sparse_grav.ipynb

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     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from gravity import Gravity, Production, Attraction, Doubly, BaseGravity\n",
    "from entropy import Unconstrained, ProductionConstrained, AttractionConstrained, DoublyConstrained\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.api import families\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline\n",
    "\n",
    "import time                                                \n",
    "\n",
    "def timeit(method):\n",
    "\n",
    "    def timed(*args, **kw):\n",
    "        ts = time.time()\n",
    "        result = method(*args, **kw)\n",
    "        te = time.time()\n",
    "        elapsed = te-ts\n",
    "\n",
    "        #print '%2.8f sec' % \\\n",
    "              #(elapsed)\n",
    "        return result, elapsed\n",
    "\n",
    "    return timed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "@timeit\n",
    "def gravity(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
    "    results = Gravity(f, o_vars, d_vars, dij, cost, framework=framework)\n",
    "    return results\n",
    "    \n",
    "@timeit    \n",
    "def production(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
    "    results = Production(f, o, d_vars, dij, 'exp', framework=framework)\n",
    "    return results\n",
    "\n",
    "@timeit    \n",
    "def attraction(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
    "    results = Attraction(f, d, o_vars, dij, 'exp', framework=framework)\n",
    "    return results\n",
    "\n",
    "@timeit    \n",
    "def doubly(f ,o, d, o_vars, d_vars, dij, cost='exp', framework='glm'):\n",
    "    results = Doubly(f, o, d, dij, 'exp', framework=framework)\n",
    "    return results\n",
    "\n"
   ]
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   "execution_count": 4,
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   "source": [
    "n = 5000\n",
    "o = np.tile(np.arange(n),n)\n",
    "d = np.repeat(np.arange(n),n)\n",
    "loc_size = np.random.randint(25000,500000, n)\n",
    "o_vars = np.tile(loc_size, n)\n",
    "d_vars = np.repeat(loc_size, n)\n",
    "dij = np.random.exponential(2500, n**2)\n",
    "f = o_vars**.3*d_vars**.4*np.exp(dij*-.00005)\n",
    "o = np.reshape(o, (-1,1))\n",
    "d = np.reshape(d, (-1,1))\n",
    "o_vars = np.reshape(o_vars, (-1,1))\n",
    "d_vars = np.reshape(d_vars, (-1,1))\n",
    "dij = np.reshape(dij, (-1,1))\n",
    "f = np.reshape(f, (-1,1))\n",
    "f = f.astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5043.84268093\n",
      "[  8.18336042e+00  -5.00054911e-05]\n"
     ]
    }
   ],
   "source": [
    "out, elapsed = doubly(f, o, d, o_vars, d_vars, dij, 'exp', framework='glm')\n",
    "print elapsed\n",
    "print out.params[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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