STAT7240_Assignment_3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-Qn65e6cPUR"
      },
      "source": [
        "In this assignment - we're going to run an NLP pipeline from end to end:\n",
        "\n",
        "* Either use the csv file provided for all wikipedia pages under the category \"natural language processing\" or generate your own here: https://petscan.wmflabs.org/ (non-comprehensive list of settings below)\n",
        "  * Page properties - set re-directs to 'No'\n",
        "  * Output - set to 'csv' \n",
        "* Download all the pages - extract the paragraph text into a pandas dataframe\n",
        "* Run LSA analysis to get main topics\n",
        "* Repeat with LDA\n",
        "* Finally - generate & refine embeddings\n",
        "\n",
        "---\n",
        "\n",
        "*Fill in the missing code from the relevant sections below, missing code is indicated by \\<FILL_CODE>*\n",
        "\n",
        "*IMPORTANT: Make sure you include the outputs or printouts for every cell in the .ipynb file that you upload.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCZy5pP2EsZy"
      },
      "source": [
        "## Load / Import data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HravXmh9cljn"
      },
      "source": [
        "### 1. Get the text data using either the csv file provided or the one you generated (2 points)\n",
        "\n",
        "For the rest of this assignment - continue to use the dataset you selected here"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "f65gVuW-cKOm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   number                  title  pageid  namespace  length         touched\n",
            "0       1                  ELIZA   10235        NaN   29186  20220910110802\n",
            "1       2   Finite-state_machine   10931        NaN   41876  20220918155749\n",
            "2       3               HAL_9000   14384        NaN   35794  20220727001747\n",
            "3       4  Information_retrieval   15271        NaN   27625  20220904163734\n",
            "4       5            Kleene_star   16750        NaN    7210  20220913212929\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "wiki_page_corpus = pd.read_csv('./wikipedia-nlp-pages.csv')\n",
        "print(wiki_page_corpus[:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "F-dTSXzuMGDN"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(866, 6)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "wiki_page_corpus.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qlmZkCoDBlW"
      },
      "source": [
        "### 2. Preprocess text into tokens (1 point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "feBKpRKBQX3X"
      },
      "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>number</th>\n",
              "      <th>title</th>\n",
              "      <th>pageid</th>\n",
              "      <th>namespace</th>\n",
              "      <th>length</th>\n",
              "      <th>touched</th>\n",
              "      <th>paragraph_tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>ELIZA</td>\n",
              "      <td>10235</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29186</td>\n",
              "      <td>20220910110802</td>\n",
              "      <td>[eliza]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Finite-state_machine</td>\n",
              "      <td>10931</td>\n",
              "      <td>NaN</td>\n",
              "      <td>41876</td>\n",
              "      <td>20220918155749</td>\n",
              "      <td>[finite, state, machine]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>HAL_9000</td>\n",
              "      <td>14384</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35794</td>\n",
              "      <td>20220727001747</td>\n",
              "      <td>[hal]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Information_retrieval</td>\n",
              "      <td>15271</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27625</td>\n",
              "      <td>20220904163734</td>\n",
              "      <td>[information, retrieval]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Kleene_star</td>\n",
              "      <td>16750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7210</td>\n",
              "      <td>20220913212929</td>\n",
              "      <td>[kleene, star]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   number                  title  pageid  namespace  length         touched  \\\n",
              "0       1                  ELIZA   10235        NaN   29186  20220910110802   \n",
              "1       2   Finite-state_machine   10931        NaN   41876  20220918155749   \n",
              "2       3               HAL_9000   14384        NaN   35794  20220727001747   \n",
              "3       4  Information_retrieval   15271        NaN   27625  20220904163734   \n",
              "4       5            Kleene_star   16750        NaN    7210  20220913212929   \n",
              "\n",
              "           paragraph_tokens  \n",
              "0                   [eliza]  \n",
              "1  [finite, state, machine]  \n",
              "2                     [hal]  \n",
              "3  [information, retrieval]  \n",
              "4            [kleene, star]  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import re\n",
        "from gensim.utils import tokenize\n",
        "from gensim.parsing.preprocessing import remove_stopwords, strip_punctuation\n",
        "custom_stopwords = ['displaystyle']\n",
        "\n",
        "def preprocess_tokenize_with_gensim(text):\n",
        "  # convert to lowercase, remove extra whitespace\n",
        "  text = text.lower().strip()\n",
        "  # remove \\n, strip symbols and punctuation \n",
        "  text = strip_punctuation(text)\n",
        "  # remove stopwords\n",
        "  text = remove_stopwords(text)\n",
        "  # tokenize\n",
        "  tokens = list(tokenize(text))\n",
        "  # remove any additional stopwords if needed (this is a custom extra step if \n",
        "  # you see words in the topics that don't belong)\n",
        "  # remove any words shorter than 2 characters\n",
        "  tokens = [token for token in tokens if token not in custom_stopwords and len(token) > 2]\n",
        "  return tokens\n",
        "\n",
        "# create tokens column\n",
        "wiki_page_corpus['paragraph_tokens'] = wiki_page_corpus['title'].apply(preprocess_tokenize_with_gensim)\n",
        "wiki_page_corpus.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GTFwmt4ijAVZ"
      },
      "source": [
        "## Run LSA with TFIDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "9ftM86E5O3Bh"
      },
      "outputs": [],
      "source": [
        "import os.path\n",
        "from gensim.corpora import Dictionary\n",
        "from gensim.models import LsiModel, TfidfModel\n",
        "import re\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gH0ROFpxD0Mj"
      },
      "source": [
        "### 3. Generate dictionary and document-term-matrix needed for computing TFIDF(1 point) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "u4U84TwIOwyU"
      },
      "outputs": [],
      "source": [
        "dictionary = Dictionary(wiki_page_corpus['paragraph_tokens'])\n",
        "corpus = [dictionary.doc2bow(text) for text in wiki_page_corpus['paragraph_tokens']]\n",
        "tfidf = TfidfModel(corpus)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4awOwQr-EMqz"
      },
      "source": [
        "### 4. Specify n_topics (at least 7), compute TFIDF matrix and train LSA model (1 point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "bhVVo7eHQ2nB"
      },
      "outputs": [],
      "source": [
        "n_topics = 10\n",
        "tfidf_corpus = tfidf[corpus]\n",
        "lsi = LsiModel(tfidf_corpus, id2word=dictionary, num_topics=n_topics)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "arP2GvpeEd90"
      },
      "source": [
        "### 5. Print top 7 topics and 7 words for every topic (1 point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "V03OXnwKQ2vc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(0, [('translation', -0.5971142607272544), ('machine', -0.5970148111237235), ('language', -0.3295565899869501), ('corpus', -0.18451525138709726), ('natural', -0.1820524406121053), ('based', -0.11277980081190185), ('neural', -0.08727640624331864)])\n",
            "(1, [('language', -0.6154347835966626), ('corpus', -0.3943217959966098), ('natural', -0.3531237440857559), ('machine', 0.3204085783387835), ('translation', 0.3021986232959472), ('processing', -0.1567719226393752), ('model', -0.10782592299555536)])\n",
            "(2, [('corpus', 0.7256188652252339), ('language', -0.3634611821492455), ('natural', -0.28776859553718237), ('national', 0.2109349721338355), ('linguistics', 0.1733937823381026), ('text', 0.1669061683014083), ('speech', 0.1402589227933959)])\n",
            "(3, [('automaton', -0.7860931324335875), ('finite', -0.4889248734250003), ('deterministic', -0.16666458387232327), ('state', -0.15557112962482988), ('büchi', -0.12409637952299955), ('nondeterministic', -0.11592631693405205), ('probabilistic', -0.0681508145438186)])\n",
            "(4, [('assistant', 0.6979813733837648), ('virtual', 0.6585702452667366), ('google', 0.12934268806065605), ('alice', 0.093101475310574), ('cortana', 0.09307362936016234), ('clova', 0.09304929542802196), ('celia', 0.09276920287487635)])\n",
            "(5, [('semantic', 0.760339650599904), ('analysis', 0.44755136068816564), ('latent', 0.23474952989395143), ('parsing', 0.15337443886221322), ('text', 0.12320172369229825), ('computational', 0.11890086793398744), ('analytics', 0.11723696561850581)])\n",
            "(6, [('text', -0.8554211685094927), ('predictive', -0.2051253555841077), ('model', -0.12215731172565263), ('corpus', 0.11586510472651045), ('english', 0.1069411538823508), ('semantic', 0.10085995676882972), ('simplification', -0.09992823191405326)])\n"
          ]
        }
      ],
      "source": [
        "# display top 7 topics and top 7 words for each topic\n",
        "for topic in lsi.show_topics(num_topics=7, num_words=7, formatted=False):\n",
        "  print(topic)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZPqBwsTjE7t"
      },
      "source": [
        "## Run LDA with the count vectorizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJAbru5NE7u7"
      },
      "source": [
        "### 6. Train an LDA model using word counts (1 points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "NgXlVKg1cyOG"
      },
      "outputs": [],
      "source": [
        "\n",
        "from gensim.models import LdaModel\n",
        "lda = LdaModel(corpus, id2word=dictionary, num_topics=n_topics)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xppQJT4fFIsY"
      },
      "source": [
        "### 7. Compare at the top 7 words for the top 7 topics from the LDA model (1 point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "7fop_kiSdNa3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1, [('corpus', 0.022556918), ('information', 0.015159833), ('language', 0.011463579), ('project', 0.011463547), ('retrieval', 0.011462624), ('word', 0.007764954), ('translator', 0.007764952)])\n",
            "(2, [('corpus', 0.025613066), ('software', 0.014344012), ('finite', 0.012965225), ('linguistic', 0.012963871), ('automaton', 0.012963666), ('english', 0.009802865), ('processing', 0.009802757)])\n",
            "(3, [('language', 0.025923941), ('engineering', 0.013122629), ('natural', 0.009922601), ('semantic', 0.009922087), ('automaton', 0.009921661), ('corpus', 0.0067222547), ('theory', 0.0067213327)])\n",
            "(7, [('automaton', 0.030969607), ('language', 0.030969474), ('natural', 0.019810943), ('finite', 0.01981091), ('assistant', 0.019808916), ('virtual', 0.01701942), ('machine', 0.017019374)])\n",
            "(4, [('text', 0.034302272), ('corpus', 0.034301773), ('inscriptionum', 0.010529217), ('analysis', 0.010528217), ('latent', 0.010527699), ('document', 0.007132592), ('semantic', 0.0071323877)])\n",
            "(8, [('translation', 0.028667431), ('machine', 0.028665403), ('english', 0.019215727), ('corpus', 0.012915851), ('recognition', 0.009765905), ('arabic', 0.009765612), ('text', 0.009765222)])\n",
            "(5, [('corpus', 0.039053958), ('language', 0.033091158), ('national', 0.018184831), ('semantic', 0.01818474), ('grammar', 0.012223093), ('analysis', 0.009242096), ('probabilistic', 0.009241902)])\n"
          ]
        }
      ],
      "source": [
        "for topic in lda.show_topics(num_topics=7, num_words=7, formatted=False):\n",
        "    print(topic)\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xBzSCuPrDPBf"
      },
      "source": [
        "### 7. Why are the topics and words different between the LDA and LSA models? (1 point)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fFL8ZS-SF6tt"
      },
      "source": [
        "Answer: LDA is probabilistic, while LSA is deterministic. They use different methodologies and assumptions to get the topics."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTNVSDpSGCt_"
      },
      "source": [
        "### 8. Visualize the topics in the LDA model you trained using pyLDAvis(1 point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "VhgudWrae8wT"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pyldavis in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (3.3.1)\n",
            "Requirement already satisfied: gensim in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (4.2.0)\n",
            "Requirement already satisfied: funcy in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (1.17)\n",
            "Requirement already satisfied: numexpr in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (2.8.3)\n",
            "Requirement already satisfied: future in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (0.18.2)\n",
            "Requirement already satisfied: setuptools in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (63.4.1)\n",
            "Requirement already satisfied: numpy>=1.20.0 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (1.21.5)\n",
            "Requirement already satisfied: jinja2 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (2.11.3)\n",
            "Requirement already satisfied: sklearn in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (0.0.post1)\n",
            "Requirement already satisfied: scikit-learn in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (1.1.2)\n",
            "Requirement already satisfied: pandas>=1.2.0 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (1.4.3)\n",
            "Requirement already satisfied: scipy in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (1.7.3)\n",
            "Requirement already satisfied: joblib in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pyldavis) (1.1.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pandas>=1.2.0->pyldavis) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from pandas>=1.2.0->pyldavis) (2022.1)\n",
            "Requirement already satisfied: Cython==0.29.28 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from gensim->pyldavis) (0.29.28)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from gensim->pyldavis) (5.2.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from jinja2->pyldavis) (2.0.1)\n",
            "Requirement already satisfied: packaging in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from numexpr->pyldavis) (21.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from scikit-learn->pyldavis) (2.2.0)\n",
            "Requirement already satisfied: six>=1.5 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->pandas>=1.2.0->pyldavis) (1.16.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\wangwilliam\\anaconda3\\lib\\site-packages (from packaging->numexpr->pyldavis) (3.0.9)\n"
          ]
        }
      ],
      "source": [
        "# Install the Python LDA Visualization package\n",
        "!pip install pyldavis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "McHOvyWne9HW"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el2024819222072593289474908259\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el2024819222072593289474908259_data = {\"mdsDat\": {\"x\": [0.13472960688768273, 0.006623032983180066, 0.05029741884747399, -0.045384727675995405, 0.033139956758910155, 0.07274113296545022, -0.005457394145143303, -0.09789678234525702, -0.10044766984260729, -0.04834457443369418], \"y\": [0.0004707778614428606, -0.001104725269302807, -0.028186536521961514, -0.11350745707859175, 0.008828870487153886, 0.09971623786991557, -0.11239900329893374, 0.05597275734684306, 0.04047168159936565, 0.04973739700406896], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [12.357938866892434, 11.476231847903632, 10.436566110239871, 10.266544038782447, 9.86789735537631, 9.631611086287652, 9.548764915821394, 9.47098741083135, 8.745151591228545, 8.198306776636363]}, \"tinfo\": {\"Term\": [\"text\", \"language\", \"automaton\", \"translation\", \"machine\", \"corpus\", \"natural\", \"finite\", \"assistant\", \"english\", \"national\", 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\"empirical\", \"engineering\", \"engineering\", \"english\", \"english\", \"english\", \"english\", \"english\", \"english\", \"entity\", \"entity\", \"entity\", \"entropy\", \"epigraphicum\", \"error\", \"etzioni\", \"eugene\", \"european\", \"european\", \"evaluation\", \"evaluation\", \"example\", \"experiment\", \"explicit\", \"expression\", \"extended\", \"extraction\", \"extraction\", \"extraction\", \"extraction\", \"extraction\", \"faith\", \"filtered\", \"finite\", \"finite\", \"finite\", \"finite\", \"finite\", \"fish\", \"francine\", \"frederick\", \"free\", \"fung\", \"generalized\", \"generation\", \"generation\", \"generation\", \"generative\", \"generator\", \"generator\", \"george\", \"global\", \"google\", \"google\", \"google\", \"google\", \"gpt\", \"graecum\", \"grammar\", \"grammar\", \"grammar\", \"grammar\", \"grammar\", \"grammar\", \"grammar\", \"grammar\", \"gregory\", \"hammadi\", \"hillel\", \"hindi\", \"historiae\", \"hovy\", \"human\", \"human\", \"ibm\", \"ibm\", \"ibm\", \"ibm\", \"ichi\", \"ilarraza\", \"image\", \"induction\", \"industry\", \"information\", \"information\", \"ink\", \"inscriptions\", \"inscriptions\", \"inscriptionum\", \"inscriptionum\", \"inscriptionum\", \"institute\", \"integration\", \"intelligence\", \"intelligent\", \"interaction\", \"interface\", \"interface\", \"iudaeae\", \"james\", \"james\", \"james\", \"jan\", \"jewish\", \"jones\", \"katz\", \"keane\", \"key\", \"kingdom\", \"kneser\", \"krzysztof\", \"laboratories\", \"lam\", \"lancaster\", \"language\", \"language\", \"language\", \"language\", \"language\", \"language\", \"language\", \"languages\", \"larry\", \"latent\", \"latent\", \"latinarum\", \"lattice\", \"law\", \"law\", \"law\", \"lee\", \"legal\", \"lennon\", \"letter\", \"lexical\", \"lexical\", \"lexical\", \"lexical\", \"lexical\", \"lexical\", \"lexicon\", \"library\", \"lillian\", \"linguae\", \"linguistic\", \"linguistic\", \"linguistic\", \"linguistics\", \"linguistics\", \"linguistics\", \"linguistics\", \"linguistics\", \"linguistics\", \"list\", \"list\", \"list\", \"list\", \"list\", \"livac\", \"localization\", \"london\", \"longman\", \"l\\u00e9on\", \"machine\", \"machine\", \"machine\", \"machine\", \"machine\", \"machine\", \"machine\", \"machine\", \"magnetic\", \"majid\", \"mallet\", \"management\", \"mandelbrot\", \"manuscript\", \"manuscript\", \"mapping\", \"margaret\", \"marie\", \"mark\", \"mark\", \"mark\", \"mark\", \"markov\", \"markov\", \"markup\", \"martha\", \"matrix\", \"matsumoto\", \"mawangdui\", \"maximum\", \"mchardy\", \"meaning\", \"medieval\", \"mere\", \"methods\", \"michael\", \"microsoft\", \"minimal\", \"mining\", \"mithriacae\", \"mitkov\", \"model\", \"model\", \"model\", \"model\", \"model\", \"model\", \"model\", \"modern\", \"modular\", \"mona\", \"monica\", \"monumentorum\", \"morphological\", \"mortals\", \"moses\", \"multiline\", \"multimodal\", \"music\", \"nag\", \"national\", \"national\", \"national\", \"national\", \"natural\", \"natural\", \"natural\", \"natural\", \"nave\", \"neo\", \"network\", \"network\", \"network\", \"network\", \"networking\", \"neural\", \"neural\", \"new\", \"ney\", \"ngram\", \"numeral\", \"ocr\", \"ocr\", \"ocr\", \"optical\", \"optical\", \"optical\", \"optical\", \"optical\", \"oriented\", \"oslo\", \"outline\", \"oxford\", \"palaestinae\", \"parsing\", \"parsing\", \"parsing\", \"parsing\", \"persian\", \"persons\", \"petrovich\", \"philipp\", \"phrase\", \"plate\", \"plate\", \"plates\", \"poolparty\", \"popping\", \"predictive\", \"privacy\", \"probabilistic\", \"probabilistic\", \"processing\", \"processing\", \"processing\", \"processing\", \"product\", \"project\", \"project\", \"project\", \"project\", \"publishing\", \"pumping\", \"punjabi\", \"quantum\", \"quranic\", \"reader\", \"readers\", \"reasoning\", \"recognition\", \"recognition\", \"recognition\", \"recognition\", \"recognition\", \"recognition\", \"recognition\", \"recursion\", \"reference\", \"regular\", \"relational\", \"religionis\", \"resolution\", \"resources\", \"retrieval\", \"retrieval\", \"robot\", \"role\", \"rule\", \"r\\u00e9sum\\u00e9\", \"safety\", \"scantron\", \"schema\", \"scientist\", \"scientist\", \"scientist\", \"scientist\", \"scottish\", \"scriptorum\", \"scrolls\", \"scrolls\", \"selectae\", \"self\", \"semantic\", \"semantic\", \"semantic\", \"semantic\", \"semantic\", \"semantic\", \"semantics\", \"semantics\", \"semantics\", \"semantics\", \"semiticarum\", \"sense\", \"sense\", \"sense\", \"separating\", \"seq\", \"shallow\", \"silk\", \"silk\", \"slonim\", \"slovenian\", \"software\", \"software\", \"software\", \"software\", \"software\", \"software\", \"software\", \"source\", \"space\", \"speculorum\", \"speech\", \"speech\", \"speech\", \"speech\", \"speech\", \"speed\", \"spoken\", \"spoken\", \"spoken\", \"stable\", \"stabler\", \"standards\", \"state\", \"state\", \"state\", \"states\", \"stenstr\\u00f6m\", \"studies\", \"studies\", \"sub\", \"supplementum\", \"survey\", \"synchronizing\", \"syntactic\", \"s\\u00e1nchez\", \"tan\", \"technologies\", \"technologies\", \"technologies\", \"teenage\", \"tehran\", \"tenten\", \"term\", \"text\", \"text\", \"text\", \"text\", \"textbook\", \"texts\", \"texts\", \"texts\", \"theorem\", \"theory\", \"theory\", \"theory\", \"theory\", \"thesaurus\", \"times\", \"tools\", \"topic\", \"topic\", \"torrey\", \"traffic\", \"transfer\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translator\", \"translator\", \"translator\", \"translator\", \"troyanskii\", \"turkey\", \"ultra\", \"unambiguous\", \"united\", \"united\", \"universal\", \"universal\", \"urban\", \"usability\", \"usage\", \"variant\", \"verifying\", \"viewer\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"voice\", \"voice\", \"voice\", \"voice\", \"v\\u00e9ronis\", \"walker\", \"wang\", \"watson\", \"website\", \"wellington\", \"william\", \"william\", \"woods\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"words\", \"words\", \"written\", \"written\", \"xian\", \"xiang\", \"yehoshua\", \"young\", \"zealand\", \"\\u014dno\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [8, 6, 1, 9, 3, 4, 7, 5, 10, 2]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
              "  s.src = url;\n",
              "  s.async = true;\n",
              "  s.onreadystatechange = s.onload = callback;\n",
              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el2024819222072593289474908259\", ldavis_el2024819222072593289474908259_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el2024819222072593289474908259\", ldavis_el2024819222072593289474908259_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el2024819222072593289474908259\", ldavis_el2024819222072593289474908259_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ],
            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "7      0.134730  0.000471       1        1  12.357939\n",
              "5      0.006623 -0.001105       2        1  11.476232\n",
              "0      0.050297 -0.028187       3        1  10.436566\n",
              "8     -0.045385 -0.113507       4        1  10.266544\n",
              "2      0.033140  0.008829       5        1   9.867897\n",
              "3      0.072741  0.099716       6        1   9.631611\n",
              "6     -0.005457 -0.112399       7        1   9.548765\n",
              "4     -0.097897  0.055973       8        1   9.470987\n",
              "9     -0.100448  0.040472       9        1   8.745152\n",
              "1     -0.048345  0.049737      10        1   8.198307, topic_info=             Term       Freq      Total Category  logprob  loglift\n",
              "33           text  12.000000  12.000000  Default  30.0000  30.0000\n",
              "10       language  27.000000  27.000000  Default  29.0000  29.0000\n",
              "78      automaton  13.000000  13.000000  Default  28.0000  28.0000\n",
              "9     translation  18.000000  18.000000  Default  27.0000  27.0000\n",
              "2         machine  18.000000  18.000000  Default  26.0000  26.0000\n",
              "..            ...        ...        ...      ...      ...      ...\n",
              "182         plate   0.638610   1.811882  Topic10  -5.5047   1.4584\n",
              "497  inscriptions   0.638604   1.852413  Topic10  -5.5047   1.4363\n",
              "651         topic   0.638604   1.783079  Topic10  -5.5047   1.4744\n",
              "517        markov   0.638601   1.813283  Topic10  -5.5047   1.4576\n",
              "569        bergen   0.638596   1.813244  Topic10  -5.5047   1.4576\n",
              "\n",
              "[542 rows x 6 columns], token_table=      Topic      Freq         Term\n",
              "term                              \n",
              "652       2  0.786250        aaron\n",
              "1080      6  0.828509        abdul\n",
              "617       8  0.540914  acquisition\n",
              "332       5  0.823557      acyclic\n",
              "881       6  0.828458         adam\n",
              "...     ...       ...          ...\n",
              "937       1  0.783087        xiang\n",
              "133       9  0.831608     yehoshua\n",
              "442       4  0.809021        young\n",
              "884       5  0.823554      zealand\n",
              "531       6  0.828449          ōno\n",
              "\n",
              "[640 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[8, 6, 1, 9, 3, 4, 7, 5, 10, 2])"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pyLDAvis.gensim_models\n",
        "pyLDAvis.enable_notebook()\n",
        "pyLDAvis.gensim_models.prepare(lda, corpus, dictionary)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EmFvslnlGXjc"
      },
      "source": [
        "### 10. Re-train the LDA model with just 9 topics & visualize with PyLDAVis again (1 point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "iXdgfhXwhTjV"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el2024819222745760806762931867\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el2024819222745760806762931867_data = {\"mdsDat\": {\"x\": [0.10436759568664807, 0.017666182100581143, -0.024538798689445345, -0.1571067314071596, 0.006008354889906214, 0.04233529976992631, -0.025462237152158923, 0.06586349741239315, -0.029133162610691106], \"y\": [0.025100584670898433, 0.14627705143070482, -0.012682573459454147, 0.016852134841255455, -0.11132054911538693, -0.020514593924216756, 0.04938116713666528, -0.02931551613515909, -0.06377770544530711], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [14.208256517622035, 13.140728288121533, 11.55779662154618, 11.029005618829046, 10.386429467552146, 10.227947058824205, 10.004687469670962, 9.902282088262917, 9.542866869570975]}, \"tinfo\": {\"Term\": [\"language\", \"corpus\", \"automaton\", \"finite\", \"machine\", \"translation\", \"semantic\", \"natural\", \"english\", \"analysis\", \"national\", \"grammar\", \"translator\", \"model\", \"scientist\", \"recognition\", \"google\", \"extraction\", \"assistant\", \"speech\", \"law\", \"linguistics\", \"william\", \"linguistic\", \"word\", \"translate\", \"retrieval\", \"statistical\", \"parsing\", \"text\", \"scientist\", \"mining\", \"croatian\", \"journal\", \"frederick\", \"methods\", \"wellington\", \"outline\", \"monumentorum\", \"applications\", \"mithriacae\", \"zealand\", \"neo\", \"intelligence\", \"london\", \"consortium\", \"religionis\", \"ichi\", \"survey\", \"assyrian\", \"analyser\", \"winograd\", \"teenage\", \"lennon\", \"lyra\", \"challenge\", \"hybrid\", \"etruscorum\", \"georgetown\", \"key\", \"corpora\", \"corpus\", \"artificial\", \"inscriptionum\", \"spoken\", \"project\", \"linguistics\", \"linguistic\", \"machine\", \"natural\", \"english\", \"processing\", \"text\", \"translation\", \"entity\", \"language\", \"international\", \"national\", \"virtual\", \"recognition\", \"word\", \"automaton\", \"lexical\", \"mark\", \"generator\", \"european\", \"markov\", 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\"separating\", \"attempto\", \"reverso\", \"extraction\", \"document\", \"grammar\", \"technologies\", \"word\", \"english\", \"network\", \"ocr\", \"mark\", \"project\", \"concordance\", \"corpus\", \"text\", \"automatic\", \"software\", \"semantic\", \"machine\", \"language\", \"translation\", \"automaton\", \"assistant\", \"corpora\", \"optical\", \"model\", \"recursive\", \"statistical\", \"morphological\", \"gpt\", \"retrieval\", \"lemma\", \"traffic\", \"rhetorical\", \"pumping\", \"standards\", \"lattice\", \"children\", \"control\", \"turkey\", \"celia\", \"technique\", \"relational\", \"architecture\", \"industry\", \"localization\", \"stenstr\\u00f6m\", \"open\", \"database\", \"languages\", \"mchardy\", \"location\", \"asymmetric\", \"numeral\", \"crowdsourcing\", \"round\", \"davies\", \"texts\", \"data\", \"information\", \"parsing\", \"software\", \"voice\", \"semantics\", \"list\", \"grammar\", \"speech\", \"translation\", \"text\", \"machine\", \"scrolls\", \"dead\", 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0.8267612876783447, 0.5598201485136656, 0.5598201485136656, 0.39260836414780614, 0.39260836414780614, 0.8260158705207074, 0.8259775585739713, 0.5432730813354287, 0.5432730813354287, 0.791193550310128, 0.7911818557149207, 0.7911717110533287, 0.8259933604472808, 0.15868278400113278, 0.4760483520033984, 0.15868278400113278, 0.15868278400113278, 0.15868278400113278, 0.27934559051868707, 0.27934559051868707, 0.27934559051868707, 0.8259930097977554, 0.8374516613645677, 0.7976467502104371, 0.7867395147942028, 0.7976514039848954, 0.7943495374975951, 0.11320757450400766, 0.22641514900801532, 0.33962272351202294, 0.11320757450400766, 0.11320757450400766, 0.11320757450400766, 0.7911847416626557, 0.7911649174532799, 0.826752252546332, 0.7976645672735134, 0.7911838129259297, 0.5183382045007989], \"Term\": [\"abbyy\", \"abdul\", \"action\", \"acyclic\", \"algorithm\", \"algorithm\", \"algorithm\", \"amazon\", \"amazon\", \"analyser\", \"analysis\", \"analysis\", \"analysis\", \"analytical\", \"annotated\", \"aperiodic\", \"applications\", \"approaches\", \"arabic\", \"arabic\", \"arabic\", \"arabic\", \"aramaic\", \"arantza\", \"architecture\", \"archive\", \"artificial\", \"artificial\", \"assistant\", \"assistant\", \"assistant\", \"assistant\", \"assistant\", \"assisted\", \"association\", \"association\", \"assyrian\", \"asymmetric\", \"attempto\", \"audio\", \"austin\", \"automatic\", \"automatic\", \"automatic\", \"automatic\", \"automatic\", \"automatic\", \"automaton\", \"automaton\", \"automaton\", \"automaton\", \"automaton\", \"automaton\", \"babel\", \"babylon\", \"bag\", \"based\", \"based\", \"based\", \"based\", \"bhurgri\", \"bible\", \"bible\", \"bible\", \"bible\", \"bidyut\", \"bonnie\", \"braille\", \"braille\", \"british\", \"bulgarian\", \"byzantinae\", \"b\\u00fcchi\", \"cache\", \"canaanite\", \"case\", \"celia\", \"center\", \"center\", \"challenge\", \"character\", \"character\", \"character\", \"character\", \"character\", \"chiew\", \"children\", \"china\", \"classic\", \"claws\", \"cmu\", \"cochin\", \"collins\", \"collocation\", \"company\", \"company\", \"comparison\", \"comparison\", \"comparison\", \"complaint\", \"computational\", \"computational\", \"computational\", \"computational\", \"conceptual\", \"concordance\", \"concordance\", \"concordance\", \"concordance\", \"concordance\", \"conference\", \"conference\", \"consortium\", \"construction\", \"construction\", \"construction\", \"contemporary\", \"context\", \"context\", \"control\", \"conversational\", \"cores\", \"corpora\", \"corpora\", \"corpora\", \"corpus\", \"corpus\", \"corpus\", \"corpus\", \"corpus\", \"corpus\", \"corpus\", \"corpus\", \"cortana\", \"croatian\", \"cross\", \"crowdsourcing\", \"curran\", \"danielle\", \"data\", \"data\", \"database\", \"databases\", \"david\", \"davies\", \"dead\", \"dead\", \"decomposition\", \"deep\", \"dependencies\", \"description\", \"deterministic\", \"deterministic\", \"different\", \"dirichlet\", \"distributed\", \"document\", \"document\", \"document\", \"document\", \"d\\u00edaz\", \"engineering\", \"engineering\", \"engineering\", \"english\", \"english\", \"english\", \"english\", \"entity\", \"entity\", \"entity\", \"entropy\", \"eric\", \"error\", \"essay\", \"etruscorum\", \"etzioni\", \"european\", \"evaluation\", \"evans\", \"example\", \"extended\", \"extraction\", \"extraction\", \"extraction\", \"extraction\", \"extraction\", \"factored\", \"filtered\", \"finite\", \"finite\", \"finite\", \"finite\", \"finite\", \"framework\", \"framework\", \"francine\", \"frederick\", \"free\", \"functional\", \"general\", \"general\", \"generalized\", \"generative\", \"generator\", \"generator\", \"generator\", \"generator\", \"generator\", \"georgetown\", \"german\", \"global\", \"google\", \"google\", \"google\", \"gpt\", \"grammar\", \"grammar\", \"grammar\", \"grammar\", \"grammar\", \"grammar\", \"gregory\", \"hammadi\", \"hillel\", \"hindi\", \"historiae\", \"history\", \"history\", \"hybrid\", \"ichi\", \"identification\", \"ilarraza\", \"image\", \"industry\", \"information\", \"information\", \"information\", \"information\", \"ink\", \"inscriptions\", \"inscriptionum\", \"inscriptionum\", \"inscriptionum\", \"institute\", \"intelligence\", \"intelligent\", \"international\", \"international\", \"international\", \"iudaeae\", \"jewish\", \"josef\", \"journal\", \"katz\", \"key\", \"kgys\", \"kingdom\", \"kneser\", \"krohn\", \"labeling\", \"laboratories\", \"lancaster\", \"language\", \"language\", \"language\", \"language\", \"language\", \"language\", \"languages\", \"latent\", \"latent\", \"latinae\", \"lattice\", \"law\", \"law\", \"layout\", \"lemma\", \"lennon\", \"letter\", \"levenshtein\", \"lex\", \"lexical\", \"lexical\", \"lexical\", \"lexical\", \"lexical\", \"lexicon\", \"library\", \"linguae\", \"linguist\", \"linguist\", \"linguistic\", \"linguistic\", \"linguistic\", \"linguistics\", \"linguistics\", \"linguistics\", \"linguistics\", \"linguistics\", \"linguistics\", \"list\", \"list\", \"list\", \"livac\", \"localization\", \"location\", \"london\", \"longman\", \"lyra\", \"machine\", \"machine\", \"machine\", \"machine\", \"machine\", \"machine\", \"machine\", \"magnetic\", \"majid\", \"mallet\", \"management\", \"mandelbrot\", \"manually\", \"manuscript\", \"mapping\", \"marie\", \"mark\", \"mark\", \"mark\", \"mark\", \"mark\", \"markov\", \"markup\", \"marti\", \"maximum\", \"mchardy\", \"medieval\", \"methods\", \"michael\", \"michael\", \"michael\", \"minimal\", \"mining\", \"mirella\", \"mithriacae\", \"model\", \"model\", \"model\", \"model\", \"model\", \"modular\", \"moens\", \"monitor\", \"monk\", \"monolingual\", \"monolingual\", \"monumentorum\", \"morphological\", \"multi\", \"multiline\", \"music\", \"nag\", \"nathan\", \"national\", \"national\", \"national\", \"native\", \"natural\", \"natural\", \"natural\", \"natural\", \"naver\", \"neo\", \"network\", \"network\", \"network\", \"network\", \"neural\", \"neural\", \"ney\", \"ngram\", \"nondeterministic\", \"number\", \"number\", \"numeral\", \"ocr\", \"ocr\", \"ocr\", \"ocr\", \"ontology\", \"open\", \"optical\", \"optical\", \"optical\", \"optical\", \"optical\", \"optical\", \"oslo\", \"outline\", \"palaestinae\", \"parsing\", \"parsing\", \"parsing\", \"parsing\", \"persons\", \"peter\", \"peter\", \"petrovich\", \"phrase\", \"phrase\", \"popping\", \"powerset\", \"presence\", \"probabilistic\", \"probabilistic\", \"probabilistic\", \"problem\", \"processing\", \"processing\", \"processing\", \"processing\", \"product\", \"project\", \"project\", \"project\", \"pumping\", \"punjabi\", \"reader\", \"readers\", \"reasoning\", \"recognition\", \"recognition\", \"recognition\", \"recognition\", \"recognition\", \"recognition\", \"recursion\", \"recursive\", \"recursive\", \"reference\", \"referring\", \"regina\", \"regulus\", \"relational\", \"religionis\", \"research\", \"research\", \"resolution\", \"retrieval\", \"retrieval\", \"reverso\", \"rhetorical\", \"rhodes\", \"robot\", \"round\", \"rule\", \"scientist\", \"scientist\", \"scottish\", \"scriptorum\", \"scrolls\", \"scrolls\", \"sea\", \"sea\", \"selectae\", \"self\", \"semantic\", \"semantic\", \"semantic\", \"semantic\", \"semantic\", \"semantic\", \"semantics\", \"semantics\", \"semantics\", \"semiticarum\", \"sense\", \"sense\", \"sense\", \"sense\", \"sentiment\", \"separating\", \"seq\", \"simplification\", \"slovenian\", \"software\", \"software\", \"software\", \"software\", \"software\", \"software\", \"software\", \"software\", \"speech\", \"speech\", \"speech\", \"speech\", \"speed\", \"spoken\", \"spoken\", \"standards\", \"state\", \"state\", \"state\", \"state\", \"states\", \"statistical\", \"stenstr\\u00f6m\", \"studies\", \"studies\", \"studies\", \"sub\", \"survey\", \"syntactic\", \"s\\u00e1nchez\", \"tagger\", \"tan\", \"taxonomy\", \"technique\", \"technologies\", \"technologies\", \"technologies\", \"teenage\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"textbook\", \"texts\", \"texts\", \"texts\", \"textual\", \"theory\", \"theory\", \"theory\", \"theory\", \"thesaurus\", \"timeline\", \"torrey\", \"traffic\", \"transducer\", \"transfer\", \"translate\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translation\", \"translator\", \"translator\", \"trigram\", \"turkey\", \"unit\", \"united\", \"united\", \"universal\", \"universal\", \"urban\", \"usability\", \"user\", \"user\", \"variant\", \"vedic\", \"verifying\", \"viewer\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"virtual\", \"voice\", \"voice\", \"voice\", \"watson\", \"website\", \"wellington\", \"william\", \"winograd\", \"woods\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"xiang\", \"yehoshua\", \"young\", \"zealand\", \"zhai\", \"zipf\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 9, 6, 3, 2, 1, 8, 7, 4]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
              "  s.src = url;\n",
              "  s.async = true;\n",
              "  s.onreadystatechange = s.onload = callback;\n",
              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el2024819222745760806762931867\", ldavis_el2024819222745760806762931867_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el2024819222745760806762931867\", ldavis_el2024819222745760806762931867_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el2024819222745760806762931867\", ldavis_el2024819222745760806762931867_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ],
            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "4      0.104368  0.025101       1        1  14.208257\n",
              "8      0.017666  0.146277       2        1  13.140728\n",
              "5     -0.024539 -0.012683       3        1  11.557797\n",
              "2     -0.157107  0.016852       4        1  11.029006\n",
              "1      0.006008 -0.111321       5        1  10.386429\n",
              "0      0.042335 -0.020515       6        1  10.227947\n",
              "7     -0.025462  0.049381       7        1  10.004687\n",
              "6      0.065863 -0.029316       8        1   9.902282\n",
              "3     -0.029133 -0.063778       9        1   9.542867, topic_info=            Term       Freq      Total Category  logprob  loglift\n",
              "10      language  27.000000  27.000000  Default  30.0000  30.0000\n",
              "21        corpus  34.000000  34.000000  Default  29.0000  29.0000\n",
              "78     automaton  13.000000  13.000000  Default  28.0000  28.0000\n",
              "1         finite   9.000000   9.000000  Default  27.0000  27.0000\n",
              "2        machine  19.000000  19.000000  Default  26.0000  26.0000\n",
              "..           ...        ...        ...      ...      ...      ...\n",
              "149  concordance   0.658000   4.902165   Topic9  -5.6266   0.3411\n",
              "31       optical   0.657988   5.523796   Topic9  -5.6267   0.2217\n",
              "238      braille   0.657966   1.856085   Topic9  -5.6267   1.3123\n",
              "153       phrase   0.657965   1.845817   Topic9  -5.6267   1.3179\n",
              "657  monolingual   0.657961   1.865552   Topic9  -5.6267   1.3072\n",
              "\n",
              "[511 rows x 6 columns], token_table=      Topic      Freq       Term\n",
              "term                            \n",
              "369       7  0.794334      abbyy\n",
              "1080      2  0.785325      abdul\n",
              "977       5  0.791192     action\n",
              "332       8  0.825984    acyclic\n",
              "455       1  0.318225  algorithm\n",
              "...     ...       ...        ...\n",
              "133       5  0.791165   yehoshua\n",
              "442       9  0.826752      young\n",
              "884       1  0.797665    zealand\n",
              "938       5  0.791184       zhai\n",
              "29        2  0.518338       zipf\n",
              "\n",
              "[596 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 9, 6, 3, 2, 1, 8, 7, 4])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Re-train the LDA model with just 9 topics & visualize with PyLDAVis again\n",
        "lda_9 = LdaModel(corpus, id2word=dictionary, num_topics=9)\n",
        "pyLDAvis.gensim_models.prepare(lda_9, corpus, dictionary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-kmEpZIko8t"
      },
      "source": [
        "### 11. Looking at the topic visualization above - should we have used a larger or smaller number of topics? Explain... (1 point)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyVMXXdBk_Vq"
      },
      "source": [
        "Answer: We should have used a larger number of topics, so that there are fewer overlaps."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFO3t_OMjJ26"
      },
      "source": [
        "## Train embeddings using word2vec"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rufAPzJtHaGk"
      },
      "source": [
        "### 12. Train a word2vec model using the common_texts variable from gensim first (1 point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "pwJvRdSBOAH9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(17, 145)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from gensim.models import Word2Vec\n",
        "from gensim.test.utils import common_texts\n",
        "\n",
        "# Initialize the Word2Vec model\n",
        "new_model = Word2Vec(vector_size=100, window=5, min_count=1, workers=4)\n",
        "\n",
        "# Build the vocabulary from the common_texts corpus\n",
        "new_model.build_vocab(common_texts)\n",
        "\n",
        "# Train the model on the common_texts corpus\n",
        "new_model.train(common_texts, total_examples=model.corpus_count, epochs=model.epochs)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "RFmPvEdQioMZ"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([-0.00515774, -0.00667028, -0.0077791 ,  0.00831315, -0.00198292,\n",
              "       -0.00685696, -0.0041556 ,  0.00514562, -0.00286997, -0.00375075,\n",
              "        0.0016219 , -0.0027771 , -0.00158482,  0.0010748 , -0.00297881,\n",
              "        0.00852176,  0.00391207, -0.00996176,  0.00626142, -0.00675622,\n",
              "        0.00076966,  0.00440552, -0.00510486, -0.00211128,  0.00809783,\n",
              "       -0.00424503, -0.00763848,  0.00926061, -0.00215612, -0.00472081,\n",
              "        0.00857329,  0.00428458,  0.0043261 ,  0.00928722, -0.00845554,\n",
              "        0.00525685,  0.00203994,  0.0041895 ,  0.00169839,  0.00446543,\n",
              "        0.00448759,  0.0061063 , -0.00320303, -0.00457706, -0.00042664,\n",
              "        0.00253447, -0.00326412,  0.00605948,  0.00415534,  0.00776685,\n",
              "        0.00257002,  0.00811904, -0.00138761,  0.00808028,  0.0037181 ,\n",
              "       -0.00804967, -0.00393476, -0.0024726 ,  0.00489447, -0.00087241,\n",
              "       -0.00283173,  0.00783599,  0.00932561, -0.0016154 , -0.00516075,\n",
              "       -0.00470313, -0.00484746, -0.00960562,  0.00137242, -0.00422615,\n",
              "        0.00252744,  0.00561612, -0.00406709, -0.00959937,  0.00154715,\n",
              "       -0.00670207,  0.0024959 , -0.00378173,  0.00708048,  0.00064041,\n",
              "        0.00356198, -0.00273993, -0.00171105,  0.00765502,  0.00140809,\n",
              "       -0.00585215, -0.00783678,  0.00123304,  0.00645651,  0.00555797,\n",
              "       -0.00897966,  0.00859466,  0.00404815,  0.00747178,  0.00974917,\n",
              "       -0.0072917 , -0.00904259,  0.0058377 ,  0.00939395,  0.00350795],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# assuming your w2v model is called new_model\n",
        "new_model.wv['computer']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "dcwWQKzxipy3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('system', 0.21617142856121063),\n",
              " ('survey', 0.044689200818538666),\n",
              " ('interface', 0.01520337350666523),\n",
              " ('time', 0.0019510575802996755),\n",
              " ('trees', -0.03284314647316933),\n",
              " ('human', -0.0742427185177803),\n",
              " ('response', -0.09317588806152344),\n",
              " ('graph', -0.09575346857309341),\n",
              " ('eps', -0.10513805598020554),\n",
              " ('user', -0.16911622881889343)]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_model.wv.most_similar('computer', topn=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ieFjxAqlHkK8"
      },
      "source": [
        "### 11. Re-train the word2vec model using our wikipedia paragraph tokens - remember you have to update your model vocabulary first (1 point)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "IEVAF0dkizJa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(40, 275)"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Update the model's vocabulary with the new sentences\n",
        "new_model.build_vocab(wiki_page_corpus, update=True)\n",
        "\n",
        "# Continue training the model with the new sentences\n",
        "new_model.train(wiki_page_corpus, total_examples=len(wiki_page_corpus), epochs=new_model.epochs)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "eBuua85gi9Ve"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-0.00515774 -0.00667028 -0.0077791   0.00831315 -0.00198292 -0.00685696\n",
            " -0.0041556   0.00514562 -0.00286997 -0.00375075  0.0016219  -0.0027771\n",
            " -0.00158482  0.0010748  -0.00297881  0.00852176  0.00391207 -0.00996176\n",
            "  0.00626142 -0.00675622  0.00076966  0.00440552 -0.00510486 -0.00211128\n",
            "  0.00809783 -0.00424503 -0.00763848  0.00926061 -0.00215612 -0.00472081\n",
            "  0.00857329  0.00428458  0.0043261   0.00928722 -0.00845554  0.00525685\n",
            "  0.00203994  0.0041895   0.00169839  0.00446543  0.00448759  0.0061063\n",
            " -0.00320303 -0.00457706 -0.00042664  0.00253447 -0.00326412  0.00605948\n",
            "  0.00415534  0.00776685  0.00257002  0.00811904 -0.00138761  0.00808028\n",
            "  0.0037181  -0.00804967 -0.00393476 -0.0024726   0.00489447 -0.00087241\n",
            " -0.00283173  0.00783599  0.00932561 -0.0016154  -0.00516075 -0.00470313\n",
            " -0.00484746 -0.00960562  0.00137242 -0.00422615  0.00252744  0.00561612\n",
            " -0.00406709 -0.00959937  0.00154715 -0.00670207  0.0024959  -0.00378173\n",
            "  0.00708048  0.00064041  0.00356198 -0.00273993 -0.00171105  0.00765502\n",
            "  0.00140809 -0.00585215 -0.00783678  0.00123304  0.00645651  0.00555797\n",
            " -0.00897966  0.00859466  0.00404815  0.00747178  0.00974917 -0.0072917\n",
            " -0.00904259  0.0058377   0.00939395  0.00350795]\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[('system', 0.21617142856121063),\n",
              " ('o', 0.1265621930360794),\n",
              " ('_', 0.10314639657735825),\n",
              " ('c', 0.08376444876194),\n",
              " ('a', 0.077409528195858),\n",
              " ('survey', 0.044689200818538666),\n",
              " ('k', 0.029901370406150818),\n",
              " ('g', 0.029808782041072845),\n",
              " ('t', 0.020089803263545036),\n",
              " ('interface', 0.01520337350666523)]"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(new_model.wv['computer'])\n",
        "new_model.wv.most_similar('computer', topn=10)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3.9.12 ('base')",
      "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": "3.9.12"
    },
    "vscode": {
      "interpreter": {
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}