reddit_w2v.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "greatest-imagination",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Special module written for this class\n",
    "#This provides access to data and to helper functions from previous weeks\n",
    "import lucem_illud #just in case, regularly update your lucem_illud with the following code: pip install git+git://github.com/UChicago-Computational-Content-Analysis/lucem_illud.git\n",
    "import gensim#For word2vec, etc\n",
    "\n",
    "import requests #for http requests\n",
    "import pandas as pd #gives us DataFrames\n",
    "import matplotlib.pyplot as plt #For graphics\n",
    "import wordcloud #Makes word clouds\n",
    "import numpy as np #For divergences/distances\n",
    "import scipy #For divergences/distances\n",
    "import seaborn as sns #makes our plots look nicer\n",
    "import sklearn.manifold #For a manifold plot\n",
    "import json #For API responses\n",
    "import urllib.parse #For joining urls\n",
    "import seaborn as sns#Makes the graphics look nicer\n",
    "import sklearn.metrics.pairwise #For cosine similarity\n",
    "import sklearn.manifold #For T-SNE\n",
    "import sklearn.decomposition #For PCA\n",
    "import datetime as dt\n",
    "import random\n",
    "\n",
    "# comp-linguistics\n",
    "import spacy\n",
    "\n",
    "#Displays the graphs\n",
    "import graphviz #You also need to install the command line graphviz\n",
    "\n",
    "#These are from the standard library\n",
    "import os.path\n",
    "import zipfile\n",
    "import subprocess\n",
    "import io\n",
    "import tempfile\n",
    "\n",
    "#This 'magic' command makes the plots work better\n",
    "#in the notebook, don't use it outside of a notebook\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "timely-potato",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "athletic-edward",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>title</th>\n",
       "      <th>text</th>\n",
       "      <th>time</th>\n",
       "      <th>ym</th>\n",
       "      <th>score</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>gme</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WSBVoteBot Log for Oct 01 2020</td>\n",
       "      <td>Every time a new submission is posted to walls...</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>202010</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>call expiring 10/2</td>\n",
       "      <td>[removed]</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>202010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Warren Buffett UNCHARMED B.H. DD inside</td>\n",
       "      <td>[removed]</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>202010</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thrown out of r/Stocks for saying I prefer FAA...</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>202010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What would you retards do?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>202010</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0                     WSBVoteBot Log for Oct 01 2020   \n",
       "1                                 call expiring 10/2   \n",
       "2            Warren Buffett UNCHARMED B.H. DD inside   \n",
       "3  Thrown out of r/Stocks for saying I prefer FAA...   \n",
       "4                         What would you retards do?   \n",
       "\n",
       "                                                text        time      ym  \\\n",
       "0  Every time a new submission is posted to walls...  2020-10-01  202010   \n",
       "1                                          [removed]  2020-10-01  202010   \n",
       "2                                          [removed]  2020-10-01  202010   \n",
       "3                                          [deleted]  2020-10-01  202010   \n",
       "4                                                NaN  2020-10-01  202010   \n",
       "\n",
       "   score  num_comments  gme  \n",
       "0      5            18    0  \n",
       "1      1             0    0  \n",
       "2      0             4    0  \n",
       "3      1             0    0  \n",
       "4      1             0    0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['title', 'text', 'time', 'ym', 'score', 'num_comments', 'gme']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "moving-butter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def na_value(x):\n",
    "    if type(x) == float:\n",
    "        return ''\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "df['title'] = df['title'].apply(na_value)\n",
    "\n",
    "np.sum(df['title'].apply(lambda x: type(x) == float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adequate-collectible",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 24s, sys: 16.2 s, total: 5min 40s\n",
      "Wall time: 7min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['tokenized_text'] = df['title'].apply(lambda x: lucem_illud.word_tokenize(x))\n",
    "df['normalized_tokens'] = df['title'].apply(lambda x: lucem_illud.normalizeTokens(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "israeli-studio",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tokenized_texts'] = df['tokenized_text'].apply(lambda x: [x])\n",
    "df['normalized_texts'] = df['normalized_tokens'].apply(lambda x: [x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "unauthorized-swiss",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1 = df[df['ym']<202101]\n",
    "df_2 = df[df['ym']>=202101]\n",
    "df_2 = df_2[df_2['ym']<202104]\n",
    "df_3 = df[df['ym']>=202104]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "about-stewart",
   "metadata": {},
   "source": [
    "# `GME W2V`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "alternate-neighborhood",
   "metadata": {},
   "outputs": [],
   "source": [
    "gme = df[df['gme']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "military-bones",
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>text</th>\n",
       "      <th>time</th>\n",
       "      <th>ym</th>\n",
       "      <th>score</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>gme</th>\n",
       "      <th>tokenized_text</th>\n",
       "      <th>normalized_tokens</th>\n",
       "      <th>tokenized_texts</th>\n",
       "      <th>normalized_texts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>REGGIE IS WITH US GME HOLDERS</td>\n",
       "      <td>[removed]</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>202010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[REGGIE, IS, WITH, US, GME, HOLDERS]</td>\n",
       "      <td>[reggie, gme, holder]</td>\n",
       "      <td>[[REGGIE, IS, WITH, US, GME, HOLDERS]]</td>\n",
       "      <td>[[reggie, gme, holder]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>GameStop Pump?</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>202010</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>[GameStop, Pump]</td>\n",
       "      <td>[gamestop, pump]</td>\n",
       "      <td>[[GameStop, Pump]]</td>\n",
       "      <td>[[gamestop, pump]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>GME More information on Ryan Cohen</td>\n",
       "      <td>\\n\\nCredit for this information goes to Just...</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>202010</td>\n",
       "      <td>37</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>[GME, More, information, on, Ryan, Cohen]</td>\n",
       "      <td>[gme, information, ryan, cohen]</td>\n",
       "      <td>[[GME, More, information, on, Ryan, Cohen]]</td>\n",
       "      <td>[[gme, information, ryan, cohen]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>What the fuck is going on with GME?</td>\n",
       "      <td>**Original Post:**\\n\\n&gt;!Can we get an actual d...</td>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>202010</td>\n",
       "      <td>59</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>[What, the, fuck, is, going, on, with, GME]</td>\n",
       "      <td>[fuck, go, gme]</td>\n",
       "      <td>[[What, the, fuck, is, going, on, with, GME]]</td>\n",
       "      <td>[[fuck, go, gme]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Gamestop Losses ---Still HOLDR with Diamond ha...</td>\n",
       "      <td>[deleted]</td>\n",
       "      <td>2020-10-03</td>\n",
       "      <td>202010</td>\n",
       "      <td>36</td>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "      <td>[Gamestop, Losses, ---Still, HOLDR, with, Diam...</td>\n",
       "      <td>[gamestop, loss, ---still, holdr, diamond, hand]</td>\n",
       "      <td>[[Gamestop, Losses, ---Still, HOLDR, with, Dia...</td>\n",
       "      <td>[[gamestop, loss, ---still, holdr, diamond, ha...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title  \\\n",
       "144                       REGGIE IS WITH US GME HOLDERS   \n",
       "239                                      GameStop Pump?   \n",
       "277                  GME More information on Ryan Cohen   \n",
       "702                 What the fuck is going on with GME?   \n",
       "1302  Gamestop Losses ---Still HOLDR with Diamond ha...   \n",
       "\n",
       "                                                   text        time      ym  \\\n",
       "144                                           [removed]  2020-10-01  202010   \n",
       "239                                           [deleted]  2020-10-01  202010   \n",
       "277     \\n\\nCredit for this information goes to Just...  2020-10-01  202010   \n",
       "702   **Original Post:**\\n\\n>!Can we get an actual d...  2020-10-02  202010   \n",
       "1302                                          [deleted]  2020-10-03  202010   \n",
       "\n",
       "      score  num_comments  gme  \\\n",
       "144       1             1    1   \n",
       "239      12            14    1   \n",
       "277      37            26    1   \n",
       "702      59           122    1   \n",
       "1302     36            83    1   \n",
       "\n",
       "                                         tokenized_text  \\\n",
       "144                [REGGIE, IS, WITH, US, GME, HOLDERS]   \n",
       "239                                    [GameStop, Pump]   \n",
       "277           [GME, More, information, on, Ryan, Cohen]   \n",
       "702         [What, the, fuck, is, going, on, with, GME]   \n",
       "1302  [Gamestop, Losses, ---Still, HOLDR, with, Diam...   \n",
       "\n",
       "                                     normalized_tokens  \\\n",
       "144                              [reggie, gme, holder]   \n",
       "239                                   [gamestop, pump]   \n",
       "277                    [gme, information, ryan, cohen]   \n",
       "702                                    [fuck, go, gme]   \n",
       "1302  [gamestop, loss, ---still, holdr, diamond, hand]   \n",
       "\n",
       "                                        tokenized_texts  \\\n",
       "144              [[REGGIE, IS, WITH, US, GME, HOLDERS]]   \n",
       "239                                  [[GameStop, Pump]]   \n",
       "277         [[GME, More, information, on, Ryan, Cohen]]   \n",
       "702       [[What, the, fuck, is, going, on, with, GME]]   \n",
       "1302  [[Gamestop, Losses, ---Still, HOLDR, with, Dia...   \n",
       "\n",
       "                                       normalized_texts  \n",
       "144                             [[reggie, gme, holder]]  \n",
       "239                                  [[gamestop, pump]]  \n",
       "277                   [[gme, information, ryan, cohen]]  \n",
       "702                                   [[fuck, go, gme]]  \n",
       "1302  [[gamestop, loss, ---still, holdr, diamond, ha...  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gme.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "fundamental-norman",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 59s, sys: 12.7 s, total: 9min 11s\n",
      "Wall time: 9min 45s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "W2V_gme = gensim.models.word2vec.Word2Vec(gme['normalized_texts'].sum(), sg=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "saving-birmingham",
   "metadata": {},
   "outputs": [],
   "source": [
    "numWords = 60\n",
    "targetWords = W2V_gme.wv.index2word[:numWords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "efficient-poultry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.32147622, -0.08278619,  0.43025297, ..., -0.08906323,\n",
       "        -0.2118545 , -0.19039042],\n",
       "       [-1.7570665 ,  1.6605484 ,  0.04754155, ...,  0.43390673,\n",
       "         0.9441768 , -1.1542293 ],\n",
       "       [ 0.07776286, -0.442557  ,  0.45298263, ...,  1.1326681 ,\n",
       "        -0.07330044, -0.50413793],\n",
       "       ...,\n",
       "       [ 0.8623421 , -0.6167868 ,  0.26313615, ...,  0.43626508,\n",
       "        -0.1965517 ,  0.27880946],\n",
       "       [-0.31808612, -0.5582547 ,  0.01866594, ..., -0.8328075 ,\n",
       "        -0.65827876,  0.07133321],\n",
       "       [-0.39970896,  0.5298294 ,  1.0586075 , ...,  1.665585  ,\n",
       "        -0.633104  , -0.25174248]], dtype=float32)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordsSubMatrix = []\n",
    "for word in targetWords:\n",
    "    wordsSubMatrix.append(W2V_gme[word])\n",
    "wordsSubMatrix = np.array(wordsSubMatrix)\n",
    "wordsSubMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "aboriginal-communication",
   "metadata": {},
   "outputs": [],
   "source": [
    "pcaWords = sklearn.decomposition.PCA(n_components = 60).fit(wordsSubMatrix)\n",
    "reducedPCA_data = pcaWords.transform(wordsSubMatrix)\n",
    "#T-SNE is theoretically better, but you should experiment\n",
    "tsneWords = sklearn.manifold.TSNE(n_components = 2).fit_transform(reducedPCA_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "divine-wrapping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OAy4ys8MlHQB8bmb9qtjV0ApFMBRCy/Fj0f088BcgB3wC/ATcBsyWzovpa4P/8dm0xLmxjetm40j6E/COmX1fyecNzcZBwBH4NPAWwLz4z+dmwPPp56Ozmd1WvS6G1iqCoRCan4lA2wa06wzcZmb3A0gqjOZ8nM5/gP8x2QB4ObVZBlgic4038U/fk83ssybpfSOZ2QvVfP7QLPyK//z/G5gf+Cs+cvkA0BsYWL2uhdYsgqEQmp8hQCdJHYDx1J3b9zGws6SH8T8iZ+HTWwCY2UeSngCul3QY8DNwWboteAboDzws6UTgQzxn5S/AMzEdESroFuB8YAIeDHUHFgaeNrOJksYCt1eve6E1i2ComZK0AnAFcJ+Z3ToDj3vIzHYqX89CBeSBW4H38WTng+podzzQA+gHjMZ/XuYoatMNuBF4DhgFnA20K5w0M5O0LXBuatcOnzbrj0+5hVARZvYO/jM9DUl7AsPN7NvK9irUCplZtfsQSpB0BT533tnM5pG0AP7HrjvQC3gQWB//Q7k28C9gcGq/WVU6HUIIIbRAMTLUfD2EL3n+pcS5D8zsUkkn4HVhjgUOxhNo+1euiyGEEELLF3WGmr/J6XaezLHCqqJfgdkBAyaa2eR0LIQQQggNFCNDzV8fSRcx7bLqrKuAvKSv8FozIYQQQmigyBkKIYQQQk2LabIQQggh1LQIhkIIIYRQ0yIYCiGEEEJNi2AohBBCCDUtgqFQdZI6SPpQ0k2S3pXUW1JXSf0lfSKpk6SFJD0k6W1JAyStlR7bXdLNkvpK+kzSMZnr7ifpNUmDJF0vqa2kQyRdnmlzqKTLqvG6QwghNA8RDIXmYkXgSmAtvPL2PvhGpDngVHwbibfMbK10P7tVxKrA1kAn4CxJs0paDdgT2MTMOgKTgH2Bu4AdJBVKEByE74kUQgihRkWdodBcfJ72JkLSe8Czad+sd4AOwLLArgBm9pykhSXNnx77mJlNACZIGoHvwr4FsB4wUBL4Hl8jzOxHSc8B20n6AJi18LwhhBBqUwRDobmYkPl+cub+ZPzn9LcSjykUyco+dlJqL+BWMzulxONuwkeXPiRGhUIIoebFNFloKV7Ep7mQ1AUYZWZj62n/LLCbpHbpMQtJWhbAzF4Flsan4u4sZ6dDCCE0fzEyFFqK7sAtkt7GN6Q9sL7GZva+pNOBpyS1wfds+zvwRWpyD9DRzEaXr8shhBBagtiOI9QkSY8Cl5vZs9XuSwghhOqKabJQUyQtIOlj4OcIhEIIIUCMDIUQQgihxsXIUAghhBBqWgRDIYQQQqhpEQyFEEIIoaZFMBRCCCGEmhbBUAghhBBqWgRDIYQQQqhpEQyFEEIIoaZFMBRCCCGEmhbBUAghhBBqWgRDIYQQQqhpEQyFEEIIoaZFMBRCKDtJh0v6QtJPkp6VtKEkk9QtnW8j6XRJn0uaKOlTSTlJqnLXQwg1IIKhEEJZSdoBuA54CtgZeBq4o6jZZUB34HZgO+AB4BLgrIp1NIRQs2LX+hBCWUl6DRhnZltkjnXHA52DgEeBb4DLzOzkTJvrgf2AxcxsfEU7HUKoKTEyFEIoG0ltgXWAPkWnHs58vyEwK3BPUZs7gbmAdcvWwRBCIIKhEEJ5LQrMAowsOj4i8/2C6XZ4UZthRedDCKEsIhgKIZTTSOA3PCjKapf5/vt0u1hRm8WKzocQQlnMUu0OhBBaLzObJOktYAfgysypHTPfvwb8CuwBvJk5vhfwY9GxZknSPsBKwIfAUOCvwKvA6ni+1NVV7F4IYToiGAohlNu5wMOSbgTuxXOIDkjnJpvZKElXATlJE4D+wBbA4cBZZvZjNTo9gwyYnLn/HfAtMGd1ulN+knoCnc1sxXra9AV+M7OulepXCI0RwVAIoazMrI+kI4FT8dVhrwJHAY8DY1OzE4HRwKHAKcBXQM7MLqt8j2ecmd0paS8zuzsd6p9uX6tWn0IIDRdL60MIFSfpcLz20HJmNqTK3QmNECNDoTWJBOoQQllJWkjS5ZK2k/RnSacAeaBPBEItX/o3HSTpF0nvSvpLiTZ7SvootXlV0gbV6GsIdYlgKIRQbr/iycU3A08CRwO3AftWs1OhSSwM3AJcDeyG50n1kbR6ps0fgPPwCuN74+kZT0tapLJdDaFukTMUQigrMxuHb7ERWp8FgAPM7BEASU8DnwOnMSXYbQ+sbWZvpzb9gS+A41K7EKouRoZCCCE01gR8OxUAzKxwf6NMm48LgVBqMwLoV9QmhKqKYCiEEEJjjbRpV+EMB5bI3B/BtIrb1CxJbSTFLE2VRTAUQgihsRaRpKJji+G5QwXtmFZxm1q2NDFKVnURDIVWR9Js1e5DCDViDjL5YJJmT/cHZNqsLGmtTJt2wKZFbWqWmX1hZi9Vux+1LobmQqsiaVNgDPBOtfsSQg0YA1ydApxhwD/xfejOy7QZBjwg6QzgZ+AMYCJweYX7GkKdIhgKrYqZ9at2H0KoId8BRwCXAasAg4Edzey9TJv3gB7AOcAywP+ArcxsZIX7GkKdogJ1CCGEEGpajAyF0IxIOgCv3fKQmX1Z7f6EEEItiJGhEEIIIdS0WE0WQgghhJoWwVAIIYQQaloEQyGEEEKoaREMhRBCCKGmRTAUQgghhJoWwVAzJ2mIpDVm8DE7SepUrj6FEEIIrUkEQ81U2sm4eAPEhtoJiGAohBBCaIAIhpqIJJN0mqSBkj6TtIWkCyS9JeldSauldu0lPS/pDUnvSbo4c43uknpJeggvWb9A0XP8S9KzkuaXNJukSyS9JmmQpNslzSNpa2AH4OR0/IBKvg8hhBBCSxPBUNMaY2YbACcBDwMvmdk6wG3AaYU2wPZmth7QEVhf0l8y19gM+JuZrWlmo9OxNpKuAtYDtjGzH4ATgR/MrJOZdQS+AU4xsyeBPsCFZtbRzG4r70sOIbRkkrpJ6lntfoRQTbEdR9O6O92+CZiZPZbuvwHskr5vC1wiaWNAQHs8KHoinf8/MxtVdN2bgVeAfW1KyfAdgPkk7Zbuz46PJoUQQghhBkQw1LR+SbeTgAmZ45OY8l4fDywIbGhmv0i6AZgj03Z8ieu+CHQBFgVGpGMCjjKz55qm6yGEWiKpHfBnYBtgJUl/Bz4F+pvZuKp2LoQKi2myylsA+DYFQksCOzbgMTcDlwLPSloiHesDHC9pTgBJ8xbykoCxwPxN3O8QWqSUi9cmfX+TpA2r3adqk3QQHvjcCewBrANcDTwOvFfFrlWNpJ6S7qt2P0J1RDBUeVcBm0h6C7gWeLYhDzKz3kB3PCDqAFyIT4sNlPQ28BJQCIZuB/aJBOoQADiL9LvOzP5mZq9WuT9VlT6EXQ/8jE+3H4H/zlgWX4n6f9XrXQjVEbvWhxBarbTw4Gj8g8Nv+DT0uWb2jKS+wEBgY2BpIAesAOyG5+DtamYfpRIXZwHbpuMDgL+b2W8VfjlNQtIeeH7jSWZ2saRuQBcz61bVjlVZSiKfx8x2m17b0PrEyFAIodUys2PSt+ub2folmixiZpvgCxxuAb5JKz174qtCAQ4E5gU2xBc7zAocUs5+l1lhleqCVe1FE5PUV9Ll6WuMpK8l/StzvoOkRyT9mM73klTneyCpi6TRKVgMrVwkUIcQalkhR+QtYK6i+9ul7/8KrAtske7PCXxXqQ6WwSvAF3jOYVt8hWtrcTCQBzYAtgb+k0YA38LLnYwGOuP/htfjAfBOxReR9FfgDrzMyb0V6XmoqgiGQgi1bAKAmU1KBd8Lq0CzK0AFnGZmd1W+e03PzMZL2g64CTihcFjSRsBDwGVmNqLOCzRvb5nZv9P3n0g6FtgcWBhYHVjWzL4BkHQI8Kqklczsk8IFJO0J3ADsbWaRP1UjYposhNDajQPmm4nHPwocJWluAEkLSVquSXpWJWb2rpltBKyCL+QYggd9JwFvpmX3LVFxrbVvgHb44pIhhUAoGQhMZMrCE/D8sd54vlgEQjUkgqEQQqNI6ljtPjTQFcDLkl5vzIPNrCdeFHVAWrn5NLBU03WveszsY+A1vJbZqsDpwJJ4hfuW6Nei+8aUv3N1rRbKHh+cvg4ulGMItSGmyUJVpJUbS5lZV0ndgf3MbMV0bqr7odnaV9KWTCk2WphiGgvMjedl/A8vGDoWr8S+cWrzNVN+/3xgZq+Vq5NmdiZwZonjXYruK/N9Xzy3pHD/fOD8cvWxOTAzS6vvzsWnlFqTD4AOktqb2bB0bH1gNuDDTLsRwF7AC8DNkg7KVP0PrVhEvqE5yAMbVbsTYYa9C0zGP1mPA54CPsYrqls6B9AP30fvpXTc8MBoCWBToFEjNqFxJK0haek6Tv8p3X5dqf5UyDN4QHS7pI6S/ojnTD2czRcCSFNpW+DvxfWptEJo5WJkKFSdmY2n9DYkoRkzs1tLHB4yncdckrl7QZN2KDTU+sANkh7Di7WuAqyatgY6AC/G+J8q9q/JmdlkSTvir6s/XnPqEbwGVan2X0r6Mz59eCVwTKl2ofWIkaFQdWm7hMH1nF9Y0iuSnpE0bzq2sqSHJf0g6btUP2SFyvW66aT6J0dUux+hZvQB/omXEjgG6IYvRd8GeAzoZGZvV613jWRmXcwsV9cxMxtiZtub2dxmNr+Z7WdmozNtu2ULLprZ52a2dKZWVWhikkzSKZIGSBoi6ZjMuZUkPSppoKR3JJ2Qjv9Z0ovp+9kkjZd0Zrr/J0kDGtOXGBkKzZqkZfHk1f8BB5jZREnt8U+0dwGb4NMxpwLPSPqDmf1UtQ4nKe/pK6AT8D1e3+RbPK9meXyp7xV4ouoP+KqXEMrOzL4H/pu+iArUocomm9lGaZup9yXdjucY3o3/zn9X0uzAi2kbq37AWpLmwUc5BwFbAuek26cb04kYGQrNlqS1gJeBJ/GaHxPTqSOBwWZ2TFoi/D5wELAIXiCvubgLGIrnQ43A+/cXYBiedNw5tXmoWh0MIYQq6w0+cocXM10Wn7pdHeglaRDwKrAosLqZTUj3u+DBz03AQmnWIIKh0Oosis/X325m/yxa0bE+sEEaHh0vaTw+8jI30JxWoE0CRuHBz7zAO3hg1w4YjucubAtsVa0OhsqTO1bS+5J+SdtGXC1p/jRVMCTTtluaSugmaUtJ/dLP/EhJt0haILVbJ00pjE7n+6RP2qWefyFJF0j6QNLP+AjRVpLi5zBUwy+Z7wvFToUvuuiY+VrezK5K7Z7GA5+u+MKNvsDOeBD1SmM6EdNkobkaA7wN7CTpqqJiaW2AZ4F/lHjc95Xo3PSYWff07bXpqy5nlL83oZm5Bh/d/AavdDwR3z2+E77vWXGtHNL57fACkNfhJQq6ActJOhn//9AP6AGsCWwPrCBpTTMrrOorTDv3BTqk9k/gHyK2A56QdLiZ3dikrzaEGfcR8L2kw8zsBvj9Z/cXMxuOB0N9gDFm9o2kp4HLgX5mVur/z3RFMBSaq1+BXfF54xckbW5mQ9O5N/BVL0PN7Je6LhBCcyNpUzwQ+hjY0MzGpOOn4su/l8D3DSu2A7CFmb2Q2rfBRxm7Av8HHGZmvTPP0wPfp2t7fE+uglvxaYi9s9uLpBGmvsBVkvqkPzghVIWZ/Za2jLlM0j/w/fNG4+kQw/EPynPgHwIAnscLoV7e2OeMabLQbKUcod3xBLkXJC2TTv0H/wT9gKSNJS2XVhFcJmmlavU3hAY4MN2eVwiE4Pef9VPqedydhUAotZ8M3J7uvpsNhJLb0u3vVcIlrY3Xzrm/eJ+11Jez8D8wuzb85YTQeGYmMxuVud/BzF5P339qZjua2Vpm9gcz61yoCWVuMTM7Pt3/wcxmzUyjzbAYGQrNWvqEsDf+y/3FNEL0uaSN8To1j+DD/F/jnw5G1321EKpunXT7UolzA/D6N6WUKkxZmDp+o8S5QtHE7LYhf0y386fVjsUWTberlTgXQqsWwVCoiuwy3pRf072e+78B+xQ9/nO8bH4ILcn86XaaaSgzmyTpuzoe90OJY7814NysmWMLp9st01dd5qnnXAitUkyThRBC5YxNt4sVn5DUlikBSzkUgqZj0/REXV8HlbEPITRLEQyFEELlvJVuO5c4txHlHa0vVObdtIzPEUKLFMFQCCFUTiGx+TRJhSkzJM0GnF89TuFRAAAgAElEQVTOJ06Jqf2AXSQdXKqNpDUltWvK55U0l6ScpBckDZM0IdVTukrSgkVt+6ZzC0m6OdVTGivpIUlLpDad07XGS/q2sBVDieedW9JZkt6T9LN8256XJMXIV5hGBEMhhGYj/SHcbfot63x8d0mPNmWfmlJaEXYDsDLwnqQrJeXxgpxz4knRk+u5xMzaB/gU6CFpkKTrJV0kqbekd/Aly8s38XMugwd6vwD3A1fhdWSOBp5LWy1kzYbXkVkH6AU8B+wIPJxKEzwFjASuxxdMnF0c3EmaD09S7w78iK9AvRMv5hd7jYVpRAJ1CCFU1pHAh8DhwBH4FgQP4vvrDcWDlbIws6GS1sMDkV2BffEaLsOA9/Gg4Z0mftqvgCXNbGT2oKT98PIAe+H1jwoWB14A9jOzSantf/Aiq32A3c3ssXT8TGAwcDxwc+Ya5+NlBc41s6kKm0pasuleWmgtNPUuByGEUB2S7sX/QE/Ay/I/CPwL/wO9OV6p+T7g5EKxTUmb4NtJrIAXDfwcWM7MtkvnLwb2wBOTvwbOMbM70rk3gf+a2U2ZPtwNfGFmJ5b79RZLNbI+Bu4ys70r/fyVJkn4yM5DhdWlkvritZCWNbMvM20741N8z5nZFkXXuQkvxjd7KsUxa7ru98CKmT0NQ6hTTJOFEJoFM9sd+BLY38zmMbP98amNtviec52ATUi5Nalq8iPAjcCCwJV41eWst4EN8SXtpwC3SFo9neuB/xElXW9BvNLzLeV4fZnnaZ8qSGePzQVcke4+WM7nrwZJG0q6X9I3kn6VZPh04Pz4SFDW6GwglAxLt/8rcflh+N+ywgq9VfHaY/0iEAoNFdNkIYRmKU1n/BlYxszGAmMlnYXnkRyP76f1nZldnR7ytKRHyNTJMbNemUs+KGkgvtv1+/hu2ZdIWsXMPgL2BgaZ2Qdlfmn/BPZOoyDfAu2BLfACiY8D95b5+StK0p/wHKCf8b3QhqTvwd+L4pyhsUzrtwacK9RUKiSmf9uI7oYaFcFQCKG5Wgr4zcy+yhz7DFgwjaQsif9hzRoCrFG4I+mfwKGpLfiIwdPgW1BIuh8fHTo53d7Q5K9iWk8DawNbAQvhf8w/xhOLr7DWl7twEr7X4Lpm9ns+VJomK8d0ZGGbk+IRpxDqFMFQCKE5ya6kGgrMImlJMytsL7EcvlP1T5K+xjcdzepQ+CblmfwbH3V53cwmS3oJX1FUcBNwp6Q7gdWBqfbsKgcze5YpG0zWguWBD7KBULIOvoKuqX0IjAc6S5otpspCQ0TOUAitWNrIdv6iYyeX+r6ZGI7nB5ECoOeBvKR5JS2OL5Xumdo+BrSTdJSkWSRtgU+dFcyLJ2KPxAci9sLzjn6XlrqPT9e8z8zGlel11bKvgJWy9YvS0vcry/FkafueW/Al/WcUny/UKwohK4KhEFopSafjox0LSLokFaDbC1hO0omSDgSWl7RndXs6lfOAwyWNkXQrXhdnFnx67HXgVeA0ADMbDWyPL08fg+cRZZOfn8QTsN/Cg6xNKT0i0wNfhn1ziXNh5l0HzAe8meoqXYcv35+VKZvNNrXT8H/30yW9Juni9Nwv4EF0iyepgySTtN102nWXNKq+NnU8rqekUhsEV4ykedJr7Fbu54ppshBar8mA4UvVB+OBxKrAV2Z2saRTgM/M7O4q9nEqZvYoUFw0cfd62vcD1qrj3GS8ps+R03naz/HaPi82vKehoczsfkn74/lBh+FL3h/AA5a3y/Sc4yRtlp5zT+BYYBzwAVNW7dWKm/BVl6EeEQyF0HpNxqeJ2uBTT0vhS5MLq28MGCrpYDOryVGRlIh9LHBNK0xcbjbSqr5eJU51KGrXpY7HD2HqXK/sue749Gnx8fHAmemrxZBv2Nu2qXKdzGwonn8X6hHTZCHUIQ1D/znd9io6117SafU89qXy97B+ZnahmfU0s2+A/niy6kAzuzBzvlcNB0L74PlEP+CFG0OouMJ0lKSdJL2Hb1uyoaSOkp6V9JOk0WnLlMVKXGI+SbdLGidpRCo/kb3+VNNkkrqkqacuku6V7/H2maSj6ujflpLelvSjfG+3PxSdn0u+z9wwSb9IGihpqxLX+YekT+R70w2WdFyJNrtK+li+l9yL+Eh2RUQwFELdOuB1bqZhZsPM7LzKdqfxzOwhM8uZ2RfV7ktzYWZ3mNncZratmU3Insv8wehepe6F2tIBuBi4ANgWLxHRF5gLz5s7Gq/M/bR8U9+sS4CfgN3wAqRnSfp7A57zRnykeOf0XNdI6lTUZpl0/fPwOlztgHtSWYTsdQ5KbXbGE+YfS6s5AZB0KF5Jvg+e53cvcGnRYo51gbtTn3ZJbe9pwOtoEjFN1spIGoIvI270Zpcz8Fx7AEuYWWudgz8Mr3j8R3w10n340u4d8f8755rZfpJeAd4AOuNbRTxRuICkE4HJZpaveO9DCC3FwkBXMxsEIOnCdHzrVHAUSR/jeX+74gsDCt4zs8PT90+mVXunSro25c3V5U4zOzdduy8epOwCvJZpsxCwiZl9ktq1wSukrwJ8KGk1PEg6yMxuTW2exHPBzgC2To/pDvQ0s3+l6z6VVrmeIumKtL3OyXi9rT3SlPXj8k18z23A+zfTYmQozIw98AqyrdUN+EaSh+DbPewBXIb/MspaGDgH+Cu++SYAkk4AiEAohDAdXxcCoaQT8FQhEAIws9fwEaPORY8t3r7lAWAJPEewPk9lrv0r8EmJxwwpBELJ++m20G4DPJfr96rpKQC7N9PPpVJ/iiur342vMlwz3e8E9CnK3XtgOq+hyUQwFGZYitZrzfvpP/nXwAJF50aa2YhUF6dwbj78E1NrHTULITSd4UX3Fy9xrNBuoaJjI+q4P70K3GOK7k8E5mhAGzLtFgfGm9lPJfo5V/pbsXjmWHEbmPJ62lP3aym7CIZaGEmLSbpZ0tCUiDY8JdmtWdRux5T09pOkdyTtWOJaW0h6MbUZL+kZSRsWtemecic6SnpU0ljgCUk98RGSZdN5k2++2Jr8im8SCr7yqqB4VUupc2OZsjFo/D9rwSR1lfRy+n8yXNK18k1iC+frzC8qPidpbkljJQ0qbpvOt5f0m6SnSp0PrVbx785v8fycYovhpQmyitsV7ldib7ZvgXnkqzKzFgN+Srl4hX4U97OQDF54PcNKtCn1HpRF/JJueW7Hi8edDGyJ11AZhE/jFHTCp20uwOeAhwP3S1qx0CBl+z+J/yfcF0+AWwh4QdJ6JZ73QWAgniB3Ab7NwfP4D/AfM1+tybt4ztBFjXmwmT2JF3i7qik7FSpqY/zfcCheMfkTvMhjqUTW6TKzH/EtP9ZOCaPFDsAD8Jpc4Rd+9yqebzNv4YCkDfBE6+KVqjsX3d8FD0AqsZx+IP435Pcc1ZRcvRtT+jkUL65ZXC9sD/xD4zuZa+1QlJy9Sxn6XFIkULc8fwROL9qNu3hedUFgAzMbDiDpTfw/xx7A+anNufiUz1aFlTSSnsYL0J3N1NsaAFxnZlMFBZK+ByaY2YCZflXNkJn9AGxWdKwvvvICYL90rHPmfJfsMTO7A7ij7J0N5bIlcICZ3V44IKkHcDD+QaQxW0rciG8eezDwZtG5bsBops0DCbXlMvzn60lJFwHzABfigcP9RW3/IOn6dHwzPMfx2OkkTzcJM/tAvq/f1fItVgbjP9urpv6T9gTsDlwv6Tt8o+I/pfOnpuRp8A+dr+Kr1XrgGy4fUu7XUBAjQy3Pa8AJko6XtHYdUzADC4EQgJmNwOdelwEfqgfWB+7NLik2szH4csYuJa4Zv5xDLfqQaYsFnoUXs9yvMRc0s4H4apt9svl3kjYCVgPuKF7qH2qLmY0ENsdrDt0JXAP0A7YsUYzxRDxH8X58Ace/gasr11sOBW7FV489jG+evJ2Z/T6CZWY3Asfgo1iP4vmU/yrUPEttXgf2wjfwfQjYCa8eXhEtemRI0vNmtnm1+1Fhe+IVVY8DLgVGSbodOCMNwcO0c8rgWzIUkt4WxHNbSs0pfwvMLWn2ol/IlZh/DqG56V9cmdrMhkr6gjq2AWmgm/Dp053wVTXgI0Xge6WFGmFm3eo4/hZ11DlL54cwJUexdz3tupOp0J1Gt6ep5l1c/btUv0pVAk/J00enrzqZ2dVMJ0gzs3uZdtVZycrjTa1FjgxJaiOpTQ0GQpjZKDM7xsyWxmveXIpH3DNSAHA0Ps/bvsS5xYEfS3wybW3J0SE0xMg6jo8AZpuJlZW98E/9BwNImhP/oDMo/REMIVRQswyG5NsfvC3pzrQS6oYUAI2SdDGeaLWMpi4xfmZq+7akw9Oxv0oaIGlQ4RrVek3lYGZD0jDjO0yp1dCQx/2Iv4e7S5q1cDzN+W6PJ0Y3xAR8i4cQWqtF6zjeDpiYPjQUcjNKjbTPV+rBZjYan9boKmlpfGXmfETidAhV0ZyDgzXw8uRrAYvgWeULAy+Y2XppuA4ASdvhK6zWM7O1gHslLYJPJXUxs454fYQ9KvsSmpak+eV72BwvaRtJm0s6F3+PHp/By52KF8N6Sr4nzm54EDQnDd/Y8B2gnaQjJXWStP4M9iGE5m7jotUtSFoKz78r7LheqMWyRInHr1PPtW/EfwcfiK/mnEA90x0hhPJpzjlDnxSGiyXdhVez/NnMHivR9s/ArYXEMjP7XtL2eJAwIP0umxNfPdWS/QK8jv/iXBb/RfoZcJyZzdDybTN7Ni2v746vdpoMvAJsNgPD9P8FOuJTdAvgc7sVmd8NoUJWwxOlb88cOxv/3VkIXD4ExuPLghdICxGQtDw+hV2Smb0g6RPg73jNlXvMrFS+X9nIt+/5m5k9U8nnDaG5ac7BUHGOiuGb0TW0vYBHzezgUo1bojQkf8R02nRo6HEzew54bjrX604m+a7o3Fg8+z+E1upp4Kb04WowvnR5E/xDyX8BzGyipP/iq3relPQwXrNr5/T4+mql9MCXTENMkYVQNc15mmxlSWun73dn2kJTWc8A3QpF0CQtCAwANk/z8UhaOA1vhxBCQ72M7zm3NL4P3yrA9Uy7xPlUfIR0NuAoYN10+5/pXL8w4vQV/nusUSStK+ktSeMk3Svp7jSFjqTtUt7kmFRJe610/HZ8uu8ReQX6Exv7/CG0dCpaNdosSOqA1xkYhNfDeRkfERlhZotk2o0q3Jd0Jr4a4zfgajO7UdJf8F9Qs+JbKxxqZsVFzkIIoSokbQP8H/BvM2torl7xNWbDK2Nfho9WbY9Xub4YL8j6RDr2Oj7ldzawiplNiGmyEFxzDobuM7NIyA2hjFJl2J7ZBQmhciQ9CWwBrGBmXzTyGpvhhfmWKtREkvQSXil9YWCUmZ2Raf8RcFjKWRpCBEMhNOucoRDCDJB0JXACXuH1ZGAffPplOWCYmV0o6QF8ynl9vHLsRsACkm42s7dLXzk0JUnL4PsBdgS2woPRRgVCyRLA10XFIb9Kt8sCB0rKFsSbjdIr30KoWc0yZyjVz4lRoRBmzOt4cu9gvNTEfPhCgnHAjqnNb2Z2GV5GYS08t+6KCIQqanl8j8BtgHvwXKSZ8S2wZFEJgKXT7VfAeWa2QOZrLjO7M51vflMDIVRBswyGQu2QdKCks9I+a5tJalftPrVg/YDj8ZGhFfDdz5dLUySFWjiFFZm/ArMzpWBgqBAz62tmMrP5zGzPtCHwzHgF3yvtH5JmkbQj0CmduxE4QtKGcnOnYrSF3dCH48FZCDUtgqFQVWZ2q5mdbWb/M7MX06ayoRFS3s8a+IKDOYCngEUl5fBtVkp5A+guqWNFOhmaXFrVtgu+w/cYPEn6UWBC2vzyUHxPqNH4qGG3zMMvAE5PK81ylex3CM1Js0ygDiGE0HiSXgWuM7Nbqt2XEFqCGBkKIYQWTtKfJLVP02QH4vlgT1S7XyG0FLGaLIQQWr5V8GTseYBPgd3M7NvqdimEliOmyUJoJEldgI3M7MLptQ0hhNB8xTRZCDMprYTrKelpScdJukLSvuncpZLyknpLWlLSxZJWlvR3Sb1Tmwer+wrKR1JbSXdIekHShZIGS+ouab90vnN67xaUNCDzuDMl7V+9nocQaklMk4Uwc7riNV0OBR7Ht0P4FegjaRDQ1sz+KemPwOH4Cq+t8ZyOHyStAXxQlZ5Xxo7AWDPbR9Im1LGxr5mNlvSJpPXxFW47Ap0r2M8QQg2LkaEQZs5XQDu8wOFvZjbBzCZT9weNfkAXPGB6DTgdD5Baq5WAgen7V/Eif9m5+WyhwBuAvwGbA6+Y2c8V6WGFSeomydK2Q4VjfSX1rVqnQqhxEQyFMHM+wisIX4dvCPw7M3sPMEmXAkcDN5jZBGBOPDB4Gt8R/eWK9riyBuNbfwBsgAc/3wNLpWPrFRqaWT9gbfy9uqmCfQwh1LhIoA4hlI2ktkBvvOjjQHz39M2APsAo4HNgHjPrltofB+xjZhtUpcMVIKkbcAteHXxIOtYXwMy6VKtfIdSyyBkKIZSNmU2StL+Z/ZpyhlY1s+HAhnU9BJ8ua3YkLQqch+8p1g4YC3wMnFXY9V1SV+AUfBRsFuAt4Ewze7YqnQ4hNEgEQyGEcrtL0iL4XmiH19VI0kV4EPHXSnVsBvUC1gFOw4OgBYB1gYUB0gq524CHgQPxvLDDgSclbR0BUQjNV0yThRBCA0gaB9xkZseVODcXnkzf38x2yBxvA7yJ7xO2YTrWjZgmC6FZiQTqEEJomNeAbpJOl9RJUjZhfmNgIeDWtCXGLJJmwX/HPgFsIGnuKvS5xZO0k6S1qt2P0LpFMBRCCA2zJ3Arvvz/VeC7VDCyPbBYanMfPj2W/ToJX0W3UMV73DrshNflCqFsImcohBAawMxG4WUU/ilpSfyP9EVAe+Dy1OxoYEDpKzC87J1s4dLo2Z1AB6Atnky/A7C5pByeg/VhOr46MAk43cweTdvjnA8MBVYFhgD7m9kPlX0VoSWKYCiEEGaQmX0NXJNWj20C9AfGAKub2dVV7VzLtjUwopB3JWkBPGn9GTPrlY5dDnxjZnumwpWvZKbRNgAONbP3UrvTgBMr/BpCCxTBUAghTIek+YHngTvwkYlx+B/evwAPmNl4SUfjOUML4dNlI4BF8UKSi5lZnSvpwu/eBvJpZeHTQKkVeF2A/QDMbIikV/F/i5+At1KxU4CewI3l7nBoHSIYCiGE6fsFzxPaH5/CmRX4ArgQuATAzHpJ+hIfibgemBcPiAbhS+7DdJjZYEnr4kFmDs/TatBDi25DmCGxtD6EEEKzkHKxvjeznyVtBFwLvAR8YGb/TW0uAyabWU7SMsAr+OjbGvho0tpm9n7aBuc3MzupKi8mtCgRDIWaJqk7MMjMHqp2X0KodZK2wkfaChv6noxX+u4BTKR0AvUZZvZISqD+N77FS0cigTrMgAiGQk2TZMCthb2xQggtUwqGTjezrtXuS2h5os5QCCGEEGpaBENhGpJWkNRH0nhJ30vqLamdJJPUM9OuTarG21/SSEkTJH0s6cxUfTd7zW7p8TtIOkXSF5J+ktRP0pqpzR6S3pb0i6SPJO1ACZL2lfRK6t+Pkl6UtGWJdodIelPSWEnj0jVvkuuQRoUADkx9M0lDMo+fRdJpkj5Mr22EpDskLVf0PF3SY/+Rvj5Jr+E9Sfs2+h8ihNBgZtY3RoVCY8VqsjCVtKFmP7xa7jXAZ/jKjsdLNJ8NOB64F7gHn7/fCjgbWAav1FvsTDwX4HJgQXzlzeOSTkuPux5fuZMD7pW0gpkNzfTvQryi70NAb3zzz/2BJyTtZmYPpnbdgJuAR9LtZGA5vFBeW2Bketzt6fUWdkofn+nrXcCuwKPAf/BVRH8Hukpa38y+LHpth+C7mV+HL/PtBvSSNIuZ3VrivQghhNAcmFl8xdfvX0xJXtyl6Pgd6XjPzDEBc5a4xi14YLRE5li39Pj/AbNljp+Ujo/Ba7EUjm+Rjp+RObZ+OnZ60fPNBryLJ0wW8uAeBN5vwOud6jVljm+dzt1SdLzQr96ZY13SsZ+AZTLH58WTOUcCs1f737bEazyi2n2Ir/iKr/hqDl8xTRaKbQ98YWYPFB2/vLihuZ/h9ymlBdPI0rP4FOx6Ja7fw8wmZu73T7cPm9nwEsdXzBzbGx/h6S1pkcIXMB/wf8CywEqp7RhgSUmbTOf11mWndHt+9qCZPYvXm9k+7UiedZ9lRovMbBw+0rUIXqW4XpmpxJka6pd0nqRD0xTfBZJWT1OXB2fObZLa7p3OdZyZ5wwhhJYsgqFQrAPwcYnjpY4haT9Jb+FTW9/joyC3p9MLlnjIF0X3x6TbqaaczOwXYAJTb265Gv4z+1l6nuzXCalNYcPMC1J/Xkr5SbdL2ktT7zRen+XwpbzzSuqeyv4XvIuP+ixa9JgPS1zng8z1KmWMmd2ITzWOwoPSa/E+F86tm9qug7/37SvYv1Al8o1lX6p2P0JobiJnKDTUNDUYJO2OBz79gSOBr/EAYl18A8tSwfakOq5f13Flvm+D7wK+bT39fBfAzD6WtBqwJT61tQVewv8dSZ3NbGw918haFzgL6ItPw2U1pC6Fpt+kyc0v39TyMjygfAP/9/kKWCGdG4AXqRuEB3X967hWqGGpDtffzGypavclhHKKYCgUGwKsXOL4KiWO7YOPCHVNIzkASFq+PF3jEzyXZ7CZDZle49SnR9IXko7Ck8IPxBOi6/N5eq52Jc6tjidajyo6vmqJtoVjn0+vv01oqJldV3TsHABJR2TOxQhBCCEQ02RhWo8Cy0rapej4cSXaTsJzeNoWDkiaAzi6TH3rnW7PL5Gvg6R2me8XKfH4N9Ptwplj44FNJH2alsOPlNQfn2IDOC/dPl9Yfg/8EXjEzCbLtwy4JLXZX9JASdumPsyLV8z9Duifygr0TUv9f5T0jqST63vBktpKulnSb5IOqq9tQYlAqEHnQu2RtEv6mf1Z0tD0s7ZIOtcdHxVdMlN6ons1+xtCucTIUCh2MbAvcIekq/H8nG2AJdL57NTQg/jS82ck3Y7npBwI/FyOjpnZAEkXAKcAq0h6ABgOLAVsjOc7FUa1npL0Pb5sfig+wnM4Pm10b+ay4/Ak7ZeAZ/DAblZgrnS/kMz8JD5FtyUeQJ2SkpCfTdcA+AbfBuBRSb3wfJwO+JL7o/Gg6b10OxLPgdoJ3+xzGpJmB+7EpwV3t1Q2IISmIGkfPJfsOOBFPD/vYqCPpM5AHpgHHwEu5JiNL3GpUCWS7jOz3ardj9YggqEwFTMbIWkzfPXYkXgO0OPAHsBgMoGOmfWWtBD+h/4y4Ft8Wf3L+IaJ5ejfqZIGpuc8AZgDGAa8BZyWaXotvuP1UXgi90h8Q8fzzey9TLu58WCpI9AZX0nXASAlW98N7IznHP2AB4CnmdkXku7FR8f+ntpdkPpzAV7D6EPgADwg+wQPuLYsmlIsmVMkaR68ltKGwF/TKrYQmtK5wJlmdnPhgKQD8EUOndKHj/H4pqjDqtXJWiXpSuAM/Hfr8/jv4lPx/MWvzOwy/EPhucAKeG7Xj1XqbosXwVCYhpl9AmyXPSap8Mnwq6K2/6F0/o2K2vUEepZ4rneL22bOzVHH8QfxoKROacXUjfW1SUbjuT8bm1nxa/tV0v/hwdCWZta3cE7SYsAG+AaSIzKPyacg6nzgEDN7WdLx+P+17tlAKLUvlYS9ED7itAKwhZm91oDXEUKDpamw5YALJZ1XoslKeJJ9qJ6XgE3xD2Fr4x+03sQ/bD6Z2owys9PTYpYd8JHk0AiRMxSmIWnOovvCl2lDmUZ8qugEPCH6C0mDJF0iqVR9pGKFpfIflDj3flGbQu2jtxvYp5vw5fARCIVyKfzuPxEfFc1+rQQ8XKV+hSn6Agfjo8pt8A9JR+Cj3HelNpa5jV3XZ0KMDIVSnpX0Af4pZA58lKgLXlTw9Wp2rKmZ2b2SXsA/VXXFf/n8S9LpZnZ+/Y/2S5Q4pjrONfSX1f349NrpkvY2s98a+LgQGiRNh38JrJZGd+sykcwCiVA5ZjZS0sr49P8ceEB0HlNqrQEsmkb2lgMOrUpHW4kIhkIpfYC98OToOfHl9t3xXJhWx8xG4KMxN0maCx+CPlvSpdQdwAxJt6szZZVawapFbT5Jt2sBzzWgS72BF4Cb8b3N9jWzuuowhdBYpwC3SfoOX1TwEz4qtBvwz1RB/TP8D+4mwEfAT2b2U7U6XGvMbM30bamCrtnzYSbFNFmYhpldaGYdzWwBM5vdzFYxs7OLttFo8dKy9fmzx9Iv+g/xDwrzMmX1zIJF7YYBr+NB42Azk5ldnVlOPwwYmJo/gCdan5VWiGX7UFe+VE/8k94ewK2lSgm0Bun9ClVgZncAO+Kjvq/gBTgvxVdHTkjNHsA3YX4En545seIdDaECYmQo1LJ5ga8lPYhvIDsaz5k4CHjKzEZJeh0fHTpF0gL4arpXzexzfEnyM8AASdfiBSgPwvdI28vMfgUwsyGSTsOX0L8u6S48aXtlvCTAH0t1zsx6SJoFXxn3m6SDzWxyWd6J6tkuJZzf1dqC7ebIzLoV3X8MeKye9hPxpfUhtGoRDIVa9v/t3XmUZWV97vHvI7Qgs0wCIoJRMYAGjcREvIjGoAlouDgEBwIYcAgqePFeUUFQEMIkKoiikICgYiACS1EjeAUkalzgRUgUlHkSGWSWqfF3/3j3weOxuruKru6qU/v7WeusXXs872FR1c95x98Ax9D6Cm0LLEdbp+tg2nwrVNVVSfagdSD/PK3/xK7ANVV1YZKtgQOBfbpzlwDbVdU3ht+oqg5NchUtQH2QNlnl1fxuIskJVdVxSZahzZw9P8nuCxiBNpaqytEvkmZc5tDfVUmSpCmbk/0QJEmSJsswJEmSes0wJEmSes0wJEmSes0wJEnTpFtgV9KYMQxJ0jRI8mHgT0cn8pQ0+zm0XuqJJAcA+wNPq6obZ7g4kjRrWDMkSZJ6zTAkSZJ6zTAkaY1WzwwAABLVSURBVFoledJcXVhW0tzkHyxpjkiyYpJDk1yV5MEkdyT5YZLXjVy6UpLjkvw6yX1JTk+yxsiz/keSU5Ncm+ShJLcmOSXJ+iPX7ZKkkrwiyceT3AzcD6zSnV85yWFJrknycJLrkxye5ElL9D+GJE2BC7VKc8exwBu77X/RAsnmwIuA04euOwX4FbAf8Ezg3cAj3b0DbwDWAI7vrn028DbgRUmeV1UPjLz3J4B7gUOBFYCHkywP/F/gWcDngCuB5wF7AZsl+Zu5tOispPFlGJLmjtcAn6+qvRZx3ZVVteNgJ0mAdyV5R1Xd3R1+f1X9ZvimJGcB3wN2AL448syHgK2q6pGh6z8APBfYoqouGzp+KXAc8ArgnKl8QElaEmwmk+aOu2g1N09bxHXHjuyfDywDbDA4MByEuqauNYHLu/f40wme+fnhINT5O+AHwC+TrDl4Aed251++qA8kzRZJNuyahJ1Ycw7qbRhKsv3C9qUxtDfwx8B1SS7p+uZMFFyuG9m/s9uuPjiQZL2uj9BdwD3Abd1rte416qoJjj0b2Hro3sFrcO3ak/lQ0lQYWvR49LmZ7BlJ9qP9YV4OWCPJLsBTaH/8766qL81g+aQpqaqvJrkQeDWtCeqtwN5JPlRVhwxd+ugCHhGAbiTYOcA6wOHAT4H7gAJOZeIvUaN9iOiuOx84aAHvd/NCP5A0gcFIxar67RJ8j3kT1HRqDutzGLoa2Ij2LXnjkXMP8offnqVZr6puBU4ATkiyAnA28JEkR0zhMc8FNgF2qaqTBge7EWBPnsJzrgRWqapzF3mltBBJrqX1M3stsBnwZ0n+EdgOmAecBby3qu4Hvt/ddkvrDsdbaB35TwH+HHgicBHwzqr6Rff8E2mDCNYA/gp4d5JTgaO79/w1cOSS/pyaOb0NQ1V15tDufwB0TQo1/A+ANA6SLAOsNNQBmqr6TZIraE1VK07hcYNv3KM1QHtPcGxhTgUOTLJDVX11pLzLA/Oq6t4pPE/99ve08HM97f+t+2mhPbSgcwjwHuDFwDXAOlV1H0CS1YCTaSMmC/hkd8+Lhp7/JuBvaeFnedpoy81pTc+PAv+2RD+dZlRvw9BEqupi4OKZLof0OKwM3JTkDOAntG+yzwd2A75ZVXd135In42fAz4Ejk2xAG1r/UmBL4I4plOkI2j9epyU5BfgR7Vv8s2lD918HnDeF56nfPl1VVyVZmxZa1qiqewCSfBT4Bi0M/YGqugs4bbCf5CPADUlW7GqToP2eDGoxH0jyRmDvqvpVd8/BwFZL4oNp5hmGpLnhN8AxtL5C29L6wV0PHAwcNpUHVdX8JNvR5g56L+2b9/nAy4DvTOE5DyZ5OfB/gB271320b+3HAJdOpVzqveu77Ya0GsrrhgJ+gCd2NY5/oGsyPgp4JW2gwKD2c01aDdPw8wfWGzlm14k5zFXrJUmzWtdn6F1V9fUk6wA30pqFH5zg2qcD1wIrDzWT7Qf8NfD6qrqpm0n9BmCjqrq26zN0e1W9b+g5V9Nqhs7o9rcB/n34uZo7eju0XpI0fqrqFuBrwKcHy8gkeWqSbbtLbqPV/PzR0G0r00Y83plkFRY8wnHYV4B9kqzdvc8Hp+szaPYxDEmSxs3OtOatHye5h9Z8uyk8NmHoQcA5Se5K8hrg47R/724FLqE1+y7KR4H/Bq6g9Xf7ynR/CM0eNpNJ0iQlWR34Mm2I9pVVNdGklpN91rXAvlV1yjQVT9LjZAdqSZq8dwAr0UYyzZ/pwkiaHjaTSdLkPQP4mUFImlsMQ5I0CUm+RuursnOS+5Kcn2T+yDUHJDl3aH+tJCckuT7JPUkuTjI64z1JVkhyVpKzk0xlgszB/bt063Ft/Tg+2sKee0D33A0nef15XfOfNFYMQ5I0CVX1auCLwElVtRKw/8Ku79bQOou2sO0W3XZX4N6R69ahdei9GXjN0CSAkpYS+wxJ0pLxQloIWnNomZTRiSY3oY1aOq6qDl2ahZP0O9YMSdKSsSFw6/B6cRN4K2328E8vlRJJmpBhSJIen/uAZZIsN3RsvaGfrwXW7ib5W5B9gMuAc5M8eRrKlCR7Jfl5koeSXJ3kvRNc9Kok3+36MT2Q5JIke2SSC9gleWaSM5LcneTeJN9O8ifTUH5pRhiGJOnxuYIWiHZL8oQkL6EtPjtwEW3h5+O7WYyfkOS5SdYdumY+8GZaIDovyVMWs0wHA7sD/wK8j7aw7seT7Di4IMk/0BY1fTpwOG1m5ftp68Udt6g3SPI04Pu0NfBOoq09dwtt0d31F7P80owwDEla4pI8J8mJSRb5j+24qKp7aR2i9wbuBvakhYPB+d8Cr6EtA3EJcBctpKw88pzfVtXutFmUL0iywWIUawXgBVV1SFUdDWxNC0TvAUiyKm0B3puBF1bVgVV1FPBS4Bxg9y7ULczHgLWAN1TVe6rqM1X198Ch/P4SGNLYMAwJaCNfknwqye1d1fmPk6yZZIskl3bHPpvkgiRv6e45Mcm+Q894S5LzhvZfkuRH3ZT4PxyuRk+yfjeU+PYkv0jyd93x9bphy4PXA4OhukmWSbJfkmuS3Jrkc0metLT+G2nxVdXbZ7oMi6Oqdqmq3Yb2T6+qZ1TVylX1+qp6b1W9Yuj8rVW1c1WtV1WrVNULq+rn3bkNh2efrqr/VVUbV9Xo6ulTcUxVPTT0zPuBHwDP7g5tQ5s08uiq+vXQdfP53Xpdr13Qw7sRctsDP62qM0dOf4JWUyaNHcOQBrYBXkz7ZrcasBvwMPBV4DPAGrR1el48mYd1q0KfAbwfWB34FHBmkuW6P6hfAy4A1qX98T06ySZVdXNVrdQNXV6FNuR4sCbQnsBf0pZC2AhYG/jwYn5uLSFJlk1yejfvzh7dsYu67QFJvtD1Nfl6kuVntLBzx9UTHLuD9vsLbdJIaL/Loy7rtgur3VmbVrP109ET3QryV02umNLsYhjSwCO0P3IbA1VVPwaeDzzaVYM/QutT8MtJPu/NwBlV9d2uGeBLtFEzW3SvFavqyKp6pKouBU4Ddhh5xsdo0z8MVoveHfhgVf2q+8b7MeD1j/cDa4nbHvh5V1MyOqQc4Maq2obW/2THCc5r6h5dxPlBB+mJFqWcjoUqJ9UBW5ptDEMCoKq+A3wW+BzwyyRHABsANwxdU8CNk3zk04Gduiayu5LcRavNWa87t9HIuZ2BdQY3J3kt7R/IHavq0aFnfnPonnNpfRem3XTP5NtTz6R1IIa26veoi4e29jVZOgY1N5tNcO653Xai2qWBW2mTRm4yeqKr3XvGH9whjQHDkB5TVUdV1ebAnwGvov1hGx0dMrx/P63D5sDwSJgbgeOrarWh1wpV9a/ductHzq1UVe8CSLIprWnutVV1+8gzXz50z6pVtep0fPYkuyb5UJJXJvkobUj0NtPx7B67kla7CG0CwlHPH9ravLJ0fJvWr2eP4aH8SZYBPtTt/tuCbu46hZ8FbJJk+5HTe9H6I0ljxzAkAJK8sOssvSxwD62/0LXAvCRvSzIvyR60Pj4DPwG2S7Jqt3bRbkPnvgy8PsnLu87ZKybZLm3dpR8BlWTPrg/RvO69N+5Gu5wBvK9rqht2AnBI1x+JJE+dxsCyPK2J4Rbg+u7nedP07L46E3hOku8AL5jg/IZJzgFeApy6VEvWU90EkHvRvtRc1H0B2IvWN++VwOer6nuLeMy+wO3AV5J8Msk7k5xE6x9oqNVYcjkODQyG3G5Eq/H5V+AU4HJaCDmCFnC+P3TPybSO1zfQ5lw5FXgZQFVd0zV1HUarUn8I+B7w3aqan2Rb4Chaf6B5tGC1J62W4FnAsUmO7d7nuqratCvDE2jzsaxNGx58DO3b7mKpqs8M7f5kcZ+nx0YovW4hl5xaVV9fWuVRU1UnJLmZFl72of3+XQG8m0nMhF1V1yXZkva7vSutn9APaL/7n6DNvC2NlbRuINLkdEPnjx8eEixNVZIDgIsMQ3q8ugkqTwGeSPuytQPw/2i1XM8DTq+qw5KsTvtCtyqt1nunbo4o6TGGIU2JYUjSbJDkibRxHY8keT9tCoGjabXbtwEX0kY07g2cX1VnJ9kJWLuqjpypcmt2splMkjSOnkxrTl+TNo/SabTpGm4BSPITWpPdZsCWSf43rRbpgpkprmYzw5CmpKq2nukySBJtLrMLq+qoJB8AlgPW75rPbqM1lV0L/Az4VlWdA4/VKEm/x2YySdLYSfI84Iu0wHM7cB1dQAI25ff7DH2ONhM+wJFVdfbSL7FmM8OQJGlOSPJfVTXRhJLSQjnPkCRJ6jVrhiRJUq9ZMyRJknrNMCRJknrNMCRJknrNMCRJknrNMCRJknrNMCRJknrNMCRJknrNMCRJknrNMCRJknrNMCRJknrNMCRJknrNMCRJknpt2ZkugCRJevySbAvst4DT/1hVP16a5RlHhiFJksbbWsBnq+rE4YNJ9gVWmZESjRmbySRJUq8ZhiRJUq8ZhiRJUq8ZhiRJUq/ZgVpSryTZqapOnulyaMGSvAl4w8jhd1TVLTNRHs191gxJ6pvbkuyfZMskH07ylJku0FQlOSBJJXlOkn9Pcn+S65Ps2p3fKcnlSe5L8t0kfzR077wkByW5NsnD3fagJPNG3mPdJF9IcnuSh5JcmuQtI9fs0pXjz5N8Mck9SW5O8qkkyy/GR9ykqrYfvIAzgZUW43nSQhmGJPXRfwIbAr8A5s9sURbLacDZwPbAxcA/JzkYeCewD7ArsDHwpaF7TurOfQHYDvgX4P3dcQCSrAicD/w18MHu+ZcBJyd52wTlOBm4CtgB+AywB/CB6fqQ0pJmM5mkXqmqb810GabR4VX1BYAkFwGvBt4ObFRV93TH1wU+meTpwMrAG4GPVNUB3TO+neRR4MAk/1RVl9JC1LOAl1XVed113+xq0Q5KckJVPTpUji9V1f7dz+cmeVH3PvsjjQFrhiRpfH1z8ENV3QncCvxwEIQ6l3fbpwFbdT+fMvKcwf5Lu+1WwE1DQWj4urWATUaOnz2yfxmwwSTKL80K1gxJ0vi6c2T/4QUcA1geWL37+Zcj1ww6Jq8+tB29ZqLrBn49sv8QsNwE92vJ2T/Ju0aOrQtcOBOFGTeGIUnqj0FoWYfWx4ehfYA7hq7beIL7R6/TLNAtw3HiDBdjrNlMJkn9cX633XHk+Ju77QVD162fZMuR695Ea4r72ZIpnjQzrBmSpJ6oqv9O8mXggCTLAt8H/oK24vmXu87T0GoZ9gS+muRDwI20wPRXwNtHOk8vCVcmOXNof1ng20v4PSeUZEPgmkVcdl1VbbjEC6MlxjAkSf2yM3A18FZgX+Bm4FDgI4MLqur+JC8FDgP+iTYK7Qpgp6oa7Xw97WZhs89NVbX+RCe6sHTe0iyMpl+qaqbLIEnSrNSFnQsXFYasGRpv9hmSJEm9ZhiSJEm9ZhiSJEm9ZhiS5pgknxjeSpIWztFk0hyQ5E9oSyhsCjwydHwzYLDS+Mm0Nac+DXyWtpjmVlX1z0u3tJI0uxiGpLlhJVoIWhl4cOj43wDHdz//T+AmYBfg3G577FIroSTNUjaTSXPD5sA9QIA1ho5/A9ite50NfAt4MXAq8JdVdfNSLqckzTrOMyRJ0gI4z1A/WDMkSZJ6zTAkSZJ6zTAkSZJ6zTAkSZJ6zaH1kiQt3FOTLGy00XVLrSRaIhxNJkmSes1mMkkaU0lqEq/LZ7qc0mxnM5kkjbctgFsWcO4vgAOXYlmksWQYkqTxdktV3TjRiSS3Le3CSOPIZjJJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrhiFJktRrrlovSePthiQLO3/F0iqINK4MQ5I0vtadxDXzl3gppDGXqprpMkiSJM0Y+wxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJkqReMwxJksZSkr0mcc0uSVZbGuXR+DIMSZLG1VOSfCDJ3yY5MslaSU5NckSSVZMcDrxqpgup2c8wJEkaV3dU1SHAJsDdwB8DPwL+A9gc+Brww5krnsaFYUiSNK7md9sHgND+TfstUMCdwNbAC2akZBorqaqZLoMkSdKMsWZIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT1mmFIkiT12v8HMHzvinVKz9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_frame_on(False)\n",
    "plt.scatter(tsneWords[:, 0], tsneWords[:, 1], alpha = 0)#Making the points invisible \n",
    "for i, word in enumerate(targetWords):\n",
    "    ax.annotate(word, (tsneWords[:, 0][i],tsneWords[:, 1][i]), size =  20 * (numWords - i) / numWords)\n",
    "plt.title('Vector Visualization gme', size = 18, color = \"darkred\")\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.savefig('w2v_tsne_gme.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "bibliographic-president",
   "metadata": {},
   "outputs": [],
   "source": [
    "Trend = dimension(W2V_gme, ['increase','soar', 'rise', 'raise', 'rocket', 'good', 'great', 'up'], \n",
    "                  ['decline', 'cut', 'drop', 'recession', 'dip', 'bad', 'flop', 'down'])\n",
    "Attitude = dimension(W2V_gme, ['crazy','urgent', 'mad', 'rush'], ['reason', 'rational', 'analysis', 'chill', 'calm'])\n",
    "Trust = dimension(W2V_gme, ['trust', 'believe', 'true', 'real', 'honest', 'sure'], \n",
    "                  ['fake', 'confuse', 'fraud', 'fool', 'doubt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "helpful-martial",
   "metadata": {},
   "outputs": [],
   "source": [
    "Companies = ['apple', 'boeing', 'facebook', 'google', 'gm', 'amazon', 'amc', 'gamestop', 'microsoft', 'tesla']\n",
    "participants = ['investor', 'analyst', 'fund', 'bank', 'government', 'reddit', 'news', 'jpmorgan', 'youtuber']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "naked-protest",
   "metadata": {},
   "outputs": [],
   "source": [
    "Comdf = makeDF(W2V3, Companies) \n",
    "Pardf = makeDF(W2V3, participants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "isolated-parking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,8))\n",
    "ax1 = fig.add_subplot(131)\n",
    "PlotDimension(ax1, Pardf, 'Trend')\n",
    "ax2 = fig.add_subplot(132)\n",
    "PlotDimension(ax2, Pardf, 'Attitude')\n",
    "ax3 = fig.add_subplot(133)\n",
    "PlotDimension(ax3, Pardf, 'Trust')\n",
    "plt.savefig('dim_proj_gme_par.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "exceptional-covering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,8))\n",
    "ax1 = fig.add_subplot(131)\n",
    "PlotDimension(ax1, Comdf, 'Trend')\n",
    "ax2 = fig.add_subplot(132)\n",
    "PlotDimension(ax2, Comdf, 'Attitude')\n",
    "ax3 = fig.add_subplot(133)\n",
    "PlotDimension(ax3, Comdf, 'Trust')\n",
    "plt.savefig('dim_proj_rb_com.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "lesbian-butterfly",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy \n",
    "\n",
    "def calc_syn0norm(model):\n",
    "    \"\"\"since syn0norm is now depricated\"\"\"\n",
    "    return (model.wv.syn0 / np.sqrt((model.wv.syn0 ** 2).sum(-1))[..., np.newaxis]).astype(np.float32)\n",
    "\n",
    "def smart_procrustes_align_gensim(base_embed, other_embed, words=None):\n",
    "    \"\"\"Procrustes align two gensim word2vec models (to allow for comparison between same word across models).\n",
    "    Code ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <wleif@stanford.edu>.\n",
    "    (With help from William. Thank you!)\n",
    "    First, intersect the vocabularies (see `intersection_align_gensim` documentation).\n",
    "    Then do the alignment on the other_embed model.\n",
    "    Replace the other_embed model's syn0 and syn0norm numpy matrices with the aligned version.\n",
    "    Return other_embed.\n",
    "    If `words` is set, intersect the two models' vocabulary with the vocabulary in words (see `intersection_align_gensim` documentation).\n",
    "    \"\"\"\n",
    "    base_embed = copy.copy(base_embed)\n",
    "    other_embed = copy.copy(other_embed)\n",
    "    # make sure vocabulary and indices are aligned\n",
    "    in_base_embed, in_other_embed = intersection_align_gensim(base_embed, other_embed, words=words)\n",
    "\n",
    "    # get the embedding matrices\n",
    "    base_vecs = calc_syn0norm(in_base_embed)\n",
    "    other_vecs = calc_syn0norm(in_other_embed)\n",
    "\n",
    "    # just a matrix dot product with numpy\n",
    "    m = other_vecs.T.dot(base_vecs) \n",
    "    # SVD method from numpy\n",
    "    u, _, v = np.linalg.svd(m)\n",
    "    # another matrix operation\n",
    "    ortho = u.dot(v) \n",
    "    # Replace original array with modified one\n",
    "    # i.e. multiplying the embedding matrix (syn0norm)by \"ortho\"\n",
    "    other_embed.wv.syn0norm = other_embed.wv.syn0 = (calc_syn0norm(other_embed)).dot(ortho)\n",
    "    return other_embed\n",
    "    \n",
    "def intersection_align_gensim(m1,m2, words=None):\n",
    "    \"\"\"\n",
    "    Intersect two gensim word2vec models, m1 and m2.\n",
    "    Only the shared vocabulary between them is kept.\n",
    "    If 'words' is set (as list or set), then the vocabulary is intersected with this list as well.\n",
    "    Indices are re-organized from 0..N in order of descending frequency (=sum of counts from both m1 and m2).\n",
    "    These indices correspond to the new syn0 and syn0norm objects in both gensim models:\n",
    "        -- so that Row 0 of m1.syn0 will be for the same word as Row 0 of m2.syn0\n",
    "        -- you can find the index of any word on the .index2word list: model.index2word.index(word) => 2\n",
    "    The .vocab dictionary is also updated for each model, preserving the count but updating the index.\n",
    "    \"\"\"\n",
    "\n",
    "    # Get the vocab for each model\n",
    "    vocab_m1 = set(m1.wv.vocab.keys())\n",
    "    vocab_m2 = set(m2.wv.vocab.keys())\n",
    "\n",
    "    # Find the common vocabulary\n",
    "    common_vocab = vocab_m1&vocab_m2\n",
    "    if words: common_vocab&=set(words)\n",
    "\n",
    "    # If no alignment necessary because vocab is identical...\n",
    "    if not vocab_m1-common_vocab and not vocab_m2-common_vocab:\n",
    "        return (m1,m2)\n",
    "\n",
    "    # Otherwise sort by frequency (summed for both)\n",
    "    common_vocab = list(common_vocab)\n",
    "    common_vocab.sort(key=lambda w: m1.wv.vocab[w].count + m2.wv.vocab[w].count,reverse=True)\n",
    "\n",
    "    # Then for each model...\n",
    "    for m in [m1,m2]:\n",
    "        # Replace old syn0norm array with new one (with common vocab)\n",
    "        indices = [m.wv.vocab[w].index for w in common_vocab]\n",
    "        old_arr = calc_syn0norm(m)\n",
    "        new_arr = np.array([old_arr[index] for index in indices])\n",
    "        m.wv.syn0norm = m.wv.syn0 = new_arr\n",
    "\n",
    "        # Replace old vocab dictionary with new one (with common vocab)\n",
    "        # and old index2word with new one\n",
    "        m.index2word = common_vocab\n",
    "        old_vocab = m.wv.vocab\n",
    "        new_vocab = {}\n",
    "        for new_index,word in enumerate(common_vocab):\n",
    "            old_vocab_obj=old_vocab[word]\n",
    "            new_vocab[word] = gensim.models.word2vec.Vocab(index=new_index, count=old_vocab_obj.count)\n",
    "        m.wv.vocab = new_vocab\n",
    "\n",
    "    return (m1,m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "defined-tattoo",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compareModels(df, category, text_column_name='normalized_sents', sort = True, embeddings_raw={}):\n",
    "    \"\"\"If you are using time as your category sorting is important\"\"\"\n",
    "    if len(embeddings_raw) == 0:\n",
    "        embeddings_raw = rawModels(df, category, text_column_name, sort)\n",
    "    cats = sorted(set(df[category]))\n",
    "    #These are much quicker\n",
    "    embeddings_aligned = {}\n",
    "    for catOuter in cats:\n",
    "        embeddings_aligned[catOuter] = [embeddings_raw[catOuter]]\n",
    "        for catInner in cats:\n",
    "            embeddings_aligned[catOuter].append(smart_procrustes_align_gensim(embeddings_aligned[catOuter][-1], embeddings_raw[catInner]))\n",
    "    return embeddings_raw, embeddings_aligned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "anonymous-mailman",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rawModels(df, category, text_column_name='normalized_sents', sort = True):\n",
    "    embeddings_raw = {}\n",
    "    cats = sorted(set(df[category]))\n",
    "    for cat in cats:\n",
    "        #This can take a while\n",
    "        print(\"Embedding {}\".format(cat), end = '\\r')\n",
    "        subsetDF = df[df[category] == cat]\n",
    "        #You might want to change the W2V parameters\n",
    "        embeddings_raw[cat] = gensim.models.word2vec.Word2Vec(subsetDF[text_column_name].sum())\n",
    "    return embeddings_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "specific-international",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding 202106\r"
     ]
    }
   ],
   "source": [
    "rawEmbeddings, comparedEmbeddings = compareModels(gme, 'ym', text_column_name='normalized_texts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "coated-corpus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([202010, 202011, 202012, 202101, 202102, 202103, 202104, 202105, 202106])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawEmbeddings.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "fitted-serve",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDivergenceDF(word, embeddingsDict):\n",
    "    dists = []\n",
    "    cats = sorted(set(embeddingsDict.keys()))\n",
    "    dists = {}\n",
    "    print(word)\n",
    "    for cat in cats:\n",
    "        dists[cat] = []\n",
    "        for embed in embeddingsDict[cat][1:]:\n",
    "            dists[cat].append(np.abs(1 - sklearn.metrics.pairwise.cosine_similarity(np.expand_dims(embeddingsDict[cat][0][word], axis = 0),\n",
    "                                                                             np.expand_dims(embed[word], axis = 0))[0,0]))\n",
    "    return pd.DataFrame(dists, index = cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "beginning-extension",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamestop\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "targetWord = 'gamestop'\n",
    "\n",
    "pltDF = getDivergenceDF(targetWord, comparedEmbeddings)\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "sns.heatmap(pltDF, ax = ax, annot = False) #set annot True for a lot more information\n",
    "ax.set_xlabel(\"Starting yearmonth\")\n",
    "ax.set_ylabel(\"Final yearmonth\")\n",
    "ax.set_title(\"Monthly linguistic change for: '{}'\".format(targetWord), fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "alpha-relation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gme\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "targetWord = 'gme'\n",
    "\n",
    "pltDF = getDivergenceDF(targetWord, comparedEmbeddings)\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "sns.heatmap(pltDF, ax = ax, annot = False) #set annot True for a lot more information\n",
    "ax.set_xlabel(\"Starting yearmonth\")\n",
    "ax.set_ylabel(\"Final yearmonth\")\n",
    "ax.set_title(\"Montly linguistic change for: '{}'\".format(targetWord), fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "industrial-chest",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}