a short demonstration of the Python-3-port of clusterpy

demo_clusterpy_with_Python3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# clusterpy with Python 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ClusterPy: Library of spatially constrained clustering algorithms\n",
      "Some functions are not available, reason: No module named 'Polygon'\n",
      "Some functions are not available, reason: No module named 'Polygon'\n"
     ]
    }
   ],
   "source": [
    "import clusterpy as cp\n",
    "import libpysal as ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer = cp.importArcData(ps.examples.get_path(\"columbus\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting variables\n",
      "Variables successfully extracted\n",
      "Running original Arisel algorithm\n",
      "Number of areas:  49\n",
      "Number of regions:  4\n",
      "INITIAL SOLUTION:  [1, 1, 1, 1, 1, 2, 3, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 0, 1, 2, 0, 2, 2, 0, 1, 1, 1, 1, 1, 1, 0, 2, 1, 0, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 1, 0, 2, 1, 1] \n",
      "INITIAL OF:  7908.486978277309\n",
      "FINAL SOLUTION:  [1, 1, 1, 1, 1, 2, 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 0, 2, 1, 0, 1, 0, 1, 1, 0, 2, 2, 0, 1, 1, 1, 0, 2, 1, 1] \n",
      "FINAL OF:  6612.971225320095\n",
      "Adding variables\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "layer.cluster('arisel', ['CRIME'], 4, wType='queen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting variables\n",
      "Variables successfully extracted\n",
      "Running original AZP algorithm (Openshaw and Rao, 1995)\n",
      "Number of areas:  49\n",
      "Number of regions:  4\n",
      "Constructing regions\n",
      "initial Solution:  [2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 0, 0, 2, 2, 2, 3, 1, 2, 1, 2, 2, 0, 2, 3, 3, 2, 1, 1, 1, 2, 3, 1, 1]\n",
      "initial O.F:  10495.717665446204\n",
      "Performing local search\n",
      "FINAL SOLUTION:  [2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 0, 0, 0, 2, 2, 3, 1, 2, 1, 2, 2, 0, 2, 3, 3, 2, 2, 1, 1, 2, 3, 1, 1]\n",
      "FINAL O.F.:  9967.595996594946\n",
      "Done\n",
      "Adding variables\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "layer.cluster('azp', ['CRIME'], 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting variables\n",
      "Variables successfully extracted\n",
      "Running max-p-regions model (Duque, Anselin and Rey, 2010)\n",
      "Local search method: Tabu Search\n",
      "Number of areas:  49\n",
      "threshold value:  100\n",
      "FINAL SOLUTION:  [0, 0, 0, 0, 0, 6, 3, 0, 6, 6, 4, 4, 3, 3, 10, 10, 2, 11, 3, 2, 7, 1, 2, 11, 11, 6, 1, 6, 11, 8, 7, 2, 1, 7, 9, 7, 8, 5, 7, 2, 2, 7, 5, 9, 9, 7, 2, 5, 9]\n",
      "FINAL OF:  0.0\n",
      "Done\n",
      "Adding variables\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "layer.cluster('maxpTabu', ['CRIME'], threshold=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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