dbscan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's setup some imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from dataviz import generate_clusters\n",
    "from dataviz import plot_clusters\n",
    "from dataviz import plot_data\n",
    "from dbscan import DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 20 randomly generated points in the range [1, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2216299e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_data(num_clusters: int, seed=None) -> np.ndarray:\n",
    "    num_points = 20\n",
    "    spread = 7\n",
    "    bounds = (1, 100)\n",
    "    clusters = generate_clusters(num_clusters,\n",
    "                                 num_points,\n",
    "                                 spread,\n",
    "                                 bounds,\n",
    "                                 bounds,\n",
    "                                 seed)\n",
    "    return np.array(clusters)\n",
    "\n",
    "num_clusters = 4\n",
    "clusters = generate_data(num_clusters, seed=1)\n",
    "plot_data(clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's choose `min_samples` to be 5 because there are **5** points per cluster,\n",
    "\n",
    "The largest distance from a point to the medoid of any cluster appears to be around **6**.\n",
    "\n",
    "Let's use that as `eps`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd2216615c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dbscan = DBSCAN(eps=6, min_samples=5)\n",
    "dbscan.fit(clusters)\n",
    "plot_clusters(clusters, dbscan.labels_, dbscan.components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Red crosses denote **core points**\n",
    "* Small black dots denote **noise points**\n",
    "\n",
    "The middle cluster was classified as noise.\n",
    "\n",
    "Let's increase `eps` from **6** to **7** to try and capture that as a cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd21dd550b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dbscan = DBSCAN(eps=7, min_samples=5)\n",
    "dbscan.fit(clusters)\n",
    "plot_clusters(clusters, dbscan.labels_, dbscan.components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 10 randomly generated points each for ranges [1, 30] and [70, 99]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_data(bounds, seed=None) -> np.ndarray:\n",
    "    num_clusters = 1\n",
    "    num_points = 10\n",
    "    spread = 7\n",
    "    clusters = generate_clusters(num_clusters,\n",
    "                                 num_points,\n",
    "                                 spread,\n",
    "                                 bounds,\n",
    "                                 bounds,\n",
    "                                 seed)\n",
    "    return np.array(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd21dc8ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_cluster = generate_data((1, 30), seed=1)\n",
    "second_cluster = generate_data((70, 99), seed=1)\n",
    "clusters = np.append(first_cluster, second_cluster, axis=0)\n",
    "plot_data(clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now each cluster has **10** points. Let's update `min_samples` to 10 and keep `eps` the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/fA0/ddG5LAKG/m47L255i3nzZzhjQtLUGwPux0V0Zq9/jLPu+yJvXft7fGPw02w46zJO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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd21dbe3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dbscan = DBSCAN(eps=7, min_samples=10)\n",
    "dbscan.fit(clusters)\n",
    "plot_clusters(clusters, dbscan.labels_, dbscan.components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 10 randomly generated points in [1, 30]\n",
    "* 50 randomly generated points in [70, 99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_data(num_points, bounds, seed=None) -> np.ndarray:\n",
    "    num_clusters = 1\n",
    "    spread = 7\n",
    "    clusters = generate_clusters(num_clusters,\n",
    "                                 num_points,\n",
    "                                 spread,\n",
    "                                 bounds,\n",
    "                                 bounds,\n",
    "                                 seed)\n",
    "    return np.array(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zwC+yGrYAXwA+K2kvcBHwcN61mZnlSRGRuoZz1tfXF/39/anLMDObSXM5yGfCmZkl4gA2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\nzBJxAJuZJfL/AV6QvSuj6yoqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd21dc2c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_cluster = generate_data(10, (1, 30), seed=1)\n",
    "second_cluster = generate_data(50, (70, 99), seed=1)\n",
    "clusters = np.append(first_cluster, second_cluster, axis=0)\n",
    "plot_data(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SSitTSpuBe4ExQJfSlARAX2BJhtokqWxyBPALwHERURURAYwF5gIPA+eU7jMBuD9DbZJU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\nkjIxgCUpEwNYkjL5P+h3h7bKfsoCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd21dc5c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dbscan = DBSCAN(eps=7, min_samples=10)\n",
    "dbscan.fit(clusters)\n",
    "plot_clusters(clusters, dbscan.labels_, dbscan.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters: 2\n"
     ]
    }
   ],
   "source": [
    "num_clusters = len(np.unique(dbscan.labels_))\n",
    "print('Number of clusters: {}'.format(num_clusters))"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}