55                0.30   \n",
            "12            (Tea)         (Coffee)                0.30                0.55   \n",
            "13            (Tea)          (Water)                0.30                0.55   \n",
            "14  (Water, Coffee)            (Tea)                0.30                0.30   \n",
            "15     (Water, Tea)         (Coffee)                0.20                0.55   \n",
            "16    (Coffee, Tea)          (Water)                0.30                0.55   \n",
            "17            (Tea)  (Water, Coffee)                0.30                0.30   \n",
            "18           (Wine)         (Coffee)                0.25                0.55   \n",
            "19           (Wine)          (Water)                0.25                0.55   \n",
            "20   (ProteinShake)         (Coffee)                0.45                0.55   \n",
            "21         (Coffee)   (ProteinShake)                0.55                0.45   \n",
            "22   (ProteinShake)          (Water)                0.45                0.55   \n",
            "23           (Soda)         (Coffee)                0.30                0.55   \n",
            "\n",
            "    support  confidence      lift  leverage  conviction  zhangs_metric  \n",
            "0      0.35    0.636364  1.818182    0.1575    1.787500       1.000000  \n",
            "1      0.35    1.000000  1.818182    0.1575         inf       0.692308  \n",
            "2      0.20    0.444444  1.269841    0.0425    1.170000       0.386364  \n",
            "3      0.20    0.571429  1.269841    0.0425    1.283333       0.326923  \n",
            "4      0.20    0.571429  1.038961    0.0075    1.050000       0.057692  \n",
            "5      0.20    0.500000  0.909091   -0.0200    0.900000      -0.142857  \n",
            "6      0.20    0.500000  0.909091   -0.0200    0.900000      -0.142857  \n",
            "7      0.30    0.750000  1.666667    0.1200    2.200000       0.666667  \n",
            "8      0.30    0.666667  1.666667    0.1200    1.800000       0.727273  \n",
            "9      0.30    0.545455  0.991736   -0.0025    0.990000      -0.018182  \n",
            "10     0.30    0.545455  0.991736   -0.0025    0.990000      -0.018182  \n",
            "11     0.30    0.545455  1.818182    0.1350    1.540000       1.000000  \n",
            "12     0.30    1.000000  1.818182    0.1350         inf       0.642857  \n",
            "13     0.20    0.666667  1.212121    0.0350    1.350000       0.250000  \n",
            "14     0.20    0.666667  2.222222    0.1100    2.100000       0.785714  \n",
            "15     0.20    1.000000  1.818182    0.0900         inf       0.562500  \n",
            "16     0.20    0.666667  1.212121    0.0350    1.350000       0.250000  \n",
            "17     0.20    0.666667  2.222222    0.1100    2.100000       0.785714  \n",
            "18     0.20    0.800000  1.454545    0.0625    2.250000       0.416667  \n",
            "19     0.20    0.800000  1.454545    0.0625    2.250000       0.416667  \n",
            "20     0.30    0.666667  1.212121    0.0525    1.350000       0.318182  \n",
            "21     0.30    0.545455  1.212121    0.0525    1.210000       0.388889  \n",
            "22     0.20    0.444444  0.808081   -0.0475    0.810000      -0.301587  \n",
            "23     0.20    0.666667  1.212121    0.0350    1.350000       0.250000  \n",
            "Time taken to complete the process using FP-Growth method:0.031816\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def time_complexity():\n",
        "    print(f\"Brute Force Time: {bfTime:.6f} seconds\")\n",
        "    print(f\"Apriori Time: {aTime:.6f} seconds\")\n",
        "    print(f\"FP-Growth Time: {fpTime:.6f} seconds\")\n",
        "\n",
        "    if bfTime < aTime and bfTime < fpTime:\n",
        "        print(\"Brute Force is the fastest method.\")\n",
        "    elif aTime < bfTime and aTime < fpTime:\n",
        "        print(\"Apriori is the fastest method.\")\n",
        "    elif fpTime < bfTime and fpTime < aTime:\n",
        "        print(\"FP-Growth is the fastest method.\")\n",
        "    else:\n",
        "        print(\"All methods take approximately the same time.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7XTrcHufisTY",
        "outputId": "ed197137-6173-4d9a-bd08-d360ee6ce162"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "global bfTime, aTime, fpTime\n",
        "time_complexity()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8dW6BhLZi7NE",
        "outputId": "e42def47-a8fd-4a16-d081-f3df7b630f6c"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Brute Force Time: 0.068779 seconds\n",
            "Apriori Time: 0.043819 seconds\n",
            "FP-Growth Time: 0.031816 seconds\n",
            "FP-Growth is the fastest method.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    }
  ]
}