yogabonito/azp-sa.ipynb

## azp-sa.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AZP-SA demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stopping condition of the simulated annealing variant of the AZP as described on pp. 430-431 in Openshaw & Rao (1995) has an issue. If there are equally good clusterings and each AZP-step (step b in the description) jumps from one to another, then there are no nonmoving steps which are required for the algorithm to terminate. That's why the implementation in [commit ee3f08f44](https://github.com/yogabonito/region/commit/ee3f08f441f7367204fc2d1d391e7e3bedacdc80) doesn't terminate in [example 3](#Example-3:-A-tricky-toy-example) below.\n",
    "\n",
    "In [commit 9502645](https://github.com/yogabonito/region/commit/95026456a70b3cfb7e4454d808c087afde314685) we have added an additional termination condition which allows the `fit` method to return even in [example 3](#Example-3:-A-tricky-toy-example) below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from region.p_regions.azp import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from libpysal import api as ps_api\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convert_from_geodataframe(gdf):\n",
    "    w = ps_api.Rook.from_dataframe(gdf)\n",
    "    graph = w.to_networkx()\n",
    "    adj = nx.to_scipy_sparse_matrix(graph)\n",
    "    neighbor_dict = w.neighbors\n",
    "    return adj, graph, neighbor_dict, w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_results(azp, gdf, invert_y_axis=True):\n",
    "    gdf[\"region\"] = azp.labels_\n",
    "    gdf.plot(column=\"region\", cmap='tab20c')\n",
    "    if invert_y_axis:\n",
    "        plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 1: 3x3 lattice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values = [726.7, 623.6, 487.3,\n",
    "          200.4, 245.0, 481.0,\n",
    "          170.9, 225.9, 226.9]\n",
    "value_arr = np.array(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71e8902b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geometry = [\n",
    "    Polygon([(x, y),\n",
    "             (x, y+1),\n",
    "             (x+1, y+1),\n",
    "             (x+1, y)]) for y in range(3) for x in range(3)\n",
    "]\n",
    "\n",
    "areas_gdf = gpd.GeoDataFrame(\n",
    "    {\"values\": values},\n",
    "    geometry=geometry)\n",
    "areas_gdf.plot(column=\"values\")\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adj, graph, neighbor_dict, w = convert_from_geodataframe(areas_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "value_dict = {area: val for area, val in zip(neighbor_dict.keys(), values)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71e864438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpsa = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2,\n",
    "                              random_state=0)\n",
    "azpsa.fit_from_dict(neighbor_dict=neighbor_dict,\n",
    "                            n_regions=2,\n",
    "                            data=value_dict,\n",
    "                            cooling_factor=0.85)\n",
    "draw_results(azpsa.azp, areas_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from GeoDataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &nbsp;&nbsp;&nbsp;&nbsp;without initial solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71aed1cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpsa = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2,\n",
    "                              random_state=0)\n",
    "azpsa.fit_from_geodataframe(gdf=areas_gdf,\n",
    "                            n_regions=2, \n",
    "                            data=[\"values\"],\n",
    "                            contiguity=\"rook\",\n",
    "                            cooling_factor=0.85)\n",
    "draw_results(azpsa.azp, areas_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from networkx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71aef3978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpsa = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2,\n",
    "                              random_state=0)\n",
    "azpsa.fit_from_networkx(graph=graph,\n",
    "                        n_regions=2,\n",
    "                        data=value_arr)\n",
    "draw_results(azpsa.azp, areas_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from scipy sparse matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71ada1eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpsa = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2,\n",
    "                              random_state=0)\n",
    "azpsa.fit_from_networkx(graph=graph,\n",
    "                 n_regions=2,\n",
    "                 data=value_arr,\n",
    "                 cooling_factor=0.85)\n",
    "draw_results(azpsa.azp, areas_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from W object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71acad898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpsa = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2,\n",
    "                              random_state=0)\n",
    "azpsa.fit_from_w(w=w,\n",
    "                 n_regions=2,\n",
    "                 data=value_arr,\n",
    "                 cooling_factor=0.85)\n",
    "draw_results(azpsa.azp, areas_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 2: Islands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "islands_geometry = [\n",
    "    Polygon([(x, y),\n",
    "             (x, y+1),\n",
    "             (x+1, y+1),\n",
    "             (x+1, y)]) for y in range(3) for x in range(3)\n",
    "] + [\n",
    "    Polygon([(x, y),\n",
    "             (x, y+1),\n",
    "             (x+1, y+1),\n",
    "             (x+1, y)]) for y in range(4,6) for x in range(5,8)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "islands_gdf = gpd.GeoDataFrame(\n",
    "    {\"values\": [726.7, 623.6, 487.3,\n",
    "                200.4, 245.0, 481.0,\n",
    "                170.9, 225.9, 226.9] +\n",
    "               [100,   120,   190,\n",
    "                175,   185,   210]},\n",
    "    geometry=islands_geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUEAAAD8CAYAAADpLRYuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71ad44e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "islands_gdf.plot(column=\"values\")\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adj, graph, neighbor_dict, w = convert_from_geodataframe(areas_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering: AZP-SA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### &nbsp;&nbsp;&nbsp;&nbsp;without initial solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUEAAAD8CAYAAADpLRYuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngpKaZgQlNc0ISmqaEZTUNCMoqWn/CybEOmJuk9kXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71af25940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpsa = AZPSimulatedAnnealing(init_temperature=1, max_iterations=2,\n",
    "                              random_state=1)\n",
    "azpsa.fit_from_geodataframe(gdf=islands_gdf,\n",
    "                            n_regions=4, \n",
    "                            data=[\"values\"],\n",
    "                            contiguity=\"rook\",\n",
    "                            cooling_factor=0.85)\n",
    "draw_results(azpsa.azp, islands_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 3: A tricky toy example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe71e7a9908>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAACSCAYAAACpHBqyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGKteEXakAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71aead7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "squares = [\n",
    "    Polygon([(x,   0),\n",
    "             (x,   1),\n",
    "             (x+1, 1),\n",
    "             (x+1, 0)]) for x in range(3)\n",
    "]\n",
    "values = [0, 1, 0]\n",
    "squares_gdf = gpd.GeoDataFrame({\"values\": values},\n",
    "                           geometry=squares)\n",
    "squares_gdf.plot(column=\"values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adj, graph, neighbor_dict, w = convert_from_geodataframe(areas_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe71accb780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpsa = AZPSimulatedAnnealing(init_temperature=1,\n",
    "                              max_iterations=3, random_state=0,\n",
    "                              repetitions_before_termination=2)\n",
    "azpsa.fit_from_geodataframe(gdf=squares_gdf,\n",
    "                            n_regions=2,\n",
    "                            data=[\"values\"],\n",
    "                            contiguity=\"rook\",\n",
    "                            cooling_factor=0.85)\n",
    "draw_results(azpsa.azp, squares_gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}