{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Analysing the Edinburgh Fringe Festival Jokes\n",
    "\n",
    "**This is the ipython notebook that revisits 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",
    "I am updating the data to include the jokes from [2015](http://www.bbc.co.uk/news/uk-scotland-edinburgh-east-fife-34039927).\n",
    "\n",
    "Here are the libraries we are going to need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "df = pandas.read_csv('jokes.csv', quotechar='\"', skipinitialspace=True) #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Year</th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2015</td>\n",
       "      <td>    Darren Walsh</td>\n",
       "      <td> 1</td>\n",
       "      <td> I just deleted all the German names off my pho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2015</td>\n",
       "      <td> Stewart Francis</td>\n",
       "      <td> 2</td>\n",
       "      <td> Kim Kardashian is saddled with a huge arse ......</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2015</td>\n",
       "      <td>       Adam Hess</td>\n",
       "      <td> 3</td>\n",
       "      <td>             Surely every car is a people carrier?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2015</td>\n",
       "      <td>    Masai Graham</td>\n",
       "      <td> 4</td>\n",
       "      <td> What's the difference between a 'hippo' and a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2015</td>\n",
       "      <td>      Dave Green</td>\n",
       "      <td> 5</td>\n",
       "      <td> If I could take just one thing to a desert isl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year           Author  Rank  \\\n",
       "0  2015     Darren Walsh     1   \n",
       "1  2015  Stewart Francis     2   \n",
       "2  2015        Adam Hess     3   \n",
       "3  2015     Masai Graham     4   \n",
       "4  2015       Dave Green     5   \n",
       "\n",
       "                                            Raw_joke  \n",
       "0  I just deleted all the German names off my pho...  \n",
       "1  Kim Kardashian is saddled with a huge arse ......  \n",
       "2              Surely every car is a people carrier?  \n",
       "3  What's the difference between a 'hippo' and a ...  \n",
       "4  If I could take just one thing to a desert isl...  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Year</th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td> 2009</td>\n",
       "      <td>        Adam Hills</td>\n",
       "      <td>  6</td>\n",
       "      <td> Going to Starbucks for coffee is like going to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td> 2009</td>\n",
       "      <td> Marcus Brigstocke</td>\n",
       "      <td>  7</td>\n",
       "      <td> To the people who've got iPhones: you just bou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td> 2009</td>\n",
       "      <td>      Rhod Gilbert</td>\n",
       "      <td>  8</td>\n",
       "      <td> A spa hotel? It's like a normal hotel, only in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td> 2009</td>\n",
       "      <td>    Dan Antopolski</td>\n",
       "      <td>  9</td>\n",
       "      <td> I've been reading the news about there being a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td> 2009</td>\n",
       "      <td>     Simon Brodkin</td>\n",
       "      <td> 10</td>\n",
       "      <td> I started so many fights at my school - I had ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year             Author  Rank  \\\n",
       "65  2009         Adam Hills     6   \n",
       "66  2009  Marcus Brigstocke     7   \n",
       "67  2009       Rhod Gilbert     8   \n",
       "68  2009     Dan Antopolski     9   \n",
       "69  2009      Simon Brodkin    10   \n",
       "\n",
       "                                             Raw_joke  \n",
       "65  Going to Starbucks for coffee is like going to...  \n",
       "66  To the people who've got iPhones: you just bou...  \n",
       "67  A spa hotel? It's like a normal hotel, only in...  \n",
       "68  I've been reading the news about there being a...  \n",
       "69  I started so many fights at my school - I had ...  "
      ]
     },
     "execution_count": 45,
     "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": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# nltk.download()  # Only do this once: needed to download the `stopwords` corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "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>Year</th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</th>\n",
       "      <th>Joke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2015</td>\n",
       "      <td>    Darren Walsh</td>\n",
       "      <td> 1</td>\n",
       "      <td> I just deleted all the German names off my pho...</td>\n",
       "      <td>       [DELETED, GERMAN, NAMES, PHONE, HANS, FREE]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2015</td>\n",
       "      <td> Stewart Francis</td>\n",
       "      <td> 2</td>\n",
       "      <td> Kim Kardashian is saddled with a huge arse ......</td>\n",
       "      <td> [KIM, KARDASHIAN, SADDLED, HUGE, ARSE, ENOUGH,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2015</td>\n",
       "      <td>       Adam Hess</td>\n",
       "      <td> 3</td>\n",
       "      <td>             Surely every car is a people carrier?</td>\n",
       "      <td>             [SURELY, EVERY, CAR, PEOPLE, CARRIER]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2015</td>\n",
       "      <td>    Masai Graham</td>\n",
       "      <td> 4</td>\n",
       "      <td> What's the difference between a 'hippo' and a ...</td>\n",
       "      <td> [DIFFERENCE, HIPPO, ZIPPO, ONE, REALLY, HEAVY,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2015</td>\n",
       "      <td>      Dave Green</td>\n",
       "      <td> 5</td>\n",
       "      <td> If I could take just one thing to a desert isl...</td>\n",
       "      <td> [COULD, TAKE, ONE, THING, DESERT, ISLAND, PROB...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year           Author  Rank  \\\n",
       "0  2015     Darren Walsh     1   \n",
       "1  2015  Stewart Francis     2   \n",
       "2  2015        Adam Hess     3   \n",
       "3  2015     Masai Graham     4   \n",
       "4  2015       Dave Green     5   \n",
       "\n",
       "                                            Raw_joke  \\\n",
       "0  I just deleted all the German names off my pho...   \n",
       "1  Kim Kardashian is saddled with a huge arse ......   \n",
       "2              Surely every car is a people carrier?   \n",
       "3  What's the difference between a 'hippo' and a ...   \n",
       "4  If I could take just one thing to a desert isl...   \n",
       "\n",
       "                                                Joke  \n",
       "0        [DELETED, GERMAN, NAMES, PHONE, HANS, FREE]  \n",
       "1  [KIM, KARDASHIAN, SADDLED, HUGE, ARSE, ENOUGH,...  \n",
       "2              [SURELY, EVERY, CAR, PEOPLE, CARRIER]  \n",
       "3  [DIFFERENCE, HIPPO, ZIPPO, ONE, REALLY, HEAVY,...  \n",
       "4  [COULD, TAKE, ONE, THING, DESERT, ISLAND, PROB...  "
      ]
     },
     "execution_count": 48,
     "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": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DECIDED',\n",
       " 'SELL',\n",
       " 'HOOVER',\n",
       " 'WELL',\n",
       " 'COLLECTING',\n",
       " 'DUST',\n",
       " 'WRITTEN',\n",
       " 'JOKE',\n",
       " 'FAT',\n",
       " 'BADGER']"
      ]
     },
     "execution_count": 50,
     "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'] <= 2014])\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": 51,
   "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": 54,
   "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>Year</th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</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> 2015</td>\n",
       "      <td>    Darren Walsh</td>\n",
       "      <td> 1</td>\n",
       "      <td> I just deleted all the German names off my pho...</td>\n",
       "      <td>       [DELETED, GERMAN, NAMES, PHONE, HANS, FREE]</td>\n",
       "      <td> {u'contains(DELETED)': False, u'contains(PHONE...</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2015</td>\n",
       "      <td> Stewart Francis</td>\n",
       "      <td> 2</td>\n",
       "      <td> Kim Kardashian is saddled with a huge arse ......</td>\n",
       "      <td> [KIM, KARDASHIAN, SADDLED, HUGE, ARSE, ENOUGH,...</td>\n",
       "      <td> {u'contains(ENOUGH)': False, u'contains(KANYE)...</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2015</td>\n",
       "      <td>       Adam Hess</td>\n",
       "      <td> 3</td>\n",
       "      <td>             Surely every car is a people carrier?</td>\n",
       "      <td>             [SURELY, EVERY, CAR, PEOPLE, CARRIER]</td>\n",
       "      <td> {u'contains(EVERY)': False, u'contains(PEOPLE)...</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2015</td>\n",
       "      <td>    Masai Graham</td>\n",
       "      <td> 4</td>\n",
       "      <td> What's the difference between a 'hippo' and a ...</td>\n",
       "      <td> [DIFFERENCE, HIPPO, ZIPPO, ONE, REALLY, HEAVY,...</td>\n",
       "      <td> {u'contains(DIFFERENCE)': False, u'contains(HI...</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2015</td>\n",
       "      <td>      Dave Green</td>\n",
       "      <td> 5</td>\n",
       "      <td> If I could take just one thing to a desert isl...</td>\n",
       "      <td> [COULD, TAKE, ONE, THING, DESERT, ISLAND, PROB...</td>\n",
       "      <td> {u'contains(THING)': True, u'contains(WOULDN)'...</td>\n",
       "      <td> True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year           Author  Rank  \\\n",
       "0  2015     Darren Walsh     1   \n",
       "1  2015  Stewart Francis     2   \n",
       "2  2015        Adam Hess     3   \n",
       "3  2015     Masai Graham     4   \n",
       "4  2015       Dave Green     5   \n",
       "\n",
       "                                            Raw_joke  \\\n",
       "0  I just deleted all the German names off my pho...   \n",
       "1  Kim Kardashian is saddled with a huge arse ......   \n",
       "2              Surely every car is a people carrier?   \n",
       "3  What's the difference between a 'hippo' and a ...   \n",
       "4  If I could take just one thing to a desert isl...   \n",
       "\n",
       "                                                Joke  \\\n",
       "0        [DELETED, GERMAN, NAMES, PHONE, HANS, FREE]   \n",
       "1  [KIM, KARDASHIAN, SADDLED, HUGE, ARSE, ENOUGH,...   \n",
       "2              [SURELY, EVERY, CAR, PEOPLE, CARRIER]   \n",
       "3  [DIFFERENCE, HIPPO, ZIPPO, ONE, REALLY, HEAVY,...   \n",
       "4  [COULD, TAKE, ONE, THING, DESERT, ISLAND, PROB...   \n",
       "\n",
       "                                            Features Funny  \n",
       "0  {u'contains(DELETED)': False, u'contains(PHONE...  True  \n",
       "1  {u'contains(ENOUGH)': False, u'contains(KANYE)...  True  \n",
       "2  {u'contains(EVERY)': False, u'contains(PEOPLE)...  True  \n",
       "3  {u'contains(DIFFERENCE)': False, u'contains(HI...  True  \n",
       "4  {u'contains(THING)': True, u'contains(WOULDN)'...  True  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Features'] = df['Joke'].apply(lambda x:extract_features(x, get_all_words(df[df['Year'] <= 2014])))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Labelling our jokes depending on what will be deemed as funny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Year</th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</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> 2015</td>\n",
       "      <td>    Darren Walsh</td>\n",
       "      <td>  1</td>\n",
       "      <td> I just deleted all the German names off my pho...</td>\n",
       "      <td>       [DELETED, GERMAN, NAMES, PHONE, HANS, FREE]</td>\n",
       "      <td> {u'contains(DELETED)': False, u'contains(PHONE...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2015</td>\n",
       "      <td> Stewart Francis</td>\n",
       "      <td>  2</td>\n",
       "      <td> Kim Kardashian is saddled with a huge arse ......</td>\n",
       "      <td> [KIM, KARDASHIAN, SADDLED, HUGE, ARSE, ENOUGH,...</td>\n",
       "      <td> {u'contains(ENOUGH)': False, u'contains(KANYE)...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2015</td>\n",
       "      <td>       Adam Hess</td>\n",
       "      <td>  3</td>\n",
       "      <td>             Surely every car is a people carrier?</td>\n",
       "      <td>             [SURELY, EVERY, CAR, PEOPLE, CARRIER]</td>\n",
       "      <td> {u'contains(EVERY)': False, u'contains(PEOPLE)...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2015</td>\n",
       "      <td>    Masai Graham</td>\n",
       "      <td>  4</td>\n",
       "      <td> What's the difference between a 'hippo' and a ...</td>\n",
       "      <td> [DIFFERENCE, HIPPO, ZIPPO, ONE, REALLY, HEAVY,...</td>\n",
       "      <td> {u'contains(DIFFERENCE)': False, u'contains(HI...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2015</td>\n",
       "      <td>      Dave Green</td>\n",
       "      <td>  5</td>\n",
       "      <td> If I could take just one thing to a desert isl...</td>\n",
       "      <td> [COULD, TAKE, ONE, THING, DESERT, ISLAND, PROB...</td>\n",
       "      <td> {u'contains(THING)': True, u'contains(WOULDN)'...</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 2015</td>\n",
       "      <td>     Mark Nelson</td>\n",
       "      <td>  6</td>\n",
       "      <td> Jesus fed 5,000 people with two fishes and a l...</td>\n",
       "      <td> [JESUS, FED, 5, 000, PEOPLE, TWO, FISHES, LOAF...</td>\n",
       "      <td> {u'contains(5)': False, u'contains(PEOPLE)': T...</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 2015</td>\n",
       "      <td>       Tom Parry</td>\n",
       "      <td>  7</td>\n",
       "      <td> Red sky at night. Shepherd's delight. Blue sky...</td>\n",
       "      <td> [RED, SKY, NIGHT, SHEPHERD, DELIGHT, BLUE, SKY...</td>\n",
       "      <td> {u'contains(BLUE)': False, u'contains(DAY)': T...</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 2015</td>\n",
       "      <td>   Alun Cochrane</td>\n",
       "      <td>  8</td>\n",
       "      <td> The first time I met my wife, I knew she was a...</td>\n",
       "      <td> [FIRST, TIME, MET, WIFE, KNEW, KEEPER, WEARING...</td>\n",
       "      <td> {u'contains(KNEW)': False, u'contains(MASSIVE)...</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> 2015</td>\n",
       "      <td>   Simon Munnery</td>\n",
       "      <td>  9</td>\n",
       "      <td>                   Clowns divorce. Custardy battle</td>\n",
       "      <td>               [CLOWNS, DIVORCE, CUSTARDY, BATTLE]</td>\n",
       "      <td> {u'contains(BATTLE)': False, u'contains(CUSTAR...</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> 2015</td>\n",
       "      <td> Grace The Child</td>\n",
       "      <td> 10</td>\n",
       "      <td> They're always telling me to live my dreams. B...</td>\n",
       "      <td> [RE, ALWAYS, TELLING, LIVE, DREAMS, WANT, NAKE...</td>\n",
       "      <td> {u'contains(DREAMS)': False, u'contains(LIVE)'...</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year           Author  Rank  \\\n",
       "0  2015     Darren Walsh     1   \n",
       "1  2015  Stewart Francis     2   \n",
       "2  2015        Adam Hess     3   \n",
       "3  2015     Masai Graham     4   \n",
       "4  2015       Dave Green     5   \n",
       "5  2015      Mark Nelson     6   \n",
       "6  2015        Tom Parry     7   \n",
       "7  2015    Alun Cochrane     8   \n",
       "8  2015    Simon Munnery     9   \n",
       "9  2015  Grace The Child    10   \n",
       "\n",
       "                                            Raw_joke  \\\n",
       "0  I just deleted all the German names off my pho...   \n",
       "1  Kim Kardashian is saddled with a huge arse ......   \n",
       "2              Surely every car is a people carrier?   \n",
       "3  What's the difference between a 'hippo' and a ...   \n",
       "4  If I could take just one thing to a desert isl...   \n",
       "5  Jesus fed 5,000 people with two fishes and a l...   \n",
       "6  Red sky at night. Shepherd's delight. Blue sky...   \n",
       "7  The first time I met my wife, I knew she was a...   \n",
       "8                    Clowns divorce. Custardy battle   \n",
       "9  They're always telling me to live my dreams. B...   \n",
       "\n",
       "                                                Joke  \\\n",
       "0        [DELETED, GERMAN, NAMES, PHONE, HANS, FREE]   \n",
       "1  [KIM, KARDASHIAN, SADDLED, HUGE, ARSE, ENOUGH,...   \n",
       "2              [SURELY, EVERY, CAR, PEOPLE, CARRIER]   \n",
       "3  [DIFFERENCE, HIPPO, ZIPPO, ONE, REALLY, HEAVY,...   \n",
       "4  [COULD, TAKE, ONE, THING, DESERT, ISLAND, PROB...   \n",
       "5  [JESUS, FED, 5, 000, PEOPLE, TWO, FISHES, LOAF...   \n",
       "6  [RED, SKY, NIGHT, SHEPHERD, DELIGHT, BLUE, SKY...   \n",
       "7  [FIRST, TIME, MET, WIFE, KNEW, KEEPER, WEARING...   \n",
       "8                [CLOWNS, DIVORCE, CUSTARDY, BATTLE]   \n",
       "9  [RE, ALWAYS, TELLING, LIVE, DREAMS, WANT, NAKE...   \n",
       "\n",
       "                                            Features  Funny  \n",
       "0  {u'contains(DELETED)': False, u'contains(PHONE...   True  \n",
       "1  {u'contains(ENOUGH)': False, u'contains(KANYE)...   True  \n",
       "2  {u'contains(EVERY)': False, u'contains(PEOPLE)...   True  \n",
       "3  {u'contains(DIFFERENCE)': False, u'contains(HI...   True  \n",
       "4  {u'contains(THING)': True, u'contains(WOULDN)'...   True  \n",
       "5  {u'contains(5)': False, u'contains(PEOPLE)': T...  False  \n",
       "6  {u'contains(BLUE)': False, u'contains(DAY)': T...  False  \n",
       "7  {u'contains(KNEW)': False, u'contains(MASSIVE)...  False  \n",
       "8  {u'contains(BATTLE)': False, u'contains(CUSTAR...  False  \n",
       "9  {u'contains(DREAMS)': False, u'contains(LIVE)'...  False  "
      ]
     },
     "execution_count": 55,
     "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": 56,
   "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>Year</th>\n",
       "      <th>Author</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Raw_joke</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> 2015</td>\n",
       "      <td>    Darren Walsh</td>\n",
       "      <td> 1</td>\n",
       "      <td> I just deleted all the German names off my pho...</td>\n",
       "      <td>       [DELETED, GERMAN, NAMES, PHONE, HANS, FREE]</td>\n",
       "      <td> {u'contains(DELETED)': False, u'contains(PHONE...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(DELETED)': False, u'contains(PHON...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2015</td>\n",
       "      <td> Stewart Francis</td>\n",
       "      <td> 2</td>\n",
       "      <td> Kim Kardashian is saddled with a huge arse ......</td>\n",
       "      <td> [KIM, KARDASHIAN, SADDLED, HUGE, ARSE, ENOUGH,...</td>\n",
       "      <td> {u'contains(ENOUGH)': False, u'contains(KANYE)...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(ENOUGH)': False, u'contains(KANYE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2015</td>\n",
       "      <td>       Adam Hess</td>\n",
       "      <td> 3</td>\n",
       "      <td>             Surely every car is a people carrier?</td>\n",
       "      <td>             [SURELY, EVERY, CAR, PEOPLE, CARRIER]</td>\n",
       "      <td> {u'contains(EVERY)': False, u'contains(PEOPLE)...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(EVERY)': False, u'contains(PEOPLE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2015</td>\n",
       "      <td>    Masai Graham</td>\n",
       "      <td> 4</td>\n",
       "      <td> What's the difference between a 'hippo' and a ...</td>\n",
       "      <td> [DIFFERENCE, HIPPO, ZIPPO, ONE, REALLY, HEAVY,...</td>\n",
       "      <td> {u'contains(DIFFERENCE)': False, u'contains(HI...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(DIFFERENCE)': False, u'contains(H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2015</td>\n",
       "      <td>      Dave Green</td>\n",
       "      <td> 5</td>\n",
       "      <td> If I could take just one thing to a desert isl...</td>\n",
       "      <td> [COULD, TAKE, ONE, THING, DESERT, ISLAND, PROB...</td>\n",
       "      <td> {u'contains(THING)': True, u'contains(WOULDN)'...</td>\n",
       "      <td> True</td>\n",
       "      <td> ({u'contains(THING)': True, u'contains(WOULDN)...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year           Author  Rank  \\\n",
       "0  2015     Darren Walsh     1   \n",
       "1  2015  Stewart Francis     2   \n",
       "2  2015        Adam Hess     3   \n",
       "3  2015     Masai Graham     4   \n",
       "4  2015       Dave Green     5   \n",
       "\n",
       "                                            Raw_joke  \\\n",
       "0  I just deleted all the German names off my pho...   \n",
       "1  Kim Kardashian is saddled with a huge arse ......   \n",
       "2              Surely every car is a people carrier?   \n",
       "3  What's the difference between a 'hippo' and a ...   \n",
       "4  If I could take just one thing to a desert isl...   \n",
       "\n",
       "                                                Joke  \\\n",
       "0        [DELETED, GERMAN, NAMES, PHONE, HANS, FREE]   \n",
       "1  [KIM, KARDASHIAN, SADDLED, HUGE, ARSE, ENOUGH,...   \n",
       "2              [SURELY, EVERY, CAR, PEOPLE, CARRIER]   \n",
       "3  [DIFFERENCE, HIPPO, ZIPPO, ONE, REALLY, HEAVY,...   \n",
       "4  [COULD, TAKE, ONE, THING, DESERT, ISLAND, PROB...   \n",
       "\n",
       "                                            Features Funny  \\\n",
       "0  {u'contains(DELETED)': False, u'contains(PHONE...  True   \n",
       "1  {u'contains(ENOUGH)': False, u'contains(KANYE)...  True   \n",
       "2  {u'contains(EVERY)': False, u'contains(PEOPLE)...  True   \n",
       "3  {u'contains(DIFFERENCE)': False, u'contains(HI...  True   \n",
       "4  {u'contains(THING)': True, u'contains(WOULDN)'...  True   \n",
       "\n",
       "                                     Labeled_Feature  \n",
       "0  ({u'contains(DELETED)': False, u'contains(PHON...  \n",
       "1  ({u'contains(ENOUGH)': False, u'contains(KANYE...  \n",
       "2  ({u'contains(EVERY)': False, u'contains(PEOPLE...  \n",
       "3  ({u'contains(DIFFERENCE)': False, u'contains(H...  \n",
       "4  ({u'contains(THING)': True, u'contains(WOULDN)...  "
      ]
     },
     "execution_count": 56,
     "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": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "classifier = nltk.NaiveBayesClassifier.train(df[df['Year'] <= 2014]['Labeled_Feature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "           contains(GOT) = True            False : True   =      2.5 : 1.0\n",
      "          contains(WELL) = True            False : True   =      2.5 : 1.0\n",
      "           contains(SAY) = True             True : False  =      2.2 : 1.0\n",
      "        contains(PEOPLE) = True            False : True   =      1.8 : 1.0\n",
      "          contains(DIDN) = True            False : True   =      1.8 : 1.0\n",
      "          contains(MADE) = True            False : True   =      1.8 : 1.0\n",
      "           contains(SEX) = True            False : True   =      1.8 : 1.0\n",
      "         contains(NEVER) = True            False : True   =      1.8 : 1.0\n",
      "          contains(KNOW) = True             True : False  =      1.7 : 1.0\n",
      "            contains(RE) = True             True : False  =      1.6 : 1.0\n"
     ]
    }
   ],
   "source": [
    "classifier.show_most_informative_features(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "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 just deleted all the German names off my pho...</td>\n",
       "      <td>  True</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> Kim Kardashian is saddled with a huge arse ......</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>             Surely every car is a people carrier?</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> What's the difference between a 'hippo' and a ...</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> If I could take just one thing to a desert isl...</td>\n",
       "      <td>  True</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> Jesus fed 5,000 people with two fishes and a l...</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> Red sky at night. Shepherd's delight. Blue sky...</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> The first time I met my wife, I knew she was a...</td>\n",
       "      <td> False</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>                   Clowns divorce. Custardy battle</td>\n",
       "      <td> False</td>\n",
       "      <td>  True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td> They're always telling me to live my dreams. B...</td>\n",
       "      <td> False</td>\n",
       "      <td> False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Raw_joke  Funny Prediction\n",
       "0  I just deleted all the German names off my pho...   True      False\n",
       "1  Kim Kardashian is saddled with a huge arse ......   True       True\n",
       "2              Surely every car is a people carrier?   True       True\n",
       "3  What's the difference between a 'hippo' and a ...   True       True\n",
       "4  If I could take just one thing to a desert isl...   True       True\n",
       "5  Jesus fed 5,000 people with two fishes and a l...  False       True\n",
       "6  Red sky at night. Shepherd's delight. Blue sky...  False       True\n",
       "7  The first time I met my wife, I knew she was a...  False      False\n",
       "8                    Clowns divorce. Custardy battle  False       True\n",
       "9  They're always telling me to live my dreams. B...  False      False"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Prediction'] = df['Features'].apply(classifier.classify)\n",
    "df[df['Year'] == 2015][['Raw_joke','Funny','Prediction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.59999999999999998"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df[df['Year'] == 2015]['Prediction'] == df[df['Year'] == 2015]['Funny']) / float(len(df[df['Year'] == 2015]))"
   ]
  },
  {
   "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": 65,
   "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'] == 2015]['Prediction'] == df[df['Year'] == 2015]['Funny']) / float(len(df[df['Year'] == 2015]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y+CAw7XylkiRpV1V2m2/KzIcBZOYvgF9UXPc+wLaO5dsjYnFm7oiI+1BMeLIaWLM7gSVJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113492f50>"
      ]
     },
     "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": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def random_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": 74,
   "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": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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PoiRJ0sR66Xl/OTM3AsjMXwO/7m9JkiSpm17C+ycR8RHgOuDh1sTMvLpvVUmSpAn1Et5r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129bb5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XkT7/UqLW/ZTW/3rAPzJZUstXklpxBsH8xeOarc3zPFe13qOnPb39CHE649rb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\naGD8C5OJnTcxXZ3aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129f5790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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r7yJu659zXE69Hn2tPBnK9UGNPpucvIcsdHbM0WKOiMJ3N4sov1oh37ixFPn7\nTXlksnxjQ0SlLqLSCi3TewDN+7/JOXeImT2yH/veDTT+ltaDG0Kr+556a9s5dwVwBjBreEdRxMCh\nR1HZvYkoDr+sWc9z2HLVvVQfuYqVG59AqXeyuzxKRmhbceiUfXjvKW/fCp1d+CzlkduuhihhtXsq\nSXtY2KKtV13mjQYGtBjLUlqa+u4BVpPVylRHt5H1tQFRPhGND7cjn88s17i4S8PteisuD4A4KUGh\njTgpLUKrbOr/wbRWxldHyGoVfK1M1t8D0Qn71EM2Mb1tPYgJM9fFhWmTMGU1ICZKikSFUvg8STtx\nsWPi786UslXGqY1uC0Hefzr+yCcw+vAv2X3vD6hu+QW1bb+ke+N59G48n7jYgc9GiZIqSakrD+Zs\nIpx9Vstb1EB7YyhHLKSd5bMaaXkPWWUPaWUPWTn/Oe0+UURS6iFp6yYu9ZC0Nd7uDmMvCh3zfo8+\n20FcKJG0ryBp613w966/KYtjIS3vnznn7gI+C3zdzBY63+SVwMXAV5xzZwO3NDx3H9DtnDs6H8T2\nJOBT8+1w+64a6WgMtcnpLIqHP5P0ri+x7Zav0nHCqyeOmr3fQzzMlEkUsvEhsvIoURRR3XYL6dh2\nCgOnMzxWwI8OE3f1Epcf/YCh5WJgoIfBQdXHUjkw9d1LlnZAWsnPf052OUdRtI+XvVfyf81QzP91\nhZZwdQyy8Tz4YHKmu/r88+Ex7+sfoP48E63wvpVd7NgxFq4ESErhErxiX+jKrxH+1V/LyBxlW0FW\nLeHL+eC3zhNoP/FYqoO/pPrwzxm+70cMP3A1pXXnUFxzZngvFvY9e+/xtRF8fca96p4w6U9thKw6\nEq5Br4Wfs47ib5SEUfq1PVvm3i6K8zkGusNlevntuLQiLP4zcV37ON6HwXwLaY3vz++4z2r46ijE\nyWPyevrZDnaiyV/umeWDy54EvAp4KvBt4P+Z2fULeF19tDnAq4HTgW4zu9Q5dyHwXsJh5ZVm9tZ5\nPoMfHBwOI0GHHwytgtz4/ZdR23YzpQ0XUFp//uQLfEbcfShxUgyv2/1AONftM0Zv+wS+vIPOk98Q\nzkXFhYmBbxIovJeW6nvpeO8ZGOhh27Y9i7rfrDqCL2/Hp2FArE+rVLdeR+XhqyAdD+smrD8/rJ2Q\nlvNQnpwKN6vW7++ZmMN+viOoqNCVB2xXmElvInC7pj3XNdm4yWqT71MdCdPvNtxufG7idMjEG8Yk\nK46huOoUClDfAAAfpklEQVRkkr5jp5wO8T4Nl/QVe8L0v9Na4/P9jocDs9F8SuNKPqlPCiSAJ0o6\niDsHHlOj+AcGembs0pg3vOuccx3AS4B/IByKDgJvMrOrF6uQ8/D1Lz2rjpCNbp3oVvK1sRDGtTE6\nT3wNccfA5KviIkn3erLRQXzeYq9u/xXle/+bwurH037kxfgsJe5a+5g8qpuLwmRpqb6XVjPrO6sM\n48s7J65q8bVxKluuofrIL8Ko7/lESQjhfPrbuNidz7zXPbVFXOjcj9MHLLiLO0z5Ox5OW1T3kI0+\nQm3oVrLRvOWetIdLDlefTNy1YXJNgfr7TGuNN9Z5mIFvPJ+5rpyvnlfba2Dd3mXKiNtXLuoAwLmE\nOUHqgxr9ZA/OxBwI+e2ZVh0EIAqrCRa692t+gtnCeyHnvC8CXglcRGh1v8TMrnLOnUwYTb7kzdW4\n2IVPOiAL3URRoYO2jc9k/J6vMH7/t+g44VWTwZ6WSceH8NXhMJua91Q3/xyIKK07L7w+KSq4RWTR\nxKUeKPWQlYfxlR2QlGjbcAGlNWdSefhq0pGHQjd0MZ8CN58SN3RN94Qla/dh7MDE9L57rVg3dfW6\nKCoAPr/kLw/LaVP4Nh4MRFEUltQtdBB3rIbeIygdcjbp6Jb8UsPbqA3eQG3wBqK2foqrT6aw6uTJ\nYK2OhgOZpI2o2E1trEo6uj2sAZBWprxfKPosS/Jm6cRMd2FQ8g58ZQ9R50DTFo/KKnvw5R0TB2CP\nhq9U8eO7yaKIKCktymRDCznn/TfAp4E3mNnEiR8zu9U59/79fudHKe5aM9ENDlBY6Sj0P47a9l9R\n3XIdpfzSryiK8eWd+S8tpDvvIhvbSmHVScTt/ZqURUSaJm7rgbYesvKucKVLoXNiYqmF8BMr17FX\nIBMnYSa4PJT3da34vd4rS0M3dVpuuJ4/xftqPvp8cqrbpHMtSedaSoc+lXT3/dS23UJth1HZ9BMq\nm35C0rORwqqTKfSfEBaRympk4zuojVWgFlqxM13z72sj+LEhsvIQ2dj2ME5pfAhf3gFJG20bn0Gh\n/6R8atqUbM9maOvNr6VfnMvXJkI7rYYJlBbp0uGJrv6sFk6XjO/Kw7xtv1rmCwnvZwGvMrMR59wG\n4HXAP5nZqJn93/34DIsiimLi9n6y8e0Tldt2+DOo7f41lU0/otB37MRlX/Xg9t5T2fxzAErr8nPj\nEVoZSESaKm5bQdy2gqy8M4R41rAIzkQIx/mleg2hnIRL7UIoN/fa6jDTX8eME9+EuetrYdnc2ii+\nNkZ9OtzCiqMprDgan5apbb+D6tAtpMMPhElsHryCQp+jsPoUkt4jw77SKlk5BLPPwzkbD/dnHHRX\n6CDu2kA2toXyfd+gtuMu2jc+M1+FLiYrDxNVR4jaB4iL+z9pz/TxCs0+rz4Z5tVpLfP6NMCdc4b5\nQsL7i0yOFB8GYuDzwIseVckXQZgEYU+4HhKIil20Hf4Myvd9jfFff4sO97tTfuHT3feRjT5MYeUJ\nxB2r1eoWkSUVt/URlVbg03J+XX3zQ3kxRFGctw5L0LYihHlleGqQJ20UB55AceAJZOUd1IZuo7rt\nlnzymtuJit2MJwXS8Z0zvEFC3LaSqOcI4vZVxO394WfHKqJCWN0xG9/O+P3fJN1xB6PDD9J25HPC\nyPcoXOaYjT6CL3aGSYL2obUcQnvHxFoXB2qSrr3DfCdZnMDASTNuv5Dw3mhmFwOY2W7gHc65mxep\nvI9a1LmGbM9vJhYSCF3nt5PuvIva4I0U15wO1FvdPwOgODEi3WsqVBFZUlEUhZZVC4uimKhtRUOQ\n78ZXR/OBXTFx20pK659Ecd35ZCObQojvuAPvY5KejUTtq/KQDkEdZrGbOzTj9n46jr+E6iPXUNn0\nE8bv/jKF1U+g7fCLQms1iqGWT0vbviqMHZjDQkLbZym1HXfiq3vyFe46J1e6K3Ts89iEfRHFc685\nsJDwzpxzp5jZLQDOuRNo3sWc+yxOitDWF1ZHiiKiKKJt4zMZHX6Q8m9+QLLiGOK2FWFqxT0PkfQd\nR9K5FiCf5k9ToYqI7K8Q5H3Q1jdjkCfdh4ZFZY54Vlj2dsfovPusm5jClWzifH5p3bkkK44JXejb\nfkm6+37aj3ru5BLRRGRj2/CVYaKOgb1WeMuqY5PX5M8W2rVxqoM3Ut1y3QwL7Ez59BMD+mYK97Cc\nbS9J7xELXkRnoRayt7cD33XObcrvDxBGnx804vb+/HrEMPlCXOqh7fCLKN9/GeVfX077cS/LR5hP\nnuv2Pl2ySw1ERB4LZg7ykXCaIJo8hxxGj/uJgXjUzzHvNTo+CZPnxAV8dZSsPERERNK5ho7H/T6V\nzT+l+vBVjN35eYprz6Z06AVh0p0oDqvd7XkI8svKFhLaWXlnWHVv8Jfhkr64RHHtE0m6D8OnY2Hq\n3in/8tMGtTGy8e3Mek1+0k6h/3EUV58y5ZK6R1XXC7nO2zlXAk4GqoCZ2QKm8ll0fq5rMrNamWzP\n5okpDL33jN/1JdLd91EYOI3a4I0kvUfR4V4eXpC0kXQdshTlblm67nhpqb6Xlup76dSDfFV/F0M7\nxiYCeV/P+WdpFT+6BZ9VJ8I33fMQ4/d9E1/eTty+mrajnkfStW7Ke5OPTm88gGiU7tlMdcs11Lbf\nAXiiYg/FtWdSHDhtwac4wjXx5YlAr4d7NrqV2vbb8gl3IGpbSXH1KVMvqZttn8C6o0/av0lanHPH\nA28AugiD1RLgCDN78oI+0eKZM7wBstFBsurIxC9DVt7F6G2fmJgUoeP4S0h6Dg+t7q4Ne89rLFPo\nj9vSUn0vLdX30lusOs/Gt5OVdzXM51Gh8tAPqW69HqI4P99+3ryL6aS77qbyyDVkww8CEHesoXjI\n2RT6T1zU0eZhhs/7qW27ldrOO/M59CHuOZziqpMprDxhxoOEucJ7Id3mXwa+DpxPmN/8WcD/7N9H\naK6oY/WUec/jthW0HfY0yg/8D0nPRpKew8N2SUnBLSLSouL2fih04ce24LOMKCnRtvG3SfqOo3z/\nZVQ2/YTazrtpP/J5xB2rprzWZ1Vq226lsuVa/PgQAEnvURQPOZuk98imDEDb65K6HXdS2xYuqSsP\nP0j5ge9QWHkchVWnkKw4akFjsRYS3rGZvSvvOr8R+ATwHeCfHt3HWXxRFBF3DJCNPjI5+nzgtDBF\nah7cPkuJ21fNtRsRETnIxYU2fPdhZGNb8dURoiihsOIokpNeS/nB71AbupXR2y+ldNjTKK45A2qj\nVLfeQHXr9WGq7CimsPoUimvPJulcs2TljpI2iqsfT3H148nKO8MldUO3UNv+K2rbf0VU7KbQfxKF\n1ScT54OrZ7KQ8B5xzrUBdwGnm9nPnXOrF+uDLLa42IkvdE5c7B9FEcXVp0w8HyUF4lL3gSqeiIgs\nkiiKSDrXklWGyca25dejt9N+1POo9R3H+APfpvLgd6gN/jJMAuNrkLRTXHcuxTVnznk5mfdZmLim\n2Bkmz/Hp5HrqWYr36UT391zrqs8lbuujtP58iuvOIxvZTG3oFqpDt1Pdcg3VLdcQd6yFoz8042sX\nEt5fAL4FvBy4xjn3TGDzPpd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YSTvwx2b2vbw1/3HgsLwu/pIwO+F7gC7n3F8R1gq/gBDa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\n8BYREWkxCm8REZEWo/AWERFpMf8/tgSl+no5RmgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129bb5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KX6dwU0mGGaieymD1VGrpEbOO1oYiFhLDJbEbR1Zx19Y7KYpGbAFqxpaP+YN5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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129bb5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8ptKLNLEspIyFYSphnApjcYUhEgou//KfmLwiztxUoLDf3WuNkIYK42Fk4pKr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\nviC2fufihvz1c62P6/K6HCEGfs80+7w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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129f5790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Gt3UeCOMNfdSyjAqxpR/LWKGSD6nU8vevebx47HhyfVy9lL8fJUqUkvge1S/X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\nIlIwCm8REZGCUXiLiIgUjMJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134084d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pcxs/a43xs9YYR+b9nF5bx/2qw+ygyUBWZTirsipfsnVrRERE5tUs2mwuJumlLdlLUj0e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\n3P1n6fkLifMQZIXSJUFF5JBjZq8Avur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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3c0ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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72FvP+PXj+3nghl4g9Pu0+oZXg9lBN1m+T39bKZ6Mhg5jSXU5Kxm8V0p7rXyi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\nFfATQv/j5xZKS0zPVYS+5B8QAuTtcbu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129bb5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+/0Gr8xyxroXD1xXoyc4KqCYphXwI8rBy3vyFTu34/FgxzWYtrB062T2k0/q9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\nrXX3P5vw+0FW5h1TPO5wYEPtiru/1N1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3c0ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jgesB8Hy3/cDRCg/91yh9e0pc+ItZVmxKLdgpBRERqZ+GCu9yMeKuLw0zdLzChovTmGub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\nUTEUvIUQQoiK8QsynmKkihfKegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129bb5d0>"
      ]
     },
     "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 0x1134084d0>"
      ]
     },
     "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": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IIcRlY86Er8ETNRZdnSDTNPGORhNZ0g0X9y7H6RZSNf29VF95BtKZuJ7XGWFt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Yz5srvMpbXM2nVSTGbIkHwr3caXbiZTIiVlbDkVuBMAA8h9zYOL9ifb7lvsNfPJ3n9x/5LEXx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1135d9450>"
      ]
     },
     "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}