inf2209-topicmodels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>year</th>\n",
       "      <th>title</th>\n",
       "      <th>event_type</th>\n",
       "      <th>pdf_name</th>\n",
       "      <th>abstract</th>\n",
       "      <th>paper_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1987</td>\n",
       "      <td>Self-Organization of Associative Database and Its Applications</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1-self-organization-of-associative-database-and-its-applications.pdf</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABASE\\nAND ITS APPLICATIONS\\nHisashi Suzuki and Suguru Arimoto\\nOsaka University, Toyonaka, Osaka 560, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>1987</td>\n",
       "      <td>A Mean Field Theory of Layer IV of Visual Cortex and Its Application to Artificial Neural Networks</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10-a-mean-field-theory-of-layer-iv-of-visual-cortex-and-its-application-to-artificial-neural-networks.pdf</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISUAL CORTEX\\nAND ITS APPLICATION TO ARTIFICIAL NEURAL NETWORKS*\\nChristopher L. Scofield\\nCenter for N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>1988</td>\n",
       "      <td>Storing Covariance by the Associative Long-Term Potentiation and Depression of Synaptic Strengths in the Hippocampus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100-storing-covariance-by-the-associative-long-term-potentiation-and-depression-of-synaptic-strengths-in-the-hippocampus.pdf</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\nLONG?TERM POTENTIATION AND DEPRESSION\\nOF SYNAPTIC STRENGTHS IN THE HIPPOCAMPUS\\nPatric K. Stanton? ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>1994</td>\n",
       "      <td>Bayesian Query Construction for Neural Network Models</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000-bayesian-query-construction-for-neural-network-models.pdf</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>Bayesian Query Construction for Neural\\nNetwork Models\\nGerhard Paass\\nJorg Kindermann\\nGerman National Research Center for Computer Science (GMD)...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001</td>\n",
       "      <td>1994</td>\n",
       "      <td>Neural Network Ensembles, Cross Validation, and Active Learning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1001-neural-network-ensembles-cross-validation-and-active-learning.pdf</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>Neural Network Ensembles, Cross\\nValidation, and Active Learning\\n\\nAnders Krogh\"\\nNordita\\nBlegdamsvej 17\\n2100 Copenhagen, Denmark\\n\\nJesper Ved...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  year  \\\n",
       "0     1  1987   \n",
       "1    10  1987   \n",
       "2   100  1988   \n",
       "3  1000  1994   \n",
       "4  1001  1994   \n",
       "\n",
       "                                                                                                                  title  \\\n",
       "0                                                        Self-Organization of Associative Database and Its Applications   \n",
       "1                    A Mean Field Theory of Layer IV of Visual Cortex and Its Application to Artificial Neural Networks   \n",
       "2  Storing Covariance by the Associative Long-Term Potentiation and Depression of Synaptic Strengths in the Hippocampus   \n",
       "3                                                                 Bayesian Query Construction for Neural Network Models   \n",
       "4                                                       Neural Network Ensembles, Cross Validation, and Active Learning   \n",
       "\n",
       "  event_type  \\\n",
       "0        NaN   \n",
       "1        NaN   \n",
       "2        NaN   \n",
       "3        NaN   \n",
       "4        NaN   \n",
       "\n",
       "                                                                                                                       pdf_name  \\\n",
       "0                                                          1-self-organization-of-associative-database-and-its-applications.pdf   \n",
       "1                     10-a-mean-field-theory-of-layer-iv-of-visual-cortex-and-its-application-to-artificial-neural-networks.pdf   \n",
       "2  100-storing-covariance-by-the-associative-long-term-potentiation-and-depression-of-synaptic-strengths-in-the-hippocampus.pdf   \n",
       "3                                                                1000-bayesian-query-construction-for-neural-network-models.pdf   \n",
       "4                                                        1001-neural-network-ensembles-cross-validation-and-active-learning.pdf   \n",
       "\n",
       "           abstract  \\\n",
       "0  Abstract Missing   \n",
       "1  Abstract Missing   \n",
       "2  Abstract Missing   \n",
       "3  Abstract Missing   \n",
       "4  Abstract Missing   \n",
       "\n",
       "                                                                                                                                              paper_text  \n",
       "0  767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABASE\\nAND ITS APPLICATIONS\\nHisashi Suzuki and Suguru Arimoto\\nOsaka University, Toyonaka, Osaka 560, ...  \n",
       "1  683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISUAL CORTEX\\nAND ITS APPLICATION TO ARTIFICIAL NEURAL NETWORKS*\\nChristopher L. Scofield\\nCenter for N...  \n",
       "2  394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\nLONG?TERM POTENTIATION AND DEPRESSION\\nOF SYNAPTIC STRENGTHS IN THE HIPPOCAMPUS\\nPatric K. Stanton? ...  \n",
       "3  Bayesian Query Construction for Neural\\nNetwork Models\\nGerhard Paass\\nJorg Kindermann\\nGerman National Research Center for Computer Science (GMD)...  \n",
       "4  Neural Network Ensembles, Cross\\nValidation, and Active Learning\\n\\nAnders Krogh\"\\nNordita\\nBlegdamsvej 17\\n2100 Copenhagen, Denmark\\n\\nJesper Ved...  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing modules\n",
    "import pandas as pd\n",
    "import os\n",
    "os.chdir('..')\n",
    "# Read data into papers\n",
    "papers = pd.read_csv('/Users/shionguha/Documents/GitHub/inf2209-humancentredtopicmodels-fa21/data/NEURIPS Papers/papers.csv')# Print head\n",
    "papers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>paper_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6404</th>\n",
       "      <td>1993</td>\n",
       "      <td>Classification of Electroencephalogram using Artificial Neural Networks</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>Classification of Electroencephalogram using\\nArtificial Neural Networks\\n\\nA C Tsoi*, D S C So*, A Sergejew**\\n*Department of Electrical Engineer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3558</th>\n",
       "      <td>2011</td>\n",
       "      <td>Additive Gaussian Processes</td>\n",
       "      <td>We introduce a Gaussian process model of functions which are additive.  An additive function is one which decomposes into a sum of low-dimensional...</td>\n",
       "      <td>Additive Gaussian Processes\\nDavid Duvenaud\\nDepartment of Engineering\\nCambridge University\\ndkd23@cam.ac.uk\\n\\nHannes Nickisch\\nMPI for Intellig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>1999</td>\n",
       "      <td>Robust Learning of Chaotic Attractors</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>Robust Learning of Chaotic Attractors\\n\\nRembrandt Bakker*\\nChemical Reactor Engineering\\nDelft Univ. of Technology\\n\\nr.bakker@stm.tudelft?nl\\n\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1995</td>\n",
       "      <td>Reorganisation of Somatosensory Cortex after Tactile Training</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>Reorganisation of Somatosensory Cortex after\\nTactile Training\\n\\nRasmus S. Petersen\\nJohn G. Taylor\\nCentre for Neural Networks, King's College L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>2002</td>\n",
       "      <td>Kernel Dependency Estimation</td>\n",
       "      <td>Abstract Missing</td>\n",
       "      <td>Kernel Dependency Estimation\\n\\nJason Weston, Olivier Chapelle, Andre Elisseeff,\\nBernhard Scholkopf and Vladimir Vapnik*\\nMax Planck Institute fo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  \\\n",
       "6404  1993   \n",
       "3558  2011   \n",
       "702   1999   \n",
       "61    1995   \n",
       "1425  2002   \n",
       "\n",
       "                                                                        title  \\\n",
       "6404  Classification of Electroencephalogram using Artificial Neural Networks   \n",
       "3558                                              Additive Gaussian Processes   \n",
       "702                                     Robust Learning of Chaotic Attractors   \n",
       "61              Reorganisation of Somatosensory Cortex after Tactile Training   \n",
       "1425                                             Kernel Dependency Estimation   \n",
       "\n",
       "                                                                                                                                                   abstract  \\\n",
       "6404                                                                                                                                       Abstract Missing   \n",
       "3558  We introduce a Gaussian process model of functions which are additive.  An additive function is one which decomposes into a sum of low-dimensional...   \n",
       "702                                                                                                                                        Abstract Missing   \n",
       "61                                                                                                                                         Abstract Missing   \n",
       "1425                                                                                                                                       Abstract Missing   \n",
       "\n",
       "                                                                                                                                                 paper_text  \n",
       "6404  Classification of Electroencephalogram using\\nArtificial Neural Networks\\n\\nA C Tsoi*, D S C So*, A Sergejew**\\n*Department of Electrical Engineer...  \n",
       "3558  Additive Gaussian Processes\\nDavid Duvenaud\\nDepartment of Engineering\\nCambridge University\\ndkd23@cam.ac.uk\\n\\nHannes Nickisch\\nMPI for Intellig...  \n",
       "702   Robust Learning of Chaotic Attractors\\n\\nRembrandt Bakker*\\nChemical Reactor Engineering\\nDelft Univ. of Technology\\n\\nr.bakker@stm.tudelft?nl\\n\\n...  \n",
       "61    Reorganisation of Somatosensory Cortex after\\nTactile Training\\n\\nRasmus S. Petersen\\nJohn G. Taylor\\nCentre for Neural Networks, King's College L...  \n",
       "1425  Kernel Dependency Estimation\\n\\nJason Weston, Olivier Chapelle, Andre Elisseeff,\\nBernhard Scholkopf and Vladimir Vapnik*\\nMax Planck Institute fo...  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the columns\n",
    "papers = papers.drop(columns=['id', 'event_type', 'pdf_name'], axis=1).sample(100)# Print out the first rows of papers\n",
    "papers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6404    classification of electroencephalogram using\\nartificial neural networks\\n\\na c tsoi* d s c so* a sergejew**\\n*department of electrical engineerin...\n",
       "3558    additive gaussian processes\\ndavid duvenaud\\ndepartment of engineering\\ncambridge university\\ndkd23@camacuk\\n\\nhannes nickisch\\nmpi for intelligen...\n",
       "702     robust learning of chaotic attractors\\n\\nrembrandt bakker*\\nchemical reactor engineering\\ndelft univ of technology\\n\\nrbakker@stmtudelftnl\\n\\njaap...\n",
       "61      reorganisation of somatosensory cortex after\\ntactile training\\n\\nrasmus s petersen\\njohn g taylor\\ncentre for neural networks king's college lond...\n",
       "1425    kernel dependency estimation\\n\\njason weston olivier chapelle andre elisseeff\\nbernhard scholkopf and vladimir vapnik*\\nmax planck institute for b...\n",
       "Name: paper_text_processed, dtype: object"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the regular expression library\n",
    "import re\n",
    "# Remove punctuation\n",
    "papers['paper_text_processed'] = \\\n",
    "papers['paper_text'].map(lambda x: re.sub('[,\\.!?]', '', x))# Convert the titles to lowercase\n",
    "papers['paper_text_processed'] = \\\n",
    "papers['paper_text_processed'].map(lambda x: x.lower())# Print out the first rows of papers\n",
    "papers['paper_text_processed'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=400x200 at 0x19313AD60>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the wordcloud library\n",
    "from wordcloud import WordCloud# Join the different processed titles together.\n",
    "long_string = ','.join(list(papers['paper_text_processed'].values))# Create a WordCloud object\n",
    "wordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue')# Generate a word cloud\n",
    "wordcloud.generate(long_string)# Visualize the word cloud\n",
    "wordcloud.to_image()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/shionguha/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import gensim\n",
    "from gensim.utils import simple_preprocess\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        # deacc=True removes punctuations\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stopwords(texts):\n",
    "    return [[word for word in simple_preprocess(str(doc)) \n",
    "             if word not in stop_words] for doc in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = papers.paper_text_processed.values.tolist()\n",
    "data_words = list(sent_to_words(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['classification', 'using', 'artificial', 'neural', 'networks', 'tsoi', 'sergejew', 'department', 'electrical', 'engineering', 'department', 'psychiatry', 'university', 'queensland', 'st', 'lucia', 'queensland', 'australia', 'abstract', 'paper', 'consider', 'problem', 'classifying', 'eeg', 'signals', 'normal', 'subjects', 'subjects', 'suffering', 'psychiatric']\n"
     ]
    }
   ],
   "source": [
    "# remove stop words\n",
    "data_words = remove_stopwords(data_words)\n",
    "print(data_words[:1][0][:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (1, 1), (2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 1), (8, 1), (9, 3), (10, 1), (11, 2), (12, 1), (13, 2), (14, 5), (15, 1), (16, 1), (17, 1), (18, 2), (19, 1), (20, 1), (21, 1), (22, 1), (23, 2), (24, 3), (25, 1), (26, 1), (27, 7), (28, 1), (29, 6)]\n"
     ]
    }
   ],
   "source": [
    "import gensim.corpora as corpora\n",
    "\n",
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_words)\n",
    "\n",
    "# Create Corpus\n",
    "texts = data_words\n",
    "\n",
    "# Term Document Frequency\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "# View\n",
    "print(corpus[:1][0][:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.005*\"model\" + 0.005*\"learning\" + 0.004*\"data\" + 0.004*\"set\" + '\n",
      "  '0.004*\"algorithm\" + 0.004*\"figure\" + 0.004*\"function\" + 0.004*\"also\" + '\n",
      "  '0.004*\"time\" + 0.003*\"using\"'),\n",
      " (1,\n",
      "  '0.010*\"model\" + 0.006*\"learning\" + 0.006*\"data\" + 0.004*\"set\" + 0.004*\"one\" '\n",
      "  '+ 0.004*\"using\" + 0.004*\"function\" + 0.004*\"number\" + 0.004*\"algorithm\" + '\n",
      "  '0.003*\"state\"'),\n",
      " (2,\n",
      "  '0.006*\"time\" + 0.005*\"model\" + 0.005*\"set\" + 0.005*\"data\" + '\n",
      "  '0.005*\"learning\" + 0.004*\"algorithm\" + 0.004*\"network\" + 0.004*\"using\" + '\n",
      "  '0.004*\"one\" + 0.003*\"two\"'),\n",
      " (3,\n",
      "  '0.006*\"algorithm\" + 0.005*\"model\" + 0.004*\"set\" + 0.004*\"time\" + '\n",
      "  '0.004*\"learning\" + 0.004*\"function\" + 0.004*\"data\" + 0.003*\"problem\" + '\n",
      "  '0.003*\"using\" + 0.003*\"number\"'),\n",
      " (4,\n",
      "  '0.005*\"learning\" + 0.005*\"algorithm\" + 0.004*\"set\" + 0.004*\"problem\" + '\n",
      "  '0.004*\"data\" + 0.004*\"function\" + 0.004*\"model\" + 0.004*\"time\" + '\n",
      "  '0.004*\"using\" + 0.003*\"one\"')]\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "\n",
    "# number of topics\n",
    "num_topics = 5\n",
    "\n",
    "# Build LDA model\n",
    "lda_model = gensim.models.LdaMulticore(corpus=corpus,\n",
    "                                       id2word=id2word,\n",
    "                                       num_topics=num_topics)\n",
    "\n",
    "# Print the Keyword in the 10 topics\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nmf demo\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.feature_extraction import text\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import word_tokenize, pos_tag\n",
    "from nltk.stem import WordNetLemmatizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/shionguha/Documents/GitHub/inf2209-humancentredtopicmodels-fa21/data/inaug_speeches.csv', encoding=\"latin1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Name</th>\n",
       "      <th>Inaugural Address</th>\n",
       "      <th>Date</th>\n",
       "      <th>text</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 153</th>\n",
       "      <th>Unnamed: 154</th>\n",
       "      <th>Unnamed: 155</th>\n",
       "      <th>Unnamed: 156</th>\n",
       "      <th>Unnamed: 157</th>\n",
       "      <th>Unnamed: 158</th>\n",
       "      <th>Unnamed: 159</th>\n",
       "      <th>Unnamed: 160</th>\n",
       "      <th>Unnamed: 161</th>\n",
       "      <th>Unnamed: 162</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>George Washington</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Thursday, April 30, 1789</td>\n",
       "      <td>Fellow-Citizens of the Senate and of the House of Representatives:    AMONG the vicissitudes incident to life no event could have fille...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>George Washington</td>\n",
       "      <td>Second Inaugural Address</td>\n",
       "      <td>Monday, March 4, 1793</td>\n",
       "      <td>Fellow Citizens:    I AM again called upon by the voice of my country to execute the functions of its Chief Magistrate. When the occas...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>John Adams</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Saturday, March 4, 1797</td>\n",
       "      <td>WHEN it was first perceived, in early times, that no middle course for America remained between unlimited submission to a foreign le...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Thomas Jefferson</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Wednesday, March 4, 1801</td>\n",
       "      <td>Friends and Fellow-Citizens:    CALLED upon to undertake the duties of the first executive office of our country, I avail myself of th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Thomas Jefferson</td>\n",
       "      <td>Second Inaugural Address</td>\n",
       "      <td>Monday, March 4, 1805</td>\n",
       "      <td>PROCEEDING, fellow-citizens, to that qualification which the Constitution requires before my entrance on the charge again conferred ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>James Madison</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Saturday, March 4, 1809</td>\n",
       "      <td>UNWILLING to depart from examples of the most revered authority, I avail myself of the occasion now presented to express the profoun...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>James Madison</td>\n",
       "      <td>Second Inaugural Address</td>\n",
       "      <td>Thursday, March 4, 1813</td>\n",
       "      <td>ABOUT to add the solemnity of an oath to the obligations imposed by a second call to the station in which my country heretofore plac...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>James Monroe</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Tuesday, March 4, 1817</td>\n",
       "      <td>I SHOULD be destitute of feeling if I was not deeply affected by the strong proof which my fellow-citizens have given me of their co...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>James Monroe</td>\n",
       "      <td>Second Inaugural Address</td>\n",
       "      <td>Monday, March 5, 1821</td>\n",
       "      <td>Fellow-Citizens:    I SHALL not attempt to describe the grateful emotions which the new and very distinguished proof of the confidence...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13</td>\n",
       "      <td>John Quincy Adams</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Friday, March 4, 1825</td>\n",
       "      <td>IN compliance with an usage coeval with the existence of our Federal Constitution, and sanctioned by the example of my predecessors ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>Andrew Jackson</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Wednesday, March 4, 1829</td>\n",
       "      <td>Fellow-Citizens:    ABOUT to undertake the arduous duties that I have been appointed to perform by the choice of a free people, I avai...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15</td>\n",
       "      <td>Andrew Jackson</td>\n",
       "      <td>Second Inaugural Address</td>\n",
       "      <td>Monday, March 4, 1833</td>\n",
       "      <td>Fellow-Citizens:    THE will of the American people, expressed through their unsolicited suffrages, calls me before you to pass throug...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16</td>\n",
       "      <td>Martin Van Buren</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Monday, March 4, 1837</td>\n",
       "      <td>Fellow-Citizens: The practice of all my predecessors imposes on me an obligation I cheerfully fulfill&lt;U+0097&gt;to accompany the first an...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17</td>\n",
       "      <td>William Henry Harrison</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Thursday, March 4, 1841</td>\n",
       "      <td>CALLED from a retirement which I had supposed was to continue for the residue of my life to fill the chief executive office of this ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>in some cases</td>\n",
       "      <td>as the powers which they respectively claim are often not defined by any distinct lines. Mischievous</td>\n",
       "      <td>however</td>\n",
       "      <td>in their tendencies as collisions of this kind may be</td>\n",
       "      <td>those which arise between the respective communities which for certain purposes compose one nation are much more so</td>\n",
       "      <td>for no such nation can long exist without the careful culture of those feelings of confidence and affection which are the effective bonds to unio...</td>\n",
       "      <td>it has been often found ineffectual. Men blinded by their passions have been known to adopt measures for their country in direct opposition to al...</td>\n",
       "      <td>then</td>\n",
       "      <td>is to destroy or keep down a bad passion by creating and fostering a good one</td>\n",
       "      <td>and this seems to be the corner stone upon which our American political architects have reared the fabric of our Government. The cement which was...</td>\n",
       "      <td>...</td>\n",
       "      <td>for the defense of those principles for which our ancestors so gloriously contended. As far as it depends upon me it shall be accomplished. All t...</td>\n",
       "      <td>nor any confidence in advance from the people but that asked for by Mr. Jefferson</td>\n",
       "      <td>\"\"to give firmness and effect to the legal administration of their affairs.\"\"    I deem the present occasion sufficiently important and solemn to...</td>\n",
       "      <td>religious liberty</td>\n",
       "      <td>and a just sense of religious responsibility are essentially connected with all true and lasting happiness; and to that good Being who has blesse...</td>\n",
       "      <td>who watched over and prospered the labors of our fathers and has hitherto preserved to us institutions far exceeding in excellence those of any o...</td>\n",
       "      <td>let us unite in fervently commending every interest of our beloved country in all future time.    Fellow-citizens</td>\n",
       "      <td>being fully invested with that high office to which the partiality of my countrymen has called me</td>\n",
       "      <td>I now take an affectionate leave of you. You will bear with you to your homes the remembrance of the pledge I have this day given to discharge al...</td>\n",
       "      <td>and I shall enter upon their performance with entire confidence in the support of a just and generous people.              \"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18</td>\n",
       "      <td>James Knox Polk</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Tuesday, March 4, 1845</td>\n",
       "      <td>Fellow-Citizens:    WITHOUT solicitation on my part, I have been chosen by the free and voluntary suffrages of my countrymen to the mo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19</td>\n",
       "      <td>Zachary Taylor</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Monday, March 5, 1849</td>\n",
       "      <td>ELECTED by the American people to the highest office known to our laws, I appear here to take the oath prescribed by the Constitutio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>Franklin Pierce</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Friday, March 4, 1853</td>\n",
       "      <td>My Countrymen:    IT a relief to feel that no heart but my own can know the personal regret and bitter sorrow over which I have been b...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21</td>\n",
       "      <td>James Buchanan</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Wednesday, March 4, 1857</td>\n",
       "      <td>Fellow-Citizens:    I APPEAR before you this day to take the solemn oath \"that I will faithfully execute the office of President of th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22</td>\n",
       "      <td>Abraham Lincoln</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Monday, March 4, 1861</td>\n",
       "      <td>Fellow-Citizens of the United States:    IN compliance with a custom as old as the Government itself, I appear before you to address y...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 163 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  \\\n",
       "0                4   \n",
       "1                5   \n",
       "2                6   \n",
       "3                7   \n",
       "4                8   \n",
       "5                9   \n",
       "6               10   \n",
       "7               11   \n",
       "8               12   \n",
       "9               13   \n",
       "10              14   \n",
       "11              15   \n",
       "12              16   \n",
       "13              17   \n",
       "14   in some cases   \n",
       "15              18   \n",
       "16              19   \n",
       "17              20   \n",
       "18              21   \n",
       "19              22   \n",
       "\n",
       "                                                                                                     Name  \\\n",
       "0                                                                                       George Washington   \n",
       "1                                                                                       George Washington   \n",
       "2                                                                                              John Adams   \n",
       "3                                                                                        Thomas Jefferson   \n",
       "4                                                                                        Thomas Jefferson   \n",
       "5                                                                                           James Madison   \n",
       "6                                                                                           James Madison   \n",
       "7                                                                                            James Monroe   \n",
       "8                                                                                            James Monroe   \n",
       "9                                                                                       John Quincy Adams   \n",
       "10                                                                                         Andrew Jackson   \n",
       "11                                                                                         Andrew Jackson   \n",
       "12                                                                                       Martin Van Buren   \n",
       "13                                                                                 William Henry Harrison   \n",
       "14   as the powers which they respectively claim are often not defined by any distinct lines. Mischievous   \n",
       "15                                                                                        James Knox Polk   \n",
       "16                                                                                         Zachary Taylor   \n",
       "17                                                                                        Franklin Pierce   \n",
       "18                                                                                         James Buchanan   \n",
       "19                                                                                        Abraham Lincoln   \n",
       "\n",
       "           Inaugural Address  \\\n",
       "0    First Inaugural Address   \n",
       "1   Second Inaugural Address   \n",
       "2          Inaugural Address   \n",
       "3    First Inaugural Address   \n",
       "4   Second Inaugural Address   \n",
       "5    First Inaugural Address   \n",
       "6   Second Inaugural Address   \n",
       "7    First Inaugural Address   \n",
       "8   Second Inaugural Address   \n",
       "9          Inaugural Address   \n",
       "10   First Inaugural Address   \n",
       "11  Second Inaugural Address   \n",
       "12         Inaugural Address   \n",
       "13         Inaugural Address   \n",
       "14                   however   \n",
       "15         Inaugural Address   \n",
       "16         Inaugural Address   \n",
       "17         Inaugural Address   \n",
       "18         Inaugural Address   \n",
       "19   First Inaugural Address   \n",
       "\n",
       "                                                      Date  \\\n",
       "0                                 Thursday, April 30, 1789   \n",
       "1                                    Monday, March 4, 1793   \n",
       "2                                  Saturday, March 4, 1797   \n",
       "3                                 Wednesday, March 4, 1801   \n",
       "4                                    Monday, March 4, 1805   \n",
       "5                                  Saturday, March 4, 1809   \n",
       "6                                  Thursday, March 4, 1813   \n",
       "7                                   Tuesday, March 4, 1817   \n",
       "8                                    Monday, March 5, 1821   \n",
       "9                                    Friday, March 4, 1825   \n",
       "10                                Wednesday, March 4, 1829   \n",
       "11                                   Monday, March 4, 1833   \n",
       "12                                   Monday, March 4, 1837   \n",
       "13                                 Thursday, March 4, 1841   \n",
       "14   in their tendencies as collisions of this kind may be   \n",
       "15                                  Tuesday, March 4, 1845   \n",
       "16                                   Monday, March 5, 1849   \n",
       "17                                   Friday, March 4, 1853   \n",
       "18                                Wednesday, March 4, 1857   \n",
       "19                                   Monday, March 4, 1861   \n",
       "\n",
       "                                                                                                                                                     text  \\\n",
       "0              Fellow-Citizens of the Senate and of the House of Representatives:    AMONG the vicissitudes incident to life no event could have fille...   \n",
       "1               Fellow Citizens:    I AM again called upon by the voice of my country to execute the functions of its Chief Magistrate. When the occas...   \n",
       "2                 WHEN it was first perceived, in early times, that no middle course for America remained between unlimited submission to a foreign le...   \n",
       "3               Friends and Fellow-Citizens:    CALLED upon to undertake the duties of the first executive office of our country, I avail myself of th...   \n",
       "4                 PROCEEDING, fellow-citizens, to that qualification which the Constitution requires before my entrance on the charge again conferred ...   \n",
       "5                 UNWILLING to depart from examples of the most revered authority, I avail myself of the occasion now presented to express the profoun...   \n",
       "6                 ABOUT to add the solemnity of an oath to the obligations imposed by a second call to the station in which my country heretofore plac...   \n",
       "7                 I SHOULD be destitute of feeling if I was not deeply affected by the strong proof which my fellow-citizens have given me of their co...   \n",
       "8               Fellow-Citizens:    I SHALL not attempt to describe the grateful emotions which the new and very distinguished proof of the confidence...   \n",
       "9                 IN compliance with an usage coeval with the existence of our Federal Constitution, and sanctioned by the example of my predecessors ...   \n",
       "10              Fellow-Citizens:    ABOUT to undertake the arduous duties that I have been appointed to perform by the choice of a free people, I avai...   \n",
       "11              Fellow-Citizens:    THE will of the American people, expressed through their unsolicited suffrages, calls me before you to pass throug...   \n",
       "12              Fellow-Citizens: The practice of all my predecessors imposes on me an obligation I cheerfully fulfill<U+0097>to accompany the first an...   \n",
       "13                CALLED from a retirement which I had supposed was to continue for the residue of my life to fill the chief executive office of this ...   \n",
       "14                                    those which arise between the respective communities which for certain purposes compose one nation are much more so   \n",
       "15              Fellow-Citizens:    WITHOUT solicitation on my part, I have been chosen by the free and voluntary suffrages of my countrymen to the mo...   \n",
       "16                ELECTED by the American people to the highest office known to our laws, I appear here to take the oath prescribed by the Constitutio...   \n",
       "17              My Countrymen:    IT a relief to feel that no heart but my own can know the personal regret and bitter sorrow over which I have been b...   \n",
       "18              Fellow-Citizens:    I APPEAR before you this day to take the solemn oath \"that I will faithfully execute the office of President of th...   \n",
       "19              Fellow-Citizens of the United States:    IN compliance with a custom as old as the Government itself, I appear before you to address y...   \n",
       "\n",
       "                                                                                                                                               Unnamed: 5  \\\n",
       "0                                                                                                                                                     NaN   \n",
       "1                                                                                                                                                     NaN   \n",
       "2                                                                                                                                                     NaN   \n",
       "3                                                                                                                                                     NaN   \n",
       "4                                                                                                                                                     NaN   \n",
       "5                                                                                                                                                     NaN   \n",
       "6                                                                                                                                                     NaN   \n",
       "7                                                                                                                                                     NaN   \n",
       "8                                                                                                                                                     NaN   \n",
       "9                                                                                                                                                     NaN   \n",
       "10                                                                                                                                                    NaN   \n",
       "11                                                                                                                                                    NaN   \n",
       "12                                                                                                                                                    NaN   \n",
       "13                                                                                                                                                    NaN   \n",
       "14   for no such nation can long exist without the careful culture of those feelings of confidence and affection which are the effective bonds to unio...   \n",
       "15                                                                                                                                                    NaN   \n",
       "16                                                                                                                                                    NaN   \n",
       "17                                                                                                                                                    NaN   \n",
       "18                                                                                                                                                    NaN   \n",
       "19                                                                                                                                                    NaN   \n",
       "\n",
       "                                                                                                                                               Unnamed: 6  \\\n",
       "0                                                                                                                                                     NaN   \n",
       "1                                                                                                                                                     NaN   \n",
       "2                                                                                                                                                     NaN   \n",
       "3                                                                                                                                                     NaN   \n",
       "4                                                                                                                                                     NaN   \n",
       "5                                                                                                                                                     NaN   \n",
       "6                                                                                                                                                     NaN   \n",
       "7                                                                                                                                                     NaN   \n",
       "8                                                                                                                                                     NaN   \n",
       "9                                                                                                                                                     NaN   \n",
       "10                                                                                                                                                    NaN   \n",
       "11                                                                                                                                                    NaN   \n",
       "12                                                                                                                                                    NaN   \n",
       "13                                                                                                                                                    NaN   \n",
       "14   it has been often found ineffectual. Men blinded by their passions have been known to adopt measures for their country in direct opposition to al...   \n",
       "15                                                                                                                                                    NaN   \n",
       "16                                                                                                                                                    NaN   \n",
       "17                                                                                                                                                    NaN   \n",
       "18                                                                                                                                                    NaN   \n",
       "19                                                                                                                                                    NaN   \n",
       "\n",
       "   Unnamed: 7  \\\n",
       "0         NaN   \n",
       "1         NaN   \n",
       "2         NaN   \n",
       "3         NaN   \n",
       "4         NaN   \n",
       "5         NaN   \n",
       "6         NaN   \n",
       "7         NaN   \n",
       "8         NaN   \n",
       "9         NaN   \n",
       "10        NaN   \n",
       "11        NaN   \n",
       "12        NaN   \n",
       "13        NaN   \n",
       "14       then   \n",
       "15        NaN   \n",
       "16        NaN   \n",
       "17        NaN   \n",
       "18        NaN   \n",
       "19        NaN   \n",
       "\n",
       "                                                                        Unnamed: 8  \\\n",
       "0                                                                              NaN   \n",
       "1                                                                              NaN   \n",
       "2                                                                              NaN   \n",
       "3                                                                              NaN   \n",
       "4                                                                              NaN   \n",
       "5                                                                              NaN   \n",
       "6                                                                              NaN   \n",
       "7                                                                              NaN   \n",
       "8                                                                              NaN   \n",
       "9                                                                              NaN   \n",
       "10                                                                             NaN   \n",
       "11                                                                             NaN   \n",
       "12                                                                             NaN   \n",
       "13                                                                             NaN   \n",
       "14   is to destroy or keep down a bad passion by creating and fostering a good one   \n",
       "15                                                                             NaN   \n",
       "16                                                                             NaN   \n",
       "17                                                                             NaN   \n",
       "18                                                                             NaN   \n",
       "19                                                                             NaN   \n",
       "\n",
       "                                                                                                                                               Unnamed: 9  \\\n",
       "0                                                                                                                                                     NaN   \n",
       "1                                                                                                                                                     NaN   \n",
       "2                                                                                                                                                     NaN   \n",
       "3                                                                                                                                                     NaN   \n",
       "4                                                                                                                                                     NaN   \n",
       "5                                                                                                                                                     NaN   \n",
       "6                                                                                                                                                     NaN   \n",
       "7                                                                                                                                                     NaN   \n",
       "8                                                                                                                                                     NaN   \n",
       "9                                                                                                                                                     NaN   \n",
       "10                                                                                                                                                    NaN   \n",
       "11                                                                                                                                                    NaN   \n",
       "12                                                                                                                                                    NaN   \n",
       "13                                                                                                                                                    NaN   \n",
       "14   and this seems to be the corner stone upon which our American political architects have reared the fabric of our Government. The cement which was...   \n",
       "15                                                                                                                                                    NaN   \n",
       "16                                                                                                                                                    NaN   \n",
       "17                                                                                                                                                    NaN   \n",
       "18                                                                                                                                                    NaN   \n",
       "19                                                                                                                                                    NaN   \n",
       "\n",
       "    ...  \\\n",
       "0   ...   \n",
       "1   ...   \n",
       "2   ...   \n",
       "3   ...   \n",
       "4   ...   \n",
       "5   ...   \n",
       "6   ...   \n",
       "7   ...   \n",
       "8   ...   \n",
       "9   ...   \n",
       "10  ...   \n",
       "11  ...   \n",
       "12  ...   \n",
       "13  ...   \n",
       "14  ...   \n",
       "15  ...   \n",
       "16  ...   \n",
       "17  ...   \n",
       "18  ...   \n",
       "19  ...   \n",
       "\n",
       "                                                                                                                                             Unnamed: 153  \\\n",
       "0                                                                                                                                                     NaN   \n",
       "1                                                                                                                                                     NaN   \n",
       "2                                                                                                                                                     NaN   \n",
       "3                                                                                                                                                     NaN   \n",
       "4                                                                                                                                                     NaN   \n",
       "5                                                                                                                                                     NaN   \n",
       "6                                                                                                                                                     NaN   \n",
       "7                                                                                                                                                     NaN   \n",
       "8                                                                                                                                                     NaN   \n",
       "9                                                                                                                                                     NaN   \n",
       "10                                                                                                                                                    NaN   \n",
       "11                                                                                                                                                    NaN   \n",
       "12                                                                                                                                                    NaN   \n",
       "13                                                                                                                                                    NaN   \n",
       "14   for the defense of those principles for which our ancestors so gloriously contended. As far as it depends upon me it shall be accomplished. All t...   \n",
       "15                                                                                                                                                    NaN   \n",
       "16                                                                                                                                                    NaN   \n",
       "17                                                                                                                                                    NaN   \n",
       "18                                                                                                                                                    NaN   \n",
       "19                                                                                                                                                    NaN   \n",
       "\n",
       "                                                                          Unnamed: 154  \\\n",
       "0                                                                                  NaN   \n",
       "1                                                                                  NaN   \n",
       "2                                                                                  NaN   \n",
       "3                                                                                  NaN   \n",
       "4                                                                                  NaN   \n",
       "5                                                                                  NaN   \n",
       "6                                                                                  NaN   \n",
       "7                                                                                  NaN   \n",
       "8                                                                                  NaN   \n",
       "9                                                                                  NaN   \n",
       "10                                                                                 NaN   \n",
       "11                                                                                 NaN   \n",
       "12                                                                                 NaN   \n",
       "13                                                                                 NaN   \n",
       "14   nor any confidence in advance from the people but that asked for by Mr. Jefferson   \n",
       "15                                                                                 NaN   \n",
       "16                                                                                 NaN   \n",
       "17                                                                                 NaN   \n",
       "18                                                                                 NaN   \n",
       "19                                                                                 NaN   \n",
       "\n",
       "                                                                                                                                             Unnamed: 155  \\\n",
       "0                                                                                                                                                     NaN   \n",
       "1                                                                                                                                                     NaN   \n",
       "2                                                                                                                                                     NaN   \n",
       "3                                                                                                                                                     NaN   \n",
       "4                                                                                                                                                     NaN   \n",
       "5                                                                                                                                                     NaN   \n",
       "6                                                                                                                                                     NaN   \n",
       "7                                                                                                                                                     NaN   \n",
       "8                                                                                                                                                     NaN   \n",
       "9                                                                                                                                                     NaN   \n",
       "10                                                                                                                                                    NaN   \n",
       "11                                                                                                                                                    NaN   \n",
       "12                                                                                                                                                    NaN   \n",
       "13                                                                                                                                                    NaN   \n",
       "14   \"\"to give firmness and effect to the legal administration of their affairs.\"\"    I deem the present occasion sufficiently important and solemn to...   \n",
       "15                                                                                                                                                    NaN   \n",
       "16                                                                                                                                                    NaN   \n",
       "17                                                                                                                                                    NaN   \n",
       "18                                                                                                                                                    NaN   \n",
       "19                                                                                                                                                    NaN   \n",
       "\n",
       "          Unnamed: 156  \\\n",
       "0                  NaN   \n",
       "1                  NaN   \n",
       "2                  NaN   \n",
       "3                  NaN   \n",
       "4                  NaN   \n",
       "5                  NaN   \n",
       "6                  NaN   \n",
       "7                  NaN   \n",
       "8                  NaN   \n",
       "9                  NaN   \n",
       "10                 NaN   \n",
       "11                 NaN   \n",
       "12                 NaN   \n",
       "13                 NaN   \n",
       "14   religious liberty   \n",
       "15                 NaN   \n",
       "16                 NaN   \n",
       "17                 NaN   \n",
       "18                 NaN   \n",
       "19                 NaN   \n",
       "\n",
       "                                                                                                                                             Unnamed: 157  \\\n",
       "0                                                                                                                                                     NaN   \n",
       "1                                                                                                                                                     NaN   \n",
       "2                                                                                                                                                     NaN   \n",
       "3                                                                                                                                                     NaN   \n",
       "4                                                                                                                                                     NaN   \n",
       "5                                                                                                                                                     NaN   \n",
       "6                                                                                                                                                     NaN   \n",
       "7                                                                                                                                                     NaN   \n",
       "8                                                                                                                                                     NaN   \n",
       "9                                                                                                                                                     NaN   \n",
       "10                                                                                                                                                    NaN   \n",
       "11                                                                                                                                                    NaN   \n",
       "12                                                                                                                                                    NaN   \n",
       "13                                                                                                                                                    NaN   \n",
       "14   and a just sense of religious responsibility are essentially connected with all true and lasting happiness; and to that good Being who has blesse...   \n",
       "15                                                                                                                                                    NaN   \n",
       "16                                                                                                                                                    NaN   \n",
       "17                                                                                                                                                    NaN   \n",
       "18                                                                                                                                                    NaN   \n",
       "19                                                                                                                                                    NaN   \n",
       "\n",
       "                                                                                                                                             Unnamed: 158  \\\n",
       "0                                                                                                                                                     NaN   \n",
       "1                                                                                                                                                     NaN   \n",
       "2                                                                                                                                                     NaN   \n",
       "3                                                                                                                                                     NaN   \n",
       "4                                                                                                                                                     NaN   \n",
       "5                                                                                                                                                     NaN   \n",
       "6                                                                                                                                                     NaN   \n",
       "7                                                                                                                                                     NaN   \n",
       "8                                                                                                                                                     NaN   \n",
       "9                                                                                                                                                     NaN   \n",
       "10                                                                                                                                                    NaN   \n",
       "11                                                                                                                                                    NaN   \n",
       "12                                                                                                                                                    NaN   \n",
       "13                                                                                                                                                    NaN   \n",
       "14   who watched over and prospered the labors of our fathers and has hitherto preserved to us institutions far exceeding in excellence those of any o...   \n",
       "15                                                                                                                                                    NaN   \n",
       "16                                                                                                                                                    NaN   \n",
       "17                                                                                                                                                    NaN   \n",
       "18                                                                                                                                                    NaN   \n",
       "19                                                                                                                                                    NaN   \n",
       "\n",
       "                                                                                                          Unnamed: 159  \\\n",
       "0                                                                                                                  NaN   \n",
       "1                                                                                                                  NaN   \n",
       "2                                                                                                                  NaN   \n",
       "3                                                                                                                  NaN   \n",
       "4                                                                                                                  NaN   \n",
       "5                                                                                                                  NaN   \n",
       "6                                                                                                                  NaN   \n",
       "7                                                                                                                  NaN   \n",
       "8                                                                                                                  NaN   \n",
       "9                                                                                                                  NaN   \n",
       "10                                                                                                                 NaN   \n",
       "11                                                                                                                 NaN   \n",
       "12                                                                                                                 NaN   \n",
       "13                                                                                                                 NaN   \n",
       "14   let us unite in fervently commending every interest of our beloved country in all future time.    Fellow-citizens   \n",
       "15                                                                                                                 NaN   \n",
       "16                                                                                                                 NaN   \n",
       "17                                                                                                                 NaN   \n",
       "18                                                                                                                 NaN   \n",
       "19                                                                                                                 NaN   \n",
       "\n",
       "                                                                                          Unnamed: 160  \\\n",
       "0                                                                                                  NaN   \n",
       "1                                                                                                  NaN   \n",
       "2                                                                                                  NaN   \n",
       "3                                                                                                  NaN   \n",
       "4                                                                                                  NaN   \n",
       "5                                                                                                  NaN   \n",
       "6                                                                                                  NaN   \n",
       "7                                                                                                  NaN   \n",
       "8                                                                                                  NaN   \n",
       "9                                                                                                  NaN   \n",
       "10                                                                                                 NaN   \n",
       "11                                                                                                 NaN   \n",
       "12                                                                                                 NaN   \n",
       "13                                                                                                 NaN   \n",
       "14   being fully invested with that high office to which the partiality of my countrymen has called me   \n",
       "15                                                                                                 NaN   \n",
       "16                                                                                                 NaN   \n",
       "17                                                                                                 NaN   \n",
       "18                                                                                                 NaN   \n",
       "19                                                                                                 NaN   \n",
       "\n",
       "                                                                                                                                             Unnamed: 161  \\\n",
       "0                                                                                                                                                     NaN   \n",
       "1                                                                                                                                                     NaN   \n",
       "2                                                                                                                                                     NaN   \n",
       "3                                                                                                                                                     NaN   \n",
       "4                                                                                                                                                     NaN   \n",
       "5                                                                                                                                                     NaN   \n",
       "6                                                                                                                                                     NaN   \n",
       "7                                                                                                                                                     NaN   \n",
       "8                                                                                                                                                     NaN   \n",
       "9                                                                                                                                                     NaN   \n",
       "10                                                                                                                                                    NaN   \n",
       "11                                                                                                                                                    NaN   \n",
       "12                                                                                                                                                    NaN   \n",
       "13                                                                                                                                                    NaN   \n",
       "14   I now take an affectionate leave of you. You will bear with you to your homes the remembrance of the pledge I have this day given to discharge al...   \n",
       "15                                                                                                                                                    NaN   \n",
       "16                                                                                                                                                    NaN   \n",
       "17                                                                                                                                                    NaN   \n",
       "18                                                                                                                                                    NaN   \n",
       "19                                                                                                                                                    NaN   \n",
       "\n",
       "                                                                                                                     Unnamed: 162  \n",
       "0                                                                                                                             NaN  \n",
       "1                                                                                                                             NaN  \n",
       "2                                                                                                                             NaN  \n",
       "3                                                                                                                             NaN  \n",
       "4                                                                                                                             NaN  \n",
       "5                                                                                                                             NaN  \n",
       "6                                                                                                                             NaN  \n",
       "7                                                                                                                             NaN  \n",
       "8                                                                                                                             NaN  \n",
       "9                                                                                                                             NaN  \n",
       "10                                                                                                                            NaN  \n",
       "11                                                                                                                            NaN  \n",
       "12                                                                                                                            NaN  \n",
       "13                                                                                                                            NaN  \n",
       "14   and I shall enter upon their performance with entire confidence in the support of a just and generous people.              \"  \n",
       "15                                                                                                                            NaN  \n",
       "16                                                                                                                            NaN  \n",
       "17                                                                                                                            NaN  \n",
       "18                                                                                                                            NaN  \n",
       "19                                                                                                                            NaN  \n",
       "\n",
       "[20 rows x 163 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>George Washington</th>\n",
       "      <td>Fellow-Citizens of the Senate and of the House of Representatives:    AMONG the vicissitudes incident to life no event could have fille...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Adams</th>\n",
       "      <td>WHEN it was first perceived, in early times, that no middle course for America remained between unlimited submission to a foreign le...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas Jefferson</th>\n",
       "      <td>Friends and Fellow-Citizens:    CALLED upon to undertake the duties of the first executive office of our country, I avail myself of th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Madison</th>\n",
       "      <td>UNWILLING to depart from examples of the most revered authority, I avail myself of the occasion now presented to express the profoun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Monroe</th>\n",
       "      <td>I SHOULD be destitute of feeling if I was not deeply affected by the strong proof which my fellow-citizens have given me of their co...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                    text\n",
       "Name                                                                                                                                                                    \n",
       "George Washington             Fellow-Citizens of the Senate and of the House of Representatives:    AMONG the vicissitudes incident to life no event could have fille...\n",
       "John Adams                       WHEN it was first perceived, in early times, that no middle course for America remained between unlimited submission to a foreign le...\n",
       "Thomas Jefferson               Friends and Fellow-Citizens:    CALLED upon to undertake the duties of the first executive office of our country, I avail myself of th...\n",
       "James Madison                    UNWILLING to depart from examples of the most revered authority, I avail myself of the occasion now presented to express the profoun...\n",
       "James Monroe                     I SHOULD be destitute of feeling if I was not deeply affected by the strong proof which my fellow-citizens have given me of their co..."
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select Rows that are first term inaugural addresses\n",
    "df = df.drop_duplicates(subset=['Name'], keep='first')# Clean Up Index\n",
    "df = df.reset_index()# Select only President's Names and their Speeches\n",
    "df = df[['Name', 'text']]# Set Index to President's Names\n",
    "df = df.set_index('Name')# Visually Inspect\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text_round1(text):\n",
    "    '''Make text lowercase, remove text in square brackets, \n",
    "    remove punctuation, remove read errors,\n",
    "    and remove words containing numbers.'''    \n",
    "    text = text.lower()\n",
    "    text = re.sub('\\[.*?\\]', ' ', text)\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), ' ', text)\n",
    "    text = re.sub('\\w*\\d\\w*', ' ', text)\n",
    "    text = re.sub('�', ' ', text)    \n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>George Washington</th>\n",
       "      <td>fellow citizens of the senate and of the house of representatives     among the vicissitudes incident to life no event could have fille...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Adams</th>\n",
       "      <td>when it was first perceived  in early times  that no middle course for america remained between unlimited submission to a foreign le...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas Jefferson</th>\n",
       "      <td>friends and fellow citizens     called upon to undertake the duties of the first executive office of our country  i avail myself of th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Madison</th>\n",
       "      <td>unwilling to depart from examples of the most revered authority  i avail myself of the occasion now presented to express the profoun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Monroe</th>\n",
       "      <td>i should be destitute of feeling if i was not deeply affected by the strong proof which my fellow citizens have given me of their co...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                    text\n",
       "Name                                                                                                                                                                    \n",
       "George Washington             fellow citizens of the senate and of the house of representatives     among the vicissitudes incident to life no event could have fille...\n",
       "John Adams                       when it was first perceived  in early times  that no middle course for america remained between unlimited submission to a foreign le...\n",
       "Thomas Jefferson               friends and fellow citizens     called upon to undertake the duties of the first executive office of our country  i avail myself of th...\n",
       "James Madison                    unwilling to depart from examples of the most revered authority  i avail myself of the occasion now presented to express the profoun...\n",
       "James Monroe                     i should be destitute of feeling if i was not deeply affected by the strong proof which my fellow citizens have given me of their co..."
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round1 = lambda x: clean_text_round1(x)\n",
    "\n",
    "# Clean Speech Text\n",
    "df[\"text\"] = df[\"text\"].apply(round1)\n",
    "\n",
    "# Visually Inspect\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>George Washington</th>\n",
       "      <td>citizen senate house representative vicissitude incident life event anxiety notification order day month hand i country voice i veneration love re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Adams</th>\n",
       "      <td>time course america submission legislature independence claim men reflection danger power fleet army contest dissension form government part count...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas Jefferson</th>\n",
       "      <td>friend citizen duty executive office country i myself presence portion fellow citizen thanks favor consciousness task talent presentiment greatnes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Madison</th>\n",
       "      <td>example authority i myself occasion profound impression call country station duty i sanction mark confidence proceeding deliberate suffrage nation...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Monroe</th>\n",
       "      <td>i destitute i proof fellow citizen confidence office function expression opinion conduct service gratification sensibility estimate importance tru...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                    text\n",
       "Name                                                                                                                                                                    \n",
       "George Washington  citizen senate house representative vicissitude incident life event anxiety notification order day month hand i country voice i veneration love re...\n",
       "John Adams         time course america submission legislature independence claim men reflection danger power fleet army contest dissension form government part count...\n",
       "Thomas Jefferson   friend citizen duty executive office country i myself presence portion fellow citizen thanks favor consciousness task talent presentiment greatnes...\n",
       "James Madison      example authority i myself occasion profound impression call country station duty i sanction mark confidence proceeding deliberate suffrage nation...\n",
       "James Monroe       i destitute i proof fellow citizen confidence office function expression opinion conduct service gratification sensibility estimate importance tru..."
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Noun extract and lemmatize function\n",
    "\n",
    "def nouns(text):\n",
    "    #Given a string of text, tokenize the text \n",
    "    #and pull out only the nouns.   \n",
    "    \n",
    "    # create mask to isolate words that are nouns\n",
    "    is_noun = lambda pos: pos[:2] == 'NN'    \n",
    "    \n",
    "    # store function to split string of words \n",
    "    # into a list of words (tokens)\n",
    "    tokenized = word_tokenize(text)    \n",
    "    \n",
    "    # store function to lemmatize each word\n",
    "    wordnet_lemmatizer = WordNetLemmatizer()    \n",
    "    \n",
    "    # use list comprehension to lemmatize all words \n",
    "    # and create a list of all nouns\n",
    "    all_nouns = [wordnet_lemmatizer.lemmatize(word) \\\n",
    "    for (word, pos) in pos_tag(tokenized) if is_noun(pos)] \n",
    "    \n",
    "    #return string of joined list of nouns\n",
    "    return ' '.join(all_nouns)\n",
    "\n",
    "# Create dataframe of only nouns from speeches\n",
    "data_nouns = pd.DataFrame(df.text.apply(nouns))\n",
    "\n",
    "# Visually Inspect\n",
    "data_nouns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>abandonment</th>\n",
       "      <th>abeyance</th>\n",
       "      <th>ability</th>\n",
       "      <th>abode</th>\n",
       "      <th>abraham</th>\n",
       "      <th>absence</th>\n",
       "      <th>absent</th>\n",
       "      <th>absolute</th>\n",
       "      <th>abstraction</th>\n",
       "      <th>abundance</th>\n",
       "      <th>...</th>\n",
       "      <th>wrongdoing</th>\n",
       "      <th>wrought</th>\n",
       "      <th>year</th>\n",
       "      <th>yes</th>\n",
       "      <th>yesterday</th>\n",
       "      <th>york</th>\n",
       "      <th>yorktown</th>\n",
       "      <th>youth</th>\n",
       "      <th>zeal</th>\n",
       "      <th>zone</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>George Washington</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.027214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Adams</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089376</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037734</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thomas Jefferson</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049273</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Madison</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James Monroe</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030786</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032494</td>\n",
       "      <td>0.049596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3153 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   abandonment  abeyance  ability  abode  abraham  absence  \\\n",
       "Name                                                                         \n",
       "George Washington          0.0       0.0      0.0    0.0      0.0      0.0   \n",
       "John Adams                 0.0       0.0      0.0    0.0      0.0      0.0   \n",
       "Thomas Jefferson           0.0       0.0      0.0    0.0      0.0      0.0   \n",
       "James Madison              0.0       0.0      0.0    0.0      0.0      0.0   \n",
       "James Monroe               0.0       0.0      0.0    0.0      0.0      0.0   \n",
       "\n",
       "                   absent  absolute  abstraction  abundance  ...  wrongdoing  \\\n",
       "Name                                                         ...               \n",
       "George Washington     0.0       0.0          0.0        0.0  ...         0.0   \n",
       "John Adams            0.0       0.0          0.0        0.0  ...         0.0   \n",
       "Thomas Jefferson      0.0       0.0          0.0        0.0  ...         0.0   \n",
       "James Madison         0.0       0.0          0.0        0.0  ...         0.0   \n",
       "James Monroe          0.0       0.0          0.0        0.0  ...         0.0   \n",
       "\n",
       "                   wrought      year  yes  yesterday  york  yorktown  youth  \\\n",
       "Name                                                                          \n",
       "George Washington      0.0  0.027214  0.0        0.0   0.0       0.0    0.0   \n",
       "John Adams             0.0  0.089376  0.0        0.0   0.0       0.0    0.0   \n",
       "Thomas Jefferson       0.0  0.000000  0.0        0.0   0.0       0.0    0.0   \n",
       "James Madison          0.0  0.000000  0.0        0.0   0.0       0.0    0.0   \n",
       "James Monroe           0.0  0.030786  0.0        0.0   0.0       0.0    0.0   \n",
       "\n",
       "                       zeal      zone  \n",
       "Name                                   \n",
       "George Washington  0.000000  0.000000  \n",
       "John Adams         0.037734  0.000000  \n",
       "Thomas Jefferson   0.049273  0.000000  \n",
       "James Madison      0.000000  0.000000  \n",
       "James Monroe       0.032494  0.049596  \n",
       "\n",
       "[5 rows x 3153 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add additional stop words since we are recreating the document-term matrix\n",
    "stop_noun = [\"america\", 'today', 'thing']\n",
    "stop_words_noun_agg = text.ENGLISH_STOP_WORDS.union(stop_noun)\n",
    "\n",
    "# Create a document-term matrix with only nouns\n",
    "\n",
    "# Store TF-IDF Vectorizer\n",
    "tv_noun = TfidfVectorizer(stop_words=stop_words_noun_agg, ngram_range = (1,1), max_df = .8, min_df = .01)\n",
    "\n",
    "\n",
    "# Fit and Transform speech noun text to a TF-IDF Doc-Term Matrix\n",
    "data_tv_noun = tv_noun.fit_transform(data_nouns.text)\n",
    "\n",
    "# Create data-frame of Doc-Term Matrix with nouns as column names\n",
    "data_dtm_noun = pd.DataFrame(data_tv_noun.toarray(), columns=tv_noun.get_feature_names())\n",
    "\n",
    "# Set President's Names as Index\n",
    "data_dtm_noun.index = df.index\n",
    "\n",
    "# Visually inspect Document Term Matrix\n",
    "data_dtm_noun.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_topics(model, feature_names, num_top_words,topic_names=None):\n",
    "#'''Given an NMF model, feature_names, and number of top words, print topic number \n",
    "#and its top feature names, up to specified number of top words.'''    \n",
    "    \n",
    "    # iterate through topics in topic-term matrix, 'H' aka\n",
    "    # model.components_\n",
    "    for ix, topic in enumerate(model.components_):        \n",
    "        \n",
    "        #print topic, topic number, and top words\n",
    "        if not topic_names or not topic_names[ix]:\n",
    "            print(\"\\nTopic \", ix)\n",
    "        else:\n",
    "            print(\"\\nTopic: '\",topic_names[ix],\"'\")\n",
    "        print(\", \".join([feature_names[i] \\\n",
    "             for i in topic.argsort()[:-num_top_words - 1:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "constitution, union, principle, congress, peace\n",
      "\n",
      "Topic  1\n",
      "american, freedom, man, peace, child\n"
     ]
    }
   ],
   "source": [
    "nmf_model = NMF(2)\n",
    "\n",
    "# Learn an NMF model for given Document Term Matrix 'V' \n",
    "# Extract the document-topic matrix 'W'\n",
    "doc_topic = nmf_model.fit_transform(data_dtm_noun)\n",
    "\n",
    "# Extract top words from the topic-term matrix 'H' \n",
    "display_topics(nmf_model, tv_noun.get_feature_names(), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "union, constitution, opinion, territory, principle\n",
      "\n",
      "Topic  1\n",
      "american, child, man, generation, job\n",
      "\n",
      "Topic  2\n",
      "peace, freedom, civilization, problem, responsibility\n",
      "\n",
      "Topic  3\n",
      "congress, business, revenue, policy, party\n",
      "\n",
      "Topic  4\n",
      "community, purpose, story, ideal, civility\n",
      "\n",
      "Topic  5\n",
      "principle, happiness, confidence, improvement, gratitude\n",
      "\n",
      "Topic  6\n",
      "dream, strength, micah, spirit, freedom\n",
      "\n",
      "Topic  7\n",
      "process, justice, woman, aspect, party\n"
     ]
    }
   ],
   "source": [
    "nmf_model = NMF(8)\n",
    "doc_topic = nmf_model.fit_transform(data_dtm_noun)\n",
    "display_topics(nmf_model, tv_noun.get_feature_names(), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BTM Example\n",
    "import numpy as np\n",
    "import bitermplus as btm\n",
    "import tmplot as tmp\n",
    "import pickle as pkl\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/shionguha/Documents/GitHub/inf2209-humancentredtopicmodels-fa21/data/SearchSnippets.txt.gz', header=None, names=['texts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "texts = df['texts'].str.strip().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PREPROCESSING\n",
    "# Obtaining terms frequency in a sparse matrix and corpus vocabulary\n",
    "X, vocabulary, vocab_dict = btm.get_words_freqs(texts)\n",
    "tf = np.array(X.sum(axis=0)).ravel()\n",
    "# Vectorizing documents\n",
    "docs_vec = btm.get_vectorized_docs(texts, vocabulary)\n",
    "docs_lens = list(map(len, docs_vec))\n",
    "# Generating biterms\n",
    "biterms = btm.get_biterms(docs_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 11.85it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████████| 12295/12295 [00:00<00:00, 111385.83it/s]\n"
     ]
    }
   ],
   "source": [
    "# INITIALIZING AND RUNNING MODEL\n",
    "model = btm.BTM(\n",
    "    X, vocabulary, seed=12321, T=8, M=20, alpha=50/8, beta=0.01)\n",
    "model.fit(biterms, iterations=20)\n",
    "p_zd = model.transform(docs_vec)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# METRICS\n",
    "perplexity = btm.perplexity(model.matrix_topics_words_, p_zd, X, 8)\n",
    "coherence = btm.coherence(model.matrix_topics_words_, X, M=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eef9b6e08f12444dadb5614242b02164",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(VBox(children=(HBox(children=(HTML(value='<b>Select a topic</b>:'), Dropdown(options=((0, 0), (…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp.report(model=model, docs=texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>topics</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>words</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aaa</th>\n",
       "      <td>3.195102e-08</td>\n",
       "      <td>3.012856e-08</td>\n",
       "      <td>3.047842e-08</td>\n",
       "      <td>3.542745e-08</td>\n",
       "      <td>3.836165e-08</td>\n",
       "      <td>2.961217e-08</td>\n",
       "      <td>2.362519e-08</td>\n",
       "      <td>4.831267e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaas</th>\n",
       "      <td>3.837318e-05</td>\n",
       "      <td>3.012856e-08</td>\n",
       "      <td>3.047842e-08</td>\n",
       "      <td>3.542745e-08</td>\n",
       "      <td>3.836165e-08</td>\n",
       "      <td>5.922729e-04</td>\n",
       "      <td>6.144912e-05</td>\n",
       "      <td>2.903592e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aaron</th>\n",
       "      <td>3.195102e-08</td>\n",
       "      <td>3.012856e-08</td>\n",
       "      <td>3.047842e-08</td>\n",
       "      <td>3.542745e-08</td>\n",
       "      <td>4.296888e-04</td>\n",
       "      <td>2.961217e-08</td>\n",
       "      <td>2.362519e-08</td>\n",
       "      <td>4.831267e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aau</th>\n",
       "      <td>3.195102e-08</td>\n",
       "      <td>3.012856e-08</td>\n",
       "      <td>3.047842e-08</td>\n",
       "      <td>3.542745e-08</td>\n",
       "      <td>3.836165e-08</td>\n",
       "      <td>2.961217e-08</td>\n",
       "      <td>2.362519e-08</td>\n",
       "      <td>4.203686e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abbreviations</th>\n",
       "      <td>7.990951e-05</td>\n",
       "      <td>3.163800e-04</td>\n",
       "      <td>3.047842e-08</td>\n",
       "      <td>3.542745e-08</td>\n",
       "      <td>3.836165e-08</td>\n",
       "      <td>2.961217e-08</td>\n",
       "      <td>2.386144e-06</td>\n",
       "      <td>4.831267e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "topics                    0             1             2             3  \\\n",
       "words                                                                   \n",
       "aaa            3.195102e-08  3.012856e-08  3.047842e-08  3.542745e-08   \n",
       "aaas           3.837318e-05  3.012856e-08  3.047842e-08  3.542745e-08   \n",
       "aaron          3.195102e-08  3.012856e-08  3.047842e-08  3.542745e-08   \n",
       "aau            3.195102e-08  3.012856e-08  3.047842e-08  3.542745e-08   \n",
       "abbreviations  7.990951e-05  3.163800e-04  3.047842e-08  3.542745e-08   \n",
       "\n",
       "topics                    4             5             6             7  \n",
       "words                                                                  \n",
       "aaa            3.836165e-08  2.961217e-08  2.362519e-08  4.831267e-08  \n",
       "aaas           3.836165e-08  5.922729e-04  6.144912e-05  2.903592e-05  \n",
       "aaron          4.296888e-04  2.961217e-08  2.362519e-08  4.831267e-08  \n",
       "aau            3.836165e-08  2.961217e-08  2.362519e-08  4.203686e-04  \n",
       "abbreviations  3.836165e-08  2.961217e-08  2.386144e-06  4.831267e-08  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs = pd.read_csv('/Users/shionguha/Documents/GitHub/inf2209-humancentredtopicmodels-fa21/data/SearchSnippets.txt.gz', header=None).values.ravel()\n",
    "phi = tmp.get_phi(model)\n",
    "phi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>docs</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>12285</th>\n",
       "      <th>12286</th>\n",
       "      <th>12287</th>\n",
       "      <th>12288</th>\n",
       "      <th>12289</th>\n",
       "      <th>12290</th>\n",
       "      <th>12291</th>\n",
       "      <th>12292</th>\n",
       "      <th>12293</th>\n",
       "      <th>12294</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topics</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.354702</td>\n",
       "      <td>0.294777</td>\n",
       "      <td>0.178074</td>\n",
       "      <td>0.332888</td>\n",
       "      <td>0.596412</td>\n",
       "      <td>0.726975</td>\n",
       "      <td>0.099094</td>\n",
       "      <td>0.257602</td>\n",
       "      <td>0.532725</td>\n",
       "      <td>0.471059</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026639</td>\n",
       "      <td>0.042030</td>\n",
       "      <td>0.057234</td>\n",
       "      <td>0.006313</td>\n",
       "      <td>0.006232</td>\n",
       "      <td>0.009572</td>\n",
       "      <td>0.001054</td>\n",
       "      <td>0.017922</td>\n",
       "      <td>0.015376</td>\n",
       "      <td>0.031247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.007173</td>\n",
       "      <td>0.021324</td>\n",
       "      <td>0.019411</td>\n",
       "      <td>0.029472</td>\n",
       "      <td>0.008740</td>\n",
       "      <td>0.011804</td>\n",
       "      <td>0.036323</td>\n",
       "      <td>0.011349</td>\n",
       "      <td>0.003909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032604</td>\n",
       "      <td>0.112638</td>\n",
       "      <td>0.032564</td>\n",
       "      <td>0.082661</td>\n",
       "      <td>0.019711</td>\n",
       "      <td>0.127421</td>\n",
       "      <td>0.109506</td>\n",
       "      <td>0.030517</td>\n",
       "      <td>0.025531</td>\n",
       "      <td>0.049617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003073</td>\n",
       "      <td>0.057144</td>\n",
       "      <td>0.013837</td>\n",
       "      <td>0.014514</td>\n",
       "      <td>0.011813</td>\n",
       "      <td>0.002588</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>0.027391</td>\n",
       "      <td>0.002325</td>\n",
       "      <td>0.005435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018470</td>\n",
       "      <td>0.140967</td>\n",
       "      <td>0.008236</td>\n",
       "      <td>0.002069</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.011241</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>0.020147</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.071262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003678</td>\n",
       "      <td>0.029281</td>\n",
       "      <td>0.010010</td>\n",
       "      <td>0.001287</td>\n",
       "      <td>0.027349</td>\n",
       "      <td>0.004351</td>\n",
       "      <td>0.018189</td>\n",
       "      <td>0.085879</td>\n",
       "      <td>0.011453</td>\n",
       "      <td>0.002965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900939</td>\n",
       "      <td>0.600111</td>\n",
       "      <td>0.677321</td>\n",
       "      <td>0.350898</td>\n",
       "      <td>0.875420</td>\n",
       "      <td>0.426372</td>\n",
       "      <td>0.672399</td>\n",
       "      <td>0.805926</td>\n",
       "      <td>0.931418</td>\n",
       "      <td>0.535746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000927</td>\n",
       "      <td>0.035162</td>\n",
       "      <td>0.001736</td>\n",
       "      <td>0.319421</td>\n",
       "      <td>0.024606</td>\n",
       "      <td>0.042996</td>\n",
       "      <td>0.019524</td>\n",
       "      <td>0.036119</td>\n",
       "      <td>0.001910</td>\n",
       "      <td>0.039332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011359</td>\n",
       "      <td>0.091567</td>\n",
       "      <td>0.037989</td>\n",
       "      <td>0.513782</td>\n",
       "      <td>0.009559</td>\n",
       "      <td>0.094109</td>\n",
       "      <td>0.084863</td>\n",
       "      <td>0.077151</td>\n",
       "      <td>0.012476</td>\n",
       "      <td>0.211272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 12295 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "docs       0         1         2         3         4         5         6      \\\n",
       "topics                                                                         \n",
       "0       0.354702  0.294777  0.178074  0.332888  0.596412  0.726975  0.099094   \n",
       "1       0.000245  0.007173  0.021324  0.019411  0.029472  0.008740  0.011804   \n",
       "2       0.003073  0.057144  0.013837  0.014514  0.011813  0.002588  0.000247   \n",
       "3       0.003678  0.029281  0.010010  0.001287  0.027349  0.004351  0.018189   \n",
       "4       0.000927  0.035162  0.001736  0.319421  0.024606  0.042996  0.019524   \n",
       "\n",
       "docs       7         8         9      ...     12285     12286     12287  \\\n",
       "topics                                ...                                 \n",
       "0       0.257602  0.532725  0.471059  ...  0.026639  0.042030  0.057234   \n",
       "1       0.036323  0.011349  0.003909  ...  0.032604  0.112638  0.032564   \n",
       "2       0.027391  0.002325  0.005435  ...  0.018470  0.140967  0.008236   \n",
       "3       0.085879  0.011453  0.002965  ...  0.900939  0.600111  0.677321   \n",
       "4       0.036119  0.001910  0.039332  ...  0.011359  0.091567  0.037989   \n",
       "\n",
       "docs       12288     12289     12290     12291     12292     12293     12294  \n",
       "topics                                                                        \n",
       "0       0.006313  0.006232  0.009572  0.001054  0.017922  0.015376  0.031247  \n",
       "1       0.082661  0.019711  0.127421  0.109506  0.030517  0.025531  0.049617  \n",
       "2       0.002069  0.000566  0.011241  0.001084  0.020147  0.003234  0.071262  \n",
       "3       0.350898  0.875420  0.426372  0.672399  0.805926  0.931418  0.535746  \n",
       "4       0.513782  0.009559  0.094109  0.084863  0.077151  0.012476  0.211272  \n",
       "\n",
       "[5 rows x 12295 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.get_theta(model).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic0</th>\n",
       "      <th>topic1</th>\n",
       "      <th>topic2</th>\n",
       "      <th>topic3</th>\n",
       "      <th>topic4</th>\n",
       "      <th>topic5</th>\n",
       "      <th>topic6</th>\n",
       "      <th>topic7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>taxes rates tax rates income tax rates lookup trust income tax rates</td>\n",
       "      <td>cancer tobacco cancers cancer tobacco cigarettes smoking lung cancer tobacco cancer percent lung cancer tobacco cancers cancer</td>\n",
       "      <td>imdb name julia roberts julia roberts imdb movies celebs</td>\n",
       "      <td>espn espn team</td>\n",
       "      <td>goalkeepers goalkeepers goalkeepers</td>\n",
       "      <td>parallel computing italy</td>\n",
       "      <td>mozilla mozilla xml html browser open-source xml html browser implementing level dom</td>\n",
       "      <td>proof fermat theorem proof fermat theorem proof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>currency converter currency exchange currency exchange rates currency converter currency insights influence exchange rates currencies currency con...</td>\n",
       "      <td>nutrition fats fatty diet dietary fats fatty foods count fat healthier eat fatty foods</td>\n",
       "      <td>lyrics lyrics girl band lyrics movie soundtrack lyrics lyrics girl band lyrics</td>\n",
       "      <td>cricket cricket cricket</td>\n",
       "      <td>diegomaradona diego maradona english</td>\n",
       "      <td>intel dual core intel core duo intel core duo extreme intel dual core dual core intel core duo intel core extreme intel core duo chips</td>\n",
       "      <td>master degree programs masters degree phd degree programs masters degree program master degree phd programs master degree</td>\n",
       "      <td>mathworld wolfram pythagorean theorem wolfram mathworld proofs fundamental theorem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>currency exchange rate conversion calculator convert money currencies exchange rates</td>\n",
       "      <td>vitamins supplements vitamins health supplements herbal supplements vitamin nutritional information vitamin supplements herbal supplements acids v...</td>\n",
       "      <td>movie character</td>\n",
       "      <td>nba basketball tickets chicago bulls tickets chicago bulls tickets chicago bulls basketball tickets chicago bulls tickets buy chicago bulls basket...</td>\n",
       "      <td>globalsecurity military world iraq navy iraqi navy iraqi naval force founded war iraqi navy coastal defense force</td>\n",
       "      <td>biologists journal cell science</td>\n",
       "      <td>finaid scholarships finaid scholarships finaid guide scholarships information scholarship searches scholarships scholarships scholarships</td>\n",
       "      <td>poetry persian poetry poetry iranian poets persian poetry poetry iranian poets persian poets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>finance income tax salary income tax indian salary income tax salary income tax india income tax indian salary income tax salary income tax india ...</td>\n",
       "      <td>breast cancer symptoms diagnosis symptoms diagnosis breast cancer symptoms skin breast cancers symptoms</td>\n",
       "      <td>movie movie</td>\n",
       "      <td>miniclip games boom boom volleyball boom boom volleyball miniclip games play games games miniclip com boom boom volleyball boom boom volleyball be...</td>\n",
       "      <td>britannica article diego armando maradona diego armando maradona encyclopaedia britannica diego armando maradona argentine football soccer player ...</td>\n",
       "      <td>anandtech anandtech intel dual core performance preview core cache chips core pentium</td>\n",
       "      <td>apache api dom document document xml standard api document interface html xml document root document tree access</td>\n",
       "      <td>andrew century poem poems poet poetry poets poet bell poetry poems poem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>income tax tax salary tax india salary salary income calculated section income tax act salary</td>\n",
       "      <td>hepatitis hepatitis hepatitis hepatitis symptoms treatment overview symptoms diagnosis treatment hepatitis hepatitis hepatitis</td>\n",
       "      <td>celebrities cruise celebrity tom cruise tom cruise clarkson tom</td>\n",
       "      <td>nba chicago bulls tickets chicago bulls tickets chicago bulls basketball ticket brokers online chicago bulls ticket broker games chicago bulls nba...</td>\n",
       "      <td>maradona diego armando maradona diego armando maradona born buenos argentina family</td>\n",
       "      <td>springerlink genre journal issn natural hazards</td>\n",
       "      <td>schools dom default xml dom tutorial xml document object model xml dom standard dom xml document node tree</td>\n",
       "      <td>root cern root roottalk roottalk roottalk formula expression formula expression value draw formula expression draw int</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                  topic0  \\\n",
       "0                                                                                  taxes rates tax rates income tax rates lookup trust income tax rates    \n",
       "1  currency converter currency exchange currency exchange rates currency converter currency insights influence exchange rates currencies currency con...   \n",
       "2                                                                  currency exchange rate conversion calculator convert money currencies exchange rates    \n",
       "3  finance income tax salary income tax indian salary income tax salary income tax india income tax indian salary income tax salary income tax india ...   \n",
       "4                                                         income tax tax salary tax india salary salary income calculated section income tax act salary    \n",
       "\n",
       "                                                                                                                                                  topic1  \\\n",
       "0                        cancer tobacco cancers cancer tobacco cigarettes smoking lung cancer tobacco cancer percent lung cancer tobacco cancers cancer    \n",
       "1                                                                nutrition fats fatty diet dietary fats fatty foods count fat healthier eat fatty foods    \n",
       "2  vitamins supplements vitamins health supplements herbal supplements vitamin nutritional information vitamin supplements herbal supplements acids v...   \n",
       "3                                               breast cancer symptoms diagnosis symptoms diagnosis breast cancer symptoms skin breast cancers symptoms    \n",
       "4                        hepatitis hepatitis hepatitis hepatitis symptoms treatment overview symptoms diagnosis treatment hepatitis hepatitis hepatitis    \n",
       "\n",
       "                                                                            topic2  \\\n",
       "0                        imdb name julia roberts julia roberts imdb movies celebs    \n",
       "1  lyrics lyrics girl band lyrics movie soundtrack lyrics lyrics girl band lyrics    \n",
       "2                                                                 movie character    \n",
       "3                                                                     movie movie    \n",
       "4                 celebrities cruise celebrity tom cruise tom cruise clarkson tom    \n",
       "\n",
       "                                                                                                                                                  topic3  \\\n",
       "0                                                                                                                                        espn espn team    \n",
       "1                                                                                                                               cricket cricket cricket    \n",
       "2  nba basketball tickets chicago bulls tickets chicago bulls tickets chicago bulls basketball tickets chicago bulls tickets buy chicago bulls basket...   \n",
       "3  miniclip games boom boom volleyball boom boom volleyball miniclip games play games games miniclip com boom boom volleyball boom boom volleyball be...   \n",
       "4  nba chicago bulls tickets chicago bulls tickets chicago bulls basketball ticket brokers online chicago bulls ticket broker games chicago bulls nba...   \n",
       "\n",
       "                                                                                                                                                  topic4  \\\n",
       "0                                                                                                                   goalkeepers goalkeepers goalkeepers    \n",
       "1                                                                                                                  diegomaradona diego maradona english    \n",
       "2                                     globalsecurity military world iraq navy iraqi navy iraqi naval force founded war iraqi navy coastal defense force    \n",
       "3  britannica article diego armando maradona diego armando maradona encyclopaedia britannica diego armando maradona argentine football soccer player ...   \n",
       "4                                                                   maradona diego armando maradona diego armando maradona born buenos argentina family    \n",
       "\n",
       "                                                                                                                                    topic5  \\\n",
       "0                                                                                                                parallel computing italy    \n",
       "1  intel dual core intel core duo intel core duo extreme intel dual core dual core intel core duo intel core extreme intel core duo chips    \n",
       "2                                                                                                         biologists journal cell science    \n",
       "3                                                   anandtech anandtech intel dual core performance preview core cache chips core pentium    \n",
       "4                                                                                         springerlink genre journal issn natural hazards    \n",
       "\n",
       "                                                                                                                                       topic6  \\\n",
       "0                                                       mozilla mozilla xml html browser open-source xml html browser implementing level dom    \n",
       "1                  master degree programs masters degree phd degree programs masters degree program master degree phd programs master degree    \n",
       "2  finaid scholarships finaid scholarships finaid guide scholarships information scholarship searches scholarships scholarships scholarships    \n",
       "3                           apache api dom document document xml standard api document interface html xml document root document tree access    \n",
       "4                                 schools dom default xml dom tutorial xml document object model xml dom standard dom xml document node tree    \n",
       "\n",
       "                                                                                                                    topic7  \n",
       "0                                                                         proof fermat theorem proof fermat theorem proof   \n",
       "1                                      mathworld wolfram pythagorean theorem wolfram mathworld proofs fundamental theorem   \n",
       "2                            poetry persian poetry poetry iranian poets persian poetry poetry iranian poets persian poets   \n",
       "3                                                 andrew century poem poems poet poetry poets poet bell poetry poems poem   \n",
       "4  root cern root roottalk roottalk roottalk formula expression formula expression value draw formula expression draw int   "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.get_top_docs(docs, model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>topic</th>\n",
       "      <th>size</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.697697</td>\n",
       "      <td>368.436646</td>\n",
       "      <td>0</td>\n",
       "      <td>12.102385</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323.020233</td>\n",
       "      <td>-110.167290</td>\n",
       "      <td>1</td>\n",
       "      <td>13.519410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-7.801935</td>\n",
       "      <td>16.974932</td>\n",
       "      <td>2</td>\n",
       "      <td>13.493259</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99.341240</td>\n",
       "      <td>-321.638245</td>\n",
       "      <td>3</td>\n",
       "      <td>10.749254</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-205.189377</td>\n",
       "      <td>-276.993134</td>\n",
       "      <td>4</td>\n",
       "      <td>10.930099</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            x           y  topic       size  label\n",
       "0   42.697697  368.436646      0  12.102385      0\n",
       "1  323.020233 -110.167290      1  13.519410      1\n",
       "2   -7.801935   16.974932      2  13.493259      2\n",
       "3   99.341240 -321.638245      3  10.749254      3\n",
       "4 -205.189377 -276.993134      4  10.930099      4"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics_coords = tmp.prepare_coords(model)\n",
    "topics_coords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-fc0eafc795904d2db9693a38bec93e7e\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-fc0eafc795904d2db9693a38bec93e7e\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-fc0eafc795904d2db9693a38bec93e7e\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300, \"stroke\": \"transparent\"}, \"axis\": {\"labelFontSize\": 13, \"titleFontSize\": 13}, \"legend\": {\"labelFontSize\": 13, \"orient\": \"bottom\", \"titleFontSize\": 13}}, \"layer\": [{\"mark\": \"rule\", \"encoding\": {\"color\": {\"value\": \"gray\"}, \"size\": {\"value\": 0.2}, \"y\": {\"type\": \"quantitative\", \"aggregate\": \"average\", \"field\": \"y\"}}}, {\"mark\": \"rule\", \"encoding\": {\"color\": {\"value\": \"gray\"}, \"size\": {\"value\": 0.2}, \"x\": {\"type\": \"quantitative\", \"aggregate\": \"average\", \"field\": \"x\"}}}, {\"mark\": {\"type\": \"circle\", \"opacity\": 0.33, \"stroke\": \"black\", \"strokeWidth\": 1}, \"encoding\": {\"size\": {\"type\": \"quantitative\", \"field\": \"size\", \"scale\": {\"range\": [0, 3000]}, \"title\": \"Marginal topic distribution\"}, \"tooltip\": [{\"type\": \"quantitative\", \"field\": \"label\"}, {\"type\": \"quantitative\", \"field\": \"size\"}], \"x\": {\"type\": \"quantitative\", \"axis\": null, \"field\": \"x\"}, \"y\": {\"type\": \"quantitative\", \"axis\": null, \"field\": \"y\"}}}, {\"mark\": {\"type\": \"text\", \"align\": \"center\", \"baseline\": \"middle\"}, \"encoding\": {\"size\": {\"value\": 13}, \"text\": {\"type\": \"quantitative\", \"field\": \"topic\"}, \"tooltip\": [{\"type\": \"quantitative\", \"field\": \"label\"}, {\"type\": \"quantitative\", \"field\": \"size\"}], \"x\": {\"type\": \"quantitative\", \"axis\": null, \"field\": \"x\"}, \"y\": {\"type\": \"quantitative\", \"axis\": null, \"field\": \"y\"}}}], \"data\": {\"name\": \"data-e331d68b9a3209a2dde215281920874d\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-e331d68b9a3209a2dde215281920874d\": [{\"x\": 42.697696685791016, \"y\": 368.4366455078125, \"topic\": 0, \"size\": 12.102384815745493, \"label\": 0}, {\"x\": 323.0202331542969, \"y\": -110.16728973388672, \"topic\": 1, \"size\": 13.519409705383813, \"label\": 1}, {\"x\": -7.801934719085693, \"y\": 16.974931716918945, \"topic\": 2, \"size\": 13.493258817556791, \"label\": 2}, {\"x\": 99.34123992919922, \"y\": -321.63824462890625, \"topic\": 3, \"size\": 10.74925383725372, \"label\": 3}, {\"x\": -205.1893768310547, \"y\": -276.9931335449219, \"topic\": 4, \"size\": 10.930098673304578, \"label\": 4}, {\"x\": 298.05755615234375, \"y\": 196.5567626953125, \"topic\": 5, \"size\": 13.699935522311096, \"label\": 5}, {\"x\": -250.4717559814453, \"y\": 274.83270263671875, \"topic\": 6, \"size\": 16.929046321359266, \"label\": 6}, {\"x\": -361.9642333984375, \"y\": -12.08669662475586, \"topic\": 7, \"size\": 8.576612307085242, \"label\": 7}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.plot_scatter_topics(topics_coords, size_col='size', label_col='label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-e169a9e0629c4faa8800b2bec7cddd4c\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-e169a9e0629c4faa8800b2bec7cddd4c\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-e169a9e0629c4faa8800b2bec7cddd4c\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"labelFontSize\": 13, \"titleFontSize\": 13}, \"legend\": {\"columns\": 1, \"labelFontSize\": 13, \"labelLimit\": 250, \"titleFontSize\": 13}}, \"data\": {\"name\": \"data-5545066d9c6665613cfb4b5f4533fd66\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Type\", \"legend\": {\"orient\": \"bottom\"}, \"scale\": {\"scheme\": \"category20\"}}, \"x\": {\"type\": \"quantitative\", \"field\": \"Probability\", \"stack\": null}, \"y\": {\"type\": \"nominal\", \"field\": \"Terms\", \"sort\": null, \"title\": null}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-5545066d9c6665613cfb4b5f4533fd66\": [{\"Terms\": \"amazon\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.04075886819443259}, {\"Terms\": \"com\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.0968799499499213}, {\"Terms\": \"books\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.02591926372231297}, {\"Terms\": \"edu\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.05149371161063871}, {\"Terms\": \"culture\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.03050983364862613}, {\"Terms\": \"democracy\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.018865582953881875}, {\"Terms\": \"theorem\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.008879818443942792}, {\"Terms\": \"theory\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.02242920021934469}, {\"Terms\": \"writing\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.010241176232962931}, {\"Terms\": \"theoretical\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.01516678441868453}, {\"Terms\": \"book\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.011067192774386062}, {\"Terms\": \"research\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.05770629788667291}, {\"Terms\": \"mathematical\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.009213779828739466}, {\"Terms\": \"wikipedia\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.07462468295813754}, {\"Terms\": \"philosophy\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.0089341401927027}, {\"Terms\": \"physics\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.014843114003217944}, {\"Terms\": \"markov\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.0054366380153165255}, {\"Terms\": \"art\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.016389766391296315}, {\"Terms\": \"online\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.03940372542760388}, {\"Terms\": \"literature\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.0070096224825252525}, {\"Terms\": \"newton\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.005009123528744453}, {\"Terms\": \"encyclopedia\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.04168019184489908}, {\"Terms\": \"index\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.02983203440182163}, {\"Terms\": \"political\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.0294871111511711}, {\"Terms\": \"posters\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.0049716355833422194}, {\"Terms\": \"paper\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.008266056392730745}, {\"Terms\": \"children\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.006101415374009807}, {\"Terms\": \"european\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.007712495851940366}, {\"Terms\": \"guide\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.01829196544285627}, {\"Terms\": \"poetry\", \"Type\": \"Marginal term probability, p(w)\", \"Probability\": 0.0038508688077457985}, {\"Terms\": \"amazon\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.02895866430533742}, {\"Terms\": \"com\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.025910134660463966}, {\"Terms\": \"books\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.018953109797361322}, {\"Terms\": \"edu\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.010928374868463065}, {\"Terms\": \"culture\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.010483898279987063}, {\"Terms\": \"democracy\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.009908977475327886}, {\"Terms\": \"theorem\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.008865423745862489}, {\"Terms\": \"theory\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.00856588517536779}, {\"Terms\": \"writing\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.008256684070341006}, {\"Terms\": \"theoretical\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.007879845223589614}, {\"Terms\": \"book\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.007256611746270001}, {\"Terms\": \"research\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.0070826861246924356}, {\"Terms\": \"mathematical\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.006430465043776562}, {\"Terms\": \"wikipedia\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.00588936310997969}, {\"Terms\": \"philosophy\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.005676787350273776}, {\"Terms\": \"physics\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.005628474677613341}, {\"Terms\": \"markov\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.005430392719705557}, {\"Terms\": \"art\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.005319273572586557}, {\"Terms\": \"online\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.005280623434458209}, {\"Terms\": \"literature\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.0052612983653940355}, {\"Terms\": \"newton\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.004956928527633294}, {\"Terms\": \"encyclopedia\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.004671883758936728}, {\"Terms\": \"index\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.004464139266496857}, {\"Terms\": \"political\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.004430320395634553}, {\"Terms\": \"posters\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.004415826593836422}, {\"Terms\": \"paper\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.003836074521911202}, {\"Terms\": \"children\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.0037780993147186795}, {\"Terms\": \"european\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.003700799038461984}, {\"Terms\": \"guide\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.003657317633067592}, {\"Terms\": \"poetry\", \"Type\": \"Conditional term probability, p(w|t)\", \"Probability\": 0.003628330029471331}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terms_probs = tmp.calc_terms_probs_ratio(phi, topic=7, lambda_=1)\n",
    "tmp.plot_terms(terms_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>taxes rates tax rates income tax rates lookup trust income tax rates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>currency converter currency exchange currency exchange rates currency converter currency insights influence exchange rates currencies currency con...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                  topic0\n",
       "0                                                                                  taxes rates tax rates income tax rates lookup trust income tax rates \n",
       "1  currency converter currency exchange currency exchange rates currency converter currency insights influence exchange rates currencies currency con..."
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_docs_topic0 = tmp.get_top_docs(docs, model=model, docs_num=2, topics=[0])\n",
    "top_docs_topic0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>table td{text-align: left !important}table th{text-align: center !important}</style><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>taxes rates tax rates income tax rates lookup trust income tax rates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>currency converter currency exchange currency exchange rates currency converter currency insights influence exchange rates currencies currency conversion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.plot_docs(top_docs_topic0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8528933d2c34a9faaa7b73f859bd700",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(VBox(children=(HBox(children=(HTML(value='<b>Select a topic</b>:'), Dropdown(options=((0, 0), (…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp.report(model, docs=docs, height=400, width=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}