demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from KMedoids import KMedoids\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_graphs(data, k_medoids):\n",
    "    colors = {0:'b*', 1:'g^',2:'ro',3:'c*', 4:'m^', 5:'yo', 6:'ko', 7:'w*'}\n",
    "    index = 0\n",
    "    for key in k_medoids.clusters.keys():\n",
    "        temp_data = k_medoids.clusters[key]\n",
    "        x = [data[i][0] for i in temp_data]\n",
    "        y = [data[i][1] for i in temp_data]\n",
    "        plt.plot(x, y, colors[index])\n",
    "        index += 1\n",
    "    plt.title('Cluster formations')\n",
    "    plt.show()\n",
    "\n",
    "    medoid_data_points = []\n",
    "    for m in k_medoids.medoids:\n",
    "        medoid_data_points.append(data[m])   \n",
    "    x = [i[0] for i in data]\n",
    "    y = [i[1] for i in data]\n",
    "    x_ = [i[0] for i in medoid_data_points]\n",
    "    y_ = [i[1] for i in medoid_data_points]\n",
    "    plt.plot(x, y, 'yo')\n",
    "    plt.plot(x_, y_, 'r*')\n",
    "    plt.title('Mediods are highlighted in red')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NdUfPRgr28+1XPR6rPelZJ2xHemwq0da0DGXlen47Niw4SHVjI/5nkXFC1SjV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\nSVJN/wXNeim4qamhxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11099f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_ = [\n",
    "    [5.2, 3.7],[5.1,4.1], [5.2, 4.2], [5, 3.7], [5.3, 4], [5.5, 3.8], [5,4], [4.7, 3.7], [4.8, 4], [4.7, 3.3], [5.2, 3.3],[4.9,3.4], [5.3,3.5]\n",
    "    ,[2.6, 1.5],[2.5, 1.2],[2.7, 1],[2.4,1.7],[2.6, 1.5],[2.7, 1.8], [2.1, 1.1], [2.2, 1.4], [2.3,0.8], [2.3, 1.3], [2.5,2]\n",
    "    , [3, 3.5], [3.5, 3.8], [3.9, 3.2], [4.5, 1.9], [4.3, 1.4], [4, 2]\n",
    "]\n",
    "n_clusters = range(2,8)\n",
    "k_medoids = [KMedoids(n_cluster=i) for i in n_clusters]\n",
    "k_medoids = [k_medoid.fit(data_) for k_medoid in k_medoids]\n",
    "loss = [k_medoid.calculate_distance_of_clusters() for k_medoid in k_medoids]\n",
    "\n",
    "# Plot elbow curve (to know best cluster count)\n",
    "plt.figure(figsize=(13,8))\n",
    "plt.plot(n_clusters,loss)\n",
    "plt.xticks(n_clusters)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss Vs No. Of clusters')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    "1. As number of clusters increases, the loss increases & then it starts decreasing. This curve differs from data set to data set\n",
    "2. For number of clusters = 2, the loss is minimum\n",
    "3. Lets take clusters = 2 as the best number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffa8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110544438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_medoids = KMedoids(n_cluster=2)\n",
    "k_medoids.fit(data_)\n",
    "plot_graphs(data_, k_medoids);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BLUmNMbglqTEGtyQ1xuCWpMYY3JLUGINbkhpjcEtSYwxuSWqMwS1JjTG4JakxBrckNcbg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\nYwxuSWqMwS1JjTG4JakxBrckNeb/A0WbZFaez7xgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1107a4c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110877e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://en.wikipedia.org/wiki/K-medoids\n",
    "data = [[2, 6], [3, 4], [3, 8], [4, 7], [6, 2], [6, 4], [7, 3], [7, 4], [8, 5], [7, 6]]\n",
    "k_medoids = KMedoids(n_cluster=2)\n",
    "k_medoids.fit(data)\n",
    "plot_graphs(data, k_medoids)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}