juanshishido · September 1, 2015 07:17
diff --git a/NLTK's Frequency Distribution Object.ipynb b/NLTK's Frequency Distribution Object.ipynb
 {
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Introduction\n\n(Be sure to start this notebook with the command \"ipython notebook --pylab inline\".)\n\nSection 1.1 of the NLTK book describes some pre-loaded books and pre-defined functions that come with them.  Section 1.2 reviews fundamental concepts about python lists and strings -- if you need to brush up on these concepts, then study this subsection carefully.  Be sure you know the difference between a *set* and a *list* and that you can work easily with python slices.\n\nThe part that I am most interested in having you focus on is Section 1.3, which introduces NLTK's frequency distribution data structure.  You need to have the books loaded and accessible from section 1.1 for this part to work.\n\n## NLTK's Frequency Distribution Object\n\nThis data structure makes it easy to tally up frequencies across words and other items, and incorporate them into list comprehensions (and later we'll see the conditional frequency distribution as well).\n\nThese are the functions supported by FreqDis:\n\n```\nfdist = FreqDist(samples) # create a frequency distribution containing the given samples\nfdist[sample] += 1 # increment the count for this sample\nfdist['monstrous'] # count of the number of times a given sample occurred\nfdist.freq('monstrous') # frequency of a given sample\nfdist.N() # total number of samples\nfdist.most_common(n) # the n most common samples and their frequencies\nfor sample in fdist: # iterate over the samples\nfdist.max() # sample with the greatest count\nfdist.tabulate() # tabulate the frequency distribution\nfdist.plot() # graphical plot of the frequency distribution\nfdist.plot(cumulative=True) # cumulative plot of the frequency distribution\nfdist1 |= fdist2 # update fdist1 with counts from fdist2\nfdist1 < fdist2 # test if samples in fdist1 occur less frequently than in fdist2\n```\nThe code below counts up all of the words in *Monty Python and the Holy Grail* (text6 in the nltk.book collection) and the final line shows the top 50 most frequent."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "%matplotlib inline\n%pprint\nimport matplotlib\nimport nltk\nfrom nltk.book import * # loads in pre-defined texts\nmp_freqdist = nltk.FreqDist(text6) # compute the frequency distribution\nmp_freqdist.most_common(50) # show the top 50 (word, frequency) pairs",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "text": "Pretty printing has been turned OFF\n*** Introductory Examples for the NLTK Book ***\nLoading text1, ..., text9 and sent1, ..., sent9\nType the name of the text or sentence to view it.\nType: 'texts()' or 'sents()' to list the materials.\ntext1: Moby Dick by Herman Melville 1851\ntext2: Sense and Sensibility by Jane Austen 1811\ntext3: The Book of Genesis\ntext4: Inaugural Address Corpus\ntext5: Chat Corpus\ntext6: Monty Python and the Holy Grail\ntext7: Wall Street Journal\ntext8: Personals Corpus\ntext9: The Man Who Was Thursday by G . K . Chesterton 1908\n",
     "name": "stdout"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[(':', 1197), ('.', 816), ('!', 801), (',', 731), (\"'\", 421), ('[', 319), (']', 312), ('the', 299), ('I', 255), ('ARTHUR', 225), ('?', 207), ('you', 204), ('a', 188), ('of', 158), ('--', 148), ('to', 144), ('s', 141), ('and', 135), ('#', 127), ('...', 118), ('Oh', 110), ('it', 107), ('is', 106), ('-', 88), ('in', 86), ('that', 84), ('t', 77), ('1', 76), ('No', 76), ('LAUNCELOT', 76), ('your', 75), ('not', 70), ('GALAHAD', 69), ('KNIGHT', 68), ('What', 65), ('FATHER', 63), ('we', 62), ('BEDEVERE', 61), ('You', 61), ('We', 60), ('this', 59), ('no', 55), ('HEAD', 54), ('Well', 54), ('have', 53), ('GUARD', 53), ('are', 52), ('Sir', 52), ('A', 50), ('And', 50)]"
     },
     "metadata": {},
     "execution_count": 1
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "**Task 1** Wow, those are some weird results.  It might make some sense to look at the actual text itself.  In the line below, write a line of code that pulls out the first 50 words of the text and shows them to you (hint: the text object is simply a list of strings)."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "text6[:50]",
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['SCENE', '1', ':', '[', 'wind', ']', '[', 'clop', 'clop', 'clop', ']', 'KING', 'ARTHUR', ':', 'Whoa', 'there', '!', '[', 'clop', 'clop', 'clop', ']', 'SOLDIER', '#', '1', ':', 'Halt', '!', 'Who', 'goes', 'there', '?', 'ARTHUR', ':', 'It', 'is', 'I', ',', 'Arthur', ',', 'son', 'of', 'Uther', 'Pendragon', ',', 'from', 'the', 'castle', 'of', 'Camelot']"
     },
     "metadata": {},
     "execution_count": 2
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "**Task 2** Now that you've looked at the text, what are two reasons for these strange results?\n\n1. Stop words have not been removed\n\n2. Text has not been normalized (e.g., to all lowercase, remove punctuation, etc.)"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "**Task 3** Address one of the problems by modifying the text of Monty Python and rerunning the frequency distribution calculation.  In the box below write your code to modify the text:"
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "import re\nfrom nltk.corpus import stopwords\n\ntext6_lower = [w.lower() for w in text6]\n\nstop_words = stopwords.words('english')\n\ntext6_clean = [re.sub('[^A-Za-z]+', '', w) for w in text6_lower if w not in stop_words]\ntext6_clean = [w for w in text6_clean if w != '']",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "**Task 4** In the box below, show the output after applying this version of the text to a FreqDist."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "text6_freqdist = nltk.FreqDist(text6_clean)\ntext6_freqdist.most_common(50)",
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[('arthur', 261), ('oh', 112), ('launcelot', 101), ('knight', 84), ('galahad', 80), ('father', 75), ('sir', 72), ('ni', 69), ('bedevere', 67), ('knights', 65), ('well', 62), ('head', 59), ('ha', 59), ('robin', 58), ('guard', 55), ('right', 55), ('yes', 53), ('villager', 47), ('boom', 45), ('come', 44), ('uh', 42), ('re', 41), ('witch', 41), ('away', 39), ('grail', 39), ('clop', 39), ('king', 38), ('one', 37), ('burn', 36), ('black', 36), ('french', 35), ('tim', 34), ('m', 34), ('us', 32), ('singing', 31), ('look', 31), ('dead', 30), ('get', 30), ('scene', 30), ('mumble', 30), ('go', 29), ('squeak', 29), ('music', 29), ('herbert', 28), ('got', 27), ('tell', 27), ('hello', 27), ('camelot', 26), ('holy', 26), ('dennis', 25)]"
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "**Task 5** How if at all has the output changed?\n\nThe list of the 50 most common words no longer includes special characters or words such as \"i\" or \"the.\" This seems to better reflect the actual, unique dialogue of the film."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "**Task 6** Following the example from the book, show a cumulative frequency plot for the words in Monty Python as newly computed, in the box below."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "import matplotlib.pyplot as plt\n\nplt.figure(figsize=(16, 8))\ntext6_freqdist.plot(50, cumulative=True)",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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Sx0c+Ur+ektRo6jLY5uZgK0mS6uG99+Duu9Mge8MNMHFi1/eHDk1D7K67phtA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      "text/plain": "<matplotlib.figure.Figure object at 0x1093b6470>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "**Task 7** In the box below, write a list comprehension that users the FreqDist you computed above to find all words in *Monty Python* that are longer than 5 characters long and occur at least 5 times (hint: the text shows how to do a variation of this).  \nShow the output sorted in alphabetical order."
  },
  {
   "metadata": {
    "collapsed": false,
    "trusted": true
   },
   "cell_type": "code",
   "source": "[w for w in set(text6_clean) if len(w) > 5 and text6_freqdist[w] > 5]",
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['domine', 'spanking', 'listen', 'course', 'narrator', 'afraid', 'villager', 'cartoon', 'guards', 'english', 'bridge', 'camelot', 'requiem', 'bedevere', 'bridgekeeper', 'questions', 'rabbit', 'master', 'killed', 'random', 'swallow', 'please', 'shrubberies', 'arthur', 'giggle', 'saying', 'officer', 'angels', 'nothing', 'chanting', 'middle', 'herbert', 'knights', 'grenade', 'father', 'galahad', 'singing', 'character', 'soldier', 'person', 'french', 'people', 'better', 'launcelot', 'squeak', 'brother', 'second', 'customer', 'minstrel', 'shrubbery', 'aaaaugh', 'knight', 'dramatic', 'dennis', 'guests', 'maynard', 'castle', 'piglet', 'enchanter', 'mumble', 'really', 'coconut', 'concorde', 'britons', 'sacred']"
     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "mimetype": "text/x-python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "file_extension": ".py",
   "name": "python",
   "version": "3.4.2",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   }
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
 }