ClusterTest DBSCAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy \n",
    "\n",
    "def bounding_box(iterable):\n",
    "    min_x, min_y = np.min(iterable[0], axis=0)\n",
    "    max_x, max_y = np.max(iterable[0], axis=0)\n",
    "    return np.array([(min_x, min_y), (max_x, min_y), (max_x, max_y), (min_x, max_y)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def old_bounding_box(coords): # not using numpy\n",
    "\n",
    "  min_x = 100000 # start with something much higher than expected min\n",
    "  min_y = 100000\n",
    "  max_x = -100000 # start with something much lower than expected max\n",
    "  max_y = -100000\n",
    "\n",
    "  for item in coords:\n",
    "    if item[0] < min_x:\n",
    "      min_x = item[0]\n",
    "\n",
    "    if item[0] > max_x:\n",
    "      max_x = item[0]\n",
    "\n",
    "    if item[1] < min_y:\n",
    "      min_y = item[1]\n",
    "\n",
    "    if item[1] > max_y:\n",
    "      max_y = item[1]\n",
    "\n",
    "  return [(min_x,min_y),(max_x,min_y),(max_x,max_y),(min_x,max_y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n",
      "0.17.1\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "import sklearn\n",
    "print sklearn.__version__\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#import ddbscan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X: ', array([[-75.16099308,  -7.66153964],\n",
      "       [-75.12323135,  -7.65167303],\n",
      "       [-75.09714143,  -7.66017874],\n",
      "       ..., \n",
      "       [-75.34499567,  -7.82617593],\n",
      "       [-75.35735406,  -7.82651602],\n",
      "       [-75.33847319,  -7.83263761]]))\n"
     ]
    }
   ],
   "source": [
    "##############################################################################\n",
    "# Generate numpy array from GLAD alert DATA \n",
    "\n",
    "from  glad_alerts import alerts\n",
    "a = alerts[\"rows\"]\n",
    "\n",
    "x = []\n",
    "for p in a:\n",
    "    x.append([p[\"long\"], p[\"lat\"]])\n",
    "\n",
    "X1 = np.array(x)\n",
    "#X = StandardScaler().fit_transform(X)\n",
    "print(\"X: \", X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading CSV File\n",
      "('Read CSV File to X:', array([[  0.828125,  15.433125],\n",
      "       [  0.828125,  15.433375],\n",
      "       [  0.828125,  15.433625],\n",
      "       ..., \n",
      "       [  0.670125,  15.351625],\n",
      "       [  0.670125,  15.351875],\n",
      "       [  0.669625,  15.351625]]))\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Read CSV from File \n",
    "=LEFT($B2,FIND(\" \", $B2))\n",
    "=RIGHT($B2,LEN($B2)-FIND(\" \", $B2))\n",
    "'''\n",
    "from numpy import genfromtxt\n",
    "#def readCSVfile(filename):\n",
    "filename = 'testclusterdata/DRC Control 1- 2016-05-01_Conv.csv'\n",
    "print \"Reading CSV File\"\n",
    "X = genfromtxt(filename, \n",
    "               delimiter=',', skip_header=1)\n",
    "\n",
    "   \n",
    "print(\"Read CSV File to X:\", X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started...\n",
      "Estimated number of clusters: 7\n",
      "('unclustered', set([0, 1, 2, 3, 4, 5, 6]))\n",
      "Silhouette Coefficient: 0.701\n"
     ]
    }
   ],
   "source": [
    "##############################################################################\n",
    "# Compute DBSCAN\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "#from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "print (\"Started...\")\n",
    "db = DBSCAN(eps=0.012, min_samples=2).fit(X)\n",
    "core_samples = db.core_sample_indices_\n",
    "labels = db.labels_\n",
    "\n",
    "# Number of clusters in labels, ignoring noise if present.\n",
    "unique_clusters = set(labels)\n",
    "\n",
    "n_clusters_ = len(unique_clusters) - (1 if -1 in labels else 0)\n",
    "print('Estimated number of clusters: %d' %n_clusters_)\n",
    "\n",
    "print (\"unclustered\",  unique_clusters)\n",
    "if -1 in labels:\n",
    "    #rint (unique_clusters[-1] )\n",
    "    print (\"unclustered points: %d.\" %labels[-1])\n",
    "\n",
    "print(\"Silhouette Coefficient: %0.3f\"\n",
    "      % metrics.silhouette_score(X, labels))\n",
    "\n",
    "###############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "##############################################################################\n",
    "# Plot result\n",
    "from scipy.spatial import ConvexHull\n",
    "import pylab as pl\n",
    "#for p in a:\n",
    "#    pl.plot(p[\"long\"], p[\"lat\"])\n",
    "\n",
    "print \"Plotting result\"\n",
    "# Black removed and is used for noise instead.\n",
    "unique_labels = set(labels)\n",
    "colors = pl.cm.Spectral(numpy.linspace(0, 1, len(unique_labels)))\n",
    "testing = zip(unique_labels, colors)\n",
    "\n",
    "#print (\"zip(unique_labels, colors)\", testing)\n",
    "\n",
    "\n",
    "for k, col in zip(unique_labels, colors):\n",
    "    if k == -1:\n",
    "        # Black used for noise.\n",
    "        col = 'k'\n",
    "        markersize = 1\n",
    "    class_members = [index[0] for index in np.argwhere(labels == k)]\n",
    "    cluster_core_samples = [index for index in core_samples\n",
    "                            if labels[index] == k]\n",
    "    for index in class_members:\n",
    "        x = X[index]\n",
    "        \n",
    "        if index in core_samples and k != -1:\n",
    "            markersize = 4\n",
    "        else:\n",
    "            markersize = 2\n",
    "        pl.plot(x[1], x[0], 'o', markerfacecolor=col,\n",
    "                markeredgecolor='k', markersize=markersize)\n",
    "       \n",
    "msg = '%s : clusters: %d' % (filename, n_clusters_)\n",
    "print(msg)\n",
    "pl.title(msg)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xrange(31)\n"
     ]
    }
   ],
   "source": [
    "print xrange(n_clusters_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1 -1  0 ..., 30 27 28]\n"
     ]
    }
   ],
   "source": [
    "print db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, -1])\n"
     ]
    }
   ],
   "source": [
    "print unique_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-75.16099308  -7.66153964]\n",
      " [-75.12323135  -7.65167303]\n",
      " [-75.09714143  -7.66017874]\n",
      " ..., \n",
      " [-75.34499567  -7.82617593]\n",
      " [-75.35735406  -7.82651602]\n",
      " [-75.33847319  -7.83263761]]\n"
     ]
    }
   ],
   "source": [
    "print X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "cluster\n",
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      "length: 7\n",
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      "length: 6\n",
      "cluster\n",
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      "length: 4\n",
      "cluster\n",
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      "length: 3\n"
     ]
    }
   ],
   "source": [
    "clusters = [X[labels == i] for i in xrange(n_clusters_)]\n",
    "for i in xrange(n_clusters_):\n",
    "    cluster = X[labels == i]\n",
    "    print i\n",
    "    #print bounding_box(cluster) \n",
    "for c in clusters:\n",
    "    print 'cluster'\n",
    "    for p in c:\n",
    "        print p[0], p[1]\n",
    "\n",
    "    print \"length:\", len(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\bunjilsrc\\code\\bunjilforestwatch\\lib\\matplotlib\\collections.py:571: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    }
   ],
   "source": [
    "#POLYGON EXAMPLE\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.patches import Polygon\n",
    "from matplotlib.collections import PatchCollection\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "patches = []\n",
    "N = 5\n",
    "\n",
    "for i in range(N):\n",
    "    polygon = Polygon(np.random.rand(N,2), True)\n",
    "    patches.append(polygon)\n",
    "\n",
    "p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.4)\n",
    "\n",
    "colors = 100*np.random.rand(len(patches))\n",
    "p.set_array(np.array(colors))\n",
    "\n",
    "ax.add_collection(p)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting boxes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:11: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 252 but corresponding boolean dimension is 4894\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 2648 is out of bounds for axis 0 with size 252",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-55-72a5981e614f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_clusters_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mcluster\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     \u001b[0mpolygon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPolygon\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mold_bounding_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcluster\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mpatches\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpolygon\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 2648 is out of bounds for axis 0 with size 252"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "#PLOT BOUNDING BOXES\n",
    "print \"Plotting boxes\"\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.patches import Polygon\n",
    "from matplotlib.collections import PatchCollection\n",
    "fig, ax = plt.subplots()\n",
    "patches = []\n",
    "\n",
    "for i in xrange(n_clusters_):\n",
    "    cluster = X[labels == i]\n",
    "    polygon = Polygon(old_bounding_box(cluster), True)\n",
    "    patches.append(polygon)\n",
    "    #print polygon\n",
    "    #pl.plot(bb[0],bb[1], 'o')\n",
    "\n",
    "p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.4)\n",
    "colors = 100*np.random.rand(len(patches))\n",
    "p.set_array(np.array(colors))\n",
    "\n",
    "#ax.add_collection(p)\n",
    "#ax.set_autoscale_on(False)\n",
    "plt.axis([-75.5, -75,-8,-7.5])\n",
    "print plt.xlim()\n",
    "msg = 'BOXES : %d' % n_clusters_\n",
    "pl.title(msg)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting both results\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\ipykernel\\__main__.py:28: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 252 but corresponding boolean dimension is 4894\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 2648 is out of bounds for axis 0 with size 252",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-59-f9925583cb0e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     26\u001b[0m                             if labels[index] == k]\n\u001b[0;32m     27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m     \u001b[0mcluster\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m     \u001b[0mpolygon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPolygon\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mold_bounding_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcluster\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 2648 is out of bounds for axis 0 with size 252"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "#PLOT BOTH\n",
    "##############################################################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.patches import Polygon\n",
    "from matplotlib.collections import PatchCollection\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "patches = []\n",
    "\n",
    "print \"Plotting both results\"\n",
    "\n",
    "# Black removed and is used for noise instead.\n",
    "unique_labels = set(labels)\n",
    "colors = pl.cm.Spectral(np.linspace(0, 1, len(unique_labels)))\n",
    "testing = zip(unique_labels, colors)\n",
    "\n",
    "for k, col in zip(unique_labels, colors):\n",
    "    if k == -1:\n",
    "        # Black used for noise.\n",
    "        col = 'k'\n",
    "        markersize = 1\n",
    "    class_members = [index[0] for index in np.argwhere(labels == k)]\n",
    "    cluster_core_samples = [index for index in core_samples\n",
    "                            if labels[index] == k]\n",
    "\n",
    "    cluster = X[labels == k]\n",
    "    polygon = Polygon(old_bounding_box(cluster), True)\n",
    "    \n",
    "    #patch = patches.PathPatch(polygon, facecolor='orange', lw=2)\n",
    "    patches.append(polygon)\n",
    "    for index in class_members:\n",
    "        x = X[index]\n",
    "        if index in core_samples and k != -1:\n",
    "            markersize = 4\n",
    "        else:\n",
    "            markersize = 2\n",
    "        pl.plot(x[0], x[1], 'o', markerfacecolor=col,\n",
    "                markeredgecolor='k', markersize=markersize)\n",
    "\n",
    "p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.01)\n",
    "colors = 100*np.random.rand(len(patches))\n",
    "p.set_array(np.array(colors))\n",
    "ax.add_collection(p)\n",
    "#ax.set_autoscale_on(False)\n",
    "#plt.axis([-75.5, -75,-8,-7.5])\n",
    "#print plt.xlim()\n",
    "msg = 'Estimated number of clusters: %d' % n_clusters_\n",
    "print(msg)\n",
    "pl.title(msg)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# ANOTHER SCAN MODEL\n",
    "import ddbscan\n",
    "\n",
    "# Create a DDBSCAN model with eps = 2 and min_pts = 5\n",
    "scan = ddbscan.DDBSCAN(0.008, 3)\n",
    "\n",
    "# Add points to model\n",
    "#data = [[1,  2], [2,  2], [1,  3], [2, 3], [3, 3], [8, 9],\n",
    "#        [7,  6], [9,  7], [6, 9], [6, 8], [5, 5], [7, 8]]\n",
    "\n",
    "#for point in data:\n",
    "#    scan.add_point(point=point, count=1, desc=\"\")\n",
    "\n",
    "from  glad_alerts import alerts\n",
    "a = alerts[\"rows\"]    \n",
    "x = []\n",
    "for p in a:\n",
    "    point = [p[\"long\"], p[\"lat\"]]\n",
    "    x.append([p[\"long\"], p[\"lat\"]])\n",
    "    scan.add_point(point=point, count=1, desc=\"\")\n",
    "\n",
    "# Compute clusters\n",
    "scan.compute()\n",
    "print \"Number of clusters %d\", len( scan.clusters)\n",
    "\n",
    "print 'Clusters found and its members points index:'\n",
    "cluster_number = 0\n",
    "for cluster in scan.clusters:\n",
    "    print '=== Cluster %d ===' % cluster_number\n",
    "    #print 'Cluster points index: %s' % list(cluster)\n",
    "    print 'Cluster size : %d' % len(cluster)\n",
    "    \n",
    "    cluster_number += 1\n",
    "\n",
    "print '\\nCluster assigned to each point:'\n",
    "for i in xrange(len(scan.points)):\n",
    "    print '=== Point: %s ===' % scan.points[i]\n",
    "    print 'Cluster: %2d' % scan.points_data[i].cluster,\n",
    "    # If a point cluster is -1, it's an anomaly\n",
    "    if scan.points_data[i].cluster == -1:\n",
    "        print '\\t <== Anomaly found!'\n",
    "    else:\n",
    "        print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
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