Lab8.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lab8.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ManikGo/ML/blob/main/Lab8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Counting for Representation\n",
        "\n",
        "### Lab Coordinator: Shreeya Pahune"
      ],
      "metadata": {
        "id": "3IgFyia30c1f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Embedding methods** (alternatively referred to as “encoding”, “vectorising”, etc) convert symbolic representations (i.e. words, emojis, categorical items, dates, times, other features, etc) into meaningful numbers (i.e. real numbers that capture underlying semantic relations between the symbols)."
      ],
      "metadata": {
        "id": "tySEJCyA0gKc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**one-hot encoding**\n",
        "\n",
        "If we wanted to embed a colour like “orange”, we could have a long vector wherein vector values correspond to the various colours of the rainbow.\n",
        "\n",
        "The location corresponding to the colour being embedded could be given the value “1” while all other values would be set to “0”. This is one of the simplest embedding methods and is known as one-hot encoding (because only one value in the vector has the value one).\n"
      ],
      "metadata": {
        "id": "T4RCuu0M02LX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Screenshot from 2022-03-22 19-04-43.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "6HAJqsE21eaV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "orange = [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\n",
        "\n",
        "green = [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]"
      ],
      "metadata": {
        "id": "Y_mijfjO1cnd"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature vectors**\n",
        "\n",
        "Imagine another type of vector that is able to represent any colour with just three values each time (lets say the values represent red, green and blue).\n"
      ],
      "metadata": {
        "id": "KmVKgzlM2G7b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "red   = [1, 0, 0]\n",
        "green = [0, 1, 0]"
      ],
      "metadata": {
        "id": "aTshDao72VSn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Document Vectors\n",
        "\n",
        "Features can also be more abstract relations, such as the context in which a word occurs (assuming words with similar contexts must have similar meanings). For instance, if we assume a document is generally on a single topic / theme, then we can safely take that entire document to be an unlabelled feature and generate distributed word vectors using documents as features."
      ],
      "metadata": {
        "id": "E40WRV682sH_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![1*Vhz5UYqvWO6iTUTuqdcEqQ.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "GkQTpwmW3G0l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning and Preprocessing step"
      ],
      "metadata": {
        "id": "02gIHeBWuLbY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Raw text must be processed and converted into a form so that it is suitable to use with various machine-learning algorithms.  \n",
        "In case of text, there are lots of things that need to be taken into account.  \n",
        "\n",
        "\n",
        "1.   Removing numbers from the text\n",
        "2.   Handling capitalization and punctuation.\n",
        "3.   Stemming and Lemmatizing text.  \n",
        "\n",
        "And most importantly, one can't just use words or images directly in algorithms; they need to be converted into vectors- a form that algorithms can understand.\n",
        "\n"
      ],
      "metadata": {
        "id": "5D__XMAMuPgm"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKNE8ggJvN0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dddf4e23-1b70-4b07-e60e-52cac30db2e2"
      },
      "source": [
        "# NLTK (or Natural Language Tool Kit) is a commonly used library for processing text.\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('wordnet')"
      ],
      "execution_count": 4,
      "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",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import numpy\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.stem import SnowballStemmer\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk import pos_tag\n",
        "from nltk.corpus import wordnet\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "def cleanText(text, lemmatize, stemmer):\n",
        "    \"\"\"Method for cleaning text from train and test data. Removes numbers, punctuation, and capitalization. Stems or lemmatizes text.\"\"\"\n",
        "\n",
        "    if isinstance(text, float):\n",
        "        text = str(text)\n",
        "    if isinstance(text, numpy.int64):\n",
        "        text = str(text)\n",
        "    try:\n",
        "        text = text.decode()\n",
        "    except AttributeError:\n",
        "        pass\n",
        "\n",
        "    soup = BeautifulSoup(text, \"lxml\")\n",
        "    text = soup.get_text()\n",
        "\n",
        "    text = re.sub(r\"[^A-Za-z]\", \" \", text)\n",
        "    text = text.lower()\n",
        "\n",
        "\n",
        "    if lemmatize:\n",
        "        wordnet_lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "        def get_tag(tag):\n",
        "            if tag.startswith('J'):\n",
        "                return wordnet.ADJ\n",
        "            elif tag.startswith('V'):\n",
        "                return wordnet.VERB\n",
        "            elif tag.startswith('N'):\n",
        "                return wordnet.NOUN\n",
        "            elif tag.startswith('R'):\n",
        "                return wordnet.ADV\n",
        "            else:\n",
        "                return ''\n",
        "\n",
        "        text_result = []\n",
        "        tokens = word_tokenize(text)  # Generate list of tokens\n",
        "        tagged = pos_tag(tokens)\n",
        "        for t in tagged:\n",
        "            try:\n",
        "                text_result.append(wordnet_lemmatizer.lemmatize(t[0], get_tag(t[1][:2])))\n",
        "            except:\n",
        "                text_result.append(wordnet_lemmatizer.lemmatize(t[0]))\n",
        "        return text_result\n",
        "\n",
        "    if stemmer:\n",
        "        text_result = []\n",
        "        tokens = word_tokenize(text)\n",
        "        snowball_stemmer = SnowballStemmer('english')\n",
        "        for t in tokens:\n",
        "            text_result.append(snowball_stemmer.stem(t))\n",
        "        return text_result"
      ],
      "metadata": {
        "id": "vCm-8pKAuWmT"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_text = \"Troubling\"\n",
        "sample_text_result = cleanText(sample_text, lemmatize=False, stemmer=True)\n",
        "sample_text_result = \" \".join(str(x) for x in sample_text_result)\n",
        "print(sample_text)\n",
        "print(sample_text_result)\n",
        "sample_text_result = cleanText(sample_text, lemmatize=True, stemmer=False)\n",
        "sample_text_result = \" \".join(str(x) for x in sample_text_result)\n",
        "print(sample_text_result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OnW8nOpkuWaJ",
        "outputId": "76b9ddc5-1d11-4f5a-b83b-5ae8faea8024"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Troubling\n",
            "troubl\n",
            "trouble\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### BAG OF WORDS \n",
        "\n",
        "A bag-of-words model, or BoW for short, is a way of extracting features from text for use in modeling, such as with machine learning algorithms.\n",
        "\n",
        "The approach is very simple and flexible, and can be used in many ways for extracting features from documents.\n",
        "\n",
        "A bag-of-words is a representation of text that describes the occurrence of words within a document. \n",
        "It is called a “bag” of words, because any information about the order or structure of words in the document is discarded. The model is only concerned with whether known words occur in the document, not where in the document."
      ],
      "metadata": {
        "id": "t7C0VRVuuomn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Functions to convert document(s) to a list of words, with the option of removing stopwords. Returns document-term matrix.\n",
        "\n",
        "def createBagOfWords(train, test, remove_stopwords, lemmatize, stemmer):\n",
        "    if remove_stopwords:\n",
        "        vectorizer = CountVectorizer(analyzer='word', input='content', stop_words=stopwords.words('english'))\n",
        "    else:\n",
        "        vectorizer = CountVectorizer(analyzer='word', input='content')\n",
        "\n",
        "    clean_train = []\n",
        "    for paragraph in train:\n",
        "        paragraph_result = cleanText(paragraph, lemmatize, stemmer)\n",
        "        paragraph = \" \".join(str(x) for x in paragraph_result)\n",
        "        clean_train.append(paragraph)\n",
        "\n",
        "    clean_test = []\n",
        "    for paragraph in test:\n",
        "        paragraph_result = cleanText(paragraph, lemmatize, stemmer)\n",
        "        paragraph = \" \".join(str(x) for x in paragraph_result)\n",
        "        clean_test.append(paragraph)\n",
        "\n",
        "    bag_of_words_train = vectorizer.fit_transform(clean_train).toarray()\n",
        "    bag_of_words_test = vectorizer.transform(clean_test).toarray()\n",
        "    return bag_of_words_train, bag_of_words_test\n"
      ],
      "metadata": {
        "id": "3HsV6oOXuVIh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TF-IDF \n",
        "TF-IDF technique is used to find meaning of sentences consisting of words and cancels out the incapabilities of Bag of Words technique which is good for text classification or for helping a machine read words in numbers.\n",
        "\n",
        "The number of times a term occurs in a document is called its Term frequency (TF).\n",
        "\n",
        " Document frequency is the number of documents in which the word is present.  Inverse DF (IDF) is the inverse of the document frequency which measures the informativeness of term *t*. \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "0EC6zxL4u8fE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def createTFIDF(train, test, remove_stopwords, lemmatize, stemmer):\n",
        "    if remove_stopwords:\n",
        "        vectorizer = TfidfVectorizer(analyzer='word', input='content', stop_words=stopwords.words('english'))\n",
        "    else:\n",
        "        vectorizer =  TfidfVectorizer(analyzer='word', input='content')\n",
        "\n",
        "    clean_train = []\n",
        "    for paragraph in train:\n",
        "        paragraph_result = cleanText(paragraph, lemmatize, stemmer)\n",
        "        paragraph = \" \".join(str(x) for x in paragraph_result)\n",
        "        clean_train.append(paragraph)\n",
        "\n",
        "    clean_test = []\n",
        "    for paragraph in test:\n",
        "        paragraph_result = cleanText(paragraph, lemmatize, stemmer)\n",
        "        paragraph = \" \".join(str(x) for x in paragraph_result)\n",
        "        clean_test.append(paragraph)\n",
        "\n",
        "    tfidf_train = vectorizer.fit_transform(clean_train).toarray()\n",
        "    tfidf_test = vectorizer.transform(clean_test).toarray()\n",
        "    return tfidf_train, tfidf_test"
      ],
      "metadata": {
        "id": "pQOWM3KyutlU"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### UNDERSTANDING THE DATA : A REVIEWS DATASET\n",
        "\n",
        "Sentiment analysis is the interpretation and classification of emotions (such as positive, negative and neutral) within text data using text analysis techniques.  \n",
        "Given below is a dataset consisting of reviews along with sentiment class (positive or negative)."
      ],
      "metadata": {
        "id": "0OrAR8lYxwGF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload the Reviews CSV file that has been shared with you.\n",
        "# Run this cell, click on the 'Choose files' button and upload the file.\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "VPlRBsaexxSk",
        "outputId": "a6a2789c-94b7-4b7e-a034-e24440c1844f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-17e37b24-7604-4a81-be60-e9bf6c610e3c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-17e37b24-7604-4a81-be60-e9bf6c610e3c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-18433fd6752e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Run this cell, click on the 'Choose files' button and upload the file.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m   \"\"\"\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m   \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m   \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m   \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m    121\u001b[0m   result = _output.eval_js(\n\u001b[1;32m    122\u001b[0m       'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[0;32m--> 123\u001b[0;31m           input_id=input_id, output_id=output_id))\n\u001b[0m\u001b[1;32m    124\u001b[0m   \u001b[0mfiles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_collections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_six\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     if (reply.get('type') == 'colab_reply' and\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('reviews.csv',sep='\\t')\n",
        "df.head(20)"
      ],
      "metadata": {
        "id": "HJVzT83Hx18B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### KNN MODEL\n",
        "\n",
        "Given below are two KNN models; in the first case we are using Bag-of-Words and in the second case we are using TF-IDF.\n",
        "Note the different metrics and parameters used in each."
      ],
      "metadata": {
        "id": "gZoe4A5Rx5OW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics, neighbors\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict\n",
        "\n",
        "## TASK - 1: Tweak the models below and see results with different parameters and distance metrics.\n",
        "\n",
        "def bow_knn():\n",
        "    \"\"\"Method for determining nearest neighbors using bag-of-words and K-Nearest Neighbor algorithm\"\"\"\n",
        "\n",
        "    training_data = pd.read_csv('reviews.csv',sep='\\t')\n",
        "    X_train, X_test, y_train, y_test = train_test_split(training_data[\"sentence\"], training_data[\"sentiment\"], test_size=0.2, random_state=5)\n",
        "    X_train, X_test = createBagOfWords(X_train, X_test, remove_stopwords=True, lemmatize=True, stemmer=False)\n",
        "    # print(X_train)\n",
        "    knn = neighbors.KNeighborsClassifier(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='euclidean', metric_params=None, n_jobs=1)\n",
        "\n",
        "    knn.fit(X_train, y_train)\n",
        "    predicted = knn.predict(X_test)\n",
        "    acc = metrics.accuracy_score(y_test, predicted)\n",
        "    print('KNN with BOW accuracy = ' + str(acc * 100) + '%')\n",
        "\n",
        "    scores = cross_val_score(knn, X_train, y_train, cv=3)\n",
        "    print(\"Cross Validation Accuracy: %0.2f\" % (scores.mean()))\n",
        "    print(scores)\n",
        "    print('\\n')\n",
        "    return predicted, y_test\n",
        "\n",
        "\n",
        "def tfidf_knn():\n",
        "    \"\"\"Method for determining nearest neighbors using tf-idf and K-Nearest Neighbor algorithm\"\"\"\n",
        "\n",
        "    training_data = pd.read_csv('reviews.csv',sep='\\t')\n",
        "    X_train, X_test, y_train, y_test = train_test_split(training_data[\"sentence\"], training_data[\"sentiment\"],\n",
        "                                                        test_size=0.2, random_state=5)\n",
        "    X_train, X_test = createTFIDF(X_train, X_test, remove_stopwords=True, lemmatize=True, stemmer=False)\n",
        "    # print(X_train)\n",
        "    knn = neighbors.KNeighborsClassifier(n_neighbors=5, weights='distance', algorithm='brute', leaf_size=30, p=2,\n",
        "                                         metric='cosine', metric_params=None, n_jobs=1)\n",
        "\n",
        "    knn.fit(X_train, y_train)\n",
        "    predicted = knn.predict(X_test)\n",
        "    acc = metrics.accuracy_score(y_test, predicted)\n",
        "    print('KNN with TFIDF accuracy = ' + str(acc * 100) + '%')\n",
        "\n",
        "    scores = cross_val_score(knn, X_train, y_train, cv=3)\n",
        "    print(\"Cross Validation Accuracy: %0.2f\" % (scores.mean()))\n",
        "    print(scores)\n",
        "    return predicted, y_test"
      ],
      "metadata": {
        "id": "2pULG0vzx11A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## KNN accuracy after using BoW\n",
        "predicted, y_test = bow_knn()"
      ],
      "metadata": {
        "id": "Glxec-DOx-hC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## KNN accuracy after using TFIDF\n",
        "predicted, y_test = tfidf_knn()"
      ],
      "metadata": {
        "id": "tzsArd34yBYs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Word2Vec"
      ],
      "metadata": {
        "id": "JDlRw-cOwql6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Word2Vec is a method to construct such an embedding. It can be obtained using two methods (both involving Neural Networks): Skip Gram and Common Bag Of Words (CBOW)."
      ],
      "metadata": {
        "id": "u0vjKAWcwtxc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## References\n",
        "\n",
        "https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa\n",
        "\n",
        "https://towardsdatascience.com/how-tf-idf-works-3dbf35e568f0"
      ],
      "metadata": {
        "id": "b7V3FJBqsugj"
      }
    }
  ]
}