02_Text_Representation_Statistical_Models.ipynb

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      "name": "02-Text Representation - Statistical Models.ipynb",
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      "display_name": "Python 3",
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      "name": "python3"
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      "mimetype": "text/x-python",
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        "<a href=\"https://colab.research.google.com/github/plaban1981/NLP-with-Python/blob/master/02_Text_Representation_Statistical_Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Text Representation with Feature Engineering\n",
        "\n",
        "### Exploring Traditional Statistical Models\n",
        "\n",
        "Feature Engineering is often known as the secret sauce to creating superior and better performing machine learning models. Just one excellent feature could be your ticket to winning a Kaggle challenge! The importance of feature engineering is even more important for unstructured, textual data because we need to convert free flowing text into some numeric representations which can then be understood by machine learning algorithms. \n",
        "\n",
        "Here we will explore the following feature engineering techniques:\n",
        "\n",
        "- Bag of Words Model (TF)\n",
        "![image.png](data:image/png;base64,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)\n",
        "- Bag of N-grams Model\n",
        "![image.png](data:image/png;base64,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)\n",
        "- TF-IDF Model\n",
        "![image.png](data:image/png;base64,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)\n",
        "- Similarity Features\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxYlyABYh9NI"
      },
      "source": [
        "* Bag of Words does not preserve the word order  so we cannot derive useful inferences\n",
        "\n",
        "* Bag of Ngrams has too many features due to which the feature set becomes too sparse and computationaly expensive. Also choosing an optimal value of N gram is not an easy task\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYC_RfbeRP6J"
      },
      "source": [
        "# Prepare a Sample Corpus\n",
        "\n",
        "Let’s now take a sample corpus of documents on which we will run most of our analyses in this article. A corpus is typically a collection of text documents usually belonging to one or more subjects or domains."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xn8eDqARP6J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0905b639-e9e0-4dc4-95d1-e91c8aac179e"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "pd.options.display.max_colwidth = 200\n",
        "\n",
        "corpus = ['The sky is blue and beautiful.',\n",
        "          'Love this blue and beautiful sky!',\n",
        "          'The quick brown fox jumps over the lazy dog.',\n",
        "          \"A king's breakfast has sausages, ham, bacon, eggs, toast and beans\",\n",
        "          'I love green eggs, ham, sausages and bacon!',\n",
        "          'The brown fox is quick and the blue dog is lazy!',\n",
        "          'The sky is very blue and the sky is very beautiful today',\n",
        "          'The dog is lazy but the brown fox is quick!'    \n",
        "]\n",
        "labels = ['weather', 'weather', 'animals', 'food', 'food', 'animals', 'weather', 'animals']\n",
        "\n",
        "corpus = np.array(corpus)\n",
        "corpus_df = pd.DataFrame({'Document': corpus, \n",
        "                          'Category': labels})\n",
        "corpus_df = corpus_df[['Document', 'Category']]\n",
        "corpus_df"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Document</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The sky is blue and beautiful.</td>\n",
              "      <td>weather</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Love this blue and beautiful sky!</td>\n",
              "      <td>weather</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The quick brown fox jumps over the lazy dog.</td>\n",
              "      <td>animals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A king's breakfast has sausages, ham, bacon, eggs, toast and beans</td>\n",
              "      <td>food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I love green eggs, ham, sausages and bacon!</td>\n",
              "      <td>food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>The brown fox is quick and the blue dog is lazy!</td>\n",
              "      <td>animals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>The sky is very blue and the sky is very beautiful today</td>\n",
              "      <td>weather</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>The dog is lazy but the brown fox is quick!</td>\n",
              "      <td>animals</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                             Document Category\n",
              "0                                      The sky is blue and beautiful.  weather\n",
              "1                                   Love this blue and beautiful sky!  weather\n",
              "2                        The quick brown fox jumps over the lazy dog.  animals\n",
              "3  A king's breakfast has sausages, ham, bacon, eggs, toast and beans     food\n",
              "4                         I love green eggs, ham, sausages and bacon!     food\n",
              "5                    The brown fox is quick and the blue dog is lazy!  animals\n",
              "6            The sky is very blue and the sky is very beautiful today  weather\n",
              "7                         The dog is lazy but the brown fox is quick!  animals"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QOMGTkvCRP6N"
      },
      "source": [
        "You can see that we have taken a few sample text documents belonging to different categories for our toy corpus. Before we talk about feature engineering, as always, we need to do some data pre-processing or wrangling to remove unnecessary characters, symbols and tokens."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAcT9NHkRP6O"
      },
      "source": [
        "# Simple Text Pre-processing\n",
        "\n",
        "Since the focus of this unit is on feature engineering, we will build a simple text pre-processor which focuses on removing special characters, extra whitespaces, digits, stopwords and lower casing the text corpus."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSIxl_PUSCvj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5444170f-468a-493b-9486-165ddeb4734c"
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OElcyG9WRP6P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79cc35ef-c3a1-4325-b50c-c74280735b3e"
      },
      "source": [
        "import nltk\n",
        "import re\n",
        "\n",
        "stop_words = nltk.corpus.stopwords.words('english')\n",
        "\n",
        "def normalize_document(doc):\n",
        "    # lower case and remove special characters\\whitespaces\n",
        "    doc = re.sub(r'[^a-zA-Z\\s]', '', doc, re.I|re.A)\n",
        "    doc = doc.lower()\n",
        "    doc = doc.strip()\n",
        "    # tokenize document\n",
        "    tokens = nltk.word_tokenize(doc)\n",
        "    # filter stopwords out of document\n",
        "    filtered_tokens = [token for token in tokens if token not in stop_words]\n",
        "    # re-create document from filtered tokens\n",
        "    doc = ' '.join(filtered_tokens)\n",
        "    return doc\n",
        "\n",
        "normalize_corpus = np.vectorize(normalize_document)\n",
        "\n",
        "norm_corpus = normalize_corpus(corpus)\n",
        "norm_corpus"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['sky blue beautiful', 'love blue beautiful sky',\n",
              "       'quick brown fox jumps lazy dog',\n",
              "       'kings breakfast sausages ham bacon eggs toast beans',\n",
              "       'love green eggs ham sausages bacon',\n",
              "       'brown fox quick blue dog lazy', 'sky blue sky beautiful today',\n",
              "       'dog lazy brown fox quick'], dtype='<U51')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMBWGlKTRP6T"
      },
      "source": [
        "# Bag of Words Model - TF\n",
        "\n",
        "This is perhaps the most simple vector space representational model for unstructured text. A vector space model is simply a mathematical model to represent unstructured text (or any other data) as numeric vectors, such that each dimension of the vector is a specific feature\\attribute. The bag of words model represents each text document as a numeric vector where each dimension is a specific word from the corpus and the value could be its frequency in the document, occurrence (denoted by 1 or 0) or even weighted values. The model’s name is such because each document is represented literally as a ‘bag’ of its own words, disregarding word orders, sequences and grammar."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7mAqxTlRP6U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae465600-75d9-49ee-d01b-328a45de8168"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "cv = CountVectorizer(min_df=0., max_df=1.)\n",
        "cv_matrix = cv.fit_transform(norm_corpus)\n",
        "cv_matrix = cv_matrix.toarray()\n",
        "cv_matrix"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              "       [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0],\n",
              "       [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n",
              "       [0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0],\n",
              "       [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1],\n",
              "       [0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDAaU4AHk-5D"
      },
      "source": [
        "## Get feature names from Count Vectorizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmQx_s67k681",
        "outputId": "4b7071af-10f4-4d17-c1cd-6bea0f44c19b"
      },
      "source": [
        "cv.get_feature_names"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method CountVectorizer.get_feature_names of CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
              "                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
              "                lowercase=True, max_df=1.0, max_features=None, min_df=0.0,\n",
              "                ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
              "                strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                tokenizer=None, vocabulary=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lPp6Bzs7RP6W"
      },
      "source": [
        "Thus you can see that our documents have been converted into numeric vectors such that each document is represented by one vector (row) in the above feature matrix. The following code will help represent this in a more easy to understand format."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVsdSiwARP6W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0b2fc67e-206e-47d4-d06c-b5f5540e5e6b"
      },
      "source": [
        "# get all unique words in the corpus\n",
        "vocab = cv.get_feature_names()\n",
        "# show document feature vectors\n",
        "pd.DataFrame(cv_matrix, columns=vocab)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bacon</th>\n",
              "      <th>beans</th>\n",
              "      <th>beautiful</th>\n",
              "      <th>blue</th>\n",
              "      <th>breakfast</th>\n",
              "      <th>brown</th>\n",
              "      <th>dog</th>\n",
              "      <th>eggs</th>\n",
              "      <th>fox</th>\n",
              "      <th>green</th>\n",
              "      <th>ham</th>\n",
              "      <th>jumps</th>\n",
              "      <th>kings</th>\n",
              "      <th>lazy</th>\n",
              "      <th>love</th>\n",
              "      <th>quick</th>\n",
              "      <th>sausages</th>\n",
              "      <th>sky</th>\n",
              "      <th>toast</th>\n",
              "      <th>today</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   bacon  beans  beautiful  blue  breakfast  ...  quick  sausages  sky  toast  today\n",
              "0      0      0          1     1          0  ...      0         0    1      0      0\n",
              "1      0      0          1     1          0  ...      0         0    1      0      0\n",
              "2      0      0          0     0          0  ...      1         0    0      0      0\n",
              "3      1      1          0     0          1  ...      0         1    0      1      0\n",
              "4      1      0          0     0          0  ...      0         1    0      0      0\n",
              "5      0      0          0     1          0  ...      1         0    0      0      0\n",
              "6      0      0          1     1          0  ...      0         0    2      0      1\n",
              "7      0      0          0     0          0  ...      1         0    0      0      0\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_1k60ZERP6Z"
      },
      "source": [
        "This should make things more clearer! You can clearly see that each column or dimension in the feature vectors represents a word from the corpus and each row represents one of our documents. The value in any cell, represents the number of times that word (represented by column) occurs in the specific document (represented by row). Hence if a corpus of documents consists of N unique words across all the documents, we would have an N-dimensional vector for each of the documents."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0DfPJwSsRP6Z"
      },
      "source": [
        "# Bag of N-Grams Model\n",
        "\n",
        "A word is just a single token, often known as a unigram or 1-gram. We already know that the Bag of Words model doesn’t consider order of words. But what if we also wanted to take into account phrases or collection of words which occur in a sequence? N-grams help us achieve that. An N-gram is basically a collection of word tokens from a text document such that these tokens are contiguous and occur in a sequence. Bi-grams indicate n-grams of order 2 (two words), Tri-grams indicate n-grams of order 3 (three words), and so on. The Bag of N-Grams model is hence just an extension of the Bag of Words model so we can also leverage N-gram based features. The following example depicts bi-gram based features in each document feature vector."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4n1N32JyRP6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "1755912f-001f-46ab-fb1f-31b2d51bc7a8"
      },
      "source": [
        "# you can set the n-gram range to 1,2 to get unigrams as well as bigrams\n",
        "bv = CountVectorizer(ngram_range=(2,2))\n",
        "bv_matrix = bv.fit_transform(norm_corpus)\n",
        "\n",
        "bv_matrix = bv_matrix.toarray()\n",
        "vocab = bv.get_feature_names()\n",
        "pd.DataFrame(bv_matrix, columns=vocab)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
            "text/plain": [
              "   bacon eggs  beautiful sky  ...  sky blue  toast beans\n",
              "0           0              0  ...         1            0\n",
              "1           0              1  ...         0            0\n",
              "2           0              0  ...         0            0\n",
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              "\n",
              "[8 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bW6F4h8QRP6c"
      },
      "source": [
        "This gives us feature vectors for our documents, where each feature consists of a bi-gram representing a sequence of two words and values represent how many times the bi-gram was present for our documents."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPwKOnnkRP6d"
      },
      "source": [
        "# TF-IDF Model\n",
        "\n",
        "There are some potential problems which might arise with the Bag of Words model when it is used on large corpora. Since the feature vectors are based on absolute term frequencies, there might be some terms which occur frequently across all documents and these may tend to overshadow other terms in the feature set. The TF-IDF model tries to combat this issue by using a scaling or normalizing factor in its computation. ___TF-IDF___ stands for __Term Frequency-Inverse Document Frequency__, which uses a combination of two metrics in its computation, namely: ___term frequency (tf)___ and ___inverse document frequency (idf)___. This technique was developed for ranking results for queries in search engines and now it is an indispensable model in the world of information retrieval and NLP.\n",
        "\n",
        "Mathematically, we can define TF-IDF as ___tfidf = tf x idf___, which can be expanded further to be represented as follows.\n",
        "\n",
        "![](https://github.com/dipanjanS/nlp_workshop_odsc19/blob/master/Module04%20-%20Text%20Representation/tfidf_eq.png?raw=1)\n",
        "\n",
        "Here, ___tfidf(w, D)___ is the TF-IDF score for word __w__ in document __D__. \n",
        "- The term ___tf(w, D)___ represents the term frequency of the word __w__ in document __D__, which can be obtained from the Bag of Words model. \n",
        "- The term ___idf(w, D)___ is the inverse document frequency for the term __w__, which can be computed as the log transform of the total number of documents in the corpus __C__ divided by the document frequency of the word __w__, which is basically the frequency of documents in the corpus where the word __w__ occurs. \n",
        "\n",
        "There are multiple variants of this model but they all end up giving quite similar results. Let’s apply this on our corpus now!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFp9zDbgRP6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "096d0536-604c-4404-8f1d-b85f1275d8ff"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "tv = TfidfVectorizer(min_df=0., max_df=1., use_idf=True)\n",
        "tv_matrix = tv.fit_transform(norm_corpus)\n",
        "tv_matrix = tv_matrix.toarray()\n",
        "\n",
        "vocab = tv.get_feature_names()\n",
        "pd.DataFrame(np.round(tv_matrix, 2), columns=vocab)"
      ],
      "execution_count": 8,
      "outputs": [
        {
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   bacon  beans  beautiful  blue  ...  sausages   sky  toast  today\n",
              "0   0.00   0.00       0.60  0.53  ...      0.00  0.60   0.00    0.0\n",
              "1   0.00   0.00       0.49  0.43  ...      0.00  0.49   0.00    0.0\n",
              "2   0.00   0.00       0.00  0.00  ...      0.00  0.00   0.00    0.0\n",
              "3   0.32   0.38       0.00  0.00  ...      0.32  0.00   0.38    0.0\n",
              "4   0.39   0.00       0.00  0.00  ...      0.39  0.00   0.00    0.0\n",
              "5   0.00   0.00       0.00  0.37  ...      0.00  0.00   0.00    0.0\n",
              "6   0.00   0.00       0.36  0.32  ...      0.00  0.72   0.00    0.5\n",
              "7   0.00   0.00       0.00  0.00  ...      0.00  0.00   0.00    0.0\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jvKPWIwRP6g"
      },
      "source": [
        "The TF-IDF based feature vectors for each of our text documents show scaled and normalized values as compared to the raw Bag of Words model values. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7xzqFWAkRP6h"
      },
      "source": [
        "# Document Similarity\n",
        "\n",
        "Document similarity is the process of using a distance or similarity based metric that can be used to identify how similar a text document is with any other document(s) based on features extracted from the documents like bag of words or tf-idf.\n",
        "\n",
        "Thus you can see that we can build on top of the tf-idf based features we engineered in the previous section and use them to generate new features which can be useful in domains like search engines, document clustering and information retrieval by leveraging these similarity based features.\n",
        "\n",
        "Pairwise document similarity in a corpus involves computing document similarity for each pair of documents in a corpus. Thus if you have C documents in a corpus, you would end up with a C x C matrix such that each row and column represents the similarity score for a pair of documents, which represent the indices at the row and column, respectively. There are several similarity and distance metrics that are used to compute document similarity. These include cosine distance/similarity, euclidean distance, manhattan distance, BM25 similarity, jaccard distance and so on. In our analysis, we will be using perhaps the most popular and widely used similarity metric,\n",
        "cosine similarity and compare pairwise document similarity based on their TF-IDF feature vectors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyToCxp9RP6i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "339787c7-7284-47b4-ec69-d79c1e2b8a61"
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "similarity_matrix = cosine_similarity(tv_matrix)\n",
        "similarity_df = pd.DataFrame(similarity_matrix)\n",
        "similarity_df"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.820599</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.192353</td>\n",
              "      <td>0.817246</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.820599</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.225489</td>\n",
              "      <td>0.157845</td>\n",
              "      <td>0.670631</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.791821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.850516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.506866</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.225489</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.506866</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.192353</td>\n",
              "      <td>0.157845</td>\n",
              "      <td>0.791821</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.115488</td>\n",
              "      <td>0.930989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.817246</td>\n",
              "      <td>0.670631</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.115488</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.850516</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.930989</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          0         1         2  ...         5         6         7\n",
              "0  1.000000  0.820599  0.000000  ...  0.192353  0.817246  0.000000\n",
              "1  0.820599  1.000000  0.000000  ...  0.157845  0.670631  0.000000\n",
              "2  0.000000  0.000000  1.000000  ...  0.791821  0.000000  0.850516\n",
              "3  0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "4  0.000000  0.225489  0.000000  ...  0.000000  0.000000  0.000000\n",
              "5  0.192353  0.157845  0.791821  ...  1.000000  0.115488  0.930989\n",
              "6  0.817246  0.670631  0.000000  ...  0.115488  1.000000  0.000000\n",
              "7  0.000000  0.000000  0.850516  ...  0.930989  0.000000  1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uq737P7GRP6l"
      },
      "source": [
        "Cosine similarity basically gives us a metric representing the cosine of the angle between the feature vector representations of two text documents. Lower the angle between the documents, the closer and more similar they are as depicted in the following figure.\n",
        "\n",
        "![](https://github.com/dipanjanS/nlp_workshop_odsc19/blob/master/Module04%20-%20Text%20Representation/cosine_depiction.png?raw=1)\n",
        "\n",
        "Looking closely at the similarity matrix clearly tells us that documents (0, 1 and 6), (2, 5 and 7) are very similar to one another and documents 3 and 4 are slightly similar to each other but the magnitude is not very strong, however still stronger than the other documents. This must indicate these similar documents have some similar features. This is a perfect example of grouping or clustering that can be solved by unsupervised learning especially when you are dealing with huge corpora of millions of text documents."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vh3zF8lKRP6n"
      },
      "source": [
        "# Bonus: Clustering using Document Similarity Features\n",
        "\n",
        "We will use a very popular partition based clustering method, K-means clustering to cluster or group these documents based on their similarity based feature representations. In K-means clustering, we have an input parameter k, which specifies the number of clusters it will output using the document features. This clustering method is a centroid based clustering method, where it tries to cluster these documents into clusters of equal variance. It tries to create these clusters by minimizing the within-cluster sum of squares measure, also known as inertia. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "wTV1UV4LvUEv",
        "outputId": "f6c5d904-e79e-43a4-f064-d399fec5b064"
      },
      "source": [
        "corpus_df"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Document</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The sky is blue and beautiful.</td>\n",
              "      <td>weather</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Love this blue and beautiful sky!</td>\n",
              "      <td>weather</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The quick brown fox jumps over the lazy dog.</td>\n",
              "      <td>animals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A king's breakfast has sausages, ham, bacon, eggs, toast and beans</td>\n",
              "      <td>food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I love green eggs, ham, sausages and bacon!</td>\n",
              "      <td>food</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>The brown fox is quick and the blue dog is lazy!</td>\n",
              "      <td>animals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>The sky is very blue and the sky is very beautiful today</td>\n",
              "      <td>weather</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>The dog is lazy but the brown fox is quick!</td>\n",
              "      <td>animals</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                             Document Category\n",
              "0                                      The sky is blue and beautiful.  weather\n",
              "1                                   Love this blue and beautiful sky!  weather\n",
              "2                        The quick brown fox jumps over the lazy dog.  animals\n",
              "3  A king's breakfast has sausages, ham, bacon, eggs, toast and beans     food\n",
              "4                         I love green eggs, ham, sausages and bacon!     food\n",
              "5                    The brown fox is quick and the blue dog is lazy!  animals\n",
              "6            The sky is very blue and the sky is very beautiful today  weather\n",
              "7                         The dog is lazy but the brown fox is quick!  animals"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5ff9gMZRP6o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "13967750-820b-473c-f356-cdf916f76c45"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "km = KMeans(n_clusters=3, random_state=0)\n",
        "km.fit_transform(similarity_matrix)\n",
        "cluster_labels = km.labels_\n",
        "cluster_labels = pd.DataFrame(cluster_labels, columns=['ClusterLabel'])\n",
        "pd.concat([corpus_df, cluster_labels], axis=1)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Document</th>\n",
              "      <th>Category</th>\n",
              "      <th>ClusterLabel</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The sky is blue and beautiful.</td>\n",
              "      <td>weather</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Love this blue and beautiful sky!</td>\n",
              "      <td>weather</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The quick brown fox jumps over the lazy dog.</td>\n",
              "      <td>animals</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A king's breakfast has sausages, ham, bacon, eggs, toast and beans</td>\n",
              "      <td>food</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I love green eggs, ham, sausages and bacon!</td>\n",
              "      <td>food</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>The brown fox is quick and the blue dog is lazy!</td>\n",
              "      <td>animals</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>The sky is very blue and the sky is very beautiful today</td>\n",
              "      <td>weather</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>The dog is lazy but the brown fox is quick!</td>\n",
              "      <td>animals</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                             Document  ... ClusterLabel\n",
              "0                                      The sky is blue and beautiful.  ...            2\n",
              "1                                   Love this blue and beautiful sky!  ...            2\n",
              "2                        The quick brown fox jumps over the lazy dog.  ...            1\n",
              "3  A king's breakfast has sausages, ham, bacon, eggs, toast and beans  ...            0\n",
              "4                         I love green eggs, ham, sausages and bacon!  ...            0\n",
              "5                    The brown fox is quick and the blue dog is lazy!  ...            1\n",
              "6            The sky is very blue and the sky is very beautiful today  ...            2\n",
              "7                         The dog is lazy but the brown fox is quick!  ...            1\n",
              "\n",
              "[8 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-NsBGloRP6q"
      },
      "source": [
        "We can see from the above output that our documents were correctly assigned to the right clusters!"
      ]
    }
  ]
}