{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import time\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "import json\n",
      "import os\n",
      "import urllib\n",
      "import urllib2\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import nltk\n",
      "from nltk.util import ngrams\n",
      "from nltk.collocations import *\n",
      "from nltk.corpus import stopwords\n",
      "\n",
      "import pandas as pd\n",
      "import math\n",
      "from scipy.stats import pearsonr\n",
      "\n",
      "user_agent = (\"Project for Data Science class v1.0\" \" /u/Valedra\" \" https://github.com/jaysayre/intelligentdolphins\")\n",
      "\n",
      "\n",
      "def json_extract(baseurl, headrs=None, params=None):\n",
      "    '''\n",
      "    Helper function to download and read json data. Takes in explanatory headers and returns json dict.\n",
      "    '''\n",
      "    if params != None:\n",
      "        form = urllib.urlencode(params)\n",
      "        url = baseurl+form\n",
      "    else:\n",
      "        url = baseurl\n",
      "    \n",
      "    if headrs != None:\n",
      "        request = urllib2.Request(url, headers=headrs)\n",
      "    else: \n",
      "        request = urllib2.Request(url)\n",
      "    return json.loads(urllib2.urlopen(request).read())\n",
      "\n",
      "def return_grams(sentence, n=[1, 3],  minlength=3):\n",
      "    gramslist = []\n",
      "    mysentencetokens_sw= nltk.word_tokenize(sentence)\n",
      "    mysentencetokens = [token for token in mysentencetokens_sw if (not token in stopwords.words('english')) and len(token) >= minlength]\n",
      "    for j in range(n[0], n[1]+1):\n",
      "        somegrams = ngrams(mysentencetokens, j)\n",
      "        for grams in somegrams:\n",
      "            gramslist.append(' '.join(grams))\n",
      "    return gramslist     \n",
      "\n",
      "def search_keyword(title, maxscores, user_agent, n=[3,3], postid='bhjfb', sort_call='relevance', t='all', subreddit=None, api_call_limit=100):\n",
      "    scores = {}\n",
      "    header = {'User-agent': user_agent}\n",
      "    for term in return_grams(title, [n[0],n[1]]): \n",
      "        post_params = {'q':term, 'sort': sort_call, 't':t, 'limit':api_call_limit}\n",
      "        if subreddit == None:\n",
      "            reddit_base = 'http://www.reddit.com/r/search/search.json?' # If we want to search all of reddit\n",
      "        else:\n",
      "            reddit_base = 'http://www.reddit.com/r/%s/search.json?' % subreddit\n",
      "            post_params.update ({'restrict_sr':'on'})\n",
      "        \n",
      "        #Makes sure maxscores isn't a float!\n",
      "        maxscores = int(maxscores) \n",
      "        api_call_limit = int(api_call_limit)\n",
      "    \n",
      "        #Since reddit only provides <= 100 calls at a time, looks at n requested and splits it up into different requests\n",
      "        if maxscores%api_call_limit != 0:\n",
      "            remainder = maxscores%api_call_limit\n",
      "            num = (maxscores/api_call_limit) +1\n",
      "        else:\n",
      "            num = maxscores/api_call_limit\n",
      "            remainder = api_call_limit\n",
      "            \n",
      "        #Makes an api call for all n entries based on the api call limit\n",
      "        for i in range(num):\n",
      "            if i == 0:\n",
      "                jsondata = json_extract(reddit_base, header, post_params)\n",
      "                tostartfrom = jsondata['data']['after']\n",
      "                for item in jsondata['data']['children']:\n",
      "                    if item['data']['score'] != 0:\n",
      "                        scores.update({item['data']['id'] : item['data']['score']})\n",
      "            elif i == num - 1:\n",
      "                post_params.update({'limit': remainder, 'after': tostartfrom}) #Indicates the post after we wish to call from\n",
      "                jsondata = json_extract(reddit_base, header, post_params)\n",
      "                for item in jsondata['data']['children']:\n",
      "                    if item['data']['score'] != 0:\n",
      "                        scores.update({item['data']['id'] : item['data']['score']})\n",
      "            else: \n",
      "                post_params.update({'after': tostartfrom}) \n",
      "                jsondata = json_extract(reddit_base, header, post_params)\n",
      "                tostartfrom = jsondata['data']['after']\n",
      "                for item in jsondata['data']['children']:\n",
      "                    if item['data']['score'] != 0:\n",
      "                        scores.update({item['data']['id'] : item['data']['score']})\n",
      "    try:\n",
      "        scores.pop(postid)\n",
      "    except:\n",
      "        pass\n",
      "        \n",
      "    return scores.values()\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this file we tried to generate trigrams in titles. \n",
      "\n",
      "Based on those we searched reddit and stored the scores of the search results. \n",
      "\n",
      "If those were zero, we tried the same thing using bigrams. \n",
      "\n",
      "We were hoping that the scores in search results would tell us anything about the score of the post we were looking at.\n",
      "\n",
      "\n",
      "\n",
      "<hr>\n",
      "\n",
      "**Since this file uses old data it should not get touched anymore and will take upwards of 30 h to run**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(\"Data/full.csv\", encoding=\"utf-8\")\n",
      "df2 = df[df['titlescore'] == '[null]']\n",
      "print len(df2)\n",
      "dfids = list(df2.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1938\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(len(df2)):\n",
      "    print df['id'][dfids[i]], i\n",
      "    b = search_keyword(df['title'][dfids[i]], 100, user_agent, n=[2,2], postid=df['id'][dfids[i]], subreddit=df['subreddit'][dfids[i]])\n",
      "    if len(b) > 0:\n",
      "        df['titlescore'][dfids[i]] = np.mean(b)\n",
      "        df['titlestd'][dfids[i]] = np.std(b)\n",
      "    print df['titlescore'][dfids[i]]\n",
      "\n",
      "#df.to_csv(\"Data/titlescorebigram.csv\", index=False, encoding='utf-8')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "\"\\nfor i in range(len(df2)):\\n    print df['id'][dfids[i]], i\\n    b = search_keyword(df['title'][dfids[i]], 100, user_agent, n=[2,2], postid=df['id'][dfids[i]], subreddit=df['subreddit'][dfids[i]])\\n    if len(b) > 0:\\n        df['titlescore'][dfids[i]] = np.mean(b)\\n        df['titlestd'][dfids[i]] = np.std(b)\\n    print df['titlescore'][dfids[i]]\\n  \""
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fulldf = pd.read_csv('Data/full.csv', encoding='utf-8')\n",
      "fulldf['titlescore'] = ['null']*len(fulldf)\n",
      "fulldf['titlestd'] = ['null']*len(fulldf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fulldf.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "Index([author, comments, downvotes, id, score, selftext, subreddit, time_created, title, type, upvotes, karma, link_karma, alchemy, titlescore, titlestd], dtype=object)"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in fulldf.index:\n",
      "    fid = fulldf['id'][i]\n",
      "    alc = df[df['id'] == fid]['titlescore']\n",
      "    fulldf['titlescore'][i] = alc\n",
      "    pop = df[df['id'] == fid]['titlestd']\n",
      "    fulldf['titlestd'][i] = pop\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "On the titlescores we tried to use CDF to predict the actual score"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$\n",
      "CDF(z) = \\frac1{2}\\left(1 + {\\rm erf}\\left(\\frac{z - \\mu}{\\sqrt{2 \\sigma^2}}\\right)\\right) \n",
      "$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calc_cdf(mean, std):\n",
      "    cdf_dem = (1.0 + math.erf((600-mean)/math.sqrt(2*(std**2))))/2.0\n",
      "    return 1 - cdf_dem "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = fulldf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#clean the data\n",
      "df['titlescore'] = df['titlescore'].apply(lambda x: x[1:-1])\n",
      "df['titlestd'] = df['titlestd'].apply(lambda x: x[1:-1])\n",
      "\n",
      "df = df[df['titlescore'] != 'null']\n",
      "df = df[df['titlescore'] != '#N/A']\n",
      "df['titlescore'] = df['titlescore'].apply(lambda x:float(x))\n",
      "df['titlestd'] = df['titlestd'].apply(lambda x:float(x))\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get the cdf\n",
      "df['probprob'] = ['null'] * len(df)\n",
      "for i in df.index:\n",
      "    df['probprob'][i] = calc_cdf(df['titlescore'][i], df['titlestd'][i])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['probprob'] = df['probprob'].astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pearsonr(df['probprob'], df['score'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unfortunately this yielded a pearsonr of ca. 10% which is nothing we want to use in our model. We still wanted to improve those numbers. We thought, normalizing the data might do the trick. For that we take the titlescore and divide it by the average score of the current type and subreddit we are looking at."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subs = list(df['subreddit'].unique()) \n",
      "types = list(df['type'].unique())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "df['titlescore'] = df['titlescore'].apply(lambda x: x[1:-1])\n",
      "\n",
      "null_df = df[df['titlescore'] == 'null']\n",
      "na_df = df[df['titlescore'] == '#N/A']\n",
      "dfids = list(null_df.index)\n",
      "for i in range(len(dfids)):\n",
      "   df['titlescore'][dfids[i]] = '0'\n",
      "dfids = list(na_df.index)\n",
      "for i in range(len(dfids)):\n",
      "   df['titlescore'][dfids[i]] = '0'\n",
      "            \n",
      "df['titlescore'] = df['titlescore'].apply(lambda x:float(x))\n",
      "'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['normalized_titlescore'] = ['null']*len(df)\n",
      "for sub in subs:\n",
      "    curr_df = df[df['subreddit'] == sub]\n",
      "    for ctype in types:\n",
      "        current_df = curr_df[curr_df['type'] == ctype]\n",
      "        curr_mean =  current_df['score'].mean()\n",
      "        dfids = list(current_df.index)\n",
      "        for i in range(len(dfids)):\n",
      "           df['normalized_titlescore'][dfids[i]] = df['titlescore'][dfids[i]]/curr_mean"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2 = pd.read_csv('Data/full2.csv', encoding='utf-8')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2= df2[df2['normalized_titlescore'] != 0]\n",
      "pearsonr(df2['normalized_titlescore'], df2['score'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(-0.13039151874449112, 7.9110685237525519e-160)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2['titlestd'] = df2['titlestd'].apply(lambda x: x[1:-1])\n",
      "df2= df2[df2['titlestd'] != 'null']\n",
      "df2= df2[df2['titlestd'] != '#N/A']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "df2['titlestd'] = df2['titlestd'].apply(lambda x:float(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(df2['titlestd'], df2['score'], c='g')\n",
      "plt.title(\"Std versus Score\")\n",
      "plt.xlabel(\"Std\")\n",
      "plt.ylabel(\"Score\")\n",
      "plt.xlim(0, 2000)\n",
      "plt.ylim(0, 8000)\n",
      "plt.show()\n",
      "r_row, p_value = pearsonr(df2['titlestd'], df2['score'])\n",
      "print \"Pearson coefficient is\" + str(r_row) + \" with a p-value of \" + str(p_value)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zCQoKIjAwkMzMzDLLK3IU+I0oKyuLpi2aklcrD7urneS3k1m8YDEdOnQgISGB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8UxWbmBGuA96xV5lvCxuFwu9u7Yy/Hjx1m3bh1P/7+neepPT9Ht+m58OulTevbsyfVXX8/8CfMJ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\n/P438luxEh8r+P383zNz5kwKCguoS62zN4EbgfS1L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N8KTli5cqU7jfkDDzzA+++/D8AH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\njWG/M+YAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x26bf6cc0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Pearson coefficient is0.153435569189 with a p-value of 3.04008690448e-221\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As you can see, neither the normalized titlescore nor the standard deviation of the scores of the search results have any statistical significance fot our model. We ended this experiment at this point as we made no progress and tried different things."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}