Part 5.2: TopicModeling_using_LDA_gensim.ipynb

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    "colab": {
      "name": "TopicModeling_LDA_gensim.ipynb",
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    },
    "kernelspec": {
      "display_name": "Python 3",
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzjpVab7zJfR"
      },
      "source": [
        "#Installing the libraray for Data Visualisation for LDA\n",
        "\n",
        "!pip install pyLDAvis "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jr997TmIzJfj"
      },
      "source": [
        "#For english letter parsing, follow the same step as above. Open commnad prompt with admin rights and run the below command\n",
        "##      python -m spacy download en "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AajM9dM2zJft"
      },
      "source": [
        "#Loading English language\n",
        "\n",
        "from spacy.lang.en import English"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SXFnMeQzJf0"
      },
      "source": [
        "#Creeating an object of English class to parse the data\n",
        "\n",
        "parser = English()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmVq6hoLzJf_"
      },
      "source": [
        "#Writing functions for data cleaning\n",
        "# Starting from tokenization\n",
        "\n",
        "def tokenize(text):\n",
        "    lda_tokens = []\n",
        "    #tokens = text\n",
        "    tokens = parser(text)\n",
        "    for token in tokens:\n",
        "        if token.orth_.isspace():\n",
        "            continue\n",
        "        elif token.like_url: #Does the token resemples URL\n",
        "            lda_tokens.append('URL')\n",
        "        elif token.orth_.startswith('@'): #Does token starts with @\n",
        "            lda_tokens.append('SCREEN_NAME')\n",
        "        else:\n",
        "            lda_tokens.append(token.lower_)\n",
        "    return lda_tokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-DiDAfBGzJgJ",
        "outputId": "8431b8b9-82dc-477b-d571-97c911a1dee9"
      },
      "source": [
        "#Importing nltk. Its a heavy library so might take time. We need only wordnet part of NLTK not complete package\n",
        "import nltk\n",
        "nltk.download('wordnet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xoUKrUEwzJgh"
      },
      "source": [
        "#Functions for Lemmma\n",
        "\n",
        "from nltk.corpus import wordnet as wn\n",
        "def get_lemma(word):\n",
        "    lemma = wn.morphy(word)\n",
        "    if lemma is None:\n",
        "        return word\n",
        "    else:\n",
        "        return lemma\n",
        "    \n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "def get_lemma2(word):\n",
        "    return WordNetLemmatizer().lemmatize(word)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "b_Aa3iU-PQG2",
        "outputId": "9658c2e7-29c9-4478-cfb1-ec072199c43f"
      },
      "source": [
        "word = 'Happpyness'\n",
        "def get_lemma(word):\n",
        "    lemma = wn.morphy(word, 'n')\n",
        "    if lemma is None:\n",
        "        return word\n",
        "    else:\n",
        "        return lemma\n",
        "get_lemma(word)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Happpyness'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "StbOaptzPm-W",
        "outputId": "3ae9fadf-6dd7-40a4-d3a0-a8b718efc9f5"
      },
      "source": [
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "def get_lemma2(word):\n",
        "    return WordNetLemmatizer().lemmatize(word)\n",
        "get_lemma(word)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Happpyness'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M66i64hszJgz",
        "outputId": "a824a1ce-15bc-4a59-c9a9-b7104fcd921f"
      },
      "source": [
        "#Accessing only the stop words and creating an object of it\n",
        "\n",
        "nltk.download('stopwords')\n",
        "en_stop = set(nltk.corpus.stopwords.words('english'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhH9XdqgzJhE"
      },
      "source": [
        "#Prepraring data for LDA\n",
        "\n",
        "def prepare_text_for_lda(text):\n",
        "    tokens = tokenize(text)\n",
        "    tokens = [token for token in tokens if len(token) > 4]\n",
        "    tokens = [token for token in tokens if token not in en_stop]\n",
        "    tokens = [get_lemma(token) for token in tokens]\n",
        "    return tokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "PS8_cW5GzJhO"
      },
      "source": [
        "#Accessing dataset. \n",
        "text_data = []\n",
        "with open('/content/drive/MyDrive/NLP IMS/Day5/day 5/dataset.csv') as f:\n",
        "    for line in f:\n",
        "        tokens = prepare_text_for_lda(line)\n",
        "        print(tokens)\n",
        "        text_data.append(tokens)\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMsBzOtHzJhV"
      },
      "source": [
        "#LDA with Gensim"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "TiIWeR3uzJhW"
      },
      "source": [
        "#Importing gensim. If this gives error, download gensim using pip command \n",
        "# !pip install gensim \n",
        "\n",
        "from gensim import corpora"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "s0CysuULzJhd"
      },
      "source": [
        "#Accessing dictionary object and making corpus\n",
        "#bag of words, keeps a count of the total occurrences of most frequently used words.\n",
        "\n",
        "#In order to work on text documents, Gensim requires the words (aka tokens) \n",
        "#be converted to unique ids. In order to achieve that, \n",
        "#Gensim lets you create a Dictionary object that maps each word to a unique id.\n",
        "\n",
        "# Corpus is typically a ‘collection of documents as a bag of words’.\n",
        "dictionary = corpora.Dictionary(text_data)\n",
        "\n",
        "corpus = [dictionary.doc2bow(text) for text in text_data]\n",
        "\n",
        "import pickle #Python object hierarchy is converted into a byte stream, \n",
        "pickle.dump(corpus, open('corpus.pkl', 'wb'))\n",
        "dictionary.save('dictionary.gensim')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "7zwdbqqizJhl",
        "outputId": "d221cefe-ad71-4778-f6ca-9ca44c0682e3"
      },
      "source": [
        "#Applying LDA\n",
        "\n",
        "import gensim\n",
        "NUM_TOPICS = 5\n",
        "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = NUM_TOPICS, id2word=dictionary, passes=15)\n",
        "ldamodel.save('model5.gensim')\n",
        "topics = ldamodel.print_topics(num_words=4)\n",
        "for topic in topics:\n",
        "    print(topic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0, '0.015*\"based\" + 0.013*\"large\" + 0.012*\"video\" + 0.012*\"image\"')\n",
            "(1, '0.020*\"network\" + 0.018*\"wireless\" + 0.016*\"sensor\" + 0.015*\"based\"')\n",
            "(2, '0.022*\"system\" + 0.020*\"design\" + 0.017*\"based\" + 0.011*\"services\"')\n",
            "(3, '0.038*\"network\" + 0.012*\"wireless\" + 0.010*\"mobile\" + 0.009*\"system\"')\n",
            "(4, '0.025*\"network\" + 0.024*\"based\" + 0.018*\"query\" + 0.016*\"using\"')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "T2N0p46WzJhr",
        "outputId": "06e2fb28-1257-4ae3-f244-5ed462c31ccf"
      },
      "source": [
        "#Testing LDA model by giving input string\n",
        "\n",
        "new_doc = 'Influence of external input on Oscillatory Cellular Nonlinear Networks dynamics.'\n",
        "\n",
        "\n",
        "new_doc = prepare_text_for_lda(new_doc)\n",
        "new_doc_bow = dictionary.doc2bow(new_doc)\n",
        "print(new_doc_bow)\n",
        "print(ldamodel.get_document_topics(new_doc_bow))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(24, 1), (82, 1), (201, 1), (357, 1), (721, 1), (722, 1), (723, 1), (724, 1)]\n",
            "[(0, 0.022228414), (1, 0.023094289), (2, 0.022377672), (3, 0.022619208), (4, 0.90968037)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "7faJ0YHizJh4",
        "outputId": "70da8ddf-c51c-4470-fa76-7688f0ab01de"
      },
      "source": [
        "#Creating visualisation of the LDA model\n",
        "\n",
        "dictionary = gensim.corpora.Dictionary.load('dictionary.gensim')\n",
        "corpus = pickle.load(open('corpus.pkl', 'rb'))\n",
        "lda = gensim.models.ldamodel.LdaModel.load('model5.gensim')\n",
        "import pyLDAvis.gensim_models\n",
        "lda_display = pyLDAvis.gensim_models.prepare(lda, corpus, dictionary, sort_topics=False)\n",
        "pyLDAvis.display(lda_display)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
            "  from collections import Iterable\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/decomposition/_lda.py:29: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  EPS = np.finfo(np.float).eps\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "\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_el724139823052651728891209806\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el724139823052651728891209806_data = {\"mdsDat\": {\"x\": [0.18623020288497077, -0.1268278339347213, 0.037507753533331245, -0.12815750004336676, 0.03124737755978604], \"y\": [0.0603717294410449, -0.03754155295094361, -0.1853527631672523, 0.08864592067947791, 0.07387666599767297], \"topics\": [1, 2, 3, 4, 5], \"cluster\": [1, 1, 1, 1, 1], \"Freq\": [18.700987896032554, 22.765668040202648, 18.55486910693896, 16.930400289842684, 23.048074666983155]}, \"tinfo\": {\"Term\": [\"network\", \"wireless\", \"design\", \"model\", \"query\", \"large\", \"sensor\", \"search\", \"services\", \"image\", \"video\", \"converter\", \"based\", \"database\", \"scale\", \"delta\", \"sigma\", \"mobile\", \"processing\", \"channel\", \"digital\", \"method\", \"continuous\", \"protocol\", \"power\", \"implementation\", \"social\", \"adaptive\", \"surface\", \"management\", \"scale\", \"light\", \"feature\", \"graphics\", \"result\", \"recommendation\", \"h.264\", \"mechanism\", 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