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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Data collection"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we provide code used to collect Twitter data for our [AAAI paper](http://www.cs.iit.edu/~culotta/pubs/culotta15predicting.pdf).\n",
      "\n",
      "Note that to this is a long running process (days) and may result in data that differs from that in the original paper. So, if you're interested in reproducing the results, I'd instead start with the [data_processing.ipynb](https://github.com/tapilab/aaai-2015-demographics/blob/master/src/data_processing.ipynb) notebook.\n",
      "\n",
      "For each brand in [brands.json](https://github.com/tapilab/aaai-2015-demographics/blob/master/data/brands.json), we collect 300 *followers*. For each of these followers, we collect up to 5,000 of their *friends* (i.e., users they follow).\n",
      "\n",
      "The results are stored in three pickle files:\n",
      "\n",
      "- `username2brand.pkl`: a dict from Twitter handle to brand demographics.\n",
      "- `id2brand.pkl`: a dict from Twitter user id to brand.\n",
      "- `brand2counts.pkl`: a dict from brand Twitter id to a Counter object. The Counter object is a dict from Twitter ID to count, representing the number times a follower of brand X is friends with user Y. E.g., `{123: {456: 10, 789: 5}}` means that of the users who follow 123, 10 also follow 456 and 5 also follow 789.\n",
      "\n",
      "MongoDB is used to store data for intermediate computations. You may want to distribute calls to `process_followers` and `process_friends` for efficiency."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pymongo import MongoClient\n",
      "\n",
      "dbconn = MongoClient('localhost', 27017)\n",
      "db = dbconn.twitter_demographics\n",
      "\n",
      "print db"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Database(MongoClient('localhost', 27017), u'twitter_demographics')\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "\n",
      "#insert twitter credentials into DB\n",
      "twitter_cred = db['twitter_cred']\n",
      "#remove all before insert\n",
      "twitter_cred.remove({})\n",
      "with open('../data/twitter-cred.json','r') as f:\n",
      "    for line in f.readlines():\n",
      "        user = json.loads(line)\n",
      "        user['is_taken'] = False\n",
      "        twitter_cred.insert(user)\n",
      "\n",
      "print 'Inserted %d users credentails'%(twitter_cred.count())    \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Inserted 4 users credentails\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#insert brand details in to db\n",
      "brands = db['brands']\n",
      "\n",
      "brands.remove({})\n",
      "with open('../data/brands.json','r') as f:\n",
      "    for line in f.readlines():\n",
      "        brands.insert(eval(line))\n",
      "        \n",
      "print 'Inserted %d brands'%(brands.count())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Inserted 1072 brands\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#helper functions\n",
      "def get_credentials():\n",
      "    \"\"\"Gets twitter credentials from DB \n",
      "    Returns:\n",
      "        Respone dict , or None if failed.\n",
      "    \"\"\"\n",
      "    twitter_cred = db['twitter_cred']\n",
      "    return twitter_cred.find_and_modify(query={'is_taken':False}, update={'$set':{'is_taken':True}}, upsert=False, sort=None)\n",
      "\n",
      "def get_brand_to_process():\n",
      "    \"\"\"Gets unprocessed brand names from DB\n",
      "    Returns:\n",
      "        brand dict or None\n",
      "    \"\"\"\n",
      "    brandObj = db['brands']\n",
      "    brand = brandObj.find_and_modify(query={'is_processed':False,'is_taken':False}, update={'$set':{'is_taken':True}}, upsert=False, sort=None, full_response=False)\n",
      "    if brand:\n",
      "        brand['brand_id'] = brand.pop('_id')\n",
      "    return brand\n",
      "\n",
      "def get_follower_to_process():\n",
      "    \"\"\"Gets unprocessed followers form DB\n",
      "    Returns:\n",
      "        follower details dict or None\n",
      "    \"\"\"\n",
      "    xmatrix = db['xmatrix']\n",
      "    followersDtls = xmatrix.find_and_modify(query={'is_processed':False,'is_taken':False}, update={'$set':{'is_taken':True}}, upsert=False, sort=None, full_response=False)\n",
      "    if followersDtls:\n",
      "        followersDtls['follower_id'] = followersDtls.pop('_id')\n",
      "    return followersDtls\n",
      "\n",
      "def add_followers_to_db(brand_id,followers_ids):\n",
      "    \"\"\"Adds followers to DB\n",
      "    Args:\n",
      "        brand_id     :brand id of which the user follows\n",
      "        followers_ids: Ids of followers of the brand\n",
      "    \"\"\"\n",
      "    xmatrix = db['xmatrix']\n",
      "    for follower_id in followers_ids:\n",
      "        xmatrix.update({'_id':follower_id},{'$addToSet':{'follows':brand_id},'$set':{'is_processed':False,'is_taken':False}},True)\n",
      "\n",
      "def add_friends_to_dB(follower_id,friends_list):\n",
      "    \"\"\"Adds friends to DB\n",
      "    Args:\n",
      "        follower_id: Id of follower \n",
      "        freiends   : list of his friends\n",
      "    \"\"\"\n",
      "    xmatrix = db['xmatrix']\n",
      "    xmatrix.update({'_id':follower_id},\n",
      "                            {'$addToSet':{\n",
      "                                'follows':{\n",
      "                                    '$each':friends_list\n",
      "                                }\n",
      "                            }\n",
      "                                          \n",
      "                        })\n",
      "\n",
      "def update_processed_flag(user_id):\n",
      "    \"\"\"updates processed flag to true\n",
      "    Args:\n",
      "        user_id: user id for which processed flag to be updated\n",
      "    \"\"\"\n",
      "    xmatrix = db['xmatrix']\n",
      "    xmatrix.update({'_id':user_id},{'$set':{'is_taken':False,'is_processed':True}})\n",
      "    \n",
      "def remove_user_from_x(self,user_id):\n",
      "    \"\"\"removes user from xmatrix\n",
      "    Args:\n",
      "        user_id: user id \n",
      "    \"\"\"\n",
      "    xmatrix = db['xmatrix']\n",
      "    xmatrix.remove({'_id':user_id})\n",
      "\n",
      "def robust_request(twitter, resource, params, max_tries=5):\n",
      "    \"\"\" If a Twitter request fails, sleep for 15 minutes.\n",
      "    Do this at most max_tries times before quitting.\n",
      "    Args:\n",
      "      twitter .... A TwitterAPI object.\n",
      "      resource ... A resource string to request.\n",
      "      params ..... A parameter dictionary for the request.\n",
      "      max_tries .. The maximum number of tries to attempt.\n",
      "    Returns:\n",
      "      A TwitterResponse object, or None if failed.\n",
      "    \"\"\"\n",
      "    for i in range(max_tries):\n",
      "        request = twitter.request(resource, params)\n",
      "        if request.status_code == 200:\n",
      "            return request\n",
      "        r = [r for r in request][0]\n",
      "        if ('code' in r and r['code'] == 34) or ('error' in r and r['error'] == 'Not authorized.'):   # 34 == user does not exist.\n",
      "            print >> sys.stderr, 'skipping bad request', resource, params\n",
      "            return None\n",
      "        else:\n",
      "            print >> sys.stderr, 'Got error:', request.text, '\\nsleeping for 15 minutes.'\n",
      "            sys.stderr.flush()\n",
      "            time.sleep(60 * 15)\n",
      "\n",
      "\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get twitter obj \n",
      "from TwitterAPI import TwitterAPI\n",
      "twitter = get_credentials()\n",
      "if twitter:\n",
      "    twitterObj = TwitterAPI(\n",
      "            twitter['api_key'],\n",
      "            twitter['api_secret'],\n",
      "            twitter ['access_token_key'],\n",
      "            twitter['access_token_secret'])\n",
      "else:\n",
      "    print >> sys.stderr,'Twitter credits not available'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_followers(user_id):\n",
      "    \"\"\"To get followers of given twitter Ids\n",
      "    Args:\n",
      "        user_id... twitter user id\n",
      "    Returns\n",
      "        followers list\n",
      "    \"\"\"\n",
      "    followers = []\n",
      "    request = robust_request(twitterObj, 'followers/ids',\n",
      "                             {'user_id': user_id, 'count': 300})\n",
      "    if request:\n",
      "        for result in request:\n",
      "            if 'ids' in result:\n",
      "                followers += result['ids']\n",
      "    return followers\n",
      "\n",
      "\n",
      "def get_friends(user_id):\n",
      "    \"\"\"To get friends of given twitter Ids\n",
      "    Args:\n",
      "        user_id... twitter user id\n",
      "    Returns\n",
      "        friends list\n",
      "    \"\"\"\n",
      "    friends = []\n",
      "    request = robust_request(twitterObj, 'friends/ids',\n",
      "                             {'user_id': user_id, 'count': 5000})\n",
      "    if request:\n",
      "        for result in request:\n",
      "            if 'ids' in result:\n",
      "                friends += result['ids']\n",
      "    return friends"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#For testing purpose\n",
      "#else set it to -1\n",
      "cut_off = 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "def process_followers():\n",
      "    \"\"\"Gets unprocessed brands from DB \n",
      "    fetch 300 followers of those brands and \n",
      "    adds it to DB. \n",
      "    Halts when all brands are processed or cutt_off is reached\n",
      "    \"\"\"\n",
      "    global cut_off\n",
      "    while True:\n",
      "        if cut_off == 0:\n",
      "            print >> sys.stderr, 'cut off reached'\n",
      "            break\n",
      "        cut_off -= 1\n",
      "        brand = get_brand_to_process()\n",
      "        if not brand:\n",
      "            print >> sys.stderr, 'No brands to process'\n",
      "            break\n",
      "        followers = get_followers(brand['brand_id'])\n",
      "        if len(followers) > 0:\n",
      "            add_followers_to_db(brand['brand_id'],followers)\n",
      "            print 'added %d followers of %s to DB'%(len(followers),brand['brand_id'])\n",
      "process_followers()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "added 300 followers of 255784266 to DB\n",
        "added 300 followers of 261927470 to DB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "added 300 followers of 268439864 to DB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "cut off reached\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#For testing purpose\n",
      "#else set it to -1\n",
      "cut_off = 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "def process_friends():\n",
      "    \"\"\"Gets unprocessed follower and get \n",
      "    5000 of their friends , adds it to DB\n",
      "    Halts when all followers are processed or cutt_off is reached\n",
      "    \"\"\"\n",
      "    global cut_off\n",
      "    while True:\n",
      "        if cut_off == 0:\n",
      "            print >> sys.stderr, 'cut off reached'\n",
      "            break\n",
      "        cut_off -= 1\n",
      "        follower = get_follower_to_process()\n",
      "        if not follower:\n",
      "            print >> sys.stderr, 'No follower to process'\n",
      "            break\n",
      "        friends = get_friends(str(follower['follower_id']))\n",
      "        if len(friends) > 0:\n",
      "            add_friends_to_dB(follower['follower_id'],friends)\n",
      "            print 'added %d followers of %s to DB'%(len(friends),follower['follower_id'])\n",
      "            update_processed_flag(str(follower['follower_id']))\n",
      "        else:\n",
      "            remove_user_from_x(str(follower['follower_id']))\n",
      "            print >> sys.stderr, 'removed user , unable to fetch friends list for %s'%(str(follower['follower_id']))\n",
      "\n",
      "process_friends()\n",
      "    \n",
      "    \n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "added 763 followers of 2891952572 to DB\n",
        "added 571 followers of 2890499755 to DB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "added 155 followers of 317563215 to DB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "cut off reached\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get all brands.\n",
      "id2brand = dict()\n",
      "username2brand = dict()\n",
      "for brand in db.brands.find():\n",
      "    id2brand[brand['_id']] = brand\n",
      "    username2brand[brand['brand_name'].lower()] = brand\n",
      "print 'read', len(id2brand), 'brands'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "read 1072 brands\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Iterate over sampled followers for each brand.\n",
      "from collections import Counter, defaultdict\n",
      "\n",
      "# Count how often each friend appears so we can remove those occuring fewer than N tims.\n",
      "friend_counts = Counter()\n",
      "count = 0\n",
      "for follower in db.xmatrix.find({'is_processed':True}):\n",
      "    friend_counts.update(follower['follows'])\n",
      "    count += 1\n",
      "    if count % 1000 == 0:\n",
      "        print 'read', count"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "read 1000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 26000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 27000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 28000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 29000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 31000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 32000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 33000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 34000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 35000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 36000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 37000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 38000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 39000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 40000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 41000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 42000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
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        " 100000\n",
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      },
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       "stream": "stdout",
       "text": [
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      },
      {
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       "stream": "stdout",
       "text": [
        " 139000\n",
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       "stream": "stdout",
       "text": [
        " 140000\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 141000\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 142000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 143000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 144000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 145000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 146000\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Filter accounts not appearing a minimum number of times.\n",
      "count_thresh = 100\n",
      "friend_set = set(f for f, v in friend_counts.iteritems() if v >= count_thresh)\n",
      "print len(friend_set), 'of', len(friend_counts), 'appear at least', count_thresh, 'times'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "67760 of 21185659 appear at least 100 times\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now construct friend counts for each brand, using the filtered set of accounts. \n",
      "brand2counts = defaultdict(lambda: Counter())\n",
      "count = 0\n",
      "for follower in db.xmatrix.find({'is_processed':True}):\n",
      "    count += 1\n",
      "    if count % 1000 == 0:\n",
      "        print 'read', count\n",
      "    brandids = set([f for f in follower['follows'] if f in id2brand])\n",
      "    friends = set(follower['follows']) & friend_set\n",
      "    for b in brandids:\n",
      "        brand2counts[b].update(friends)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "read 1000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13000\n",
        "read"
       ]
      },
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       "stream": "stdout",
       "text": [
        " 14000\n",
        "read"
       ]
      },
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       "stream": "stdout",
       "text": [
        " 15000\n",
        "read"
       ]
      },
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       "stream": "stdout",
       "text": [
        " 16000\n",
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       ]
      },
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       "stream": "stdout",
       "text": [
        " 17000\n",
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       ]
      },
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        " 18000\n",
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       "stream": "stdout",
       "text": [
        " 20000\n",
        "read"
       ]
      },
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        " 21000\n",
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        " 22000\n",
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       "stream": "stdout",
       "text": [
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        " 100000\n",
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       "stream": "stdout",
       "text": [
        " 139000\n",
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      {
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       "stream": "stdout",
       "text": [
        " 140000\n",
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 141000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 142000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 143000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 144000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 145000\n",
        "read"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 146000\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Print the top accounts for the first brand.\n",
      "print brand2counts.keys()[0], sorted(brand2counts.values()[0].items(), key=lambda x: -x[1])[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "15650816 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[(15650816, 626), (35764757, 322), (15846407, 306), (25525507, 283), (90420314, 273)]\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Read the demographics data for each brand.\n",
      "import json\n",
      "username2demo = dict()\n",
      "for line in open('../data/demo.json', 'rt'):\n",
      "    js = json.loads(line)\n",
      "    username2demo[js['twitter'].lower()] = js\n",
      "print 'read', len(username2demo), 'demographics'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "read 1513 demographics\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Add demographics to each brand dict.\n",
      "for username, brand in username2brand.iteritems():\n",
      "    brand['demo'] = username2demo[username]\n",
      "    if not 'Female' in brand['demo']:\n",
      "        print brand['brand_name']  # , brand['demo']['Female']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "stltoday\n",
        "World_Wildlife\n",
        "BettyBuzz\n",
        "TeamRankings\n",
        "InsideHoops\n",
        "GrindTV\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set self reference counts to 0.\n",
      "for brand in brand2counts:\n",
      "    brand2counts[brand][brand] = 0.\n",
      "# Now, the brand id should not appear in the count dict.\n",
      "print brand2counts.keys()[0], sorted(brand2counts.values()[0].items(), key=lambda x: -x[1])[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "15650816 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[(35764757, 322), (15846407, 306), (25525507, 283), (90420314, 273), (34381878, 251)]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Pickle everything\n",
      "from functools import partial\n",
      "import pickle\n",
      "pickle.dump(username2brand, open('username2brand.pkl', 'wb'))\n",
      "pickle.dump(id2brand,open('id2brand.pkl','wb'))\n",
      "pickle.dump(dict([(b,c) for b,c in brand2counts.iteritems()]), open('brand2counts.pkl', 'wb'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}