Capstone/Capstone - Clickbait.ipynb

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   "source": [
    "# DSI Capstone Project - _'To Click Or Not To Click, That Is The Question'_\n",
    "\n",
    "### by Ezekiel Kuang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://cdn01.vulcanpost.com/wp-uploads/2016/08/clickbait1.jpg\" width=\"1000\" height=\"1000\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url= \"https://cdn01.vulcanpost.com/wp-uploads/2016/08/clickbait1.jpg\", width=1000, height=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Executive Summary\n",
    "\n",
    "<p><li type=\"disc\">In the digital era, anyone can write and publish content online. Content proliferation has led to a rampant increase in the amount of clickbait, whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page.</li></p>\n",
    "\n",
    "<p><li type=\"disc\">The goal of my project was to find out whether a particular article was clickbait or not, without having to read through the article manually.</li></p>\n",
    "\n",
    "<p><li type=\"disc\">Using a corpus of annotated articles provided by the Bauhaus-Universität Weimar in Germany, success would be measured by how accurate it would predict clickbait articles given the prior condition that they were indeed clickbait. </li></p>\n",
    "\n",
    "<p><li type=\"disc\">My findings suggest that simplistic classifiers could do most of the heavy lifting. The use of an LSTM RNN model could help to further increase its scores, but overtraining the model could lead to poor scores if it is used to predict a brand new corpus of text. </li></p>\n",
    "\n",
    "<p><li type=\"disc\">In the course of doing this project, I am conscious of the fact that this is a very small dataset of ~20,000 articles and contains barely enough words in the corpus to train the model to a satisfactory level. Obtaining a larger corpus is limited by the fact that each article requires manual annotation (for clickbait or non-clickbait), but it would certainly have helped if the model had a wider range of words to learn from.</li></p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   },
   "source": [
    "## A Brief Outline of the Steps Involved\n",
    "\n",
    "1. Data Preprocessing\n",
    "2. Model Selection and Implementation\n",
    "3. Comparison of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"alert alert-block alert-info\"><h2>Data Preprocessing </h2></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Import necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1 µs, total: 4 µs\n",
      "Wall time: 7.87 µs\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import pickle\n",
    "\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer, TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict\n",
    "from sklearn.metrics import accuracy_score, precision_score, confusion_matrix, classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import roc_curve, auc, average_precision_score, roc_auc_score\n",
    "from sklearn.naive_bayes import MultinomialNB, BernoulliNB, GaussianNB\n",
    "\n",
    "\n",
    "%matplotlib inline \n",
    "%time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true,
    "slideshow": {
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   "outputs": [],
   "source": [
    "# There are two JSONL files provided, import them to see what they look like\n",
    "\n",
    "instances = pd.read_json('instances.jsonl', encoding='utf8', lines=True)\n",
    "truth = pd.read_json('truth.jsonl', encoding='utf8', lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
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       "0  [UK’s response to modern slavery leaving victi...   \n",
       "1                                     [this is good]   \n",
       "2  [The \"forgotten\" Trump roast: Relive his bruta...   \n",
       "3             [Meet the happiest #dog in the world!]   \n",
       "4  [Tokyo's subway is shut down amid fears over a...   \n",
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       "\n",
       "                                      targetCaptions  \\\n",
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       "1  [In this July 1, 2010 file photo, Dr. Charmain...   \n",
       "2  [President Trump will not attend this year's W...   \n",
       "3                    [Maru , Maru, Maru, Maru, Maru]   \n",
       "4  [All nine lines of Tokyo's subway system were ...   \n",
       "\n",
       "                                   targetDescription  \\\n",
       "0  “Inexcusable” failures in the UK’s system for ...   \n",
       "1  President Donald Trump has appointed pro-life ...   \n",
       "2  President Trump won't be at this year's White ...   \n",
       "3  The article is about Maru, a husky dog who has...   \n",
       "4  The temporary suspension, which lasted ten min...   \n",
       "\n",
       "                                      targetKeywords  \\\n",
       "0  modern slavery, Department For Work And Pensio...   \n",
       "1  Americans United for Life, Dr. Charmaine Yoest...   \n",
       "2  trump whcd, whcd, white house correspondents d...   \n",
       "3      Maru, husky, dogs, pandas, furball, instagram   \n",
       "4        Tokyo,subway,shut,fears,North,Korean,attack   \n",
       "\n",
       "                                    targetParagraphs  \\\n",
       "0  [Thousands of modern slavery victims have not ...   \n",
       "1  [President Donald Trump has appointed the pro-...   \n",
       "2  [When the White House correspondents’ dinner i...   \n",
       "3  [Adorable is probably an understatement. This ...   \n",
       "4  [One of Tokyo's major subways systems says it ...   \n",
       "\n",
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       "0  ‘Inexcusable’ failures in UK’s response to mod...  \n",
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       "4  Tokyo's subway is shut down amid fears over an...  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are many columns in 'instances', but the one column I am most interested in is 'targetParagraphs', which contains the full article text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>id</th>\n",
       "      <th>truthClass</th>\n",
       "      <th>truthJudgments</th>\n",
       "      <th>truthMean</th>\n",
       "      <th>truthMedian</th>\n",
       "      <th>truthMode</th>\n",
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       "      <th>0</th>\n",
       "      <td>858464162594172928</td>\n",
       "      <td>clickbait</td>\n",
       "      <td>[1.0, 1.0, 1.0, 1.0, 1.0]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>858462320779026432</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.3333333333, 0.0, 0.3333333333, 0.0, 0.0]</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>858460992073863168</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.3333333333, 0.6666666666, 1.0, 0.0, 0.0]</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>858459539296980992</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.0, 0.6666666666, 0.0, 0.3333333333, 0.33333...</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>858455355948384256</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id    truthClass  \\\n",
       "0  858464162594172928     clickbait   \n",
       "1  858462320779026432  no-clickbait   \n",
       "2  858460992073863168  no-clickbait   \n",
       "3  858459539296980992  no-clickbait   \n",
       "4  858455355948384256  no-clickbait   \n",
       "\n",
       "                                      truthJudgments  truthMean  truthMedian  \\\n",
       "0                          [1.0, 1.0, 1.0, 1.0, 1.0]   1.000000     1.000000   \n",
       "1        [0.3333333333, 0.0, 0.3333333333, 0.0, 0.0]   0.133333     0.000000   \n",
       "2        [0.3333333333, 0.6666666666, 1.0, 0.0, 0.0]   0.400000     0.333333   \n",
       "3  [0.0, 0.6666666666, 0.0, 0.3333333333, 0.33333...   0.266667     0.333333   \n",
       "4                          [0.0, 0.0, 0.0, 0.0, 0.0]   0.000000     0.000000   \n",
       "\n",
       "   truthMode  \n",
       "0   1.000000  \n",
       "1   0.000000  \n",
       "2   0.000000  \n",
       "3   0.333333  \n",
       "4   0.000000  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Each article is classified as 'clickbait' or 'no-clickbait', shown in the 'truthClass' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19538 entries, 0 to 19537\n",
      "Data columns (total 9 columns):\n",
      "id                   19538 non-null int64\n",
      "postMedia            19538 non-null object\n",
      "postText             19538 non-null object\n",
      "postTimestamp        19538 non-null object\n",
      "targetCaptions       19538 non-null object\n",
      "targetDescription    19538 non-null object\n",
      "targetKeywords       19538 non-null object\n",
      "targetParagraphs     19538 non-null object\n",
      "targetTitle          19538 non-null object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 1.5+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print instances.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "instances.sort_values(by=['id'], ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19538 entries, 0 to 19537\n",
      "Data columns (total 6 columns):\n",
      "id                19538 non-null int64\n",
      "truthClass        19538 non-null object\n",
      "truthJudgments    19538 non-null object\n",
      "truthMean         19538 non-null float64\n",
      "truthMedian       19538 non-null float64\n",
      "truthMode         19538 non-null float64\n",
      "dtypes: float64(3), int64(1), object(2)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "truth.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confirms that both files do not have any blank values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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       "      <th>19537</th>\n",
       "      <td>804113781580328960</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.3333333333, 0.0]</td>\n",
       "      <td>0.066667</td>\n",
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       "      <th>19536</th>\n",
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       "      <td>[1.0, 0.0, 0.6666666666, 1.0, 1.0]</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19535</th>\n",
       "      <td>804121272967983104</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19534</th>\n",
       "      <td>804123103995580416</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.3333333333, 0.0]</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19533</th>\n",
       "      <td>804126501117435904</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.0, 0.6666666666, 0.0, 0.0, 0.0]</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id    truthClass  \\\n",
       "19537  804113781580328960  no-clickbait   \n",
       "19536  804119512010424320     clickbait   \n",
       "19535  804121272967983104  no-clickbait   \n",
       "19534  804123103995580416  no-clickbait   \n",
       "19533  804126501117435904  no-clickbait   \n",
       "\n",
       "                                          truthJudgments  truthMean  \\\n",
       "19537                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]   0.066667   \n",
       "19536                 [1.0, 0.0, 0.6666666666, 1.0, 1.0]   0.733333   \n",
       "19535  [0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...   0.333333   \n",
       "19534                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]   0.066667   \n",
       "19533                 [0.0, 0.6666666666, 0.0, 0.0, 0.0]   0.133333   \n",
       "\n",
       "       truthMedian  truthMode  \n",
       "19537     0.000000        0.0  \n",
       "19536     1.000000        1.0  \n",
       "19535     0.333333        0.0  \n",
       "19534     0.000000        0.0  \n",
       "19533     0.000000        0.0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truth.sort_values(by=['id'], ascending=True, inplace=True)\n",
    "truth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Merging the two dataframes together by id\n",
    "corpus = pd.merge(instances, truth, on=['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>postMedia</th>\n",
       "      <th>postText</th>\n",
       "      <th>postTimestamp</th>\n",
       "      <th>targetCaptions</th>\n",
       "      <th>targetDescription</th>\n",
       "      <th>targetKeywords</th>\n",
       "      <th>targetParagraphs</th>\n",
       "      <th>targetTitle</th>\n",
       "      <th>truthClass</th>\n",
       "      <th>truthJudgments</th>\n",
       "      <th>truthMean</th>\n",
       "      <th>truthMedian</th>\n",
       "      <th>truthMode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>804113781580328960</td>\n",
       "      <td>[]</td>\n",
       "      <td>[Panama Papers: Europol links 3,500 names to s...</td>\n",
       "      <td>Thu Dec 01 00:03:59 +0000 2016</td>\n",
       "      <td>[Europol]</td>\n",
       "      <td>Law enforcement agency analysis uncovers proba...</td>\n",
       "      <td>European Union,Panama Papers,Organised crime,W...</td>\n",
       "      <td>[Law enforcement agency analysis uncovers prob...</td>\n",
       "      <td>Panama Papers: Europol links 3,500 names to su...</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.3333333333, 0.0]</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804119512010424320</td>\n",
       "      <td>[media/photo_804119509338640385.jpg]</td>\n",
       "      <td>[The key to truly great chicken soup]</td>\n",
       "      <td>Thu Dec 01 00:26:46 +0000 2016</td>\n",
       "      <td>[Chicken Noodle Soup Warm and comforting, this...</td>\n",
       "      <td>For the best rendition of this American classi...</td>\n",
       "      <td>Cooking and Cookbooks,Soups,Poultry</td>\n",
       "      <td>[Warm and comforting, this soup is a classic t...</td>\n",
       "      <td>A Superior Chicken Soup</td>\n",
       "      <td>clickbait</td>\n",
       "      <td>[1.0, 0.0, 0.6666666666, 1.0, 1.0]</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>804121272967983104</td>\n",
       "      <td>[]</td>\n",
       "      <td>[Afghan policewomen face down their fears to s...</td>\n",
       "      <td>Thu Dec 01 00:33:45 +0000 2016</td>\n",
       "      <td>[The battle to recruit women to the Afghan Pol...</td>\n",
       "      <td>The Afghan women risking all to join the police.</td>\n",
       "      <td></td>\n",
       "      <td>[Afghanistan is one of the most dangerous plac...</td>\n",
       "      <td>100 Women 2016: On the frontline with the wome...</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>804123103995580416</td>\n",
       "      <td>[]</td>\n",
       "      <td>[Conservatives are watching less football this...</td>\n",
       "      <td>Thu Dec 01 00:41:02 +0000 2016</td>\n",
       "      <td>[BuzzFeed Hires Mondelez Marketing Executive, ...</td>\n",
       "      <td>Many factors are dragging down NFL ratings, in...</td>\n",
       "      <td>nfl,ratings,american football,political,genera...</td>\n",
       "      <td>[Explanations for this year’s sagging National...</td>\n",
       "      <td>Older Viewers and Conservatives Are Watching L...</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.3333333333, 0.0]</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>804126501117435904</td>\n",
       "      <td>[media/photo_804125377400553474.jpg]</td>\n",
       "      <td>[Richard Sherman weighs in on Cam Newton’s str...</td>\n",
       "      <td>Thu Dec 01 00:54:32 +0000 2016</td>\n",
       "      <td>[Richard Sherman Says 'Karma' Caught Up to Cam...</td>\n",
       "      <td>Seattle Seahawks news, rumors, scores, schedul...</td>\n",
       "      <td>Football, NFL, NFC West, Seattle Seahawks, Bre...</td>\n",
       "      <td>[Seattle Seahawks cornerback Richard Sherman m...</td>\n",
       "      <td>Seattle Seahawks Richard Sherman Says 'Karma' ...</td>\n",
       "      <td>no-clickbait</td>\n",
       "      <td>[0.0, 0.6666666666, 0.0, 0.0, 0.0]</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                             postMedia  \\\n",
       "0  804113781580328960                                    []   \n",
       "1  804119512010424320  [media/photo_804119509338640385.jpg]   \n",
       "2  804121272967983104                                    []   \n",
       "3  804123103995580416                                    []   \n",
       "4  804126501117435904  [media/photo_804125377400553474.jpg]   \n",
       "\n",
       "                                            postText  \\\n",
       "0  [Panama Papers: Europol links 3,500 names to s...   \n",
       "1              [The key to truly great chicken soup]   \n",
       "2  [Afghan policewomen face down their fears to s...   \n",
       "3  [Conservatives are watching less football this...   \n",
       "4  [Richard Sherman weighs in on Cam Newton’s str...   \n",
       "\n",
       "                    postTimestamp  \\\n",
       "0  Thu Dec 01 00:03:59 +0000 2016   \n",
       "1  Thu Dec 01 00:26:46 +0000 2016   \n",
       "2  Thu Dec 01 00:33:45 +0000 2016   \n",
       "3  Thu Dec 01 00:41:02 +0000 2016   \n",
       "4  Thu Dec 01 00:54:32 +0000 2016   \n",
       "\n",
       "                                      targetCaptions  \\\n",
       "0                                          [Europol]   \n",
       "1  [Chicken Noodle Soup Warm and comforting, this...   \n",
       "2  [The battle to recruit women to the Afghan Pol...   \n",
       "3  [BuzzFeed Hires Mondelez Marketing Executive, ...   \n",
       "4  [Richard Sherman Says 'Karma' Caught Up to Cam...   \n",
       "\n",
       "                                   targetDescription  \\\n",
       "0  Law enforcement agency analysis uncovers proba...   \n",
       "1  For the best rendition of this American classi...   \n",
       "2   The Afghan women risking all to join the police.   \n",
       "3  Many factors are dragging down NFL ratings, in...   \n",
       "4  Seattle Seahawks news, rumors, scores, schedul...   \n",
       "\n",
       "                                      targetKeywords  \\\n",
       "0  European Union,Panama Papers,Organised crime,W...   \n",
       "1                Cooking and Cookbooks,Soups,Poultry   \n",
       "2                                                      \n",
       "3  nfl,ratings,american football,political,genera...   \n",
       "4  Football, NFL, NFC West, Seattle Seahawks, Bre...   \n",
       "\n",
       "                                    targetParagraphs  \\\n",
       "0  [Law enforcement agency analysis uncovers prob...   \n",
       "1  [Warm and comforting, this soup is a classic t...   \n",
       "2  [Afghanistan is one of the most dangerous plac...   \n",
       "3  [Explanations for this year’s sagging National...   \n",
       "4  [Seattle Seahawks cornerback Richard Sherman m...   \n",
       "\n",
       "                                         targetTitle    truthClass  \\\n",
       "0  Panama Papers: Europol links 3,500 names to su...  no-clickbait   \n",
       "1                           A Superior Chicken Soup      clickbait   \n",
       "2  100 Women 2016: On the frontline with the wome...  no-clickbait   \n",
       "3  Older Viewers and Conservatives Are Watching L...  no-clickbait   \n",
       "4  Seattle Seahawks Richard Sherman Says 'Karma' ...  no-clickbait   \n",
       "\n",
       "                                      truthJudgments  truthMean  truthMedian  \\\n",
       "0                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]   0.066667     0.000000   \n",
       "1                 [1.0, 0.0, 0.6666666666, 1.0, 1.0]   0.733333     1.000000   \n",
       "2  [0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...   0.333333     0.333333   \n",
       "3                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]   0.066667     0.000000   \n",
       "4                 [0.0, 0.6666666666, 0.0, 0.0, 0.0]   0.133333     0.000000   \n",
       "\n",
       "   truthMode  \n",
       "0        0.0  \n",
       "1        1.0  \n",
       "2        0.0  \n",
       "3        0.0  \n",
       "4        0.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-alert alert-warning\">Since I am only interested in whether a particular article is clickbait or not, I will need to determine a baseline for my classification scores</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate baseline accuracy for classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no-clickbait    14777\n",
       "clickbait        4761\n",
       "Name: truthClass, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus['truthClass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline accuracy is 0.756321015457\n"
     ]
    }
   ],
   "source": [
    "# rename clickbait/no-clickbait values to 1 and 0\n",
    "corpus['truthClass'].replace('clickbait', 1, inplace=True)\n",
    "corpus['truthClass'].replace('no-clickbait', 0, inplace=True)\n",
    "baseline_acc = 1. - corpus.truthClass.mean()\n",
    "print 'Baseline accuracy is ' + str(baseline_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><p>I will proceed to drop all features that will not be used. postText, targetCaptions, targetKeywords, targetDescription are columns that also contain text information but I dropped them as they are subsets of words that will be found in the targetPara column (i.e. the full article)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpus.drop(['postMedia','postText','postTimestamp','targetCaptions','targetKeywords','targetDescription'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>804126501117435904</td>\n",
       "      <td>[Seattle Seahawks cornerback Richard Sherman m...</td>\n",
       "      <td>Seattle Seahawks Richard Sherman Says 'Karma' ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.0, 0.6666666666, 0.0, 0.0, 0.0]</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                   targetParagraphs  \\\n",
       "0  804113781580328960  [Law enforcement agency analysis uncovers prob...   \n",
       "1  804119512010424320  [Warm and comforting, this soup is a classic t...   \n",
       "2  804121272967983104  [Afghanistan is one of the most dangerous plac...   \n",
       "3  804123103995580416  [Explanations for this year’s sagging National...   \n",
       "4  804126501117435904  [Seattle Seahawks cornerback Richard Sherman m...   \n",
       "\n",
       "                                         targetTitle  truthClass  \\\n",
       "0  Panama Papers: Europol links 3,500 names to su...           0   \n",
       "1                           A Superior Chicken Soup            1   \n",
       "2  100 Women 2016: On the frontline with the wome...           0   \n",
       "3  Older Viewers and Conservatives Are Watching L...           0   \n",
       "4  Seattle Seahawks Richard Sherman Says 'Karma' ...           0   \n",
       "\n",
       "                                      truthJudgments  truthMean  truthMedian  \\\n",
       "0                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]   0.066667     0.000000   \n",
       "1                 [1.0, 0.0, 0.6666666666, 1.0, 1.0]   0.733333     1.000000   \n",
       "2  [0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...   0.333333     0.333333   \n",
       "3                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]   0.066667     0.000000   \n",
       "4                 [0.0, 0.6666666666, 0.0, 0.0, 0.0]   0.133333     0.000000   \n",
       "\n",
       "   truthMode  \n",
       "0        0.0  \n",
       "1        1.0  \n",
       "2        0.0  \n",
       "3        0.0  \n",
       "4        0.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>para</th>\n",
       "      <th>title</th>\n",
       "      <th>clickbait</th>\n",
       "      <th>judgements</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>804113781580328960</td>\n",
       "      <td>[Law enforcement agency analysis uncovers prob...</td>\n",
       "      <td>Panama Papers: Europol links 3,500 names to su...</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.3333333333, 0.0]</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804119512010424320</td>\n",
       "      <td>[Warm and comforting, this soup is a classic t...</td>\n",
       "      <td>A Superior Chicken Soup</td>\n",
       "      <td>1</td>\n",
       "      <td>[1.0, 0.0, 0.6666666666, 1.0, 1.0]</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>804121272967983104</td>\n",
       "      <td>[Afghanistan is one of the most dangerous plac...</td>\n",
       "      <td>100 Women 2016: On the frontline with the wome...</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>804123103995580416</td>\n",
       "      <td>[Explanations for this year’s sagging National...</td>\n",
       "      <td>Older Viewers and Conservatives Are Watching L...</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.3333333333, 0.0]</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>804126501117435904</td>\n",
       "      <td>[Seattle Seahawks cornerback Richard Sherman m...</td>\n",
       "      <td>Seattle Seahawks Richard Sherman Says 'Karma' ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.0, 0.6666666666, 0.0, 0.0, 0.0]</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                               para  \\\n",
       "0  804113781580328960  [Law enforcement agency analysis uncovers prob...   \n",
       "1  804119512010424320  [Warm and comforting, this soup is a classic t...   \n",
       "2  804121272967983104  [Afghanistan is one of the most dangerous plac...   \n",
       "3  804123103995580416  [Explanations for this year’s sagging National...   \n",
       "4  804126501117435904  [Seattle Seahawks cornerback Richard Sherman m...   \n",
       "\n",
       "                                               title  clickbait  \\\n",
       "0  Panama Papers: Europol links 3,500 names to su...          0   \n",
       "1                           A Superior Chicken Soup           1   \n",
       "2  100 Women 2016: On the frontline with the wome...          0   \n",
       "3  Older Viewers and Conservatives Are Watching L...          0   \n",
       "4  Seattle Seahawks Richard Sherman Says 'Karma' ...          0   \n",
       "\n",
       "                                          judgements      mean    median  mode  \n",
       "0                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]  0.066667  0.000000   0.0  \n",
       "1                 [1.0, 0.0, 0.6666666666, 1.0, 1.0]  0.733333  1.000000   1.0  \n",
       "2  [0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...  0.333333  0.333333   0.0  \n",
       "3                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]  0.066667  0.000000   0.0  \n",
       "4                 [0.0, 0.6666666666, 0.0, 0.0, 0.0]  0.133333  0.000000   0.0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns for easier reference\n",
    "headers = ['id','para','title','clickbait','judgements','mean','median','mode']\n",
    "corpus.columns = headers\n",
    "        \n",
    "corpus.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'Watch Live:\\xa0Joe Biden Honored on Senate Floor',\n",
       " u\"The judge in the murder trial of a white former patrolman who fatally shot a black motorist decided Wednesday to let the jury consider a lesser charge of manslaughter, meaning prosecutors won't have to prove Michael Slager acted out of malice when he shot Walter Scott in the back.\",\n",
       " u\"Slager is charged in the April 2015 shooting death of Walter Scott, who fled from a traffic stop and resisted arrest. The final moments of their encounter were captured on a bystander's cellphone video that shocked the nation.\",\n",
       " u\"While the jury was visiting the scene of the shooting on Wednesday morning, Judge Clifton Newman told attorneys he would grant the prosecution's request to let jurors consider manslaughter as well as murder.\",\n",
       " u\"South Carolina law defines murder as the unlawful taking of life with malice. In Slager's case \\u2014 because the prosecution is alleging no aggravating circumstances that could bring a death sentence \\u2014 murder carries a penalty of 30 years to life.\",\n",
       " u'The prosecution contends that by shooting Scott repeatedly in the back as the motorist tried to run away, Slager showed evidence of malice.',\n",
       " u'Manslaughter is the unlawful killing of another without malice, punishable by two to 30 years in prison.',\n",
       " u'Defense attorney Andy Savage did not object in court to the addition of the lesser charge.',\n",
       " u'After hearing from 55 witnesses, the jurors were taken to the crime scene Wednesday. The vacant lot in North Charleston was the last step before closing arguments Wednesday afternoon in the month-long trial.',\n",
       " u\"The jury \\u2014 11 white people and one black man \\u2014 have already seen a bystander's cellphone video of the shooting multiple times over the course of the trial.\",\n",
       " u'Circuit Judge Clifton Newman told the jurors not to take notes or discuss what they see at the scene. Court officials and one representative each from the defense and prosecution went along, but the media was not allowed to attend.']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus['para'][9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Because of the nature of JSONL files, after loading into the Pandas dataframe, each article appears as a list of strings. Every item in this list corresponds to a single sentence in the article. They are encoded in the UTF-8 format, which throws up the strange characters like \\xa0 and \\u2014.\n",
    "<br><br>\n",
    "Another point to note is that not all article lines are encoded in UTF-8. Some of them are already in ASCII format. Certain articles contain a mixed encoding of the two.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unicodetoascii(text):\n",
    "    \"\"\"function to substitute non-ASCII characters to ASCII\"\"\"\n",
    "    TEXT = (text.\n",
    "    \t\treplace('\\xe2\\x80\\x99', \"'\").\n",
    "            replace('\\xc3\\xa9', 'e').\n",
    "            replace('\\xe2\\x80\\x90', '-').\n",
    "            replace('\\xe2\\x80\\x91', '-').\n",
    "            replace('\\xe2\\x80\\x92', '-').\n",
    "            replace('\\xe2\\x80\\x93', '-').\n",
    "            replace('\\xe2\\x80\\x94', '-').\n",
    "            replace('\\xe2\\x80\\x94', '-').\n",
    "            replace('\\xe2\\x80\\x98', \"'\").\n",
    "            replace('\\xe2\\x80\\x9b', \"'\").\n",
    "            replace('\\xe2\\x80\\x9c', '\"').\n",
    "            replace('\\xe2\\x80\\x9c', '\"').\n",
    "            replace('\\xe2\\x80\\x9d', '\"').\n",
    "            replace('\\xe2\\x80\\x9e', '\"').\n",
    "            replace('\\xe2\\x80\\x9f', '\"').\n",
    "            replace('\\xe2\\x80\\xa6', '...').\n",
    "            replace('\\xe2\\x80\\xb2', \"'\").\n",
    "            replace('\\xe2\\x80\\xb3', \"'\").\n",
    "            replace('\\xe2\\x80\\xb4', \"'\").\n",
    "            replace('\\xe2\\x80\\xb5', \"'\").\n",
    "            replace('\\xe2\\x80\\xb6', \"'\").\n",
    "            replace('\\xe2\\x80\\xb7', \"'\").\n",
    "            replace('\\xe2\\x81\\xba', \"+\").\n",
    "            replace('\\xe2\\x81\\xbb', \"-\").\n",
    "            replace('\\xe2\\x81\\xbc', \"=\").\n",
    "            replace('\\xe2\\x81\\xbd', \"(\").\n",
    "            replace('\\xe2\\x81\\xbe', \")\").\n",
    "            replace('\\xc2\\xa0', ' ').\n",
    "            replace('\\\\', '')\n",
    "\n",
    "                 )\n",
    "    return TEXT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This will generate a clean list of articles, all encoded in ASCII format\n",
    "final = []    \n",
    "for p in range(len(corpus['para'])): \n",
    "    arty = ''\n",
    "    for l in range(len(corpus['para'][p])): \n",
    "        try:\n",
    "            t = unicodetoascii(corpus['para'][p][l].encode('utf-8', 'ignore').lower()) \n",
    "            arty += ' ' + t\n",
    "            \n",
    "        except:\n",
    "            t = unicodetoascii(corpus['para'][p][l].encode('ascii', 'ignore').lower())\n",
    "            arty += ' ' + t\n",
    "    final.append(arty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### By now every element in the 'final' list contains a single unicode string of characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert list to dataframe, for merging into main dataframe later\n",
    "final_series = pd.Series(final)\n",
    "df = pd.DataFrame(final_series, columns=['Words'])\n",
    "\n",
    "# character count for each article, place into dataframe to merge later\n",
    "char_count = [len(final[i]) for i in range(len(final))]\n",
    "char_pd = pd.DataFrame(char_count)\n",
    "\n",
    "# merge everything\n",
    "new_corpus = pd.merge(corpus, df, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_corpus['Words'] = [str(x) for x in new_corpus['Words']] # converts unicode datatype to string\n",
    "new_corpus['char_count'] = char_pd \n",
    "\n",
    "# I want to get a word count as well, so split the string in new_corpus.Words by space, then count the number of words\n",
    "new_corpus['word_count'] = new_corpus['Words'].apply(lambda x: len(x.split(\" \"))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>para</th>\n",
       "      <th>title</th>\n",
       "      <th>clickbait</th>\n",
       "      <th>judgements</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
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       "      <th>Words</th>\n",
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       "  </thead>\n",
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       "      <td>804113781580328960</td>\n",
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       "      <td>[Warm and comforting, this soup is a classic t...</td>\n",
       "      <td>A Superior Chicken Soup</td>\n",
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       "      <td>warm and comforting, this soup is a classic t...</td>\n",
       "      <td>7810</td>\n",
       "      <td>1363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>804121272967983104</td>\n",
       "      <td>[Afghanistan is one of the most dangerous plac...</td>\n",
       "      <td>100 Women 2016: On the frontline with the wome...</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...</td>\n",
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       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>afghanistan is one of the most dangerous plac...</td>\n",
       "      <td>933</td>\n",
       "      <td>164</td>\n",
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       "      <td>804123103995580416</td>\n",
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       "      <td>Older Viewers and Conservatives Are Watching L...</td>\n",
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       "      <td>3202</td>\n",
       "      <td>528</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>804126501117435904</td>\n",
       "      <td>[Seattle Seahawks cornerback Richard Sherman m...</td>\n",
       "      <td>Seattle Seahawks Richard Sherman Says 'Karma' ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[0.0, 0.6666666666, 0.0, 0.0, 0.0]</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>seattle seahawks cornerback richard sherman m...</td>\n",
       "      <td>893</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id                                               para  \\\n",
       "0  804113781580328960  [Law enforcement agency analysis uncovers prob...   \n",
       "1  804119512010424320  [Warm and comforting, this soup is a classic t...   \n",
       "2  804121272967983104  [Afghanistan is one of the most dangerous plac...   \n",
       "3  804123103995580416  [Explanations for this year’s sagging National...   \n",
       "4  804126501117435904  [Seattle Seahawks cornerback Richard Sherman m...   \n",
       "\n",
       "                                               title  clickbait  \\\n",
       "0  Panama Papers: Europol links 3,500 names to su...          0   \n",
       "1                           A Superior Chicken Soup           1   \n",
       "2  100 Women 2016: On the frontline with the wome...          0   \n",
       "3  Older Viewers and Conservatives Are Watching L...          0   \n",
       "4  Seattle Seahawks Richard Sherman Says 'Karma' ...          0   \n",
       "\n",
       "                                          judgements      mean    median  \\\n",
       "0                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]  0.066667  0.000000   \n",
       "1                 [1.0, 0.0, 0.6666666666, 1.0, 1.0]  0.733333  1.000000   \n",
       "2  [0.6666666666, 0.6666666666, 0.0, 0.0, 0.33333...  0.333333  0.333333   \n",
       "3                 [0.0, 0.0, 0.0, 0.3333333333, 0.0]  0.066667  0.000000   \n",
       "4                 [0.0, 0.6666666666, 0.0, 0.0, 0.0]  0.133333  0.000000   \n",
       "\n",
       "   mode                                              Words  char_count  \\\n",
       "0   0.0   law enforcement agency analysis uncovers prob...        4755   \n",
       "1   1.0   warm and comforting, this soup is a classic t...        7810   \n",
       "2   0.0   afghanistan is one of the most dangerous plac...         933   \n",
       "3   0.0   explanations for this year's sagging national...        3202   \n",
       "4   0.0   seattle seahawks cornerback richard sherman m...         893   \n",
       "\n",
       "   word_count  \n",
       "0         730  \n",
       "1        1363  \n",
       "2         164  \n",
       "3         528  \n",
       "4         161  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_corpus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R/f3zZ656aRYNDQ00XYLmKEOyJPWQiHgn0MrMByJiPCT3ZeZYfXsUWAIsBva2PfVo4+Nj\nExoZ2T8DlUvNaLVGmy5BDZroS5IhWZJ6y7uAsYhYDbyOasvEcNvjA8AzwL769kTj42OSdMJxT7Ik\n9ZDMXJmZF2TmKuCvgHcAWyJiVT1lLbAV2AGsiIiFEbEEWEb1o77twMXFXEk64RiSJan3fQi4MSK+\nDZwM3JeZTwKbqELwg8D1mXkAuB04MyK2Ae8BbmyoZklqlNstJKlH1avJ4y44yuN3AHcUY/uB3+pu\nZZI097mSLEmSJBUMyZIkSVKh4+0W9aWFfoNqf9ttVNfgvIspnOh0rEVLkiRJ3dTRSnL9K+lzqU5q\nugA4jemd6CRJkiTNWZ1ut1gD7AS+Anwd2MwLT3RaDZxNfaJTZu4Fxk90kiRJkuasTrdbnAq8ArgE\neCXwNWDeNE50mpBHnOp45hGnkiQd/zoNyU8Bj2fmc0BGxAGqLRfjJjvRaUIecarjmUecntj8kiRJ\nvaHT7RbbgIsioi8iXga8BPjLaZzoJEmSJM1ZHa0kZ+bmiFhJFYLnAVcBfw3cEREnA7uoTnQ6HBHj\nJzrN4/kTnSRJkqQ5q+NLwGXmtUcZntKJTpIkSdJc5mEikiRJUsGQLEmSJBUMyZIkSVLBkCxJkiQV\nDMmSJElSwZAsSZIkFQzJkiRJUsGQLEmSJBUMyZIkSVLBkCxJkiQVDMmSJElSwZAsSZIkFQzJkiRJ\nUsGQLEmSJBUMyZIkSVLBkCxJkiQVDMmSJElSwZAsSZIkFQzJkiRJUsGQLEmSJBUMyZIkSVKhv+kC\npKO58GNv4ZTT7m+6jGm78GNvaboESZI0A/rGxsaaruEFWq3RuVeUZtXw8GIu+eBXmy5j2jZvvIw9\ne/Y1XYYaNDQ00Nd0DbPNnq233/1ulpz295suY9r2/vRp7r38M02XoQZN1LNdSZYkScfkG7/3heNy\nYeMbGy8DQ7JehHuSJUmSpMIxrSRHxDDwA+BNwCHgLmAMeBS4KjOPRMQ64Ir68Q2ZufmYKpYkSZK6\nrOOV5Ig4Cfg08PN6aCOwPjNXAH3ApRGxFLgaOA9YA9wcEQuOrWRJkiSpu45lJfnjwKeAD9f3zwIe\nqm9vAS4EDgPbM/MgcDAidgPLge9N9MKDg4vo759/DKVJzRkaGmi6BEmSdIw6CskR8U6glZkPRMR4\nSO7LzPFfOI8CS4DFwN62p46PT2hkZH8nZUlzQqs12nQJapBfkiSpN3S6kvwuYCwiVgOvA+4Bhtse\nHwCeAfbVt8txSZIkac7qKCRn5srx2xHxTeC9wMciYlVmfhNYC/xXYAdwU0QsBBYAy6h+1CdJkiTN\nWTN5neQPAXdExMnALuC+zDwcEZuArVQ/Erw+Mw/M4HtKkiRJM+6YQ3Jmrmq7e8FRHr8DuONY30eS\nJEmaLR4mIkmSJBUMyZIkSVJhJvckS5IaVh/0dCdwOtUPpjcAjzHFE1Ej4hTgXqorFo0Cl2dma7Y/\nhyQ1zZVkSeotbweeqk8/vQj4JNM7EfVKYGc99x5gfQOfQZIaZ0iWpN7yReCG+nYf1SpxeSLqauBs\n6hNRM3MvMH4i6vnA/cVcSTrhuN1CknpIZj4LEBEDwH1UK8Efn8aJqO3jUzoldXBwEf3982ekfmm2\neUqmXowhWZJ6TEScBnwFuC0zPxcRt7Y9PNmJqO3jUzoldWRk/0yULTWi1RptugQ1aKIvSW63kKQe\nEhG/DHwDuC4z76yHH4mIVfXttVQHPO0AVkTEwohYwvMnom4HLi7mStIJx5VkSeotHwEGgRsiYnxv\n8jXApqmciBoRtwN3R8Q24DngbbP/ESSpeYZkSeohmXkNVSguTelE1MzcD/xWd6qTpOOH2y0kSZKk\ngiFZkiRJKhiSJUmSpIIhWZIkSSoYkiVJkqSCIVmSJEkqGJIlSZKkgiFZkiRJKhiSJUmSpIIn7mlO\nWvrSl7N542VNlzFtS1/68qZLkKRZZ89WL+obGxtruoYXaLVG515R6hnDw4vZs2df02WoRw0NDfQ1\nXcNss2erm+zZ6qaJerbbLSRJkqSCIVmSJEkqGJIlSZKkQkc/3IuIk4A7gdOBBcAG4DHgLmAMeBS4\nKjOPRMQ64ArgELAhMzcfe9mSJElS93S6kvx24KnMXAFcBHwS2Aisr8f6gEsjYilwNXAesAa4OSIW\nHHvZkiRJUvd0egm4LwL31bf7qFaJzwIeqse2ABcCh4HtmXkQOBgRu4HlwPc6rliSJEnqso5CcmY+\nCxARA1RheT3w8cwcvwzQKLAEWAzsbXvq+PiEBgcX0d8/v5PSpCkZGhpougRJkjSHdXyYSEScBnwF\nuC0zPxcRt7Y9PAA8A+yrb5fjExoZ2d9pWdKUtFqjTZegHuUXMEnqDR3tSY6IXwa+AVyXmXfWw49E\nxKr69lpgK7ADWBERCyNiCbCM6kd9kiRJ0pzV6UryR4BB4IaIuKEeuwbYFBEnA7uA+zLzcERsogrM\n84DrM/PAsRYtSZIkdZPHUuuE4xGn6iaPpZZmlj1b3eSx1JIkSdI0GJIlSZKkgiFZkiRJKhiSJUmS\npIIhWZIkSSoYkiVJkqSCIVmSJEkqGJIlSZKkgiFZkiRJKhiSJUmSpIIhWZIkSSoYkiVJkqSCIVmS\nJEkqGJIlSZKkgiFZkiRJKhiSJUmSpIIhWZIkSSoYkiVJkqSCIVmSJEkqGJIlSZKkgiFZkiRJKhiS\nJUmSpIIhWZIkSSoYkiVJkqRCf7ffICLmAbcBvwIcBN6dmbu7/b6SpM7YtyVpdlaSLwMWZuYbgX8F\n/NtZeE9JUufs25JOeF1fSQbOB+4HyMzvRMTrZ+E9dQJZufIcHn9817SeMzy8eErzzjhjGQ8//N1O\nypKOZ/ZtddV0+/ZUezbYtzVzZiMkLwb2tt0/HBH9mXnoxZ4wOLiI/v753a9MPWHXrseaLkHqNdPq\n2/ZsTZd9W8eD2QjJ+4CBtvvzJgrIACMj+7tbkU5oQ0MDtFqjTZehHjU0NDD5pLlvWn3bnq1usmer\nmybq2bOxJ3k7cDFARLwB2DkL7ylJ6px9W9IJbzZWkr8CvCkivgX0Af9iFt5TktQ5+7akE17XQ3Jm\nHgHe2+33kSTNDPu2JHmYiCRJkvQChmRJkiSpYEiWJEmSCoZkSZIkqWBIliRJkgqGZEmSJKlgSJYk\nSZIKfWNjY03XIEmSJM0priRLkiRJBUOyJEmSVDAkS5IkSQVDsiRJklQwJEuSJEkFQ7IkSZJUMCRL\nkiRJBUOyGhMR74yIP5qF93lft99jOiJiZUQsb7oOSZprIuI7EXH6LL2XvVgTMiTrRLC+6QIK7wJe\n1nQRknSCsxdrQp64p1kREacA/wF4BXAy8D4ggHcCzwFDwO2Z+acR8U+Bq4CTgDHgN4F/BNxSz/1T\n4OdHmfMU8CfA2fV7fLR+3keBzwDXAJ8CXk31BXF9Zn4zIh4FfgQ8l5lvbav5HOAT9dz/Dfw2cEb9\nHoeBA8C6+vHPZ+Yb6ud9B3hr/dleCQzXn/sDwN8C/xnYA1ySmT85tn+zktSciPgBsBYYoerBqzLz\nhxHxQ+CNVL3+rcAh4OHMvC4ifh84F/gl4HeAtwMXAT+l6tmrM/PHbe9hL1YjXEnWbHkv8OPMfCNV\n0zqnHv8FsIYq5L6/HnsN8OuZeT7wWP04wMLMXJGZn32ROZcBp2bm2cCvAa/PzJuApzPzXwLvBv42\nM1cClwL/vn7dXwL+TXtArn0aeFdmnkPVTJcBdwDvy8wLgNuAjZN87oOZuZYqoH8gM38A3A9ca1OW\n1AP+E1X/PR/4a2B1RLyWauHhNcBbqALxucCrI+KS+nm7MvNc4CXASuCfAO8ABo7yHvZiNaK/6QJ0\nwghgC0Bm/k/gExHxTuCHmTkWEU8Ci+q5e4C7I+JZqtWCb9fj2fZ6R5sT43MzcwS4oajhHwMr6lUJ\ngP6IOPUorz1uaWbuql/vzwAi4mWZ+Vf14w8DR9tT3dd2+5H6nz8FFh5lriQdz74MXA/8pP7n1VQL\ncF+i6s3fycxfAETEVuDM+nnjPfc1wPcz8wiwLyJ2HuU97MVqhCvJmi27qFYKiIhXRcTn6vH/b79P\nRCwBbqRabX431baK8UZ3ZJI57e+xJCIeqJ83/vzHgf+Ymauo/jz4ReDp9tcu/J+IeHX9etdFxG/W\nY+M/9LiAarXkADAcEfMj4u9R/Vlv3NH2Mx3B/+9J6gGZ+SjwKqptbn9O9Ze5S+vbjwPnRER/RPRR\nrRj/qH7qeM99DDg7IuZFxEuA1x7lbezFaoT/49Bs+TTwqoh4CLiHF//T2D5gO9WK8FaqAFz+sOLF\n5nwNGImIbcADVHvYAB6LiHvrGs6oa/gW8L/q1YsXcwVwZz3/V6ma/jrgk/WKyPif7Z4E/gL4HtWf\nAHdP8u/iu8AfRcSySeZJ0vHgm0Cr7qcPAXsy8+8ycyfwBap+vQP4MfDV9ifWq8FbqPrn56n+Sliy\nF6sR/nBPkiRJKriSLEmSJBUMyZIkSVLBkCxJkiQVDMmSJElSwZAsSZIkFQzJkiRJUsGQLEmSJBX+\nH9RJwBQ/TskwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a4e3ff150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot to show the character and word distribution among articles\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
    "\n",
    "bplot1 = axes[0].boxplot(new_corpus.word_count,\n",
    "                         vert=True,   # vertical box aligmnent\n",
    "                         patch_artist=True, sym='', labels=['character count'])   # fill with color\n",
    "\n",
    "bplot2 = axes[1].boxplot(new_corpus.char_count,\n",
    "                         vert=True,   # vertical box aligmnent\n",
    "                         patch_artist=True, sym='', labels=['word count'])   # fill with color\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional EDA on the corpus I am using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Wordcloud representation of the words most frequently encountered \n",
    "# in clickbait articles\n",
    "from wordcloud import WordCloud\n",
    "cw = new_corpus.loc[new_corpus['clickbait']==1]\n",
    "new_cw = cw['Words'].reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AJbml73hxRXlnCgLcc68txnGgL9dcayYzSyRzgAjvchjSS604W7x4Ov3ICbKS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WqlnnNKV4ZDeYIgIAiKMFnSKAcINyr0qBU6FIISf6i/51KikmJXwMb+tnfwBLrD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jzX2p+TvNkKkk4fHHT3TqR8I7SuKBz/PucTKPpS917iBKFfM4OPo7gzeSibVj0chUNKFO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apPkY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\nxemHxBFmPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f842e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate wordcloud with a cap of 250 words. The more frequent the word count, the larger the size of the word\n",
    "clickwords_wc = WordCloud(width=512, height=512, relative_scaling=0.5, max_words=250, collocations=False).generate(str(cw['Words']))\n",
    "plt.figure(figsize = (10,8), facecolor='k')\n",
    "plt.imshow(clickwords_wc)\n",
    "plt.axis('off')\n",
    "plt.tight_layout(pad=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word2Vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-alert alert-warning\">The Word2Vec model generates takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Before feeding the corpus into the Word2Vec model, I will need to lemmatize and tokenize each word first\n",
    "\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import RegexpTokenizer, sent_tokenize\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "tokenizer = RegexpTokenizer('[\\'a-zA-Z]+')\n",
    "\n",
    "# Lemmatize words, but do not remove stopwords to preserve the order of words for context\n",
    "doc = []\n",
    "for i in range(len(new_corpus['Words'])):\n",
    "    docwords = [lemmatizer.lemmatize(t) for t in tokenizer.tokenize(new_corpus['Words'][i])]\n",
    "    doc.append(docwords) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_corpus['word_break'] = doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Specify Word2Vec number of features (i.e. the number of dimensions each word will be mapped to)\n",
    "num_features = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from gensim.models.word2vec import Word2Vec\n",
    "w2v_ngram = Word2Vec(new_corpus['word_break'], size=num_features, min_count=1, window=10, sg=1)\n",
    "\n",
    "# trains the model with 300 features, \n",
    "# minimum number of counts for a word to be included in the model is 1\n",
    "# the model will run through 10 words at a time\n",
    "# sg=1 refers to the use of skipgram model instead of the CBOW(continuous bag-of-words) method.\n",
    "# CBOW does not take into account the order in which the words are presented\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136827"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See how many words are in the vocabulary\n",
    "len(w2v_ngram.wv.vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the words that are most related to the top occurring words from the wordcloud above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('strawpedos', 0.7235246300697327),\n",
       " ('mazel', 0.6788522005081177),\n",
       " ('notalotofpeopleknowthat', 0.6756964921951294),\n",
       " ('tpaycheck', 0.6750880479812622),\n",
       " ('moneyworld', 0.6750779151916504),\n",
       " ('screenshare', 0.6749424934387207),\n",
       " ('kwelkernbc', 0.671303391456604),\n",
       " ('refraining', 0.6686923503875732),\n",
       " ('depleting', 0.6654508113861084),\n",
       " ('notarized', 0.6652501225471497)]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_ngram.most_similar(['create', 'sign', 'log', 'post', 'new', 'year'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('creating', 0.6075267195701599),\n",
       " ('reinvent', 0.5619786977767944),\n",
       " ('crowdsourcing', 0.5613309741020203),\n",
       " ('strawpedos', 0.5567101240158081),\n",
       " ('grander', 0.5509200096130371),\n",
       " ('experiential', 0.5493941903114319),\n",
       " ('reorient', 0.545639157295227),\n",
       " ('conjure', 0.5444437265396118),\n",
       " ('build', 0.5434606671333313),\n",
       " (\"cvc's\", 0.5428497195243835)]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_ngram.most_similar(['create'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('newsletter', 0.6009889841079712),\n",
       " ('onparenting', 0.5445947647094727),\n",
       " ('terkel', 0.5290687084197998),\n",
       " ('bloombergbriefs', 0.5285654664039612),\n",
       " ('connect', 0.520905613899231),\n",
       " ('strawpedos', 0.5094450116157532),\n",
       " ('moneyworld', 0.5000960826873779),\n",
       " (\"grim's\", 0.49627047777175903),\n",
       " ('piping', 0.49604693055152893),\n",
       " ('theplaylist', 0.49180951714515686)]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_ngram.most_similar(['sign'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('strawpedos', 0.5301008224487305),\n",
       " ('cyberintrusion', 0.5061978101730347),\n",
       " ('create', 0.49343323707580566),\n",
       " ('mazel', 0.4799206852912903),\n",
       " ('kwelkernbc', 0.47766053676605225),\n",
       " ('slumbering', 0.47247809171676636),\n",
       " ('weckerly', 0.4607362449169159),\n",
       " (\"utility's\", 0.45820555090904236),\n",
       " ('deregister', 0.45747244358062744),\n",
       " ('inputting', 0.45716285705566406)]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_ngram.most_similar(['log'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('shared', 0.6130218505859375),\n",
       " (\"p'bone\", 0.6009458303451538),\n",
       " ('bonebone', 0.5824652910232544),\n",
       " ('sydneysmashcakes', 0.5721464157104492),\n",
       " ('blackvoices', 0.5717799067497253),\n",
       " ('designerdaddy', 0.559134840965271),\n",
       " ('teatrzoo', 0.5530307292938232),\n",
       " ('huffington', 0.5522246360778809),\n",
       " ('jul', 0.5513942241668701),\n",
       " ('shawnmendes', 0.5506025552749634)]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_ngram.most_similar(['post'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('york', 0.6854352951049805),\n",
       " ('fallsburg', 0.5223125219345093),\n",
       " (\"cuomo's\", 0.5210436582565308),\n",
       " ('lennihan', 0.5157123804092407),\n",
       " ('brainstorm', 0.5140136480331421),\n",
       " ('matternet', 0.5065895318984985),\n",
       " ('streeteasy', 0.504841685295105),\n",
       " ('sundararajan', 0.5036032795906067),\n",
       " ('madcap', 0.5019736289978027),\n",
       " ('katzenstein', 0.5001546144485474)]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_ngram.most_similar(['new'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('month', 0.7071107625961304),\n",
       " (u'decade', 0.6619080901145935),\n",
       " (\"dowling's\", 0.5775504112243652),\n",
       " ('mcglone', 0.5734220147132874),\n",
       " ('breezed', 0.5716580152511597),\n",
       " ('expended', 0.5632145404815674),\n",
       " ('rosier', 0.5620723962783813),\n",
       " ('skyrocketed', 0.5616158843040466),\n",
       " ('psnb', 0.5609381794929504),\n",
       " ('ministering', 0.5573949813842773)]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_ngram.most_similar(['year'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('yahoo', 0.7812060117721558),\n",
       " ('twinsterphoto', 0.672163724899292),\n",
       " ('finance', 0.6356829404830933),\n",
       " ('moneyquestions', 0.5619872808456421),\n",
       " ('sonnenfeldt', 0.5548639893531799),\n",
       " ('uriel', 0.5398631691932678),\n",
       " (\"ofmcdonald's\", 0.5356080532073975),\n",
       " (\"ballard's\", 0.5348875522613525),\n",
       " ('hahm', 0.529039740562439),\n",
       " ('minshew', 0.5264647006988525)]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_ngram.most_similar(['firefox'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w2v_ngram.save('./Capstone')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PyLDA for Topic Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-alert alert-warning\">A topic model is a type of statistical model for discovering the abstract \"topics\" that occur in a collection of documents. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This non-commercial license of GraphLab Create for academic use is assigned to ezekiel.kuang@gmail.com and will expire on October 17, 2018.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1510147475.log\n"
     ]
    }
   ],
   "source": [
    "# meant to be exploratory and not immediately useful to the classification task at hand\n",
    "from __future__ import division\n",
    "\n",
    "import graphlab as gl\n",
    "import pandas as pd\n",
    "import pyLDAvis\n",
    "import pyLDAvis.graphlab \n",
    "\n",
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sa = gl.SArray(doc) # converts the corpus into SArray format for the LDA model to work\n",
    "sa = gl.text_analytics.count_words(sa) # word frequency count, similar to CountVectorizer. Returns a list of dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Learning a topic model</pre>"
      ],
      "text/plain": [
       "Learning a topic model"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>       Number of documents     19538</pre>"
      ],
      "text/plain": [
       "       Number of documents     19538"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>           Vocabulary size    136827</pre>"
      ],
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       "           Vocabulary size    136827"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>   Running collapsed Gibbs sampling</pre>"
      ],
      "text/plain": [
       "   Running collapsed Gibbs sampling"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+---------------+----------------+-----------------+</pre>"
      ],
      "text/plain": [
       "+-----------+---------------+----------------+-----------------+"
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time  | Tokens/Second  | Est. Perplexity |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time  | Tokens/Second  | Est. Perplexity |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+---------------+----------------+-----------------+</pre>"
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      "text/plain": [
       "+-----------+---------------+----------------+-----------------+"
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      "text/html": [
       "<pre>+-----------+---------------+----------------+-----------------+</pre>"
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       "+-----------+---------------+----------------+-----------------+"
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   ],
   "source": [
    "# creates topic model with 100 topics. \n",
    "# Had tried with 50 topics before but the result was that many topic clusters were just prepositions\n",
    "topic_model = gl.topic_model.create(sa, num_topics=100, num_iterations=200) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ezekielkuang/anaconda/lib/python2.7/site-packages/pyLDAvis/_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    }
   ],
   "source": [
    "# generates graph of topics and saves as a html file so it can be viewed later on without retraining\n",
    "pylda = pyLDAvis.graphlab.prepare(topic_model, sa)\n",
    "pyLDAvis.save_html(pylda, 'lda.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topics observed:\n",
    "7 - business<br>\n",
    "8 - family <br>\n",
    "9 - police matters<br>\n",
    "11,16,23,29,25,33,48 - politics/government<br>\n",
    "14 - sports<br>\n",
    "19 - russia<br>\n",
    "24,56,57 - football<br>\n",
    "26 - middle east<br>\n",
    "27 - movies, books and tv<br>\n",
    "28 - health<br>\n",
    "32 - places<br>\n",
    "35 - music and dance<br>\n",
    "37 - taxes and retirement<br>\n",
    "39 - climate change<br>\n",
    "40 - gender issues<br>\n",
    "41 - flights<br>\n",
    "43 - dining<br>\n",
    "44 - weather<br>\n",
    "45 - social media<br>\n",
    "51 - vacation<br>\n",
    "58 - australia<br>\n",
    "59 - transport<br>\n",
    "65 - india<br>\n",
    "68 - education<br>\n",
    "71 - animals<br>\n",
    "72 - religion<br>\n",
    "81 - social media<br>\n",
    "83 - royalty<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">__A note about the terms in PyLDA graph:__<br><br>\n",
    "Lift is defined as the ratio of a term’s probability within a topic to its marginal probability across the corpus. \n",
    "Lambda determines the weight given to the probability of term w under topic k relative to its lift(measuring both on the log scale)<br><br>\n",
    "\n",
    "When lambda = 1, words are ranked solely on probability. Conversely, they are ranked solely by lift when lambda = 0.\n",
    "Lift is the measure of how high the ratio of red to blue bars is (i.e. how exclusive the word is to that given topic). Probability is given by the absolute width of red bars.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-alert alert-info\">\n",
    "<h2>Model Selection and Implementation</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models to be used:\n",
    "#### Logistic Regression\n",
    "#### Naive Bayes Classifiers\n",
    "#### SVMs\n",
    "#### Random Forest Classifiers\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since this is a classification problem, I defined the X and y as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = new_corpus['clickbait'].values\n",
    "X = new_corpus['Words'].values\n",
    "# Split dataset into train and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Will be creating many more pipelines to fit the models from here onward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "log_pipeline = Pipeline([\n",
    "    ('bow', CountVectorizer()),  # strings to token integer counts\n",
    "    ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores\n",
    "    ('logit', LogisticRegression()),  # train on TF-IDF vectors w/ logistic regression\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">Theoretically I could have combined the CountVectorizer and TfidfTransformer steps into one using TfidfVectorizer, but I kept them separate because doing so allows me to specify certain parameters at each step. Will be handy when I do bigrams later.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fit, predict and score the model\n",
    "log_fit = log_pipeline.fit(X_train, y_train)\n",
    "log_pred = log_fit.predict(X_test)\n",
    "log_pred_proba = log_fit.predict_proba(X_test)\n",
    "logr = log_fit.score(X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.96      0.89      4468\n",
      "          1       0.73      0.32      0.45      1394\n",
      "\n",
      "avg / total       0.80      0.81      0.78      5862\n",
      "\n",
      "0.396414294035\n",
      "0.641842388854\n",
      "0.810644831116\n"
     ]
    }
   ],
   "source": [
    "# Detailed results\n",
    "print classification_report(y_test, log_pred)\n",
    "print average_precision_score(y_test, log_pred)\n",
    "print roc_auc_score(y_test, log_pred)\n",
    "print accuracy_score(y_test, log_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RoonFEXmfkkgSZwHXPdM/O0EopZoCdwG1gSTgU6VUD611XF4LS0hIuaRgfP0W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\nmX5u+k8ZoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a47551290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "fpr, tpr, _ = roc_curve(y_test, log_pred_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve\n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('Receiver operating characteristic for log regression', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">The dataset is imbalanced, with roughly three non-clickbait articles for every clickbait article present. I will use random undersampling to balance the dataset. In random undersampling, the majority class (non-clickbait articles) are undersampled.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.747867621972\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.77      0.82      4468\n",
      "          1       0.48      0.67      0.56      1394\n",
      "\n",
      "avg / total       0.79      0.75      0.76      5862\n",
      "\n",
      "0.400186970698\n",
      "0.722566595038\n",
      "0.747867621972\n"
     ]
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import make_pipeline as make_pipeline_imb\n",
    "\n",
    "logpipe = make_pipeline_imb(\n",
    "    TfidfVectorizer(),  # integer counts to weighted TF-IDF scores\n",
    "    RandomUnderSampler(), # undersamples majority class\n",
    "    LogisticRegression()  \n",
    ")\n",
    "\n",
    "logpipe.fit(X_train, y_train)\n",
    "logpipe_pred = logpipe.predict(X_test)\n",
    "logpipe_pred_proba = logpipe.predict_proba(X_test)\n",
    "log2 = logpipe.score(X_test, y_test)\n",
    "print log2\n",
    "print classification_report(y_test, logpipe_pred)\n",
    "print average_precision_score(y_test, logpipe_pred)\n",
    "print roc_auc_score(y_test, logpipe_pred)\n",
    "print accuracy_score(y_test, logpipe_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-alert alert-warning\">\n",
    "Support numbers do not appear to change! Is there a problem? <br><br>\n",
    "Not at all, as I have opted to do the rebalancing BEFORE the classification. <br><br>\n",
    "For RandomUnderSampler(), by default it will resample all classes available to achieve a balanced dataset. In my case, since it is a binary classification, it will undersample the majority class (clickbait articles) in the TRAINING set and fit them into the model. The classification report only outputs samples from the TEST set, which is why the numbers remain the same.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xvIL8axQul79ANGsQQadWNahbpYxJiYQ7O3LkMNWqVcfHx4fIyEjmzfvc7Ehe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\nS5OEUkopt/4fccrzp4lYqBUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a54b88890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, logpipe_pred_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve \n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('ROC for undersampled log regression', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Would using bigrams help? The pipeline below specifies using bigrams for the CountVectorizer class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "log_pipeline2 = Pipeline([\n",
    "    ('bow', CountVectorizer(ngram_range=(1, 2), stop_words='english')), # strings to token integer counts up to bigrams\n",
    "    ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores\n",
    "    ('logit', LogisticRegression()),  # train on TF-IDF vectors w/ log regression\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.809280109178\n"
     ]
    }
   ],
   "source": [
    "log_fit2 = log_pipeline2.fit(X_train, y_train)\n",
    "log_pred2 = log_fit2.predict(X_test)\n",
    "log_pred_proba2 = log_fit2.predict_proba(X_test)\n",
    "logr2 = log_fit2.score(X_test, y_test)\n",
    "print logr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.81      0.97      0.89      4468\n",
      "          1       0.76      0.29      0.42      1394\n",
      "\n",
      "avg / total       0.80      0.81      0.77      5862\n",
      "\n",
      "0.38887707889\n",
      "0.630335887658\n",
      "0.810644831116\n"
     ]
    }
   ],
   "source": [
    "print classification_report(y_test, log_pred2)\n",
    "print average_precision_score(y_test, log_pred2)\n",
    "print roc_auc_score(y_test, log_pred2)\n",
    "print accuracy_score(y_test, log_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bigrams do not appear to improve the scores for log regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes classifiers, using unigram up to trigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mnnb_pipeline1 = Pipeline([\n",
    "    ('bow', CountVectorizer(ngram_range=(1, 1), stop_words='english')),  # strings to token integer counts\n",
    "    ('multinom', MultinomialNB()),  # train on TF-IDF vectors w/ multinomial Naive Bayes\n",
    "])\n",
    "\n",
    "mnnb_pipeline2 = Pipeline([\n",
    "    ('bow', CountVectorizer(ngram_range=(1, 3), stop_words='english')),  # strings to token integer counts up to trigrams\n",
    "    ('multinom', MultinomialNB()),  # train on TF-IDF vectors w/ multinomial Naive Bayes\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.750085295121\n"
     ]
    }
   ],
   "source": [
    "mnnb_fit1 = mnnb_pipeline1.fit(X_train, y_train)\n",
    "mnnb_pred1 = mnnb_fit1.predict(X_test)\n",
    "mnnb_pred1_proba = mnnb_fit1.predict_proba(X_test)\n",
    "mnb1 = mnnb_fit1.score(X_test, y_test)\n",
    "print mnb1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.87      0.80      0.83      4468\n",
      "          1       0.48      0.61      0.54      1394\n",
      "\n",
      "avg / total       0.77      0.75      0.76      5862\n",
      "\n",
      "0.384331418177\n",
      "0.700331160916\n",
      "0.750085295121\n"
     ]
    }
   ],
   "source": [
    "print classification_report(y_test, mnnb_pred1)\n",
    "print average_precision_score(y_test, mnnb_pred1)\n",
    "print roc_auc_score(y_test, mnnb_pred1)\n",
    "print accuracy_score(y_test, mnnb_pred1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sdUgeqzSO1KXA/5RSv2K0OQxQSvUGwrXWM5RSHwA/K6XSgd3AnFKISYgSd+RU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\nS5OEUkoptzRJKKWUckuThFJKKbf+H4Sj9s34jtaQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a38286990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, mnnb_pred1_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve \n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('Receiver operating characteristic for Multinomial NB1', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.783350392358\n"
     ]
    }
   ],
   "source": [
    "mnnb_fit2 = mnnb_pipeline2.fit(X_train, y_train)\n",
    "mnnb_pred2 = mnnb_fit2.predict(X_test)\n",
    "mnnb_pred2_proba = mnnb_fit2.predict_proba(X_test)\n",
    "mnb2 = mnnb_fit2.score(X_test, y_test)\n",
    "print mnb2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.79      0.98      0.87      4468\n",
      "          1       0.69      0.16      0.26      1394\n",
      "\n",
      "avg / total       0.76      0.78      0.73      5862\n",
      "\n",
      "0.311093507705\n",
      "0.569893963\n",
      "0.783350392358\n"
     ]
    }
   ],
   "source": [
    "print classification_report(y_test, mnnb_pred2)\n",
    "print average_precision_score(y_test, mnnb_pred2)\n",
    "print roc_auc_score(y_test, mnnb_pred2)\n",
    "print accuracy_score(y_test, mnnb_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tt0t4/PFGzu6+JUFkL3mrhhAijdNxCXQaujJNgnj/uZo+kyCSkpLo3bsHnTq1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\nJAkhhBAOSZIQQgjh0P8DNLR6TnD1q1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a6e788550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, mnnb_pred2_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve \n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('Receiver operating characteristic for Multinomial NB2', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.707267144319\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.71      0.79      4468\n",
      "          1       0.43      0.71      0.53      1394\n",
      "\n",
      "avg / total       0.78      0.71      0.73      5862\n",
      "\n",
      "0.373436643526\n",
      "0.70703754677\n",
      "0.707267144319\n"
     ]
    }
   ],
   "source": [
    "# Multinomial NB with random undersampling\n",
    "\n",
    "mnnbpipe = make_pipeline_imb(\n",
    "    TfidfVectorizer(),  # integer counts to weighted TF-IDF scores\n",
    "    RandomUnderSampler(),\n",
    "    MultinomialNB()  # train on TF-IDF vectors w/ multinomial Naive Bayes\n",
    ")\n",
    "\n",
    "mnnbpipe.fit(X_train, y_train)\n",
    "mnnbpipe_pred = mnnbpipe.predict(X_test)\n",
    "mnnbpipe_pred_proba = mnnbpipe.predict_proba(X_test)\n",
    "mnnb2 = mnnbpipe.score(X_test, y_test)\n",
    "print mnnb2\n",
    "print classification_report(y_test, mnnbpipe_pred)\n",
    "print average_precision_score(y_test, mnnbpipe_pred)\n",
    "print roc_auc_score(y_test, mnnbpipe_pred)\n",
    "print accuracy_score(y_test, mnnbpipe_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f8tz+ePcmhIYEElUmRHou+YBDhw5SvXoNLBYLlSpVYs6cL80OyadIkhAex263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\nmiSUUko5pUlCKaWUU5oklFJKOfX/wYlvz+Ee/sQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3b98d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, mnnbpipe_pred_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve \n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('ROC for undersampled Multinomial NB2', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Bernoulli NB\n",
    "bern_pipeline = Pipeline([\n",
    "    ('bow', CountVectorizer()), # strings to token integer counts\n",
    "    ('bern', BernoulliNB()),  # train on TF-IDF vectors w/ Bernoulli Naive Bayes\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.770044353463\n"
     ]
    }
   ],
   "source": [
    "bern_fit = bern_pipeline.fit(X_train, y_train)\n",
    "bern_pred = bern_fit.predict(X_test)\n",
    "bern_pred_proba = bern_fit.predict_proba(X_test)\n",
    "bern = bern_fit.score(X_test, y_test)\n",
    "print bern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.90      0.86      4468\n",
      "          1       0.52      0.36      0.43      1394\n",
      "\n",
      "avg / total       0.75      0.77      0.75      5862\n",
      "\n",
      "0.340495128023\n",
      "0.628534299061\n",
      "0.770044353463\n"
     ]
    }
   ],
   "source": [
    "print classification_report(y_test, bern_pred)\n",
    "print average_precision_score(y_test, bern_pred)\n",
    "print roc_auc_score(y_test, bern_pred)\n",
    "print accuracy_score(y_test, bern_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hVd6adTlBtLulGkUC5DrIVwQFBTF37kJ++ukH+vYdYHY4XkN+QcJrxCWkOIa7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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8ff15f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, bern_pred_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve \n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('Receiver operating characteristic for Bernoulli NB', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.742238143978\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.86      0.79      0.82      4468\n",
      "          1       0.47      0.59      0.52      1394\n",
      "\n",
      "avg / total       0.77      0.74      0.75      5862\n",
      "\n",
      "0.372450922651\n",
      "0.689260887882\n",
      "0.742238143978\n"
     ]
    }
   ],
   "source": [
    "# Bernoulli NB with random undersampling\n",
    "\n",
    "bernpipe = make_pipeline_imb(\n",
    "    TfidfVectorizer(),  # integer counts to weighted TF-IDF scores\n",
    "    RandomUnderSampler(),\n",
    "    BernoulliNB()  # train on TF-IDF vectors w/ Bernoulli Naive Bayes\n",
    ")\n",
    "\n",
    "bernpipe.fit(X_train, y_train)\n",
    "bernpipe_pred = bernpipe.predict(X_test)\n",
    "bernpipe_pred_proba = bernpipe.predict_proba(X_test)\n",
    "bern2 = bernpipe.score(X_test, y_test)\n",
    "print bern2\n",
    "print classification_report(y_test, bernpipe_pred)\n",
    "print average_precision_score(y_test, bernpipe_pred)\n",
    "print roc_auc_score(y_test, bernpipe_pred)\n",
    "print accuracy_score(y_test, bernpipe_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o0TIpEF5KioTwqLR0W5YC0bBGGYZ1ayRnsPiR48eP0a1bR3bs2M5VV1VhyZLl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa6396690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, bernpipe_pred_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve \n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('ROC for undersampled Bernoulli NB', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Would SVMs perform better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SVM using linear kernel\n",
    "\n",
    "from sklearn.svm import LinearSVC\n",
    "svc_pipeline = Pipeline([\n",
    "    ('bow', CountVectorizer()),  # strings to token integer counts\n",
    "    ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores\n",
    "    ('linsvc', LinearSVC()),  # train on TF-IDF vectors w/ SVM\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.796997611737\n"
     ]
    }
   ],
   "source": [
    "svc_fit = svc_pipeline.fit(X_train, y_train)\n",
    "svc_pred = svc_fit.predict(X_test)\n",
    "svc = svc_fit.score(X_test, y_test)\n",
    "print svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.92      0.87      4468\n",
      "          1       0.61      0.41      0.49      1394\n",
      "\n",
      "avg / total       0.78      0.80      0.78      5862\n",
      "\n",
      "0.389231134155\n",
      "0.662996163376\n",
      "0.796997611737\n"
     ]
    }
   ],
   "source": [
    "print classification_report(y_test, svc_pred)\n",
    "print average_precision_score(y_test, svc_pred)\n",
    "print roc_auc_score(y_test, svc_pred)\n",
    "print accuracy_score(y_test, svc_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.71340839304\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.72      0.79      4468\n",
      "          1       0.43      0.68      0.53      1394\n",
      "\n",
      "avg / total       0.77      0.71      0.73      5862\n",
      "\n",
      "0.371725495126\n",
      "0.702182361033\n",
      "0.71340839304\n"
     ]
    }
   ],
   "source": [
    "# SVM with random undersampling\n",
    "\n",
    "svcpipe = make_pipeline_imb(\n",
    "    TfidfVectorizer(),  # integer counts to weighted TF-IDF scores\n",
    "    RandomUnderSampler(),\n",
    "    LinearSVC()  # train on TF-IDF vectors w/ SVM\n",
    ")\n",
    "\n",
    "svcpipe.fit(X_train, y_train)\n",
    "svcpipe_pred = svcpipe.predict(X_test)\n",
    "svc2 = svcpipe.score(X_test, y_test)\n",
    "print svc2\n",
    "print classification_report(y_test, svcpipe_pred)\n",
    "print average_precision_score(y_test, svcpipe_pred)\n",
    "print roc_auc_score(y_test, svcpipe_pred)\n",
    "print accuracy_score(y_test, svcpipe_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What about Random Forest Classifiers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "randomforest_pipeline = Pipeline([\n",
    "    ('bow', CountVectorizer()),  # strings to token integer counts\n",
    "    ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores\n",
    "    ('random', RandomForestClassifier()),  # train on TF-IDF vectors w/ multinomial Naive Bayes\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.791879904469\n"
     ]
    }
   ],
   "source": [
    "randfor_fit = randomforest_pipeline.fit(X_train, y_train)\n",
    "randfor_pred = randfor_fit.predict(X_test)\n",
    "randfor_proba = randfor_fit.predict_proba(X_test)\n",
    "randomforest = randfor_fit.score(X_test, y_test)\n",
    "print randomforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.97      0.88      4468\n",
      "          1       0.70      0.22      0.33      1394\n",
      "\n",
      "avg / total       0.78      0.79      0.75      5862\n",
      "\n",
      "0.338567923674\n",
      "0.594244068132\n",
      "0.791879904469\n"
     ]
    }
   ],
   "source": [
    "print classification_report(y_test, randfor_pred)\n",
    "print average_precision_score(y_test, randfor_pred)\n",
    "print roc_auc_score(y_test, randfor_pred)\n",
    "print accuracy_score(y_test, randfor_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KfQ78rpTKBPYAk4ohkxC2O3wqlcWr41i59di5YTNUrQr06FSblvVlaIjC+umn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\npyRJCCGEcEqShBBCCKf+H3bgnZ9KpUIXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8357e090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, randfor_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve \n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('Receiver operating characteristic for Random Forest', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.719379051518\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.77      0.81      4468\n",
      "          1       0.43      0.56      0.49      1394\n",
      "\n",
      "avg / total       0.75      0.72      0.73      5862\n",
      "\n",
      "0.345193849674\n",
      "0.663654118109\n",
      "0.719379051518\n"
     ]
    }
   ],
   "source": [
    "# Random Forest with random undersampling\n",
    "\n",
    "rfcpipe = make_pipeline_imb(\n",
    "    TfidfVectorizer(),  # integer counts to weighted TF-IDF scores\n",
    "    RandomUnderSampler(),\n",
    "    RandomForestClassifier()  # train on TF-IDF vectors w/ Random Forest Classifier\n",
    ")\n",
    "\n",
    "rfcpipe.fit(X_train, y_train)\n",
    "rfcpipe_pred = rfcpipe.predict(X_test)\n",
    "rfcpipe_pred_proba = rfcpipe.predict_proba(X_test)\n",
    "rfc2 = rfcpipe.score(X_test, y_test)\n",
    "print rfc2\n",
    "print classification_report(y_test, rfcpipe_pred)\n",
    "print average_precision_score(y_test, rfcpipe_pred)\n",
    "print roc_auc_score(y_test, rfcpipe_pred)\n",
    "print accuracy_score(y_test, rfcpipe_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D4YQpNzeX2267mR9+WExISAhdu3a3OlK1JUVCiApkGAZ//n2MuJ/jSc3MBaBD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a837a76d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, rfcpipe_pred_proba[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot of a ROC curve \n",
    "plt.figure(figsize=[6,6])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=2)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=2)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=13)\n",
    "plt.ylabel('True Positive Rate', fontsize=13)\n",
    "plt.title('ROC for undersampled Random Forest', fontsize=13)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           f1-score  precision  recall  support\n",
      "0              0.82       0.88    0.77   4468.0\n",
      "1              0.56       0.48    0.67   1394.0\n",
      "avg/total      0.69       0.68    0.72   5862.0\n",
      "           f1-score  precision  recall  support\n",
      "0              0.79       0.89    0.71   4468.0\n",
      "1              0.53       0.43    0.71   1394.0\n",
      "avg/total      0.66       0.66    0.71   5862.0\n",
      "           f1-score  precision  recall  support\n",
      "0              0.82       0.86    0.79   4468.0\n",
      "1              0.52       0.47    0.59   1394.0\n",
      "avg/total      0.67       0.67    0.69   5862.0\n",
      "           f1-score  precision  recall  support\n",
      "0              0.79       0.88    0.72   4468.0\n",
      "1              0.53       0.43    0.68   1394.0\n",
      "avg/total      0.66       0.66    0.70   5862.0\n",
      "           f1-score  precision  recall  support\n",
      "0              0.81       0.85    0.77   4468.0\n",
      "1              0.49       0.43    0.56   1394.0\n",
      "avg/total      0.65       0.64    0.67   5862.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "def score_extraction(report, pred, classes):\n",
    "    \n",
    "    \"\"\"function to convert the results of sklearn's classification report into a dataframe\"\"\"\n",
    "    \n",
    "    clf_rep = precision_recall_fscore_support(y_test, pred)\n",
    "    out_dict = {\n",
    "                 \"precision\" :clf_rep[0].round(2)\n",
    "                ,\"recall\" : clf_rep[1].round(2)\n",
    "                ,\"f1-score\" : clf_rep[2].round(2)\n",
    "                ,\"support\" : clf_rep[3]\n",
    "                }\n",
    "    out_df = pd.DataFrame(out_dict, index = classes.classes_)\n",
    "    avg_tot = (out_df.apply(lambda x: round(x.mean(), 2) if x.name!=\"support\" else  round(x.sum(), 2)).to_frame().T)\n",
    "    avg_tot.index = [\"avg/total\"]\n",
    "    out_df = out_df.append(avg_tot)\n",
    "    print out_df\n",
    "    return out_df\n",
    "\n",
    "se_log = score_extraction(y_test, logpipe_pred, logpipe)\n",
    "se_mnnb = score_extraction(y_test, mnnbpipe_pred, mnnbpipe)\n",
    "se_bern = score_extraction(y_test, bernpipe_pred, bernpipe)\n",
    "se_svc = score_extraction(y_test, svcpipe_pred, svcpipe)\n",
    "se_rfc = score_extraction(y_test, rfcpipe_pred, rfcpipe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# selecting best model and output the predictions and actual texts into a csv file for cool visual representation\n",
    "cfd = pd.DataFrame({'expected':y_test, 'predicted':mnnbpipe_pred,'text':[text[:1000] + '...' for text in X_test]})\n",
    "cfd.to_csv('cfd.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h2> Conclusions so far</h2><br>\n",
    "1. Balancing the datasets significantly improves the models' recall scores, at the expense of overall f1 scores <br>\n",
    "2. When considering the recall scores for undersampled models, Multinomial NB > SVM > Log Regression > Bernoulli NB > Random Forest<br>\n",
    "3. Bigrams do not appear to improve model scores, which is surprising (or it could be my own oversight in implementing)\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to add in visualizations for the above later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save to pickle file\n",
    "new_corpus.to_pickle('new_corpus.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "f = open('./new_corpus.pickle','rb')\n",
    "new_corpus = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not being satisfied with the scores that I obtained above, I sought to test out using LSTM RNNs for classifying the clickbait articles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.layers import Dense, Input, LSTM, Embedding, Flatten, Dropout\n",
    "from keras.models import Model, Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "# Loads pre-trained word vectors from the Glove dataset from the Stanford NLP Group\n",
    "# This dataset contains 6 bn tokens with a vocabulary of 400k, taken from Wikipedia 2014 + Gigaword 5 articles\n",
    "# Each word may be expressed as a vector with 50, 100, 200 or 300 dimensions (100 in this case)\n",
    "# The result is a dictionary, embeddings_index with words as its key and the 100-d vector as its value\n",
    "\n",
    "embeddings_index = {}\n",
    "glove_data = '../Glove/glove.6B.100d.txt'\n",
    "f = open(glove_data)\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    value = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = value\n",
    "f.close()\n",
    "\n",
    "print('Loaded %s word vectors.' % len(embeddings_index))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Instantiate Tokenizer class and use it to tokenize the strings of text \n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts(new_corpus['Words']) # method to fit the text to train on\n",
    "X2 = tokenizer.texts_to_sequences(new_corpus['Words']) # list of word indexes, where the word of rank i in the dataset (starting at 1) has index i)\n",
    "X2 = pad_sequences(X2, maxlen=100) # limits maximum sequence length of 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-alert alert-warning\">\n",
    "The steps above has the effect of expressing each article in the corpus into a sequence of 100 numbers, where every number is a reference to the word's index, as shown below:</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19538, 100)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3127,  9404,     1,  2739,   821,     5,   490,  1680,    11,\n",
       "        15488,  5411,    19,     3,   603,  3681,   736,  2805,   434,\n",
       "         7374,     6, 11265,     5,     1,   454,     7,    78,  7403,\n",
       "          170,    15,   684,  4050,     4,  6520,   587,   602,   880,\n",
       "         1084,  5987,     8,   281, 11072,  2522,     4,   315,  2212,\n",
       "        27957,  1556,     2,   557,  6178, 15489, 24442,     1,   412,\n",
       "          409,     5, 25210,     1,   712,   660,  2893,   232,    98,\n",
       "            1, 19526,    18,   590,    40,  1429,   225,  3041,    74,\n",
       "          659,   942,     5,  2893,   131,     1,   712,   943,     9,\n",
       "            1,  2040,     5,    28,  2003,     5,   382,   677,     2,\n",
       "            1, 11834,  3840,     4,     5,   382,  3323,    19,    78,\n",
       "         1225],\n",
       "       [   30,     2,    21, 37530,  8161,     2,  6018,   154,    21,\n",
       "         4021,     8,   402,   231,     8,     1,    88,  2536,  3144,\n",
       "         6018,    22,  3419,    34,   146,  9609,    11,    38,   241,\n",
       "            3, 40165,  7747,   170,    15, 16854,  3121, 70274,  3933,\n",
       "           38, 23079,    31,    46, 10773,  3072,     4,  9680,    19,\n",
       "            1,  6018,     4,    55,     1,  2135,  6018,   786, 16030,\n",
       "           38,   203,   456,    64,  1469, 10178, 31843,    17,     1,\n",
       "          186,  8791,     6, 59262,     5,     1,  4587,  3144,  3072,\n",
       "          364,     1,   887,    10, 91390,  3144,  3072,    10,   501,\n",
       "           48,    12,   427,     2,  6018,   419, 17759,    16,    29,\n",
       "           62,    23,     2,  3934,    12,  3419,  3144,  6018,    19,\n",
       "         8247]], dtype=int32)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embedding_dimension = 100\n",
    "word_index = tokenizer.word_index # dictionary mapping corpus words (str) to their rank/index (int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The embedding_matrix matrix maps words to vectors in the specified embedding dimension shown above (100)\n",
    "embedding_matrix = np.zeros((len(word_index) + 1, embedding_dimension)) # set up array of zeros with dimensions equal to vocabulary size and embedding dimensions\n",
    "for word, i in word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word) # returns value (100-d vectors) for the given key (word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        embedding_matrix[i] = embedding_vector[:embedding_dimension]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156613, 100)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# End up with an embedding matrix equivalent to the vocabulary size. \n",
    "# Each word in the vocabulary is represented by a 100-d vector\n",
    "embedding_matrix.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Specifies parameters for the embedding layer to be used in the model\n",
    "embedding_layer = Embedding(embedding_matrix.shape[0],\n",
    "                            embedding_matrix.shape[1],\n",
    "                            weights=[embedding_matrix],\n",
    "                            input_length=100, trainable=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Building the model\n",
    "model = Sequential()\n",
    "model.add(embedding_layer)\n",
    "model.add(LSTM(128)) \n",
    "model.add(Dropout(0.2)) # to prevent overfitting\n",
    "model.add(Dense(1, activation='sigmoid')) \n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 100, 100)          15661300  \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 128)               117248    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 15,778,677\n",
      "Trainable params: 117,377\n",
      "Non-trainable params: 15,661,300\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 15630 samples, validate on 3908 samples\n",
      "Epoch 1/10\n",
      "15630/15630 [==============================] - 174s - loss: 0.5001 - acc: 0.7858 - val_loss: 0.4914 - val_acc: 0.7963\n",
      "Epoch 2/10\n",
      "15630/15630 [==============================] - 160s - loss: 0.4720 - acc: 0.7939 - val_loss: 0.4814 - val_acc: 0.8032\n",
      "Epoch 3/10\n",
      "15630/15630 [==============================] - 164s - loss: 0.4671 - acc: 0.7984 - val_loss: 0.4664 - val_acc: 0.8035\n",
      "Epoch 4/10\n",
      "15630/15630 [==============================] - 160s - loss: 0.4549 - acc: 0.8007 - val_loss: 0.4632 - val_acc: 0.8068\n",
      "Epoch 5/10\n",
      "15630/15630 [==============================] - 158s - loss: 0.4466 - acc: 0.8052 - val_loss: 0.4691 - val_acc: 0.8060\n",
      "Epoch 6/10\n",
      "15630/15630 [==============================] - 162s - loss: 0.4408 - acc: 0.8073 - val_loss: 0.4650 - val_acc: 0.7996\n",
      "Epoch 7/10\n",
      "15630/15630 [==============================] - 217s - loss: 0.4304 - acc: 0.8111 - val_loss: 0.4754 - val_acc: 0.8035\n",
      "Epoch 8/10\n",
      "15630/15630 [==============================] - 229s - loss: 0.4219 - acc: 0.8148 - val_loss: 0.4736 - val_acc: 0.7986\n",
      "Epoch 9/10\n",
      "15630/15630 [==============================] - 218s - loss: 0.4123 - acc: 0.8184 - val_loss: 0.4908 - val_acc: 0.8012\n",
      "Epoch 10/10\n",
      "15630/15630 [==============================] - 171s - loss: 0.3946 - acc: 0.8285 - val_loss: 0.4896 - val_acc: 0.7838\n",
      "{'acc': [0.78579654509793928, 0.79385796545868259, 0.79840051182858551, 0.80070377479969346, 0.80518234167355263, 0.80729366603449837, 0.81113243762758858, 0.81484325013706793, 0.81836212413553544, 0.82853486885722449], 'loss': [0.50007242813220176, 0.47204671154324246, 0.46708727615884843, 0.45489051550989035, 0.44662692896921669, 0.44076137044684521, 0.43042028166892354, 0.42185769929995692, 0.41231950638161513, 0.39457578716259772], 'val_acc': [0.79631525076765608, 0.80322415557830096, 0.80348004094165815, 0.80680655066530194, 0.80603889457523026, 0.79964176049129987, 0.80348004094165815, 0.798618219037871, 0.80117707267144322, 0.78377686796315249], 'val_loss': [0.49135306382984562, 0.48138755169471187, 0.46641934820915076, 0.46320242986098281, 0.46913036585098156, 0.46495269026946628, 0.4754382204130948, 0.4736136574462102, 0.49082506471671006, 0.48956910764060707]}\n",
      "19538/19538 [==============================] - 75s    \n",
      "Accuracy: 82.61%\n",
      "CPU times: user 3 µs, sys: 2 µs, total: 5 µs\n",
      "Wall time: 18.8 µs\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())\n",
    "m1 = model.fit(X2, y, epochs=10, batch_size=64, validation_split=0.2, shuffle=True)\n",
    "print m1.history\n",
    "# Final evaluation of the model\n",
    "scores = model.evaluate(X2, y, verbose=1)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))\n",
    "\n",
    "%time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "Training the model for 10 epochs is probably too much, as the accuracy starts to peak at epoch 4 and then goes downwards from there<br><br>\n",
    "Experimentation with many different configurations of batch size, number of epochs and vector dimensions have not made much difference to the accuracy, as they give similar results with accuracy peaking out at ~80-85%\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<h3> Overall Observations and Conclusions</h3><br>\n",
    "1. A good model relies on having a very large corpus of text to learn its vocabulary from. Compared to datasets used in standard packages in NLP (e.g. Glove), my corpus appears to be too small. But large datasets have a tradeoff in that they take significantly longer times and computing resources to train. I believe my model's level of accuracy is still acceptable, relative to the training time taken.<br><br>\n",
    "2. The use of simplistic Naive Bayes classifiers in predicting clickbait articles can do most of the heavy lifting, getting up to 70% of the job done (after balancing the dataset). The use of neural networks can help, as seen in my use of a LSTM RNN model, but I wonder whether the model will do as well when exposed to a completely new corpus.<br><br>\n",
    "3. There are many potential areas for improvement. For one, there are already papers published on the use of character-level RNNs, where every token is a single letter in the corpus. I did not consider the implementation of this model since my project is a classification problem, not a text-generative one. Another way to refine the model would be to introduce additional feature engineering that may help in classifying, e.g. using POS(part-of-speech) tagging to allow the model to better understand context within a sentence. Last but not least. <br><br>\n",
    "4. Text data is unstructured, messy and hard to preprocess. There are dozens of scenarios that a regex has to take into account for it to properly clean the data. It is also hard to visualize given its high-dimensionality. Many errors can be made in between the steps of preprocessing and model training, without an easy way to spot them.<br><br>\n",
    "5. At the end of the day, as with all models, (garbage in = garbage out). Admittedly, my preprocessing steps only sought to remove basic items such as punctuation and unicode format. I note that articles may sometimes contain email addresses and emojis. There were also several words with spelling errors that I did not correct. A thorough cleaning of the dataset should be able to factor in all the above to ensure a better score.\n",
    "\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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