MIH Profiling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "from MultiIndexHash import MultiIndexHash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from MultiIndexHash import MultiIndexHash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "feat = pickle.load(open('Data/feature_matrix/FC6_full_trained.p','rb'))\n",
    "f = feat > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.10 s\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start = time.time()\n",
    "MIH = MultiIndexHash(f)\n",
    "print('{:.2f} s'.format(time.time()-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext line_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%lprun -f MultiIndexHash.r_search MIH.r_search(f[400,:],1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  False,\n",
       "  True)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(MIH.tables[0].keys())[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True, False, False, False, False, False, False,\n",
       "         True,  True, False, False,  True, False],\n",
       "       [ True,  True, False, False,  True, False, False,  True, False,\n",
       "        False, False,  True, False, False, False],\n",
       "       [False,  True,  True,  True, False,  True, False, False, False,\n",
       "         True,  True, False,  True,  True,  True],\n",
       "       [False, False, False, False,  True, False, False, False, False,\n",
       "         True, False, False,  True, False,  True]], dtype=bool)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MIH.lookup[0][:4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MacOSFile(object):\n",
    "    def __init__(self, f):\n",
    "        self.f = f\n",
    "\n",
    "    def __getattr__(self, item):\n",
    "        return getattr(self.f, item)\n",
    "\n",
    "    def read(self, n):\n",
    "        if n >= (1 << 31):\n",
    "            buffer = bytearray(n)\n",
    "            pos = 0\n",
    "            while pos < n:\n",
    "                size = min(n - pos, 1 << 31 - 1)\n",
    "                chunk = self.f.read(size)\n",
    "                buffer[pos:pos + size] = chunk\n",
    "                pos += size\n",
    "            return buffer\n",
    "        return self.f.read(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('Data/feature_matrix/fc6_full_set.p','rb') as f:\n",
    "    feat = pickle.load(MacOSFile(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = feat > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "232.73 s\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start = time.time()\n",
    "MIH = MultiIndexHash(f)\n",
    "print('{:.2f} s'.format(time.time()-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext snakeviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "*** Profile stats marshalled to file '/var/folders/xg/61d36gvd7c74014sl8wkr2bw0000gn/T/tmp19w1l0wy'. \n"
     ]
    }
   ],
   "source": [
    "%snakeviz MIH.k_nn(f[400,:],10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "*** Profile stats marshalled to file '/var/folders/xg/61d36gvd7c74014sl8wkr2bw0000gn/T/tmpmkt7okka'. \n"
     ]
    }
   ],
   "source": [
    "%snakeviz MIH.r_search(f[400,:],1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext line_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'r_search'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-82ed7e797bbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mMultiIndexHash\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mr_search\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'r_search'"
     ]
    }
   ],
   "source": [
    "from MultiIndexHash import r_search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "%lprun -f MultiIndexHash.r_search MIH.r_search(f[400,:],1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%lprun -f MultiIndexHash.k_nn MIH.k_nn(f[400,:],10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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