crawler_scheduler_beta.json

{
 "metadata": {
  "name": "",
  "signature": "sha256:99aeb3cf149c3a930a669cacd6b4bcffd6947445d6319bb5ace164909709711f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "\n",
      "df = pd.read_csv('data/short_ranks.csv')\n",
      "df.rename(columns={'Unnamed: 0': 'url'}, inplace=True)\n",
      "df['global_rank'] = df.Global.str.replace(',', '').astype(float)\n",
      "df.sort('global_rank', ascending=True, inplace=True)\n",
      "df.index = range(1, len(df)+1) # index acts as the rank, so must start at 1\n",
      "\n",
      "# Given an expected number of daily observations, generate Beta samples \n",
      "# (between 0 and 1) and map them to ranks (by multiplying by the # of ranks);\n",
      "# when you reach a point where the # of ranks to visit is equal to the # of \n",
      "# remaining observations, simply visit everything once.\n",
      "def generate_daily_traffic(alpha, beta, n_daily_obs):\n",
      "    global df\n",
      "    visited_ranks = set()\n",
      "    n_ranks = len(df)\n",
      "    n_remaining_obs = n_daily_obs\n",
      "    while len(visited_ranks) < n_ranks:\n",
      "        n_remaining_obs -= 1\n",
      "        # probalistic part\n",
      "        if n_remaining_obs >= (n_ranks - len(visited_ranks)):\n",
      "            rank = int(np.ceil(np.random.beta(alpha, beta) * n_ranks))\n",
      "            visited_ranks.add(rank)\n",
      "            yield df.ix[rank, 'url'], rank\n",
      "        # deterministic part, i.e. visit every unvisited site once\n",
      "        else:\n",
      "            remaining_ranks = set(df.index) - visited_ranks\n",
      "            for rank in remaining_ranks:\n",
      "                visited_ranks.add(rank)\n",
      "                yield df.ix[rank, 'url'], rank\n",
      "\n",
      "ranks = []\n",
      "visited_urls = set()\n",
      "for url, rank in generate_daily_traffic(0.5, 5, 500000):\n",
      "    visited_urls.add(url)\n",
      "    ranks.append(rank)\n",
      "\n",
      "assert len(visited_urls) == len(df)\n",
      "print('# visited URLs =', len(visited_urls))\n",
      "print('# daily observations =', len(ranks))\n",
      "plt.hist(ranks, bins=100);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "# visited URLs = 110583\n",
        "# daily observations = 500000\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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STJgwIVOmTMmtt96a1tbWJElnZ2e+9a1vZfr06bnjjjuSJBdddFF6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\nAJB7QiwAAAAAcu//A3E9s7RU03ugAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1127f7f10>"
       ]
      }
     ],
     "prompt_number": 1
    }
   ],
   "metadata": {}
  }
 ]
}