Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topics - Easy Topic Modeling in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1. Preperations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading modules from DARIAH-Topics library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "/usr/local/lib/python3.5/dist-packages/funcy/decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n"
     ]
    }
   ],
   "source": [
    "from dariah_topics import preprocessing as pre\n",
    "from dariah_topics import visualization as visual\n",
    "from dariah_topics import mallet as mal\n",
    "from dariah_topics import evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load required functions from Gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gensim.models import LdaModel\n",
    "from gensim.corpora import MmCorpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Activating inline output in Jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Reading a corpus of documents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defining the path to the corpus folder\n",
    "\n",
    "In the present example code, we are using a folder of 'txt' documents provided with the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = \"corpus_txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List all documents in the folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# List documents in folder\n",
    "doclist = pre.create_document_list(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us check the list of documents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['corpus_txt/Poe_ThePurloinedLetter.txt',\n",
       " 'corpus_txt/Lovecraft_TheShunnedHouse.txt',\n",
       " 'corpus_txt/Kipling_ThyServantaDog.txt',\n",
       " 'corpus_txt/Howard_ShadowsintheMoonlight.txt',\n",
       " 'corpus_txt/Howard_SchadowsinZamboula.txt',\n",
       " 'corpus_txt/Poe_TheCaskofAmontillado.txt',\n",
       " 'corpus_txt/Kipling_TheEndofthePassage.txt',\n",
       " 'corpus_txt/Howard_TheDevilinIron.txt',\n",
       " 'corpus_txt/Lovecraft_AttheMountainofMadness.txt',\n",
       " 'corpus_txt/Doyle_AStudyinScarlet.txt',\n",
       " 'corpus_txt/Doyle_TheSignoftheFour.txt',\n",
       " 'corpus_txt/Doyle_TheHoundoftheBaskervilles.txt',\n",
       " 'corpus_txt/Kipling_TheJungleBook.txt',\n",
       " 'corpus_txt/Poe_TheMasqueoftheRedDeath.txt',\n",
       " 'corpus_txt/Howard_GodsoftheNorth.txt',\n",
       " 'corpus_txt/Doyle_AScandalinBohemia.txt',\n",
       " 'corpus_txt/Poe_EurekaAProsePoem.txt']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doclist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate document labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc_labels = list(pre.get_labels(doclist))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read listed documents from folder\n",
    "\n",
    "The list of documents can also be manipulated before giving it to the following function to select specific documents from the folder. In the general case shown here, all documents in the folder are read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corpus = pre.read_from_txt(doclist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, the corpus is generator object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3. Tokenize corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc_tokens = [list(pre.tokenize(doc)) for doc in list(corpus)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4. Create a sparse bag-of-words model\n",
    "\n",
    "The LDA topic model is based on a bag-of-words model of the corpus. To improve performance in large corpora, actual words and document titels are replaced by indices in the actual bag-of-words model. It is therefore necessary to create dictionaries for mapping these indices in advance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "id_types, doc_ids = pre.create_dictionaries(doc_labels, doc_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create market matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sparse_bow = pre.create_mm(doc_labels, doc_tokens, id_types, doc_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5. Feature selection and/or removal\n",
    "\n",
    "In topic modeling, it is often usefull (if not vital) to remove some types before modeling. In this example, the 100 most frequent words and the hapax legomena in the corpus are listed and removed. Alternatively, the 'feature_list' containing all features that are to be removed from the corpus can be replaced by, or combined with a simple stop word list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List the 100 most frequent words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mfw100 = pre.find_stopwords(sparse_bow, id_types, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### List hapax legomena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hapax_list = pre.find_hapax(sparse_bow, id_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine lists and remove content from bag-of-words model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_list = set(mfw100).union(hapax_list)\n",
    "clean_term_frequency = pre.remove_features(sparse_bow, id_types, feature_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6. Save bag-of-words model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pre.save_bow_mm(sparse_bow, output_path=\"gb_plain\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Model creation\n",
    "\n",
    "The actual topic modeling is done with external state-of-the art LDA implementations. In this example, we are relying on Gensim."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Load corpus object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mm = MmCorpus(\"gb_plain.mm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Rearrange dictionaries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc2id = {value : key for key, value in doc_ids.items()}\n",
    "type2id = {value : key for key, value in id_types.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3. Generate LDA model\n",
    "\n",
    "**Warning: this step can take quite a while!** Meaning something between some seconds and some hours depending on corpus size and the number of passes. Our example short stories corpus should be done within a minute or two at 'passes = 10'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = LdaModel(corpus=mm, id2word=type2id, num_topics=10, passes = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4. Create document-topic matrix\n",
    "\n",
    "The generated model object can now be translated into a human-readable document-topic matrix (that is a actually a pandas data frame) that constitutes our principle exchange format for topic modeling results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc_topic = visual.create_doc_topic(mm, model, doc_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model visualization and evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. The document-topic matrix in a heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aFDewnoy2V+1nM/vU7VTG5MbXc1Tq750oaVvc8P5H9e6cItr7Gp7kEtzArvfs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OO3uC3+icfmanXl1mybBXu2Zi9t69MOcmqFE4H9IuK2dMx+wNO47QsR6P2wsfc0jStuM5nMCCEL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa49dddec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap = visual.doc_topic_heatmap(doc_topic)\n",
    "heatmap.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Topics in a single document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mZlaRkyZrKh0dHY0OwczMrCInTWZmZmZ1cNJkZmZmVoemSJok3Slps6RdksY3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\niAA2Vujn9ojY0JfBVRi7sz/7r5ekAyL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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3fd2ecc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual.plot_doc_topics(doc_topic, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Topic words in a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word 1</th>\n",
       "      <th>Word 2</th>\n",
       "      <th>Word 3</th>\n",
       "      <th>Word 4</th>\n",
       "      <th>Word 5</th>\n",
       "      <th>Word 6</th>\n",
       "      <th>Word 7</th>\n",
       "      <th>Word 8</th>\n",
       "      <th>Word 9</th>\n",
       "      <th>Word 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Topic 1</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>of-life</td>\n",
       "      <td>niagara</td>\n",
       "      <td>eagle</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>love-locks</td>\n",
       "      <td>liked</td>\n",
       "      <td>syllable</td>\n",
       "      <td>trumpeted</td>\n",
       "      <td>records</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 2</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>of-life</td>\n",
       "      <td>niagara</td>\n",
       "      <td>eagle</td>\n",
       "      <td>liked</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>love-locks</td>\n",
       "      <td>cushion</td>\n",
       "      <td>trumpeted</td>\n",
       "      <td>syllable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 3</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>of-life</td>\n",
       "      <td>niagara</td>\n",
       "      <td>eagle</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>liked</td>\n",
       "      <td>love-locks</td>\n",
       "      <td>trumpeted</td>\n",
       "      <td>syllable</td>\n",
       "      <td>records</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 4</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>niagara</td>\n",
       "      <td>eagle</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>of-life</td>\n",
       "      <td>love-locks</td>\n",
       "      <td>vi</td>\n",
       "      <td>mention</td>\n",
       "      <td>syllable</td>\n",
       "      <td>mauled</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 5</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>of-life</td>\n",
       "      <td>niagara</td>\n",
       "      <td>eagle</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>liked</td>\n",
       "      <td>cushion</td>\n",
       "      <td>expedient</td>\n",
       "      <td>trumpeted</td>\n",
       "      <td>love-locks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 6</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>of-life</td>\n",
       "      <td>niagara</td>\n",
       "      <td>eagle</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>liked</td>\n",
       "      <td>trumpeted</td>\n",
       "      <td>cushion</td>\n",
       "      <td>love-locks</td>\n",
       "      <td>syllable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 7</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>niagara</td>\n",
       "      <td>of-life</td>\n",
       "      <td>eagle</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>expedient</td>\n",
       "      <td>mauled</td>\n",
       "      <td>love-locks</td>\n",
       "      <td>trumpeted</td>\n",
       "      <td>syllable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 8</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>niagara</td>\n",
       "      <td>of-life</td>\n",
       "      <td>eagle</td>\n",
       "      <td>love-locks</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>syllable</td>\n",
       "      <td>cushion</td>\n",
       "      <td>trumpeted</td>\n",
       "      <td>liked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 9</th>\n",
       "      <td>of-life</td>\n",
       "      <td>fo'c</td>\n",
       "      <td>eagle</td>\n",
       "      <td>liked</td>\n",
       "      <td>unutterably</td>\n",
       "      <td>niagara</td>\n",
       "      <td>trumpeted</td>\n",
       "      <td>sleepiness</td>\n",
       "      <td>mauled</td>\n",
       "      <td>de_scend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Topic 10</th>\n",
       "      <td>fo'c</td>\n",
       "      <td>of-life</td>\n",
       "      <td>niagara</td>\n",
       "      <td>eagle</td>\n",
       "      <td>de_scend</td>\n",
       "      <td>love-locks</td>\n",
       "      <td>syllable</td>\n",
       "      <td>liked</td>\n",
       "      <td>cushion</td>\n",
       "      <td>trumpeted</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Word 1   Word 2   Word 3    Word 4       Word 5      Word 6  \\\n",
       "Topic 1      fo'c  of-life  niagara     eagle     de_scend  love-locks   \n",
       "Topic 2      fo'c  of-life  niagara     eagle        liked    de_scend   \n",
       "Topic 3      fo'c  of-life  niagara     eagle     de_scend       liked   \n",
       "Topic 4      fo'c  niagara    eagle  de_scend      of-life  love-locks   \n",
       "Topic 5      fo'c  of-life  niagara     eagle     de_scend       liked   \n",
       "Topic 6      fo'c  of-life  niagara     eagle     de_scend       liked   \n",
       "Topic 7      fo'c  niagara  of-life     eagle     de_scend   expedient   \n",
       "Topic 8      fo'c  niagara  of-life     eagle   love-locks    de_scend   \n",
       "Topic 9   of-life     fo'c    eagle     liked  unutterably     niagara   \n",
       "Topic 10     fo'c  of-life  niagara     eagle     de_scend  love-locks   \n",
       "\n",
       "              Word 7      Word 8      Word 9     Word 10  \n",
       "Topic 1        liked    syllable   trumpeted     records  \n",
       "Topic 2   love-locks     cushion   trumpeted    syllable  \n",
       "Topic 3   love-locks   trumpeted    syllable     records  \n",
       "Topic 4           vi     mention    syllable      mauled  \n",
       "Topic 5      cushion   expedient   trumpeted  love-locks  \n",
       "Topic 6    trumpeted     cushion  love-locks    syllable  \n",
       "Topic 7       mauled  love-locks   trumpeted    syllable  \n",
       "Topic 8     syllable     cushion   trumpeted       liked  \n",
       "Topic 9    trumpeted  sleepiness      mauled    de_scend  \n",
       "Topic 10    syllable       liked     cushion   trumpeted  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = visual.topicwords_in_df(model)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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