nlp/101/count_vectorize_bag_of_words.ipynb

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    "colab": {
      "name": "count vectorize - bag of words",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/manishiitg/ML_Experments/blob/master/nlp/101/count_vectorize_bag_of_words.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JRm79lY8uy2q",
        "colab_type": "text"
      },
      "source": [
        "**Trying out bag of words representation **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWxNhWwllU16",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import spacy \n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "id": "3TRrLjGJlbja",
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          "height": 69
        }
      },
      "source": [
        "# bag of words very simple example \n",
        "\n",
        "sentence = \"NLP is awesome\"\n",
        "count_vectorizer = CountVectorizer()  \n",
        "count_vectorizer.fit(sentence.split())\n",
        "matrix = count_vectorizer.transform(sentence.split())\n",
        "print(matrix.todense())"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 1]\n",
            " [0 1 0]\n",
            " [1 0 0]]\n"
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          "name": "stdout"
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "# bag of words very simple example \n",
        "\n",
        "sentences = [\"NLP is awesome\",\"I want to learn NLP\"]\n",
        "count_vectorizer = CountVectorizer()  \n",
        "count_vectorizer.fit(sentences)\n",
        "\n",
        "new_sentense = \"How to learn NLP?\"\n",
        "\n",
        "matrix = count_vectorizer.transform(new_sentense.split())\n",
        "print(matrix.todense())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 0 0 0]\n",
            " [0 0 0 0 1 0]\n",
            " [0 0 1 0 0 0]\n",
            " [0 0 0 1 0 0]]\n"
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      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Qxe5Iiz1n58H",
        "colab_type": "code",
        "outputId": "588d2ded-960f-47b8-c87b-bae92def83dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.datasets import fetch_20newsgroups\n",
        "\n",
        "news = fetch_20newsgroups(subset=\"train\")\n",
        "\n",
        "print(news.keys())\n",
        "\n",
        "df = pd.DataFrame(news['data'])\n",
        "print(df.head())\n",
        "\n",
        "\n",
        "count_vectorizer = CountVectorizer(\n",
        "    analyzer=\"word\", tokenizer=nltk.word_tokenize,\n",
        "    preprocessor=None, stop_words='english', max_features=1000, max_df=.9)  \n",
        "count_vectorizer.fit(news[\"data\"])\n",
        "\n",
        "# matrix = count_vectorizer.transform(new_sentense.split())\n",
        "# print(matrix.todense())\n",
        "print(count_vectorizer.get_feature_names())\n",
        "print(count_vectorizer.vocabulary_)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading 20news dataset. This may take a few minutes.\n",
            "Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['data', 'filenames', 'target_names', 'target', 'DESCR'])\n",
            "                                                   0\n",
            "0  From: lerxst@wam.umd.edu (where's my thing)\\nS...\n",
            "1  From: guykuo@carson.u.washington.edu (Guy Kuo)...\n",
            "2  From: twillis@ec.ecn.purdue.edu (Thomas E Will...\n",
            "3  From: jgreen@amber (Joe Green)\\nSubject: Re: W...\n",
            "4  From: jcm@head-cfa.harvard.edu (Jonathan McDow...\n",
            "['!', '#', '$', '%', '&', \"'\", \"''\", \"'ax\", \"'d\", \"'ll\", \"'m\", \"'re\", \"'s\", \"'ve\", '*', '**', '***', '*/', '+', '-', '-+', '--', '..', '...', '/', '/*', '/3t', '0', '0d', '0t-', '1', '1.', '1.1', '10', '100', '11', '12', '13', '14', '145', '15', '16', '17', '18', '19', '1992', '1993', '1d9', '2', '2.', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '3.1', '30', '31', '32', '33', '34', '35', '4', '40', '45', '5', '50', '6', '60', '7', '8', '80', '9', '93', ';', '<', '=', '>', '?', '[', '\\\\', ']', '^', '_', '_/', '`', '``', '`p', 'a.', 'a86', 'able', 'accept', 'access', 'according', 'act', 'action', 'actually', 'add', 'address', 'administration', 'advance', 'advice', 'age', 'ago', 'agree', 'ah', 'air', 'al', 'algorithm', 'allow', 'allowed', 'america', 'american', 'andrew', 'anonymous', 'answer', 'anybody', 'apparently', 'appears', 'apple', 'application', 'applications', 'appreciated', 'apr', 'april', 'area', 'argument', 'armenia', 'armenian', 'armenians', 'arms', 'article', 'article-i.d', 'ask', 'asked', 'asking', 'assume', 'atheists', 'attack', 'author', 'authority', 'available', 'average', 'avoid', 'away', 'b', 'b8f', 'bad', 'banks', 'base', 'baseball', 'based', 'basic', 'basis', 'bbs', 'belief', 'believe', 'best', 'better', 'bible', 'big', 'bike', 'bit', 'bits', 'black', 'blood', 'board', 'bob', 'body', 'book', 'books', 'boston', 'bought', 'box', 'break', 'brian', 'build', 'building', 'built', 'bus', 'business', 'buy', 'c', 'c.', 'ca', 'cable', 'california', 'called', 'came', 'canada', 'car', 'card', 'cards', 'care', 'carry', 'cars', 'case', 'cases', 'cause', 'center', 'certain', 'certainly', 'chance', 'change', 'changed', 'cheap', 'check', 'chicago', 'child', 'children', 'chip', 'chips', 'choice', 'chris', 'christ', 'christian', 'christianity', 'christians', 'church', 'citizens', 'city', 'claim', 'claims', 'class', 'clear', 'clearly', 'clinton', 'clipper', 'close', 'code', 'college', 'color', 'colorado', 'come', 'comes', 'coming', 'command', 'comments', 'commercial', 'common', 'communications', 'community', 'company', 'complete', 'completely', 'computer', 'computing', 'condition', 'congress', 'consider', 'considered', 'contact', 'continue', 'control', 'controller', 'copy', 'corporation', 'correct', 'cost', 'country', 'couple', 'course', 'court', 'cover', 'create', 'created', 'crime', 'current', 'currently', 'cut', 'd', 'd.', 'dan', 'data', 'date', 'dave', 'david', 'day', 'days', 'db', 'dead', 'deal', 'death', 'decided', 'defense', 'deleted', 'department', 'dept', 'des', 'design', 'designed', 'details', 'development', 'device', 'devices', 'did', 'die', 'difference', 'different', 'difficult', 'digital', 'directly', 'directory', 'disclaimer', 'discussion', 'disk', 'display', 'distribution', 'division', 'dod', 'does', 'doing', 'dos', 'doubt', 'doug', 'dr.', 'drive', 'driver', 'drivers', 'drives', 'drugs', 'e', 'e-mail', 'e.', 'early', 'earth', 'easily', 'east', 'easy', 'ed', 'education', 'effect', 'electronic', 'email', 'encryption', 'end', 'enforcement', 'engine', 'engineering', 'entire', 'entry', 'eric', 'error', 'especially', 'event', 'events', 'evidence', 'exactly', 'example', 'excellent', 'exist', 'existence', 'exists', 'expect', 'experience', 'explain', 'f', 'face', 'fact', 'faith', 'family', 'fan', 'faq', 'far', 'fast', 'faster', 'father', 'fax', 'fbi', 'federal', 'feel', 'field', 'figure', 'file', 'files', 'final', 'finally', 'fine', 'firearms', 'folks', 'follow', 'following', 'food', 'force', 'form', 'format', 'frank', 'free', 'freedom', 'friend', 'ftp', 'function', 'future', 'g', 'game', 'games', 'gary', 'gas', 'gave', 'general', 'generally', 'george', 'germany', 'gets', 'getting', 'given', 'gives', 'giz', 'gm', 'gmt', 'goal', 'god', 'goes', 'going', 'good', 'gordon', 'got', 'government', 'graphics', 'great', 'greek', 'ground', 'group', 'groups', 'guess', 'gun', 'guns', 'guy', 'h', 'half', 'hand', 'happen', 'happened', 'happy', 'hard', 'hardware', 'having', 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'reply', 'reply-to', 'report', 'request', 'require', 'required', 'research', 'response', 'rest', 'result', 'results', 'return', 'richard', 'right', 'rights', 'road', 'robert', 'roger', 'room', 'rule', 'rules', 'run', 'running', 'runs', 'russian', 's', 's.', 'safety', 'said', 'sale', 'san', 'save', 'saw', 'say', 'saying', 'says', 'school', 'science', 'scientific', 'scott', 'screen', 'scsi', 'season', 'second', 'secret', 'section', 'secure', 'security', 'seen', 'sell', 'send', 'sense', 'sent', 'serial', 'series', 'server', 'service', 'services', 'set', 'shall', 'short', 'shot', 'similar', 'simple', 'simply', 'single', 'site', 'situation', 'size', 'small', 'smith', 'society', 'software', 'solution', 'somebody', 'son', 'soon', 'sorry', 'sort', 'sound', 'sounds', 'source', 'sources', 'south', 'space', 'speak', 'special', 'specific', 'speed', 'st.', 'standard', 'start', 'started', 'state', 'statement', 'states', 'station', 'stephanopoulos', 'steve', 'stop', 'story', 'street', 'strong', 'studies', 'study', 'stuff', 'stupid', 'suggest', 'summary', 'sun', 'support', 'supposed', 'sure', 'systems', 't', 'taken', 'takes', 'taking', 'talk', 'talking', 'tape', 'tax', 'team', 'teams', 'technical', 'technology', 'tell', 'test', 'texas', 'text', 'thank', 'thanks', 'theory', 'thing', 'things', 'think', 'thinking', 'thomas', 'thought', 'tim', 'time', 'times', 'tin', 'title', 'today', 'told', 'tom', 'took', 'toronto', 'total', 'tried', 'true', 'truth', 'try', 'trying', 'turkey', 'turkish', 'turks', 'turn', 'tv', 'type', 'u', 'u.s.', 'uk', 'understand', 'unfortunately', 'unit', 'united', 'univ', 'university', 'unix', 'unless', 'usa', 'use', 'used', 'useful', 'usenet', 'user', 'users', 'uses', 'using', 'usually', 'uunet', 'v', 'value', 'values', 'various', 'version', 'video', 'view', 'views', 'voice', 'vs', 'w', 'want', 'wanted', 'wants', 'war', 'washington', 'water', 'way', 'ways', 'weapons', 'week', 'weeks', 'went', 'west', 'western', 'white', 'widget', 'willing', 'win', 'window', 'windows', 'wish', 'wo', 'women', 'wondering', 'word', 'words', 'work', 'working', 'works', 'world', 'worse', 'worth', 'write', 'writes', 'written', 'wrong', 'wrote', 'x', 'x-newsreader', 'y', 'year', 'years', 'yes', 'york', 'young', 'z', '{', '|', '||', '}']\n",
            "{\"'s\": 12, 'thing': 889, 'car': 196, '!': 0, '?': 84, 'nntp-posting-host': 637, 'university': 925, 'college': 236, '15': 40, 'wondering': 973, 'saw': 788, 'day': 283, 'looked': 557, 'early': 330, 'called': 193, 'really': 741, 'small': 825, 'rest': 764, 'body': 174, 'know': 520, 'model': 604, 'engine': 343, 'years': 991, 'history': 453, 'info': 479, 'looking': 558, 'e-mail': 328, 'thanks': 887, '-': 19, '--': 21, 'guy': 433, 'final': 381, 'summary': 864, 'keywords': 515, 'article-i.d': 138, 'washington': 954, '11': 35, 'number': 641, 'send': 806, 'message': 597, 'speed': 844, 'add': 103, 'cards': 198, 'disk': 311, 'm': 567, 'especially': 349, 'days': 284, 'network': 630, 'knowledge': 521, 'base': 155, \"n't\": 617, '<': 81, '>': 83, 'thomas': 893, 'e': 327, 'questions': 731, '...': 23, 'engineering': 344, 'computer': 251, 'distribution': 313, 'usa': 928, 'folks': 385, 'mac': 570, 'plus': 691, 'finally': 382, 'gave': 404, 'life': 543, 'way': 956, \"'m\": 10, 'market': 583, 'new': 631, 'machine': 571, 'bit': 168, 'maybe': 587, 'somebody': 830, 'answer': 121, '*': 14, 'does': 316, 'anybody': 122, \"'d\": 8, 'heard': 446, 'supposed': 867, 'make': 576, '``': 92, \"''\": 6, 'access': 98, 'price': 710, 'line': 548, 'like': 545, 'ones': 653, 'just': 509, 'went': 961, 'recently': 747, 'display': 312, 'probably': 714, 'got': 422, 'feel': 376, 'better': 164, 'looks': 559, 'great': 425, 'good': 420, 'opinions': 656, 'people': 675, 'use': 929, 'worth': 981, 'taking': 873, 'size': 824, 'money': 606, 'hit': 454, 'real': 740, 'question': 730, \"'ve\": 13, 'machines': 572, 'actually': 102, 'uses': 935, ';': 80, 'advance': 106, 'email': 339, \"'ll\": 9, 'post': 701, 'news': 632, 'reading': 739, 'time': 896, 'tom': 902, '\\\\': 86, 'truth': 908, 'joe': 506, 'systems': 869, 'division': 314, '14': 38, 'world': 979, 'x-newsreader': 988, 'tin': 898, '[': 85, 'version': 943, '1.1': 32, ']': 87, 'robert': 772, 'wrote': 986, 'writes': 983, 'article': 137, 'graphics': 424, 'chip': 216, 'far': 369, 'stuff': 861, 'goes': 418, 'pretty': 708, 'nice': 635, 'command': 242, 'points': 693, 'information': 480, 'corporation': 262, 'person': 678, 'sense': 807, 'launch': 532, 'ma': 569, '23': 53, 'clear': 230, '&': 4, 'memory': 593, 'error': 348, 'sorry': 833, 'known': 522, 'yes': 992, 'knew': 519, \"'\": 5, 'software': 828, 'things': 890, 'right': 769, 'values': 941, 'set': 814, 'code': 235, 'tell': 882, 'second': 799, 'ideas': 465, '$': 2, 'c.': 189, 'd.': 277, 'john': 507, 'power': 704, 'weapons': 958, 'makes': 577, 'cost': 264, 'need': 625, 'control': 259, 'government': 423, 'individual': 478, 'result': 765, \"'re\": 11, 'coming': 241, 'say': 789, 'believe': 162, 'hard': 440, 'support': 866, 'gas': 403, 'agree': 110, 'hope': 458, 'course': 267, 'doug': 320, 'says': 791, 'means': 589, 'street': 857, 'doubt': 319, 'using': 936, 'killed': 517, 'year': 990, 'easily': 332, 'reasonable': 743, 'mean': 588, 'read': 738, 'argument': 132, 'point': 692, 'allowed': 116, 'later': 531, 'given': 411, 'consider': 255, 'class': 229, 'speak': 841, 'company': 248, 'write': 982, 'today': 900, 'special': 842, 'brian': 181, 'reply-to': 757, 'chicago': 213, '12': 36, 'request': 759, 'thank': 886, 'directly': 307, 'thought': 894, 'sure': 868, 'instead': 484, 'trying': 910, 'file': 379, 'ide': 463, 'vs': 948, 'scsi': 797, 'state': 849, 'pc': 673, 'april': 130, '27': 57, '20': 50, '%': 3, 'faster': 371, '7': 75, 'devices': 300, 'long': 554, 'problems': 716, 'love': 565, 'stupid': 862, 'performance': 676, 'numbers': 642, 'list': 549, 'statement': 850, '{': 996, '}': 999, 'controller': 260, 'note': 640, 'exist': 356, 'mode': 603, 'fast': 370, 'data': 279, 'correct': 263, 'posted': 702, 'newsgroup': 633, 'ibm': 462, 'available': 147, 'ftp': 396, '#': 1, 'problem': 715, 'apple': 125, 'said': 784, 'interface': 489, 'think': 891, 'true': 907, 'understand': 920, 'going': 419, 'reference': 750, 'digital': 306, '21': 51, '1': 30, 'win': 967, 'help': 448, '10': 33, 'ca': 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    {
      "cell_type": "code",
      "metadata": {
        "id": "wFAtifCbpPx_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
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}