test_spdot.ipynb

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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
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   "source": [
    "import numpy as np\n",
    "from scipy import sparse as sp\n",
    "from pysal.spreg.utils import spdot\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",
    "\n",
    "        print '%2.2f sec' % \\\n",
    "              (te-ts)\n",
    "        return result\n",
    "\n",
    "    return timed\n",
    "\n",
    "#a*b != np.dot(a,b) but (c*d).sum() == c.dot(d).sum()\n",
    "#and c*d != d*c or c.dot(d) != d.dot(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@timeit\n",
    "def densedot(a,b):\n",
    " return np.dot(a,b.T)\n",
    "\n",
    "@timeit\n",
    "def sparsedot(a,b):\n",
    " return a.dot(b.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#When sparse arrays are moderately sparse\n",
    "a = np.random.randint(1,100, (3000,3000))\n",
    "b = np.random.randint(1,100, (3000,3000))\n",
    "c = sp.rand(3000,3000, .5)\n",
    "d = sp.rand(3000, 3000, .5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.19 sec\n",
      "67523117094780\n",
      "11.85 sec\n",
      "1688132086.31\n"
     ]
    }
   ],
   "source": [
    "print np.sum(densedot(a,b)) #dense op with dense arrays\n",
    "print densedot(c,d).sum()   #dense op with sparse arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.58 sec\n",
      "67523117094780\n",
      "11.81 sec\n",
      "1688132086.31\n"
     ]
    }
   ],
   "source": [
    "print np.sum(sparsedot(a,b)) #sparse op with dense arrays\n",
    "print sparsedot(c,d).sum()   #sparse op with sparse arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.17 sec\n",
      "13.97 sec\n",
      "True\n",
      "11.47 sec\n",
      "11.55 sec\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print np.allclose(densedot(a,b),sparsedot(a,b))\n",
    "print np.allclose(densedot(c,d).toarray(),sparsedot(c,d).toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#When sparse arrays are actually dense\n",
    "c = sp.rand(3000,3000, .99)\n",
    "d = sp.rand(3000, 3000, .99)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.73 sec\n",
      "67523117094780\n",
      "42.78 sec\n",
      "6618433757.47\n"
     ]
    }
   ],
   "source": [
    "print np.sum(densedot(a,b)) #dense op with dense arrays\n",
    "print densedot(c,d).sum()   #dense op with sparse arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.83 sec\n",
      "67523117094780\n",
      "42.89 sec\n",
      "6618433757.47\n"
     ]
    }
   ],
   "source": [
    "print np.sum(sparsedot(a,b)) #sparse op with dense arrays\n",
    "print sparsedot(c,d).sum()   #sparse op with sparse arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.88 sec\n",
      "16.01 sec\n",
      "True\n",
      "45.83 sec\n",
      "43.77 sec\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print np.allclose(densedot(a,b),sparsedot(a,b))\n",
    "print np.allclose(densedot(c,d).toarray(),sparsedot(c,d).toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#When sparse arrays are very sparse\n",
    "c = sp.rand(3000,3000, .1)\n",
    "d = sp.rand(3000, 3000, .1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.71 sec\n",
      "67523117094780\n",
      "0.74 sec\n",
      "67566516.6591\n"
     ]
    }
   ],
   "source": [
    "print np.sum(densedot(a,b)) #dense op with dense arrays\n",
    "print densedot(c,d).sum()   #dense op with sparse arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.45 sec\n",
      "67523117094780\n",
      "0.71 sec\n",
      "67566516.6591\n"
     ]
    }
   ],
   "source": [
    "print np.sum(sparsedot(a,b)) #sparse op with dense arrays\n",
    "print sparsedot(c,d).sum()   #sparse op with sparse arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.44 sec\n",
      "14.68 sec\n",
      "True\n",
      "0.76 sec\n",
      "0.70 sec\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print np.allclose(densedot(a,b),sparsedot(a,b))\n",
    "print np.allclose(densedot(c,d).toarray(),sparsedot(c,d).toarray())"
   ]
  }
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