om-henners/minimum spanning tree.ipynb

## minimum spanning tree.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:b6fdb0cd57fdaca572372fa1c9596092fe5f3a6d4ba9019de39ec3795dc7d2eb"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Minimum spanning tree of random points\n",
      "\n",
      "Given points calculate the distance between them using delaunay to limit the number of connections. Then get the minimum spanning tree."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy.spatial\n",
      "from matplotlib import pyplot as plt\n",
      "import networkx as nx\n",
      "import itertools"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pts = np.random.randint(0, 100, (15, 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(pts[:, 0], pts[:, 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.collections.PathCollection at 0x8967ef0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x8913cc0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "delaunay = scipy.spatial.Delaunay(pts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = nx.Graph()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for tri in delaunay.simplices:\n",
      "    #print list(itertools.combinations(tri, 2))\n",
      "    for edge in itertools.combinations(tri, 2):\n",
      "        dist= scipy.spatial.distance.euclidean(pts[edge[0]], pts[edge[1]])\n",
      "        g.add_edge(edge[0], edge[1], dist=dist)\n",
      "len(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "15"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pos = {i:pts[i] for i in g}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(g, pos=pos)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/Z/Xq1Tod99atW3h7e7NhwwZq1Kih07Fzq3Llyvj5+TFq1Ch27dqldDh5wqxZ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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x894d5f8>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g2 = nx.minimum_spanning_tree(g, weight=\"dist\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(g2, pos=pos)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2LKs2bSIkJMSsMiUHUY9XxANVqVKFe+65R6ObLRYdHc19zZox4NQp3r1G6AIE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8972e48>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now some playin with a genetic algorithm to build calculate the TSP using the original graph"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from deap import base, creator, tools, algorithms\n",
      "import random"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "creator.create(\"Individual\", list, fitness=creator.FitnessMin)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "toolbox = base.Toolbox()\n",
      "toolbox.register(\"indices\", random.sample, g.nodes(), len(g))\n",
      "toolbox.register(\"individual\", tools.initIterate, creator.Individual, toolbox.indices)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "toolbox.individual()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[4, 10, 1, 5, 12, 3, 8, 9, 0, 7, 13, 2, 6, 14, 11]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "toolbox.population(n=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "[[14, 0, 8, 6, 11, 5, 2, 1, 3, 4, 9, 7, 12, 10, 13],\n",
        " [2, 11, 9, 0, 3, 4, 13, 6, 1, 10, 14, 7, 12, 5, 8],\n",
        " [5, 1, 14, 6, 7, 9, 4, 11, 8, 10, 3, 13, 0, 2, 12],\n",
        " [2, 6, 14, 7, 9, 8, 13, 5, 4, 11, 10, 0, 12, 3, 1],\n",
        " [0, 5, 4, 10, 6, 8, 12, 9, 3, 2, 1, 13, 14, 11, 7]]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def evaluate(individual):\n",
      "    \n",
      "    return (sum([\n",
      "        nx.shortest_path_length(g, a, b, weight=\"dist\")\n",
      "        for a, b in itertools.izip(individual, individual[1:])# + individual[:1])\n",
      "    ]), )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "toolbox.register(\"mate\", tools.cxPartialyMatched)\n",
      "toolbox.register(\"mutate\", tools.mutShuffleIndexes, indpb=0.1)\n",
      "toolbox.register(\"select\", tools.selTournament, tournsize=3)\n",
      "toolbox.register(\"evaluate\", evaluate)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pop = toolbox.population(n=100)\n",
      "CXPB = 0.1\n",
      "MUTPB = 0.1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hall_of_fame = tools.HallOfFame(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats = tools.Statistics(key=lambda ind: ind.fitness.values)\n",
      "stats.register(\"avg\", np.mean)\n",
      "stats.register(\"std\", np.std)\n",
      "stats.register(\"min\", np.min)\n",
      "stats.register(\"max\", np.max)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=100, stats=stats, verbose=True)\n",
      "pop, logbook = algorithms.eaMuPlusLambda(\n",
      "    pop, toolbox, 20, 30, cxpb=0.5, mutpb=0.2, ngen=1000, stats=stats, verbose=False, halloffame=hall_of_fame)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gen, fit_mins, size_avgs = logbook.select(\"gen\", \"min\", \"avg\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax1 = plt.subplots()\n",
      "line1 = ax1.plot(gen, fit_mins, \"b-\", label=\"Minimum Fitness\")\n",
      "ax1.set_xlabel(\"Generation\")\n",
      "ax1.set_ylabel(\"Fitness\", color=\"b\")\n",
      "for tl in ax1.get_yticklabels():\n",
      "    tl.set_color(\"b\")\n",
      "\n",
      "ax2 = ax1.twinx()\n",
      "line2 = ax2.plot(gen, size_avgs, \"r-\", label=\"Average Size\")\n",
      "ax2.set_ylabel(\"Size\", color=\"r\")\n",
      "for tl in ax2.get_yticklabels():\n",
      "    tl.set_color(\"r\")\n",
      "\n",
      "lns = line1 + line2\n",
      "labs = [l.get_label() for l in lns]\n",
      "ax1.legend(lns, labs, loc=\"center right\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "<matplotlib.legend.Legend at 0xa35ad30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jRo3w559/4uDBgzh48CD+/PNP9O3bF6tWrQIAjB07FmvWrMHHH3+MsWPHGt47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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa35a780>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "best_path = hall_of_fame.items[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g3 = nx.Graph()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for a, b in zip(best_path, best_path[1:]): # + best_path[:1]):\n",
      "    g3.add_edge(a, b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(g3, pos=pos)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bsiM9neyG0cUAvwJJQBoQABwE2mVpF6DV0tnvacOzRGG2Z88e7ty5w+DBg9Uu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\nBzYmJtKte3cu3bwpq9LlczK4qoCKiYm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       "text": [
        "<matplotlib.figure.Figure at 0x8d0c278>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}