notebook/call_cplex_parallel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/danqing0703/Google_Drive/research_2017Spring/code_review/CPS_TRC\n"
     ]
    }
   ],
   "source": [
    "cd ../"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from src.models.cplex_MPI_final import cplex_CPS\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# select 600 people for visualization\n",
    "X = np.loadtxt('notebook/data/x.csv')\n",
    "Y = np.loadtxt('notebook/data/y.csv')\n",
    "X = X[0:600]\n",
    "Y = Y[0:600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# filepath and filename shall be changed to run the code\n",
    "filepath = '/Users/danqing0703/Google_Drive/research_2017Spring/code_review/CPS_TRC/output/cplex'\n",
    "filename = 'cplex_par_run_1.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Part 1] community assignment for 600 people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = cplex_CPS(X, Y, filepath, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people for assignment\n",
      "Number of people for assignment\n",
      "300\n",
      "300\n",
      "Number of potential social center\n",
      "Number of potential social center\n",
      "300\n",
      "300\n",
      "total running time 318.106790066\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "t0 = time.time()\n",
    "A.run()\n",
    "t1 = time.time()\n",
    "print 'total running time',t1-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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NgRZppgoKCshxG3j/Rui1s7fshx/hqHth2foMysrDwQYoIiItSlrmQJvZdmbW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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138b8810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "community = A.DF['C'].tolist()\n",
    "X = A.DF['X'].tolist()\n",
    "Y = A.DF['Y'].tolist()\n",
    "plt.figure(3,figsize=(12,8)) \n",
    "plt.scatter(X,Y,c=community,s=50)\n",
    "plt.title('location visualization',fontsize =30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Part 2]community assignment for 1200 people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# select 600 people for visualization\n",
    "X = np.loadtxt('notebook/data/x.csv')\n",
    "Y = np.loadtxt('notebook/data/y.csv')\n",
    "X = X[0:1200]\n",
    "Y = Y[0:1200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# filepath and filename shall be changed to run the code\n",
    "filepath = '/Users/danqing0703/Google_Drive/research_2017Spring/code_review/CPS_TRC/output/cplex'\n",
    "filename = 'cplex_par_run_2.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A = cplex_CPS(X, Y, filepath, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people for assignment\n",
      "300\n",
      "Number of people for assignment\n",
      "Number of people for assignment\n",
      "300\n",
      "Number of people for assignment\n",
      "Number of potential social center\n",
      "300\n",
      "300\n",
      "300\n",
      "Number of potential social center\n",
      "Number of potential social center\n",
      "Number of potential social center\n",
      "300\n",
      "300\n",
      "300\n",
      "total running time 408.66482091\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "t0 = time.time()\n",
    "A.run()\n",
    "t1 = time.time()\n",
    "print 'total running time',t1-t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}