nltk_ch1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "Import basic Natural Language Toolkit libraries.\n",
    "Going through the NLTK book: https://www.nltk.org/book/ch01.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Introductory Examples for the NLTK Book ***\n",
      "Loading text1, ..., text9 and sent1, ..., sent9\n",
      "Type the name of the text or sentence to view it.\n",
      "Type: 'texts()' or 'sents()' to list the materials.\n",
      "text1: Moby Dick by Herman Melville 1851\n",
      "text2: Sense and Sensibility by Jane Austen 1811\n",
      "text3: The Book of Genesis\n",
      "text4: Inaugural Address Corpus\n",
      "text5: Chat Corpus\n",
      "text6: Monty Python and the Holy Grail\n",
      "text7: Wall Street Journal\n",
      "text8: Personals Corpus\n",
      "text9: The Man Who Was Thursday by G . K . Chesterton 1908\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.book import FreqDist\n",
    "from nltk.text import Text\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set variables for the notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "RHT_XML_CORPUS_PATH='/mnt/VMs/virtualenv/nltkenv/share/nltk_data/corpora/redhat-training'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_ids = [x for x in os.listdir(RHT_XML_CORPUS_PATH) if x.endswith('.xml')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rht_corpus = nltk.corpus.reader.xmldocs.XMLCorpusReader(RHT_XML_CORPUS_PATH,\n",
    "                                                        file_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `words()` method generates a list of words from the corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "['DO180',\n",
       " 'Introduction',\n",
       " 'to',\n",
       " 'Containers',\n",
       " ',',\n",
       " 'Kubernetes',\n",
       " ',',\n",
       " 'and',\n",
       " 'Red',\n",
       " 'Hat',\n",
       " 'OpenShift',\n",
       " 'Student',\n",
       " 'Workbook',\n",
       " 'OCP',\n",
       " '4',\n",
       " '.',\n",
       " '0',\n",
       " '1',\n",
       " '20190613',\n",
       " '20190613',\n",
       " 'Zach',\n",
       " 'Gutterman',\n",
       " 'Dan',\n",
       " 'Kolepp',\n",
       " 'Eduardo',\n",
       " 'Ramirez',\n",
       " 'Ronco',\n",
       " 'Jordi',\n",
       " 'Sola',\n",
       " 'Alaball',\n",
       " 'Seth',\n",
       " 'Kenlon',\n",
       " 'Dave',\n",
       " 'Sacco',\n",
       " 'Connie',\n",
       " 'Petlitzer',\n",
       " 'Contributors',\n",
       " ':',\n",
       " 'Michael',\n",
       " 'Jarrett',\n",
       " 'and',\n",
       " 'Forrest',\n",
       " 'Taylor',\n",
       " 'Copyright',\n",
       " 'The',\n",
       " 'contents',\n",
       " 'of',\n",
       " 'this',\n",
       " 'course',\n",
       " 'and',\n",
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       " ',',\n",
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       " 'without',\n",
       " 'any',\n",
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       " 'from',\n",
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       " 'or',\n",
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       " 'the',\n",
       " 'use',\n",
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       " 'misuse',\n",
       " 'of',\n",
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       " 'or',\n",
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       " 'contained',\n",
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       " '.',\n",
       " 'If',\n",
       " 'you',\n",
       " 'believe',\n",
       " 'Red',\n",
       " 'Hat',\n",
       " 'training',\n",
       " 'materials',\n",
       " 'are',\n",
       " 'being',\n",
       " 'used',\n",
       " ',',\n",
       " 'copied',\n",
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       " 'distributed',\n",
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       " '+',\n",
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       " '+',\n",
       " '1',\n",
       " '(',\n",
       " '919',\n",
       " ')',\n",
       " '754',\n",
       " '-',\n",
       " '3700',\n",
       " '.',\n",
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       " ',',\n",
       " 'Red',\n",
       " 'Hat',\n",
       " 'Enterprise',\n",
       " 'Linux',\n",
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       " 'the',\n",
       " 'Shadowman',\n",
       " 'logo',\n",
       " ',',\n",
       " 'JBoss',\n",
       " ',',\n",
       " 'Hibernate',\n",
       " ',',\n",
       " 'Fedora',\n",
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       " 'the',\n",
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       " 'in',\n",
       " 'the',\n",
       " 'United',\n",
       " 'States',\n",
       " 'and',\n",
       " 'other',\n",
       " 'countries',\n",
       " '.',\n",
       " 'Linux',\n",
       " 'Java',\n",
       " 'XFS',\n",
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       " 'respective',\n",
       " 'owners',\n",
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       " 'Document',\n",
       " 'Conventions',\n",
       " '\"',\n",
       " 'References',\n",
       " '\"',\n",
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       " 'where',\n",
       " 'to',\n",
       " 'find',\n",
       " 'external',\n",
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       " 'to',\n",
       " 'a',\n",
       " 'subject',\n",
       " '.',\n",
       " 'Note',\n",
       " '\"',\n",
       " 'Notes',\n",
       " '\"',\n",
       " 'are',\n",
       " 'tips',\n",
       " ',',\n",
       " 'shortcuts',\n",
       " 'or',\n",
       " 'alternative',\n",
       " 'approaches',\n",
       " 'to',\n",
       " 'the',\n",
       " 'task',\n",
       " 'at',\n",
       " 'hand',\n",
       " '.',\n",
       " 'Ignoring',\n",
       " 'a',\n",
       " 'note',\n",
       " 'should',\n",
       " 'have',\n",
       " 'no',\n",
       " 'negative',\n",
       " 'consequences',\n",
       " ',',\n",
       " 'but',\n",
       " 'you',\n",
       " 'might',\n",
       " 'miss',\n",
       " 'out',\n",
       " 'on',\n",
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       " 'your',\n",
       " 'life',\n",
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       " '.',\n",
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       " 'Important',\n",
       " '\"',\n",
       " 'boxes',\n",
       " 'detail',\n",
       " 'things',\n",
       " 'that',\n",
       " 'are',\n",
       " 'easily',\n",
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       " ':',\n",
       " 'configuration',\n",
       " 'changes',\n",
       " 'that',\n",
       " 'only',\n",
       " 'apply',\n",
       " 'to',\n",
       " 'the',\n",
       " 'current',\n",
       " 'session',\n",
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       " 'or',\n",
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       " 'that',\n",
       " 'need',\n",
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       " 'an',\n",
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       " ':',\n",
       " 'Classroom',\n",
       " 'Machines',\n",
       " 'Machine',\n",
       " 'name',\n",
       " 'IP',\n",
       " 'addresses',\n",
       " 'Role',\n",
       " 'content',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " 'materials',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " 'classroom',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '254',\n",
       " '.',\n",
       " '254',\n",
       " ',',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '252',\n",
       " '.',\n",
       " '254',\n",
       " 'Classroom',\n",
       " 'utility',\n",
       " 'server',\n",
       " 'workstation',\n",
       " '.',\n",
       " 'lab',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " 'workstation',\n",
       " 'X',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '250',\n",
       " '.',\n",
       " '254',\n",
       " ',',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '252',\n",
       " '.',\n",
       " 'X',\n",
       " 'Student',\n",
       " 'graphical',\n",
       " 'workstation',\n",
       " 'lb',\n",
       " '.',\n",
       " 'lab',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '250',\n",
       " '.',\n",
       " '42',\n",
       " 'Load',\n",
       " 'balancer',\n",
       " 'for',\n",
       " 'the',\n",
       " 'OpenShift',\n",
       " 'Container',\n",
       " 'Platform',\n",
       " 'cluster',\n",
       " 'master0',\n",
       " '.',\n",
       " 'lab',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '250',\n",
       " '.',\n",
       " '11',\n",
       " 'OpenShift',\n",
       " 'Container',\n",
       " 'Platform',\n",
       " 'cluster',\n",
       " 'server',\n",
       " 'worker0',\n",
       " '.',\n",
       " 'lab',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '250',\n",
       " '.',\n",
       " '51',\n",
       " 'OpenShift',\n",
       " 'Container',\n",
       " 'Platform',\n",
       " 'cluster',\n",
       " 'node',\n",
       " 'worker1',\n",
       " '.',\n",
       " 'lab',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '250',\n",
       " '.',\n",
       " '52',\n",
       " 'OpenShift',\n",
       " 'Container',\n",
       " 'Platform',\n",
       " 'cluster',\n",
       " 'node',\n",
       " 'services',\n",
       " '.',\n",
       " 'lab',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " 'registry',\n",
       " '.',\n",
       " 'lab',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '250',\n",
       " '.',\n",
       " '13',\n",
       " 'Classroom',\n",
       " 'private',\n",
       " 'registry',\n",
       " 'The',\n",
       " 'environment',\n",
       " 'runs',\n",
       " 'a',\n",
       " 'central',\n",
       " 'utility',\n",
       " 'server',\n",
       " ',',\n",
       " 'classroom',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " 'content',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " 'materials',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " 'The',\n",
       " 'workstation',\n",
       " '.',\n",
       " 'lab',\n",
       " '.',\n",
       " 'example',\n",
       " '.',\n",
       " 'com',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '250',\n",
       " '.',\n",
       " '0',\n",
       " '/',\n",
       " '24',\n",
       " '172',\n",
       " '.',\n",
       " '25',\n",
       " '.',\n",
       " '252',\n",
       " '.',\n",
       " '0',\n",
       " '/',\n",
       " '24',\n",
       " 'workstation',\n",
       " '.',\n",
       " 'lab',\n",
       " ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rht_corpus.words()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Text` class provides lots of handy methods for examining a text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "do180 = Text(rht_corpus.words())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `concordance` method displays instances of the word with context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying 25 of 368 matches:\n",
      "tainers , Kubernetes , and Red Hat OpenShift Student Workbook OCP 4 . 0 1 20190\n",
      "tainers , Kubernetes , and Red Hat OpenShift DO180 : Introduction to Containers\n",
      "tainers , Kubernetes , and Red Hat OpenShift is a hands - on course that teache\n",
      "man , Kubernetes , and the Red Hat OpenShift Container Platform . One of the ke\n",
      "ations and microservices . Red Hat OpenShift Container Platform is an implement\n",
      " on a Kubernetes cluster using the OpenShift Container Platform . Demonstrate b\n",
      "e of containers , Kubernetes , and OpenShift . Audience System Administrators D\n",
      "5 . 250 . 42 Load balancer for the OpenShift Container Platform cluster master0\n",
      " example . com 172 . 25 . 250 . 11 OpenShift Container Platform cluster server \n",
      " example . com 172 . 25 . 250 . 51 OpenShift Container Platform cluster node wo\n",
      " example . com 172 . 25 . 250 . 52 OpenShift Container Platform cluster node se\n",
      "hat Application User name Password OpenShift web console kubeadmin The password\n",
      "containers orchestrated by Red Hat OpenShift Container Platform . Objectives De\n",
      " of orchestrating applications and OpenShift Container Platform . Sections Over\n",
      " Quiz ) Overview of Kubernetes and OpenShift ( and Quiz ) Overview of Container\n",
      "mages . Overview of Kubernetes and OpenShift Objectives After completing this s\n",
      "hestration tool . Describe Red Hat OpenShift Container Platform ( RHOCP ). Limi\n",
      "changes in the application state . OpenShift Overview Red Hat OpenShift Contain\n",
      "state . OpenShift Overview Red Hat OpenShift Container Platform ( RHOCP ) is a \n",
      "evelopers . Beginning with Red Hat OpenShift v4 , hosts in an OpenShift cluster\n",
      "Red Hat OpenShift v4 , hosts in an OpenShift cluster all use Red Hat Enterprise\n",
      " this course , the terms RHOCP and OpenShift are used to refer to the Red Hat O\n",
      "t are used to refer to the Red Hat OpenShift Container Platform . OpenShift Fea\n",
      "Hat OpenShift Container Platform . OpenShift Features OpenShift adds the follow\n",
      "iner Platform . OpenShift Features OpenShift adds the following features to a K\n"
     ]
    }
   ],
   "source": [
    "word_token = 'OpenShift'\n",
    "do180.concordance(word_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "do180.similar(word_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dispersion plot of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "do180.dispersion_plot(['Container', 'OpenShift', 'Podman'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<FreqDist with 3904 samples and 50343 outcomes>\n"
     ]
    }
   ],
   "source": [
    "fdist1 = FreqDist(do180)\n",
    "print(fdist1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most Common:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc054c71940>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist1.most_common(50)\n",
    "fdist1.plot(50, cumulative=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do180.collocations()\n",
    "# The above method is broken; and will be fixed in a newer version on NLTK..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}