unsupervised_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised Learning\n",
    "This notebook demonstrates unsupervised learning techniques. \n",
    "\n",
    "If you are interested in identifying hidden patterns in data, specifically unlabelled (or unstructured) data, then this notebook will help guide you through unsupervised learning from data with Python. We'll use some key Python statistics and machine learning libaries:\n",
    "\n",
    "* `sci-kit learn`\n",
    "* `statsmodels`\n",
    "\n",
    "For data, we've already collected over 200 speeches from the U.S. Secretary of Defense from the last 4 years. If you're curious to learn about webscraping or how this data was acquired, check out the notebook `data_collection.ipynb`. This is _text_, so it is inherently unstructured data. We'll use some basic natural language processing (NLP) techniques to convert this text into structured data.\n",
    "\n",
    "Let's get started by importing some packages we'll need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "\n",
    "from sklearn import feature_extraction\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's make sure that we have loaded NLTK's (a key Python NLP library) stopwords:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/brandon/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stopwords are words that are common to language and are important in context, but don't carry significant semantic meaning with respect to an overall text. For example, if you were looking at a news article about nuclear weapons in North Korea you could determine what the article was about based on the frequency of words like \"nuclear\", \"weapons\", and \"North Korea\". You wouldn't be able to learn much from words like \"who\", \"when\", \"our\", \"me\", etc.\n",
    "\n",
    "Let's load nltk's English stopwords as variable called `stopwords`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\"]\n"
     ]
    }
   ],
   "source": [
    "stopwords = stopwords.words('english')\n",
    "print(stopwords[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenization and stemming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we are going to define some functions to help us resolve words like \"ran\", \"running\", \"runs\" into a canonical (or _stemmed_) form (e.g. \"run\"). We'll use the NLTK [Snowball Stemmer](https://www.nltk.org/_modules/nltk/stem/snowball.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer(\"english\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'run'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stemmer.stem('runs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "            if len(token) >= 4:\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",
    "            if len(token) >= 4:\n",
    "                filtered_tokens.append(token)\n",
    "    return filtered_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we'll also need to **_tokenize_** the speeches. This is the process of converting text into words.\n",
    "\n",
    "For example, we would take \n",
    "```\n",
    "\"The quick brown fox jumped over the lazy dog.\"\n",
    "```\n",
    "\n",
    "and tokenize it into:\n",
    "```\n",
    "[\"The\", \"quick\", \"brown\", \"fox\", \"jumped\", \"over\", \"the\", \"lazy\", \"dog\", \".\"]\n",
    "```\n",
    "\n",
    "This lets us generate frequency counts of words within speeches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing our data\n",
    "Let's read in the speeches as a dataframe and take a look at what we're dealing with!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>speech</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>December 25, 2018</td>\n",
       "      <td>\\nOn behalf of the Department of State, I woul...</td>\n",
       "      <td>On the Occasion of Christmas</td>\n",
       "      <td>http://www.state.gov/secretary/remarks/2018/12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>December 21, 2018</td>\n",
       "      <td>\\nQUESTION: Thanks very much for taking the ti...</td>\n",
       "      <td>Interview With Steve Inskeep of NPR</td>\n",
       "      <td>http://www.state.gov/secretary/remarks/2018/12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>\\nQUESTION: Good morning, Secretary. How are y...</td>\n",
       "      <td>Interview With Brian Grimmett of KMUW Wichita ...</td>\n",
       "      <td>http://www.state.gov/secretary/remarks/2018/12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>\\nQUESTION: (Inaudible.)\\n\\n\\nSECRETARY POMPEO...</td>\n",
       "      <td>Interview With Bryce Dolan of 550 KFRM-AM</td>\n",
       "      <td>http://www.state.gov/secretary/remarks/2018/12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>December 20, 2018</td>\n",
       "      <td>\\nQUESTION: Pompeo is with us now. I don’t kno...</td>\n",
       "      <td>Interview With Laura Ingraham of The Laura Ing...</td>\n",
       "      <td>http://www.state.gov/secretary/remarks/2018/12...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                date                                             speech  \\\n",
       "0  December 25, 2018  \\nOn behalf of the Department of State, I woul...   \n",
       "1  December 21, 2018  \\nQUESTION: Thanks very much for taking the ti...   \n",
       "2  December 20, 2018  \\nQUESTION: Good morning, Secretary. How are y...   \n",
       "3  December 20, 2018  \\nQUESTION: (Inaudible.)\\n\\n\\nSECRETARY POMPEO...   \n",
       "4  December 20, 2018  \\nQUESTION: Pompeo is with us now. I don’t kno...   \n",
       "\n",
       "                                               title  \\\n",
       "0                       On the Occasion of Christmas   \n",
       "1                Interview With Steve Inskeep of NPR   \n",
       "2  Interview With Brian Grimmett of KMUW Wichita ...   \n",
       "3          Interview With Bryce Dolan of 550 KFRM-AM   \n",
       "4  Interview With Laura Ingraham of The Laura Ing...   \n",
       "\n",
       "                                                 url  \n",
       "0  http://www.state.gov/secretary/remarks/2018/12...  \n",
       "1  http://www.state.gov/secretary/remarks/2018/12...  \n",
       "2  http://www.state.gov/secretary/remarks/2018/12...  \n",
       "3  http://www.state.gov/secretary/remarks/2018/12...  \n",
       "4  http://www.state.gov/secretary/remarks/2018/12...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('datasets/SecState_Speeches.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can tokenize and stem the speeches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "totalvocab_stemmed = []\n",
    "totalvocab_tokenized = []\n",
    "\n",
    "for i in df.speech:\n",
    "    allwords_stemmed = tokenize_and_stem(i)\n",
    "    totalvocab_stemmed.extend(allwords_stemmed)\n",
    "\n",
    "    allwords_tokenized = tokenize_only(i)\n",
    "    totalvocab_tokenized.extend(allwords_tokenized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to generate a vocabulary: this will be a mapping from stemmed version of words back to their original form. That way we can lookup a stemmed word and back into what it actually was (in human readable form)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 186901 items in our vocabulary.\n"
     ]
    }
   ],
   "source": [
    "vocab = pd.DataFrame({'words': totalvocab_tokenized}, index = totalvocab_stemmed)\n",
    "print('There are ' + str(vocab.shape[0]) + ' items in our vocabulary.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf-idf and calculating document similarity\n",
    "To get a ff-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.\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",
    "We will use this tf-idf matrix as a structured representation of the relationship between documents. We can use this to perform unsupervised learning: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brandon/anaconda/envs/p3/lib/python3.5/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['abov', 'afterward', 'alon', 'alreadi', 'alway', 'anoth', 'anyon', 'anyth', 'anywher', 'becam', 'becaus', 'becom', 'befor', 'besid', 'describ', 'dure', 'els', 'elsewher', 'empti', 'everi', 'everyon', 'everyth', 'everywher', 'fifti', 'forti', 'henc', 'hereaft', 'herebi', 'howev', 'hundr', 'inde', 'mani', 'meanwhil', 'moreov', 'nobodi', 'noon', 'noth', 'nowher', 'onc', 'onli', 'otherwis', 'ourselv', 'perhap', 'pleas', 'sever', 'sinc', 'sincer', 'sixti', 'someon', 'someth', 'sometim', 'somewher', 'themselv', 'thenc', 'thereaft', 'therebi', 'therefor', 'togeth', 'twelv', 'twenti', 'veri', 'whatev', 'whenc', 'whenev', 'wherea', 'whereaft', 'wherebi', 'wherev', 'yourselv'] not in stop_words.\n",
      "  'stop_words.' % sorted(inconsistent))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.77 s, sys: 66.4 ms, total: 8.83 s\n",
      "Wall time: 8.85 s\n",
      "(384, 575)\n"
     ]
    }
   ],
   "source": [
    "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=0.1, stop_words='english',\n",
    "                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))\n",
    "\n",
    "%time tfidf_matrix = tfidf_vectorizer.fit_transform(df.speech) #fit the vectorizer to speeches\n",
    "\n",
    "print(tfidf_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the tf-idf matrix has 204 rows (one for each speech) and 1,593 columns (one for each word which met the thresholds we specified for our `TfidfVectorizer`.\n",
    "\n",
    "We can get all the terms from our tf-idf matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "terms = tfidf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-means clustering\n",
    "Now we're ready to perform clustering on our speeches. We'll pick an arbitrary, but human understandable, number of clusters. Let's try for 12 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.75 s, sys: 8.26 ms, total: 2.75 s\n",
      "Wall time: 2.76 s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "num_clusters = 6\n",
    "\n",
    "km = KMeans(n_clusters=num_clusters)\n",
    "\n",
    "%time km.fit(tfidf_matrix)\n",
    "\n",
    "clusters = km.labels_.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to add the cluster labels to our dataset. This let's us see how many speeches ended up in each cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0     37\n",
       "1     44\n",
       "2     72\n",
       "3     74\n",
       "4     19\n",
       "5    138\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cluster'] = clusters\n",
    "df.groupby('cluster')['cluster'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can inspect the top terms in each of these clusters and compare them with the speech titles. This should give us a good sense of whether or not our clustering was successful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mTop terms per cluster:\u001b[0m\n",
      "\n",
      "\u001b[94m\u001b[1mCluster 0 Words:\u001b[0m\n",
      "\t-secretary\n",
      "\t-north\n",
      "\t-korea\n",
      "\t-north\n",
      "\t-president\n",
      "\t-thanks\n",
      "\t-question\n",
      "\t-china\n",
      "\t-pompeo\n",
      "\t-secretary\n",
      "\n",
      "\u001b[92m\u001b[1mCluster 0 Speech Titles:\n",
      "\u001b[0m\n",
      "\t- Press Availability at the U.S. - Canada 2+2 Ministerial\n",
      "\t- Press Availability With Secretary of Defense James Mattis, Chinese Politburo Member Yang Jiechi, and\n",
      "\t- Remarks to Traveling Press\n",
      "\t- Remarks at the Conference on Prosperity and Security in Central America\n",
      "\t- Remarks With Chinese Director of the Central Foreign Affairs Commission Office Yang Jiechi Before Th\n",
      "\t- Remarks With Chinese Foreign Minister Wang Yi Before Their Meeting\n",
      "\t- Remarks With South Korean President Moon Jae-in\n",
      "\t- Remarks With Japanese Prime Minister Shinzo Abe\n",
      "\t- Remarks at a Meeting on the Democratic People's Republic of Korea\n",
      "\t- On the Outcome of Summit Meeting Between President Moon and Chairman Kim\n",
      "\n",
      "\n",
      "\n",
      "\u001b[94m\u001b[1mCluster 1 Words:\u001b[0m\n",
      "\t-embedded_plugin_status\n",
      "\t-embedded_plugin_q\n",
      "\t-embedded_plugin_status\n",
      "\t-typeof\n",
      "\t-.push\n",
      "\t-embedded_plugin_q\n",
      "\t-undefined\n",
      "\t-else\n",
      "\t-function\n",
      "\t-else\n",
      "\n",
      "\u001b[92m\u001b[1mCluster 1 Speech Titles:\n",
      "\u001b[0m\n",
      "\t- Press Availability at the United Nations\n",
      "\t- Press Availability at NATO Headquarters\n",
      "\t- Remarks With NATO Secretary General Jens Stoltenberg Before Their Meeting\n",
      "\t- Restoring the Role of the Nation-State in the Liberal International Order\n",
      "\t- Remarks at the PEPFAR Faith Communities and HIV Technical Summit\n",
      "\t- Remarks to the Press\n",
      "\t- Secretary of State Michael R. Pompeo and Ukrainian Foreign Minister Pavlo Klimkin Statements to the \n",
      "\t- Remarks at the 36th Annual Jewish Institute for National Security of America Awards Dinner\n",
      "\t- Remarks to the Media\n",
      "\t- Remarks at the 13th Annual Values Voter Summit\n",
      "\n",
      "\n",
      "\n",
      "\u001b[94m\u001b[1mCluster 2 Words:\u001b[0m\n",
      "\t-question\n",
      "\t-secretary\n",
      "\t-pompeo\n",
      "\t-secretary\n",
      "\t-president\n",
      "\t-think\n",
      "\t-very\n",
      "\t-make\n",
      "\t-country\n",
      "\t-time\n",
      "\n",
      "\u001b[92m\u001b[1mCluster 2 Speech Titles:\n",
      "\u001b[0m\n",
      "\t- Interview With Steve Inskeep of NPR\n",
      "\t- Interview With Brian Grimmett of KMUW Wichita Public Radio\n",
      "\t- Interview With Bryce Dolan of 550 KFRM-AM\n",
      "\t- Interview With Laura Ingraham of The Laura Ingraham Show\n",
      "\t- Interview With Steve and Ted in the Morning of KNSS Radio\n",
      "\t- Interview With Fox & Friends\n",
      "\t- Interview With Hugh Hewitt of the Hugh Hewitt Show\n",
      "\t- Interview With Andrew Yates of Bott Radio Network\n",
      "\t- Interview With Lukas & Careth In The Morning, B98 FM\n",
      "\t- Interview With Gesell Tobias of Voice of America Latin America\n",
      "\n",
      "\n",
      "\n",
      "\u001b[94m\u001b[1mCluster 3 Words:\u001b[0m\n",
      "\t-freedom\n",
      "\t-peaceful\n",
      "\t-government\n",
      "\t-world\n",
      "\t-u.s.\n",
      "\t-internal\n",
      "\t-today\n",
      "\t-support\n",
      "\t-human\n",
      "\t-department\n",
      "\n",
      "\u001b[92m\u001b[1mCluster 3 Speech Titles:\n",
      "\u001b[0m\n",
      "\t- On the Occasion of Christmas\n",
      "\t- United States Action To Confront Illegal Immigration\n",
      "\t- Bulgaria's Defense Modernization\n",
      "\t- Conclusion of Yemen Consultations in Sweden\n",
      "\t- Religious Freedom Designations\n",
      "\t- Human Rights Day\n",
      "\t- On Senegal's Millennium Challenge Compact\n",
      "\t- International Anticorruption Day\n",
      "\t- On the Passing of President George H. W. Bush\n",
      "\t- The U.S.-Saudi Partnership Is Vital\n",
      "\n",
      "\n",
      "\n",
      "\u001b[94m\u001b[1mCluster 4 Words:\u001b[0m\n",
      "\t-iran\n",
      "\t-iranian\n",
      "\t-regime\n",
      "\t-nuclear\n",
      "\t-missiles\n",
      "\t-activity\n",
      "\t-sanctions\n",
      "\t-secretary\n",
      "\t-threat\n",
      "\t-question\n",
      "\n",
      "\u001b[92m\u001b[1mCluster 4 Speech Titles:\n",
      "\u001b[0m\n",
      "\t- Remarks at the United Nations Security Council Meeting on Iran\n",
      "\t- Iran Test Launches Ballistic Missile Violating UN Security Council Ban\n",
      "\t- Iran's President Works To Further Isolate Iranian Regime From Both World and Iranian People\n",
      "\t- Interview With Hadi Nili of BBC Persian\n",
      "\t- Press Availability With Secretary of Treasury Steven T. Mnuchin\n",
      "\t- Briefing on Iran Sanctions\n",
      "\t- Confronting Iran: The Trump Administration's Strategy\n",
      "\t- Extradition of Iranian Official Asadollah Assadi for Role in Paris Terrorist Plot \n",
      "\t- On U.S. Appearance Before the International Court of Justice\n",
      "\t- Threats to American Personnel and Facilities in Iraq\n",
      "\n",
      "\n",
      "\n",
      "\u001b[94m\u001b[1mCluster 5 Words:\u001b[0m\n",
      "\t-independent\n",
      "\t-celebrating\n",
      "\t-congratulations\n",
      "\t-wish\n",
      "\t-behalf\n",
      "\t-government\n",
      "\t-government\n",
      "\t-government\n",
      "\t-behalf\n",
      "\t-behalf\n",
      "\n",
      "\u001b[92m\u001b[1mCluster 5 Speech Titles:\n",
      "\u001b[0m\n",
      "\t- Bhutan National Day\n",
      "\t- Kazakhstan's Independence Day\n",
      "\t- The Republic of Kenya's National Day\n",
      "\t- Burkina Faso's National Day\n",
      "\t- Bicentennial Statement to Cabo Verde\n",
      "\t- Finland National Day\n",
      "\t- Thailand National Day\n",
      "\t- Central African Republic National Day\n",
      "\t- Barbados Independence Day\n",
      "\t- Lao People's Democratic Republic National Day\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class color:\n",
    "   PURPLE = '\\033[95m'\n",
    "   CYAN = '\\033[96m'\n",
    "   DARKCYAN = '\\033[36m'\n",
    "   BLUE = '\\033[94m'\n",
    "   GREEN = '\\033[92m'\n",
    "   YELLOW = '\\033[93m'\n",
    "   RED = '\\033[91m'\n",
    "   BOLD = '\\033[1m'\n",
    "   UNDERLINE = '\\033[4m'\n",
    "   END = '\\033[0m'\n",
    "\n",
    "print(\"{0}Top terms per cluster:{1}\".format(color.BOLD,color.END))\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(\"{0}Cluster {1} Words:{2}\".format(color.BLUE + color.BOLD, i, color.END))\n",
    "    \n",
    "    for ind in order_centroids[i, :10]:\n",
    "        word = str(vocab.loc[terms[ind].split(' ')].values.tolist()[0][0])\n",
    "        print('\\t-{0}'.format(word))\n",
    "    print('')\n",
    "    \n",
    "    print(\"{0}Cluster {1} Speech Titles:\\n{2}\".format(color.GREEN + color.BOLD, i, color.END))\n",
    "    titles = df[df['cluster'] == i].title\n",
    "    for title in titles[:10]:\n",
    "        print('\\t- {0}'.format(title[:100]))\n",
    "    print('\\n\\n')\n",
    "    \n",
    "print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing our clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use t-Distributed Stochastic Neighbor Embedding, or [t-SNE ](https://lvdmaaten.github.io/tsne/), for dimensionality reduction. This let's us project highly dimensional data, such as our tfidf matrix, into 2 dimensional space. This will let us visualize the clustering in 2-dimensions. Remember that much is lost projecting data with over 1500 features into 2d space!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = tfidf_matrix.toarray()\n",
    "projection = TSNE().fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1190a8240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('poster')\n",
    "sns.set_color_codes()\n",
    "plot_kwds = {'marker': 'o', 'alpha' : 0.75, 's' : 150, 'linewidths':0}\n",
    "\n",
    "labels = clusters\n",
    "palette = sns.color_palette(palette='deep', n_colors=np.unique(labels).max() + 1)\n",
    "colors = [palette[x] if x >= 0 else (0.75, 0.75, 0.75) for x in labels]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax.margins(0.05)\n",
    "\n",
    "plt.scatter(*projection.T, c=colors, **plot_kwds)\n",
    "frame = plt.gca()\n",
    "frame.axes.get_xaxis().set_visible(False)\n",
    "frame.axes.get_yaxis().set_visible(False)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}