Map Reduce.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:cf1eea379a3851fcc505e15314ad5c3f65736aa31501327101d34335f7cfa210"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython import display\n",
      "display.Image('https://mikecvet.files.wordpress.com/2010/07/mrfigure.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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eAmaa7bWpa7RNBWJvRb2MeTHziuXlqje0rhzWnPeiX42SBYnQECIJEERA8EWL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\nRK5CYII=\n",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.Image at 0x7fe3e1fdd110>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = [1, 2, 3]\n",
      "b = [4, 5, 6, 7]\n",
      "c = [8, 9, 1, 2, 3]\n",
      "L = map(lambda x:len(x), [a, b, c])\n",
      " \n",
      "# L == [3, 4, 5]\n",
      "N = reduce(lambda x, y: x+y, L)\n",
      "# N == 12\n",
      "# Or, if we want to be fancy and do it in one line\n",
      "N = reduce(lambda x, y: x+y, map(lambda x:len(x), [a, b, c]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "12"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "from multiprocessing import Pool\n",
      "\n",
      "def sanitize(w):\n",
      "    \"\"\"\n",
      "    If a token has been identified to contain\n",
      "    non-alphanumeric characters, such as punctuation,\n",
      "    assume it is leading or trailing punctuation\n",
      "    and trim them off. Other internal punctuation\n",
      "    is left intact.\n",
      "    \"\"\"\n",
      "# Strip punctuation from the front\n",
      "    while len(w) > 0 and not w[0].isalnum():\n",
      "        w = w[1:]\n",
      "\n",
      "    # String punctuation from the back\n",
      "    while len(w) > 0 and not w[-1].isalnum():\n",
      "        w = w[:-1]\n",
      "\n",
      "    return w\n",
      "\n",
      "def load(path):\n",
      "    \"\"\"\n",
      "    Load the contents the file at the given\n",
      "    path into a big string and return it.\n",
      "    \"\"\"\n",
      "    word_list = []\n",
      "    f = open(path, \"r\")\n",
      "    for line in f:\n",
      "        word_list.append (line)\n",
      "        \n",
      "    # Efficiently concatenate Python string objects\n",
      "    return (''.join(word_list)).split ()\n",
      "def chunks(l, n):\n",
      "    \"\"\"\n",
      "    A generator function for chopping up a given list into chunks of\n",
      "    length n.\n",
      "    \"\"\"\n",
      "\n",
      "    for i in xrange(0, len(l), n):\n",
      "        yield l[i:i+n]\n",
      "\n",
      "def tuple_sort (a, b):\n",
      "    \"\"\"\n",
      "    Sort tuples by term frequency, and then alphabetically.\n",
      "    \"\"\"\n",
      "    if a[1] < b[1]:\n",
      "        return 1\n",
      "    elif a[1] > b[1]:\n",
      "        return -1\n",
      "    else:\n",
      "        return cmp(a[0], b[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def Map(L):\n",
      "    \"\"\"\n",
      "    Given a list of tokens, return a list of tuples of\n",
      "    titlecased (or proper noun) tokens and a count of '1'.\n",
      "    Also remove any leading or trailing punctuation from\n",
      "    each token.\n",
      "    \"\"\"\n",
      "    results = []\n",
      "    for w in L:\n",
      "        # True if w contains non-alphanumeric characters\n",
      "        if not w.isalnum():\n",
      "            w = sanitize(w)\n",
      "        # True if w is a title-cased token\n",
      "        if w.istitle():\n",
      "            results.append ((w, 1))\n",
      "    return results \n",
      "\n",
      "def Partition(L):\n",
      "    \"\"\"\n",
      "    Group the sublists of (token, 1) pairs into a term-frequency-list\n",
      "    map, so that the Reduce operation later can work on sorted\n",
      "    term counts. The returned result is a dictionary with the structure\n",
      "    {token : [(token, 1), ...] .. }\n",
      "    \"\"\"\n",
      "    tf = {}\n",
      "    for sublist in L:\n",
      "        for p in sublist:\n",
      "            # Append the tuple to the list in the map\n",
      "            try:\n",
      "                tf[p[0]].append (p)\n",
      "            except KeyError:\n",
      "                tf[p[0]] = [p]\n",
      "    return tf\n",
      "\n",
      "def Reduce(Mapping):\n",
      "    \"\"\"\n",
      "    Given a (token, [(token, 1) ...]) tuple, collapse all the\n",
      "    count tuples from the Map operation into a single term frequency\n",
      "    number for this token, and return a final tuple (token, frequency).\n",
      "    \"\"\"\n",
      "    return (Mapping[0], sum(pair[1] for pair in Mapping[1]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load file, stuff it into a string\n",
      "text = load('big.txt')\n",
      " \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def compute_with_cores(cores=1):\n",
      "    # Build a pool of 8 processes\n",
      "    pool = Pool(processes=cores)\n",
      "\n",
      "    # Fragment the string data into 8 chunks\n",
      "    partitioned_text = list(chunks(text, len(text) /cores))\n",
      "\n",
      "    # Generate count tuples for title-cased tokens\n",
      "    single_count_tuples = pool.map(Map, partitioned_text)\n",
      "\n",
      "    # Organize the count tuples; lists of tuples by token key\n",
      "    token_to_tuples = Partition(single_count_tuples)\n",
      "\n",
      "    # Collapse the lists of tuples into total term frequencies\n",
      "    term_frequencies = pool.map(Reduce, token_to_tuples.items())\n",
      "\n",
      "    # Sort the term frequencies in nonincreasing order\n",
      "    term_frequencies.sort (tuple_sort)\n",
      "\n",
      "    for pair in term_frequencies[:3]:\n",
      "        print pair[0], \":\", pair[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "compute_with_cores(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n",
        "1 loops, best of 3: 1.81 s per loop\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "compute_with_cores(4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n",
        "1 loops, best of 3: 1.6 s per loop\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "compute_with_cores(8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I : 7182\n",
        "The : 7116\n",
        "He : 2309\n",
        "1 loops, best of 3: 1.85 s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}