chapter4.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UdayLab/Hands-on-Pattern-Mining/blob/main/chapter4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Chapter 4: Pattern Discovery in Transactional Databases"
      ],
      "metadata": {
        "id": "14HciOZCFrYm"
      },
      "id": "14HciOZCFrYm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install PAMI repository"
      ],
      "metadata": {
        "id": "OXAsDHIFFwxx"
      },
      "id": "OXAsDHIFFwxx"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "initial_id",
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "initial_id",
        "outputId": "712ce502-6650-453c-de40-effe0fda6b3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hBuilding wheels for collected packages: JsonForm, JsonSir\n",
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            "Successfully built JsonForm JsonSir\n",
            "Installing collected packages: JsonSir, validators, python-easyconfig, cramjam, sphinxcontrib-jquery, fastparquet, sphinx-rtd-theme, JsonForm, discord.py, resource, pami\n",
            "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 cramjam-2.9.0 discord.py-2.4.0 fastparquet-2024.11.0 pami-2024.11.15.1 python-easyconfig-0.1.7 resource-0.2.1 sphinx-rtd-theme-3.0.2 sphinxcontrib-jquery-4.1 validators-0.34.0\n"
          ]
        }
      ],
      "source": [
        "!pip install pami"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download the sample transactional database"
      ],
      "metadata": {
        "id": "aXMwnQmBF4rr"
      },
      "id": "aXMwnQmBF4rr"
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -nc https://web-ext.u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9i5Wvom4F-kr",
        "outputId": "f2d02062-dbea-439c-99ca-62e26ca9068e"
      },
      "id": "9i5Wvom4F-kr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-12-03 05:57:12--  https://web-ext.u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv\n",
            "Resolving web-ext.u-aizu.ac.jp (web-ext.u-aizu.ac.jp)... 163.143.103.34\n",
            "Connecting to web-ext.u-aizu.ac.jp (web-ext.u-aizu.ac.jp)|163.143.103.34|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4019277 (3.8M) [text/csv]\n",
            "Saving to: ‘Transactional_T10I4D100K.csv’\n",
            "\n",
            "Transactional_T10I4 100%[===================>]   3.83M  3.88MB/s    in 1.0s    \n",
            "\n",
            "2024-12-03 05:57:14 (3.88 MB/s) - ‘Transactional_T10I4D100K.csv’ saved [4019277/4019277]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discovering frequent patterns using FP-growth"
      ],
      "metadata": {
        "id": "tiKA5YNBGkZZ"
      },
      "id": "tiKA5YNBGkZZ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "```python\n",
        "from PAMI.frequentPattern.basic import FPGrowth  as alg\n",
        "\n",
        "obj = alg.FPGrowth(iFile='inputFileName',minSup=minimumSupportvalue,sep='\\t')\n",
        "obj.mine()\n",
        "obj.save('outputFileName')\n",
        "\n",
        "frequentPatternsDF= obj.getPatternsAsDataFrame()\n",
        "print('#Patterns: ' + str(len(frequentPatternsDF)))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "RwAbDmPAGpLZ"
      },
      "id": "RwAbDmPAGpLZ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 1"
      ],
      "metadata": {
        "id": "p1h7KQYiG9ae"
      },
      "id": "p1h7KQYiG9ae"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.frequentPattern.basic import FPGrowth  as alg\n",
        "\n",
        "obj = alg.FPGrowth(iFile='Transactional_T10I4D100K.csv',minSup=300,sep='\\t')\n",
        "obj.mine()\n",
        "obj.save('frequentPatternsAtMinSupCount300.txt')\n",
        "\n",
        "frequentPatternsDF= obj.getPatternsAsDataFrame()\n",
        "print('#Patterns: ' + str(len(frequentPatternsDF)))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aonyqnmUGgyJ",
        "outputId": "36f1488a-1b7f-4079-c24c-9b773f8180f6"
      },
      "id": "aonyqnmUGgyJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frequent patterns were generated successfully using frequentPatternGrowth algorithm\n",
            "#Patterns: 4540\n",
            "Runtime: 11.018220901489258\n",
            "Memory (RSS): 562528256\n",
            "Memory (USS): 540168192\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discovering Closed Frequent Patterns"
      ],
      "metadata": {
        "id": "vww7aVdQHP3Z"
      },
      "id": "vww7aVdQHP3Z"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.frequentPattern.closed import CHARM  as alg\n",
        "\n",
        "obj = alg.CHARM(iFile='inputFileName', minSup=minimumSupportValue)\n",
        "obj.mine()\n",
        "obj.save('outputFileName')\n",
        "\n",
        "closedFPsDF= obj.getPatternsAsDataFrame()\n",
        "\n",
        "print('#Patterns: ' + str(len(obj.getPatternsAsDataFrame())))  \n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "y-Lm5Na9HTvT"
      },
      "id": "y-Lm5Na9HTvT"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 2"
      ],
      "metadata": {
        "id": "TBNI5kT3HqbH"
      },
      "id": "TBNI5kT3HqbH"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.frequentPattern.closed import CHARM  as alg\n",
        "\n",
        "obj = alg.CHARM(iFile='Transactional_T10I4D100K.csv', minSup=300)\n",
        "obj.mine()\n",
        "obj.save('closedFrequentPatterns.txt')\n",
        "\n",
        "closedFPsDF= obj.getPatternsAsDataFrame()\n",
        "\n",
        "print('#Patterns: ' + str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljtbQUopF1fE",
        "outputId": "4f37d742-281f-47c3-9d19-991f06e2d5b5"
      },
      "id": "ljtbQUopF1fE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Closed Frequent patterns were generated successfully using CHARM algorithm\n",
            "#Patterns: 2856\n",
            "Runtime: 11.586620092391968\n",
            "Memory (RSS): 581001216\n",
            "Memory (USS): 558632960\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discovering Maximal Frequent Patterns"
      ],
      "metadata": {
        "id": "NWzSMDsjH3hs"
      },
      "id": "NWzSMDsjH3hs"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "```python\n",
        "from PAMI.frequentPattern.maximal import MaxFPGrowth  as alg\n",
        "\n",
        "obj = alg.MaxFPGrowth(iFile='inputFileName', minSup=minimumSupportValue)\n",
        "obj.mine()\n",
        "obj.save('outputFileName')\n",
        "\n",
        "maximalFPsDF= obj.getPatternsAsDataFrame()\n",
        "\n",
        "print('#Patterns: ' + str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "ww4Vv1_pH694"
      },
      "id": "ww4Vv1_pH694"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 3"
      ],
      "metadata": {
        "id": "rOeQDaIKIXWi"
      },
      "id": "rOeQDaIKIXWi"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.frequentPattern.maximal import MaxFPGrowth  as alg\n",
        "\n",
        "obj = alg.MaxFPGrowth(iFile='Transactional_T10I4D100K.csv', minSup=300)\n",
        "obj.mine()\n",
        "obj.save('maximalFrequentPatternsAtMinSupCount100.txt')\n",
        "\n",
        "maximalFPsDF= obj.getPatternsAsDataFrame()\n",
        "\n",
        "print('#Patterns: ' + str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-px1Mv6H6gZ",
        "outputId": "da253725-2c81-4acc-98be-5bf8de520a99"
      },
      "id": "_-px1Mv6H6gZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximal Frequent patterns were generated successfully using MaxFp-Growth algorithm \n",
            "#Patterns: 1292\n",
            "Runtime: 11.646287202835083\n",
            "Memory (RSS): 539312128\n",
            "Memory (USS): 516968448\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discovering Top-k Frequently Occurring Patterns"
      ],
      "metadata": {
        "id": "9r52n1lwIqwO"
      },
      "id": "9r52n1lwIqwO"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.frequentPattern.topk import FAE  as alg\n",
        "\n",
        "obj = alg.FAE(iFile='inputFileName', k=number of frequently patterns needed)\n",
        "obj.mine()\n",
        "obj.save('outputFileName')\n",
        "\n",
        "print('#Patterns: ' + str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "bFQ93rypIwdJ"
      },
      "id": "bFQ93rypIwdJ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 4"
      ],
      "metadata": {
        "id": "8A-ig8K-JBsZ"
      },
      "id": "8A-ig8K-JBsZ"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.frequentPattern.topk import FAE  as alg\n",
        "\n",
        "obj = alg.FAE(iFile='Transactional_T10I4D100K.csv', k=1000)\n",
        "obj.mine()\n",
        "obj.save('topkFrequentPatterns.txt')\n",
        "\n",
        "print('#Patterns: ' + str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FUEpvCYhIv3r",
        "outputId": "da2df095-2cbc-44dd-9dd4-923bee43a411"
      },
      "id": "FUEpvCYhIv3r",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " TopK frequent patterns were successfully generated using FAE algorithm.\n",
            "#Patterns: 1000\n",
            "Runtime: 19.14928388595581\n",
            "Memory (RSS): 366190592\n",
            "Memory (USS): 343724032\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rare Item Problem - Calculating the items' MIS values"
      ],
      "metadata": {
        "id": "Gc_k_tfwJTOD"
      },
      "id": "Gc_k_tfwJTOD"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.extras.calculateMISValues import usingBeta as ub\n",
        "cd = ub.usingBeta(iFile='inputFileName',\n",
        "        beta=percentageOfItemsFrequency, LS=lowestMinimumSupportForAnItem) #using default tab separator\n",
        "cd.calculateMIS()\n",
        "cd.save('outputFileName')\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "sems1MQUJbaR"
      },
      "id": "sems1MQUJbaR"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 5"
      ],
      "metadata": {
        "id": "fvoG0ZTlJeDk"
      },
      "id": "fvoG0ZTlJeDk"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.calculateMISValues import usingBeta as ub\n",
        "cd = ub.usingBeta(iFile='Transactional_T10I4D100K.csv',\n",
        "        beta=0.5, LS=100) #using default tab separator\n",
        "cd.calculateMIS()\n",
        "cd.save('MIS.txt')"
      ],
      "metadata": {
        "id": "bIA9BiDwJatI"
      },
      "id": "bIA9BiDwJatI",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rare Item Problem - Mining Frequent Patterns using Multiple Minimum Support Values"
      ],
      "metadata": {
        "id": "7Vq4trmIKL-J"
      },
      "id": "7Vq4trmIKL-J"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.multipleMinimumSupportBasedFrequentPattern.basic \\\n",
        "    import CFPGrowthPlus as alg\n",
        "\n",
        "obj = alg.CFPGrowthPlus(iFile='inputFileName',\n",
        "        MIS='MIS.txt')  #using default tab separator\n",
        "obj.mine()         \n",
        "obj.save('outputFileName')\n",
        "print('Total No of patterns: ' +\n",
        "    str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "k4z2tnqxKUiq"
      },
      "id": "k4z2tnqxKUiq"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 6"
      ],
      "metadata": {
        "id": "Ybolv5HeKX0n"
      },
      "id": "Ybolv5HeKX0n"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.multipleMinimumSupportBasedFrequentPattern.basic \\\n",
        "    import CFPGrowthPlus as alg\n",
        "\n",
        "obj = alg.CFPGrowthPlus(iFile='Transactional_T10I4D100K.csv',\n",
        "        MIS='MIS.txt')  #using default tab separator\n",
        "obj.mine()\n",
        "obj.save('frequentPatternsMultipleMinimumSupports.txt')\n",
        "print('Total No of patterns: ' +\n",
        "    str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNFmwbO3KT8p",
        "outputId": "5b9882a3-1ff6-4b75-ce5c-3e914b5ae402"
      },
      "id": "dNFmwbO3KT8p",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frequent patterns were generated successfully using Conditional Frequent Pattern Growth algorithm\n",
            "Total No of patterns: 4947\n",
            "Runtime: 17.537547826766968\n",
            "Memory (RSS): 700235776\n",
            "Memory (USS): 677855232\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discovering Correlated Patterns"
      ],
      "metadata": {
        "id": "bmNkbtS_LIAR"
      },
      "id": "bmNkbtS_LIAR"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.correlatedPattern.basic import CoMine as alg\n",
        "\n",
        "obj = alg.CoMine(iFile='inputFileName',\n",
        "        minSup=minimumSupportValue, minAllConf=minimumAllConfidenceValue)\n",
        "obj.mine()\n",
        "obj.save('correlatedPatterns.txt')\n",
        "\n",
        "print('#Patterns: ' + str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "-KMikDejLNj8"
      },
      "id": "-KMikDejLNj8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 7"
      ],
      "metadata": {
        "id": "VeWLau1ELQvt"
      },
      "id": "VeWLau1ELQvt"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.correlatedPattern.basic import CoMine as alg\n",
        "\n",
        "obj = alg.CoMine(iFile='Transactional_T10I4D100K.csv', minSup=300, minAllConf=0.5)\n",
        "obj.mine()\n",
        "obj.save('correlatedPatterns.txt')\n",
        "\n",
        "print('#Patterns: ' + str(len(obj.getPatternsAsDataFrame())))\n",
        "print('Runtime: ' + str(obj.getRuntime()))\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lanvH9puLL_F",
        "outputId": "89905d9f-fe58-42fd-9e1f-a2860fe96bc3"
      },
      "id": "lanvH9puLL_F",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlated patterns were generated successfully using CoMine algorithm\n",
            "#Patterns: 723\n",
            "Runtime: 15.577804803848267\n",
            "Memory (RSS): 657141760\n",
            "Memory (USS): 634855424\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discovering Relative Frequent Patterns"
      ],
      "metadata": {
        "id": "oiCPzdFxLwQ2"
      },
      "id": "oiCPzdFxLwQ2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.relativeFrequentPattern.basic import RSFPGrowth as alg\n",
        "\n",
        "obj = alg.RSFPGrowth(iFile='inputFileName', minSup=minimumSupportCount, minRS=minimumRelativeSupportValue)\n",
        "\n",
        "obj.mine()\n",
        "obj.save('outputFileName')\n",
        "\n",
        "relativeFrequentPatternsDF= obj.getPatternsAsDataFrame()\n",
        "print('#Patterns: ' + str(len(relativeFrequentPatternsDF)))\n",
        "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "NgoNQlG9L7nH"
      },
      "id": "NgoNQlG9L7nH"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 8"
      ],
      "metadata": {
        "id": "9pz77qyYL-9T"
      },
      "id": "9pz77qyYL-9T"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.relativeFrequentPattern.basic import RSFPGrowth as alg\n",
        "\n",
        "obj = alg.RSFPGrowth(iFile='Transactional_T10I4D100K.csv', minSup=300, minRS=0.75)\n",
        "\n",
        "obj.mine()\n",
        "obj.save('relativeFrequentPatterns.txt')\n",
        "\n",
        "relativeFrequentPatternsDF= obj.getPatternsAsDataFrame()\n",
        "print('#Patterns: ' + str(len(relativeFrequentPatternsDF)))\n",
        "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RG3r3mWELz67",
        "outputId": "d48d2d29-7435-4248-e5f5-922577127a26"
      },
      "id": "RG3r3mWELz67",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relative support frequent patterns were generated successfully using RSFPGrowth algorithm\n",
            "#Patterns: 4540\n",
            "Runtime: 29.90667486190796\n",
            "Memory (RSS): 574488576\n",
            "Memory (USS): 552030208\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discovering Fault-Tolerant Frequent Patterns"
      ],
      "metadata": {
        "id": "v2NKfNVSMciE"
      },
      "id": "v2NKfNVSMciE"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.faultTolerantFrequentPattern.basic import FTFPGrowth as alg\n",
        "\n",
        "obj = alg.FTFPGrowth(iFile='inputFileName', minSup=minimumSupportValue, itemSup=minimumSupportAnItemHasToMaintain, minLength=minimumLengthOfAnItemset, faultTolerance=faultTolerantValue, sep=\"\\t\")\n",
        "obj.mine()\n",
        "\n",
        "print('#Patterns: ' + str(len(relativeFrequentPatternsDF)))\n",
        "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "MhOhZwq3MgQ2"
      },
      "id": "MhOhZwq3MgQ2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 9"
      ],
      "metadata": {
        "id": "2-GzQqFuMj3-"
      },
      "id": "2-GzQqFuMj3-"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.faultTolerantFrequentPattern.basic import FTFPGrowth as alg\n",
        "\n",
        "obj = alg.FTFPGrowth(iFile='Transactional_T10I4D100K.csv', minSup=100, itemSup=100, minLength=3, faultTolerance=1, sep=\"\\t\")\n",
        "\n",
        "obj.mine()\n",
        "\n",
        "print('#Patterns: ' + str(len(relativeFrequentPatternsDF)))\n",
        "print('Runtime: ' + str(obj.getRuntime())) #measure the runtime\n",
        "print('Memory (RSS): ' + str(obj.getMemoryRSS()))\n",
        "print('Memory (USS): ' + str(obj.getMemoryUSS()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nSkzk503Mfhs",
        "outputId": "934499cc-952d-4fe7-b0bf-0900b612d377"
      },
      "id": "nSkzk503Mfhs",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frequent patterns were generated successfully using frequentPatternGrowth algorithm\n",
            "#Patterns: 4540\n",
            "Runtime: 18.587899923324585\n",
            "Memory (RSS): 930197504\n",
            "Memory (USS): 907669504\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Discovering Association Rules From the Frequent Patterns"
      ],
      "metadata": {
        "id": "8PigaEY6Ng3t"
      },
      "id": "8PigaEY6Ng3t"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generic\n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from PAMI.AssociationRules.basic import confidence as alg\n",
        "\n",
        "obj = alg.confidence('inputFileName', minConf=minimumConfidenceValue)\n",
        "obj.mine()\n",
        "obj.printResults()\n",
        "obj.save(\"outputFileName\")\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "jeFj4OWVNmtm"
      },
      "id": "jeFj4OWVNmtm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example 10"
      ],
      "metadata": {
        "id": "EZEFTHfsNsLR"
      },
      "id": "EZEFTHfsNsLR"
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.AssociationRules.basic import confidence as alg\n",
        "\n",
        "obj = alg.confidence('frequentPatternsAtMinSupCount300.txt', minConf=0.75)\n",
        "obj.mine()\n",
        "obj.printResults()\n",
        "obj.save(\"associationRulesconfidence.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5otyL8jjNkdh",
        "outputId": "ef9153df-00a8-4e5f-f123-2748a445c955"
      },
      "id": "5otyL8jjNkdh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Association rules successfully  generated from frequent patterns \n",
            "Total number of Association Rules: 22984\n",
            "Total Memory in USS: 903217152\n",
            "Total Memory in RSS 925667328\n",
            "Total ExecutionTime in ms: 0.036444902420043945\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}