cluster_analysis_web.ipynb

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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[home](http://www.brandonrose.org)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Document Clustering with Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src='header_short.jpg'>\n",
      "\n",
      "In this guide, I will explain how to cluster a set of documents using Python. My motivating example is to identify the latent structures within the synopses of the top 100 films of all time (per an IMDB list). See [the original post](http://www.brandonrose.org/top100) for a more detailed discussion on the example. This guide covers:\n",
      "\n",
      "<ul>\n",
      "<li> tokenizing and stemming each synopsis\n",
      "<li> transforming the corpus into vector space using [tf-idf](http://en.wikipedia.org/wiki/Tf%E2%80%93idf)\n",
      "<li> calculating cosine distance between each document as a measure of similarity\n",
      "<li> clustering the documents using the [k-means algorithm](http://en.wikipedia.org/wiki/K-means_clustering)\n",
      "<li> using [multidimensional scaling](http://en.wikipedia.org/wiki/Multidimensional_scaling) to reduce dimensionality within the corpus\n",
      "<li> plotting the clustering output using [matplotlib](http://matplotlib.org/) and [mpld3](http://mpld3.github.io/)\n",
      "<li> conducting a hierarchical clustering on the corpus using [Ward clustering](http://en.wikipedia.org/wiki/Ward%27s_method)\n",
      "<li> plotting a Ward dendrogram\n",
      "<li> topic modeling using [Latent Dirichlet Allocation (LDA)](http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)\n",
      "</ul>\n",
      "\n",
      "Note that my [github repo](https://github.com/brandomr/document_cluster) for the whole project is available. The 'cluster_analysis' workbook is fully functional; the 'cluster_analysis_web' workbook has been trimmed down for the purpose of creating this walkthrough. Feel free to download the repo and use 'cluster_analysis' to step through the guide yourself."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Contents"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<ul>\n",
      "<li>[Stopwords, stemming, and tokenization](#Stopwords,-stemming,-and-tokenizing)\n",
      "<li>[Tf-idf and document similarity](#Tf-idf-and-document-similarity)\n",
      "<li>[K-means clustering](#K-means-clustering)\n",
      "<li>[Multidimensional scaling](#Multidimensional-scaling)\n",
      "<li>[Visualizing document clusters](#Visualizing-document-clusters)\n",
      "<li>[Hierarchical document clustering](#Hierarchical-document-clustering)\n",
      "<li>[Latent Dirichlet Allocation (LDA)](#Latent-Dirichlet-Allocation)\n",
      "</ul>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But first, I import everything I am going to need up front"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import nltk\n",
      "import re\n",
      "import os\n",
      "import codecs\n",
      "from sklearn import feature_extraction\n",
      "import mpld3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For the purposes of this walkthrough, imagine that I have 2 primary lists:\n",
      "<ul>\n",
      "<li> *'titles'*: the titles of the films in their rank order<li> *'synopses'*: the synopses of the films matched to the 'titles' order\n",
      "</ul>\n",
      "\n",
      "In the full workbook that I posted to github you can walk through the import of these lists, but for brevity just keep in mind that for the rest of this walk-through I will focus on using these two lists. Of primary importance is the _**'synopses'**_ list; *'titles'* is mostly used for labeling purposes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print titles[:10] #first 10 titles"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['The Godfather', 'The Shawshank Redemption', \"Schindler's List\", 'Raging Bull', 'Casablanca', \"One Flew Over the Cuckoo's Nest\", 'Gone with the Wind', 'Citizen Kane', 'The Wizard of Oz', 'Titanic']\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print synopses[0][:200] #first 200 characters in first synopses (for 'The Godfather')\n",
      "print\n",
      "print"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Plot [edit] [ [ edit edit ] ] \n",
        " On the day of his only daughter's wedding, Vito Corleone hears requests in his role as the Godfather, the Don of a New York crime family. Vito's youngest son, Michael, \n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Stopwords, stemming, and tokenizing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This section is focused on defining some functions to manipulate the synopses. First, I load [NLTK's](http://www.nltk.org/) list of English stop words. [Stop words](http://en.wikipedia.org/wiki/Stop_words) are words like \"a\", \"the\", or \"in\" which don't convey significant meaning. I'm sure there are much better explanations of this out there."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load nltk's English stopwords as variable called 'stopwords'\n",
      "stopwords = nltk.corpus.stopwords.words('english')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print stopwords[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your']\n"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next I import the [Snowball Stemmer](http://snowball.tartarus.org/) which is actually part of NLTK. [Stemming](http://en.wikipedia.org/wiki/Stemming) is just the process of breaking a word down into its root."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load nltk's SnowballStemmer as variabled 'stemmer'\n",
      "from nltk.stem.snowball import SnowballStemmer\n",
      "stemmer = SnowballStemmer(\"english\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Below I define two functions:\n",
      "\n",
      "<ul>\n",
      "<li> *tokenize_and_stem*: tokenizes (splits the synopsis into a list of its respective words (or tokens) and also stems each token <li> *tokenize_only*: tokenizes the synopsis only\n",
      "</ul>\n",
      "\n",
      "I use both these functions to create a dictionary which becomes important in case I want to use stems for an algorithm, but later convert stems back to their full words for presentation purposes. Guess what, I do want to do that!\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# here I define a tokenizer and stemmer which returns the set of stems in the text that it is passed\n",
      "\n",
      "def tokenize_and_stem(text):\n",
      "    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n",
      "    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n",
      "    filtered_tokens = []\n",
      "    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n",
      "    for token in tokens:\n",
      "        if re.search('[a-zA-Z]', token):\n",
      "            filtered_tokens.append(token)\n",
      "    stems = [stemmer.stem(t) for t in filtered_tokens]\n",
      "    return stems\n",
      "\n",
      "\n",
      "def tokenize_only(text):\n",
      "    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n",
      "    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n",
      "    filtered_tokens = []\n",
      "    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n",
      "    for token in tokens:\n",
      "        if re.search('[a-zA-Z]', token):\n",
      "            filtered_tokens.append(token)\n",
      "    return filtered_tokens"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Below I use my stemming/tokenizing and tokenizing functions to iterate over the list of synopses to create two vocabularies: one stemmed and one only tokenized. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#not super pythonic, no, not at all.\n",
      "#use extend so it's a big flat list of vocab\n",
      "totalvocab_stemmed = []\n",
      "totalvocab_tokenized = []\n",
      "for i in synopses:\n",
      "    allwords_stemmed = tokenize_and_stem(i) #for each item in 'synopses', tokenize/stem\n",
      "    totalvocab_stemmed.extend(allwords_stemmed) #extend the 'totalvocab_stemmed' list\n",
      "    \n",
      "    allwords_tokenized = tokenize_only(i)\n",
      "    totalvocab_tokenized.extend(allwords_tokenized)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using these two lists, I create a pandas DataFrame with the stemmed vocabulary as the index and the tokenized words as the column. The benefit of this is it provides an efficient way to look up a stem and return a full token. The downside here is that stems to tokens are one to many: the stem 'run' could be associated with 'ran', 'runs', 'running', etc. For my purposes this is fine--I'm perfectly happy returning the first token associated with the stem I need to look up."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vocab_frame = pd.DataFrame({'words': totalvocab_tokenized}, index = totalvocab_stemmed)\n",
      "print 'there are ' + str(vocab_frame.shape[0]) + ' items in vocab_frame'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "there are 312209 items in vocab_frame\n"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You'll notice there is clearly some repetition here. I could clean it up, but there are only 312209 items in the DataFrame which isn't huge overhead in looking up a stemmed word based on the stem-index."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print vocab_frame.head()\n",
      "print\n",
      "print\n",
      "print\n",
      "print"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     words\n",
        "plot  plot\n",
        "edit  edit\n",
        "edit  edit\n",
        "edit  edit\n",
        "on      on\n",
        "\n",
        "\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Tf-idf and document similarity"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src='http://www.jiem.org/index.php/jiem/article/viewFile/293/252/2402' align='right' style=\"margin-left:10px\">\n",
      "\n",
      "Here, I define term frequency-inverse document frequency (tf-idf) vectorizer parameters and then convert the *synopses* list into a tf-idf matrix. \n",
      "\n",
      "To get a Tf-idf matrix, first count word occurrences by document. This is transformed into a document-term matrix (dtm). This is also just called a term frequency matrix. An example of a dtm is here at right.\n",
      "\n",
      "Then apply the term frequency-inverse document frequency weighting: words that occur frequently within a document but not frequently within the corpus receive a higher weighting as these words are assumed to contain more meaning in relation to the document.\n",
      "\n",
      "A couple things to note about the parameters I define below:\n",
      "\n",
      "<ul>\n",
      "<li> max_df: this is the maximum frequency within the documents a given feature can have to be used in the tfi-idf matrix. If the term is in greater than 80% of the documents it probably cares little meanining (in the context of film synopses)\n",
      "<li> min_idf: this could be an integer (e.g. 5) and the term would have to be in at least 5 of the documents to be considered. Here I pass 0.2; the term must be in at least 20% of the document. I found that if I allowed a lower min_df I ended up basing clustering on names--for example \"Michael\" or \"Tom\" are names found in several of the movies and the synopses use these names frequently, but the names carry no real meaning.\n",
      "<li> ngram_range: this just means I'll look at unigrams, bigrams and trigrams. See [n-grams](http://en.wikipedia.org/wiki/N-gram)\n",
      "</ul>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction.text import TfidfVectorizer\n",
      "\n",
      "#define vectorizer parameters\n",
      "tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000,\n",
      "                                 min_df=0.2, stop_words='english',\n",
      "                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))\n",
      "\n",
      "%time tfidf_matrix = tfidf_vectorizer.fit_transform(synopses) #fit the vectorizer to synopses\n",
      "\n",
      "print(tfidf_matrix.shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 29.1 s, sys: 468 ms, total: 29.6 s\n",
        "Wall time: 37.8 s\n",
        "(100, 563)\n"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*terms* is just a list of the features used in the tf-idf matrix. This is a vocabulary"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "terms = tfidf_vectorizer.get_feature_names()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*dist* is defined as 1 - the cosine similarity of each document. Cosine similarity is measured against the tf-idf matrix and can be used to generate a measure of similarity between each document and the other documents in the corpus (each synopsis among the synopses). Subtracting it from 1 provides cosine distance which I will use for plotting on a euclidean (2-dimensional) plane.\n",
      "\n",
      "Note that with *dist* it is possible to evaluate the similarity of any two or more synopses."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.metrics.pairwise import cosine_similarity\n",
      "dist = 1 - cosine_similarity(tfidf_matrix)\n",
      "print\n",
      "print"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "K-means clustering"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now onto the fun part. Using the tf-idf matrix, you can run a slew of clustering algorithms to better understand the hidden structure within the synopses. I first chose [k-means](http://en.wikipedia.org/wiki/K-means_clustering). K-means initializes with a pre-determined number of clusters (I chose 5). Each observation is assigned to a cluster (cluster assignment) so as to minimize the within cluster sum of squares. Next, the mean of the clustered observations is calculated and used as the new cluster centroid. Then, observations are reassigned to clusters and  centroids recalculated in an iterative process until the algorithm reaches convergence.\n",
      "\n",
      "I found it took several runs for the algorithm to converge a global optimum as k-means is susceptible to reaching local optima. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.cluster import KMeans\n",
      "\n",
      "num_clusters = 5\n",
      "\n",
      "km = KMeans(n_clusters=num_clusters)\n",
      "\n",
      "%time km.fit(tfidf_matrix)\n",
      "\n",
      "clusters = km.labels_.tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 232 ms, sys: 6.64 ms, total: 239 ms\n",
        "Wall time: 305 ms\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I use joblib.dump to pickle the model, once it has converged and to reload the model/reassign the labels as the clusters."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.externals import joblib\n",
      "\n",
      "#uncomment the below to save your model \n",
      "#since I've already run my model I am loading from the pickle\n",
      "\n",
      "#joblib.dump(km,  'doc_cluster.pkl')\n",
      "\n",
      "km = joblib.load('doc_cluster.pkl')\n",
      "clusters = km.labels_.tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here, I create a dictionary of titles, ranks, the synopsis, the cluster assignment, and the genre [rank and genre were scraped from IMDB].\n",
      "\n",
      "I convert this dictionary to a Pandas DataFrame for easy access. I'm a huge fan of [Pandas](http://pandas.pydata.org/) and recommend taking a look at some of its awesome functionality which I'll use below, but not describe in a ton of detail."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "films = { 'title': titles, 'rank': ranks, 'synopsis': synopses, 'cluster': clusters, 'genre': genres }\n",
      "\n",
      "frame = pd.DataFrame(films, index = [clusters] , columns = ['rank', 'title', 'cluster', 'genre'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['cluster'].value_counts() #number of films per cluster (clusters from 0 to 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "4    26\n",
        "0    25\n",
        "2    21\n",
        "1    16\n",
        "3    12\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped = frame['rank'].groupby(frame['cluster']) #groupby cluster for aggregation purposes\n",
      "\n",
      "grouped.mean() #average rank (1 to 100) per cluster"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "cluster\n",
        "0          47.200000\n",
        "1          58.875000\n",
        "2          49.380952\n",
        "3          54.500000\n",
        "4          43.730769\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that **clusters 4 and 0** have the lowest rank, which indicates that they, on average, contain films that were ranked as \"better\" on the top 100 list.\n",
      "\n",
      "\n",
      "Here is some fancy indexing and sorting on each cluster to identify which are the top *n* (I chose *n*=6) words that are nearest to the cluster centroid. This gives a good sense of the main topic of the cluster."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from __future__ import print_function\n",
      "\n",
      "print(\"Top terms per cluster:\")\n",
      "print()\n",
      "#sort cluster centers by proximity to centroid\n",
      "order_centroids = km.cluster_centers_.argsort()[:, ::-1] \n",
      "\n",
      "for i in range(num_clusters):\n",
      "    print(\"Cluster %d words:\" % i, end='')\n",
      "    \n",
      "    for ind in order_centroids[i, :6]: #replace 6 with n words per cluster\n",
      "        print(' %s' % vocab_frame.ix[terms[ind].split(' ')].values.tolist()[0][0].encode('utf-8', 'ignore'), end=',')\n",
      "    print() #add whitespace\n",
      "    print() #add whitespace\n",
      "    \n",
      "    print(\"Cluster %d titles:\" % i, end='')\n",
      "    for title in frame.ix[i]['title'].values.tolist():\n",
      "        print(' %s,' % title, end='')\n",
      "    print() #add whitespace\n",
      "    print() #add whitespace\n",
      "    \n",
      "print()\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Top terms per cluster:\n",
        "\n",
        "Cluster 0 words: family, home"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", mother, war"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", house, dies"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ",\n",
        "\n",
        "Cluster 0 titles: Schindler's List, One Flew Over the Cuckoo's Nest, Gone with the Wind, The Wizard of Oz, Titanic, Forrest Gump, E.T. the Extra-Terrestrial, The Silence of the Lambs, Gandhi, A Streetcar Named Desire, The Best Years of Our Lives, My Fair Lady, Ben-Hur, Doctor Zhivago, The Pianist, The Exorcist, Out of Africa, Good Will Hunting, Terms of Endearment, Giant, The Grapes of Wrath, Close Encounters of the Third Kind, The Graduate, Stagecoach, Wuthering Heights,\n",
        "\n",
        "Cluster 1 words: police, car"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", killed, murders"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", driving"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", house,\n",
        "\n",
        "Cluster 1 titles: Casablanca, Psycho, Sunset Blvd., Vertigo, Chinatown, Amadeus, High Noon, The French Connection, Fargo, Pulp Fiction, The Maltese Falcon, A Clockwork Orange, Double Indemnity, Rebel Without a Cause, The Third Man, North by Northwest,\n",
        "\n",
        "Cluster 2 words: father"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", new, york"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", new, brothers"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", apartments"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ",\n",
        "\n",
        "Cluster 2 titles: The Godfather, Raging Bull, Citizen Kane, The Godfather: Part II, On the Waterfront, 12 Angry Men, Rocky, To Kill a Mockingbird, Braveheart, The Good, the Bad and the Ugly, The Apartment, Goodfellas, City Lights, It Happened One Night, Midnight Cowboy, Mr. Smith Goes to Washington, Rain Man, Annie Hall, Network, Taxi Driver, Rear Window,\n",
        "\n",
        "Cluster 3 words: george, dance"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", singing"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", john, love"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", perform,\n",
        "\n",
        "Cluster 3 titles: West Side Story,"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Singin' in the Rain, It's a Wonderful Life, Some Like It Hot, The Philadelphia Story, An American in Paris, The King's Speech, A Place in the Sun, Tootsie, Nashville, American Graffiti, Yankee Doodle Dandy,\n",
        "\n",
        "Cluster 4 words: killed, soldiers"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", captain"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", men, army"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ", command,\n",
        "\n",
        "Cluster 4 titles: The Shawshank Redemption,"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Lawrence of Arabia, The Sound of Music, Star Wars, 2001: A Space Odyssey, The Bridge on the River Kwai, Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb, Apocalypse Now, The Lord of the Rings: The Return of the King, Gladiator, From Here to Eternity, Saving Private Ryan, Unforgiven, Raiders of the Lost Ark, Patton, Jaws, Butch Cassidy and the Sundance Kid, The Treasure of the Sierra Madre, Platoon, Dances with Wolves, The Deer Hunter, All Quiet on the Western Front, Shane, The Green Mile, The African Queen, Mutiny on the Bounty,\n",
        "\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Multidimensional scaling"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is some code to convert the dist matrix into a 2-dimensional array using [multidimensional scaling](http://en.wikipedia.org/wiki/Multidimensional_scaling). I won't pretend I know a ton about MDS, but it was useful for this purpose. Another option would be to use [principal component analysis](http://en.wikipedia.org/wiki/Principal_component_analysis). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os  # for os.path.basename\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib as mpl\n",
      "\n",
      "from sklearn.manifold import MDS\n",
      "\n",
      "MDS()\n",
      "\n",
      "# convert two components as we're plotting points in a two-dimensional plane\n",
      "# \"precomputed\" because we provide a distance matrix\n",
      "# we will also specify `random_state` so the plot is reproducible.\n",
      "mds = MDS(n_components=2, dissimilarity=\"precomputed\", random_state=1)\n",
      "\n",
      "pos = mds.fit_transform(dist)  # shape (n_components, n_samples)\n",
      "\n",
      "xs, ys = pos[:, 0], pos[:, 1]\n",
      "print()\n",
      "print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Visualizing document clusters"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this section, I demonstrate how you can visualize the document clustering output using matplotlib and mpld3 (a matplotlib wrapper for D3.js). \n",
      "\n",
      "First I define some dictionaries for going from cluster number to color and to cluster name. I based the cluster names off the words that were closest to each cluster centroid."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#set up colors per clusters using a dict\n",
      "cluster_colors = {0: '#1b9e77', 1: '#d95f02', 2: '#7570b3', 3: '#e7298a', 4: '#66a61e'}\n",
      "\n",
      "#set up cluster names using a dict\n",
      "cluster_names = {0: 'Family, home, war', \n",
      "                 1: 'Police, killed, murders', \n",
      "                 2: 'Father, New York, brothers', \n",
      "                 3: 'Dance, singing, love', \n",
      "                 4: 'Killed, soldiers, captain'}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, I plot the labeled observations (films, film titles) colored by cluster using matplotlib. I won't get into too much detail about the matplotlib plot, but I tried to provide some helpful commenting."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#some ipython magic to show the matplotlib plots inline\n",
      "%matplotlib inline \n",
      "\n",
      "#create data frame that has the result of the MDS plus the cluster numbers and titles\n",
      "df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=titles)) \n",
      "\n",
      "#group by cluster\n",
      "groups = df.groupby('label')\n",
      "\n",
      "\n",
      "# set up plot\n",
      "fig, ax = plt.subplots(figsize=(17, 9)) # set size\n",
      "ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
      "\n",
      "#iterate through groups to layer the plot\n",
      "#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label\n",
      "for name, group in groups:\n",
      "    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, \n",
      "            label=cluster_names[name], color=cluster_colors[name], \n",
      "            mec='none')\n",
      "    ax.set_aspect('auto')\n",
      "    ax.tick_params(\\\n",
      "        axis= 'x',          # changes apply to the x-axis\n",
      "        which='both',      # both major and minor ticks are affected\n",
      "        bottom='off',      # ticks along the bottom edge are off\n",
      "        top='off',         # ticks along the top edge are off\n",
      "        labelbottom='off')\n",
      "    ax.tick_params(\\\n",
      "        axis= 'y',         # changes apply to the y-axis\n",
      "        which='both',      # both major and minor ticks are affected\n",
      "        left='off',      # ticks along the bottom edge are off\n",
      "        top='off',         # ticks along the top edge are off\n",
      "        labelleft='off')\n",
      "    \n",
      "ax.legend(numpoints=1)  #show legend with only 1 point\n",
      "\n",
      "#add label in x,y position with the label as the film title\n",
      "for i in range(len(df)):\n",
      "    ax.text(df.ix[i]['x'], df.ix[i]['y'], df.ix[i]['title'], size=8)  \n",
      "\n",
      "    \n",
      "    \n",
      "plt.show() #show the plot\n",
      "\n",
      "#uncomment the below to save the plot if need be\n",
      "#plt.savefig('clusters_small_noaxes.png', dpi=200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kyJFUr14dAwMDqlWrVuByac9er3h9+bSsRitfl1wrADQaaOXrgk/Lai8/MCGEEEKI5yAT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UAAAg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+77FU6O4CgF+WBVXiU5kyZQoffvgh0dHRVKhQgfXr11OhQgX8/f05ePAgZcqUYdWqVbRu3Ror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\nIiIiIiIiIiIiIiJt1f8BbDm1ufp3iu4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10eb68d90>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The clustering plot looks great, but it pains my eyes to see overlapping labels. Having some experience with [D3.js](http://d3js.org/) I knew one solution would be to use a browser based/javascript interactive. Fortunately, I recently stumbled upon [mpld3](https://mpld3.github.io/) a matplotlib wrapper for D3. Mpld3 basically let's you use matplotlib syntax to create web interactives. It has a really easy, high-level API for adding tooltips on mouse hover, which is what I am interested in.\n",
      "\n",
      "It also has some nice functionality for zooming and panning. The below javascript snippet basicaly defines a custom location for where the zoom/pan toggle resides. Don't worry about it too much and you actually don't need to use it, but it helped for formatting purposes when exporting to the web later. The only thing you might want to change is the x and y attr for the position of the toolbar."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#define custom toolbar location\n",
      "class TopToolbar(mpld3.plugins.PluginBase):\n",
      "    \"\"\"Plugin for moving toolbar to top of figure\"\"\"\n",
      "\n",
      "    JAVASCRIPT = \"\"\"\n",
      "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
      "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
      "    TopToolbar.prototype.constructor = TopToolbar;\n",
      "    function TopToolbar(fig, props){\n",
      "        mpld3.Plugin.call(this, fig, props);\n",
      "    };\n",
      "\n",
      "    TopToolbar.prototype.draw = function(){\n",
      "      // the toolbar svg doesn't exist\n",
      "      // yet, so first draw it\n",
      "      this.fig.toolbar.draw();\n",
      "\n",
      "      // then change the y position to be\n",
      "      // at the top of the figure\n",
      "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
      "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
      "\n",
      "      // then remove the draw function,\n",
      "      // so that it is not called again\n",
      "      this.fig.toolbar.draw = function() {}\n",
      "    }\n",
      "    \"\"\"\n",
      "    def __init__(self):\n",
      "        self.dict_ = {\"type\": \"toptoolbar\"}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is the actual creation of the interactive scatterplot. I won't go into much more detail about it since it's pretty much a straightforward copy of one of the mpld3 examples, though I use a pandas groupby to group by cluster, then iterate through the groups as I layer the scatterplot. Note that relative to doing this with raw D3, mpld3 is much simpler to integrate into your Python workflow. If you click around the rest of my website you'll see that I do love D3, but for basic interactives I will probably use mpld3 a lot going forward.\n",
      "\n",
      "Note that mpld3 lets you define some custom CSS, which I use to style the font, the axes, and the left margin on the figure."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#create data frame that has the result of the MDS plus the cluster numbers and titles\n",
      "df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=titles)) \n",
      "\n",
      "#group by cluster\n",
      "groups = df.groupby('label')\n",
      "\n",
      "#define custom css to format the font and to remove the axis labeling\n",
      "css = \"\"\"\n",
      "text.mpld3-text, div.mpld3-tooltip {\n",
      "  font-family:Arial, Helvetica, sans-serif;\n",
      "}\n",
      "\n",
      "g.mpld3-xaxis, g.mpld3-yaxis {\n",
      "display: none; }\n",
      "\n",
      "svg.mpld3-figure {\n",
      "margin-left: -200px;}\n",
      "\"\"\"\n",
      "\n",
      "# Plot \n",
      "fig, ax = plt.subplots(figsize=(14,6)) #set plot size\n",
      "ax.margins(0.03) # Optional, just adds 5% padding to the autoscaling\n",
      "\n",
      "#iterate through groups to layer the plot\n",
      "#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label\n",
      "for name, group in groups:\n",
      "    points = ax.plot(group.x, group.y, marker='o', linestyle='', ms=18, \n",
      "                     label=cluster_names[name], mec='none', \n",
      "                     color=cluster_colors[name])\n",
      "    ax.set_aspect('auto')\n",
      "    labels = [i for i in group.title]\n",
      "    \n",
      "    #set tooltip using points, labels and the already defined 'css'\n",
      "    tooltip = mpld3.plugins.PointHTMLTooltip(points[0], labels,\n",
      "                                       voffset=10, hoffset=10, css=css)\n",
      "    #connect tooltip to fig\n",
      "    mpld3.plugins.connect(fig, tooltip, TopToolbar())    \n",
      "    \n",
      "    #set tick marks as blank\n",
      "    ax.axes.get_xaxis().set_ticks([])\n",
      "    ax.axes.get_yaxis().set_ticks([])\n",
      "    \n",
      "    #set axis as blank\n",
      "    ax.axes.get_xaxis().set_visible(False)\n",
      "    ax.axes.get_yaxis().set_visible(False)\n",
      "\n",
      "    \n",
      "ax.legend(numpoints=1) #show legend with only one dot\n",
      "\n",
      "mpld3.display() #show the plot\n",
      "\n",
      "#uncomment the below to export to html\n",
      "#html = mpld3.fig_to_html(fig)\n",
      "#print(html)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "\n",
        "<style>\n",
        "\n",
        "text.mpld3-text, div.mpld3-tooltip {\n",
        "  font-family:Arial, Helvetica, sans-serif;\n",
        "}\n",
        "\n",
        "g.mpld3-xaxis, g.mpld3-yaxis {\n",
        "display: none; }\n",
        "\n",
        "svg.mpld3-figure {\n",
        "margin-left: -200px;}\n",
        "\n",
        "text.mpld3-text, div.mpld3-tooltip {\n",
        "  font-family:Arial, Helvetica, sans-serif;\n",
        "}\n",
        "\n",
        "g.mpld3-xaxis, g.mpld3-yaxis {\n",
        "display: none; }\n",
        "\n",
        "svg.mpld3-figure {\n",
        "margin-left: -200px;}\n",
        "\n",
        "text.mpld3-text, div.mpld3-tooltip {\n",
        "  font-family:Arial, Helvetica, sans-serif;\n",
        "}\n",
        "\n",
        "g.mpld3-xaxis, g.mpld3-yaxis {\n",
        "display: none; }\n",
        "\n",
        "svg.mpld3-figure {\n",
        "margin-left: -200px;}\n",
        "\n",
        "text.mpld3-text, div.mpld3-tooltip {\n",
        "  font-family:Arial, Helvetica, sans-serif;\n",
        "}\n",
        "\n",
        "g.mpld3-xaxis, g.mpld3-yaxis {\n",
        "display: none; }\n",
        "\n",
        "svg.mpld3-figure {\n",
        "margin-left: -200px;}\n",
        "\n",
        "text.mpld3-text, div.mpld3-tooltip {\n",
        "  font-family:Arial, Helvetica, sans-serif;\n",
        "}\n",
        "\n",
        "g.mpld3-xaxis, g.mpld3-yaxis {\n",
        "display: none; }\n",
        "\n",
        "svg.mpld3-figure {\n",
        "margin-left: -200px;}\n",
        "\n",
        "</style>\n",
        "\n",
        "<div id=\"fig_el3692546428098727999978051\"></div>\n",
        "<script>\n",
        "function mpld3_load_lib(url, callback){\n",
        "  var s = document.createElement('script');\n",
        "  s.src = url;\n",
        "  s.async = true;\n",
        "  s.onreadystatechange = s.onload = callback;\n",
        "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
        "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "}\n",
        "\n",
        "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
        "   // already loaded: just create the figure\n",
        "   !function(mpld3){\n",
        "       \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "       mpld3.draw_figure(\"fig_el3692546428098727999978051\", {\"axes\": [{\"xlim\": [-0.70189582032870046, 0.71051473536226761], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Family, home, war\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.94086021505376327], \"rotation\": -0.0, \"id\": \"el369254642580240\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Police, killed, murders\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.88799283154121844], \"rotation\": -0.0, \"id\": \"el369254642615248\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Father, New York, brothers\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.83512544802867383], \"rotation\": -0.0, \"id\": \"el369254643716240\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Dance, singing, love\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.782258064516129], \"rotation\": -0.0, \"id\": \"el369254643456528\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Killed, soldiers, captain\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.72939068100358417], \"rotation\": -0.0, \"id\": \"el369254643458896\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-0.70189582032870046, 0.71051473536226761], \"ylim\": [-0.70830339802964049, 0.69370701811001245], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": [\"M\", \"L\", \"L\", \"L\", \"L\", \"Z\"], \"yindex\": 1, \"coordinates\": \"axes\", \"dasharray\": \"10,0\", \"zorder\": 1000001.0, \"alpha\": 1, \"xindex\": 0, \"data\": \"data07\", \"id\": \"el369254600344144\"}], \"sharey\": [], \"sharex\": [], \"axesbgalpha\": null, \"axes\": [{\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": null, \"position\": \"bottom\", \"nticks\": 0, \"tickvalues\": []}, {\"scale\": \"linear\", \"tickformat\": \"\", \"grid\": {\"gridOn\": false}, \"fontsize\": null, \"position\": \"left\", \"nticks\": 0, \"tickvalues\": []}], \"lines\": [], \"markers\": [{\"edgecolor\": \"none\", \"facecolor\": \"#1B9E77\", \"edgewidth\": 0.5, \"yindex\": 1, \"coordinates\": \"data\", \"zorder\": 2, \"markerpath\": [[[0.0, 9.0], [2.3868279, 9.0], [4.676218837063681, 8.051703224294176], [6.3639610306789285, 6.3639610306789285], [8.051703224294176, 4.676218837063681], [9.0, 2.3868279], [9.0, 0.0], [9.0, -2.3868279], [8.051703224294176, 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0.008757361949769692], [0.4755390685857624, -0.4356393509319867], [0.27427215751846695, -0.2940897904508699], [-0.32213342140019896, 0.33175887910243107], [0.3241754832842173, -0.45547190176887226], [0.18462105786497715, 0.5797922578764358]]}, \"id\": \"el369254642809872\"});\n",
        "   }(mpld3);\n",
        "}else if(typeof define === \"function\" && define.amd){\n",
        "   // require.js is available: use it to load d3/mpld3\n",
        "   require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
        "   require([\"d3\"], function(d3){\n",
        "      window.d3 = d3;\n",
        "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
        "         \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "         mpld3.draw_figure(\"fig_el3692546428098727999978051\", {\"axes\": [{\"xlim\": [-0.70189582032870046, 0.71051473536226761], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Family, home, war\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.94086021505376327], \"rotation\": -0.0, \"id\": \"el369254642580240\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Police, killed, murders\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.88799283154121844], \"rotation\": -0.0, \"id\": \"el369254642615248\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Father, New York, brothers\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.83512544802867383], \"rotation\": -0.0, \"id\": \"el369254643716240\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Dance, singing, love\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.782258064516129], \"rotation\": -0.0, \"id\": \"el369254643456528\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Killed, soldiers, captain\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.72939068100358417], \"rotation\": -0.0, \"id\": \"el369254643458896\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-0.70189582032870046, 0.71051473536226761], \"ylim\": [-0.70830339802964049, 0.69370701811001245], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, \"pathcodes\": 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0.008757361949769692], [0.4755390685857624, -0.4356393509319867], [0.27427215751846695, -0.2940897904508699], [-0.32213342140019896, 0.33175887910243107], [0.3241754832842173, -0.45547190176887226], [0.18462105786497715, 0.5797922578764358]]}, \"id\": \"el369254642809872\"});\n",
        "      });\n",
        "    });\n",
        "}else{\n",
        "    // require.js not available: dynamically load d3 & mpld3\n",
        "    mpld3_load_lib(\"https://mpld3.github.io/js/d3.v3.min.js\", function(){\n",
        "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.2.js\", function(){\n",
        "                 \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "    mpld3.register_plugin(\"htmltooltip\", HtmlTooltipPlugin);\n",
        "    HtmlTooltipPlugin.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    HtmlTooltipPlugin.prototype.constructor = HtmlTooltipPlugin;\n",
        "    HtmlTooltipPlugin.prototype.requiredProps = [\"id\"];\n",
        "    HtmlTooltipPlugin.prototype.defaultProps = {labels:null, hoffset:0, voffset:10};\n",
        "    function HtmlTooltipPlugin(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    HtmlTooltipPlugin.prototype.draw = function(){\n",
        "       var obj = mpld3.get_element(this.props.id);\n",
        "       var labels = this.props.labels;\n",
        "       var tooltip = d3.select(\"body\").append(\"div\")\n",
        "                    .attr(\"class\", \"mpld3-tooltip\")\n",
        "                    .style(\"position\", \"absolute\")\n",
        "                    .style(\"z-index\", \"10\")\n",
        "                    .style(\"visibility\", \"hidden\");\n",
        "\n",
        "       obj.elements()\n",
        "           .on(\"mouseover\", function(d, i){\n",
        "                              tooltip.html(labels[i])\n",
        "                                     .style(\"visibility\", \"visible\");})\n",
        "           .on(\"mousemove\", function(d, i){\n",
        "                    tooltip\n",
        "                      .style(\"top\", d3.event.pageY + this.props.voffset + \"px\")\n",
        "                      .style(\"left\",d3.event.pageX + this.props.hoffset + \"px\");\n",
        "                 }.bind(this))\n",
        "           .on(\"mouseout\",  function(d, i){\n",
        "                           tooltip.style(\"visibility\", \"hidden\");});\n",
        "    };\n",
        "    \n",
        "    mpld3.register_plugin(\"toptoolbar\", TopToolbar);\n",
        "    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);\n",
        "    TopToolbar.prototype.constructor = TopToolbar;\n",
        "    function TopToolbar(fig, props){\n",
        "        mpld3.Plugin.call(this, fig, props);\n",
        "    };\n",
        "\n",
        "    TopToolbar.prototype.draw = function(){\n",
        "      // the toolbar svg doesn't exist\n",
        "      // yet, so first draw it\n",
        "      this.fig.toolbar.draw();\n",
        "\n",
        "      // then change the y position to be\n",
        "      // at the top of the figure\n",
        "      this.fig.toolbar.toolbar.attr(\"x\", 150);\n",
        "      this.fig.toolbar.toolbar.attr(\"y\", 400);\n",
        "\n",
        "      // then remove the draw function,\n",
        "      // so that it is not called again\n",
        "      this.fig.toolbar.draw = function() {}\n",
        "    }\n",
        "    \n",
        "                 mpld3.draw_figure(\"fig_el3692546428098727999978051\", {\"axes\": [{\"xlim\": [-0.70189582032870046, 0.71051473536226761], \"yscale\": \"linear\", \"axesbg\": \"#FFFFFF\", \"texts\": [{\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Family, home, war\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.94086021505376327], \"rotation\": -0.0, \"id\": \"el369254642580240\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Police, killed, murders\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.88799283154121844], \"rotation\": -0.0, \"id\": \"el369254642615248\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Father, New York, brothers\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.83512544802867383], \"rotation\": -0.0, \"id\": \"el369254643716240\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Dance, singing, love\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.782258064516129], \"rotation\": -0.0, \"id\": \"el369254643456528\"}, {\"v_baseline\": \"auto\", \"h_anchor\": \"start\", \"color\": \"#000000\", \"text\": \"Killed, soldiers, captain\", \"coordinates\": \"axes\", \"zorder\": 1000003.0, \"alpha\": 1, \"fontsize\": 12.0, \"position\": [0.78209605414746564, 0.72939068100358417], \"rotation\": -0.0, \"id\": \"el369254643458896\"}], \"zoomable\": true, \"images\": [], \"xdomain\": [-0.70189582032870046, 0.71051473536226761], \"ylim\": [-0.70830339802964049, 0.69370701811001245], \"paths\": [{\"edgecolor\": \"#000000\", \"facecolor\": \"#FFFFFF\", \"edgewidth\": 1.0, 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        "            })\n",
        "         });\n",
        "}\n",
        "</script>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "<IPython.core.display.HTML at 0x114c1c4d0>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Hierarchical document clustering"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that I was successfuly able to cluster and plot the documents using k-means, I wanted to try another clustering algorithm. I chose the [Ward clustering algorithm](http://en.wikipedia.org/wiki/Ward%27s_method) because it offers hierarchical clustering. Ward clustering is an agglomerative clustering method, meaning that at each stage, the pair of clusters with minimum between-cluster distance are merged. I used the precomputed cosine distance matrix (*dist*) to calclate a linkage_matrix, which I then plot as a dendrogram. \n",
      "\n",
      "Note that this method returned 3 primary clusters, with the largest cluster being split into about 4 major subclusters. Note that the cluster in red contains many of the \"Killed, soldiers, captain\" films. *Braveheart* and *Gladiator* are within the same low-level cluster which is interesting as these are probably my two favorite movies."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.cluster.hierarchy import ward, dendrogram\n",
      "\n",
      "linkage_matrix = ward(dist) #define the linkage_matrix using ward clustering pre-computed distances\n",
      "\n",
      "fig, ax = plt.subplots(figsize=(15, 20)) # set size\n",
      "ax = dendrogram(linkage_matrix, orientation=\"right\", labels=titles);\n",
      "\n",
      "plt.tick_params(\\\n",
      "    axis= 'x',          # changes apply to the x-axis\n",
      "    which='both',      # both major and minor ticks are affected\n",
      "    bottom='off',      # ticks along the bottom edge are off\n",
      "    top='off',         # ticks along the top edge are off\n",
      "    labelbottom='off')\n",
      "\n",
      "plt.tight_layout() #show plot with tight layout\n",
      "\n",
      "#uncomment below to save figure\n",
      "plt.savefig('ward_clusters.png', dpi=200) #save figure as ward_clusters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XiSyPUkELiKBEwWLiHSJP5b8PRKCk2Ict3CNJTg+RJEmSSlhMrA+xYTo/jOYD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E/sAGRKMu82ZuehHxN3z1t8Sl3LIS8e3pwqptivapdhOR5TGIaHj+gQg8tBINl/bUurCv1Vje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\nje6AJEmSuqb+5/SnZUFLo7tRj5FE8ADgprQ/vUK9YgY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6ZlpIkiSp2zHTQpI6h+YyLSZk\nWTZwtaxEkiRJWn1qFXmUJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEkS/wdC\nzE77qJ1osQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d583e90>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<p> </p></br>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Latent Dirichlet Allocation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This section focuses on using [Latent Dirichlet Allocation (LDA)](http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) to learn yet more about the hidden structure within the top 100 film synopses. LDA is a probabilistic topic model that assumes documents are a mixture of topics and that each word in the document is attributable to the document's topics. There is quite a good high-level overview of probabilistic topic models by one of the big names in the field, David Blei, available in the [Communications of the ACM here](http://delivery.acm.org/10.1145/2140000/2133826/p77-blei.pdf?ip=68.48.185.120&id=2133826&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&CFID=612398453&CFTOKEN=48760790&__acm__=1419436704_2d47aefe0700e44f81eb822df659a341). Incidentally, Blei was one of the authors of the seminal paper on LDA.\n",
      "\n",
      "For my implementaiton of LDA, I use the [Gensim pacakage](https://radimrehurek.com/gensim/). I'm going to preprocess the synopses a bit differently here, and first I define a function to remove any proper noun."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#strip any proper names from a text...unfortunately right now this is yanking the first word from a sentence too.\n",
      "import string\n",
      "def strip_proppers(text):\n",
      "    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n",
      "    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent) if word.islower()]\n",
      "    return \"\".join([\" \"+i if not i.startswith(\"'\") and i not in string.punctuation else i for i in tokens]).strip()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since the above function is just based on capitalization, it is prone to remove words at the beginning of sentences. So, I wrote the below function using NLTK's part of speech tagger. However, it took way too long to run across all synopses, so I stuck with the above."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#strip any proper nouns (NNP) or plural proper nouns (NNPS) from a text\n",
      "from nltk.tag import pos_tag\n",
      "\n",
      "def strip_proppers_POS(text):\n",
      "    tagged = pos_tag(text.split()) #use NLTK's part of speech tagger\n",
      "    non_propernouns = [word for word,pos in tagged if pos != 'NNP' and pos != 'NNPS']\n",
      "    return non_propernouns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here I run the actual text processing (removing of proper nouns, tokenization, removal of stop words)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from gensim import corpora, models, similarities \n",
      "\n",
      "#remove proper names\n",
      "%time preprocess = [strip_proppers(doc) for doc in synopses]\n",
      "\n",
      "#tokenize\n",
      "%time tokenized_text = [tokenize_and_stem(text) for text in preprocess]\n",
      "\n",
      "#remove stop words\n",
      "%time texts = [[word for word in text if word not in stopwords] for text in tokenized_text]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 12.9 s, sys: 148 ms, total: 13 s\n",
        "Wall time: 15.9 s\n",
        "CPU times: user 15.1 s, sys: 172 ms, total: 15.3 s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Wall time: 19.3 s\n",
        "CPU times: user 4.56 s, sys: 39.2 ms, total: 4.6 s"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Wall time: 5.95 s\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Below are some Gensim specific conversions; I also filter out extreme words (see inline comment)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#create a Gensim dictionary from the texts\n",
      "dictionary = corpora.Dictionary(texts)\n",
      "\n",
      "#remove extremes (similar to the min/max df step used when creating the tf-idf matrix)\n",
      "dictionary.filter_extremes(no_below=1, no_above=0.8)\n",
      "\n",
      "#convert the dictionary to a bag of words corpus for reference\n",
      "corpus = [dictionary.doc2bow(text) for text in texts]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The actual model runs below. I took 100 passes to ensure convergence, but you can see that it took my machine 13 minutes to run. My chunksize is larger than the corpus so basically all synopses are used per pass. I should optimize this, and Gensim has the capacity to run in parallel. I'll likely explore this further as I use the implementation on larger corpora."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%time lda = models.LdaModel(corpus, num_topics=5, \n",
      "                            id2word=dictionary, \n",
      "                            update_every=5, \n",
      "                            chunksize=10000, \n",
      "                            passes=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CPU times: user 9min 53s, sys: 5.87 s, total: 9min 59s\n",
        "Wall time: 13min 1s\n"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Each topic has a set of words that defines it, along with a certain probability."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lda.show_topics()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "[u'0.006*men + 0.005*kill + 0.004*soldier + 0.004*order + 0.004*patient + 0.004*night + 0.003*priest + 0.003*becom + 0.003*new + 0.003*speech',\n",
        " u\"0.006*n't + 0.005*go + 0.005*fight + 0.004*doe + 0.004*home + 0.004*famili + 0.004*car + 0.004*night + 0.004*say + 0.004*next\",\n",
        " u\"0.005*ask + 0.005*meet + 0.005*kill + 0.004*say + 0.004*friend + 0.004*car + 0.004*love + 0.004*famili + 0.004*arriv + 0.004*n't\",\n",
        " u'0.009*kill + 0.006*soldier + 0.005*order + 0.005*men + 0.005*shark + 0.004*attempt + 0.004*offic + 0.004*son + 0.004*command + 0.004*attack',\n",
        " u'0.004*kill + 0.004*water + 0.004*two + 0.003*plan + 0.003*away + 0.003*set + 0.003*boat + 0.003*vote + 0.003*way + 0.003*home']"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here, I convert the topics into just a list of the top 20 words in each topic. You can see a similar breakdown of topics as I identified using k-means including a war/family topic and a more clearly war/epic topic."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "topics_matrix = lda.show_topics(formatted=False, num_words=20)\n",
      "topics_matrix = np.array(topics_matrix)\n",
      "\n",
      "topic_words = topics_matrix[:,:,1]\n",
      "for i in topic_words:\n",
      "    print([str(word) for word in i])\n",
      "    print()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['men', 'kill', 'soldier', 'order', 'patient', 'night', 'priest', 'becom', 'new', 'speech', 'friend', 'decid', 'young', 'ward', 'state', 'front', 'would', 'home', 'two', 'father']\n",
        "\n",
        "[\"n't\", 'go', 'fight', 'doe', 'home', 'famili', 'car', 'night', 'say', 'next', 'ask', 'day', 'want', 'show', 'goe', 'friend', 'two', 'polic', 'name', 'meet']\n",
        "\n",
        "['ask', 'meet', 'kill', 'say', 'friend', 'car', 'love', 'famili', 'arriv', \"n't\", 'home', 'two', 'go', 'father', 'money', 'call', 'polic', 'apart', 'night', 'hous']\n",
        "\n",
        "['kill', 'soldier', 'order', 'men', 'shark', 'attempt', 'offic', 'son', 'command', 'attack', 'water', 'friend', 'ask', 'fire', 'arriv', 'wound', 'die', 'battl', 'death', 'fight']\n",
        "\n",
        "['kill', 'water', 'two', 'plan', 'away', 'set', 'boat', 'vote', 'way', 'home', 'run', 'ship', 'would', 'destroy', 'guilti', 'first', 'attack', 'go', 'use', 'forc']\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 86
    }
   ],
   "metadata": {}
  }
 ]
}