LDA.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LDA.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "15_LiAvSzSu9b8hYkKc-yGT1b--tvSDVj",
      "authorship_tag": "ABX9TyMwIZFas7RYx13/LEDA9rSH"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoeTc2LS2NUJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import fetch_20newsgroups\n",
        "import spacy\n",
        "import gensim"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EtEBdns32rfx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "newsgroups_train = fetch_20newsgroups('/content/drive/My Drive/Datasets',subset='train',\n",
        "                                     remove=('headers', 'footers', 'quotes'))"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3fZg0vzH3C7e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "newsgroups_test = fetch_20newsgroups('/content/drive/My Drive/Datasets',subset='test',\n",
        "                                     remove=('headers', 'footers', 'quotes'))"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iTAGxYpI3Wr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "c31e1e23-b40c-4963-e1c2-6c897e09f019"
      },
      "source": [
        "import pprint\n",
        "for i in range(2):\n",
        "  print(newsgroups_train.data[i])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "I was wondering if anyone out there could enlighten me on this car I saw\n",
            "the other day. It was a 2-door sports car, looked to be from the late 60s/\n",
            "early 70s. It was called a Bricklin. The doors were really small. In addition,\n",
            "the front bumper was separate from the rest of the body. This is \n",
            "all I know. If anyone can tellme a model name, engine specs, years\n",
            "of production, where this car is made, history, or whatever info you\n",
            "have on this funky looking car, please e-mail.\n",
            "A fair number of brave souls who upgraded their SI clock oscillator have\n",
            "shared their experiences for this poll. Please send a brief message detailing\n",
            "your experiences with the procedure. Top speed attained, CPU rated speed,\n",
            "add on cards and adapters, heat sinks, hour of usage per day, floppy disk\n",
            "functionality with 800 and 1.4 m floppies are especially requested.\n",
            "\n",
            "I will be summarizing in the next two days, so please add to the network\n",
            "knowledge base if you have done the clock upgrade and haven't answered this\n",
            "poll. Thanks.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UIvSadZB6Yq8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "668ce997-db76-4db1-cf34-8ee38f2932ab"
      },
      "source": [
        "len(newsgroups_train.data)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11314"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IYDHpgDW2xWU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "def preprocess(text):\n",
        "  m_text = re.sub(r'\\s+',' ',text)\n",
        "  m_text = m_text.strip()\n",
        "  m_text = re.sub(r'\\S*@\\S*' , '' ,m_text)\n",
        "  m_text = re.sub(r'https?://\\S+|www\\.\\S+','',m_text)\n",
        "  m_text = re.sub(r'[^a-zA-Z_\\s+]','',m_text)\n",
        "  return m_text"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hI8gx3E209h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import spacy\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "def tokenizer(text):\n",
        "    m_text = preprocess(text)\n",
        "    doc = nlp(m_text)\n",
        "    doc = [token for token in doc if not (token.is_punct or token.is_stop or len(token.text) < 3 or len(token.text)>12) ]\n",
        "    doc = [token.lemma_ if token.pos_ is not 'PRON' else token.lower_ for token in doc]\n",
        "    return doc"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31UvKeeI276R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = [tokenizer(txt) for txt in newsgroups_train['data']]\n",
        "test_data =  [tokenizer(txt) for txt in newsgroups_test['data']]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbygJZtk_6ZE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "67515c9b-0ff8-4d26-e3a3-6e2b2b57f541"
      },
      "source": [
        "for i in range(5):\n",
        "  print(train_data[i])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['wonder', 'enlighten', 'car', 'see', 'day', 'door', 'sport', 'car', 'look', 'late', 'early', 'call', 'Bricklin', 'door', 'small', 'addition', 'bumper', 'separate', 'rest', 'body', 'know', 'tellme', 'model', 'engine', 'specs', 'year', 'production', 'car', 'history', 'info', 'funky', 'look', 'car', 'email']\n",
            "['fair', 'number', 'brave', 'soul', 'upgrade', 'clock', 'oscillator', 'share', 'experience', 'poll', 'send', 'brief', 'message', 'detail', 'experience', 'procedure', 'speed', 'attain', 'cpu', 'rate', 'speed', 'add', 'card', 'adapter', 'heat', 'sink', 'hour', 'usage', 'day', 'floppy', 'disk', 'floppy', 'especially', 'request', 'summarize', 'day', 'add', 'network', 'knowledge', 'base', 'clock', 'upgrade', 'answer', 'poll', 'thank']\n",
            "['folk', 'mac', 'plus', 'finally', 'give', 'ghost', 'weekend', 'start', 'life', 'way', 'sooo', 'market', 'new', 'machine', 'bit', 'sooner', 'intend', 'look', 'pick', 'powerbook', 'maybe', 'bunch', 'question', 'hopefully', 'somebody', 'answer', 'anybody', 'know', 'dirt', 'round', 'powerbook', 'expect', 'hear', 'suppose', 'appearence', 'summer', 'hear', 'anymore', 'access', 'macleak', 'wonder', 'anybody', 'info', 'anybody', 'hear', 'rumor', 'price', 'drop', 'powerbook', 'line', 'like', 'one', 'duos', 'go', 'recently', 'impression', 'display', 'probably', 'swing', 'get', 'disk', 'feel', 'well', 'display', 'yea', 'look', 'great', 'store', 'wow', 'good', 'solicit', 'opinion', 'people', 'use', 'daytoday', 'worth', 'take', 'disk', 'size', 'money', 'hit', 'active', 'display', 'realize', 'real', 'subjective', 'question', 'play', 'machine', 'computer', 'store', 'breifly', 'figure', 'opinion', 'somebody', 'actually', 'use', 'machine', 'daily', 'prove', 'helpful', 'hellcat', 'perform', 'thank', 'bunch', 'advance', 'info', 'email', 'ill', 'post', 'summary', 'news', 'reading', 'time', 'premium', 'final', 'corner', '   ', 'Tom', 'Willis', '   ', 'Purdue', 'Electrical', 'Engineering']\n",
            "['Weiteks', 'addressphone', 'number', 'like', 'information', 'chip']\n",
            "['article', 'Tom', 'Baker', 'expect', 'error', 'basically', 'know', 'bug', 'warning', 'system', 'software', 'thing', 'check', 'right', 'value', 'set', 'till', 'launch', 'suchlike', 'fix', 'code', 'possibly', 'introduce', 'new', 'bug', 'tell', 'crew', 'warning', 'liftoff', 'ignore']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HT_NYIbd8STf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.models import Phrases\n",
        "from gensim.models.phrases import Phraser\n",
        "def bigrams(data):\n",
        "    bigram = Phrases(data)\n",
        "    bigram_model = Phraser(bigram)\n",
        "    return bigram_model\n",
        "\n",
        "bigram_model = bigrams(train_data)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtEIDdEuAzW7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_bigram = [bigram_model[doc] for doc in train_data]\n",
        "test_bigram = [bigram_model[doc] for doc in test_data]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYgeJjsIAFjZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "e7055d2b-75ab-48bb-94b5-0f7df615b8f4"
      },
      "source": [
        "for i in range(5):\n",
        "  print(train_bigram[i])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['wonder', 'enlighten', 'car', 'see', 'day', 'door', 'sport_car', 'look', 'late', 'early', 'call', 'Bricklin', 'door', 'small', 'addition', 'bumper', 'separate', 'rest', 'body', 'know', 'tellme', 'model', 'engine', 'specs', 'year', 'production', 'car', 'history', 'info', 'funky', 'look', 'car', 'email']\n",
            "['fair', 'number', 'brave', 'soul', 'upgrade', 'clock_oscillator', 'share', 'experience', 'poll', 'send', 'brief', 'message', 'detail', 'experience', 'procedure', 'speed', 'attain', 'cpu', 'rate', 'speed', 'add', 'card', 'adapter', 'heat_sink', 'hour', 'usage', 'day', 'floppy_disk', 'floppy', 'especially', 'request', 'summarize', 'day', 'add', 'network', 'knowledge', 'base', 'clock', 'upgrade', 'answer', 'poll', 'thank']\n",
            "['folk', 'mac', 'plus', 'finally', 'give', 'ghost', 'weekend', 'start', 'life', 'way', 'sooo', 'market', 'new', 'machine', 'bit', 'sooner', 'intend', 'look', 'pick', 'powerbook', 'maybe', 'bunch', 'question', 'hopefully', 'somebody', 'answer', 'anybody_know', 'dirt', 'round', 'powerbook', 'expect', 'hear', 'suppose', 'appearence', 'summer', 'hear', 'anymore', 'access', 'macleak', 'wonder', 'anybody', 'info', 'anybody', 'hear_rumor', 'price', 'drop', 'powerbook', 'line', 'like', 'one', 'duos', 'go', 'recently', 'impression', 'display', 'probably', 'swing', 'get', 'disk', 'feel', 'well', 'display', 'yea', 'look', 'great', 'store', 'wow', 'good', 'solicit', 'opinion', 'people', 'use', 'daytoday', 'worth', 'take', 'disk', 'size', 'money', 'hit', 'active', 'display', 'realize', 'real', 'subjective', 'question', 'play', 'machine', 'computer', 'store', 'breifly', 'figure', 'opinion', 'somebody', 'actually', 'use', 'machine', 'daily', 'prove', 'helpful', 'hellcat', 'perform', 'thank', 'bunch', 'advance', 'info', 'email', 'ill', 'post_summary', 'news', 'reading', 'time', 'premium', 'final', 'corner', '   ', 'Tom', 'Willis', '   ', 'Purdue', 'Electrical_Engineering']\n",
            "['Weiteks', 'addressphone', 'number', 'like', 'information', 'chip']\n",
            "['article', 'Tom', 'Baker', 'expect', 'error', 'basically', 'know', 'bug', 'warning', 'system', 'software', 'thing', 'check', 'right', 'value', 'set', 'till', 'launch', 'suchlike', 'fix', 'code', 'possibly', 'introduce', 'new', 'bug', 'tell', 'crew', 'warning', 'liftoff', 'ignore']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6sakAe9a86Cn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.corpora import Dictionary\n",
        "dictionary = Dictionary(documents= train_bigram, prune_at=None)\n",
        "dictionary.filter_extremes(no_below=5,no_above= .3 ,keep_n=None)  # use Dictionary to remove un-relevant tokens\n",
        "dictionary.compactify()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYADf-EoCTxr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bow_train_data = [dictionary.doc2bow(doc) for doc in train_bigram]\n",
        "bow_test_data = [dictionary.doc2bow(doc) for doc in test_bigram]"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJ0qXSR7E-Sq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "35a54f46-e495-4789-a2d0-92f51570a0d6"
      },
      "source": [
        "bow_train_data[3]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(49, 1), (105, 1), (152, 1), (153, 1)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irSlGd6P9i6P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.models import LdaMulticore\n",
        "num_topics = 20\n",
        "\n",
        "LDA = LdaMulticore(\n",
        "    corpus=bow_train_data, num_topics=num_topics, id2word=dictionary, eval_every=None, passes=20, batch=True\n",
        "    ,per_word_topics = True )"
      ],
      "execution_count": 232,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kuig6leGAQib",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_topics = LDA.top_topics(bow_train_data ,topn =7)"
      ],
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNcJ4rsfOALL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "633423c8-5dbe-4787-f753-90de7f50522d"
      },
      "source": [
        "import pickle\n",
        "pickle.dump(bow_train_data, open('/content/drive/My Drive/LDA/bow_train_corpus.pkl', 'wb'))\n",
        "pickle.dump(bow_test_data, open('/content/drive/My Drive/LDA/bow_test_corpus.pkl', 'wb'))\n",
        "LDA.save('/content/drive/My Drive/LDA/model.gensim')\n",
        "dictionary.save('/content/drive/My Drive/LDA/gensim_dictionary.gensim')"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:254: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kbY9fa46BnL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pyLDAvis.gensim\n",
        "import pyLDAvis\n",
        "# Visualize the topics\n",
        "pyLDAvis.enable_notebook()\n",
        "LDAvis_prepared = pyLDAvis.gensim.prepare(LDA, bow_train_data, dictionary)"
      ],
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VEx26rYPmzx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "61483d5c-e241-4774-97e1-0cf359c1c418"
      },
      "source": [
        "LDAvis_prepared"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el38051404299537886883651274665\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el38051404299537886883651274665_data = {\"mdsDat\": {\"x\": [-0.12188412974073308, 0.11671251718725825, -0.16356547482821873, 0.135294518000672, -0.02782398746755101, 0.033861168556813925, -0.11836238698887372, 0.19575414280242703, -0.12137457631030478, -0.06862001202659283, 0.006875698857538621, -0.04586631304129947, 0.09359870039172219, -0.06215157817148929, 0.10179631569627404, -0.10509739563606642, 0.18657201380168983, -0.024928747813289617, -0.10390800135267157, 0.0931175280826953], \"y\": [0.02379144594503425, 0.09576355078547613, -0.01670182585799785, 0.14641283694219617, -0.07561602451886719, 0.11212932573710802, 0.031694684998997864, 0.023341429971707427, 0.017153453855741276, 0.035472001676520315, 0.0036835777583854234, 0.035294622707593946, -0.03302621296881278, 0.05249040176910701, 0.0826942970947694, -0.13129787967470738, -0.30531492718169045, -0.10383501904609002, -0.03318375571345397, 0.03905401571898198], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [14.08559513092041, 9.455368041992188, 9.09850788116455, 7.555154800415039, 6.491547584533691, 6.287770748138428, 5.378445148468018, 5.117361068725586, 4.233366966247559, 4.134365081787109, 3.710458517074585, 3.694326162338257, 3.540961742401123, 3.1669676303863525, 2.675126791000366, 2.663907766342163, 2.5744638442993164, 2.5308127403259277, 1.9569555521011353, 1.6485438346862793]}, \"tinfo\": {\"Term\": [\"   \", \"     \", \"file\", \"    \", \"game\", \"God\", \"program\", \"people\", \"team\", \"car\", \"use\", \"key\", \"government\", \"think\", \"window\", \"say\", \"drive\", \"know\", \"entry\", \"player\", \"card\", \"year\", \"Jesus\", \"chip\", \"Armenians\", \"system\", \"server\", \"      \", \"play\", \"post\", \"Christianity\", \"atheism\", \"atheist\", \"salvation\", \"morality\", \"Jesus_Christ\", \"Christ\", \"God_exist\", \"believe_God\", 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\"torque\", \"torque\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"town\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"trial\", \"troop\", \"troop\", \"troop\", \"troop\", \"troop\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"true\", \"truelove_serve\", \"trust_NSA\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"truth\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"tube\", \"tube\", \"tube\", \"tube\", \"tube\", \"tube\", \"tube\", \"tube\", \"tumor\", \"turbine\", \"turkish\", \"turkish\", \"turkish\", \"turkish\", \"turkish_genocide\", \"turkish_government\", \"turkish_government\", \"turkish_kurdish\", \"turkish_kurdish\", \"turkish_minority\", \"turkish_village\", \"tvtwm\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type_font\", \"type_font\", \"ultrasound\", \"ultrasound\", \"unassiste\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"universe\", \"universe\", \"universe\", \"universe\", \"universe\", \"universe\", \"unlock_message\", \"unreadable\", \"upgrade\", \"upgrade\", \"upgrade\", \"upgrade\", \"upgrade\", \"upgrade\", \"upgrade\", \"upgrade\", \"upgrade\", \"uprising\", \"uranium\", \"uranium\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"uudecode\", \"uudecode\", \"vaccine\", \"vaginal\", \"valve\", \"valve\", \"van\", \"van\", \"van\", \"van\", \"vat\", \"vault\", \"vault\", \"vegetable\", \"verse\", \"verse\", \"verse\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"version\", \"vesa\", \"video_card\", \"video_card\", \"video_card\", \"video_card\", \"video_driver\", \"village\", \"village\", \"village\", \"village\", \"visual\", \"visual\", \"visual\", \"visual\", \"visual\", \"vitamin\", \"vitamin\", \"vitamin\", \"voltage\", \"voltage\", \"volume_number\", \"volume_number\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"wait_period\", \"wait_period\", \"wait_period\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warning\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"watt\", \"watt\", \"watt\", \"watt\", \"wave\", \"wave\", \"wave\", \"wave\", \"wave\", \"wave\", \"wave\", \"wave\", \"wave\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"way\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon_mass\", \"weight_gain\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"well\", \"wheel\", \"wheel\", \"wheel\", \"wheel\", \"wheel\", \"wheel\", \"wheel\", \"wheel\", \"wheel\", \"wheel\", \"whitespace\", \"whitespace\", \"widget\", \"widget\", \"widget\", \"widget_set\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win\", \"win_game\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window_manager\", \"window_manager\", \"window_manager\", \"window_manager\", \"windshield\", \"winner\", \"winner\", \"winner\", \"winner\", \"winner\", \"winner\", \"winner\", \"winner\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wire\", \"wiring\", \"wiring\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world_Mary\", \"worship\", \"worship\", \"worship\", \"wound\", \"wound\", \"wound\", \"wound\", \"wound\", \"wound\", \"wound\", \"wound\", \"wrench\", \"wrench\", \"wrench\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"xSoviet_Armenia\", \"xSoviet_Armenia\", \"xSoviet_armenian\", \"xbase\", \"xdm\", \"xterm\", \"xterm\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"yesterday\", \"yesterday\", \"yesterday\", \"yesterday\", \"yesterday\", \"yesterday\", \"yesterday\", \"yesterday\", \"yesterday\", \"yesterday\", \"young_people\", \"young_people\", \"young_people\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [17, 7, 14, 16, 3, 15, 19, 9, 5, 10, 12, 8, 20, 11, 1, 6, 2, 4, 18, 13]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
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              "  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_el38051404299537886883651274665\", ldavis_el38051404299537886883651274665_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://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el38051404299537886883651274665\", ldavis_el38051404299537886883651274665_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el38051404299537886883651274665\", ldavis_el38051404299537886883651274665_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ],
            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "16    -0.121884  0.023791       1        1  14.085595\n",
              "6      0.116713  0.095764       2        1   9.455368\n",
              "13    -0.163565 -0.016702       3        1   9.098508\n",
              "15     0.135295  0.146413       4        1   7.555155\n",
              "2     -0.027824 -0.075616       5        1   6.491548\n",
              "14     0.033861  0.112129       6        1   6.287771\n",
              "18    -0.118362  0.031695       7        1   5.378445\n",
              "8      0.195754  0.023341       8        1   5.117361\n",
              "4     -0.121375  0.017153       9        1   4.233367\n",
              "9     -0.068620  0.035472      10        1   4.134365\n",
              "11     0.006876  0.003684      11        1   3.710459\n",
              "7     -0.045866  0.035295      12        1   3.694326\n",
              "19     0.093599 -0.033026      13        1   3.540962\n",
              "10    -0.062152  0.052490      14        1   3.166968\n",
              "0      0.101796  0.082694      15        1   2.675127\n",
              "5     -0.105097 -0.131298      16        1   2.663908\n",
              "1      0.186572 -0.305315      17        1   2.574464\n",
              "3     -0.024929 -0.103835      18        1   2.530813\n",
              "17    -0.103908 -0.033184      19        1   1.956956\n",
              "12     0.093118  0.039054      20        1   1.648544, topic_info=       Term         Freq        Total Category  logprob  loglift\n",
              "62           2894.000000  2894.000000  Default  30.0000  30.0000\n",
              "679           990.000000   990.000000  Default  29.0000  29.0000\n",
              "210    file  1754.000000  1754.000000  Default  28.0000  28.0000\n",
              "678           804.000000   804.000000  Default  27.0000  27.0000\n",
              "1455   game  1047.000000  1047.000000  Default  26.0000  26.0000\n",
              "...     ...          ...          ...      ...      ...      ...\n",
              "62             42.219467  2894.010010  Topic20  -5.6513  -0.1222\n",
              "172    tell    38.140350  1652.825073  Topic20  -5.7529   0.3363\n",
              "300    work    38.740814  2630.372559  Topic20  -5.7373  -0.1127\n",
              "112     new    36.792881  1682.729614  Topic20  -5.7889   0.2824\n",
              "105    like    37.753231  3402.296875  Topic20  -5.7631  -0.3959\n",
              "\n",
              "[1610 rows x 6 columns], token_table=       Topic      Freq          Term\n",
              "term                                \n",
              "62         1  0.007256              \n",
              "62         2  0.025570              \n",
              "62         3  0.083621              \n",
              "62         4  0.013131              \n",
              "62         5  0.006220              \n",
              "...      ...       ...           ...\n",
              "567       12  0.090300     yesterday\n",
              "567       19  0.024627     yesterday\n",
              "10733      3  0.140002  young_people\n",
              "10733      7  0.031112  young_people\n",
              "10733     12  0.808901  young_people\n",
              "\n",
              "[7708 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[17, 7, 14, 16, 3, 15, 19, 9, 5, 10, 12, 8, 20, 11, 1, 6, 2, 4, 18, 13])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4xxvvEQGZat",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "217a03bb-23ee-40c1-cedc-9bf4dc0070bf"
      },
      "source": [
        "for i in range(10):\n",
        "  print(top_topics[i])"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "([(0.013780172, 'post'), (0.010773995, 'people'), (0.008151971, 'like'), (0.007919357, 'group'), (0.0074083675, 'think'), (0.0072529586, 'right'), (0.0062818513, 'know')], -1.2682225647518988)\n",
            "([(0.01671948, 'entry'), (0.014209497, 'file'), (0.014061238, 'program'), (0.011123775, 'line'), (0.010171141, 'problem'), (0.008263295, 'use'), (0.006917783, 'system')], -1.2712718974767643)\n",
            "([(0.019815465, 'game'), (0.015184599, 'team'), (0.012752844, 'year'), (0.010867448, 'play'), (0.010864883, 'good'), (0.009931654, 'player'), (0.008976983, 'think')], -1.3256276033541878)\n",
            "([(0.01158912, 'God'), (0.008576355, 'people'), (0.008176537, 'think'), (0.0075558797, 'know'), (0.0064504975, 'Jesus'), (0.006187106, 'believe'), (0.00576525, 'say')], -1.3865729483554852)\n",
            "([(0.010859024, 'drive'), (0.010739261, 'card'), (0.009631535, 'use'), (0.009246996, 'problem'), (0.008278383, 'work'), (0.0075654243, 'system'), (0.0074819387, 'know')], -1.3908445482360863)\n",
            "([(0.01128554, 'armenian'), (0.009634438, 'Armenians'), (0.0072683822, 'turkish'), (0.0071939556, 'Turkey'), (0.0067762434, 'Jews'), (0.004981845, 'people'), (0.0044235126, 'Nazis')], -1.3926062179697443)\n",
            "([(0.011918273, 'people'), (0.011593926, 'say'), (0.009384511, 'go'), (0.008636929, 'know'), (0.008129146, 'come'), (0.0064086895, 'kill'), (0.00603392, 'like')], -1.404975596830457)\n",
            "([(0.017552001, 'people'), (0.011631929, 'government'), (0.010309371, 'gun'), (0.009445439, 'right'), (0.0074558267, 'law'), (0.007168676, 'think'), (0.0071248617, 'want')], -1.4366691572812325)\n",
            "([(0.0079639815, 'patient'), (0.0067161326, 'disease'), (0.005009539, 'year'), (0.0047835773, 'doctor'), (0.004572874, 'use'), (0.0045024497, 'time'), (0.004262836, 'cause')], -1.5146682441021262)\n",
            "([(0.010137685, 'space'), (0.008860343, 'launch'), (0.008697745, 'satellite'), (0.0076747653, 'NASA'), (0.007214204, 'mission'), (0.0066284975, 'Space'), (0.0060350876, 'orbit')], -1.5463037736539713)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zh3YMXt8BDgd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_log_perplexity = LDA.log_perplexity(bow_train_data)\n",
        "test_log_perplexity = LDA.log_perplexity(bow_test_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q924iY26M-cY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "92af44ad-4022-4a35-f89f-7975b0f2e4f0"
      },
      "source": [
        "print(\"Train log-perplexity score :\" , round(train_log_perplexity,2))\n",
        "print(\"Test log-perplexity score :\" , round(test_log_perplexity,2))"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train log-perplexity score : -8.13\n",
            "Test log-perplexity score : -8.49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UVOwuHsKD1X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "801ea947-d72f-4f09-e296-ec7d589bb7a8"
      },
      "source": [
        "newsgroups_train.target_names"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['alt.atheism',\n",
              " 'comp.graphics',\n",
              " 'comp.os.ms-windows.misc',\n",
              " 'comp.sys.ibm.pc.hardware',\n",
              " 'comp.sys.mac.hardware',\n",
              " 'comp.windows.x',\n",
              " 'misc.forsale',\n",
              " 'rec.autos',\n",
              " 'rec.motorcycles',\n",
              " 'rec.sport.baseball',\n",
              " 'rec.sport.hockey',\n",
              " 'sci.crypt',\n",
              " 'sci.electronics',\n",
              " 'sci.med',\n",
              " 'sci.space',\n",
              " 'soc.religion.christian',\n",
              " 'talk.politics.guns',\n",
              " 'talk.politics.mideast',\n",
              " 'talk.politics.misc',\n",
              " 'talk.religion.misc']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihHiS_yzNcjt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.models import CoherenceModel\n",
        "# Compute Coherence Score\n",
        "coherence_model_lda = CoherenceModel(model=LDA, texts= test_bigram, coherence='c_v' , topn=4)\n",
        "coherence = coherence_model_lda.get_coherence()"
      ],
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spr-Zgtbq9G7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d2560c52-b6c9-4d3f-e8a9-9f371f9b4530"
      },
      "source": [
        "print('Test Coherence Score: ', round(coherence , 2))"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Coherence Score:  0.67\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XoTkuM0ORViB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_LDA_vectors(bow_corpus):\n",
        "  vectors = []\n",
        "  for i in range(len(bow_corpus)):\n",
        "      top_topics = LDA.get_document_topics(bow_corpus[i] , minimum_probability=0.0)\n",
        "      topic_vec = [top_topics[i][1] for i in range(20)]\n",
        "      vectors.append(topic_vec)\n",
        "  return vectors"
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwcZ_tnySS3O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_vectors = get_LDA_vectors(bow_train_data)\n",
        "test_vectors = get_LDA_vectors(bow_test_data)"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f913vmuMS9f2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "ae85f575-5cf4-498d-f9b7-e05ed822b685"
      },
      "source": [
        "train_vectors[40]"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4337626,\n",
              " 0.0005952382,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.112444766,\n",
              " 0.0005952381,\n",
              " 0.15745255,\n",
              " 0.2868163,\n",
              " 0.0005952381,\n",
              " 0.0005952382,\n",
              " 0.0005952381,\n",
              " 0.0005952381,\n",
              " 0.0005952381]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl9WwlXKWD1B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labels = newsgroups_train.target\n",
        "test_labels = newsgroups_test.target"
      ],
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zaBET63mSiX0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scalar = StandardScaler()\n",
        "scalar.fit(train_vectors)\n",
        "scaled_train = scalar.transform(train_vectors)\n",
        "scaled_test = scalar.transform(test_vectors)"
      ],
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XiNENgvrWVYV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "outputId": "cbe2f461-12a0-4da2-eb18-d36c8de38ba1"
      },
      "source": [
        "svc = SVC(verbose=5) \n",
        "params = {'C':[1,2,5,6], 'kernel' : ['rbf']}\n",
        "model = GridSearchCV(svc, params , n_jobs= -1 ,verbose = 10,cv = 2)\n",
        "model = model.fit(scaled_train,train_labels)"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 2 folds for each of 4 candidates, totalling 8 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    5.8s\n",
            "[Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:   11.8s\n",
            "[Parallel(n_jobs=-1)]: Done   6 out of   8 | elapsed:   17.9s remaining:    6.0s\n",
            "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:   23.8s remaining:    0.0s\n",
            "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:   23.8s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[LibSVM]"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cPrQLNfYNby",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c3d736cf-e8a5-41a6-a0b4-6238a6b43211"
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 6, 'kernel': 'rbf'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuBNPf-9bEEY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svc = SVC(C = 6 , kernel = 'rbf')\n",
        "svc = svc.fit(scaled_train,train_labels)\n",
        "pred_labels =svc.predict(scaled_test)"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AIhubfUvXKeF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "a2a41446-da39-4478-ee90-528f55b25081"
      },
      "source": [
        "from sklearn.metrics import accuracy_score as accuracy\n",
        "accuracy = accuracy(pred_labels,test_labels)\n",
        "print('Accuracy :',accuracy)\n",
        "print('f1_score :',metrics.f1_score(test_labels, pred_labels, average='weighted'))"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.3835634625597451\n",
            "f1_score : 0.3653025167225441\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9K3zJCqViG4S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "tfidf = TfidfVectorizer(analyzer= 'word', tokenizer=tokenizer)\n",
        "tfidf_train = tfidf.fit_transform(newsgroups_train.data)\n",
        "tfidf_test = tfidf.transform(newsgroups_test.data)\n",
        "\n",
        "classifier_NB = MultinomialNB(alpha=0.01)\n",
        "classifier_NB.fit(tfidf_train , train_labels)\n",
        "\n",
        "pred = classifier_NB.predict(tfidf_test)"
      ],
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fW23FslWnUXj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "c98ee8fb-b063-4747-e062-cc0eb3a8a474"
      },
      "source": [
        "accuracy = accuracy(test_labels,pred)\n",
        "print('Accuracy :',accuracy)\n",
        "print('f1_score :',metrics.f1_score(test_labels, pred, average='weighted'))"
      ],
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy : 0.6872012745618693\n",
            "f1_score : 0.6832558535521507\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}