{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Analysing the Edinburgh Fringe Festival Jokes\n",
    "\n",
    "**This is the ipython notebook for the blog post: [Python, natural language processing and predicting funny](http://vknight.org/unpeudemath/code/2015/06/14/natural-language-and-predicting-funny/)**.\n",
    "\n",
    "Here are the libraries we are going to need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "import pandas  # To handle our data nicely\n",
    "import nltk  # For all the clever stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Loading and tidying the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>     Tim Vine</td>\n",
       "      <td> 1</td>\n",
       "      <td> I've decided to sell my Hoover... well it was ...</td>\n",
       "      <td> 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td> Masai Graham</td>\n",
       "      <td> 2</td>\n",
       "      <td> I've written a joke about a fat badger but I c...</td>\n",
       "      <td> 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>    Rob Auton</td>\n",
       "      <td> 1</td>\n",
       "      <td> I heard a rumour that Cadbury is bringing out ...</td>\n",
       "      <td> 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>   Alex Horne</td>\n",
       "      <td> 2</td>\n",
       "      <td> I used to work in a shoe-recycling shop. It wa...</td>\n",
       "      <td> 2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>  Alfie Moore</td>\n",
       "      <td> 3</td>\n",
       "      <td> I'm in a same-sex marriage... the sex is alway...</td>\n",
       "      <td> 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Author  Rank                                           Raw_joke  \\\n",
       "0       Tim Vine     1  I've decided to sell my Hoover... well it was ...   \n",
       "1   Masai Graham     2  I've written a joke about a fat badger but I c...   \n",
       "10     Rob Auton     1  I heard a rumour that Cadbury is bringing out ...   \n",
       "11    Alex Horne     2  I used to work in a shoe-recycling shop. It wa...   \n",
       "12   Alfie Moore     3  I'm in a same-sex marriage... the sex is alway...   \n",
       "\n",
       "    Year  \n",
       "0   2014  \n",
       "1   2014  \n",
       "10  2013  \n",
       "11  2013  \n",
       "12  2013  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pandas.read_json('jokes.json') # Loading the json file\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td> Simon Brodkin</td>\n",
       "      <td> 10</td>\n",
       "      <td> I started so many fights at my school - I had ...</td>\n",
       "      <td> 2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 </th>\n",
       "      <td> Scott Capurro</td>\n",
       "      <td>  7</td>\n",
       "      <td> Scotland had oil but it's running out thanks t...</td>\n",
       "      <td> 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7 </th>\n",
       "      <td>    Jason Cook</td>\n",
       "      <td>  8</td>\n",
       "      <td> I've been married for 10 years I haven't made ...</td>\n",
       "      <td> 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8 </th>\n",
       "      <td> Felicity Ward</td>\n",
       "      <td>  9</td>\n",
       "      <td> This show is about perception and perspective....</td>\n",
       "      <td> 2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9 </th>\n",
       "      <td>  Masai Graham</td>\n",
       "      <td>  2</td>\n",
       "      <td> I've written a joke about a fat badger but I c...</td>\n",
       "      <td> 2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Author  Rank                                           Raw_joke  \\\n",
       "59  Simon Brodkin    10  I started so many fights at my school - I had ...   \n",
       "6   Scott Capurro     7  Scotland had oil but it's running out thanks t...   \n",
       "7      Jason Cook     8  I've been married for 10 years I haven't made ...   \n",
       "8   Felicity Ward     9  This show is about perception and perspective....   \n",
       "9    Masai Graham     2  I've written a joke about a fat badger but I c...   \n",
       "\n",
       "    Year  \n",
       "59  2009  \n",
       "6   2014  \n",
       "7   2014  \n",
       "8   2014  \n",
       "9   2013  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting rid of the common word and tokenising the jokes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# nltk.download()  # Only do this once: needed to download the `stopwords` corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "commonwords = [e.upper() for e in set(nltk.corpus.stopwords.words('english'))] # <- Need to download the corpus: import nltk; nltk.download()\n",
    "commonwords.extend(['M', 'VE'])\n",
    "tokenizer = nltk.tokenize.RegexpTokenizer(r'\\w+')  # To be able to strip out unwanted things in strings\n",
    "string_to_list = lambda x: [el.upper() for el in tokenizer.tokenize(x) if el.upper() not in commonwords]\n",
    "df['Joke'] = df['Raw_joke'].apply(string_to_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "      <th>Year</th>\n",
       "      <th>Joke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>     Tim Vine</td>\n",
       "      <td> 1</td>\n",
       "      <td> I've decided to sell my Hoover... well it was ...</td>\n",
       "      <td> 2014</td>\n",
       "      <td>   [DECIDED, SELL, HOOVER, WELL, COLLECTING, DUST]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td> Masai Graham</td>\n",
       "      <td> 2</td>\n",
       "      <td> I've written a joke about a fat badger but I c...</td>\n",
       "      <td> 2014</td>\n",
       "      <td>    [WRITTEN, JOKE, FAT, BADGER, COULDN, FIT, SET]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>    Rob Auton</td>\n",
       "      <td> 1</td>\n",
       "      <td> I heard a rumour that Cadbury is bringing out ...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [HEARD, RUMOUR, CADBURY, BRINGING, ORIENTAL, C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>   Alex Horne</td>\n",
       "      <td> 2</td>\n",
       "      <td> I used to work in a shoe-recycling shop. It wa...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [USED, WORK, SHOE, RECYCLING, SHOP, SOLE, DEST...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>  Alfie Moore</td>\n",
       "      <td> 3</td>\n",
       "      <td> I'm in a same-sex marriage... the sex is alway...</td>\n",
       "      <td> 2013</td>\n",
       "      <td>                      [SEX, MARRIAGE, SEX, ALWAYS]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Author  Rank                                           Raw_joke  \\\n",
       "0       Tim Vine     1  I've decided to sell my Hoover... well it was ...   \n",
       "1   Masai Graham     2  I've written a joke about a fat badger but I c...   \n",
       "10     Rob Auton     1  I heard a rumour that Cadbury is bringing out ...   \n",
       "11    Alex Horne     2  I used to work in a shoe-recycling shop. It wa...   \n",
       "12   Alfie Moore     3  I'm in a same-sex marriage... the sex is alway...   \n",
       "\n",
       "    Year                                               Joke  \n",
       "0   2014    [DECIDED, SELL, HOOVER, WELL, COLLECTING, DUST]  \n",
       "1   2014     [WRITTEN, JOKE, FAT, BADGER, COULDN, FIT, SET]  \n",
       "10  2013  [HEARD, RUMOUR, CADBURY, BRINGING, ORIENTAL, C...  \n",
       "11  2013  [USED, WORK, SHOE, RECYCLING, SHOP, SOLE, DEST...  \n",
       "12  2013                       [SEX, MARRIAGE, SEX, ALWAYS]  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training our classifier\n",
    "\n",
    "**From here on in we use the jokes up until 2013 as the training set.**\n",
    "\n",
    "We start by getting the entire set of words in all the jokes from the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'HEARD',\n",
       " u'RUMOUR',\n",
       " u'CADBURY',\n",
       " u'BRINGING',\n",
       " u'ORIENTAL',\n",
       " u'CHOCOLATE',\n",
       " u'BAR',\n",
       " u'COULD',\n",
       " u'CHINESE',\n",
       " u'WISPA']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Year'] = df['Year'].apply(int)\n",
    "\n",
    "def get_all_words(dataframe):\n",
    "    \"\"\"\n",
    "    A function that gets all the words from the Joke column in a given dataframe\n",
    "    \"\"\"\n",
    "    all_words = []\n",
    "    for jk in dataframe['Joke']:\n",
    "        all_words.extend(jk)\n",
    "    return all_words\n",
    "\n",
    "all_words = get_all_words(df[df['Year'] <= 2013])\n",
    "all_words[:10]  # The first ten words in our training data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a function to extract features from a given joke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def extract_features(joke, all_words):\n",
    "    words = set(joke)\n",
    "    features = {}\n",
    "    for word in words:\n",
    "        features['contains(%s)' % word] = (word in all_words)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "      <th>Year</th>\n",
       "      <th>Joke</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>     Tim Vine</td>\n",
       "      <td> 1</td>\n",
       "      <td> I've decided to sell my Hoover... well it was ...</td>\n",
       "      <td> 2014</td>\n",
       "      <td>   [DECIDED, SELL, HOOVER, WELL, COLLECTING, DUST]</td>\n",
       "      <td> {u'contains(DUST)': False, u'contains(COLLECTI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td> Masai Graham</td>\n",
       "      <td> 2</td>\n",
       "      <td> I've written a joke about a fat badger but I c...</td>\n",
       "      <td> 2014</td>\n",
       "      <td>    [WRITTEN, JOKE, FAT, BADGER, COULDN, FIT, SET]</td>\n",
       "      <td> {u'contains(SET)': True, u'contains(WRITTEN)':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>    Rob Auton</td>\n",
       "      <td> 1</td>\n",
       "      <td> I heard a rumour that Cadbury is bringing out ...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [HEARD, RUMOUR, CADBURY, BRINGING, ORIENTAL, C...</td>\n",
       "      <td> {u'contains(ORIENTAL)': True, u'contains(CHOCO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>   Alex Horne</td>\n",
       "      <td> 2</td>\n",
       "      <td> I used to work in a shoe-recycling shop. It wa...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [USED, WORK, SHOE, RECYCLING, SHOP, SOLE, DEST...</td>\n",
       "      <td> {u'contains(DESTROYING)': True, u'contains(SOL...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>  Alfie Moore</td>\n",
       "      <td> 3</td>\n",
       "      <td> I'm in a same-sex marriage... the sex is alway...</td>\n",
       "      <td> 2013</td>\n",
       "      <td>                      [SEX, MARRIAGE, SEX, ALWAYS]</td>\n",
       "      <td> {u'contains(MARRIAGE)': True, u'contains(SEX)'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Author  Rank                                           Raw_joke  \\\n",
       "0       Tim Vine     1  I've decided to sell my Hoover... well it was ...   \n",
       "1   Masai Graham     2  I've written a joke about a fat badger but I c...   \n",
       "10     Rob Auton     1  I heard a rumour that Cadbury is bringing out ...   \n",
       "11    Alex Horne     2  I used to work in a shoe-recycling shop. It wa...   \n",
       "12   Alfie Moore     3  I'm in a same-sex marriage... the sex is alway...   \n",
       "\n",
       "    Year                                               Joke  \\\n",
       "0   2014    [DECIDED, SELL, HOOVER, WELL, COLLECTING, DUST]   \n",
       "1   2014     [WRITTEN, JOKE, FAT, BADGER, COULDN, FIT, SET]   \n",
       "10  2013  [HEARD, RUMOUR, CADBURY, BRINGING, ORIENTAL, C...   \n",
       "11  2013  [USED, WORK, SHOE, RECYCLING, SHOP, SOLE, DEST...   \n",
       "12  2013                       [SEX, MARRIAGE, SEX, ALWAYS]   \n",
       "\n",
       "                                             Features  \n",
       "0   {u'contains(DUST)': False, u'contains(COLLECTI...  \n",
       "1   {u'contains(SET)': True, u'contains(WRITTEN)':...  \n",
       "10  {u'contains(ORIENTAL)': True, u'contains(CHOCO...  \n",
       "11  {u'contains(DESTROYING)': True, u'contains(SOL...  \n",
       "12  {u'contains(MARRIAGE)': True, u'contains(SEX)'...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Features'] = df['Joke'].apply(lambda x:extract_features(x, get_all_words(df[df['Year'] <= 2013])))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Labelling our jokes depending on what will be deemed as funny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "      <th>Year</th>\n",
       "      <th>Joke</th>\n",
       "      <th>Features</th>\n",
       "      <th>Funny</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>          Tim Vine</td>\n",
       "      <td> 1</td>\n",
       "      <td> I've decided to sell my Hoover... well it was ...</td>\n",
       "      <td> 2014</td>\n",
       "      <td>   [DECIDED, SELL, HOOVER, WELL, COLLECTING, DUST]</td>\n",
       "      <td> {u'contains(DUST)': False, u'contains(COLLECTI...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td>      Masai Graham</td>\n",
       "      <td> 2</td>\n",
       "      <td> I've written a joke about a fat badger but I c...</td>\n",
       "      <td> 2014</td>\n",
       "      <td>    [WRITTEN, JOKE, FAT, BADGER, COULDN, FIT, SET]</td>\n",
       "      <td> {u'contains(SET)': True, u'contains(WRITTEN)':...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>         Rob Auton</td>\n",
       "      <td> 1</td>\n",
       "      <td> I heard a rumour that Cadbury is bringing out ...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [HEARD, RUMOUR, CADBURY, BRINGING, ORIENTAL, C...</td>\n",
       "      <td> {u'contains(ORIENTAL)': True, u'contains(CHOCO...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>        Alex Horne</td>\n",
       "      <td> 2</td>\n",
       "      <td> I used to work in a shoe-recycling shop. It wa...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [USED, WORK, SHOE, RECYCLING, SHOP, SOLE, DEST...</td>\n",
       "      <td> {u'contains(DESTROYING)': True, u'contains(SOL...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>       Alfie Moore</td>\n",
       "      <td> 3</td>\n",
       "      <td> I'm in a same-sex marriage... the sex is alway...</td>\n",
       "      <td> 2013</td>\n",
       "      <td>                      [SEX, MARRIAGE, SEX, ALWAYS]</td>\n",
       "      <td> {u'contains(MARRIAGE)': True, u'contains(SEX)'...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>          Tim Vine</td>\n",
       "      <td> 4</td>\n",
       "      <td> My friend told me he was going to a fancy dres...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [FRIEND, TOLD, GOING, FANCY, DRESS, PARTY, ITA...</td>\n",
       "      <td> {u'contains(GOING)': True, u'contains(PARTY)':...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>      Gary Delaney</td>\n",
       "      <td> 5</td>\n",
       "      <td> I can give you the cause of anaphylactic shock...</td>\n",
       "      <td> 2013</td>\n",
       "      <td>      [GIVE, CAUSE, ANAPHYLACTIC, SHOCK, NUTSHELL]</td>\n",
       "      <td> {u'contains(ANAPHYLACTIC)': True, u'contains(N...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>         Phil Wang</td>\n",
       "      <td> 6</td>\n",
       "      <td> The Pope is a lot like Doctor Who. He never di...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [POPE, LOT, LIKE, DOCTOR, NEVER, DIES, KEEPS, ...</td>\n",
       "      <td> {u'contains(REPLACED)': True, u'contains(NEVER...</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td> Marcus Brigstocke</td>\n",
       "      <td> 7</td>\n",
       "      <td> You know you are fat when you hug a child and ...</td>\n",
       "      <td> 2013</td>\n",
       "      <td>               [KNOW, FAT, HUG, CHILD, GETS, LOST]</td>\n",
       "      <td> {u'contains(LOST)': True, u'contains(CHILD)': ...</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>     Liam Williams</td>\n",
       "      <td> 8</td>\n",
       "      <td>                 The universe implodes. No matter.</td>\n",
       "      <td> 2013</td>\n",
       "      <td>                      [UNIVERSE, IMPLODES, MATTER]</td>\n",
       "      <td> {u'contains(MATTER)': True, u'contains(IMPLODE...</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Author  Rank  \\\n",
       "0            Tim Vine     1   \n",
       "1        Masai Graham     2   \n",
       "10          Rob Auton     1   \n",
       "11         Alex Horne     2   \n",
       "12        Alfie Moore     3   \n",
       "13           Tim Vine     4   \n",
       "14       Gary Delaney     5   \n",
       "15          Phil Wang     6   \n",
       "16  Marcus Brigstocke     7   \n",
       "17      Liam Williams     8   \n",
       "\n",
       "                                             Raw_joke  Year  \\\n",
       "0   I've decided to sell my Hoover... well it was ...  2014   \n",
       "1   I've written a joke about a fat badger but I c...  2014   \n",
       "10  I heard a rumour that Cadbury is bringing out ...  2013   \n",
       "11  I used to work in a shoe-recycling shop. It wa...  2013   \n",
       "12  I'm in a same-sex marriage... the sex is alway...  2013   \n",
       "13  My friend told me he was going to a fancy dres...  2013   \n",
       "14  I can give you the cause of anaphylactic shock...  2013   \n",
       "15  The Pope is a lot like Doctor Who. He never di...  2013   \n",
       "16  You know you are fat when you hug a child and ...  2013   \n",
       "17                  The universe implodes. No matter.  2013   \n",
       "\n",
       "                                                 Joke  \\\n",
       "0     [DECIDED, SELL, HOOVER, WELL, COLLECTING, DUST]   \n",
       "1      [WRITTEN, JOKE, FAT, BADGER, COULDN, FIT, SET]   \n",
       "10  [HEARD, RUMOUR, CADBURY, BRINGING, ORIENTAL, C...   \n",
       "11  [USED, WORK, SHOE, RECYCLING, SHOP, SOLE, DEST...   \n",
       "12                       [SEX, MARRIAGE, SEX, ALWAYS]   \n",
       "13  [FRIEND, TOLD, GOING, FANCY, DRESS, PARTY, ITA...   \n",
       "14       [GIVE, CAUSE, ANAPHYLACTIC, SHOCK, NUTSHELL]   \n",
       "15  [POPE, LOT, LIKE, DOCTOR, NEVER, DIES, KEEPS, ...   \n",
       "16                [KNOW, FAT, HUG, CHILD, GETS, LOST]   \n",
       "17                       [UNIVERSE, IMPLODES, MATTER]   \n",
       "\n",
       "                                             Features  Funny  \n",
       "0   {u'contains(DUST)': False, u'contains(COLLECTI...   True  \n",
       "1   {u'contains(SET)': True, u'contains(WRITTEN)':...   True  \n",
       "10  {u'contains(ORIENTAL)': True, u'contains(CHOCO...   True  \n",
       "11  {u'contains(DESTROYING)': True, u'contains(SOL...   True  \n",
       "12  {u'contains(MARRIAGE)': True, u'contains(SEX)'...   True  \n",
       "13  {u'contains(GOING)': True, u'contains(PARTY)':...   True  \n",
       "14  {u'contains(ANAPHYLACTIC)': True, u'contains(N...   True  \n",
       "15  {u'contains(REPLACED)': True, u'contains(NEVER...  False  \n",
       "16  {u'contains(LOST)': True, u'contains(CHILD)': ...  False  \n",
       "17  {u'contains(MATTER)': True, u'contains(IMPLODE...  False  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "funny_threshold = 5\n",
    "df['Rank'] = df['Rank'].apply(int)\n",
    "df['Funny'] = df['Rank'] <= funny_threshold\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a labeled feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "      <th>Year</th>\n",
       "      <th>Joke</th>\n",
       "      <th>Features</th>\n",
       "      <th>Funny</th>\n",
       "      <th>Labeled_Feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 </th>\n",
       "      <td>     Tim Vine</td>\n",
       "      <td> 1</td>\n",
       "      <td> I've decided to sell my Hoover... well it was ...</td>\n",
       "      <td> 2014</td>\n",
       "      <td>   [DECIDED, SELL, HOOVER, WELL, COLLECTING, DUST]</td>\n",
       "      <td> {u'contains(DUST)': False, u'contains(COLLECTI...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(DUST)': False, u'contains(COLLECT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 </th>\n",
       "      <td> Masai Graham</td>\n",
       "      <td> 2</td>\n",
       "      <td> I've written a joke about a fat badger but I c...</td>\n",
       "      <td> 2014</td>\n",
       "      <td>    [WRITTEN, JOKE, FAT, BADGER, COULDN, FIT, SET]</td>\n",
       "      <td> {u'contains(SET)': True, u'contains(WRITTEN)':...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(SET)': True, u'contains(WRITTEN)'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>    Rob Auton</td>\n",
       "      <td> 1</td>\n",
       "      <td> I heard a rumour that Cadbury is bringing out ...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [HEARD, RUMOUR, CADBURY, BRINGING, ORIENTAL, C...</td>\n",
       "      <td> {u'contains(ORIENTAL)': True, u'contains(CHOCO...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(ORIENTAL)': True, u'contains(CHOC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>   Alex Horne</td>\n",
       "      <td> 2</td>\n",
       "      <td> I used to work in a shoe-recycling shop. It wa...</td>\n",
       "      <td> 2013</td>\n",
       "      <td> [USED, WORK, SHOE, RECYCLING, SHOP, SOLE, DEST...</td>\n",
       "      <td> {u'contains(DESTROYING)': True, u'contains(SOL...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(DESTROYING)': True, u'contains(SO...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>  Alfie Moore</td>\n",
       "      <td> 3</td>\n",
       "      <td> I'm in a same-sex marriage... the sex is alway...</td>\n",
       "      <td> 2013</td>\n",
       "      <td>                      [SEX, MARRIAGE, SEX, ALWAYS]</td>\n",
       "      <td> {u'contains(MARRIAGE)': True, u'contains(SEX)'...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(MARRIAGE)': True, u'contains(SEX)...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Author  Rank                                           Raw_joke  \\\n",
       "0       Tim Vine     1  I've decided to sell my Hoover... well it was ...   \n",
       "1   Masai Graham     2  I've written a joke about a fat badger but I c...   \n",
       "10     Rob Auton     1  I heard a rumour that Cadbury is bringing out ...   \n",
       "11    Alex Horne     2  I used to work in a shoe-recycling shop. It wa...   \n",
       "12   Alfie Moore     3  I'm in a same-sex marriage... the sex is alway...   \n",
       "\n",
       "    Year                                               Joke  \\\n",
       "0   2014    [DECIDED, SELL, HOOVER, WELL, COLLECTING, DUST]   \n",
       "1   2014     [WRITTEN, JOKE, FAT, BADGER, COULDN, FIT, SET]   \n",
       "10  2013  [HEARD, RUMOUR, CADBURY, BRINGING, ORIENTAL, C...   \n",
       "11  2013  [USED, WORK, SHOE, RECYCLING, SHOP, SOLE, DEST...   \n",
       "12  2013                       [SEX, MARRIAGE, SEX, ALWAYS]   \n",
       "\n",
       "                                             Features Funny  \\\n",
       "0   {u'contains(DUST)': False, u'contains(COLLECTI...  True   \n",
       "1   {u'contains(SET)': True, u'contains(WRITTEN)':...  True   \n",
       "10  {u'contains(ORIENTAL)': True, u'contains(CHOCO...  True   \n",
       "11  {u'contains(DESTROYING)': True, u'contains(SOL...  True   \n",
       "12  {u'contains(MARRIAGE)': True, u'contains(SEX)'...  True   \n",
       "\n",
       "                                      Labeled_Feature  \n",
       "0   ({u'contains(DUST)': False, u'contains(COLLECT...  \n",
       "1   ({u'contains(SET)': True, u'contains(WRITTEN)'...  \n",
       "10  ({u'contains(ORIENTAL)': True, u'contains(CHOC...  \n",
       "11  ({u'contains(DESTROYING)': True, u'contains(SO...  \n",
       "12  ({u'contains(MARRIAGE)': True, u'contains(SEX)...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Labeled_Feature'] = zip(df['Features'],df['Funny'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating our classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(df[df['Year'] <= 2013]['Labeled_Feature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "           contains(GOT) = True            False : True   =      2.4 : 1.0\n",
      "          contains(KNOW) = True             True : False  =      1.7 : 1.0\n",
      "        contains(PEOPLE) = True            False : True   =      1.7 : 1.0\n",
      "           contains(SEX) = True            False : True   =      1.7 : 1.0\n",
      "         contains(NEVER) = True            False : True   =      1.7 : 1.0\n",
      "            contains(RE) = True             True : False  =      1.6 : 1.0\n",
      "        contains(FRIEND) = True             True : False  =      1.6 : 1.0\n",
      "           contains(SAY) = True             True : False  =      1.6 : 1.0\n",
      "        contains(BOUGHT) = True             True : False  =      1.6 : 1.0\n",
      "           contains(ONE) = True             True : False  =      1.5 : 1.0\n"
     ]
    }
   ],
   "source": [
    "classifier.show_most_informative_features(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joke = 'Why was 10 afraid of 7? Because 7 8 9'\n",
    "classifier.classify(extract_features(string_to_list(joke), get_all_words(df[df['Year'] <= 2013])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joke = 'Your mother is ...'\n",
    "classifier.classify(extract_features(string_to_list(joke), get_all_words(df[df['Year'] <= 2013])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The real test comes from applying our classifier to [this year's jokes](http://www.bbc.co.uk/news/uk-scotland-edinburgh-east-fife-28838287)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raw_joke</th>\n",
       "      <th>Funny</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> I've decided to sell my Hoover... well it was ...</td>\n",
       "      <td>  True</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> I've written a joke about a fat badger but I c...</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> Always leave them wanting more my uncle used t...</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> I was given some Sudoku toilet paper. It didn'...</td>\n",
       "      <td>  True</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> I wanted to do a show about feminism. But my h...</td>\n",
       "      <td>  True</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> Money can't buy you happiness? Well check this...</td>\n",
       "      <td> False</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> Scotland had oil but it's running out thanks t...</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> I've been married for 10 years I haven't made ...</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> This show is about perception and perspective....</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Raw_joke  Funny Prediction\n",
       "0  I've decided to sell my Hoover... well it was ...   True      False\n",
       "1  I've written a joke about a fat badger but I c...   True       True\n",
       "2  Always leave them wanting more my uncle used t...   True       True\n",
       "3  I was given some Sudoku toilet paper. It didn'...   True      False\n",
       "4  I wanted to do a show about feminism. But my h...   True      False\n",
       "5  Money can't buy you happiness? Well check this...  False      False\n",
       "6  Scotland had oil but it's running out thanks t...  False       True\n",
       "7  I've been married for 10 years I haven't made ...  False       True\n",
       "8  This show is about perception and perspective....  False       True"
      ]
     },
     "execution_count": 798,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Prediction'] = df['Features'].apply(classifier.classify)\n",
    "df[df['Year'] == 2014][['Raw_joke','Funny','Prediction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33333333333333331"
      ]
     },
     "execution_count": 799,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df[df['Year'] == 2014]['Prediction'] == df[df['Year'] == 2014]['Funny']) / float(len(df[df['Year'] == 2014]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapping all of the above in a function to see if we can identify how our classifier performs based on a funniness threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def accuracy(funny_threshold):\n",
    "    \"\"\"\n",
    "    A function to return the accuracy of our predictor\n",
    "    \"\"\"\n",
    "    df['Funny'] = df['Rank'] <= funny_threshold  # Changing the threshold\n",
    "    df['Labeled_Feature'] = zip(df['Features'], df['Funny'])  # Re create labeled features\n",
    "    classifier = nltk.NaiveBayesClassifier.train(df[df['Year'] <= 2013]['Labeled_Feature'])  # Train classifier\n",
    "    df['Prediction'] = df['Features'].apply(classifier.classify)  # Apply classifier\n",
    "    return sum(df[df['Year'] == 2014]['Prediction'] == df[df['Year'] == 2014]['Funny']) / float(len(df[df['Year'] == 2014]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 801,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn as sns  # Making our plots look nicer easier (seaborn does a lot more: check it out)\n",
    "import matplotlib.pyplot as plt  # Plots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3dLYDAz3bizNzZ3n7hcB+wL8B9wWWRcSPMvNjU51s+fJlLFmyR4W4u2dwcGDmO/VR0/NB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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11494cd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(0, 11)\n",
    "y = [accuracy(n) for n in x]\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.set_context(rc={\"figure.figsize\": (8, 4)})\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel('Funny Threshold')\n",
    "plt.ylabel('Accuracy of classifier')\n",
    "sns.plt.ylim(0,1.05)\n",
    "sns.plt.xlim(min(x) - .5, max(x) + .5)\n",
    "plt.xticks(x)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapping everything in another function to see the effect of the testing data set\n",
    "\n",
    "We used previous years to train for this year. Here we will just use random samples of a variety of size of the data to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def accuracy(ratio_of_data=.8, funny_threshold=5):\n",
    "    \"\"\"\n",
    "    A function to return the accuracy of our predictor based on \n",
    "    the percentage of rows and threshold of funniness used as a training set\n",
    "    \"\"\"\n",
    "    n = max(1, min(int(ratio_of_data * len(df)), len(df) - 1))  # Makes sure we don't have a stupid number of rows\n",
    "    df['Funny'] = df['Rank'] <= funny_threshold  # Threshold funniness\n",
    "\n",
    "    training_rows = random.sample(df.index, n)  # Identify the rows used for the training\n",
    "    all_words = get_all_words(df.ix[training_rows])  # Identify all the words\n",
    "    df['Features'] = df['Joke'].apply(lambda x:extract_features(x, all_words))  # Extract the features\n",
    "    df['Labeled_Feature'] = zip(df['Features'],df['Funny'])  # Re create labeled features\n",
    "    \n",
    "    classifier = nltk.NaiveBayesClassifier.train(df.ix[training_rows]['Labeled_Feature'])  # Train classifier\n",
    "    df['Prediction'] = df['Features'].apply(classifier.classify)  # Apply classifier\n",
    "    return sum(df.drop(training_rows)['Prediction'] == df.drop(training_rows)['Funny']) / float((len(df) - n))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a plot of the accuracy for varying ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ratio_experiment_data(funny_threshold=5, number_of_steps=10, number_of_repetitions=20):\n",
    "    \"\"\"\n",
    "    Use `funny_threshold` as the funny threshold.\n",
    "    Repeat all this for a granularity given by `number_of_steps` and repeating each experiment `number_of_repetitions` times.\n",
    "    \"\"\"\n",
    "    steps = [x / float(number_of_steps) for x in range(number_of_steps)]\n",
    "    return [[accuracy(ratio_of_data=x, funny_threshold=funny_threshold) for x in steps] \n",
    "            for k in range(number_of_repetitions)], steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot(funny_threshold=5, number_of_steps=10, number_of_repetitions=20, color='blue'):\n",
    "    data, steps = random_experiment_data(funny_threshold=funny_threshold, \n",
    "                                         number_of_steps=number_of_steps, \n",
    "                                         number_of_repetitions=number_of_repetitions)\n",
    "\n",
    "    fig = plt.figure()\n",
    "    sns.tsplot(data, steps, color=color)\n",
    "    plt.xlabel('Ratio of data used for training (%s repetitions)' % number_of_repetitions)\n",
    "    plt.ylabel('Accuracy')\n",
    "    sns.plt.ylim(0,1)\n",
    "    plt.title('Accuracy with funny threshold: %s ' % funny_threshold)\n",
    "    plt.show()\n",
    "    return data, steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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PoiRJ0sR66Xl/OTM3AsjMXwO/7m9JkiSpm17C+ycR8RHgOuDh1sTMvLpvVUmSpAn1Et5r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f90890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BpzT8i58hXrSEtvZGuk+dXYL2gwMUd71A6cUdpIcPhI2ZDJnLLJl1G4lXrjlr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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148dfd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3LepzXxHV5ruHRsiyjAn7PMnw72g/7rnk15wA+C9j3NbT8DjAtDJBOrqdKMpR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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114317a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ct/eewrfw3pNQImUQmB4XniQJm6qXUUmPY7L1PUYbXz5gH+150PNicsFjKXiLiMgPJO89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\ncyeZ2ZO6nbiFhNJqQavYSV7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B6iITRCxlIoQnAV9YmSStuBcCf9w5AcwyLfvczGzBwGFm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\naE3aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a8f2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KlFiRrmVlYf1kUM/nBLSdjXv5aeVK9mUPUKKbc7uewMnF8w75PFnIKCVlBpLVlNPuQ0J7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\n+h0lxN/1Z9y9kvcKfJz4txqAHe5+fb7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113410710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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klJKpjddJklCmdNC+SklKlRrbsl2UQ5kVSR8rkxWH9d1tJ3hnZnaOu98AYGb3A3QDERE5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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d54950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RDi1b8Hb3APzurM03zrHfB5crDSIiIqvR6rkru4iIyN2EgreIiEiX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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148dfd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/sKvC5j21vU0fwLKuHBsGSxw5UOTIwRIre/Nkmb9xKPkH5inXMraVa1z78B5u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\nMqF08U7gT0Ob4TPxgepz+Ha9c9l7AU+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141f84d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vn/Srn/ZtVTcF6f1gkuNMlZ7h0Zih4WRsqdZD/kZ8C0KxNPXvrrPDn0B7F2TS\n/0MW9vgAWnvSrgY70uvoo4hDgnWxlLBrt1+Gtvo3PHLo6y7uzbBsiT8xDQzG9A/6Eb+T5U0QQGeH\nD+jdNYG9rdUvW1uuGWxVrt5Pb1cqfjDixO1RJaGnO6RvUZYlizMsWZShb1H2kAJMFI9fepjLHjq+\nIY5hcDhmtODztBooypWErdvLY60z1QABsKzPD35c2tfCz28eYP/B+PAgniTkc7422nYELXpJktA/\nGDM4GKWtKgGjhZg77ylyxz0FRgsJuSycfkoLy/r8e13cmyGfP/x7VW3tCEP85ZbpCPKWfDA2+LNU\nGZ9RrPazn8poIeb2uwvctqFAuQK9PSFPvrANOzk/9tw4TmhvD1nYE5LN1v/ey+V0tPhonH4XD09L\nOcrxs5sOcu+DRcoVX1A745QWzju75ZDLo5IkGfu+LejOzEprTKHor06aLoiD74vecH+RDfcVxn4v\nK5dlOWGVn8Ni+ZKju6opSRJ+cP0w9z1Y4oRVWV7ynK4pC2FJkpDNBixfkmXp0u7mDt7lSpxsuOsg\nQeivifv61YOccmKOFzyrk+VLsnM6Q85vi2YI3rWq/ZpxVNO0HVQDuG/nrgb0sGZSmPEA7/erBsVq\n6T2Ok7Fm9ihmvGRf079dfSxOg00l8v3JSU0Nr/bEWir7/vxqUB8cjimWQnY+VuRAfzw2UKdWGMKC\nan/mggwLe0IWLsikfZYBe/dH7NhdGQvY+w4ceozODn8yqP4t68tOGjSqLRv9gzEDgzEHB6OxwN4/\n6NP7eE4bubQP3XcHwMDQ4Uve9vb4muiSsaDum4trA0w1CdXm8P6BaCxY17bAtOQDTlid46Q1/rLD\n6lUpvb3t7Ns3zAMPl7j59tHJg3jaL9zTFdIxzQjtOE44OBCN5U0QBAwNx/z6rgIb7itQLvtWl/PO\nauG8s1sP62P27ycgm/WFhmzGB+m21vpaO+I4oVCIKaRzeZcqUCkf3k1SNTQSc/Ptfv7sOPYFmqc+\nqZ21q3I1x/Q19EW9UwfPOE4YGPLdWaXS5C0CxWLMQ4+Uufeh4ljTeHfneNN4bV4kiW8t6ur0/fSN\nGF8xMupbISqV6ZfhjKKEBx8p8Zt7i2yrGe2ez8GalTnWrsyxdlWOhQtmTmeSJFx/8wi331Vk+ZIM\nL7+ie8om+SRJaG0JWbI4Ux0L09zBe8euYrJz1zAAV/9okAc3lfndF3SxdlWOVcsbsIqHNF3wPh5V\nA365FPvXCAiuAAAeU0lEQVSaZuyDevXxKEqoRD7wL1zYzkD/KOBrrAcHIvYf9Cea/Qd90+f+g9GM\nTfMAuRws78uyfGmWZUuyLO/L0tU5WcBgbHR+VBlvpp+uFhdFvvm9f8AH80Ix9k3c2eogvPGJd3yQ\nToN1Npj0sppKlLDvQMSevRG791XYvS9iz77D32drS8CSxZnxoL4oQ6GYpAG7dEhhZfHCDCet8QF7\nxdIsmUwwVtjKZf0lREuWdLJ5y0A6HoIZg3guF9DdFR5yNUUc+9HgQ8PjM4Ad6I+49TcF7nVFojQA\nXnhuK+ee3ko+H4y1/OTzaf6k/f2zPdK/OmFJoZhQKSeUI986VU0n+EuVfvHrUR7YWAL8qO6nPamd\npX3jteA48d0pCxeMB/Gh4SgdTwBBcHgQLFcSNm0uc//GIpu2+AHGACesznPu6XnWnTChabyav50h\nXVOM+p5tg0MR/TXdGtMZLcRs3VFh87Yym7eXOdA//l3rbA9Yu8oH8rUrc3R2HF6RrA6uXtSb4coX\ndk05QDCOEzo7wkNakps+eD+6tZjs2zdM/2DEVV/rp29Rhte8tJuuTr+whsw+Be+5E0UJ3T0dbN8x\nOHairTaHBsGhtafRQnxIMD/Q76+TXbwwk9aqM4ecaKvHDyY2PaeX20zsUqhE44WNSuRbHaLYPx5H\nvtCRJJPNKT3eJRHXjNKHQ8cUBDWtHWFA2qzu96seL0l87X/3vigN5hV27/V9uJPJZvGr363Jjc2z\nD9XZyHztu7XVjy2o1g6r3+/avumZgniSJIQZH2RKpYTh0YRqFuzeW+GW3xRwD5dIEt9K8sQntHKm\ntZDN+BadbNbX4ucqQE1mcDjiYH+Utp74xO/aU+GGW0bYvM1/EKednOfJT2yjt6emoJIktLUGFIvJ\nWOtCrShK2LK9zH0bS2x8pETJT9HAot4Mp6/Lc9q6PCed0HXYgLWWFj8t6bEacNw/ENE/GE36nqZ8\nzqCfkGrztjKbt5UPGWy4qDfD2pVZ1q7KsXpFjgc2FvnRDSN0d4a88kXdhxWiq+IkSa8MOPS7MW+C\n9/U3j3DbhgLPfUYHp63Ls2ZFrqlmEWomCt5za7L8juN0usdSMmNQh8P7SfPZgGzOj4KfrWVck8S3\nHpRKfhR3Evua16GXxtUOBJz591lbS6z2iVdn5au9BrpU8lNM7t7rg3o2Ayet8SfJbPbQWm1rS3VG\nvsnf98T8rgbx6tUIMwXxapq27Sxzy50FNm3x0apvUYaL1rdy6kl5wjA4LgLUZAYGfc0zrglam7eV\nueGWEXbtiQhDOOf0Fi4+v23KOTSqA73uf6iE21Qau4SzuzPktHV5Tj8lT9/CzGED1uLYFwQaMbr/\naFSvpx8cio+4qT5JEvbsj9JAXmHbzvLYINDqANj21oArX9TNwikGviUxLFqUmTSf50Xw3rlziE9/\n5SDZLPzRqxbQ2hKwYqmazBtFwXtuHUl+TwzqUZKQzfjR2e119pM2g0rFX+5WqgnoEwsuUZz4S83y\nIW2tPmDXU2CYKr8H0ubUmZrT9x+MuOXOwlh/6MplWS5a38qJa3J+7ETkB371dM3tqlVHIkmStOY5\nHrSSJOHBTWVuvHWEA/0xuSxccE4rFz6hjZa02X/3voj7HyrxwMOlsUHE7a0Bti7P6evyrFg6+bXS\n3T1tlIsFFnSHcz45Tj38pZMRQ8PJ2OQxRyqKEnY8VmHzdh/Mh4ZjXvjsTpb1Td5CnCQJy/qmHrc1\nL4L3dT/fx09+McKlF7ZxcVoC9iuvSCMoeM8t5Xd9agdnhQF0dmTqupRsopnye2DQN6dOFsRrnbQm\nx5PWt7JqeY7q+bSj3Q8sbJZCVHXA3cDQ+IpXUZRwtyvyy9tGGR7xNeXTTs6zeXt5LA/y+YBTT8xx\n2roW1q6cfBR2FPlBf22tAaecvIB9+4bm9L0djSjyQXx45PCZ0WZTEPipn6cb1T9V8G6azuI4Sbj9\nrgKZDJx7RgtJEkzZdyAi81cYBrS3Z2ifYlKq2dLdlaG7KzMWxE9fl+e0k/M88LAfgdzdGXLhE1pZ\nujjrm9FJ6Eovn2u2KWbDMGDhgiwLuv3gweERP3L8CWe0csYpLdxxd4FbflPgznuLZDNw6kk5Tl/X\nwklrcocVnKpdFy0tfrR8V8d4S1Cz5EsmE9C3KMuCbr/g0shIMu0gziOVJH6A3rK+o19Mq2mCt9tY\n4OBAzNmn5eloC8nnm+eLICLNqxrE+wciBtIgfkY6D3V1QFx3V2bKubGbSRj6oNXbE7O/P2ZkJCaf\nC7jovDbOPaOFXXsqrFiaoyV/eMAGaG0NaG/L0FFn18XxLpcLWbo4HLskbiSdGCp8HC0qSZLQ1uov\nhXw8hYGmCd6/uNU3b51/ditxnEx7zaWIyGyrzjznJ7KJyGUDerqyx9UgtNmSzYYsWRRS6vKXKo4W\nfMA5cfX4TJZRnJANobXVXwM/H/OhKgz94LrqVLqDQ34622Jx+tnuJkqSZNaukGqa4P3wo0XWrMzS\nt8g3UWkFMRGZa0EwfhL/bZDPhyztCykUfRAvFHxzb1trQEf7459CthllMgELerIs6PF94wNDftrZ\nUjkZmzBoMnGcsHCBb8WZDU0TvMGPeARmZWF0ERGpT2tLyPIlYdpNoHNvVSYT0NuTpXdiIJ8w21yc\nwNLF2SOaancmTRO8X/fyxSzujfwMNGoyFxGZcwrcU6sN5JWKn+J4tJAQxQkr+rKzvuRs0wTv005p\nY9++YcIgmdd9KyIi0tyy2ZCFCxobp5ouCrarr1tERH7LNVUkjOOErkkmfRcREflt0lSRMJsNZr3f\nQEREpNk0VSRsb9NgCRERkYYNWDOzEPgkcA5QBN7gnHu4ZvuFwMfwa9JtB17jnCtNdbxKJT5sqTQR\nEZHfRo2seb8IyDvnLgH+Eh+oATCzAPgs8Drn3FOAnwAnTnewjrbmmeRfRESkkRoZvC8FrgVwzt0C\nXFCz7VRgH/AuM/sZsMA556Y72JI+Lf0pIiICjQ3e3cBAzf0obUoHWAxcAnwCeCZwuZldNt3BVOsW\nERHxZuzzNrNlzrldR3HsAaCr5n7onKsuhLsP2FitbZvZtfia+fXTHbCvr2u6zdIAyvO5pfyeW8rv\nuac8nx31DFi70cweBL4IfNc5V67z2DcBVwDfMLOLgLtqtm0COs3s5HQQ21OAz810wD17But8aZkN\nfX1dyvM5pPyeW8rvuac8P3JTFXbqaTY/FfhH4HeAB83s38zsghmeA/AdoGBmN+EHq73TzK40sz9M\nR5X/AfA1M7sV2OKc+2E9b0REROS3XVBdRH0mZtYGvBz4OyAG9gBvc87d3LjkHSJRiW1uqZQ8t5Tf\nc0v5PfeU50eur69r0gFfM9a8zexZZvYloNq8/XLn3BrgdcA3ZzORIiIiMrN6+rzfB3weeItzbrj6\noHPubjP7aMNSJiIiIpOqp8/7uUCnc27YzFaa2YfMrB3AOffPjU2eiIiITFRP8P4asDy9PZg+58sN\nS5GIiIhMq55m87XOuSsAnHMDwHvMbENjkyUiIiJTqafmHZvZOdU7ZnY6MOUCIiIiItJY9dS83w38\nyMy2p/f7gFc3LkkiIiIynRmDt3Pux2a2BjgbKPuHXLHhKRMREZFJ1TO3+WnAW4AOfDN7xsxOcM49\ntdGJExERkcPV0+f9n8ABYD1wJ7AE0FSmIiIix0g9wTt0zr0f+B/gDuCFwLMbmioRERGZUj3Be9jM\nWoAHgfPT/u7FjU2WiIiITKWe0eZfAf4beCXwKzN7DrCjoakSERGRKdVT874ReIlzbg/wdOCzwIsb\nmSgRERGZWj017/90zp0G4JzbCmxtbJJERERkOvUE73vN7H3ALcBo9UHn3A0NS5WIiIhMqZ7gvQi4\nLP2rNfG+iIiIzIF6Zlh7+hykQ0REROpUzwxr10/ycOKce0YD0iMiIiIzqKfZ/G9qbufwk7QcaExy\nREREZCb1NJv/bMJD15nZrcB7G5IiERERmVY9zeZrau4GwFnAwoalSERERKZVT7P5DUCS3k6AvcDb\nG5YiERERmdaMM6w5504ATnXOnQicBjzDOadVxURERI6RGYO3mb0cv5oYwBrgATN7UUNTJSIiIlOq\nZ27z9wLPBHDObQTO49AR6CIiIjKH6gneOefcY9U7zrndDUyPiIiIzKCeAWs3mdnXga/iR5u/HLi5\noakSERGRKdUTvN+KH13+RqCMH33+yUYmSkRERKZWV7M5MOqcuwJ4B36hknqCvoiIiDRAPcH7a8Dy\n9PZA+pwvNyxFIiIiMq16atBr01o3zrkB4D1mtqGxyRIREZGp1FPzjs3snOodMzsdKDUuSSIiIjKd\nemre7wZ+ZGbb0/uLgVc3LkkiIiIynXqmR/0xfma1NwFXAzsBTY8qIiJyjNSzqthJ+MvEXgcsAP4e\neEFjkyUiIiJTmTJ4m9lL8LXt9cB38U3lVznnNDWqiIjIMTRdzfub6d8lzrmHAMwsmWb/Q5hZiJ/M\n5RygCLzBOffwJPt9FtjnnPurI0m4iIjIb6vp+rzPAbYCN5rZr8zsTziyyVleBOSdc5cAfwl8bOIO\nZvZG4CzG1wsXERGRGUwZvJ1z9zjn/hRYBfwD8HRgqZldY2bPq+PYlwLXpse6BbigdqOZXQI8EfgM\nfs50ERERqcOMNWnnXAU/yvxqM1uC7/v+B+CaGZ7ajZ+RrSoys9A5F5vZcuB9wIuBV9Sb2L6+rnp3\nlVmiPJ9byu+5pfyee8rz2XFEc5Sny4H+U/o3kwGg9lMKnXNxevtl+OvFfwAsA9rN7H7n3L9Pd8A9\newaPJLnyOPX1dSnP55Dye24pv+ee8vzITVXYaeQCIzcBVwDfMLOLgLuqG5xznwA+AWBmrwVOmylw\ni4iIiNfI4P0d4FlmdlN6//VmdiXQ6Zy7asK+GrAmIiJSp4YFb+dcArx5wsMPTrLflxqVBhERkfmo\nnoVJRERE5Dii4C0iItJkFLxFRESajIK3iIhIk1HwFhERaTIK3iIiIk1GwVtERKTJKHiLiIg0GQVv\nERGRJqPgLSIi0mQUvEVERJqMgreIiEiTUfAWERFpMgreIiIiTUbBW0REpMkoeIuIiDQZBW8REZEm\no+AtIiLSZBS8RUREmoyCt4iISJNR8BYREWkyCt4iIiJNRsFbRESkySh4i4iINBkFbxERkSaj4C0i\nItJkFLxFRESajIK3iIhIk1HwFhERaTIK3iIiIk1GwVtERKTJKHiLiIg0GQVvERGRJqPgLSIi0mQU\nvEVERJqMgreIiEiTyTbqwGYWAp8EzgGKwBuccw/XbL8S+GOgAtwNvMU5lzQqPSIiIvNFI2veLwLy\nzrlLgL8EPlbdYGZtwIeApzvnngz0AM9vYFpERETmjUYG70uBawGcc7cAF9RsKwAXO+cK6f0sMNrA\ntIiIiMwbjQze3cBAzf0obUrHOZc45/YAmNnbgQ7n3I8bmBYREZF5o2F93vjA3VVzP3TOxdU7aSD/\nCLAOeGk9B+zr65p5J5lVyvO5pfyeW8rvuac8nx2NDN43AVcA3zCzi4C7Jmz/DL75/MX1DlTbs2dw\ndlMo0+rr61KezyHl99xSfs895fmRm6qw08jg/R3gWWZ2U3r/9ekI807gNuD3gRuAn5oZwL84577b\nwPSIiIjMCw0L3mlt+s0THn6w5namUa8tIiIyn2mSFhERkSaj4C0iItJkFLxFRESajIK3iIhIk1Hw\nFhERaTIK3iIiIk1GwVtERKTJKHiLiIg0GQVvERGRJqPgLSIi0mQUvEVERJqMgreIiEiTUfAWERFp\nMgreIiIiTUbBW0REpMkoeIuIiDQZBW8REZEmo+AtIiLSZBS8RUREmoyCt4iISJNR8BYREWkyCt4i\nIiJNRsFbRESkySh4i4iINBkFbxERkSaj4C0iItJkFLxFRESajIK3iIhIk1HwFhERaTIK3iIiIk1G\nwVtERKTJKHiLiIg0GQVvERGRJqPgLSIi0mQUvEVERJqMgreIiEiTUfAWERFpMtlGHdjMQuCTwDlA\nEXiDc+7hmu1XAO8FKsAXnHOfa1RaRERE5pNG1rxfBOSdc5cAfwl8rLrBzHLAPwHPAp4G/JGZLWlg\nWkREROaNRgbvS4FrAZxztwAX1Gw7HdjonOt3zpWBXwBPbWBaRERE5o1GBu9uYKDmfpQ2pVe39dds\nGwR6GpgWERGReaNhfd74wN1Vcz90zsXp7f4J27qAAzMcL+jr65phF5ltyvO5pfyeW8rvuac8nx2N\nrHnfBDwXwMwuAu6q2fYAcIqZ9ZpZHt9kfnMD0yIiIjJvBEmSNOTAZhYwPtoc4PXA+UCnc+4qM3s+\n8D58AeLzzrlPNSQhIiIi80zDgreIiIg0hiZpERERaTIK3iIiIk1GwVtERKTJNPJSsaOiaVXnVh35\nfSXwx/j8vht4i3NOAyWO0kz5XbPfZ4F9zrm/muMkzjt1fMcvxM8AGQDbgdc450rHIq3zQR35/WLg\nr4EEfw7/9DFJaJM7HmvemlZ1bk2X323Ah4CnO+eejJ9I5/nHJJXzx5T5XWVmbwTOwp/c5PGb7jse\nAJ8FXuecewrwE+DEY5LK+WOm73j1HH4p8Kdmpgm6jsLxGLw1rercmi6/C8DFzrlCej8LjM5t8uad\n6fIbM7sEeCLwGXxNUB6/6fL8VGAf8C4z+xmwwDnn5jyF88u033GgDCwA2vDfcRVSj8LxGLw1rerc\nmjK/nXOJc24PgJm9Hehwzv34GKRxPpkyv81sOX7ug7ehwD2bpjunLAYuAT4BPBO43Mwum+P0zTfT\n5Tf4mvjtwD3A951ztftKnY7H4D3b06rK9KbLb8wsNLOPApcDL53rxM1D0+X3y/DB5AfAXwCvNLPX\nzHH65qPp8nwfvjXPOecq+BrjxJqiHJkp89vM1uALp2uBE4ClZvayOU/hPHA8Bm9Nqzq3pstv8M23\nLcCLa5rP5ehNmd/OuU845y5wzl0GfBj4mnPu349NMueV6b7jm4BOMzs5vf8UfI1Qjt50+d0KREAx\nDei78U3ocoSOuxnWNK3q3Jouv4Hb0r8bap7yL865785pIueRmb7fNfu9FjDn3F/PfSrnlzrOKdXC\nUgDc5Jx757FJ6fxQR36/E3glfkzNRuAP01YPOQLHXfAWERGR6R2PzeYiIiIyDQVvERGRJqPgLSIi\n0mQUvEVERJqMgreIiEiTUfAWERFpMgreMmfM7AQzK5nZnenfBjN7xMw+UMdzr6+5fecspecLZubM\n7BXT7PNuM3v/DMf5GzN78myk6WiZ2cvM7P+b5PHnmtmjZvblozzuEb83M7vGzJZNs/18M7tqqu1H\n+FqrzOwL6e3XmNmOmu/Xh9LHF6Rpus/Mfm5mS2fjtetM3xPN7MPp7SvM7G/S22P5amZXmdl5s/ia\nf2Jmz5ut48nx6bhbElTmve3OufXVO+l83g+Z2ddnWBDiadUbtc9/nF4LtMwwQUQ9EyE8Ffjp7CRp\n1r0M+LvaCWCO0BG/N+fctIHDOXc78IdHmZ6JPo5fXhL8tKbvdM7954R9/hb4uXPueWb2auBfgN+d\npdefyRnAUgDn3PeB76ePj+Wrc2628qLq34AbzOw6LW06fyl4y7G2Iv1/0MyywKeAM/EnPAe8BPgI\ngJnd7Jy72Mxi51xoZu3AVfiZnGLgo865Q2qY6YIIHweegQ/EX3bOfcTMvoefUetWM3t2dQGW9Dl/\nCrwR2A/sAu5MH38b8GqgI329V+BXALsAuMrMXgIswgeLdqAX+HPn3DcnpOmLwPXOuS+l96vv53Lg\nH9N0HgCudM7tS+c3/2N8S9ntwFudc0UzexXwf4Ah/ExVhQmv8wbghfjFNmLgRvzyl73AMPAO59xt\naXoWAScDf+acuyZ9/msmvLf/h58L/Mz0vT9lYn445x4ws0fxha3LgN9JX+8k4EfOubea2dOB9zvn\nLktX8rolPVYf8Hbn3LVmtgr4Kn7qzLuBpznnVk94f+uA5c65B9OHngisM7O/Sp/zdufcQfxUnU9J\n9/kP4N/MLOOciyZ8JmN5gJ+285/Sz3Ev8Ebn3KNpeu/GL2bSCvyJc+66tDb/aWB1mhd/hZ+d8INA\nh5n9NX6t8Kfjg/bEfH2/c+7n6X6vwk8h+iPgz4E1wHfS110PPAb87/Rz/0L6eQB80jn3Oedc2cx+\ngZ/F7IvIvKRmc5lrK9ImzfvNbA9+vfAXO+d2ABcDhXQd4HX4JQOf45x7B4Bz7uIJx/oAsMc5dzY+\nOH/AzM6esM+bgJXA2fiT+0vN7LnOuRekxzxvQuC+AF8rXI8/0a4AEjPrwgfCp6Wv913gLenc47cB\nb3DO3YNfdOEPnHPnA2/AT+U70VS1+ffgg8SF+BraeWZ2Znqci9MWhz3Au81sBfDRNI1PSvPqkOM6\n5z4HfA94r3Pu88BXgI87584F3gl8M10jIEnz8Yxq4E6fP/G9JcAG59xp+DnBD8uPmvdXTcvF+ALY\nOcAVZnbWJHmRSz/zd+ILPuBrx19P0/pN/Gc40fPxBZKqHfgg+ARgKz4ogv8Md6bvqYJfOKNvknTs\ncc6dAVwHfA5feDofH8Svqtkvmz7+KuBLZpZL0/sF59wFab58Bh+A3wtc7Zz7e9LlL9MC5sR8Tczs\nucAVwHn47986/PeXNP8+lub1wfS1LwZ6nXPn4VdEu7Tm/dwAvGCSPJN5QsFb5tqONAidAXwZyAPX\nAzjnbgQ+ZWZvBf4VOAU/x/pULgM+nz53H3A1PphN3OeL6fKmo/ja3OXTHPPpwH8754bThVi+BgTO\nuUF8TeaVZvYP+JNsR83zqkt4vho4x8z+D/CuCfvM5HvAd83sE8D9zrnr0vSfAtyS9vW/ADD8ifuX\nzrnH0gUevsg0y4iaWSdwcnVe+nSd5f3pscDXfutxS/r8evPjl2lejuID/sJJjnlt+v+9Ndufif9+\nkKb54CTPWwdsq95xzr3EOffr9O5HSBfHYPJ8iSd5rJoHp+JbCr6f5vmHgRNr9vt0+nq/wRcKzknT\n+8F0/x/gWzVPTl+79vWn+owC/Gf9NedcMW0V+AL+u5oAu51zG9J978Hn0z2Amdm1+O/dX9Qcbwv+\neyPzlIK3HBPOuQTfPLkUeDeAmb0AH1yrzYE3MP261uGE7SGQOYp9asUc+ruI0rStBn6FX6v4Gnyw\nrN2vWtP8Bb5J9Dbg75j8N5ZU05TW2gBwzn0cX3jYCHwkbUINgf9yzq1PCz1PAt5Re4zadE5jYj6Q\n3q92ndW7Ytxomu7J8mOyz6r2uFO1OBRqtlePETH95wT+s6p+Pt3pghdVIVBOb28Hlqf7ZdM075sm\nHRlgU02en4/vo66qzesQqKT/X1bznEvxzdxHYrLv6mSfT4IvUO7HN5l/Al8Iu8PMetJ9ykxeQJF5\nQsFbjpm0dvFu4K/TPsPL8YHqS/h+vacyfgKPzGziyfynwB8AmNlifHPlzybZ57Xm1yVvx9cWr2dq\nPwFeYGY9aZNyda3hC4CHnHP/AvwaX6urpqcC5MxsIb62837n3LXAs5k8AO1lvJ/yRdUHzexmoCt9\njY/jm05/BrzYzPrMr9b0KXzw/gVwcTraOgCunOY94ZwbAB42sxenr3URvuA00/KXFSA3yeMXcnh+\n1DOGZrrCWK3r8J8VZvYcJl828mH8utDg+/D/zMyemN5/G/Dt9PYPgOq66K8Abqjt757EA8DCmlH2\nv48vVFbT/6o0XRcw3if/U+Ct6eNnAhvw/eVlJs+XyfL1p8CVZtaaFjJezzSDBdMR5V9Juzr+GF/o\nXZVuPhFfCJR5SsFb5trEftn/wdfgPoTvV7zSzH6N7zO8mvHmyquB35hZS80xPog/yd4F/Bz427Qp\ns9Zn8E2rG4A78P2PV0+WljQ9G/B9ybfiA+S2dL8fAaGZ3QP8T/p6J6RPuxbflGr4vtJ7zewm/Mm0\nxczaJrzMp4CnmdkG/MCnHenj7wG+aGa34fu53++cuwv4G/xJvBpoP+yc2w28OU3Xr/E1s5lGxr8a\neEeaX/8KvMQ5V62dTvXca/FdGRPHG/zPNPlRPV5t3/dMj9duB/gT/PiEO4CXM3mz+X+TdpOkwfgV\nwKfN7D7gCfjBXuD7nS9K0/om0iA71Ws754r4AWEfSz+j1+ADeHWfdWZ2O/4zf0XabfH29DU2AF8H\nXuWcG8J/jy5KuxZq3/fEfE3SIPzf+Fabe4BH8LXqYJL8SvD5PmJm9+Kb/L/lnLs33X4ZfhyCzFNa\nElREjjtm9nbgx865+9NroD+TDuSbuN+3gPfVBK1Gp+t64C+cc7fOxesdjbTF6CbgkprCmcwzulRM\nRI5HDwFfTy9xKzD1deHvxLfAvG6O0tUM3gZ8SIF7flPNW0REpMmoz1tERKTJKHiLiIg0GQVvERGR\nJqPgLSIi0mQUvEVERJqMgreIiEiT+f8BNiX5/MEnedUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a7aa10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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o2+Wz8pw0/RtTtC5LjD9HGEYk0x6ZtmOPjxdH4y7xvOtFqAZ2cSxk3wNuxrlD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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113410710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3ANdFxKuB+/palSRJmlAvPe9rgd0zcy6wHXA6sFs/i5IkSRPrpef95czcGCAzfwn8sr8l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\nGbmMV+72lQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d54950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "steps = 30\n",
    "repetitions = 50\n",
    "stacked_data = []\n",
    "clrs = sns.color_palette(\"hls\", 11)\n",
    "for n in range(11):\n",
    "    stacked_data.append(plot(funny_threshold=n, number_of_steps=steps, number_of_repetitions=repetitions, color=clrs[n - 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are all the above on a single plot (not terrible helpful)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hQB/hwDn8o4fh6OFoI9vB6u4hub6XsH0jleQGMlacm+5UlPfYnDps038snLlC8vrbE2y+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\n7HGiVcgR7K543DUZcbC+QSbMJuR0KJkeY5HZtNtHJKdaV3TbqxPNQlyUQ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kS6NeY6QckNjVc245xiNDPdC80Ux5cynlUCNYd1yUUmROyEUoX/FeK7EsLbWhVfBvjQ9BGRa0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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b3f4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(rc={\"figure.figsize\": (10, 10)})\n",
    "plt.figure()\n",
    "for n in range(11):\n",
    "    sns.tsplot(*stacked_data[n], color=clrs[n - 1], condition=\"$n=%s$\" % (n))\n",
    "    plt.xlabel('% of data')\n",
    "    plt.ylabel('Accuracy')\n",
    "    sns.plt.ylim(0,1)\n",
    "    plt.title('Accuracy with funny threshold: $n$' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.8"
  },
  "name": "nlp-of-jokes.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}