got_generate_some_word_embeddings_general_and_got_specific.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mRunning cells with 'Python 3.9.16 64-bit' requires ipykernel package.\n",
      "Run the following command to install 'ipykernel' into the Python environment. \n",
      "Command: '/usr/local/bin/python3.9 -m pip install ipykernel -U --user --force-reinstall'"
     ]
    }
   ],
   "source": [
    "!pip install gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in /usr/local/lib/python3.11/site-packages (3.8.1)\n",
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      "Requirement already satisfied: sklearn in /usr/local/lib/python3.11/site-packages (0.0.post4)\n",
      "Collecting en-core-web-sm==3.5.0\n",
      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl (12.8 MB)\n",
      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m0m eta \u001b[36m0:00:01\u001b[0m0:01\u001b[0m:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: spacy<3.6.0,>=3.5.0 in /usr/local/lib/python3.11/site-packages (from en-core-web-sm==3.5.0) (3.5.2)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.0.12)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.0.4)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.0.9)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.7)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.0.8)\n",
      "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (8.1.10)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.1.1)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.4.6)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.8)\n",
      "Requirement already satisfied: typer<0.8.0,>=0.3.0 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.7.0)\n",
      "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.10.1)\n",
      "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (6.3.0)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (4.65.0)\n",
      "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.24.3)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.29.0)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.10.7)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.1.2)\n",
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      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.11/site-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.3.0)\n",
      "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.11/site-packages (from pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (4.5.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.1.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.11/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.26.15)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/site-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2022.12.7)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.11/site-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.7.9)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.11/site-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.0.4)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.11/site-packages (from typer<0.8.0,>=0.3.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (8.1.3)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/site-packages (from jinja2->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.1.2)\n",
      "Installing collected packages: en-core-web-sm\n",
      "Successfully installed en-core-web-sm-3.5.0\n",
      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the package via spacy.load('en_core_web_sm')\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk\n",
    "!pip install pandas\n",
    "!pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None \n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n",
    "\n",
    "from gensim.models import word2vec\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "script = pd.read_csv('Game_of_Thrones_Script_count_senti.csv')\n",
    "script_text = script[[\"Name\", \"Sentence\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waymar royce</td>\n",
       "      <td>What do you expect? They're savages. One lot s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>will</td>\n",
       "      <td>I've never seen wildlings do a thing like this...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>waymar royce</td>\n",
       "      <td>How close did you get?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>will</td>\n",
       "      <td>Close as any man would.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gared</td>\n",
       "      <td>We should head back to the wall.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>23906</th>\n",
       "      <td>brienne</td>\n",
       "      <td>I think we can all agree that ships take prece...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23907</th>\n",
       "      <td>bronn</td>\n",
       "      <td>I think that's a very presumptuous statement.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23908</th>\n",
       "      <td>tyrion lannister</td>\n",
       "      <td>I once brought a jackass and a honeycomb into ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23909</th>\n",
       "      <td>man</td>\n",
       "      <td>The Queen in the North!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23910</th>\n",
       "      <td>all</td>\n",
       "      <td>The Queen in the North! The Queen in the North...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23911 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name                                           Sentence\n",
       "0          waymar royce  What do you expect? They're savages. One lot s...\n",
       "1                  will  I've never seen wildlings do a thing like this...\n",
       "2          waymar royce                             How close did you get?\n",
       "3                  will                            Close as any man would.\n",
       "4                 gared                   We should head back to the wall.\n",
       "...                 ...                                                ...\n",
       "23906           brienne  I think we can all agree that ships take prece...\n",
       "23907             bronn      I think that's a very presumptuous statement.\n",
       "23908  tyrion lannister  I once brought a jackass and a honeycomb into ...\n",
       "23909               man                            The Queen in the North!\n",
       "23910               all  The Queen in the North! The Queen in the North...\n",
       "\n",
       "[23911 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "script_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/mdargatz/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>waymar royce</td>\n",
       "      <td>expect theyre savages lot steals goat lot they...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>will</td>\n",
       "      <td>ive wildlings ive life</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>waymar royce</td>\n",
       "      <td>close</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>will</td>\n",
       "      <td>close</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gared</td>\n",
       "      <td>head back wall</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           Name                                           Sentence\n",
       "0  waymar royce  expect theyre savages lot steals goat lot they...\n",
       "1          will                             ive wildlings ive life\n",
       "2  waymar royce                                              close\n",
       "3          will                                              close\n",
       "4         gared                                     head back wall"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "\n",
    "STOP_WORDS = nltk.corpus.stopwords.words()\n",
    "\n",
    "def clean_sentence(val):\n",
    "    \"remove chars that are not letters or numbers, downcase, then remove stop words\"\n",
    "    regex = re.compile('([^\\s\\w]|_)+')\n",
    "    sentence = regex.sub('', val).lower()\n",
    "    sentence = sentence.split(\" \")\n",
    "    \n",
    "    for word in list(sentence):\n",
    "        if word in STOP_WORDS:\n",
    "            sentence.remove(word)  \n",
    "            \n",
    "    sentence = \" \".join(sentence)\n",
    "    return sentence\n",
    "\n",
    "def clean_dataframe(data):\n",
    "    \"drop nans, then apply 'clean_sentence' function to question1 and 2\"\n",
    "    data = data.dropna(how=\"any\")\n",
    "    \n",
    "    for col in ['Sentence']:\n",
    "        data[col] = data[col].apply(clean_sentence)\n",
    "    \n",
    "    return data\n",
    "\n",
    "data = clean_dataframe(script_text)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'iteritems'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/ll/tlyhkxyx7qj42_ffkhwd31z00000gn/T/ipykernel_4175/1223568995.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mcorpus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_list\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcorpus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mcorpus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_corpus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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     12\u001b[0m \u001b[0mcorpus\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/ll/tlyhkxyx7qj42_ffkhwd31z00000gn/T/ipykernel_4175/1223568995.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbuild_corpus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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      2\u001b[0m     \u001b[0;34m\"Creates a list of lists containing words from each sentence\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mcorpus\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[1;32m      4\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Sentence'\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----> 5\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0msentence\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\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      6\u001b[0m             \u001b[0mword_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msentence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\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;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mcorpus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_list\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[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.11/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5985\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\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   5986\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'iteritems'"
     ]
    }
   ],
   "source": [
    "def build_corpus(data):\n",
    "    \"Creates a list of lists containing words from each sentence\"\n",
    "    corpus = []\n",
    "    for col in ['Sentence']:\n",
    "        for sentence in data[col].iteritems():\n",
    "            word_list = sentence[1].split(\" \")\n",
    "            corpus.append(word_list)\n",
    "            \n",
    "    return corpus\n",
    "\n",
    "corpus = build_corpus(data)        \n",
    "corpus[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas==1.5 in /usr/local/lib/python3.11/site-packages (1.5.0)\r\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.11/site-packages (from pandas==1.5) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/site-packages (from pandas==1.5) (2023.3)\r\n",
      "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.11/site-packages (from pandas==1.5) (1.24.3)\r\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/site-packages (from python-dateutil>=2.8.1->pandas==1.5) (1.16.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas==1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tsne_plot(model):\n",
    "    \"Creates and TSNE model and plots it\"\n",
    "    labels = []\n",
    "    tokens = []\n",
    "\n",
    "    for word in model.wv.vocab:\n",
    "        tokens.append(model[word])\n",
    "        labels.append(word)\n",
    "    \n",
    "    tsne_model = TSNE(perplexity=40, n_components=2, init='pca', n_iter=2500, random_state=23)\n",
    "    new_values = tsne_model.fit_transform(tokens)\n",
    "\n",
    "    x = []\n",
    "    y = []\n",
    "    for value in new_values:\n",
    "        x.append(value[0])\n",
    "        y.append(value[1])\n",
    "        \n",
    "    plt.figure(figsize=(16, 16)) \n",
    "    for i in range(len(x)):\n",
    "        plt.scatter(x[i],y[i])\n",
    "        plt.annotate(labels[i],\n",
    "                     xy=(x[i], y[i]),\n",
    "                     xytext=(5, 2),\n",
    "                     textcoords='offset points',\n",
    "                     ha='right',\n",
    "                     va='bottom')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Google's pre-trained Word2Vec model.\n",
    "import gensim\n",
    "model = gensim.models.KeyedVectors.load_word2vec_format('./GoogleNews-vectors-negative300.bin.gz', binary=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne_plot(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = model.most_similar('king', topn=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_similarity(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = 'Helvetica'\n",
    "\n",
    "# set the style of the axes and the text color\n",
    "plt.rcParams['axes.edgecolor']='#333F4B'\n",
    "plt.rcParams['axes.linewidth']=0.8\n",
    "plt.rcParams['xtick.color']='#333F4B'\n",
    "plt.rcParams['ytick.color']='#333F4B'\n",
    "plt.rcParams['text.color']='#333F4B'\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.plot(data[\"word\"],data[\"number\"]*100, \"o\", markersize=24, color='#007ACC', alpha=0.9)\n",
    "plt.bar(data[\"word\"],data[\"number\"]*100, alpha=0.4, width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.most_similar(positive=['woman', 'king'], negative=['man'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.most_similar(positive=['mother', 'father'], topn=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_got = word2vec.Word2Vec(corpus, size=64, window=5, min_count=20, workers=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_got.most_similar(\"jon\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_got.most_similar(\"stark\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_got.most_similar(\"tywin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_got.most_similar(positive=['tywin', 'son'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_got.most_similar(positive=['jaime', 'cersei', 'tyrion'], topn=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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