examples/example0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "example0.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RaneemQaddoura/EvoNP/blob/master/examples/example0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xDHnOXVpdhqM"
      },
      "source": [
        "<h1>Evolutionary Algorithm with Nearest Point (EvoNP)</h1>\n",
        "An efficient evolutionary algorithm with a nearest neighbor search technique for clustering analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ez_-PBAldmnx"
      },
      "source": [
        "EvoNP is a clustering algorithm which aims at grouping similar data points to the same cluster and dissimilar data points to different clusters. It is based on the evolution behavior of genetic algorithm and the Nearest Neighbor Search (NNS) technique."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2GG8uYydpYC"
      },
      "source": [
        "<h2>Installation</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74qavDbAduJM"
      },
      "source": [
        "Python 3.xx is required"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60ih1JvtdxS5"
      },
      "source": [
        "<h2>GitHub</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBV9uLU8d0kj"
      },
      "source": [
        "Clone the Git repository from GitHub:\n",
        "git clone https://github.com/RaneemQaddoura/EvoNP.git"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9cptk_KxsuG"
      },
      "source": [
        "!git clone https://github.com/RaneemQaddoura/EvoNP.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXGpBJ2QyaFf"
      },
      "source": [
        "# Change working directory\n",
        "import os\n",
        "os.chdir(\"EvoNP/\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xb2orhr8yg0N"
      },
      "source": [
        "#Install NumPy, SciPy, sklearn, pandas, treelib, and matplotlib\n",
        "!pip install -r requirements.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxI2S08PzFuR"
      },
      "source": [
        "os.chdir(\"src\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "quKX4VLgrsk2"
      },
      "source": [
        "import EvoNP\n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "warnings.simplefilter(action='ignore', category=RuntimeWarning)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XknvbftizNXM"
      },
      "source": [
        "format = '%m/%d/%y %H:%M:%S'\n",
        "\n",
        "#initializing variables\n",
        "directory = \"../datasets/\" # the directory where the dataset is stored\n",
        "nChromosomes = 20 \n",
        "nGenerations = 50 \n",
        "crossoverProbability = 0.8\n",
        "mutationProbability = 0.001\n",
        "\n",
        "filename = \"aggregation.csv\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RkToNhYRzSX3"
      },
      "source": [
        "# Read the dataset file and generate the points list and true values\n",
        "data = np.genfromtxt(directory + filename, delimiter=',')\n",
        "\n",
        "nPoints, nValues = data.shape #Number of points and Number of values for each point\n",
        "nValues = nValues - 1 #Dimension value\n",
        "k = len(np.unique(data[:,-1]))#k: Number of clusters\n",
        "points = data[:,:-1] #list of points\n",
        "labelsTrue = data[:,-1] #List of actual cluster of each points (last field)\n",
        "     \n",
        "popSize = (nChromosomes,k) # The population will have nChromosomes chromosome where each chromosome has chromosomeLength genes.\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I92d34BZn6tR"
      },
      "source": [
        "bestChromosomeInAllGenerations, bestLabelsPredInAllGenerations, bestFitnessInAllGenerations, allBestFitness = EvoNP.run(points, nPoints, k, nChromosomes, nGenerations, crossoverProbability, mutationProbability)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGddoNOhn419",
        "outputId": "3ffbb396-2c1e-45b4-a884-8c346f2ce8bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "print(\"HS: \" + str(float(\"%0.2f\"%metrics.homogeneity_score(labelsTrue,bestLabelsPredInAllGenerations[bestChromosomeInAllGenerations]))))\n",
        "print(\"CS: \" + str(float(\"%0.2f\"%metrics.completeness_score(labelsTrue,bestLabelsPredInAllGenerations[bestChromosomeInAllGenerations]))))\n",
        "print(\"VM: \" + str(float(\"%0.2f\"%metrics.v_measure_score(labelsTrue,bestLabelsPredInAllGenerations[bestChromosomeInAllGenerations]))))\n",
        "print(\"AMI: \" + str(float(\"%0.2f\"%metrics.adjusted_mutual_info_score(labelsTrue,bestLabelsPredInAllGenerations[bestChromosomeInAllGenerations]))))\n",
        "print(\"ARI: \" + str(float(\"%0.2f\"%metrics.adjusted_rand_score(labelsTrue,bestLabelsPredInAllGenerations[bestChromosomeInAllGenerations]))))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "HS: 0.85\n",
            "CS: 0.81\n",
            "VM: 0.83\n",
            "AMI: 0.83\n",
            "ARI: 0.66\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJjcfIXRn1hW",
        "outputId": "db6b28bd-d875-40d4-c650-5d8e417bfaa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "# plot fitness progression\n",
        "allGenerations = [x+1 for x in range(nGenerations)]\n",
        "plt.plot(allGenerations, allBestFitness)\n",
        "plt.title(filename[:-4])\n",
        "plt.xlabel('Generations')\n",
        "plt.ylabel('Fitness')\n",
        "plt.grid()\n",
        "plt.show()\n",
        "plt.clf()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRUYaf0h-Fh2"
      },
      "source": [
        "<h2>Citing EvoNP</h2>\n",
        "Qaddoura, R., Faris, H. & Aljarah, I. An efficient evolutionary algorithm with a nearest neighbor search technique for clustering analysis. J Ambient Intell Human Comput (2020). "
      ]
    }
  ]
}