word_space_model_and_tfidf_from_scratch.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2FuO82RTBftK"
      },
      "source": [
        "# 1. Vector Space Model of Word Meaning\n",
        "\n",
        "The goal of this problem set is to make you familiar with vector space model of word meaning. You may reuse some of functions you coded in Assignment 1. \n",
        "\n",
        "### Warning: This assignment may take substantial time to run if you are not optimizing your code. Make sure you have plenty of time to run if you are new to programming.\n",
        "\n",
        "Go to https://drive.google.com/drive/folders/1Pe6D713L9S0GWwb_XzeLXAeNZOrBqZaN?usp=sharing and click add shortcut to drive. This will add the data required for this problem set to your Google drive.\n",
        "\n",
        "<img src=\"https://drive.google.com/uc?id=1LqHisiziX8Ri94Xs6Cv8mhx6vivFM3kS\" alt=\"Drawing\" height=\"300\"/>\n",
        "\n",
        "Caution: Since this is real language on Twitter and deals with current events, some of it could be disturbing. In the later section of the course, we will deal with ethics and what is appropriate and what is not. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "l01x8imjnaN8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UtZEcHthBeXz"
      },
      "source": [
        "Run the below code snippet. It will generate a URL which generates an authorization code.* Enter it below to give Colab access to your Google drive. \n",
        "\n",
        "*Copy function may not work. If so, manually copy the authorization code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "KW-dce7oJlyr",
        "outputId": "22c6d93d-b1ca-458a-a9ec-80e24c4a6ab7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ni2pYuuQKaHY"
      },
      "source": [
        "When you run the `ls` command below, you should see the files in the tweets folder.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYENtyc7SOxA",
        "outputId": "7d0653b8-48ae-43c1-b859-3521ae0fa31c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20000_tweets.jsonl\n",
            "20000_tweets.txt\n",
            "covid-tweets-2020-08-10-2020-08-21.tokenized.txt\n",
            "covid-tweets-2020-08-10-2020-08-21.trigrams.txt\n",
            "GoogleNews-vectors-negative300.bin.gz\n",
            "stop_words.txt\n"
          ]
        }
      ],
      "source": [
        "!ls \"/content/drive/My Drive/tweets\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZMOmElPSPHk",
        "outputId": "095f488c-8dfb-45d3-9bcc-caa5ee22985e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "312878\n"
          ]
        }
      ],
      "source": [
        "# let's read tweets. These tweets are already tokenized and cleaned (Assignment 1)\n",
        "tweets = open(\"/content/drive/My Drive/tweets/covid-tweets-2020-08-10-2020-08-21.tokenized.txt\", \"r\").read().split(\"\\n\")\n",
        "tweets = [tweet.split() for tweet in tweets]\n",
        "\n",
        "print(len(tweets))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVkL6n6jU1LU"
      },
      "source": [
        "## Problem 1.1: Word space model\n",
        "\n",
        "Compute the most important context words of `ventilator`. Use Pointwise Mutual Information (PMI) to rank the context words (Refer to Lecture 4).\n",
        "\n",
        "We define context as up to 3 words to the left and 3 words to the right. Ignore stop words and words that do not start with [a-z#]. Also ignore words that are not in the top 1000 frequent words.\n",
        "\n",
        "These context words define the dimensions of the vector space model. Represent each word as a vector (dictionary/counter) of context words with PMI as the importance of the context word. Print the top 20 context words for each.\n",
        "\n",
        "This is the sample output I got for `ventilator`. Your numbers need not match mine but the ranked list should look close to what I have.\n",
        "\n",
        "```\n",
        "[('put', 18.280538283196606), ('wearing', 17.587373569812726), ('even', 17.58651933524197), ('like', 17.402738298715878), ('covid', 17.172590097063086), ('patients', 16.894419647496004), ('use', 16.894298589380956), ('die', 16.89426559608771), ('days', 16.89415252713107), ('needed', 16.489137134110106), ('month', 16.48907033839664), ('weeks', 16.488913820220848), ('away', 16.48879303327717), ('week', 16.488739054051933), ('person', 16.488678720881293), ('good', 16.488160838026904), ('deaths', 16.487822204799755), ('go', 16.487564042558112), ('would', 16.48707075078768), ('one', 16.48706217686235), ('get', 16.486565870239033)]\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0SbP-yfiaL3"
      },
      "source": [
        "Let's first load stop words."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PFoeDtZPTy-g",
        "outputId": "5c9cb8f6-1d6a-4294-b4b8-d673d42bba80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "134\n"
          ]
        }
      ],
      "source": [
        "stop_words = set()\n",
        "def load_stop_words():\n",
        "  words = open(\"/content/drive/My Drive/tweets/stop_words.txt\", \"r\").read().split(\"\\n\")\n",
        "  for word in words:\n",
        "    stop_words.add(word.strip())\n",
        "\n",
        "load_stop_words()\n",
        "print(len(stop_words))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJF6PXSHQBiR"
      },
      "source": [
        "Let's build the word vectors"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch\n",
        "random1=torch.rand(3,3,3)\n",
        "max=torch.max(random1, 1)\n",
        "print(random1)\n",
        "max.values"
      ],
      "metadata": {
        "id": "m7BonDRXGFI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQ896-ClQBKM",
        "outputId": "57ae27da-31bb-468d-9109-0dff54e9f1d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "('patient', 8.208049670672274)\n",
            "('hospital', 7.828532887744695)\n",
            "('put', 7.70227502401739)\n",
            "('patients', 7.260058509812107)\n",
            "('spent', 7.216329148436841)\n",
            "('end', 7.1998174369751755)\n",
            "('enjoy', 7.163342866806889)\n",
            "('tried', 7.143470720095273)\n",
            "('brain', 6.990086773972683)\n",
            "('critical', 6.930980156715512)\n",
            "('needed', 6.817994527950766)\n",
            "('month', 6.797663529665058)\n",
            "('wo', 6.7187213773813586)\n",
            "('weeks', 6.6952663568812305)\n",
            "('failure', 6.652977849729515)\n",
            "('days', 6.649703779500786)\n",
            "('beyond', 6.282158884700785)\n",
            "('respect', 6.277819948976449)\n",
            "('cnn', 6.277819948976449)\n",
            "('trumps', 6.277819948976449)\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "import math\n",
        "from collections import Counter \n",
        "import itertools\n",
        "from itertools import islice\n",
        "\n",
        "#pattern1= re.compile('^[^a-z#]')\n",
        "pattern2= re.compile('^[a-z]*’[a-z]{0,1}')\n",
        "pattern3= re.compile('^[a-z]*\\'[a-z]{0,1}')\n",
        "pattern4= re.compile('\\d{1,}')\n",
        "#pattern5= re.compile('[’”“‘]')\n",
        "pattern6= re.compile('^[a-z#]{2,}')\n",
        "corpus=[]#Duplicate a new corpus\n",
        "corpus=[[word for word in tweet if word not in stop_words] for tweet in tweets]\n",
        "corpus=[[word for word in tweet if word in re.findall(pattern6,word)] for tweet in corpus]\n",
        "corpus=[[word for word in tweet if word not in re.findall(pattern2,word)] for tweet in corpus]\n",
        "corpus=[[word for word in tweet if word not in re.findall(pattern3,word)] for tweet in corpus]\n",
        "corpus=[[word for word in tweet if word not in re.findall(pattern4,word)] for tweet in corpus]\n",
        "#corpus=[[word for word in tweet if word not in re.findall(pattern5,word)] for tweet in corpus]\n",
        "merge=list(itertools.chain.from_iterable(corpus))\n",
        "tweets_count=list(itertools.chain.from_iterable(tweets))\n",
        "count=Counter(merge)\n",
        "total_count=len(tweets_count)\n",
        "def cofreq2(word1,word2,cop):\n",
        "    cofreq=0\n",
        "    for tweet in cop:\n",
        "      for word in tweet:\n",
        "        if word==word1 and word2 in tweet: #Check the index of word1 \n",
        "          pos1=tweet.index(word1) - 3\n",
        "          pos2=tweet.index(word1) + 3\n",
        "          if tweet.index(word2) in range(pos1,pos2): #If the word 2 is in range, cofreq+=1\n",
        "            cofreq+=1\n",
        "    if cofreq==0:\n",
        "      return 1 #Prevent non-type error\n",
        "    else:\n",
        "      return cofreq\n",
        "def PMI(word1, word2):\n",
        "  # You have to store frequencies of individual words and (word, context word) \n",
        "  # pairs to compute this. You can compute them beforehand in order to avoid \n",
        "  # counting every time when this function is called.\n",
        "  # Write your code\n",
        "  freq_word1=count[word1]#Find unigrams\n",
        "  freq_word2=count[word2]\n",
        "  cofreq=0\n",
        "  cofreq+=cofreq2(word1,word2,corpus)             \n",
        "  return math.log2((cofreq*total_count)/(freq_word1*freq_word2)) \n",
        "    \n",
        "def build_word_vector(word):\n",
        "  # Write your code\n",
        "  vocab=[word for word, word_count in count.most_common(1000)]\n",
        "  word_vector=[]\n",
        "  word_vector=[tuple((cword,PMI(word,cword))) for cword in vocab]\n",
        "  sorted_vec=sorted(word_vector, key=lambda x: x[1], reverse=True)\n",
        "  return sorted_vec\n",
        "\n",
        "\n",
        "def print_top_dimensions(word_vector, n):\n",
        "  # print top n dimensions sorted in the order of importance.\n",
        "    for i in range(0,n):\n",
        "      print(word_vector[i])\n",
        " \n",
        "v1 = build_word_vector('ventilator')\n",
        "print_top_dimensions(v1, 20) # print top 20 dimensions along with their weights\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "window=2\n",
        "i=[1,2,3,4,5,6,7,8]\n",
        "b=i[window:-window]\n",
        "print(b)\n",
        "mylist=[]\n",
        "for num in b:\n",
        "  ind=i.index(num)\n",
        "  former=[i[number] for number in range(ind-window,ind)]\n",
        "  after=[i[number] for number in range(ind+1,ind+window+1)]\n",
        "  temp=former+after\n",
        "  mylist.append(temp)\n",
        "print([])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuDMIWqMfCGt",
        "outputId": "e3c6519d-d102-4fec-8028-948e91b650ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3, 4, 5, 6]\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indices=[1, 6, 7, 436, 2, 1, 176, 4, 3, 46, 8, 1, 6]\n",
        "window_size=2\n",
        "currents=indices[window_size:-window_size]\n",
        "surroundings=[]\n",
        "print(currents)\n",
        "print(len(indices))\n",
        "for i in range(window_size,len(indices)-window_size):\n",
        "      former=[indices[f_number] for f_number in range(i-window_size,i)]\n",
        "      after=[indices[a_number] for a_number in range(i+1,i+window_size+1)]\n",
        "      temp=former+after\n",
        "      surroundings.append(temp)\n",
        "print(currents)\n",
        "print(surroundings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9yDxXWm3aPP1",
        "outputId": "fd23d93b-aac2-4fd0-c718-fb9840678adc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7, 436, 2, 1, 176, 4, 3, 46, 8]\n",
            "13\n",
            "[7, 436, 2, 1, 176, 4, 3, 46, 8]\n",
            "[[1, 6, 436, 2], [6, 7, 2, 1], [7, 436, 1, 176], [436, 2, 176, 4], [2, 1, 4, 3], [1, 176, 3, 46], [176, 4, 46, 8], [4, 3, 8, 1], [3, 46, 1, 6]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indi=[1, 6, 7, 436, 2, 1, 176, 4, 3, 46, 8, 1, 6]\n",
        "for i in range(0,len(indi)):\n",
        "  print(indi[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPhZhZKE9Ali",
        "outputId": "dfd1edca-6fb3-48b3-e757-443c14582fd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "6\n",
            "7\n",
            "436\n",
            "2\n",
            "1\n",
            "176\n",
            "4\n",
            "3\n",
            "46\n",
            "8\n",
            "1\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLZlAXuUVQXj"
      },
      "source": [
        "## Problem 1.2: Compute the word similarity between words using Cosine Similarity.\n",
        "\n",
        "Compute cosine similarity between the following pair of words: \n",
        "```\n",
        "('ventilator', 'covid-19')\n",
        "('ventilator', 'lockdown')\n",
        "('ventilator', 'mask')\n",
        "('ventilator', 'ppe')\n",
        "```\n",
        "\n",
        "Outputs of my code are:\n",
        "```\n",
        "('ventilator', 'covid-19') 0.17076006036635358\n",
        "('ventilator', 'lockdown') ...\n",
        "('ventilator', 'mask') 0.19229601085517933\n",
        "('ventilator', 'ppe') ...\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4x3CVEnsctn",
        "outputId": "abee5266-a331-4869-fdee-15fb06a37552"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "('ventilator', 'covid-19') 0.7351879330489419\n",
            "('ventilator', 'lockdown') 0.8620622191430917\n",
            "('ventilator', 'mask') 0.8806266225724617\n",
            "('ventilator', 'ppe') 0.9271256269421272\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from numpy import dot\n",
        "from numpy.linalg import norm\n",
        "def cosine_similarity(vector1, vector2):\n",
        "  # write your code\n",
        "  vec1=[i[1] for i in vector1]\n",
        "  vec2=[i[1] for i in vector2]\n",
        "  arr1=np.array(vec1)\n",
        "  arr2=np.array(vec2)\n",
        "  result = dot(arr1, arr2)/(norm(arr1)*norm(arr2))\n",
        "  return result \n",
        "\n",
        "ventilator = build_word_vector('ventilator')\n",
        "covid19 = build_word_vector('covid-19')\n",
        "lockdown = build_word_vector('lockdown')\n",
        "mask = build_word_vector('mask')\n",
        "ppe = build_word_vector('ppe')\n",
        "\n",
        "print(('ventilator', 'covid-19'), cosine_similarity(ventilator, covid19))\n",
        "print(('ventilator', 'lockdown'), cosine_similarity(ventilator, lockdown))\n",
        "print(('ventilator', 'mask'), cosine_similarity(ventilator, mask))\n",
        "print(('ventilator', 'ppe'), cosine_similarity(ventilator, ppe))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yDJHApLrmLnw"
      },
      "source": [
        "## Problem 1.3: What can you tell about these words from the similarities?\n",
        "\n",
        "1. `ventilator` when compared with `covid-19, lockdown, mask, ppe`\n",
        "2. `pandemic` when compared with `covid-19, lockdown, mask, ppe`\n",
        "3. `president` compared with `trump, biden`\n",
        "4. `trudeau` compared with `trump, biden`\n",
        "\n",
        "\n",
        "('ventilator', 'covid-19') 0.7351879330489419\n",
        "('ventilator', 'lockdown') 0.8620622191430917\n",
        "('ventilator', 'mask') 0.8806266225724617\n",
        "('ventilator', 'ppe') 0.9271256269421272\n",
        "\n",
        "1. All these words are related but ppe and mask has the largest cosine similarity value because they're all medical supplies while the other two are not.\n",
        "\n",
        "2. pandemic is most similar to covid-19 because pandemic is caused by covid-19 and ppe is only one dimension of the covid. \n",
        "\n",
        "3 and 4. ('president', 'trump') 0.9950780835806367\n",
        "('president', 'biden') 0.9975171466111247\n",
        "('trudeau', 'trump') 0.9652466707198312\n",
        "('trudeau', 'biden') 0.9520173225124738\n",
        "\n",
        "When we're comparing President with Trump and Biden, the pair with Biden has higher cosine similarity is maybe because Biden is the current president thus the average PMI in Biden word vector is higher. Trudeau and Trump has higher cosine similarity than Trudeau and Biden is probabaly because Trump has been in office longer than Biden as the head of state so there's more information in common with the leader of Canada, Trudeau\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-TRJ-3IVc0PX",
        "outputId": "81513153-ccc5-4b45-88fc-a20adb414c77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "('president', 'trump') 0.9950780835806367\n",
            "('president', 'biden') 0.9975171466111247\n",
            "('trudeau', 'trump') 0.9652466707198312\n",
            "('trudeau', 'biden') 0.9520173225124738\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from numpy import dot\n",
        "from numpy.linalg import norm\n",
        "def cosine_similarity(vector1, vector2):\n",
        "  # write your code\n",
        "  vec1=[i[1] for i in vector1]\n",
        "  vec2=[i[1] for i in vector2]\n",
        "  arr1=np.array(vec1)\n",
        "  arr2=np.array(vec2)\n",
        "  result = dot(arr1, arr2)/(norm(arr1)*norm(arr2))\n",
        "  return result \n",
        "\n",
        "president = build_word_vector('president')\n",
        "trump = build_word_vector('trump')\n",
        "biden = build_word_vector('biden')\n",
        "trudeau = build_word_vector('trudeau')\n",
        "\n",
        "\n",
        "print(('president', 'trump'), cosine_similarity(president, trump))\n",
        "print(('president', 'biden'), cosine_similarity(president, biden))\n",
        "print(('trudeau', 'trump'), cosine_similarity(trudeau, trump))\n",
        "print(('trudeau', 'biden'), cosine_similarity(trudeau, biden))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Q-FpH2k7akG"
      },
      "source": [
        "# Let's play with word2vec\n",
        "\n",
        "First let's load word2vec. I am using [gensim](https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html) but feel free to use any libraries or tools."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indices=[1,2,3,4,5,6,7,8,9]\n",
        "window_size=2\n",
        "currents=indices[window_size:-window_size]\n",
        "surrondings=[]\n",
        "for num in currents:\n",
        "    ind=indices.index(num)\n",
        "    former=[indices[number] for number in range(ind-window_size,ind)]\n",
        "    after=[indices[number] for number in range(ind+1,ind+window_size+1)]\n",
        "    temp=former+after\n",
        "    surrondings.append(temp)\n",
        "print(surrondings)\n",
        "print(currents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lup2QvWkDEA2",
        "outputId": "08624e44-0903-4302-9a9a-248fcddc9590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1, 2, 4, 5], [2, 3, 5, 6], [3, 4, 6, 7], [4, 5, 7, 8], [5, 6, 8, 9]]\n",
            "[3, 4, 5, 6, 7]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "listA=[]\n",
        "listB=[1,2]\n",
        "listA.extend(listB)\n",
        "print(listA)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuO7e5pzIaBJ",
        "outputId": "9cc5ffea-853c-499e-c1cb-7c2db83cc7fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "11Jcaf1T9mHU",
        "outputId": "6b51bc81-3a0c-488d-fbf9-20959bbd77c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-cec7ac6f8b29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mEMBEDDING_FILE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/content/drive/My Drive/tweets/GoogleNews-vectors-negative300.bin.gz'\u001b[0m \u001b[0;31m# from above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mword2vec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKeyedVectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_word2vec_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEMBEDDING_FILE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinary\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[0;32m/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mload_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype)\u001b[0m\n\u001b[1;32m   1436\u001b[0m         return _load_word2vec_format(\n\u001b[1;32m   1437\u001b[0m             \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfvocab\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfvocab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinary\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0municode_errors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0municode_errors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1438\u001b[0;31m             limit=limit, datatype=datatype)\n\u001b[0m\u001b[1;32m   1439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_keras_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_embeddings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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/gensim/models/utils_any2vec.py\u001b[0m in \u001b[0;36m_load_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"loading projection weights from %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmart_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfin\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    172\u001b[0m         \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_unicode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0mvocab_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# throws for invalid file format\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/smart_open/smart_open_lib.py\u001b[0m in \u001b[0;36msmart_open\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    496\u001b[0m     \u001b[0mignore_ext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mignore_extension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_extension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\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[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/smart_open/smart_open_lib.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(uri, mode, buffering, encoding, errors, newline, closefd, opener, ignore_ext, compression, transport_params)\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mve\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m     \u001b[0mbinary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_open_binary_stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinary_mode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransport_params\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    236\u001b[0m     \u001b[0mdecompressed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mso_compression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompression_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinary_mode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/smart_open/smart_open_lib.py\u001b[0m in \u001b[0;36m_open_binary_stream\u001b[0;34m(uri, mode, transport_params)\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0mscheme\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_sniff_scheme\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m     \u001b[0msubmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscheme\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m     \u001b[0mfobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_uri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransport_params\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    399\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'name'\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    400\u001b[0m         \u001b[0mfobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muri\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/smart_open/local_file.py\u001b[0m in \u001b[0;36mopen_uri\u001b[0;34m(uri_as_string, mode, transport_params)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_uri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri_as_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransport_params\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     33\u001b[0m     \u001b[0mparsed_uri\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse_uri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri_as_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0mfobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed_uri\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'uri_path'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\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     35\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/My Drive/tweets/GoogleNews-vectors-negative300.bin.gz'"
          ]
        }
      ],
      "source": [
        "from gensim.models import KeyedVectors\n",
        "\n",
        "EMBEDDING_FILE = '/content/drive/My Drive/tweets/GoogleNews-vectors-negative300.bin.gz' # from above\n",
        "word2vec = KeyedVectors.load_word2vec_format(EMBEDDING_FILE, binary=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FXHjFq6upAUA"
      },
      "source": [
        "## Problem 1.4: Compute the top 5 similar words of `ventilator` using word2vec?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xvff7yDBFUMg",
        "outputId": "ba52e08b-e787-40e6-97eb-03fd1e9a5ccd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "respirator: 0.7865\n",
            "mechanical_ventilator: 0.7064\n",
            "intensive_care: 0.6810\n",
            "ventilators: 0.6684\n",
            "breathing_tube: 0.6666\n"
          ]
        }
      ],
      "source": [
        "# Write your code here\n",
        "result = word2vec.most_similar(positive=['ventilator'], topn=5)\n",
        "most_similar_key, similarity = result[0]  \n",
        "print(f\"{most_similar_key}: {similarity:.4f}\")\n",
        "most_similar_key, similarity = result[1]  \n",
        "print(f\"{most_similar_key}: {similarity:.4f}\")\n",
        "most_similar_key, similarity = result[2]  \n",
        "print(f\"{most_similar_key}: {similarity:.4f}\")\n",
        "most_similar_key, similarity = result[3]  \n",
        "print(f\"{most_similar_key}: {similarity:.4f}\")\n",
        "most_similar_key, similarity = result[4]  \n",
        "print(f\"{most_similar_key}: {similarity:.4f}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufUX14RV2bCY"
      },
      "source": [
        "# Problem 1.5: Word analogy\n",
        "\n",
        "If I told you the plural of `car` is `cars`, can you automatically find the plural of `hypothesis` and `man` using word2vec.\n",
        "\n",
        "Similarly, if I told you a newborn `dog` is called `puppy`, can you automatically find what are the newborn words of `cat` and `sheep` using word2vec?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "yCSJiYLnW1yU",
        "outputId": "23098a46-90ab-470f-b52b-be200372239e"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-494441d4ae08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mmost_similar_key\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimilarity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;31m#The first element is the most similar word to the our input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"{most_similar_key}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_analogy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'car'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'cars'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'hypothesis'\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[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_analogy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'car'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'cars'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'man'\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      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_analogy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dog'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'puppy'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'cat'\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<ipython-input-2-494441d4ae08>\u001b[0m in \u001b[0;36mword_analogy\u001b[0;34m(word1, word2, word3)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Write your code here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mword_analogy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mword2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mword3\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----> 3\u001b[0;31m   \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mword2vec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmost_similar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositive\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mword3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#Calculate the word vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m   \u001b[0mmost_similar_key\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msimilarity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;31m#The first element is the most similar word to the our input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"{most_similar_key}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'word2vec' is not defined"
          ]
        }
      ],
      "source": [
        "# Write your code here\n",
        "def word_analogy(word1, word2, word3):\n",
        "  result = word2vec.most_similar(positive=[word2, word3], negative=[word1])#Calculate the word vectors \n",
        "  most_similar_key, similarity = result[0]#The first element is the most similar word to the our input  \n",
        "  return f\"{most_similar_key}\"\n",
        "print(word_analogy('car','cars','hypothesis'))  \n",
        "print(word_analogy('car','cars','man'))  \n",
        "print(word_analogy('dog','puppy','cat'))\n",
        "print(word_analogy('dog','puppy','sheep'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXX-DQe2oTcH"
      },
      "source": [
        "# 2. Topic Models \n",
        "\n",
        "The goal of this part is to make you familiar with topic models. You may reuse some of functions you coded for the previous assignments.\n",
        "\n",
        "## Data Download and Setup\n",
        "\n",
        "Let us start by downloading the news section of the Brown corpus:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zrStv7GPOixj",
        "outputId": "9780988c-784f-4eb5-e4af-c582f6b10eec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package brown to /root/nltk_data...\n",
            "[nltk_data]   Package brown is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('brown')\n",
        "from nltk.corpus import brown\n",
        "documents = [brown.words(fileid) for fileid in brown.fileids()]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7m5pYgzSqJIq"
      },
      "source": [
        "Let us inspect some of the documents:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCvgANNlUOjs",
        "outputId": "250b7a6c-8a79-4b72-d563-2bb99528f83a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The news section of the Brown corpus contains 500 documents.\n",
            "Document 0 has 2242 words: ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...]\n",
            "Document 1 has 2277 words: ['Austin', ',', 'Texas', '--', 'Committee', 'approval', ...]\n",
            "Document 2 has 2275 words: ['Several', 'defendants', 'in', 'the', 'Summerdale', ...]\n"
          ]
        }
      ],
      "source": [
        "print(\"The news section of the Brown corpus contains {} documents.\".format(len(documents)))\n",
        "for i in range(3):\n",
        "  document = documents[i]\n",
        "  print(\"Document {} has {} words: {}\".format(i, len(document), document))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hfx34Lg4Vi9l"
      },
      "source": [
        "Finally, let us download a list of stopwords for later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlizEa3mVnWR",
        "outputId": "9f67069f-c326-4937-b466-1710d3ba9615"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
        }
      ],
      "source": [
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "stopwords_list = stopwords.words('english')\n",
        "print(stopwords_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fk-pkKhvWTMA"
      },
      "source": [
        "## Problem 2.1: Document-Term Matrix\n",
        "\n",
        "Create a document-term matrix with tf-idf. You should preprocess documents by: 1) lowercasing words, 2) excluding stopwords, and 3) including alphanumeric strings only.\n",
        "\n",
        "```python\n",
        "import numpy as np\n",
        "import re \n",
        "pattern=re.compile('^[a-zA-Z0-9]*$')\n",
        "documents=[[word.lower() for word in document] for document in documents]\n",
        "documents=[[word for word not in stopwords_list] for document in documents]\n",
        "documents=[[word for word not in re.findall(pattern,word)] for document in documents]\n",
        "\n",
        "\n",
        "\n",
        "def create_tfidf_matrix(documents: List[List[str]]) -> (np.array, List[str]):\n",
        "  # Args:\n",
        "  #   documents: list of documents, each document being a list of words.\n",
        "  # Outputs:\n",
        "  #   tfidf_matrix: np.array of shape (num_documents, vocabulary_size)\n",
        "  #   vocabulary: a list of terms corresponding to the columns of the matrix.\n",
        "\n",
        "tfidf_matrix, vocabulary = create_tfidf_matrix(documents)\n",
        "```\n",
        "\n",
        "How sparse is this matrix? Calculate the ratio between cells with value 0 and the total number of cells. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "90kVZdvndex0"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "pattern=re.compile('^[a-zA-Z0-9]*$')\n",
        "pattern1= re.compile('\\d{1,}')\n",
        "documents=[[word.lower() for word in document] for document in documents]\n",
        "documents=[[word for word in document if word not in stopwords_list] for document in documents]\n",
        "documents=[[word for word in document if word in re.findall(pattern,word)] for document in documents]\n",
        "documents=[[word for word in document if word not in re.findall(pattern1,word)] for document in documents]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dz5rHBNIDuSJ",
        "outputId": "43512c7d-6692-4271-873d-ea9e0387c406"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sparsity: 0.9824832698957634\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import re \n",
        "from collections import Counter\n",
        "from typing import List\n",
        "import itertools\n",
        "from itertools import chain\n",
        "pattern=re.compile('^[a-zA-Z0-9]*$')\n",
        "pattern1= re.compile('\\d{1,}')\n",
        "documents=[[word.lower() for word in document] for document in documents]\n",
        "documents=[[word for word in document if word not in stopwords_list] for document in documents]\n",
        "documents=[[word for word in document if word in re.findall(pattern,word)] for document in documents]\n",
        "documents=[[word for word in document if word not in re.findall(pattern1,word)] for document in documents]\n",
        "corpus=list(itertools.chain.from_iterable(documents))\n",
        "vocab=[s for x in documents for s in set(x)]\n",
        "wordoc_count=Counter(vocab)\n",
        "unique_words=list(set(corpus))\n",
        "word_freq=Counter(corpus)\n",
        "def tfval(document):\n",
        "  word_tf_values = {}\n",
        "  doc_freq = Counter(document) #occurences for words in a single document\n",
        "  for k,v in doc_freq.items(): \n",
        "    word_tf = v/len(document) #calculate the tf value\n",
        "    word_tf_values[k] = word_tf\n",
        "  return word_tf_values  #Dict of tf in a document\n",
        "def idfval(docs):              \n",
        "  word_idf_values = {}\n",
        "  for k,v in wordoc_count.items(): #Count of words in the corpus as key\n",
        "    word_idf_values[k] = np.log(len(documents)/v) #Calculate idf \n",
        "  return word_idf_values #Dict of idf in a document\n",
        "\n",
        "def create_tfidf_matrix(documents: List[List[str]]) -> (np.array, List[str]):\n",
        "  # Args:\n",
        "  #   documents: list of documents, each document being a list of words.\n",
        "  # Outputs:\n",
        "  #   tfidf_matrix: np.array of shape (num_documents, vocabulary_size)\n",
        "  #   vocabulary: a list of terms corresponding to the columns of the matrix.\n",
        "  nrow=len(documents)\n",
        "  ncolumn=len(unique_words)\n",
        "  mat=np.zeros((nrow,ncolumn))\n",
        "  idf=idfval(documents)\n",
        "  zero_counter=0\n",
        "  for i in range(0,ncolumn-1):\n",
        "    for j in range(0,nrow-1):\n",
        "      mat[j,i]=idf[unique_words[i]]\n",
        "  for i in range(0,ncolumn-1):\n",
        "    for j in range(0,nrow-1):\n",
        "      if unique_words[i] in documents[j]:\n",
        "        tf=tfval(documents[j])\n",
        "        mat[j,i]=mat[j,i]*tf[unique_words[i]]\n",
        "      else: \n",
        "        mat[j,i]=0\n",
        "        zero_counter+=1\n",
        "  sparsity=zero_counter/(nrow*ncolumn)\n",
        "  print(\"Sparsity: {}\".format(sparsity))\n",
        "  return mat,unique_words\n",
        "\n",
        "  \n",
        "\n",
        "tfidf_matrix, vocabulary = create_tfidf_matrix(documents)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gilMYo2eWTWD"
      },
      "source": [
        "## Problem 2.2: Latent Semantic Analysis\n",
        "\n",
        "We perform LSA to obtain document embeddings `U` and term embeddings `VT`.\n",
        "\n",
        "```python\n",
        "from sklearn.utils.extmath import randomized_svd\n",
        "\n",
        "U, Sigma, VT = randomized_svd(tfidf_matrix, \n",
        "                              n_components=10,\n",
        "                              n_iter=100,\n",
        "                              random_state=42)\n",
        "```\n",
        "\n",
        "Define a function to find the 5 most relevant terms for each of the 10 latent dimensions (tip: you should make use of VT and the vocabulary).\n",
        "\n",
        "```python\n",
        "def extract_salient_words(VT: np.array, \n",
        "                  vocabulary: List[str]\n",
        "                  ) -> salient_words: dict[int, List[str]]:\n",
        "  # Args:\n",
        "  #  VT: a numpy array of size (n_components, vocabulary_size)\n",
        "  #  vocabulary: a list of words of size vocabulary_size\n",
        "  # Outputs:\n",
        "  #  salient_words: a dictionary with the latent dimension indices as keys and a list of its 5 most salient words as values\n",
        "\n",
        "salient_words = extract_salient_words(VT, vocabulary)\n",
        "\n",
        "for key, value in salient_words:\n",
        "  print(\"Concept {}: {}\".format(str(key), \" \".join(value)))\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pf2P98urNvmH"
      },
      "outputs": [],
      "source": [
        "from sklearn.utils.extmath import randomized_svd\n",
        "\n",
        "U, Sigma, VT = randomized_svd(tfidf_matrix, \n",
        "                              n_components=20,\n",
        "                              n_iter=100,\n",
        "                              random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0sJAYQHNvPw",
        "outputId": "4ba78dcb-1944-4d5e-d681-330767052110"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Concept 0: af polynomial operator theorem q\n",
            "Concept 1: af polynomial operator theorem diagonalizable\n",
            "Concept 2: anode holder arc temperature plug\n",
            "Concept 3: phil anode mike deegan jess\n",
            "Concept 4: staining nonspecific clover sections wtv\n",
            "Concept 5: dictionary text cell platform information\n",
            "Concept 6: platform gyro accelerometer leveling axis\n",
            "Concept 7: q tangent secants c curve\n",
            "Concept 8: feed phil milligrams cattle grams\n",
            "Concept 9: phil mike deegan eddie anniston\n"
          ]
        }
      ],
      "source": [
        "#import numpy as np\n",
        "from typing import List\n",
        "from typing import Dict\n",
        "def extract_salient_words(VT: np.array, \n",
        "                  vocabulary: List[str],\n",
        "                  ):\n",
        "  # Args:\n",
        "  #  VT: a numpy array of size (n_components, vocabulary_size)\n",
        "  #  vocabulary: a list of words of size vocabulary_size\n",
        "  # Outputs:\n",
        "  #  salient_words: a dictionary with the latent dimension indices as keys and a list of its 5 most salient words as values\n",
        "  salient_words={}\n",
        "  for i in range(0,10):\n",
        "    list_word=[]\n",
        "    indices = (-VT[i]).argsort()[:5]#\n",
        "    for index in indices:\n",
        "      list_word.append(unique_words[index])\n",
        "    key=i\n",
        "    salient_words[key]=list_word\n",
        "  return salient_words\n",
        "\n",
        "salient_words = extract_salient_words(VT, vocabulary)\n",
        "\n",
        "for key, value in salient_words.items():\n",
        "  print(\"Concept {}: {}\".format(str(key), \" \".join(value)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SyNdaiPLpUHS"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtEdR0wjZ-JN"
      },
      "source": [
        "## Problem 2.3: Document Retrieval\n",
        "\n",
        "Given a text query, view this as a mini document, and compare it to your documents in the low-dimensional space.\n",
        "\n",
        "First, we need to create a vector $\\mathbf{d}$, which is the tf-idf representation of the query. Then we project it into the semantic space with the linear map $\\Sigma^{-1} V^\\top$ learned above. This gives us a pseudo-document embedding $\\hat{\\mathbf{d}}$.\n",
        "\n",
        "$$\\hat{\\mathbf{d}} = \\Sigma^{-1} V^\\top \\mathbf{d}$$\n",
        "\n",
        "Then, you will need to implement a function to calculate the cosine similarity between this embedded query and all the document embeddings.\n",
        "\n",
        "Retrieve the indices of the top-3 documents with the highest cosine similarity with the following queries:\n",
        "\n",
        "\n",
        "```python\n",
        "query1 = ['T.', 'V.', 'Barker', 'developed', 'the', 'classification-angle', 'system']\n",
        "query2 = ['imitation', 'vs.', 'formalism', 'in', 'philosophical', 'debates']\n",
        "query3 = ['Krim', 'attended', 'the', 'University', 'of', 'North', 'Carolina', 'to', 'follow', 'Thomas', 'Wolfe']\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ClqkiFqmRlP7",
        "outputId": "bb84a41e-9936-4dac-ea84-2b8f5223b0e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{318: 0.26151359547699154, 43: 0.20450920257641628, 137: 0.17101291633164023}\n",
            "{204: 0.4379294938807412, 244: 0.20250985822362316, 57: 0.06022114785186182}\n",
            "{70: 0.032977641298781925, 16: 0.03169558941804979, 197: 0.029062905706194967}\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from numpy import dot\n",
        "from numpy.linalg import norm\n",
        "from operator import itemgetter\n",
        "query1 = ['T.', 'V.', 'Barker', 'developed', 'the', 'classification-angle', 'system']\n",
        "query2 = ['imitation', 'vs.', 'formalism', 'in', 'philosophical', 'debates']\n",
        "query3 = ['Krim', 'attended', 'the', 'University', 'of', 'North', 'Carolina', 'to', 'follow', 'Thomas', 'Wolfe']\n",
        "\n",
        "idf=idfval(documents)\n",
        "def tfidfvec2(query):\n",
        "  query=[word.lower() for word in query]\n",
        "  query=[word for word in query if word not in stopwords_list]\n",
        "  unique=list(set(query))\n",
        "  word_c=Counter(query)\n",
        "  unique=list(set(query))\n",
        "  ncolumn=len(unique)\n",
        "  mat=[]\n",
        "  dic={}\n",
        "  tf=tfval(query)\n",
        "  for word in query:\n",
        "    tf=tfval(query)\n",
        "    if word not in idf.keys():\n",
        "      dic[word]=0\n",
        "    else:\n",
        "      dic[word]=tf[word]*idf[word]\n",
        "  return dic\n",
        "           \n",
        "\n",
        "def cosine_embedding(query,document):\n",
        "  docscore=0\n",
        "  tfdoc={}\n",
        "  numerator=0\n",
        "  vecquery=[]\n",
        "  vecdoc=[]\n",
        "\n",
        "  for word in document:\n",
        "      tfdoc=tfval(document)\n",
        "      tfidf=tfidfvec2(query)\n",
        "      docscore=tfdoc[word]*idf[word]\n",
        "      vecdoc.append(tuple((word,docscore)))\n",
        "      if word in query:\n",
        "        queryWordScore = tfidf[word]   \n",
        "        vecquery.append(tuple((word,queryWordScore)))\n",
        "      else:\n",
        "        vecquery.append(tuple((word,0)))\n",
        "\n",
        "  vecquery=[i[1] for i in vecquery]\n",
        "  vecdoc=[i[1] for i in vecdoc]\n",
        "  arr1=np.array(vecquery)\n",
        "  arr2=np.array(vecdoc)\n",
        "  if dot(arr1,arr2)!=0 and (norm(arr1)*norm(arr2))!=0:\n",
        "    result = dot(arr1, arr2)/(norm(arr1)*norm(arr2))\n",
        "  else:\n",
        "    result=0\n",
        "  return result\n",
        "\n",
        "def calculate_cosine(query):\n",
        "  cosine={}\n",
        "  for document in documents:\n",
        "      cosine[documents.index(document)]=cosine_embedding(query,document)\n",
        "  res = dict(sorted(cosine.items(), key = itemgetter(1), reverse = True)[:3])\n",
        "  return res\n",
        "print(calculate_cosine(query1))\n",
        "print(calculate_cosine(query2))\n",
        "print(calculate_cosine(query3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v8kIWyhpPL4D"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hE7VtK5a0m5"
      },
      "source": [
        "## Problem 2.4: Document Clustering\n",
        "\n",
        "```python\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "num_clusters = 10\n",
        "km = KMeans(n_clusters=num_clusters)\n",
        "document_embeddings = U * Sigma\n",
        "km.fit(document_embeddings)\n",
        "clusters = km.labels_.tolist()\n",
        "print(clusters)\n",
        "```\n",
        "\n",
        "Let us now plot the document embeddings and their clusters:\n",
        "\n",
        "```python\n",
        "import umap\n",
        "embedding = umap.UMAP(n_neighbors=100, min_dist=0.5, random_state=42).fit_transform(document_embeddings)\n",
        "\n",
        "plt.figure(figsize=(7,5))\n",
        "plt.scatter(embedding[:, 0], embedding[:, 1], c=clusters, s=20, edgecolor='none')\n",
        "plt.show()\n",
        "```\n",
        "\n",
        "What are the differences you observe by using a different number of `n_components` in LSA or `n_clusters` in K-Means?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LyxBdSOgZv-y",
        "outputId": "ea2cd7ff-698a-4094-a491-cb18f0d22ba5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[3, 3, 19, 3, 3, 3, 3, 19, 3, 3, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 19, 3, 19, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 19, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 0, 4, 17, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 3, 3, 3, 3, 11, 19, 3, 3, 3, 3, 3, 3, 0, 3, 3, 0, 3, 3, 0, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 0, 3, 3, 3, 0, 3, 3, 3, 0, 0, 3, 0, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 3, 3, 3, 0, 3, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 3, 0, 0, 3, 3, 0, 3, 3, 3, 0, 3, 0, 3, 3, 3, 0, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3, 0, 19, 3, 3, 19, 19, 19, 19, 3, 19, 19, 4, 19, 3, 19, 3, 19, 19, 3, 3, 19, 19, 19, 7, 19, 3, 3, 19, 3, 19, 3, 4, 9, 4, 4, 3, 4, 4, 3, 4, 3, 3, 15, 3, 14, 3, 6, 3, 1, 8, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 16, 3, 3, 3, 3, 3, 19, 3, 19, 19, 3, 19, 19, 19, 19, 3, 19, 3, 19, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 0, 3, 3, 3, 3, 3, 3, 3, 13, 3, 3, 3, 3, 3, 18, 3, 3, 4, 4, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 3]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "num_clusters = 10\n",
        "km = KMeans(n_clusters=num_clusters)\n",
        "document_embeddings = U * Sigma\n",
        "km.fit(document_embeddings)\n",
        "clusters = km.labels_.tolist()\n",
        "print(clusters)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "tYqLVWB0ZvuF",
        "outputId": "60b34da5-e86a-4386-da9b-26da5d354a7d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "import umap\n",
        "embedding = umap.UMAP(n_neighbors=100, min_dist=0.5, random_state=42).fit_transform(document_embeddings)\n",
        "\n",
        "plt.figure(figsize=(7,5))\n",
        "plt.scatter(embedding[:, 0], embedding[:, 1], c=clusters, s=20, edgecolor='none')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KIZet-oAbx_2"
      },
      "source": [
        "When we keep the n_components constant and change the n_clusters from 10 to 20 we found the colour distribution in the clustering changed. Then I kept n_clusters constant(=10) and change n_components from 10 to 20, the shape of the clustering changed. Finally, I tried n_clusters=n_components=20. This time we obeserved the change in colour too.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPsynu8PYHQI"
      },
      "source": [
        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)\n",
        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)\n",
        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)\n",
        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)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "word_space_model_and_tfidf_from_scratch.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}