NLP_Basics/Bag of Words/Bag_of_Words(BOW).ipynb

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      "name": "Bag of Words(BOW).ipynb",
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      "name": "python"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mittalsharad/NLP/blob/main/NLP_Basics/Bag%20of%20Words/Bag_of_Words(BOW).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UcOhq3QstSZ6"
      },
      "source": [
        "# Bag of words Model"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "nvU8VKvvtd4U"
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      "source": [
        "## Implementing Bag of Words Algorithm with Python\n",
        "\n",
        "In this section, we are going to implement a bag of words algorithm with Python. Also, this is a very basic implementation to understand how bag of words algorithm work, so I would not recommend using this in your project, instead use the method described in the next section."
      ]
    },
    {
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        "id": "_EtfFFcas548",
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      "source": [
        "def vectorize(tokens):\n",
        "    ''' This function takes list of words in a sentence as input \n",
        "    and returns a vector of size of filtered_vocab.It puts 0 if the \n",
        "    word is not present in tokens and count of token if present.'''\n",
        "    vector=[]\n",
        "    for w in filtered_vocab:\n",
        "        vector.append(tokens.count(w))\n",
        "    return vector\n",
        "def unique(sequence):\n",
        "    '''This functions returns a list in which the order remains \n",
        "    same and no item repeats.Using the set() function does not \n",
        "    preserve the original ordering,so i didnt use that instead'''\n",
        "    seen = set()\n",
        "    return [x for x in sequence if not (x in seen or seen.add(x))]\n",
        "#create a list of stopwords.You can import stopwords from nltk too\n",
        "stopwords=[\"to\",\"is\",\"a\"]\n",
        "#list of special characters.You can use regular expressions too\n",
        "special_char=[\",\",\":\",\" \",\";\",\".\",\"?\"]\n",
        "#Write the sentences in the corpus,in our case, just two \n",
        "string1=\"Welcome to My Blog, Now start learning\"\n",
        "string2=\"Learning is a very good practice\"\n",
        "#convert them to lower case\n",
        "string1=string1.lower()\n",
        "string2=string2.lower()\n",
        "#split the sentences into tokens\n",
        "tokens1=string1.split()\n",
        "tokens2=string2.split()\n",
        "print(tokens1)\n",
        "print(tokens2)\n",
        "#create a vocabulary list\n",
        "vocab=unique(tokens1+tokens2)\n",
        "print(vocab)\n",
        "#filter the vocabulary list\n",
        "filtered_vocab=[]\n",
        "for w in vocab: \n",
        "    if w not in stopwords and w not in special_char: \n",
        "        filtered_vocab.append(w)\n",
        "print(filtered_vocab)\n",
        "#convert sentences into vectords\n",
        "vector1=vectorize(tokens1)\n",
        "print(vector1)\n",
        "vector2=vectorize(tokens2)\n",
        "print(vector2)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['welcome', 'to', 'my', 'blog,', 'now', 'start', 'learning']\n",
            "['learning', 'is', 'a', 'very', 'good', 'practice']\n",
            "['welcome', 'to', 'my', 'blog,', 'now', 'start', 'learning', 'is', 'a', 'very', 'good', 'practice']\n",
            "['welcome', 'my', 'blog,', 'now', 'start', 'learning', 'very', 'good', 'practice']\n",
            "[1, 1, 1, 1, 1, 1, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 1, 1, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJOU6anWt4gT"
      },
      "source": [
        "## Create a Bag of Words Model with Sklearn\n",
        "We can use the `CountVectorizer()` function from the Sklearn library to easily implement the above BoW model using Python."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBnmY2yatsWe",
        "outputId": "e4adb3ca-5975-4285-acff-b4561e77c56a"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        " \n",
        "sentence_1=\"This is a good job.I will not miss it for anything\"\n",
        "sentence_2=\"This is not good at all\"\n",
        " \n",
        " \n",
        " \n",
        "CountVec = CountVectorizer(ngram_range=(1,1), # to use bigrams ngram_range=(2,2)\n",
        "                           stop_words='english')\n",
        "#transform\n",
        "Count_data = CountVec.fit_transform([sentence_1,sentence_2])\n",
        " \n",
        "#create dataframe\n",
        "cv_dataframe=pd.DataFrame(Count_data.toarray(),columns=CountVec.get_feature_names())\n",
        "print(cv_dataframe)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   good  job  miss\n",
            "0     1    1     1\n",
            "1     1    0     0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gR24klAiut1f",
        "outputId": "7f2aeaf5-e3c6-403c-b10a-78385e886ad7"
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ys5TbZ2guFpz",
        "outputId": "caceb1d4-3d53-42a7-8fa6-15dcc11bbb6e"
      },
      "source": [
        "import nltk\n",
        "\n",
        "paragraph =  \"\"\"I have three visions for India. In 3000 years of our history, people from all over \n",
        "               the world have come and invaded us, captured our lands, conquered our minds. \n",
        "               From Alexander onwards, the Greeks, the Turks, the Moguls, the Portuguese, the British,\n",
        "               the French, the Dutch, all of them came and looted us, took over what was ours. \n",
        "               Yet we have not done this to any other nation. We have not conquered anyone. \n",
        "               We have not grabbed their land, their culture, \n",
        "               their history and tried to enforce our way of life on them. \n",
        "               Why? Because we respect the freedom of others.That is why my \n",
        "               first vision is that of freedom. I believe that India got its first vision of \n",
        "               this in 1857, when we started the War of Independence. It is this freedom that\n",
        "               we must protect and nurture and build on. If we are not free, no one will respect us.\n",
        "               My second vision for India’s development. For fifty years we have been a developing nation.\n",
        "               It is time we see ourselves as a developed nation. We are among the top 5 nations of the world\n",
        "               in terms of GDP. We have a 10 percent growth rate in most areas. Our poverty levels are falling.\n",
        "               Our achievements are being globally recognised today. Yet we lack the self-confidence to\n",
        "               see ourselves as a developed nation, self-reliant and self-assured. Isn’t this incorrect?\n",
        "               I have a third vision. India must stand up to the world. Because I believe that unless India \n",
        "               stands up to the world, no one will respect us. Only strength respects strength. We must be \n",
        "               strong not only as a military power but also as an economic power. Both must go hand-in-hand. \n",
        "               My good fortune was to have worked with three great minds. Dr. Vikram Sarabhai of the Dept. of \n",
        "               space, Professor Satish Dhawan, who succeeded him and Dr. Brahm Prakash, father of nuclear material.\n",
        "               I was lucky to have worked with all three of them closely and consider this the great opportunity of my life. \n",
        "               I see four milestones in my career\"\"\"\n",
        "               \n",
        "               \n",
        "# Cleaning the texts\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "ps = PorterStemmer()\n",
        "wordnet=WordNetLemmatizer()\n",
        "sentences = nltk.sent_tokenize(paragraph)\n",
        "print(\"Sentences after Sent_Tokenization: \")\n",
        "print(sentences)\n",
        "\n",
        "corpus = []\n",
        "for i in range(len(sentences)):\n",
        "    review = re.sub('[^a-zA-Z]', ' ', sentences[i])\n",
        "    review = review.lower()\n",
        "    review = review.split()\n",
        "    review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]\n",
        "    review = ' '.join(review)\n",
        "    corpus.append(review)\n",
        "\n",
        "print(\"Words after preprocessing i.e removing unwanted chars, lowering the case, and removing stop words: \") \n",
        "print(corpus)   \n",
        "# Creating the Bag of Words model\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(max_features = 1500)\n",
        "X = cv.fit_transform(corpus).toarray()\n",
        "print(\"Bag of words :\")\n",
        "print(X)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sentences after Sent_Tokenization: \n",
            "['I have three visions for India.', 'In 3000 years of our history, people from all over \\n               the world have come and invaded us, captured our lands, conquered our minds.', 'From Alexander onwards, the Greeks, the Turks, the Moguls, the Portuguese, the British,\\n               the French, the Dutch, all of them came and looted us, took over what was ours.', 'Yet we have not done this to any other nation.', 'We have not conquered anyone.', 'We have not grabbed their land, their culture, \\n               their history and tried to enforce our way of life on them.', 'Why?', 'Because we respect the freedom of others.That is why my \\n               first vision is that of freedom.', 'I believe that India got its first vision of \\n               this in 1857, when we started the War of Independence.', 'It is this freedom that\\n               we must protect and nurture and build on.', 'If we are not free, no one will respect us.', 'My second vision for India’s development.', 'For fifty years we have been a developing nation.', 'It is time we see ourselves as a developed nation.', 'We are among the top 5 nations of the world\\n               in terms of GDP.', 'We have a 10 percent growth rate in most areas.', 'Our poverty levels are falling.', 'Our achievements are being globally recognised today.', 'Yet we lack the self-confidence to\\n               see ourselves as a developed nation, self-reliant and self-assured.', 'Isn’t this incorrect?', 'I have a third vision.', 'India must stand up to the world.', 'Because I believe that unless India \\n               stands up to the world, no one will respect us.', 'Only strength respects strength.', 'We must be \\n               strong not only as a military power but also as an economic power.', 'Both must go hand-in-hand.', 'My good fortune was to have worked with three great minds.', 'Dr. Vikram Sarabhai of the Dept.', 'of \\n               space, Professor Satish Dhawan, who succeeded him and Dr. Brahm Prakash, father of nuclear material.', 'I was lucky to have worked with all three of them closely and consider this the great opportunity of my life.', 'I see four milestones in my career']\n",
            "Words after preprocessing i.e removing unwanted chars, lowering the case, and removing stop words: \n",
            "['three vision india', 'year histori peopl world come invad us captur land conquer mind', 'alexand onward greek turk mogul portugues british french dutch came loot us took', 'yet done nation', 'conquer anyon', 'grab land cultur histori tri enforc way life', '', 'respect freedom other first vision freedom', 'believ india got first vision start war independ', 'freedom must protect nurtur build', 'free one respect us', 'second vision india develop', 'fifti year develop nation', 'time see develop nation', 'among top nation world term gdp', 'percent growth rate area', 'poverti level fall', 'achiev global recognis today', 'yet lack self confid see develop nation self reliant self assur', 'incorrect', 'third vision', 'india must stand world', 'believ unless india stand world one respect us', 'strength respect strength', 'must strong militari power also econom power', 'must go hand hand', 'good fortun work three great mind', 'dr vikram sarabhai dept', 'space professor satish dhawan succeed dr brahm prakash father nuclear materi', 'lucki work three close consid great opportun life', 'see four mileston career']\n",
            "Bag of words :\n",
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 1 1 0]\n",
            " [0 1 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFr6cYPkuZMm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}