lotr_embeddings.ipynb

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  "metadata": {
    "colab": {
      "name": "lotr_embeddings.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMDjQHCH3RGiBsRWX+1GKPV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/neelchoudhury/nlp_notebooks/blob/master/lotr_embeddings.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvk2GoELilua"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zW3h1A2swXru"
      },
      "source": [
        "## LOTR Embeddings\n",
        "\n",
        "Ideally, I guess this should be called Middle-Earth Embeddings.\n",
        "I am going to use the following books to create embeddings - \n",
        "\n",
        "1. The LOTR Trilogy\n",
        "2. The Hobbit\n",
        "3. The Silmarillion\n",
        "\n",
        "I will add other books later, to get a better view. Maybe a good idea is also to Scrape LOTR Wiki websites to get more textual information about the Middle-Earth World."
      ]
    },
    {
      "cell_type": "code",
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        "id": "L7v3KplLw32p",
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        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 140
        }
      },
      "source": [
        "from google.colab import files\n",
        "files = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a8de8c9a-0519-47ab-bfea-8940f470bd22\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a8de8c9a-0519-47ab-bfea-8940f470bd22\">\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> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving HobbitBook.json to HobbitBook.json\n",
            "Saving LordOfTheRingsBook.json to LordOfTheRingsBook.json\n",
            "Saving TheSilmarillion.txt to TheSilmarillion.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFCOc45Mx9jm",
        "outputId": "7e78e5bd-a3ee-4aa7-fa71-222e75c5e0ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(files)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWfGTwktw7qC",
        "outputId": "0441b3e7-7f6a-409d-cd27-047926943dcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "for k,_ in files.items():\n",
        "  print(k)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "HobbitBook.json\n",
            "LordOfTheRingsBook.json\n",
            "TheSilmarillion.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWX_xHasx7He"
      },
      "source": [
        "hobbit = files['HobbitBook.json']\n",
        "lotr = files['LordOfTheRingsBook.json']\n",
        "silmarillion = files['TheSilmarillion.txt']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7g93gIUyQoO",
        "outputId": "7b14b9e7-63f0-464a-f539-3cf7f3b75616",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(hobbit)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bytes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5IVvi2LyR6l",
        "outputId": "13c7ca64-04cf-48ae-d0d6-4469d9595580",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(silmarillion)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bytes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOLXuY6vyXZt"
      },
      "source": [
        "import json\n",
        "\n",
        "hobbit_json = json.loads(hobbit)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yr5pa2kHys_U",
        "outputId": "6aba7bbd-234b-44ba-8fa5-f841e4c9ba38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(hobbit_json)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "472TCOaWyw6M"
      },
      "source": [
        "lotr_json = json.loads(lotr)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Gha2XOEA5rS"
      },
      "source": [
        "silmarillion_text = silmarillion.decode(\"latin1\", errors=\"ignore\")"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHTzWztbA-7U",
        "outputId": "a8d19690-d09a-402a-ab42-9b25bea5a84c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "silmarillion_text[0:40]"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'FOREWORD\\r\\nThe Silmarillion, now publishe'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNO2ERaSBGxT",
        "outputId": "9e689b33-36b1-4888-ee58-00a6d05e5b01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "silmarillion[2740:2747]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b' of T\\xfar'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UA3JpvLBBlSE",
        "outputId": "85a3388c-8291-4ce0-ac34-318994c8cded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "silmarillion_text[2740:2750]"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' of Túrin '"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rn0FN9usDNln"
      },
      "source": [
        "import re\n",
        "\n",
        "silmarillion_text_clean = re.sub(r'\\r|\\n', r' ', silmarillion_text.strip().lower())\n",
        "silmarillion_text_clean = re.sub(r'chapter\\s+\\d+', r'', silmarillion_text_clean)\n",
        "# silmarillion_text_clean = re.sub(r'[,:;\\.!]', r'', silmarillion_text_clean)\n",
        "# silmarillion_text_clean = re.sub(r'[\"\\']', r' ', silmarillion_text_clean)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dvQ8e9HEEeU",
        "outputId": "e4ecd7dd-4db0-427a-d6de-cc103f9e1251",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "silmarillion_text_clean[0:200]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'foreword  the silmarillion, now published four years after the death of its author, is an account of the elder days, or the first age of the world. in the lord of the rings were narrated the great eve'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8l29jviSHDBZ",
        "outputId": "8848c23e-dcc2-4698-e2de-419fa6619085",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import nltk\n",
        "\n",
        "nltk.download(\"punkt\")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80UhxXNUEI2O"
      },
      "source": [
        "\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2IZXYZdG1FT"
      },
      "source": [
        "silmarillion_sent = sent_tokenize(silmarillion_text_clean)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jeezOaRG-sx",
        "outputId": "3474cd9c-80c8-436b-e17e-ca0700cc64d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(silmarillion_sent)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5498"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlTru517HJSY",
        "outputId": "331821d4-5311-4131-de24-319ee281303f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "silmarillion_sent[0]"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'foreword  the silmarillion, now published four years after the death of its author, is an account of the elder days, or the first age of the world.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GzVUYEA-HMs4"
      },
      "source": [
        "silmarillion_tokens = []\n",
        "\n",
        "for sent in silmarillion_sent:\n",
        "  silmarillion_tokens.append(word_tokenize(sent))"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d42B0u8DH16f",
        "outputId": "1b2a6514-206d-423c-c673-d977743e76fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "silmarillion_tokens[0]"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['foreword',\n",
              " 'the',\n",
              " 'silmarillion',\n",
              " ',',\n",
              " 'now',\n",
              " 'published',\n",
              " 'four',\n",
              " 'years',\n",
              " 'after',\n",
              " 'the',\n",
              " 'death',\n",
              " 'of',\n",
              " 'its',\n",
              " 'author',\n",
              " ',',\n",
              " 'is',\n",
              " 'an',\n",
              " 'account',\n",
              " 'of',\n",
              " 'the',\n",
              " 'elder',\n",
              " 'days',\n",
              " ',',\n",
              " 'or',\n",
              " 'the',\n",
              " 'first',\n",
              " 'age',\n",
              " 'of',\n",
              " 'the',\n",
              " 'world',\n",
              " '.']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWaue2eoH4LP"
      },
      "source": [
        "import gensim\n",
        "from gensim.models.phrases import Phrases, Phraser\n",
        "\n",
        "bigrams = Phrases(silmarillion_tokens)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9f32l-flH_Yn",
        "outputId": "380350db-225a-4d82-8d63-54e2e65073d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "silmarillion_final_tokens = []\n",
        "\n",
        "for sent in silmarillion_tokens:\n",
        "  phrases = bigrams[sent]\n",
        "  silmarillion_final_tokens.append(phrases)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/models/phrases.py:598: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
            "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEcuzRjuINk2",
        "outputId": "c751bfbf-d6e4-437c-a3a9-e843db9b5389",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "silmarillion_final_tokens[0]"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['foreword',\n",
              " 'the',\n",
              " 'silmarillion',\n",
              " ',',\n",
              " 'now',\n",
              " 'published',\n",
              " 'four',\n",
              " 'years',\n",
              " 'after',\n",
              " 'the',\n",
              " 'death',\n",
              " 'of',\n",
              " 'its',\n",
              " 'author',\n",
              " ',',\n",
              " 'is',\n",
              " 'an',\n",
              " 'account',\n",
              " 'of',\n",
              " 'the',\n",
              " 'elder_days',\n",
              " ',',\n",
              " 'or',\n",
              " 'the',\n",
              " 'first_age',\n",
              " 'of',\n",
              " 'the',\n",
              " 'world',\n",
              " '.']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n96Y6XUXIYLW"
      },
      "source": [
        "import gensim.models\n",
        "\n",
        "model = gensim.models.Word2Vec(sentences=silmarillion_final_tokens)"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXdbokCMIcnG",
        "outputId": "5a28c155-9950-4279-b2c7-cf2fe7e86df8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model.wv.most_similar('fingolfin')"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('fingon', 0.9995997548103333),\n",
              " ('most', 0.9995915293693542),\n",
              " ('because', 0.9995751976966858),\n",
              " ('doriath', 0.9995064735412598),\n",
              " ('hithlum', 0.9994909763336182),\n",
              " ('finarfin', 0.9994851350784302),\n",
              " ('rose', 0.9994783401489258),\n",
              " ('orcs', 0.9994781613349915),\n",
              " ('deeds', 0.9994683265686035),\n",
              " ('valinor', 0.999459981918335)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "te4mt0cPJWnF"
      },
      "source": [
        "hobbit_text = \"\"\n",
        "\n",
        "for block in hobbit_json:\n",
        "  hobbit_text += block['ChapterData'] + '\\r\\n'"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5RLKRB9Js7V"
      },
      "source": [
        "lotr_text = \"\"\n",
        "\n",
        "for block in lotr_json:\n",
        "  lotr_text += block['ChapterData'] + '\\r\\n'"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jW-OAxzfOtwU",
        "outputId": "9bb2ed59-5c7f-4edf-e840-58fb6dfa16ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(silmarillion_text)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "839630"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NTbWH4YOjbs",
        "outputId": "bf17f472-2e0c-4388-ca67-b88159a53517",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(lotr_text)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2479084"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNh6BJ4iOpDc",
        "outputId": "2d41584e-cdad-4a02-b4d7-331384ddbb64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(hobbit_text)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "522163"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KH1axZcRJ28c"
      },
      "source": [
        "all_text = silmarillion_text + hobbit_text + lotr_text"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwJRpypGJ9yc",
        "outputId": "1d924c7b-5f13-4c30-d634-9686822772a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(all_text)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3840877"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgsa1raIPvUq"
      },
      "source": [
        "all_text_clean = re.sub(r'\\r|\\n', r' ', all_text.strip().lower())\n",
        "all_text_clean = re.sub(r'chapter\\s+\\d+', r'', all_text_clean)"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjtjPwTwJ_Gb"
      },
      "source": [
        "all_sent = sent_tokenize(all_text_clean)"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ep2bz3AYNJlI",
        "outputId": "e14e4dc8-2476-4353-dbcb-724138e604d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "all_sent[-3252:-3245]"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['they were out on the plain now and drawing near the entrance to udun.',\n",
              " 'some way in front of it, before the gate at the bridge-end, the road from the west converged with others coming from the south, and from barad-dur.',\n",
              " 'along all the roads troops were moving; for the captains of the west were advancing and the dark lord was speeding his forces north.',\n",
              " 'so it chanced that several companies came together at the road-meeting, in the dark beyond the light of the watch-fires on the wall.',\n",
              " 'at once there was great jostling and cursing as each troop tried to get first to the gate and the ending of their march.',\n",
              " 'though the drivers yelled and plied their whips, scuffles broke out and some blades were drawn.',\n",
              " 'a troop of heavy-armed uruks from barad-dur charged into the durthang line and threw them into confusion.']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GX8kiX1WLAKR",
        "outputId": "556a09b1-8594-4f88-b66a-31d6991c5147",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(all_sent)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "44991"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuBLdXrRLBOh"
      },
      "source": [
        "all_tokens = []\n",
        "cnt_all_tokens = 0\n",
        "\n",
        "for sent in all_sent:\n",
        "  tokens = word_tokenize(sent)\n",
        "  all_tokens.append(tokens)\n",
        "  cnt_all_tokens += len(tokens)"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EKJrhwqJLJDZ",
        "outputId": "2ca7de40-c888-4890-e903-ec42b43d6a1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cnt_all_tokens"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "841313"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SskmS4jSLK1B"
      },
      "source": [
        "all_bigrams = Phrases(all_sent)"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPQK2qIgLolx",
        "outputId": "da6eb670-01ae-429d-ea1a-fe172e758ebf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "all_bigram_tokens = []\n",
        "cnt_bigram_tokens = 0\n",
        "\n",
        "for sent in all_tokens:\n",
        "  bigram_tokens = bigrams[sent]\n",
        "  all_bigram_tokens.append(bigram_tokens)\n",
        "  cnt_bigram_tokens += len(bigram_tokens)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/models/phrases.py:598: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
            "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzDZu0CWMgHU",
        "outputId": "14351c7d-913b-40e0-fdbb-50fb76e23528",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cnt_bigram_tokens"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "817949"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sebuBlXPPXMb",
        "outputId": "6294352e-06c2-453a-cda7-425f18ddfdd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "all_bigram_tokens[-3252:-3245]"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['they',\n",
              "  'were',\n",
              "  'out',\n",
              "  'on',\n",
              "  'the',\n",
              "  'plain',\n",
              "  'now',\n",
              "  'and',\n",
              "  'drawing',\n",
              "  'near',\n",
              "  'the',\n",
              "  'entrance',\n",
              "  'to',\n",
              "  'udun',\n",
              "  '.'],\n",
              " ['some',\n",
              "  'way',\n",
              "  'in',\n",
              "  'front',\n",
              "  'of',\n",
              "  'it',\n",
              "  ',',\n",
              "  'before',\n",
              "  'the',\n",
              "  'gate',\n",
              "  'at',\n",
              "  'the',\n",
              "  'bridge-end',\n",
              "  ',',\n",
              "  'the',\n",
              "  'road',\n",
              "  'from',\n",
              "  'the',\n",
              "  'west',\n",
              "  'converged',\n",
              "  'with',\n",
              "  'others',\n",
              "  'coming',\n",
              "  'from',\n",
              "  'the',\n",
              "  'south',\n",
              "  ',',\n",
              "  'and',\n",
              "  'from',\n",
              "  'barad-dur',\n",
              "  '.'],\n",
              " ['along',\n",
              "  'all',\n",
              "  'the',\n",
              "  'roads',\n",
              "  'troops',\n",
              "  'were',\n",
              "  'moving',\n",
              "  ';',\n",
              "  'for',\n",
              "  'the',\n",
              "  'captains',\n",
              "  'of',\n",
              "  'the',\n",
              "  'west',\n",
              "  'were',\n",
              "  'advancing',\n",
              "  'and',\n",
              "  'the',\n",
              "  'dark_lord',\n",
              "  'was',\n",
              "  'speeding',\n",
              "  'his',\n",
              "  'forces',\n",
              "  'north',\n",
              "  '.'],\n",
              " ['so',\n",
              "  'it_chanced',\n",
              "  'that',\n",
              "  'several',\n",
              "  'companies',\n",
              "  'came',\n",
              "  'together',\n",
              "  'at',\n",
              "  'the',\n",
              "  'road-meeting',\n",
              "  ',',\n",
              "  'in',\n",
              "  'the',\n",
              "  'dark',\n",
              "  'beyond',\n",
              "  'the',\n",
              "  'light',\n",
              "  'of',\n",
              "  'the',\n",
              "  'watch-fires',\n",
              "  'on',\n",
              "  'the',\n",
              "  'wall',\n",
              "  '.'],\n",
              " ['at',\n",
              "  'once',\n",
              "  'there',\n",
              "  'was',\n",
              "  'great',\n",
              "  'jostling',\n",
              "  'and',\n",
              "  'cursing',\n",
              "  'as',\n",
              "  'each',\n",
              "  'troop',\n",
              "  'tried',\n",
              "  'to',\n",
              "  'get',\n",
              "  'first',\n",
              "  'to',\n",
              "  'the',\n",
              "  'gate',\n",
              "  'and',\n",
              "  'the',\n",
              "  'ending',\n",
              "  'of',\n",
              "  'their',\n",
              "  'march',\n",
              "  '.'],\n",
              " ['though',\n",
              "  'the',\n",
              "  'drivers',\n",
              "  'yelled',\n",
              "  'and',\n",
              "  'plied',\n",
              "  'their',\n",
              "  'whips',\n",
              "  ',',\n",
              "  'scuffles',\n",
              "  'broke',\n",
              "  'out',\n",
              "  'and',\n",
              "  'some',\n",
              "  'blades',\n",
              "  'were_drawn',\n",
              "  '.'],\n",
              " ['a',\n",
              "  'troop',\n",
              "  'of',\n",
              "  'heavy-armed',\n",
              "  'uruks',\n",
              "  'from',\n",
              "  'barad-dur',\n",
              "  'charged',\n",
              "  'into',\n",
              "  'the',\n",
              "  'durthang',\n",
              "  'line',\n",
              "  'and',\n",
              "  'threw',\n",
              "  'them',\n",
              "  'into',\n",
              "  'confusion',\n",
              "  '.']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFvwZLm8MpIu"
      },
      "source": [
        "model_three_books = gensim.models.Word2Vec(sentences=all_bigram_tokens)"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-6hi0MLMzWm",
        "outputId": "aedad9c6-977b-4e88-fba6-1e9972b37346",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('frodo')"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('sam', 0.9811313152313232),\n",
              " ('pippin', 0.9463716149330139),\n",
              " ('bilbo', 0.9423794746398926),\n",
              " ('gollum', 0.9355931282043457),\n",
              " ('gandalf', 0.9116418361663818),\n",
              " ('merry', 0.8822979927062988),\n",
              " ('boromir', 0.8775188326835632),\n",
              " ('aragorn', 0.8722878694534302),\n",
              " ('faramir', 0.8624827861785889),\n",
              " ('strider', 0.8527661561965942)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7sUCpuHM7Ue",
        "outputId": "de3e2f7b-e181-419e-f7a9-d8509d0e60d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('gandalf')"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('strider', 0.9559337496757507),\n",
              " ('boromir', 0.9453694224357605),\n",
              " ('aragorn', 0.9385251998901367),\n",
              " ('faramir', 0.937170684337616),\n",
              " ('merry', 0.9339939951896667),\n",
              " ('bilbo', 0.9133778214454651),\n",
              " ('frodo', 0.9116418361663818),\n",
              " ('pippin', 0.9091291427612305),\n",
              " ('sam', 0.9045179486274719),\n",
              " ('gimli', 0.8939559459686279)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_n2wOs2oQD3B",
        "outputId": "f38787d0-c7c9-4306-e87f-541f30f80616",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('sauron')"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('morgoth', 0.9273006319999695),\n",
              " ('saruman', 0.8866622447967529),\n",
              " ('ilúvatar', 0.8389760255813599),\n",
              " ('melkor', 0.8328588008880615),\n",
              " ('that_time', 0.822688102722168),\n",
              " ('enemy', 0.8195509314537048),\n",
              " ('ulmo', 0.8155595660209656),\n",
              " ('fëanor', 0.8135055899620056),\n",
              " ('beginning', 0.8030401468276978),\n",
              " ('substance', 0.7994720935821533)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T81JoyjbQJJo",
        "outputId": "04677e8a-f710-4abe-b79d-5fb76552ef74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('glaurung')"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('maedhros', 0.9555717706680298),\n",
              " ('fingon', 0.9531328678131104),\n",
              " ('weeping', 0.9515796303749084),\n",
              " ('celegorm', 0.9502866268157959),\n",
              " ('tuor', 0.9493952393531799),\n",
              " ('mîm', 0.9433969259262085),\n",
              " ('despair', 0.9427868723869324),\n",
              " ('finishing', 0.9427171945571899),\n",
              " ('melian', 0.9419878125190735),\n",
              " ('gasp', 0.941307544708252)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJ1EKgXFQQAp",
        "outputId": "0bdb796f-0571-40c4-8dbb-b7725341b13c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('ancalagon')"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('fortress', 0.9654216766357422),\n",
              " ('text_as', 0.9558560848236084),\n",
              " ('green_mound', 0.9533474445343018),\n",
              " ('torrent', 0.9515771865844727),\n",
              " ('quenya_name', 0.9505199790000916),\n",
              " ('tombs', 0.9495702981948853),\n",
              " ('founded', 0.9477051496505737),\n",
              " ('girdle', 0.9438568353652954),\n",
              " ('fastness', 0.943145215511322),\n",
              " ('d_)', 0.9412729740142822)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVpwuk4SQVNZ",
        "outputId": "da1cfcc3-ecad-4296-87d5-1ea9b8e5d871",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('smaug')"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('difference', 0.958035945892334),\n",
              " ('laws', 0.9550260305404663),\n",
              " ('unlikely', 0.9517083168029785),\n",
              " (\"'nothing\", 0.9511921405792236),\n",
              " ('shelob', 0.9493365287780762),\n",
              " ('pinch', 0.9490600824356079),\n",
              " ('riddles', 0.9458019733428955),\n",
              " ('troll', 0.9456537961959839),\n",
              " ('fatty', 0.9434322118759155),\n",
              " ('thief', 0.9426092505455017)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwkUwUpCQdtu",
        "outputId": "0caeaa2d-7d9e-46c2-bc41-0f7b7dca9653",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('elrond')"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('denethor', 0.9357262253761292),\n",
              " ('theoden', 0.9018715023994446),\n",
              " ('glóin', 0.8980854749679565),\n",
              " ('galadriel', 0.8768000602722168),\n",
              " ('eomer', 0.8759504556655884),\n",
              " ('thorin', 0.8740214109420776),\n",
              " ('balin', 0.8638989925384521),\n",
              " ('eomund', 0.8636239767074585),\n",
              " ('butterbur', 0.8514596223831177),\n",
              " ('éomer', 0.8510818481445312)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ovKsfshdQiKq",
        "outputId": "1ec84104-0a7f-4af0-a5e8-d06ced3427b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('gollum')"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('frodo', 0.9355930685997009),\n",
              " ('bilbo', 0.9346021413803101),\n",
              " ('sam', 0.9125850200653076),\n",
              " ('boromir', 0.8865759968757629),\n",
              " ('gandalf', 0.8857231736183167),\n",
              " ('pippin', 0.8610973358154297),\n",
              " ('strider', 0.8465977907180786),\n",
              " ('faramir', 0.8461841344833374),\n",
              " ('he', 0.8436329364776611),\n",
              " ('treebeard', 0.83555006980896)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVfvsSRbRBm4",
        "outputId": "d4768aaa-0392-42ce-d13f-c094e51dadf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('shire')"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('rivendell', 0.8692772388458252),\n",
              " ('eldar', 0.857909083366394),\n",
              " ('peace', 0.8325352668762207),\n",
              " ('war', 0.83222895860672),\n",
              " ('enemy', 0.8309415578842163),\n",
              " ('ways', 0.8300570249557495),\n",
              " ('fight', 0.821245551109314),\n",
              " ('friendship', 0.8193013668060303),\n",
              " ('southfarthing', 0.8191047310829163),\n",
              " ('youth', 0.8149329423904419)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXDqwm8jRGAP",
        "outputId": "70f9df7c-e76a-46dd-ca0b-7fbcbdafbc28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('gondor')"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('rohan', 0.889488697052002),\n",
              " ('kings', 0.8645479083061218),\n",
              " ('beleriand', 0.8291288614273071),\n",
              " ('moria', 0.8205814957618713),\n",
              " ('lords', 0.8183269500732422),\n",
              " ('languages', 0.8126538991928101),\n",
              " ('children', 0.8110241293907166),\n",
              " ('lórien', 0.8083497285842896),\n",
              " ('descendants', 0.8074419498443604),\n",
              " ('aman', 0.805523693561554)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dzez2CsRRVtv",
        "outputId": "f483f452-4648-4194-d818-52059e36d69e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('mordor')"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('borders', 0.9196503162384033),\n",
              " ('mirkwood', 0.9182040691375732),\n",
              " ('isengard', 0.9105217456817627),\n",
              " ('aman', 0.9073267579078674),\n",
              " ('beleriand', 0.8923590779304504),\n",
              " ('ford', 0.8876895904541016),\n",
              " ('ruin', 0.887398362159729),\n",
              " ('downs', 0.8872861266136169),\n",
              " ('woods', 0.8851507306098938),\n",
              " ('moria', 0.8776147961616516)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5g9MRYPsRc1P",
        "outputId": "e7f4da57-e785-48d3-c888-c4a4d79c517f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('orcs')"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('riders', 0.8830175399780273),\n",
              " ('valar', 0.8718794584274292),\n",
              " ('goblins', 0.8650541305541992),\n",
              " ('dwarves', 0.8378405570983887),\n",
              " ('eldar', 0.8375397324562073),\n",
              " ('ents', 0.8293201923370361),\n",
              " ('others', 0.8286027908325195),\n",
              " ('middle-earth', 0.824885368347168),\n",
              " ('those', 0.8205505013465881),\n",
              " ('pursuers', 0.8203446865081787)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6tbfPqGReVP",
        "outputId": "b6487295-838b-4d45-e5f2-737056a17492",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('hobbits')"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('others', 0.8941866755485535),\n",
              " ('dwarves', 0.832573652267456),\n",
              " ('they', 0.8317553400993347),\n",
              " ('goblins', 0.7809754610061646),\n",
              " ('orcs', 0.7736722230911255),\n",
              " ('pursuers', 0.7668974995613098),\n",
              " ('company', 0.7522684335708618),\n",
              " ('travellers', 0.7400654554367065),\n",
              " ('spiders', 0.713121771812439),\n",
              " ('voices', 0.7116217017173767)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIfWuh9KRkmm",
        "outputId": "e1f9ccfc-6089-4e95-a11e-c2c367d21b53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar('hobbit')"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('dwarf', 0.8909330368041992),\n",
              " ('baggins', 0.8609179258346558),\n",
              " ('wizard', 0.8376719355583191),\n",
              " ('boromir', 0.8161255717277527),\n",
              " ('g', 0.8131849765777588),\n",
              " ('butterbur', 0.8084607720375061),\n",
              " ('bilbo', 0.8066343069076538),\n",
              " ('man', 0.8059030771255493),\n",
              " ('sake', 0.8029026985168457),\n",
              " ('thorin', 0.799715518951416)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIVvGffESBzJ"
      },
      "source": [
        "from sklearn.decomposition import IncrementalPCA\n",
        "from sklearn.manifold import TSNE\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "\n",
        "def reduce_dimensions(model):\n",
        "    num_dimensions = 2  # final num dimensions (2D, 3D, etc)\n",
        "\n",
        "    vectors = [] # positions in vector space\n",
        "    labels = [] # keep track of words to label our data again later\n",
        "    for word in model.wv.vocab:\n",
        "        vectors.append(model.wv[word])\n",
        "        labels.append(word)\n",
        "\n",
        "    # convert both lists into numpy vectors for reduction\n",
        "    vectors = np.asarray(vectors)\n",
        "    labels = np.asarray(labels)\n",
        "\n",
        "    # reduce using t-SNE\n",
        "    vectors = np.asarray(vectors)\n",
        "    tsne = TSNE(n_components=num_dimensions, random_state=0)\n",
        "    vectors = tsne.fit_transform(vectors)\n",
        "\n",
        "    x_vals = [v[0] for v in vectors]\n",
        "    y_vals = [v[1] for v in vectors]\n",
        "    return x_vals, y_vals, labels\n",
        "\n",
        "\n",
        "x_vals, y_vals, labels = reduce_dimensions(model_three_books)"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57dM9IyuRmhn",
        "outputId": "9d28698f-a8a9-4198-b826-2274998fba6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "%matplotlib inline\n",
        "\n",
        "random.seed(0)\n",
        "print(\"before\")\n",
        "plt.figure(figsize=(12, 12))\n",
        "plt.scatter(x_vals, y_vals)\n",
        "\n",
        "# Label randomly subsampled 25 data points\n",
        "#\n",
        "\n",
        "\n",
        "indices = list(range(len(labels)))\n",
        "selected_indices = random.sample(indices, 25)\n",
        "for i in selected_indices:\n",
        "    plt.annotate(labels[i], (x_vals[i], y_vals[i]))\n",
        "plt.show();\n",
        "print(\"after\")"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "before\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "after\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URUgxEAUR7iG",
        "outputId": "671033f8-d86c-49ca-9df0-5aec82a6123c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import time\n",
        "\n",
        "st = time.time()\n",
        "model_three_books.train(all_bigram_tokens, total_words=model_three_books.corpus_total_words, epochs=100)\n",
        "et = time.time()\n",
        "\n",
        "print(et-st)"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "89.28402495384216\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZApyqdBnTY2b",
        "outputId": "2cd34ec9-6a0f-4d4b-c5c2-1c1c0f3a58e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "model_three_books.wv.most_similar(\"smaug\")"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/matutils.py:737: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
            "  if np.issubdtype(vec.dtype, np.int):\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('dragon', 0.5151079297065735),\n",
              " ('goblins', 0.4186294674873352),\n",
              " ('dori', 0.4024121165275574),\n",
              " ('morgoth', 0.3964056968688965),\n",
              " ('shelob', 0.39089828729629517),\n",
              " ('balin', 0.37216487526893616),\n",
              " ('bombur', 0.3710308372974396),\n",
              " ('bilbo', 0.36994537711143494),\n",
              " ('everybody', 0.36615467071533203),\n",
              " ('enemy', 0.36508235335350037)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41hgo_1uaAsM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}