yogabonito/azp_basic_tabu.ipynb

## azp_basic_tabu.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AZP-BasicTabu demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic tabu version of the AZP as described on p. 432 in Openshaw & Rao (1995) is lacking a stopping condition. In [commit 8e56e23](https://github.com/yogabonito/region/commit/8e56e23e2c669b2b676fd8db97044c63cdb7d84d) we have added a termination condition: If a clustering has been visited a certain number of times (defined by the `repetitions_before_termination` argument), a termination flag is set allowing only improving moves from that point on. When no improving move is left, the result is returned.\n",
    "\n",
    "With this modification we get the optimal solutions in all of our three examples 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": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4PSM+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffaefe626a0>"
      ]
     },
     "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": {},
   "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 0x7ffaefe476a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpbt = AZPBasicTabu(random_state=1,\n",
    "                     tabu_length=20,\n",
    "                     repetitions_before_termination=2)\n",
    "azpbt.fit_from_dict(neighbor_dict=neighbor_dict,\n",
    "                            n_regions=2,\n",
    "                            data=value_dict)\n",
    "draw_results(azpbt, 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 0x7ffaec49b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpbt = AZPBasicTabu(random_state=1,\n",
    "                     tabu_length=20,\n",
    "                     repetitions_before_termination=2)\n",
    "azpbt.fit_from_geodataframe(gdf=areas_gdf,\n",
    "                            n_regions=2,\n",
    "                            data=[\"values\"],\n",
    "                            contiguity=\"rook\")\n",
    "draw_results(azpbt, 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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\n68S9kQN3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffaec444c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpbt = AZPBasicTabu(random_state=1,\n",
    "                     tabu_length=20,\n",
    "                     repetitions_before_termination=2)\n",
    "azpbt.fit_from_networkx(graph=graph,\n",
    "                        n_regions=2,\n",
    "                        data=value_arr)\n",
    "draw_results(azpbt, 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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\n68S9kQN3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffaec3529b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpbt = AZPBasicTabu(random_state=1,\n",
    "                     tabu_length=20,\n",
    "                     repetitions_before_termination=2)\n",
    "azpbt.fit_from_scipy_sparse_matrix(adj=adj,\n",
    "                                   n_regions=2,\n",
    "                                   data=value_arr)\n",
    "draw_results(azpbt, 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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\n68S9kQN3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffaf09c35f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpbt = AZPBasicTabu(random_state=1,\n",
    "                     tabu_length=20,\n",
    "                     repetitions_before_termination=2)\n",
    "azpbt.fit_from_w(w=w,\n",
    "                 n_regions=2,\n",
    "                 data=value_arr)\n",
    "draw_results(azpbt, 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 0x7ffaec288e10>"
      ]
     },
     "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"
   ]
  },
  {
   "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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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffaefe62da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpbt = AZPBasicTabu(tabu_length=3, random_state=1,\n",
    "                     repetitions_before_termination=2)\n",
    "azpbt.fit_from_geodataframe(gdf=islands_gdf,\n",
    "                            n_regions=4,\n",
    "                            data=[\"values\"],\n",
    "                            contiguity=\"rook\",)\n",
    "draw_results(azpbt, 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 0x7ffaec29add8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAACSCAYAAACpHBqyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGKteEXakAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffaefe13438>"
      ]
     },
     "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": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAACSCAYAAACpHBqyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffaec3e49e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "azpbt = AZPBasicTabu(tabu_length=3, random_state=0,\n",
    "                     repetitions_before_termination=2)\n",
    "azpbt.fit_from_geodataframe(gdf=squares_gdf,\n",
    "                            n_regions=2,\n",
    "                            data=[\"values\"],\n",
    "                            contiguity=\"rook\")\n",
    "draw_results(azpbt, 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
}