1. Text Representation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> \n",
    "        <h1>Natural Language Processing From Scratch</h1>\n",
    "        <h2>Text Representation</h2>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "        @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lesson we will see in some details how we can best represent text in our application. Let's start by importing the modules we will be using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "import gzip\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the default style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose a well known nursery rhyme, that has the added distinction of having been the first audio ever recorded, to be the short snippet of text that we will use in our examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\"\"Mary had a little lamb, little lamb,\n",
    "    little lamb. Mary had a little lamb\n",
    "    whose fleece was white as snow.\n",
    "    And everywhere that Mary went\n",
    "    Mary went, Mary went. Everywhere\n",
    "    that Mary went,\n",
    "    The lamb was sure to go\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenization\n",
    "\n",
    "The first step in any analysis is to tokenize the text. What this means is that we will extract all the individual words in the text. For the sake of simplicity, we will assume that our text is well formed and that our words are delimited either by white space or punctuation characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n"
     ]
    }
   ],
   "source": [
    "print(string.punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_words(text):\n",
    "    temp = text.split() # Split the text on whitespace\n",
    "    text_words = []\n",
    "\n",
    "    for word in temp:\n",
    "        # Remove any punctuation characters present in the beginning of the word\n",
    "        while word[0] in string.punctuation:\n",
    "            word = word[1:]\n",
    "\n",
    "        # Remove any punctuation characters present in the end of the word\n",
    "        while word[-1] in string.punctuation:\n",
    "            word = word[:-1]\n",
    "\n",
    "        # Append this word into our list of words.\n",
    "        text_words.append(word.lower())\n",
    "        \n",
    "    return text_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this step we now have our text represented as an array of individual, lowercase, words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mary', 'had', 'a', 'little', 'lamb', 'little', 'lamb', 'little', 'lamb', 'mary', 'had', 'a', 'little', 'lamb', 'whose', 'fleece', 'was', 'white', 'as', 'snow', 'and', 'everywhere', 'that', 'mary', 'went', 'mary', 'went', 'mary', 'went', 'everywhere', 'that', 'mary', 'went', 'the', 'lamb', 'was', 'sure', 'to', 'go']\n"
     ]
    }
   ],
   "source": [
    "text_words = extract_words(text)\n",
    "print(text_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw during the video, this is a wasteful way to represent text. We can be much more efficient by representing each word by a number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_dict = {}\n",
    "word_list = []\n",
    "vocabulary_size = 0\n",
    "text_tokens = []\n",
    "\n",
    "for word in text_words:\n",
    "    # If we are seeing this word for the first time, create an id for it and added it to our word dictionary\n",
    "    if word not in word_dict:\n",
    "        word_dict[word] = vocabulary_size\n",
    "        word_list.append(word)\n",
    "        vocabulary_size += 1\n",
    "    \n",
    "    # add the token corresponding to the current word to the tokenized text.\n",
    "    text_tokens.append(word_dict[word])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we were tokenizing our text, we also generated a dictionary **word_dict** that maps words to integers and a **word_list** that maps each integer to the corresponding word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word list: ['mary', 'had', 'a', 'little', 'lamb', 'whose', 'fleece', 'was', 'white', 'as', 'snow', 'and', 'everywhere', 'that', 'went', 'the', 'sure', 'to', 'go'] \n",
      "\n",
      " Word dictionary:\n",
      "{'a': 2,\n",
      " 'and': 11,\n",
      " 'as': 9,\n",
      " 'everywhere': 12,\n",
      " 'fleece': 6,\n",
      " 'go': 18,\n",
      " 'had': 1,\n",
      " 'lamb': 4,\n",
      " 'little': 3,\n",
      " 'mary': 0,\n",
      " 'snow': 10,\n",
      " 'sure': 16,\n",
      " 'that': 13,\n",
      " 'the': 15,\n",
      " 'to': 17,\n",
      " 'was': 7,\n",
      " 'went': 14,\n",
      " 'white': 8,\n",
      " 'whose': 5}\n"
     ]
    }
   ],
   "source": [
    "print(\"Word list:\", word_list, \"\\n\\n Word dictionary:\")\n",
    "pprint(word_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two datastructures already proved their usefulness when we converted our text to a list of tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 3, 4, 3, 4, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 0, 14, 0, 14, 0, 14, 12, 13, 0, 14, 15, 4, 7, 16, 17, 18]\n"
     ]
    }
   ],
   "source": [
    "print(text_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, while this representation is convenient for memory reasons it has some severe limitations. Perhaps the most important of which is the fact that computers naturally assume that numbers can be operated on mathematically (by addition, subtraction, etc) in a way that doesn't match our understanding of words.\n",
    "\n",
    "## One-hot encoding\n",
    "\n",
    "One typical way of overcoming this difficulty is to represent each word by a one-hot encoded vector where every element is zero except the one corresponding to a specific word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot(word, word_dict):\n",
    "    \"\"\"\n",
    "        Generate a one-hot encoded vector corresponding to *word*\n",
    "    \"\"\"\n",
    "    \n",
    "    vector = np.zeros(len(word_dict))\n",
    "    vector[word_dict[word]] = 1\n",
    "    \n",
    "    return vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, for example, the word \"fleece\" would be represented by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "fleece_hot = one_hot(\"fleece\", word_dict)\n",
    "print(fleece_hot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This vector has every element set to zero, except element 6, since:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(word_dict[\"fleece\"])\n",
    "fleece_hot[6] == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(fleece_hot.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag of words\n",
    "\n",
    "We can now use the one-hot encoded vector for each word to produce a vector representation of our original text, by simply adding up all the one-hot encoded vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6. 2. 2. 4. 5. 1. 1. 2. 1. 1. 1. 1. 2. 2. 4. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "text_vector1 = np.zeros(vocabulary_size)\n",
    "\n",
    "for word in text_words:\n",
    "    hot_word = one_hot(word, word_dict)\n",
    "    text_vector1 += hot_word\n",
    "    \n",
    "print(text_vector1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In practice, we can also easily skip the encoding step at the word level by using the *word_dict* defined above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6. 2. 2. 4. 5. 1. 1. 2. 1. 1. 1. 1. 2. 2. 4. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "text_vector = np.zeros(vocabulary_size)\n",
    "\n",
    "for word in text_words:\n",
    "    text_vector[word_dict[word]] += 1\n",
    "    \n",
    "print(text_vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naturally, this approach is completely equivalent to the previous one and has the added advantage of being more efficient in terms of both speed and memory requirements.\n",
    "\n",
    "This is known as the __bag of words__ representation of the text. It should be noted that these vectors simply contains the number of times each word appears in our document, so we can easily tell that the word *mary* appears exactly 6 times in our little nursery rhyme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vector[word_dict[\"mary\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more pythonic (and efficient) way of producing the same result is to use the standard __Counter__ module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mary',\n",
       " 'had',\n",
       " 'a',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'mary',\n",
       " 'had',\n",
       " 'a',\n",
       " 'little',\n",
       " 'lamb',\n",
       " 'whose',\n",
       " 'fleece',\n",
       " 'was',\n",
       " 'white',\n",
       " 'as',\n",
       " 'snow',\n",
       " 'and',\n",
       " 'everywhere',\n",
       " 'that',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'everywhere',\n",
       " 'that',\n",
       " 'mary',\n",
       " 'went',\n",
       " 'the',\n",
       " 'lamb',\n",
       " 'was',\n",
       " 'sure',\n",
       " 'to',\n",
       " 'go']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'mary': 6,\n",
      "         'lamb': 5,\n",
      "         'little': 4,\n",
      "         'went': 4,\n",
      "         'had': 2,\n",
      "         'a': 2,\n",
      "         'was': 2,\n",
      "         'everywhere': 2,\n",
      "         'that': 2,\n",
      "         'whose': 1,\n",
      "         'fleece': 1,\n",
      "         'white': 1,\n",
      "         'as': 1,\n",
      "         'snow': 1,\n",
      "         'and': 1,\n",
      "         'the': 1,\n",
      "         'sure': 1,\n",
      "         'to': 1,\n",
      "         'go': 1})\n"
     ]
    }
   ],
   "source": [
    "word_counts = Counter(text_words)\n",
    "pprint(word_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From which we can easily generate the __text_vector__ and __word_dict__ data structures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "items = list(word_counts.items())\n",
    "\n",
    "# Extract word dictionary and vector representation\n",
    "word_dict2 = dict([[items[i][0], i] for i in range(len(items))])\n",
    "text_vector2 = [items[i][1] for i in range(len(items))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts['mary']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's take a look at them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6., 2., 2., 4., 5., 1., 1., 2., 1., 1., 1., 1., 2., 2., 4., 1., 1.,\n",
       "       1., 1.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text vector: [6, 2, 2, 4, 5, 1, 1, 2, 1, 1, 1, 1, 2, 2, 4, 1, 1, 1, 1] \n",
      "\n",
      "Word dictionary:\n",
      "{'a': 2,\n",
      " 'and': 11,\n",
      " 'as': 9,\n",
      " 'everywhere': 12,\n",
      " 'fleece': 6,\n",
      " 'go': 18,\n",
      " 'had': 1,\n",
      " 'lamb': 4,\n",
      " 'little': 3,\n",
      " 'mary': 0,\n",
      " 'snow': 10,\n",
      " 'sure': 16,\n",
      " 'that': 13,\n",
      " 'the': 15,\n",
      " 'to': 17,\n",
      " 'was': 7,\n",
      " 'went': 14,\n",
      " 'white': 8,\n",
      " 'whose': 5}\n"
     ]
    }
   ],
   "source": [
    "print(\"Text vector:\", text_vector2, \"\\n\\nWord dictionary:\")\n",
    "pprint(word_dict2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results using this approach are slightly different than the previous ones, because the words are mapped to different integer ids but the corresponding values are the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for word in word_dict.keys():\n",
    "    if text_vector[word_dict[word]] != text_vector2[word_dict2[word]]:\n",
    "        print(\"Error!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, there are no differences!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Term Frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bag of words vector representation introduced above relies simply on the frequency of occurence of each word. Following a long tradition of giving fancy names to simple ideas, this is known as __Term Frequency__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intuitively, we expect the the frequency with which a given word is mentioned should correspond to the relevance of that word for the piece of text we are considering. For example, **Mary** is a pretty important word in our little nursery rhyme and indeed it is the one that occurs the most often:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('mary', 6),\n",
       " ('lamb', 5),\n",
       " ('little', 4),\n",
       " ('went', 4),\n",
       " ('had', 2),\n",
       " ('a', 2),\n",
       " ('was', 2),\n",
       " ('everywhere', 2),\n",
       " ('that', 2),\n",
       " ('whose', 1),\n",
       " ('fleece', 1),\n",
       " ('white', 1),\n",
       " ('as', 1),\n",
       " ('snow', 1),\n",
       " ('and', 1),\n",
       " ('the', 1),\n",
       " ('sure', 1),\n",
       " ('to', 1),\n",
       " ('go', 1)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(items, key=lambda x:x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, it's hard to draw conclusions from such a small piece of text. Let us consider a significantly larger piece of text, the first 100 MB of the english Wikipedia from: http://mattmahoney.net/dc/textdata. For the sake of convenience, text8.gz has been included in this repository in the **data/** directory. We start by loading it's contents into memory as an array of words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for line in gzip.open(\"data/text8.gz\", 'rt'):\n",
    "    data.extend(line.strip().split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at the first 50 words in this large corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anarchism',\n",
       " 'originated',\n",
       " 'as',\n",
       " 'a',\n",
       " 'term',\n",
       " 'of',\n",
       " 'abuse',\n",
       " 'first',\n",
       " 'used',\n",
       " 'against',\n",
       " 'early',\n",
       " 'working',\n",
       " 'class',\n",
       " 'radicals',\n",
       " 'including',\n",
       " 'the',\n",
       " 'diggers',\n",
       " 'of',\n",
       " 'the',\n",
       " 'english',\n",
       " 'revolution',\n",
       " 'and',\n",
       " 'the',\n",
       " 'sans',\n",
       " 'culottes',\n",
       " 'of',\n",
       " 'the',\n",
       " 'french',\n",
       " 'revolution',\n",
       " 'whilst',\n",
       " 'the',\n",
       " 'term',\n",
       " 'is',\n",
       " 'still',\n",
       " 'used',\n",
       " 'in',\n",
       " 'a',\n",
       " 'pejorative',\n",
       " 'way',\n",
       " 'to',\n",
       " 'describe',\n",
       " 'any',\n",
       " 'act',\n",
       " 'that',\n",
       " 'used',\n",
       " 'violent',\n",
       " 'means',\n",
       " 'to',\n",
       " 'destroy',\n",
       " 'the']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the top 10 most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the 1061396\n",
      "of 593677\n",
      "and 416629\n",
      "one 411764\n",
      "in 372201\n",
      "a 325873\n",
      "to 316376\n",
      "zero 264975\n",
      "nine 250430\n",
      "two 192644\n"
     ]
    }
   ],
   "source": [
    "counts = Counter(data)\n",
    "\n",
    "sorted_counts = sorted(list(counts.items()), key=lambda x:x[1], reverse=True)\n",
    "\n",
    "for word, count in sorted_counts[:10]:\n",
    "    print(word, count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surprisingly, we find that the most common words are not particularly meaningful. Indeed, this is a common occurence in Natural Language Processing. The most frequent words are typically auxiliaries required due to gramatical rules.\n",
    "\n",
    "On the other hand, there is also a large number of words that occur very infrequently as can be easily seen by glancing at the word freqency distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3300x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist = Counter(counts.values())\n",
    "dist = list(dist.items())\n",
    "dist.sort(key=lambda x:x[0])\n",
    "dist = np.array(dist)\n",
    "\n",
    "norm = np.dot(dist.T[0], dist.T[1])\n",
    "\n",
    "plt.loglog(dist.T[0], dist.T[1]/norm)\n",
    "plt.xlabel(\"count\")\n",
    "plt.ylabel(\"P(count)\")\n",
    "plt.title(\"Word frequency distribution\")\n",
    "plt.gcf().set_size_inches(11, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One common technique to simplify NLP tasks is to remove what are known as Stopwords, words that are very frequent but not meaningful. If we simply remove the most common 100 words, we significantly reduce the amount of data we have to consider while losing little information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = set([word for word, count in sorted_counts[:100]])\n",
    "\n",
    "clean_data = []\n",
    "\n",
    "for word in data:\n",
    "    if word not in stopwords:\n",
    "        clean_data.append(word)\n",
    "\n",
    "print(\"Original size:\", len(data))\n",
    "print(\"Clean size:\", len(clean_data))\n",
    "print(\"Reduction:\", 1-len(clean_data)/len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_data[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow, our dataset size was reduced almost in half!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In practice, we don't simply remove the most common words in our corpus but rather a manually curate list of stopwords. Lists for dozens of languages and applications can easily be found online."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Term Frequency/Inverse Document Frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way of determining of the relative importance of a word is to see how often it appears across multiple documents. Words that are relevant to a specific topic are more likely to appear in documents about that topic and much less in documents about other topics. On the other hand, less meaningful words (like **the**) will be common across documents about any subject."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To measure the document frequency of a word we will need to have multiple documents. For the sake of simplicity, we will treat each sentence of our nursery rhyme as an individual document:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_text = text.split('.')\n",
    "corpus_words = []\n",
    "\n",
    "for document in corpus_text:\n",
    "    doc_words = extract_words(document)\n",
    "    corpus_words.append(doc_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our corpus is represented as a list of word lists, where each list is just the word representation of the corresponding sentence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pprint(corpus_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now calculate the number of documents in which each word appears:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "document_count = {}\n",
    "\n",
    "for document in corpus_words:\n",
    "    word_set = set(document)\n",
    "    \n",
    "    for word in word_set:\n",
    "        document_count[word] = document_count.get(word, 0) + 1\n",
    "\n",
    "pprint(document_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the word __Mary__ appears in all 4 of our documents, making it useless when it comes to distinguish between the different sentences. On the other hand, words like __white__ which appear in only one document are very discriminative. Using this approach we can define a new quantity, the ___Inverse Document Frequency__ that tells us how frequent a word is across the documents in a specific corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inv_doc_freq(corpus_words):\n",
    "    number_docs = len(corpus_words)\n",
    "    \n",
    "    document_count = {}\n",
    "\n",
    "    for document in corpus_words:\n",
    "        word_set = set(document)\n",
    "\n",
    "        for word in word_set:\n",
    "            document_count[word] = document_count.get(word, 0) + 1\n",
    "    \n",
    "    IDF = {}\n",
    "    \n",
    "    for word in document_count:\n",
    "        IDF[word] = np.log(number_docs/document_count[word])\n",
    "        \n",
    "    return IDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where we followed the convention of using the logarithm of the inverse document frequency. This has the numerical advantage of avoiding to have to handle small fractional numbers. \n",
    "\n",
    "We can easily see that the IDF gives a smaller weight to the most common words and a higher weight to the less frequent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IDF = inv_doc_freq(corpus_words)\n",
    "\n",
    "pprint(IDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected **Mary** has the smallest weight of all words 0, meaning that it is effectively removed from the dataset. You can consider this as a way of implicitly identify and remove stopwords. In case you do want to keep even the words that appear in every document, you can just add a 1. to the argument of the logarithm above:\n",
    "\n",
    "\\begin{equation}\n",
    "\\log\\left[1+\\frac{N_d}{N_d\\left(w\\right)}\\right]\n",
    "\\end{equation}\n",
    "\n",
    "When we multiply the term frequency of each word by it's inverse document frequency, we have a good way of quantifying how relevant a word is to understand the meaning of a specific document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tf_idf(corpus_words):\n",
    "    IDF = inv_doc_freq(corpus_words)\n",
    "    \n",
    "    TFIDF = []\n",
    "    \n",
    "    for document in corpus_words:\n",
    "        TFIDF.append(Counter(document))\n",
    "    \n",
    "    for document in TFIDF:\n",
    "        for word in document:\n",
    "            document[word] = document[word]*IDF[word]\n",
    "            \n",
    "    return TFIDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf(corpus_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we finally have a vector representation of each of our documents that takes the informational contributions of each word into account. Each of these vectors provides us with a unique representation of each document, in the context (corpus) in which it occurs, making it posssible to define the similarity of two documents, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Porter Stemmer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is still, however, one issue with our approach to representing text. Since we treat each word as a unique token and completely independently from all others, for large documents we will end up with many variations of the same word such as verb conjugations, the corresponding adverbs and nouns, etc. \n",
    "\n",
    "One way around this difficulty is to use stemming algorithm to reduce words to their root (or stem) version. The most famous Stemming algorithm is known as the **Porter Stemmer** and was introduced by Martin Porter in 1980 [Program 14, 130 (1980)](https://dl.acm.org/citation.cfm?id=275705)\n",
    "\n",
    "The algorithm starts by defining consonants (C) and vowels (V):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "V = set('aeiouy')\n",
    "C = set('bcdfghjklmnpqrstvwxz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stem of a word is what is left of that word after a speficic ending has been removed. A function to do this is easy to implement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stem(suffix, word):\n",
    "    \"\"\"\n",
    "        Extract the stem of a word\n",
    "    \"\"\"\n",
    "    \n",
    "    if word.lower().endswith(suffix.lower()): # Case insensitive comparison\n",
    "        return word[:-len(suffix)]\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also defines words (or stems) to be sequences of vowels and consonants of the form:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation}\n",
    "[C](VC)^m[V]\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where $m$ is called the **measure** of the word and [] represent optional sections. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure(orig_word):\n",
    "    \"\"\"\n",
    "        Calculate the \"measure\" m of a word or stem, according to the Porter Stemmer algorthim\n",
    "    \"\"\"\n",
    "    \n",
    "    word = orig_word.lower()\n",
    "\n",
    "    optV = False\n",
    "    optC = False\n",
    "    VC = False\n",
    "    m = 0\n",
    "\n",
    "    pos = 0\n",
    "\n",
    "    # We can think of this implementation as a simple finite state machine\n",
    "    # looks for sequences of vowels or consonants depending of the state\n",
    "    # in which it's in, while keeping track of how many VC sequences it\n",
    "    # has encountered.\n",
    "    # The presence of the optional V and C portions is recorded in the\n",
    "    # optV and optC booleans.\n",
    "    \n",
    "    # We're at the initial state.\n",
    "    # gobble up all the optional consonants at the beginning of the word\n",
    "    while pos < len(word) and word[pos] in C:\n",
    "        pos += 1\n",
    "        optC = True\n",
    "\n",
    "    while pos < len(word):\n",
    "        # Now we know that the next state must be a vowel\n",
    "        while pos < len(word) and word[pos] in V:\n",
    "            pos += 1\n",
    "            optV = True\n",
    "\n",
    "        # Followed by a consonant\n",
    "        while pos < len(word) and word[pos] in C:\n",
    "            pos += 1\n",
    "            optV = False\n",
    "        \n",
    "        # If a consonant was found, then we matched VC\n",
    "        # so we should increment m by one. Otherwise, \n",
    "        # optV remained true and we simply had a dangling\n",
    "        # V sequence.\n",
    "        if not optV:\n",
    "            m += 1\n",
    "\n",
    "    return m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider a simple example. The word __crepusculars__ should have measure 4:\n",
    "\n",
    "[cr] (ep) (usc) (ul) (ars)\n",
    "\n",
    "and indeed it does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = \"crepusculars\"\n",
    "print(measure(word))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Porter algorithm sequentially applies a series of transformation rules over a series of 5 steps (step 1 is divided in 3 substeps and step 5 in 2). The rules are only applied if a certain condition is true. \n",
    "\n",
    "In addition to possibily specifying a requirement on the measure of a word, conditions can make use of different boolean functions as well: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ends_with(char, stem):\n",
    "    \"\"\"\n",
    "        Checks the ending of the word\n",
    "    \"\"\"\n",
    "    return stem[-1] == char\n",
    "\n",
    "def double_consonant(stem):\n",
    "    \"\"\"\n",
    "        Checks the ending of a word for a double consonant\n",
    "    \"\"\"\n",
    "    if len(stem) < 2:\n",
    "        return False\n",
    "\n",
    "    if stem[-1] in C and stem[-2] == stem[-1]:\n",
    "        return True\n",
    "\n",
    "    return False\n",
    "\n",
    "def contains_vowel(stem):\n",
    "    \"\"\"\n",
    "        Checks if a word contains a vowel or not\n",
    "    \"\"\"\n",
    "    return len(set(stem) & V) > 0 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define a function to apply a specific rule to a word or stem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_rule(condition, suffix, replacement, word):\n",
    "    \"\"\"\n",
    "        Apply Porter Stemmer rule.\n",
    "        if \"condition\" is True replace \"suffix\" by \"replacement\" in \"word\"\n",
    "    \"\"\"\n",
    "    \n",
    "    stem = get_stem(suffix, word)\n",
    "\n",
    "    if stem is not None and condition is True:\n",
    "        # Remove the suffix\n",
    "        word = stem\n",
    "\n",
    "        # Add the replacement suffix, if any\n",
    "        if replacement is not None:\n",
    "            word += replacement\n",
    "\n",
    "    return word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see how rules can be applied. For example, this rule, from step 1b is successfully applied to __pastered__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = \"plastered\"\n",
    "suffix = \"ed\"\n",
    "stem = get_stem(suffix, word)\n",
    "apply_rule(contains_vowel(stem), suffix, None, word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While try applying the same rule to **bled** will fail to pass the condition resulting in no change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = \"bled\"\n",
    "suffix = \"ed\"\n",
    "stem = get_stem(suffix, word)\n",
    "apply_rule(contains_vowel(stem), suffix, None, word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more complex example, we have, in Step 4:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = \"adoption\"\n",
    "suffix = \"ion\"\n",
    "stem = get_stem(suffix, word)\n",
    "apply_rule(measure(stem) > 1 and (ends_with(\"s\", stem) or ends_with(\"t\", stem)), suffix, None, word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ends_with(\"t\", stem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In total, the Porter Stemmer algorithm (for the English language) applies several dozen rules (see https://tartarus.org/martin/PorterStemmer/def.txt for a complete list). Implementing all of them is both tedious and error prone, so we abstain from providing a full implementation of the algorithm here. High quality implementations can be found in all major NLP libraries such as [NLTK](http://www.nltk.org/howto/stem.html).\n",
    "\n",
    "The dificulties of defining matching rules to arbitrary text cannot be fully resolved without the use of Regular Expressions (typically implemented as Finite State Machines like our __measure__ implementation above), a more advanced topic that is beyond the scope of this course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}