3B_Topic_modeling_gensim.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Topic Modeling Lab\n",
    "This notebook uses LDA in GenSim module. The original notebook uses LDA in SKLearn instead.  \n",
    "We want to use this notebook to compare two different LDA implementations in 3_Topic_modeling.ipynb. \n",
    "Please send any comments to CMPLXSYS251-Fall2024@umich.edu.\n",
    "\n",
    "## Contents\n",
    "1. [Setup](#Section-1%3A-Setup)\n",
    "    1. [Import](#1.1-Import-Packages)\n",
    "    1. [Download](#1.2-Download-and-Prepare-Data)\n",
    "    1. [Read](#1.3-Read-Data)\n",
    "    1. [Pick Your Data](#1.4-Pick-Your-Data)\n",
    "    1. [Clean Text](#1.5-Clean-Text)\n",
    "1. [Converting Text to Numbers](#Section-2%3A-Converting-Text-to-Numbers)\n",
    "1. [LDA Topic Model](#Section-3%3A-LDA-Topic-Model)\n",
    "1. [Interpret Topics](#Section-4%3A-Interpret-Topics)\n",
    "1. [Topic Quality](#Section-5%3A-Topic-Quality)\n",
    "1. [Topic Popularity](#Section-6%3A-Topic-Popularity)\n",
    "1. [NMF Topic Model](#Section-7%3A-NMF-Topic-Model)\n",
    "1. [What We Learned](#Section-7%3A-What-We-Learned)\n",
    "\n",
    "## Section 0: Background\n",
    "- In this lab, we'll learn about topic modeling. Topic modeling uses statistics to understand what text is about, that is, to find the topics in text.\n",
    "- We'll use the online dating profile text that OKCupid made public as our example, but of course topic modeling can be used on any text.\n",
    "\n",
    "@Author: [Jeff Lockhart](http://www-personal.umich.edu/~jwlock/) & [Ed Platt](https://elplatt.com/), with some code adapted from [Aneesha Bakharia](https://medium.com/@aneesha/topic-modeling-with-scikit-learn-e80d33668730)'s example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Section 1: Setup\n",
    "### 1.1 Import Packages\n",
    "- Packages contain code others have written to make our work easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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      "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in /opt/conda/lib/python3.11/site-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (7.0.4)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.11/site-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.3)\n",
      "Requirement already satisfied: marisa-trie>=0.7.7 in /opt/conda/lib/python3.11/site-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.2.0)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.11/site-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.11/site-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.17.2)\n",
      "Requirement already satisfied: wrapt in /opt/conda/lib/python3.11/site-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.16.0)\n",
      "Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.2)\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 lxml\n",
    "!pip install spacy\n",
    "!pip install gensim\n",
    "!pip install nltk\n",
    "!pip install tqdm\n",
    "!pip install pyLDAvis\n",
    "!python -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pprint import pprint\n",
    "\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "from bs4 import BeautifulSoup\n",
    "import spacy\n",
    "\n",
    "import gensim\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "import gensim.corpora as corpora\n",
    "\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "import time\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Download and Prepare Data\n",
    "This code checks whether you have the data. If you don't, it will download and prepare it for you. To see how it works, look at lab `1 Data munging` which explains it in detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ready to go!\n"
     ]
    }
   ],
   "source": [
    "%run -i \"download_and_clean_data.py\"\n",
    "print('Ready to go!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>age_group</th>\n",
       "      <th>age</th>\n",
       "      <th>body</th>\n",
       "      <th>alcohol_use</th>\n",
       "      <th>drug_use</th>\n",
       "      <th>edu</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>height_group</th>\n",
       "      <th>industry</th>\n",
       "      <th>kids</th>\n",
       "      <th>...</th>\n",
       "      <th>essay0</th>\n",
       "      <th>essay1</th>\n",
       "      <th>essay2</th>\n",
       "      <th>essay3</th>\n",
       "      <th>essay4</th>\n",
       "      <th>essay5</th>\n",
       "      <th>essay6</th>\n",
       "      <th>essay7</th>\n",
       "      <th>essay8</th>\n",
       "      <th>essay9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>overweight</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>HS</td>\n",
       "      <td>multiple</td>\n",
       "      <td>over_6</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>about me:&lt;br /&gt;\\n&lt;br /&gt;\\ni would love to think...</td>\n",
       "      <td>currently working as an international agent fo...</td>\n",
       "      <td>making people laugh.&lt;br /&gt;\\nranting about a go...</td>\n",
       "      <td>the way i look. i am a six foot half asian, ha...</td>\n",
       "      <td>books:&lt;br /&gt;\\nabsurdistan, the republic, of mi...</td>\n",
       "      <td>food.&lt;br /&gt;\\nwater.&lt;br /&gt;\\ncell phone.&lt;br /&gt;\\n...</td>\n",
       "      <td>duality and humorous things</td>\n",
       "      <td>trying to find someone to hang out with. i am ...</td>\n",
       "      <td>i am new to california and looking for someone...</td>\n",
       "      <td>you want to be swept off your feet!&lt;br /&gt;\\nyou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>average</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>White</td>\n",
       "      <td>under_6</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>i am a chef: this is what that means.&lt;br /&gt;\\n1...</td>\n",
       "      <td>dedicating everyday to being an unbelievable b...</td>\n",
       "      <td>being silly. having ridiculous amonts of fun w...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>i am die hard christopher moore fan. i don't r...</td>\n",
       "      <td>delicious porkness in all of its glories.&lt;br /...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>i am very open and will share just about anyth...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_group  age        body alcohol_use drug_use      edu race_ethnicity  \\\n",
       "0         20   22  overweight         yes       no       HS       multiple   \n",
       "1         30   35     average         yes      yes  unknown          White   \n",
       "\n",
       "  height_group industry kids  ...  \\\n",
       "0       over_6    other   no  ...   \n",
       "1      under_6    other   no  ...   \n",
       "\n",
       "                                              essay0  \\\n",
       "0  about me:<br />\\n<br />\\ni would love to think...   \n",
       "1  i am a chef: this is what that means.<br />\\n1...   \n",
       "\n",
       "                                              essay1  \\\n",
       "0  currently working as an international agent fo...   \n",
       "1  dedicating everyday to being an unbelievable b...   \n",
       "\n",
       "                                              essay2  \\\n",
       "0  making people laugh.<br />\\nranting about a go...   \n",
       "1  being silly. having ridiculous amonts of fun w...   \n",
       "\n",
       "                                              essay3  \\\n",
       "0  the way i look. i am a six foot half asian, ha...   \n",
       "1                                                NaN   \n",
       "\n",
       "                                              essay4  \\\n",
       "0  books:<br />\\nabsurdistan, the republic, of mi...   \n",
       "1  i am die hard christopher moore fan. i don't r...   \n",
       "\n",
       "                                              essay5  \\\n",
       "0  food.<br />\\nwater.<br />\\ncell phone.<br />\\n...   \n",
       "1  delicious porkness in all of its glories.<br /...   \n",
       "\n",
       "                        essay6  \\\n",
       "0  duality and humorous things   \n",
       "1                          NaN   \n",
       "\n",
       "                                              essay7  \\\n",
       "0  trying to find someone to hang out with. i am ...   \n",
       "1                                                NaN   \n",
       "\n",
       "                                              essay8  \\\n",
       "0  i am new to california and looking for someone...   \n",
       "1  i am very open and will share just about anyth...   \n",
       "\n",
       "                                              essay9  \n",
       "0  you want to be swept off your feet!<br />\\nyou...  \n",
       "1                                                NaN  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles = pd.read_csv('data/clean_profiles.tsv', sep='\\t')\n",
    "profiles.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Pick Your Data\n",
    "- Pick which section of the profiles you want to analyze.\n",
    "#### Options:\n",
    "- `text` - All of the text from a profile (**Recommended**)\n",
    "- `essay0` - My self summary (**Recommended**)\n",
    "- `essay1` - What I’m doing with my life\n",
    "- `essay2` - I’m really good at\n",
    "- `essay3` - The first thing people usually notice about me\n",
    "- `essay4` - Favorite books, movies, show, music, and food\n",
    "- `essay5` - The six things I could never do without\n",
    "- `essay6` - I spend a lot of time thinking about\n",
    "- `essay7` - On a typical Friday night I am\n",
    "- `essay8` - The most private thing I am willing to admit\n",
    "- `essay9` - You should message me if...\n",
    "\n",
    "#### Replace `essay0` in the cell below with the essay you want to look at.\n",
    "- `text` and `essay0` are both recommended, but it's your choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_section_to_use = 'essay0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Clean Text \n",
    "- For this lab, it is not so important that you understand this code. \n",
    "- For now, just run it and move on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some of the essays have just a link in the text. BeautifulSoup sees that and gets \n",
    "# the wrong idea. This line hides those warnings.\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module='bs4')\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning, module='bs4.builder._lxml')\n",
    "def clean(text):\n",
    "    if pd.isnull(text):\n",
    "        t = np.nan\n",
    "    else:\n",
    "        t = BeautifulSoup(text, 'lxml').get_text()\n",
    "        t = t.lower()\n",
    "\n",
    "        bad_words = ['http', 'www', '\\nnan']\n",
    "\n",
    "        for b in bad_words:\n",
    "            t = t.replace(b, '')\n",
    "    if t == '':\n",
    "        t = np.nan\n",
    "    \n",
    "    return t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaning up profile text for essay0 ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/58785 [00:00<?, ?it/s]/tmp/ipykernel_2028/512017477.py:9: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n",
      "  t = BeautifulSoup(text, 'lxml').get_text()\n",
      "/tmp/ipykernel_2028/512017477.py:9: MarkupResemblesLocatorWarning: The input looks more like a URL than markup. You may want to use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  t = BeautifulSoup(text, 'lxml').get_text()\n",
      "100%|██████████| 58785/58785 [00:11<00:00, 4914.78it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We started with 58785 profiles.\n",
      "Dropping profiles that didn't write anything for the essay we chose...\n",
      "We have 53396 profiles left.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print('Cleaning up profile text for', profile_section_to_use, '...')\n",
    "profiles['clean'] = profiles[profile_section_to_use].progress_apply(clean)\n",
    "\n",
    "print('We started with', profiles.shape[0], 'profiles.')\n",
    "print(\"Dropping profiles that didn't write anything for the essay we chose...\")\n",
    "profiles.dropna(axis=0, subset=['clean'], inplace=True)\n",
    "\n",
    "text_cols = ['text', 'essay0', 'essay1', 'essay2', 'essay3', 'essay4', \n",
    "             'essay5', 'essay6', 'essay7', 'essay8', 'essay9']\n",
    "profiles.drop(columns=text_cols, inplace=True)\n",
    "\n",
    "#what we will use as our documents, here the cleaned up text of each profile\n",
    "documents = profiles['clean'].values\n",
    "\n",
    "print('We have', documents.shape[0], 'profiles left.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove punctuation/lower casing\n",
    "\n",
    "- Next, let’s perform a simple preprocessing on the content of paper_text column to make them more amenable for analysis, and reliable results.\n",
    "- To do that, we’ll use a regular expression to remove any punctuation, and then lowercase the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    about me:\\n\\ni would love to think that i was ...\n",
       "1    i am a chef: this is what that means\\n1 i am a...\n",
       "2    i'm not ashamed of much but writing public tex...\n",
       "3               i work in a library and go to school  \n",
       "4    hey how's it going currently vague on the prof...\n",
       "Name: clean, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the regular expression library\n",
    "\n",
    "# Remove punctuation\n",
    "profiles['clean'] = profiles['clean'].map(lambda x: re.sub(r'[,.!?]', '', x))\n",
    "\n",
    "# Convert the text to lowercase\n",
    "profiles['clean'] = profiles['clean'].map(lambda x: x.lower())\n",
    "\n",
    "# Print out the first rows of profiles\n",
    "profiles['clean'].head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 2: Converting Text to Numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** **\n",
    "#### Step 1: Tokenize words and further clean-up text\n",
    "** **\n",
    "Let’s tokenize each sentence into a list of words, removing punctuations and unnecessary characters altogether.\n",
    "\n",
    "- Here we tell it to only use the 1,000 most popular words, ignoring stop words like \"a\" and \"of\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 30 cleaned words of essay 0 from profile 0 are:\n",
      "\n",
      " ['about', 'me', 'would', 'love', 'to', 'think', 'that', 'was', 'some', 'some', 'kind', 'of', 'intellectual', 'either', 'the', 'dumbest', 'smart', 'guy', 'or', 'the', 'smartest', 'dumb', 'guy', 'can', 'say', 'can', 'tell', 'the', 'difference', 'love']\n"
     ]
    }
   ],
   "source": [
    "# [:1] selects the first profile, and [:30] selects the first 30 words from that profile\n",
    "# You can try to change the parameters and see what happens\n",
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n",
    "\n",
    "data = profiles.clean.values.tolist()\n",
    "data_words = list(sent_to_words(data))\n",
    "\n",
    "print(\"The first 30 cleaned words of essay 0 from profile 0 are:\\n\\n\", data_words[:1][0][:30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** **\n",
    "#### Step 2: Remove Stopwords, Make Bigrams and Lemmatize\n",
    "** **\n",
    "\n",
    "The phrase models are ready. Let’s define the functions to remove the stopwords, make trigrams and lemmatization and call them sequentially."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    " \n",
    "#### Short Answer No.1\n",
    "- (1) Why do we want to ignore stop words? (1-2 sentences)\n",
    "- (2) Give 3 more examples of stop words \n",
    "    \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])\n",
    "\n",
    "def remove_stopwords(texts):\n",
    "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove Stop Words\n",
    "data_words_nostops = remove_stopwords(data_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** **\n",
    "#### Step 3: Phrase Modeling: Bigram and Trigram Models\n",
    "** **\n",
    "\n",
    "Bigrams are two words frequently occurring together in the document. Trigrams are 3 words frequently occurring. Some examples in our example are: 'back_bumper', 'oil_leakage', 'maryland_college_park' etc.\n",
    "\n",
    "Gensim's Phrases model can build and implement the bigrams, trigrams, quadgrams and more. The two important arguments to Phrases are min_count and threshold.\n",
    "\n",
    "*The higher the values of these param, the harder it is for words to be combined.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "\n",
    "# Faster way to get a sentence clubbed as a trigram/bigram\n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### See what words are being counted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions for bigrams, trigrams and lemmatization\n",
    "\n",
    "def make_bigrams(texts):\n",
    "    return [bigram_mod[doc] for doc in texts]\n",
    "\n",
    "def make_trigrams(texts):\n",
    "    return [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    return texts_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's call the functions in order. Note that calling these functions might take a couple of time, generally around 2 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first 30 words of essay 0 from profile 0 (after lemmatization) are:\n",
      "\n",
      " ['love', 'think', 'kind', 'intellectual', 'dumbest', 'smart', 'guy', 'smart', 'dumb', 'guy', 'say', 'tell', 'difference', 'love', 'talk', 'idea', 'concept', 'forge', 'odd', 'metaphor', 'instead', 'recite', 'cliche', 'simularitie', 'underwater', 'salt', 'favorite', 'word', 'salt', 'way']\n",
      "--- 214.81752371788025 seconds ---\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "# Form Bigrams\n",
    "data_words_bigrams = make_bigrams(data_words_nostops)\n",
    "\n",
    "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
    "nlp = spacy.load(\"en_core_web_sm\", disable=['parser', 'ner'])\n",
    "\n",
    "# Do lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "print(\"The first 30 words of essay 0 from profile 0 (after lemmatization) are:\\n\\n\", data_lemmatized[:1][0][:30])\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### See an example of how a profile's text is encoded\n",
    "- `n` is the profile number you want to look at. Change the value of `n` and re-run the code to see different profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 2), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 2), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 2), (27, 1), (28, 1), (29, 1)]\n"
     ]
    }
   ],
   "source": [
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Create Corpus\n",
    "texts = data_lemmatized\n",
    "\n",
    "# Term Document Frequency\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "# View\n",
    "print(corpus[:1][0][:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile text:\n",
      "life is about the little things. i love to laugh. it's easy to do\n",
      "when one can find beauty and humor in the ugly. this perspective\n",
      "makes for a more gratifying life. it's a gift. we are here to play.\n",
      "\n",
      "\n",
      "BOW (Bag-of-Words) vector:\n",
      "laugh \t 1\n",
      "life \t 2\n",
      "little \t 1\n",
      "love \t 1\n",
      "make \t 1\n",
      "thing \t 1\n",
      "find \t 1\n",
      "beauty \t 1\n",
      "easy \t 1\n",
      "gift \t 1\n",
      "gratifying \t 1\n",
      "humor \t 1\n",
      "perspective \t 1\n",
      "play \t 1\n",
      "ugly \t 1\n"
     ]
    }
   ],
   "source": [
    "def show_bow_vector(bow, dictionary):\n",
    "    for word_id, count in bow:\n",
    "        print(f\"{dictionary[word_id]} \\t {count}\")\n",
    "\n",
    "# Assuming you want to show the n-th document\n",
    "n = 6\n",
    "print(f\"Profile text:\\n{documents[n]}\\n\")\n",
    "\n",
    "print(\"\\nBOW (Bag-of-Words) vector:\")\n",
    "show_bow_vector(corpus[n], id2word)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.2 \n",
    "\n",
    "- (1) Given the essay questions decribed above, list 5 words that you predict to be the most popular in essay4. \n",
    "    \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_section_to_use = 'essay4'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaning up profile text for essay4 ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/58785 [00:00<?, ?it/s]/tmp/ipykernel_2028/512017477.py:9: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n",
      "  t = BeautifulSoup(text, 'lxml').get_text()\n",
      "  4%|▍         | 2452/58785 [00:00<00:09, 5774.90it/s]/tmp/ipykernel_2028/512017477.py:9: MarkupResemblesLocatorWarning: The input looks more like a URL than markup. You may want to use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  t = BeautifulSoup(text, 'lxml').get_text()\n",
      "100%|██████████| 58785/58785 [00:09<00:00, 5974.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We started with 58785 profiles.\n",
      "Dropping profiles that didn't write anything for the essay we chose...\n",
      "Our transformed text has 53396 rows and 53396 columns.\n",
      "The first few words (after lemmatization) are:\n",
      " ['aesop', 'cry', 'feel', 'game', 'hail', 'make', 'man', 'operation', 'rock', 'show', 'throne', 'want', 'alternative', 'die', 'draw', 'fan', 'hard', 'humor', 'involve', 'kind']\n",
      "--- 230.86880731582642 seconds ---\n"
     ]
    }
   ],
   "source": [
    "# In this cell, let's find the top 10 most popular words and list them alphabetically. \n",
    "# Look the sample codes above and try fill in the \"???\".\n",
    "# This would generally take around 140 seconds.\n",
    "start_time = time.time()\n",
    "\n",
    "profiles = pd.read_csv('data/clean_profiles.tsv', sep='\\t')\n",
    "\n",
    "print('Cleaning up profile text for', profile_section_to_use, '...')\n",
    "profiles['clean'] = profiles[profile_section_to_use].progress_apply(clean)\n",
    "\n",
    "print('We started with', profiles.shape[0], 'profiles.')\n",
    "print(\"Dropping profiles that didn't write anything for the essay we chose...\")\n",
    "profiles.dropna(axis=0, subset=['clean'], inplace=True)\n",
    "\n",
    "# Remove unused text columns\n",
    "text_cols = ['text', 'essay0', 'essay1', 'essay2', 'essay3', 'essay4', \n",
    "             'essay5', 'essay6', 'essay7', 'essay8', 'essay9']\n",
    "profiles.drop(columns=text_cols, inplace=True)\n",
    "\n",
    "# Documents we will work with (cleaned text)\n",
    "documents = profiles['clean'].values\n",
    "\n",
    "# Split each document into tokens (list of words) for Gensim\n",
    "data_words = [doc.split() for doc in documents]\n",
    "data_words_nostops = remove_stopwords(data_words)\n",
    "data_words_bigrams = make_bigrams(data_words_nostops)\n",
    "\n",
    "# Do lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "# Create a Gensim dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Filter out words that occur in less than 10 documents, or more than 50% of documents\n",
    "id2word.filter_extremes(no_below=10, no_above=0.5)\n",
    "\n",
    "texts = data_lemmatized\n",
    "\n",
    "#corpus = ???\n",
    "\n",
    "# Print the total number of rows and columns (dictionary size)\n",
    "print(f\"Our transformed text has {len(corpus)} rows and {len(corpus)} columns.\")\n",
    "\n",
    "print(\"The first few words (after lemmatization) are:\\n\", list(id2word.token2id.keys())[:20])\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "    \n",
    "- (2) Did any of your predicted words made to top 10? (No right or wrong answer!) \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleaning up profile text for essay0 ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/58785 [00:00<?, ?it/s]/tmp/ipykernel_2028/512017477.py:9: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n",
      "  t = BeautifulSoup(text, 'lxml').get_text()\n",
      "/tmp/ipykernel_2028/512017477.py:9: MarkupResemblesLocatorWarning: The input looks more like a URL than markup. You may want to use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
      "  t = BeautifulSoup(text, 'lxml').get_text()\n",
      "100%|██████████| 58785/58785 [00:08<00:00, 7238.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We started with 58785 profiles.\n",
      "Dropping profiles that didn't write anything for the essay we chose...\n",
      "Our transformed text has 53396 rows and 53396 columns.\n",
      "The first few words (after lemmatization) are:\n",
      " ['able', 'attention_span', 'bend', 'blow', 'choice', 'cliche', 'concept', 'consider', 'conversation', 'cool', 'decide', 'deep', 'difference', 'dress', 'dumb', 'everyday', 'extra_point', 'favorite', 'forge', 'funny']\n"
     ]
    }
   ],
   "source": [
    "# Now let's focus on essay0 again. \n",
    "# This would take another 140 seconds.\n",
    "\n",
    "profile_section_to_use = 'essay0'\n",
    "\n",
    "profiles = pd.read_csv('data/clean_profiles.tsv', sep='\\t')\n",
    "\n",
    "print('Cleaning up profile text for', profile_section_to_use, '...')\n",
    "profiles['clean'] = profiles[profile_section_to_use].progress_apply(clean)\n",
    "\n",
    "print('We started with', profiles.shape[0], 'profiles.')\n",
    "print(\"Dropping profiles that didn't write anything for the essay we chose...\")\n",
    "profiles.dropna(axis=0, subset=['clean'], inplace=True)\n",
    "\n",
    "# Remove unused text columns\n",
    "text_cols = ['text', 'essay0', 'essay1', 'essay2', 'essay3', 'essay4', \n",
    "             'essay5', 'essay6', 'essay7', 'essay8', 'essay9']\n",
    "profiles.drop(columns=text_cols, inplace=True)\n",
    "\n",
    "# Documents we will work with (cleaned text)\n",
    "documents = profiles['clean'].values\n",
    "\n",
    "# Split each document into tokens (list of words) for Gensim\n",
    "data_words = [doc.split() for doc in documents]\n",
    "data_words_nostops = remove_stopwords(data_words)\n",
    "data_words_bigrams = make_bigrams(data_words_nostops)\n",
    "\n",
    "# Do lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "# Create a Gensim dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Filter out words that occur in less than 10 documents, or more than 50% of documents\n",
    "id2word.filter_extremes(no_below=10, no_above=0.5)\n",
    "\n",
    "texts = data_lemmatized\n",
    "\n",
    "#corpus = ???\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "# Print the total number of rows and columns (dictionary size)\n",
    "print(f\"Our transformed text has {len(corpus)} rows and {len(corpus)} columns.\")\n",
    "\n",
    "print(\"The first few words (after lemmatization) are:\\n\", list(id2word.token2id.keys())[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 3: LDA Topic Model\n",
    "\n",
    "- LDA stands for Latent Dirichlet Allocation. The statistical math behind it is complicated, but its goals are simple:\n",
    "    - find groups of words that often show up together and call those groups topics. \n",
    "    - find topics that can be used to tell documents apart, i.e. topics that are in some documents but not others.\n",
    "- LDA is the most popular method for topic modeling.\n",
    "- [Learn more](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) about LDA\n",
    "\n",
    "### Step 1: Decide how many topics we want to find\n",
    "\n",
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.3\n",
    "- How would the number of topics you choose affect the result in your opinion? (Think about extreme cases like you only pick 1 topic, vs if you pick 10,000 topics.) (1-2 sentences)\n",
    "- Choose 1 essay and describe the types of topics you would expect to find.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#how many topics we want our model to find\n",
    "ntopics = 15\n",
    "nshow = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Run the LDA algorithm\n",
    "- LDA can be a little slow. We'll use a faster method later on.\n",
    "- We have everything required to train the base LDA model. In addition to the corpus and dictionary, you need to provide the number of topics as well. Apart from that, alpha and eta are hyperparameters that affect sparsity of the topics. According to the Gensim docs, both defaults to 1.0/num_topics prior (we'll use default for the base model).\n",
    "- chunksize controls how many documents are processed at a time in the training algorithm. Increasing chunksize will speed up training, at least as long as the chunk of documents easily fit into memory.\n",
    "- passes controls how often we train the model on the entire corpus (set to 10). Another word for passes might be \"epochs\". iterations is somewhat technical, but essentially it controls how often we repeat a particular loop over each document. It is important to set the number of \"passes\" and \"iterations\" high enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing LDA on vectors. This may take a while...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Build LDA model\n",
    "print('Performing LDA on vectors. This may take a while...')\n",
    "lda_model = gensim.models.LdaMulticore(corpus=corpus,\n",
    "                                       id2word=id2word,\n",
    "                                       num_topics=ntopics, \n",
    "                                       random_state=100,\n",
    "                                       chunksize=100,\n",
    "                                       passes=10,\n",
    "                                       per_word_topics=True)\n",
    "\n",
    "lda_topics = lda_model.get_topics()\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** **\n",
    "The above LDA model is built with 15 different topics where each topic is a combination of keywords and each keyword contributes a certain weightage to the topic.\n",
    "\n",
    "You can see the keywords for each topic and the weightage(importance) of each keyword using `lda_model.print_topics()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.060*\"time\" + 0.047*\"work\" + 0.023*\"spend\" + 0.019*\"friend\" + 0.017*\"life\" '\n",
      "  '+ 0.016*\"day\" + 0.016*\"get\" + 0.016*\"enjoy\" + 0.015*\"try\" + 0.013*\"go\"'),\n",
      " (1,\n",
      "  '0.029*\"life\" + 0.025*\"love\" + 0.015*\"people\" + 0.012*\"world\" + 0.011*\"live\" '\n",
      "  '+ 0.010*\"learn\" + 0.009*\"experience\" + 0.008*\"thing\" + 0.007*\"nature\" + '\n",
      "  '0.007*\"feel\"'),\n",
      " (2,\n",
      "  '0.017*\"get\" + 0.011*\"day\" + 0.010*\"make\" + 0.009*\"love\" + 0.008*\"eat\" + '\n",
      "  '0.008*\"dog\" + 0.007*\"good\" + 0.007*\"go\" + 0.007*\"car\" + 0.007*\"ride\"'),\n",
      " (3,\n",
      "  '0.016*\"read\" + 0.012*\"book\" + 0.011*\"half\" + 0.008*\"also\" + 0.007*\"nerdy\" + '\n",
      "  '0.007*\"nerd\" + 0.007*\"geeky\" + 0.007*\"geek\" + 0.006*\"language\" + '\n",
      "  '0.006*\"speak\"'),\n",
      " (4,\n",
      "  '0.036*\"art\" + 0.023*\"work\" + 0.018*\"music\" + 0.016*\"study\" + 0.015*\"artist\" '\n",
      "  '+ 0.015*\"write\" + 0.014*\"design\" + 0.013*\"school\" + 0.011*\"creative\" + '\n",
      "  '0.011*\"film\"'),\n",
      " (5,\n",
      "  '0.012*\"word\" + 0.008*\"well\" + 0.006*\"find\" + 0.005*\"self\" + 0.005*\"however\" '\n",
      "  '+ 0.005*\"often\" + 0.004*\"say\" + 0.004*\"also\" + 0.004*\"quite\" + '\n",
      "  '0.004*\"human\"'),\n",
      " (6,\n",
      "  '0.028*\"thing\" + 0.027*\"think\" + 0.023*\"people\" + 0.021*\"make\" + 0.019*\"get\" '\n",
      "  '+ 0.016*\"say\" + 0.016*\"know\" + 0.015*\"really\" + 0.013*\"try\" + 0.013*\"good\"'),\n",
      " (7,\n",
      "  '0.071*\"play\" + 0.052*\"love\" + 0.047*\"music\" + 0.035*\"watch\" + 0.031*\"movie\" '\n",
      "  '+ 0.031*\"sport\" + 0.030*\"go\" + 0.026*\"game\" + 0.021*\"also\" + 0.015*\"enjoy\"'),\n",
      " (8,\n",
      "  '0.037*\"look\" + 0.036*\"relationship\" + 0.029*\"woman\" + 0.025*\"man\" + '\n",
      "  '0.021*\"want\" + 0.020*\"date\" + 0.013*\"open\" + 0.011*\"partner\" + 0.010*\"sex\" '\n",
      "  '+ 0.010*\"interested\"'),\n",
      " (9,\n",
      "  '0.058*\"year\" + 0.039*\"move\" + 0.038*\"live\" + 0.020*\"love\" + 0.019*\"city\" + '\n",
      "  '0.018*\"work\" + 0.017*\"go\" + 0.016*\"grow\" + 0.016*\"travel\" + 0.016*\"back\"'),\n",
      " (10,\n",
      "  '0.038*\"pretty\" + 0.028*\"go\" + 0.027*\"guy\" + 0.021*\"really\" + 0.021*\"get\" + '\n",
      "  '0.019*\"good\" + 0.016*\"person\" + 0.016*\"easy\" + 0.016*\"humor\" + '\n",
      "  '0.015*\"people\"'),\n",
      " (11,\n",
      "  '0.051*\"love\" + 0.030*\"enjoy\" + 0.029*\"new\" + 0.024*\"go\" + 0.022*\"travel\" + '\n",
      "  '0.019*\"good\" + 0.017*\"thing\" + 0.016*\"friend\" + 0.013*\"try\" + 0.012*\"food\"'),\n",
      " (12,\n",
      "  '0.013*\"heart\" + 0.012*\"come\" + 0.009*\"day\" + 0.009*\"know\" + 0.009*\"never\" + '\n",
      "  '0.009*\"man\" + 0.008*\"old\" + 0.008*\"eye\" + 0.007*\"see\" + 0.007*\"first\"'),\n",
      " (13,\n",
      "  '0.036*\"love\" + 0.034*\"life\" + 0.023*\"good\" + 0.019*\"friend\" + 0.018*\"look\" '\n",
      "  '+ 0.016*\"fun\" + 0.015*\"person\" + 0.013*\"laugh\" + 0.013*\"family\" + '\n",
      "  '0.012*\"enjoy\"'),\n",
      " (14,\n",
      "  '0.041*\"people\" + 0.039*\"new\" + 0.038*\"meet\" + 0.028*\"m\" + 0.028*\"know\" + '\n",
      "  '0.025*\"get\" + 0.025*\"look\" + 0.024*\"go\" + 0.020*\"want\" + 0.020*\"friend\"')]\n"
     ]
    }
   ],
   "source": [
    "# Print the Keyword in the 15 topics\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Model Perplexity and Coherence Score\n",
    "\n",
    "Let's calculate the baseline coherence score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coherence Score:  0.41726468346497025\n"
     ]
    }
   ],
   "source": [
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('Coherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** **\n",
    "#### Step 3: Visualize Results\n",
    "** **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el20282814713837577447699360382\" style=\"background-color:white;\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el20282814713837577447699360382_data = {\"mdsDat\": {\"x\": [0.18016434478118076, 0.13050641605542607, 0.023776621534330317, -0.024659653003948026, 0.09796172717128292, -0.03008881164503272, 0.10375454486719021, 0.14566027852009178, 0.12505469630632557, -0.03359581781847375, 0.06417374607955242, -0.1966769030793905, -0.2081631229559165, -0.12520711093752865, -0.25266095587508974], \"y\": [0.14734912972223252, -0.09259311248818088, -0.151839870051647, -0.006500314054784676, 0.07584225645119189, 0.023588931538162432, 0.054359831584362216, -0.09810809494043016, -0.08950330205426961, -0.17338800409535554, 0.26978849839900454, -0.11595029115904355, 0.24252055299593506, -0.11744184476997002, 0.03187563292279314], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [11.554440297751572, 11.18087233626944, 10.505291995623205, 9.418998029724273, 9.030718360349928, 8.227576038925106, 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      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "11     0.180164  0.147349       1        1  11.554440\n",
       "13     0.130506 -0.092593       2        1  11.180872\n",
       "6      0.023777 -0.151840       3        1  10.505292\n",
       "1     -0.024660 -0.006500       4        1   9.418998\n",
       "9      0.097962  0.075842       5        1   9.030718\n",
       "2     -0.030089  0.023589       6        1   8.227576\n",
       "0      0.103755  0.054360       7        1   6.749603\n",
       "14     0.145660 -0.098108       8        1   6.558255\n",
       "10     0.125055 -0.089503       9        1   4.913974\n",
       "8     -0.033596 -0.173388      10        1   4.597696\n",
       "7      0.064174  0.269788      11        1   4.214376\n",
       "5     -0.196677 -0.115950      12        1   4.151539\n",
       "4     -0.208163  0.242521      13        1   3.432148\n",
       "12    -0.125207 -0.117442      14        1   3.199329\n",
       "3     -0.252661  0.031876      15        1   2.265184, topic_info=             Term          Freq         Total Category  logprob  loglift\n",
       "238          play  10309.000000  10309.000000  Default  30.0000  30.0000\n",
       "81           year  16865.000000  16865.000000  Default  29.0000  29.0000\n",
       "104           new  20448.000000  20448.000000  Default  28.0000  28.0000\n",
       "254         music  11441.000000  11441.000000  Default  27.0000  27.0000\n",
       "80           work  20089.000000  20089.000000  Default  26.0000  26.0000\n",
       "..            ...           ...           ...      ...      ...      ...\n",
       "43           make    280.854027  19877.301785  Topic15  -5.3319  -0.4720\n",
       "175          side    212.044591   2792.381492  Topic15  -5.6129   1.2097\n",
       "8    conversation    215.160172   3857.759267  Topic15  -5.5984   0.9011\n",
       "17       favorite    206.319254   3930.247561  Topic15  -5.6403   0.8405\n",
       "42           love    211.387855  50632.802151  Topic15  -5.6160  -1.6911\n",
       "\n",
       "[1192 rows x 6 columns], token_table=      Topic      Freq     Term\n",
       "term                          \n",
       "990       2  0.180654  ability\n",
       "990       3  0.052950  ability\n",
       "990       4  0.281882  ability\n",
       "990       6  0.063852  ability\n",
       "990       9  0.007787  ability\n",
       "...     ...       ...      ...\n",
       "1193     13  0.970598  youtube\n",
       "5941      1  0.972748    yummy\n",
       "5941      8  0.009826    yummy\n",
       "3550      6  0.140710   zombie\n",
       "3550     15  0.851668   zombie\n",
       "\n",
       "[4306 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[12, 14, 7, 2, 10, 3, 1, 15, 11, 9, 8, 6, 5, 13, 4])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "\n",
    "LDAvis_prepared = gensimvis.prepare(lda_model, corpus, id2word)\n",
    "\n",
    "LDAvis_prepared\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 4: Interpret Topics\n",
    "#### Some helper functions \n",
    "Don't worry about how these work right now. Just run them and scroll down. We'll use them to make our analysis easier later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_topic(topic, n_words=10):\n",
    "    words = [word for word, _ in topic[:n_words]]  # Get top `n_words` words from the topic\n",
    "    return \" \".join(words)\n",
    "\n",
    "def display_topics(lda_model, n_words=10):\n",
    "    topics = lda_model.show_topics(num_topics=-1, num_words=n_words, formatted=False)\n",
    "    for topic_idx, topic in topics:\n",
    "        print(\"Topic {}: {}\".format(topic_idx, describe_topic(topic, n_words)))\n",
    "\n",
    "def get_topic_words(lda_model, topic_id, num_words=10):\n",
    "    # Get the top words for a specific topic\n",
    "    topic = lda_model.show_topic(topic_id, topn=num_words)\n",
    "    words = [word for word, _ in topic]  # Extract the words only\n",
    "    return set(words)\n",
    "\n",
    "def find_intersection(idxa, idxb, n):\n",
    "    a = set()\n",
    "    b = set()\n",
    "    both = set()\n",
    "    i = 0\n",
    "    while len(both) < n:\n",
    "        a.add(idxa[i])\n",
    "        b.add(idxb[i])\n",
    "        both = a.intersection(b)\n",
    "        i += 1\n",
    "    return list(both)\n",
    "\n",
    "def compare_topic_overlap(lda_model, topic_id_a, topic_id_b, num_words=nshow):\n",
    "    topic_a_words = get_topic_words(lda_model, topic_id_a, num_words)\n",
    "    topic_b_words = get_topic_words(lda_model, topic_id_b, num_words)\n",
    "    \n",
    "    # Find the intersection (overlap) between the two sets of words\n",
    "    overlap = topic_a_words.intersection(topic_b_words)\n",
    "    \n",
    "    print(f\"Common words between Topic {topic_id_a} and Topic {topic_id_b}:\")\n",
    "    print(overlap)\n",
    "\n",
    "def compare_topic_difference(lda_model, topic_id_a, topic_id_b, num_words=nshow):\n",
    "    topic_a_words = get_topic_words(lda_model, topic_id_a, num_words)\n",
    "    topic_b_words = get_topic_words(lda_model, topic_id_b, num_words)\n",
    "    \n",
    "    # Find the difference (words in Topic A but not in Topic B)\n",
    "    unique_to_a = topic_a_words.difference(topic_b_words)\n",
    "    unique_to_b = topic_b_words.difference(topic_a_words)\n",
    "    \n",
    "    print(f\"Words unique to Topic {topic_id_a}: {unique_to_a}\")\n",
    "    print(f\"Words unique to Topic {topic_id_b}: {unique_to_b}\")\n",
    "\n",
    "def blue_matrix(cells, xl, yl, t, x_labels=None):\n",
    "    n = cells.shape[0]\n",
    "    plt.figure()\n",
    "    fig, ax = plt.subplots(figsize=(8,8))\n",
    "    plt.imshow(cells, cmap='Blues')\n",
    "    ax.xaxis.tick_top()\n",
    "    ax.xaxis.set_label_position('top') \n",
    "    if x_labels is not None:\n",
    "        plt.xticks(range(n), x_labels)\n",
    "    else:\n",
    "        plt.xticks(range(n))\n",
    "    plt.yticks(range(n))\n",
    "    plt.ylabel(yl)\n",
    "    plt.xlabel(xl)\n",
    "    plt.title(t)\n",
    "    # show a colorbar legend\n",
    "    plt.colorbar()\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Show our topics with the top words in each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0: time work spend friend life day get enjoy try go\n",
      "Topic 1: life love people world live learn experience thing nature feel\n",
      "Topic 2: get day make love eat dog good go car ride\n",
      "Topic 3: read book half also nerdy nerd geeky geek language speak\n",
      "Topic 4: art work music study artist write design school creative film\n",
      "Topic 5: word well find self however often say also quite human\n",
      "Topic 6: thing think people make get say know really try good\n",
      "Topic 7: play love music watch movie sport go game also enjoy\n",
      "Topic 8: look relationship woman man want date open partner sex interested\n",
      "Topic 9: year move live love city work go grow travel back\n",
      "Topic 10: pretty go guy really get good person easy humor people\n",
      "Topic 11: love enjoy new go travel good thing friend try food\n",
      "Topic 12: heart come day know never man old eye see first\n",
      "Topic 13: love life good friend look fun person laugh family enjoy\n",
      "Topic 14: people new meet m know get look go want friend\n"
     ]
    }
   ],
   "source": [
    "display_topics(lda_model, n_words=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.4\n",
    "- Pick three topics.\n",
    "- Look at the words that make up each one. Say which one it is, and briefly answer these questions about it (3-5 sentences per topic):\n",
    "    - What does the topic seem to be about?\n",
    "    - Do any of the other topics seem similar to this one?\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Examine the words that make two topics similar or different\n",
    "- We can also compare two topics to each other by looking at words that are common in both, or words that are common in one but not the other.\n",
    "- Try changing `topic_a` and `topic_b` to different topic numbers.\n",
    "- Notice the `how` option will let you see either the `overlap` or `difference` between two topics\n",
    "    - Notice also that the difference between topics a and b is not the same as between b and a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Common words between Topic 0 and Topic 2:\n",
      "{'get', 'go', 'day'}\n",
      "Words unique to Topic 0: {'friend', 'work', 'time', 'spend', 'try', 'enjoy', 'life'}\n",
      "Words unique to Topic 2: {'dog', 'make', 'car', 'good', 'ride', 'eat', 'love'}\n"
     ]
    }
   ],
   "source": [
    "topic_a = 0\n",
    "topic_b = 2\n",
    "\n",
    "compare_topic_overlap(lda_model, topic_a, topic_b)\n",
    "compare_topic_difference(lda_model, topic_a, topic_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Interpret these topics\n",
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.5\n",
    "- This part is for you to do: code can't do it for you.\n",
    "- Try to come up with a short, catchy name for each topic and write it down.\n",
    "    - For example, if the words were \"san francisco city moved living born years raised lived live\", you might call it \"places lived\" because the topic seems to be about where people currently live (San Francisco) and where they were born / raised / moved from.  \n",
    "    \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...\n",
    "\n",
    "- Topic 0:\n",
    "- Topic 1: \n",
    "- Topic 2:\n",
    "- Topic 3: \n",
    "- Topic 4:\n",
    "- Topic 5: \n",
    "- Topic 6:\n",
    "- Topic 7: \n",
    "- Topic 8: \n",
    "- Topic 9:\n",
    "- Topic 10: \n",
    "- Topic 11:\n",
    "- Topic 12: \n",
    "- Topic 13:\n",
    "- Topic 14: \n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Check whether your interpretations match with the text\n",
    "\n",
    "#### Helper functions\n",
    "- Run this code and scroll down. \n",
    "- You don't need to understand these details right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_profiles_from_topics(data, transformed, topic_a, topic_b=None, pick_from=10):\n",
    "    #get our data ready\n",
    "    df = pd.DataFrame(transformed)\n",
    "    df = df.sort_values(by=topic_a, ascending=False)\n",
    "    n = df.shape[0]\n",
    "    \n",
    "    if topic_b is None:\n",
    "        #if we only want things high in one topic, take randomly from the top\n",
    "        keep = df.head(pick_from).sample(1)\n",
    "        pid = keep.index.values[0]\n",
    "    else:\n",
    "        #if we want things high in two topics, find them and pick one of the top\n",
    "        idxb = df.sort_values(by=topic_b, ascending=False).index.values\n",
    "        both = find_intersection(df.index.values, idxb, pick_from)\n",
    "        keep = df.loc[both, :].sample(1)\n",
    "        pid = keep.index.values[0]    \n",
    "        \n",
    "    #output text to show our results\n",
    "    match_text = 'Profile number ' + str(pid)\n",
    "    match_text += ' has more of topic ' + str(topic_a)\n",
    "    match_text += ' than {:.2f}%'.format(100 - (np.where(df.index==pid)[0][0] / n)*100)\n",
    "    match_text += ' of other profiles'\n",
    "    if topic_b is not None:\n",
    "        match_text += ' and more of topic ' + str(topic_b)\n",
    "        match_text += ' than {:.2f}%'.format(100 - (np.where(idxb==pid)[0][0] / n)*100)\n",
    "        match_text += ' of other profiles.'\n",
    "        \n",
    "    #print results\n",
    "    text = data[pid]\n",
    "    print(match_text)\n",
    "    print('Here is the text:\\n\\n', text)\n",
    "    return\n",
    "\n",
    "def visualize_profile(profile_topics, profile_id, num_topics):\n",
    "    # Extract the topic distribution for the given profile\n",
    "    topic_distribution = np.zeros(num_topics)\n",
    "    \n",
    "    for topic_id, prob in profile_topics[profile_id]:\n",
    "        topic_distribution[topic_id] = prob\n",
    "    \n",
    "    # Plot a stem diagram for the topic distribution\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.xticks(range(num_topics))\n",
    "    plt.xlabel('Topic number')\n",
    "    plt.ylabel('How much of profile is about each topic')\n",
    "    plt.title('Profile #'+str(profile_id))\n",
    "    plt.stem(range(num_topics), topic_distribution)\n",
    "    \n",
    "    plt.show()\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate what portion of each profile is about each topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get topic distributions for each document\n",
    "\n",
    "profile_topics = [lda_model.get_document_topics(bow, minimum_probability=0) for bow in corpus]\n",
    "\n",
    "\n",
    "num_topics = lda_model.num_topics\n",
    "profile_topics_matrix = np.array([[prob for topic_id, prob in lda_model.get_document_topics(bow, minimum_probability=0)] for bow in corpus])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at the text of a profile that has a lot of a particular topic\n",
    "- This function randomly picks one of the top few profiles for a topic, so each time you run it you will see a different example.\n",
    "    - If you want it to pick from more or less topics, change the value of `pick_from`\n",
    "    - If you want to see a different topic, change the value of `topic_a`\n",
    "    - **Hint:** you can press `ctrl`+`enter` over and over to keep re-running the code in this cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile number 26661 has more of topic 7 than 98.89% of other profiles\n",
      "Here is the text:\n",
      "\n",
      " i'm an animation major who enjoys having fun but a book, movie and\n",
      "a nap is always good too. i love travel and have been out of the\n",
      "country a couple of times. before moving to california i had lived\n",
      "in hawaii the majority of my life.\n"
     ]
    }
   ],
   "source": [
    "get_profiles_from_topics(documents, profile_topics, topic_a=7, pick_from=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.6\n",
    "    \n",
    "- For each of the 3 topics you chose in **Step 1**, use the function get_profiles_from_topics defined above to find a document that strongly aligns with the topic. \n",
    "- Then discuss if the document truly aligns with the topic? (3-5 sentences) \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at the text of a profile that matches two topics well at the same time\n",
    "- Note that some topics might not happen together very often. If this is the case, the examples we find of both together might not be very good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile number 49964 has more of topic 1 than 97.77% of other profiles and more of topic 2 than 96.41% of other profiles.\n",
      "Here is the text:\n",
      "\n",
      " i am an adventurous bohemian soul seeking same for eclectic dining,\n",
      "witty banter, and outdoor excursions. my roots are in the wilds of\n",
      "montana but my curious ways have allowed me to live in multiple\n",
      "countries. this means i learned how to play poker and drink whiskey\n",
      "at an early age.. and now can order said whiskey (and wine) in\n",
      "multiple languages.\n",
      "\n",
      "although i've lived across the world i'm now a true sf gal full of\n",
      "contradictions: i belong to a fancy winery in sonoma but relish a\n",
      "1am hunt for burritos in the mission. i have high heels from j.\n",
      "choo but wear running shoes to work. i kick box but bring my\n",
      "punching partners homemade cookies. i'm strong and independent but\n",
      "also an affectionate romantic that relishes old fashioned chivalry.\n"
     ]
    }
   ],
   "source": [
    "get_profiles_from_topics(documents, profile_topics, topic_a=1, topic_b=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### See how much of a profile is about each topic\n",
    "- Try looking at some of the profiles you just found:\n",
    "    - Make the `pid` equal to the profile number from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"i am an adventurous bohemian soul seeking same for eclectic dining,\\nwitty banter, and outdoor excursions. my roots are in the wilds of\\nmontana but my curious ways have allowed me to live in multiple\\ncountries. this means i learned how to play poker and drink whiskey\\nat an early age.. and now can order said whiskey (and wine) in\\nmultiple languages.\\n\\nalthough i've lived across the world i'm now a true sf gal full of\\ncontradictions: i belong to a fancy winery in sonoma but relish a\\n1am hunt for burritos in the mission. i have high heels from j.\\nchoo but wear running shoes to work. i kick box but bring my\\npunching partners homemade cookies. i'm strong and independent but\\nalso an affectionate romantic that relishes old fashioned chivalry.\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pid = 49964\n",
    "visualize_profile(profile_topics, pid, num_topics) # visualizing the topic distribution for the profile with ID pid.\n",
    "\n",
    "\n",
    "### NEW\n",
    "documents[pid]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.7\n",
    "\n",
    "- Does the topic classification make sense to you? If so, which of the words in the example printed above are the most representitave of each of the topics? (3-5 sentences)\n",
    "    \n",
    "- Do your topic names match what you are seeing in the text? \n",
    "- Did any of your interpretations change after reading some profiles? (You would do so by changing pid and create several different charts. Then, based on the result, conclude if you want to make any changes to your topic names) \n",
    "    - If you need to update your topic names, do so here.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 5: Topic Quality\n",
    "Let's see how good the topics we found are.\n",
    "\n",
    "### Step 1: See if the topics are each about different things.\n",
    "We want each topic to be about something different than the other topics. We can check this by comparing the words in each topic to the words in all the others. How to interpret:\n",
    "- Each square shows how similar two topics are. Darker means more similar, and lighter means more different.\n",
    "- The square in the very top left shows how similar topic 0 is to topic 0 (i.e. how similar it is to itself). \n",
    "- The square next to it in the top row shows how similar topic 0 is to topic 1, and so on. \n",
    "- For any two topics, you can see how similar they are by finding their numbers on the edges and seeing where they intersect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_topics(components):\n",
    "    sim = cosine_similarity(components)\n",
    "    blue_matrix(sim, xl='Topic number', yl='Topic number',\n",
    "                t = 'Word Similarity between Topics')  # heatmap\n",
    "    return\n",
    "\n",
    "plot_topics(lda_topics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: See if different topics show up in different profiles\n",
    "The point is to tell profiles apart based on what topics they're about, so we need to check whether the topics appear in different profiles.\n",
    "- This shows us something that looks similar to the topic similarity we saw before, but this time:\n",
    "    - We **don't** compare topics based on which words they use\n",
    "    - We **do** compare topics based on how often they appear in the same profile as one another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Flatten the topic distributions for each profile into a matrix\n",
    "profile_topics_flattened = np.array([np.array([t[1] for t in profile]) for profile in profile_topics])\n",
    "\n",
    "# Convert to DataFrame and compute correlation\n",
    "def topic_cooccurance(topics):\n",
    "    co = pd.DataFrame(topics).corr()\n",
    "    blue_matrix(co, xl='Topic number', yl='Topic number', t='Topic Co-occurrence in Profiles')\n",
    "\n",
    "# Call the function with the flattened topic matrix\n",
    "topic_cooccurance(profile_topics_flattened)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note that the topics are mostly uncorrelated. \n",
    "- The cells in the figure above are mostly very light blue\n",
    "- This doesn't mean that, for instance, topic 1 and 2 never show up in the same profile.\n",
    "- It does mean, however, that seeing any particular topic doesn't mean we're especially likely to also see any other topic.\n",
    "\n",
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.8\n",
    "- Why is the diagonal line so dark? (1-2 sentences)\n",
    "\n",
    "- Would you say that this topic classification result is good? Why? (explain in 1-3 sentences)\n",
    "\n",
    "- Do you think that the two figures above are sufficient to conclude that the topic quality is perfect? Why? (3-5 sentences)\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 6: Topic Popularity\n",
    "\n",
    "#### Helper functions to visualize and compare topics\n",
    "- Run this code and scroll down. The details of how it works aren't our focus right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def common_topics_bars(profile_topics):\n",
    "    df = pd.DataFrame([{t[0]: t[1] for t in topics} for topics in profile_topics])\n",
    "    \n",
    "    popularity = df.mean().rename_axis('Topic')\n",
    "    \n",
    "    popularity = popularity.sort_values(ascending=False)\n",
    "    \n",
    "    popularity.plot.bar(title='Topic Popularity')\n",
    "    plt.xlabel('Topic')\n",
    "    plt.ylabel('Average Probability')\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "def rank_groups(data, trait, topic):\n",
    "    groups = data[trait].value_counts().index.values\n",
    "    result = {}\n",
    "    \n",
    "    # Rank groups by their mean for the specific topic\n",
    "    for g in groups:\n",
    "        result[g] = data[data[trait] == g][topic].mean()\n",
    "    \n",
    "    r = pd.DataFrame.from_dict(result, orient='index')\n",
    "    r.columns = [topic]\n",
    "    r = r.sort_values(by=topic, ascending=False)\n",
    "    \n",
    "    return r.round(3)\n",
    "\n",
    "def top_topics(data, trait, value, n_top_topics=3, distinctive=False):\n",
    "    topics = [col for col in data if col.startswith('topic_')]\n",
    "    vals = {}\n",
    "    means = {}\n",
    "    if distinctive:\n",
    "        for t in topics:\n",
    "            means[t] = data[t].mean()\n",
    "    else:\n",
    "        for t in topics:\n",
    "            means[t] = 1\n",
    "    \n",
    "    data = data[data[trait] == value]\n",
    "    \n",
    "    for t in topics:\n",
    "        vals[t] = data[t].mean() / means[t]\n",
    "    vals = pd.DataFrame.from_dict(vals, orient='index')    \n",
    "    vals = vals.sort_values(by=0, ascending=False).head(n_top_topics)\n",
    "\n",
    "    return list(vals.index.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall most common topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_topics_bars(profile_topics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who is a topic most popular with?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Merge our information about topics with our information about people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>age_group</th>\n",
       "      <th>age</th>\n",
       "      <th>body</th>\n",
       "      <th>alcohol_use</th>\n",
       "      <th>drug_use</th>\n",
       "      <th>edu</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>height_group</th>\n",
       "      <th>industry</th>\n",
       "      <th>kids</th>\n",
       "      <th>...</th>\n",
       "      <th>topic_5</th>\n",
       "      <th>topic_6</th>\n",
       "      <th>topic_7</th>\n",
       "      <th>topic_8</th>\n",
       "      <th>topic_9</th>\n",
       "      <th>topic_10</th>\n",
       "      <th>topic_11</th>\n",
       "      <th>topic_12</th>\n",
       "      <th>topic_13</th>\n",
       "      <th>topic_14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>overweight</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>HS</td>\n",
       "      <td>multiple</td>\n",
       "      <td>over_6</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.481732</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.073849</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.056642</td>\n",
       "      <td>0.000641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>average</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>White</td>\n",
       "      <td>under_6</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.208464</td>\n",
       "      <td>0.033179</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.082837</td>\n",
       "      <td>0.390253</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.270755</td>\n",
       "      <td>0.001451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>thin</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Grad_Pro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>under_6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141563</td>\n",
       "      <td>0.256084</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.029052</td>\n",
       "      <td>0.091089</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.052276</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>0.226373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>thin</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>HS</td>\n",
       "      <td>White</td>\n",
       "      <td>under_6</td>\n",
       "      <td>education</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.013335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>fit</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>BA</td>\n",
       "      <td>multiple</td>\n",
       "      <td>under_6</td>\n",
       "      <td>creative</td>\n",
       "      <td>no</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.089820</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.002902</td>\n",
       "      <td>0.746309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age_group  age        body alcohol_use drug_use       edu race_ethnicity  \\\n",
       "0         20   22  overweight         yes       no        HS       multiple   \n",
       "1         30   35     average         yes      yes   unknown          White   \n",
       "2         30   38        thin         yes      yes  Grad_Pro            NaN   \n",
       "3         20   23        thin         yes      yes        HS          White   \n",
       "4         20   29         fit         yes       no        BA       multiple   \n",
       "\n",
       "  height_group   industry kids  ...   topic_5   topic_6   topic_7   topic_8  \\\n",
       "0       over_6      other   no  ...  0.000641  0.481732  0.000641  0.000641   \n",
       "1      under_6      other   no  ...  0.001451  0.208464  0.033179  0.001451   \n",
       "2      under_6        NaN   no  ...  0.141563  0.256084  0.000670  0.000670   \n",
       "3      under_6  education   no  ...  0.013335  0.013335  0.013335  0.013335   \n",
       "4      under_6   creative   no  ...  0.002902  0.002902  0.002902  0.002902   \n",
       "\n",
       "    topic_9  topic_10  topic_11  topic_12  topic_13  topic_14  \n",
       "0  0.000641  0.073849  0.000641  0.000641  0.056642  0.000641  \n",
       "1  0.001451  0.082837  0.390253  0.001451  0.270755  0.001451  \n",
       "2  0.029052  0.091089  0.000670  0.052276  0.000670  0.226373  \n",
       "3  0.013335  0.013335  0.013335  0.013335  0.013335  0.013335  \n",
       "4  0.002902  0.089820  0.002902  0.002902  0.002902  0.746309  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_topics_prob = [[prob for _, prob in doc] for doc in profile_topics]\n",
    "topic_info = pd.DataFrame(profile_topics_prob).add_prefix('topic_')\n",
    "together = profiles.merge(topic_info, left_index=True, right_index=True)\n",
    "together.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: See the groups that have the most text about a given topic\n",
    "- The numbers here show how much of a profile, on average, is about a specific topic. For example, if you don't have a pet, you probably wouldn't be writing about your pet (topic X). \n",
    "\n",
    "#### Play around with the code in the next few cells:\n",
    "- Pick 3 of the traits we have data for. Here are the options (information we know about users from their profiles):\n",
    "    - `age_group` categories: ['10', '20', '30', '40', '50']\n",
    "    - `body` categories: ['average', 'fit', 'thin', 'overweight', 'unknown']\n",
    "    - `alcohol_use` categories: ['yes', 'no']\n",
    "    - `drug_use` categories: ['yes', 'no']\n",
    "    - `edu` (highest degree completed) categories: ['`<HS`', 'HS', 'BA', 'Grad_Pro', 'unknown'] \n",
    "    - `race_ethnicity` categories: ['Asian', 'Black', 'Latinx', 'White', 'multiple', 'other']\n",
    "    - `height_group` (whether someone is over or under six feet tall) categories: ['under_6', 'over_6']\n",
    "    - `industry` (what field they work in) categories: ['STEM', 'business', 'education', 'creative', 'med_law', 'other'] \n",
    "    - `kids` (whether they have children) categories: ['yes', 'no']\n",
    "    - `orientation` categories: ['straight', 'gay', 'bisexual']\n",
    "    - `pets_likes` (what pets they like) categories: ['both', 'dogs', 'cats', 'neither']\n",
    "    - `pets_has` (what pets they have) categories: ['both', 'dogs', 'cats', 'neither']\n",
    "    - `pets_any` (whether they have pets or not) categories: ['yes', 'no']\n",
    "    - `religion` categories: ['christianity', 'catholicism', 'judaism', 'buddhism', 'none', 'other'] \n",
    "    - `sex` categories: ['m', 'f']\n",
    "    - `smoker` categories: ['yes', 'no']\n",
    "    - `languages` categories: ['multiple', 'English_only'] \n",
    "- Change the topics and values in the code in the next few cells to explore how the trait you chose relates to the topics.\n",
    "    \n",
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.9\n",
    "\n",
    "- Before you run the code, predict what will the result be (1-2 sentences)\n",
    "\n",
    "- Write down which traits you chose to look at, and what you learned about different groups of people from the topics they used. What topics do they have in common? What topics make them different? Does this make sense given the groups? Why or why not? Remember: we interpreted the topics above, so explain your findings in terms of content, not just topic numbers. (1-2 paragraphs)\n",
    "\n",
    "- Does the actual result match your prediction? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>topic_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HS</th>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grad_Pro</th>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;HS</th>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          topic_5\n",
       "HS          0.040\n",
       "unknown     0.038\n",
       "Grad_Pro    0.038\n",
       "<HS         0.037\n",
       "BA          0.037"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rank_groups(together, trait='edu', topic='topic_5') ## we use this as an example, and ask students to find 3 more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: See the topics that are most common for a given group\n",
    "- This example shows most common topics for different education groups.\n",
    "- You can change the arguments to compare different groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['topic_11', 'topic_13', 'topic_9']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show most popular topics for High School graduates\n",
    "top_topics(data=together, trait='edu', value='HS', n_top_topics=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['topic_11', 'topic_13', 'topic_9']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show most popular topics for High School graduates\n",
    "top_topics(data=together, trait='edu', value='BA', n_top_topics=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: See the topics that distinguish a group from other groups\n",
    "- This example shows most distinctive topics for different education levels.\n",
    "- You can change the arguments to compare different groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['topic_4', 'topic_5', 'topic_8']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_topics(data=together, trait='edu', value='HS', n_top_topics=3, distinctive=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['topic_8', 'topic_4', 'topic_12']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_topics(data=together, trait='edu', value='Grad_Pro', n_top_topics=3, distinctive=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section 7: NMF Topic Model\n",
    "- NMF is an alternative to LDA\n",
    "- NMF stands for Non-Negative Matrix Factorization. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Expand for more on how NMF works"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "- `Factoring` is something you may have done in math class before, for example:\n",
    "    - $ 10 $ can be factored as $ 2 \\times 5 $\n",
    "    - $ x^2+3x+2 $ can be factored as $ (x+2)(x+1)$\n",
    "- When we convert the text into numbers for the computer, it gets stored as something called a `matrix`.\n",
    "    - The matrix is `non-negative` because we can't have negative numbers of words: all the word counts are zero or more.\n",
    "- It is not important right now how exactly we find factors for these matrices, but if you're curious, you can learn more about it in a Linear Algebra class.\n",
    "- It turns out that finding factors for text is a really good way of finding topics. This makes sense intuitively: factors are simple things we can combine to get the more complicated output, and topics are simple things people combine to write profiles.\n",
    "- [Learn more](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization#Text_mining) about NMF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Convert text to numbers the computer understands\n",
    "- NMF takes \"tf-idf vectors\" as input. Tf-idf stands for \"text frequency - inverse document frequency.\" \n",
    "    - Text frequency is the same as the count vectors we used for LDA above: how often does each word appear in the text?\n",
    "    - Inverse document frequency means we divide (\"inverse\") by the number of documents the word is in. (If everyone uses the word, it isn't very helpful for figuring out what makes people different. So this measurement looks for words that are used a lot in some documents, and not at all in others.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizing text by TF-IDF...\n",
      "Our transformed text has 53396 rows and 1000 columns.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=1000, stop_words='english')\n",
    "\n",
    "print(\"Vectorizing text by TF-IDF...\")\n",
    "tfidf_text = tfidf_vectorizer.fit_transform(documents)\n",
    "\n",
    "tmp = tfidf_text.get_shape()\n",
    "print(\"Our transformed text has\", tmp[0], \"rows and\", tmp[1], \"columns.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The features are mostly the same as count vectors, because they are just the common words in the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first few words (alphabetically) are:\n",
      " ['10' '11' '12' '15' '20' '30' 'ability' 'able' 'abroad' 'absolutely'\n",
      " 'act' 'active' 'activities' 'activity' 'actually' 'add' 'admit'\n",
      " 'adventure' 'adventures' 'adventurous']\n"
     ]
    }
   ],
   "source": [
    "tfidf_words = tfidf_vectorizer.get_feature_names_out()\n",
    "print(\"The first few words (alphabetically) are:\\n\", tfidf_words[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The values are different: the counts have been divided by the documents they show up in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile text:\n",
      " hey how's it going? currently vague on the profile i know, more to\n",
      "come soon. looking to meet new folks outside of my circle of\n",
      "friends. i'm pretty responsive on the reply tip, feel free to drop\n",
      "a line. cheers.\n",
      "\n",
      "TF-IDF vector:\n",
      "free \t 0.25\n",
      "feel \t 0.22\n",
      "outside \t 0.26\n",
      "folks \t 0.36\n",
      "meet \t 0.2\n",
      "soon \t 0.31\n",
      "come \t 0.25\n",
      "profile \t 0.26\n",
      "currently \t 0.26\n",
      "going \t 0.17\n",
      "hey \t 0.3\n",
      "pretty \t 0.19\n",
      "friends \t 0.15\n",
      "new \t 0.14\n",
      "looking \t 0.16\n",
      "line \t 0.33\n",
      "know \t 0.17\n"
     ]
    }
   ],
   "source": [
    "n = 4\n",
    "\n",
    "print('Profile text:\\n', documents[n])\n",
    "print('\\nTF-IDF vector:')\n",
    "\n",
    "def show_vector(x, words):\n",
    "    rows,cols = x.nonzero()\n",
    "    for row,col in zip(rows,cols):\n",
    "        print(words[col], '\\t', x[row,col].round(2))\n",
    "        \n",
    "show_vector(tfidf_text[n], words=tfidf_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Build a topic model using NMF\n",
    "\n",
    "- NMF is faster than LDA and often works a little better for small documents like we have here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing NMF on vectors...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from sklearn.decomposition import NMF\n",
    "\n",
    "sklearn_major_ver = int(sklearn.__version__.split('.')[0])\n",
    "\n",
    "if (sklearn_major_ver < 1):\n",
    "    model = NMF(n_components=ntopics, alpha=.1, l1_ratio=.5, init='nndsvd', random_state= 7)\n",
    "else:\n",
    "    # after sklearn v1.0, parameter alpha was removed, alpha_W and alpha_H with different approach.  \n",
    "    model = NMF(n_components=ntopics, alpha_W=.0001, alpha_H='same', l1_ratio=.5, init='nndsvd', random_state= 7)\n",
    "\n",
    "print('Performing NMF on vectors...')\n",
    "nmf = model.fit(tfidf_text)\n",
    "nmf_topics = nmf.components_\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Show our topics with the top words in each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0: time people don things really friends know enjoy think person\n",
      "Topic 1: bay area moved years sf east ago born school grew\n",
      "Topic 2: just ask know moved want message meet really trying say\n",
      "Topic 3: new people meet meeting things trying places friends try city\n",
      "Topic 4: like things don movies stuff play people watch feel fun\n",
      "Topic 5: im dont lol chill guy thats person laid nice kinda\n",
      "Topic 6: love laugh travel outdoors family food cook dance sports hiking\n",
      "Topic 7: life live enjoy fullest share enjoying living simple short loving\n",
      "Topic 8: san francisco city moved living years born raised lived diego\n",
      "Topic 9: looking fun guy loving meet girl nice friends relationship share\n",
      "Topic 10: good food conversation time humor company sense enjoy wine friends\n",
      "Topic 11: going easy guy enjoy person laid pretty fun movies friends\n",
      "Topic 12: work hard play working time live job sf company school\n",
      "Topic 13: ve lived years got told past seen traveled spent far\n",
      "Topic 14: music art live movies listening listen food play playing dancing\n"
     ]
    }
   ],
   "source": [
    "def describe_NMF_topic(topic, feature_names, n_words=10):\n",
    "    words = []\n",
    "    # sort the words in the topic by importance\n",
    "    topic = topic.argsort() \n",
    "    # select the n_words most important words\n",
    "    topic = topic[:-n_words - 1:-1]\n",
    "    # for each important word, get it's name (i.e. the word) from our list of names\n",
    "    for i in topic:\n",
    "        words.append(feature_names[i])\n",
    "    # print the topic number and its most important words, separated by spaces\n",
    "    return \" \".join(words)\n",
    "\n",
    "\n",
    "def display_NMF_topics(components, feature_names, n_words=10):\n",
    "    # loop through each topic (component) in the model; show its top words\n",
    "    for topic_idx, topic in enumerate(components):\n",
    "        print(\"Topic {}:\".format(topic_idx), \n",
    "              describe_NMF_topic(topic, feature_names, n_words))\n",
    "    return\n",
    "    \n",
    "display_NMF_topics(nmf_topics, tfidf_words, nshow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Compare topics to each other\n",
    "We can compare topics visually by plotting the similarity of each topic's chosen words to each other topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_topics(nmf_topics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Examine the words that make two topics similar or different\n",
    "We can also compare two topics to each other by looking at words that are common in both, or words that are common in one but not the other. Try changing topic_a and topic_b to different topic numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overlap between 1 and 8: years year lived ago living raised moved college grew born\n",
      "difference between 1 and 8: outside past 10 running outdoors place pretty places recent playing\n",
      "difference between 8 and 1: picture play plus played poetry playful point plan pictures planning\n"
     ]
    }
   ],
   "source": [
    "def find_intersection(idxa, idxb, n):\n",
    "    a = set()\n",
    "    b = set()\n",
    "    both = set()\n",
    "    i = 0\n",
    "    while len(both) < n:\n",
    "        a.add(idxa[i])\n",
    "        b.add(idxb[i])\n",
    "        both = a.intersection(b)\n",
    "        i += 1\n",
    "    return list(both)\n",
    "\n",
    "def compare_topic_words(topics, a, b, words, how='overlap', n_words=nshow):\n",
    "    b_sort = False\n",
    "    if how == 'difference':\n",
    "        b_sort = True\n",
    "    \n",
    "    dfa = pd.DataFrame(topics, columns=words).T\n",
    "    idxa = dfa.sort_values(by=a, ascending=False).index.values\n",
    "    idxb = dfa.sort_values(by=b, ascending=b_sort).index.values\n",
    "    both = find_intersection(idxa, idxb, n=n_words)\n",
    "    \n",
    "    out = how + ' between ' + str(a) + ' and ' + str(b) + ':'\n",
    "    for w in both:\n",
    "        out += ' ' + w\n",
    "    print(out)\n",
    "    return\n",
    "\n",
    "topic_a = 1\n",
    "topic_b = 8\n",
    "\n",
    "compare_topic_words(nmf_topics, topic_a, topic_b, tfidf_words, n_words=nshow, how='overlap')\n",
    "compare_topic_words(nmf_topics, topic_a, topic_b, tfidf_words, n_words=nshow, how='difference')\n",
    "compare_topic_words(nmf_topics, topic_b, topic_a, tfidf_words, n_words=nshow, how='difference')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Interpret these topics\n",
    "\n",
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.10\n",
    "    \n",
    "- This part is for you to do: code can't do it for you.\n",
    "- Look at the list of important words for each topic, and think about these questions.\n",
    "    - What do the words have in common?\n",
    "    - What could someone write that would use most of those words?\n",
    "    - What does this topic seem to be about?\n",
    "- Try to come up with a short, catchy name for each topic.\n",
    "    - For example, if the words were \"san francisco city moved living born years raised lived live\", you might call it \"places lived\" because the topic seems to be about where people currently live (San Francisco) and where they were born / raised / moved from. \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Compare the topics from LDA and NMF\n",
    "\n",
    "#### Helper function to make a graph for us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion(x, y, x_label='', y_label='', t_label = '', sort=True):\n",
    "    n = x.shape[0]\n",
    "    corrs = cosine_similarity(x, y)\n",
    "    topic_similarity = pd.DataFrame(corrs)\n",
    "    new_order=None\n",
    "    \n",
    "    if sort:\n",
    "        matches = []\n",
    "        pairs = {}\n",
    "\n",
    "        for i in range(n):\n",
    "            for j in range(n):\n",
    "                tmp = {}\n",
    "                tmp['i'] = i\n",
    "                tmp['j'] = j\n",
    "                tmp['match'] = corrs[i][j]\n",
    "                matches.append(tmp)\n",
    "\n",
    "        matches = pd.DataFrame(matches).sort_values(by='match', ascending=False)\n",
    "\n",
    "        for row in matches.iterrows():\n",
    "            i = row[1]['i']\n",
    "            j = row[1]['j']\n",
    "            if i not in pairs.keys():\n",
    "                if j not in pairs.values():\n",
    "                    pairs[i] = row[1]['j']\n",
    "\n",
    "        new_order = list(range(n))\n",
    "        for k in pairs.keys():\n",
    "            new_order[int(k)] = int(pairs[k])\n",
    "\n",
    "        topic_similarity = topic_similarity[new_order]\n",
    "\n",
    "    blue_matrix(topic_similarity, xl=x_label, yl=y_label,\n",
    "                t = t_label,\n",
    "                x_labels=new_order)\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### See how similar the words in each topic from LDA are to the words in each topic from NMF\n",
    "- The NMF topics are along the X axis and the LDA are along the Y axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iHI6Lj4+XdOn9frkuXbrI39/ffrtBgwaSZK8cFTXDyczpefPmKSQkxN4dXKpUKT3wwANav3699uzZc81z/v777+rbt68iIiLk7++vwMBAtWnTRpLyvP8sFkueCmeDBg0cnn9ycnK+74/rMWXKFB06dCjfCU9XGjRokLZs2aKdO3dq3rx5qlatmlq3bn3VxyxatEibN2922JwNsSmo3MlH0dHRDvsHDhyoc+fO5Zk41LdvX4eu8JiYGLVo0SLPmPfLJSUlKTs7+6o9D7fccovKlCmjkSNHas6cOfr555+v8xkB5iPJ9FHt2rXTr7/+qiNHjig5OVlNmza1JyK5Y4fS09OVnJysgIAA3XHHHZIujYcMCAhQhQoVHM5nsVgUERGhEydOOOyPjIzM0/aJEycUERGRZ7+zfVfTrFkzjRw5Uh999JGOHDmip59+Wvv27XPadXWlsmXLOtwOCgq66n5Xl4qYNm2aHn/8cd1+++365JNP9P3332vz5s26++677V3Kl3P2OrmqR48eqlq1qt566y1Jl5ZyOXv2bIG6yqVLk1OulPszyf25/vHHHzp16pSCgoIUGBjosKWlpRUowY+JiVG1atW0evVq+x/n3CTz0KFD2r17t1avXq2QkBC1aNHC3q4kPfvss3nafeKJJyTJ3vYff/yhf/3rX3mOyx2XemWM5cqVc7httVolyenPqSjkJnO5M59/++03rVu3Tl26dJFhGDp16pROnTql+++/X9L/Zpzn58yZM2rVqpU2bdqkCRMmaM2aNdq8ebOWLVsmKe/zKlGiRJ4Zqlar1eE9f+LEiau+P1zVokUL3XPPPZo8ebJOnjx5zeNbt26t6tWr6+2339Z7772nQYMGXXPpn9q1a6tZs2YOW2GcOHHC6e9p7s/tys++/D7jrjzucrnjmq82GSg8PFxr165Vo0aN9Pzzz6tu3bqKiorS6NGjr2voEArJ4mfO5iOYXe6j2rVrp2nTpmnNmjVas2aNOnfubL8vN6Fct26dfUJQbgJarlw5ZWVl6dixYw6JpmEYSktL06233urQjrM/BOXKlXM6YcDVSQSXCwwM1OjRo/X666/rp59+uu7zuMv777+vtm3bavbs2Q778xsz5Y618vz8/DRkyBA9//zzeu211zRr1izdddddqlmzZoEef/mkm1y5P5PcRKx8+fIqV66cvvrqK6fnCA0NLVBbueP/1q5dq5ycHLVt21ahoaGKiopSUlKSVq9erVatWtkTvvLly0uSEhISdO+99zo9Z+7zLF++vBo0aKCJEyc6PS6/ZWzMcP78ea1evVrVqlWzJxbvvvuuDMPQxx9/7HQNzoULF2rChAkO1dfLffvttzpy5IjWrFljr15KKtTaquXKldMPP/yQZ39hfmcTExNVr149TZo0qUDHP/TQQ3rxxRdlsVg0YMCA6273epUrV85hsmSuI0eOSPrfezRXfp9xV36puVzuZ+qhQ4fyVEwvV79+fS1ZskSGYejHH3/UggULNG7cOIWEhGjUqFEFej6AN/CddBkOWrduLX9/f3388cfatWuXw2zp8PBw+6zJffv2OSxdlLuMyPvvv+9wvk8++URnz57Ns5adM+3atdOuXbu0Y8cOh/1XTlrJj7MPeul/3YDekERYLBZ7gpTrxx9/dLoQtSuuVWV75JFHFBQUpH79+mn37t0FnkwlXUqAP/30U4d9H374ofz8/Oxdk127dtWJEyeUnZ2dp0rUrFkzh4TWarXmG2f79u31xx9/aPr06WrevLk9Ob3rrru0fPlybd682d5VLl1KIKtXr64dO3Y4bbdZs2b2c3Tt2lU//fSTqlWr5vQ4b3h/SJcmpjz55JM6ceKERo4cad+3cOFCVatWTcnJyXm2Z555Rqmpqfryyy/zPW/uF5Yr339vv/32dcfarl27fN8f16tWrVoaNGiQ3nzzzQINRxkwYIC6deum5557TjfddNN1t3u97rrrLnsCf7lFixapRIkSeZY6Wrx4scNQiP3792vDhg1XXZmiY8eO8vf3z/PlND8Wi0UNGzbU66+/rtKlS2vbtm0Ff0KAF6CS6aPCwsLUpEkTrVixQn5+fvbxmLnatGmj6dOnS3JcH7NDhw7q1KmTRo4cKZvNppYtW+rHH3/U6NGj1bhxY/Xv3/+abQ8fPlzvvvuuunTpogkTJqhSpUr64IMP9J///KdAsXfq1EmVK1dWt27dVKtWLeXk5CglJUWvvfaaSpUq5TAb2ixdu3bV+PHjNXr0aLVp00a7d+/WuHHjFBsbW6hZrvXr15ckvfHGGxowYIACAwNVs2ZNe4JVunRpPfjgg5o9e7ZiYmIKtBZhrnLlyunxxx/XgQMHVKNGDX3xxRd655139Pjjj6tKlSqSLs30/eCDD9S5c2cNGzZMt912mwIDA3Xo0CElJyerR48e6tmzpz3WJUuWaOnSpbr55psVHBxsj//OO++UxWLR119/bV/WSrqUfOZWqS5PMqVLSVJ8fLw6deqkgQMH6qabbtKff/6pX375Rdu2bbPPvB43bpySkpLUokULDR06VDVr1tSFCxe0b98+ffHFF5ozZ45b1ybMb7mjNm3a2CtTf/zxh77//nsZhqHTp0/bF2PfsWOHnn76afus9y+//FJHjhzRlClTnCYj9erV08yZMzVv3jx17drVabstWrRQmTJl9Nhjj2n06NEKDAzUBx98kOdLnSsefPBBvf7663rwwQc1ceJEVa9eXV988YXTq4O5YsyYMfrggw+UnJyskiVLXvXYqKgorVixolDtFcbo0aPt431ffvlllS1bVh988IE+//xzTZ06VeHh4Q7HHz16VD179tSjjz6q9PR0jR49WsHBwUpISMi3japVq+r555/X+PHjdf78ef3lL39ReHi4fv75Zx0/flxjx47VZ599plmzZumee+6xXw0pd4mnDh06FPXLgCtZLJc2T7fpI0gyfVi7du20efNmNW7cOM9SIG3atNHrr7+uoKAg+7g46dI35xUrVmjMmDGaP3++Jk6cqPLly6t///6aNGlSnuqJMxEREVq7dq2GDRumxx9/XCVKlFDPnj01c+ZM9ejR45qPf/HFF7Vy5Uq9/vrrSk1NVUZGhiIjI9W+fXslJCTYJ+uY6YUXXtC5c+c0b948TZ06VXXq1NGcOXO0fPnyAk2OyU/btm2VkJCghQsX6p133lFOTo6Sk5MdEpLevXtr9uzZevzxx/OsG3o1EREReuutt/Tss89q586dKlu2rJ5//nmHJNDf31+ffvqp3njjDb333ntKTExUQECAKleurDZt2tiTSEkaO3asUlNT9eijj+r06dOKiYnRvn37JF1KaBs1aqTt27c7JJO5/597/+XatWunH374QRMnTtTw4cN18uRJlStXTnXq1FGvXr3sx0VGRmrLli0aP368XnnlFR06dEihoaGKjY3V3XffrTJlyhT4NSmIy68wdbnLfy65Xd9+fn4qVaqUYmJiFBcXpzlz5jhUwObNm6egoCA99NBDTs9Zvnx59ezZUx9//LH++OMPp+Mky5Urp88//1zPPPOM/vrXv6pkyZLq0aOHli5det2Xhi1RooS+/fZbDRs2TKNGjZLFYlHHjh21ZMkSh88HV0VFRWn48OEF7jI3U82aNbVhwwY9//zzGjJkiM6fP6/atWtr/vz5Tq9qNWnSJG3evFkPPfSQbDabbrvtNi1ZssThCl7OjBs3TtWrV9ebb76pfv36KSAgQNWrV9fQoUMlSdWrV1fp0qU1depUHTlyREFBQapZs6YWLFhgyjACoDAshrOpjwC81jPPPKPZs2fr4MGDVx3/BcD91qxZo3bt2umjjz6yT9aC77HZbAoPD5e1yZOy+F+7uOJORnaGMrbNVHp6+lXXii0OqGQCxcT333+vX3/9VbNmzdLgwYNJMAEAXo0kEygm4uLiVKJECXXt2lUTJkwwOxwA8H2MySwUkkygmGBkC2C+tm3b8rsIFBBLGAEAAMDtqGQCAAA4ZcYVeHyn/uc7zwQAAABeg0omAACAM0z8KRQqmQCKhX379slisSglJcXsUFy2Zs0aWSyWQl1fHACKG5JMwAcMHDhQFotFkydPdti/YsUK+7Wupf8lO2XKlNGFCxccjv3hhx9ksVicHn/l9uKLL+YbS+5Vo9wtOjpaqampqlevntvPDQBwP5JMwEcEBwdrypQpOnny5DWPDQ0N1fLlyx32vfvuu/ZrmF9p9+7dSk1NtW+jRo1yS8yu8Pf3V0REhAICGOWTKzMz0+wQAN9m8TNn8xG+80yAG1z79u0VERGhxMTEax47YMAAvfvuu/bb58+f15IlS/K9NnLFihUVERFh30qVKuX0uKpVq0qSevbsKYvFYr8tSbNnz1a1atXs12J+7733HB5rsVg0e/ZsxcfHKyQkRLGxsfroo4/s9zvrLt+1a5e6dOmisLAwhYaGqlWrVvq///s/p7HlVmW/+eYbNWvWTCVKlFCLFi20e/du+zEDBw7UPffc4/C44cOHO1w7vm3btnrqqac0fPhwlSlTRpUqVdLcuXN19uxZPfTQQwoNDVW1atX05Zdf5onh3//+txo2bKjg4GDdfvvt2rlzp8P9GzZsUOvWrRUSEqLo6GgNHTpUZ8+edXh9J0yYoIEDByo8PFyPPvqo0+cKAN6AJBPwEf7+/po0aZLefPNNHTp06KrH9u/fX+vXr9eBAwckSZ988omqVq2qJk2aFCqGzZs3S5Lmz5+v1NRU++3ly5dr2LBheuaZZ/TTTz9p8ODBeuihh5ScnOzw+Jdeekn33XefduzYob/+9a/6y1/+ol9++cVpW4cPH1br1q0VHBysb7/9Vlu3btWgQYOUlZV11RhfeOEFvfbaa9qyZYsCAgI0aNAgl5/nwoULVb58ef3www966qmn9Pjjj+uBBx5QixYttG3bNnXq1En9+/fXuXPnHB733HPP6dVXX9XmzZtVsWJFde/eXRcvXpQk7dy5U506ddK9996rH3/8UUuXLtV3332nJ5980uEcr7zyiurVq6etW7fqpZdecjl2AC7Infjj6c1HkGQCPqRnz55q1KiRRo8efdXjKlasqPj4eC1YsEDSpa7yqyVblStXVqlSpezbiRMnnB5XoUIFSVLp0qUVERFhv/3qq69q4MCBeuKJJ1SjRg2NGDFC9957r1599VWHxz/wwAN65JFHVKNGDY0fP17NmjXTm2++6bStt956S+Hh4VqyZImaNWumGjVq6KGHHlLNmjWv+twnTpyoNm3aqE6dOho1apQ2bNiQZ3zqtTRs2FAvvviiqlevroSEBIWEhKh8+fJ69NFHVb16db388ss6ceKEfvzxR4fHjR49Wh06dFD9+vW1cOFC/fHHH/ZhC6+88or69u2r4cOHq3r16mrRooVmzJihRYsWOcR355136tlnn9Utt9yiW265xaW4AcCTSDIBHzNlyhQtXLhQP//881WPGzRokBYsWKDff/9dGzduVL9+/fI9dv369UpJSbFvZcqUcSmmX375RS1btnTY17JlyzxVyri4uDy386tkpqSkqFWrVgoMDHQplgYNGtj/PzIyUpJ09OjR6z6Hv7+/ypUrp/r169v3VapUyel5L39+ZcuWVc2aNe3Pb+vWrVqwYIFDMt+pUyfl5ORo79699sc1a9bMpVgBFAJjMguFEfSAj2ndurU6deqk559/XgMHDsz3uM6dO2vw4MF6+OGH1a1bN5UrVy7fY2NjY1W6dOlCxWW5ogvIMIw8+wryuFwhISHXFcflSWnuuXNyciRJfn5+ea5Lndudnd85cs9ztfNezeXHDh48WEOHDs1zzOUTskqWLHnNcwKAN/CddBmA3eTJk/Wvf/1LGzZsyPcYf39/9e/fX2vWrLmucYn5CQwMVHZ2tsO+2rVr67vvvnPYt2HDBtWuXdth3/fff5/ndq1atZy206BBA61fv95pEni9KlSooNTUVId97lyX8/Lnd/LkSf3666/259ekSRPt2rXL3g1++RYUFOS2GADAU0gyAR9Uv3599evXL9/xjLnGjx+vY8eOqVOnTm5ru2rVqvrmm2+UlpZmX07pueee04IFCzRnzhzt2bNH06ZN07Jly/Tss886PPajjz7Su+++q19//VWjR4/WDz/8kGfiS64nn3xSNptNffr00ZYtW7Rnzx699957DrPFXXXnnXdqy5YtWrRokfbs2aPRo0frp59+uu7zXWncuHH65ptv9NNPP2ngwIEqX768fTb7yJEjtXHjRg0ZMkQpKSnas2ePPv30Uz311FNuax+Ai5j4UygkmYCPGj9+fJ6u3ysFBQWpfPnyBeq2LqjXXntNSUlJio6OVuPGjSVJ99xzj9544w298sorqlu3rt5++23Nnz/fYWkgSRo7dqyWLFmiBg0aaOHChfrggw9Up04dp+2UK1dO3377rc6cOaM2bdqoadOmeuedd1weo3m5Tp066aWXXtLf//533XrrrTp9+rQefPDB6z7flSZPnqxhw4apadOmSk1N1aeffmqvUjZo0EBr167Vnj171KpVKzVu3FgvvfSSfdwoABQ3FuNaf4UAwAMsFouWL1+eZ51KAPA0m82m8PBwWeNGyRJg9WjbRlaGMjZOVnp6usLCwjzatrtRyQQAAIDbkWQCAADA7VjCCIBXYOQOAK9jsXh+3Uom/gAAAAD5o5IJAADgjJ/l0ubpNn0ElUwAAAC4HUnmNcyaNUuxsbEKDg5W06ZNtX79eo+2v27dOnXr1k1RUVGyWCxasWKFR9tPTEzUrbfeqtDQUFWsWFH33HNPoRa7vh6zZ89WgwYNFBYWprCwMMXFxenLL7/0aAxXSkxMlMVi0fDhwz3a7pgxY2SxWBy2iIgIj8YgSYcPH9Zf//pXlStXTiVKlFCjRo20detWj7VftWrVPK+DxWLRkCFDPBZDVlaWXnzxRcXGxiokJEQ333yzxo0bV6BLSbrT6dOnNXz4cMXExCgkJEQtWrTQ5s2bi6y9a30mGYahMWPGKCoqSiEhIWrbtq127drl0RiWLVumTp062deAdedVmwoSw8WLFzVy5EjVr19fJUuWVFRUlB588EEdOXLEYzFIlz4vatWqpZIlS6pMmTJq3769Nm3a5NEYLjd48GBZLBZNnz7drTHAe5FkXsXSpUs1fPhwvfDCC9q+fbtatWql+Ph4HThwwGMxnD17Vg0bNtTMmTM91ubl1q5dqyFDhuj7779XUlKSsrKy1LFjR509e9ZjMVSuXFmTJ0/Wli1btGXLFt15553q0aOH2/9wFdTmzZs1d+5cNWjQwJT269atq9TUVPu2c+dOj7Z/8uRJtWzZUoGBgfryyy/1888/67XXXiv0tc1dsXnzZofXICkpSZL0wAMPeCyGKVOmaM6cOZo5c6Z++eUXTZ06Va+88so1r7Lkbo888oiSkpL03nvvaefOnerYsaPat2+vw4cPF0l71/pMmjp1qqZNm6aZM2dq8+bNioiIUIcOHXT69GmPxXD27Fm1bNlSkydPdlubrsRw7tw5bdu2TS+99JK2bdumZcuW6ddff1X37t09FoMk1ahRQzNnztTOnTv13XffqWrVqurYsaOOHTvmsRhyrVixQps2bVJUVJTb2vYIi585m49gMfaruP3229WkSRPNnj3bvq927dq65557lJiY6PF4vGGx6mPHjqlixYpau3atWrdubVocZcuW1SuvvKKHH37Yo+2eOXNGTZo00axZszRhwgQ1atTIo9/Kx4wZoxUrVhRJZaagRo0apX//+98er+pfzfDhw/XZZ59pz549br160dV07dpVlSpV0rx58+z77rvvPpUoUULvvfeeR2I4f/68QkNDtXLlSnXp0sW+v1GjRuratasmTJhQpO1f+ZlkGIaioqI0fPhwjRw5UpKUkZGhSpUqacqUKRo8eHCRx3C5ffv2KTY2Vtu3b1ejRo3c3nZBYsi1efNm3Xbbbdq/f7+qVKliSgy5C4yvXr1ad911l8diOHz4sG6//XatWrVKXbp00fDhwz3eC+Qq+2LsrV6UJSDYo20bWReUsX4Ci7H7sszMTG3dulUdO3Z02N+xY0dt2LDBpKjMl56eLulSkmeG7OxsLVmyRGfPnlVcXJzH2x8yZIi6dOmi9u3be7ztXHv27FFUVJRiY2PVp08f/f777x5t/9NPP1WzZs30wAMPqGLFimrcuLHeeecdj8ZwuczMTL3//vsaNGiQxxJMSbrjjjv0zTff6Ndff5Uk7dixQ9999506d+7ssRiysrKUnZ2t4GDHP4IhISH67rvvPBZHrr179yotLc3hc9NqtapNmzY39OemdOmz02KxeLTif7nMzEzNnTtX4eHhatiwocfazcnJUf/+/fXcc8+pbt26HmvXbbh2eaEwuzwfx48fV3Z2tipVquSwv1KlSkpLSzMpKnMZhqERI0bojjvuUL169Tza9s6dOxUXF6cLFy6oVKlSWr58eb7XtC4qS5Ys0bZt24p0vNu13H777Vq0aJFq1KihP/74QxMmTFCLFi20a9culStXziMx/P7775o9e7ZGjBih559/Xj/88IOGDh0qq9Xq1ut8F9SKFSt06tQpDRw40KPtjhw5Uunp6apVq5b8/f2VnZ2tiRMn6i9/+YvHYggNDVVcXJzGjx+v2rVrq1KlSlq8eLE2bdqk6tWreyyOXLmfjc4+N/fv3+/xeLzFhQsXNGrUKPXt29fjlanPPvtMffr00blz5xQZGamkpCSVL1/eY+1PmTJFAQEBGjp0qMfahPcgybyGKysjhmF4tFriTZ588kn9+OOPplRIatasqZSUFJ06dUqffPKJBgwYoLVr13os0Tx48KCGDRumr7/+Ok/VyJPi4+Pt/1+/fn3FxcWpWrVqWrhwoUaMGOGRGHJyctSsWTNNmjRJktS4cWPt2rVLs2fPNiXJnDdvnuLj4z0+1mvp0qV6//339eGHH6pu3bpKSUnR8OHDFRUVpQEDBngsjvfee0+DBg3STTfdJH9/fzVp0kR9+/bVtm3bPBbDlfjc/J+LFy+qT58+ysnJ0axZszzefrt27ZSSkqLjx4/rnXfeUa9evbRp0yZVrFixyNveunWr3njjDW3btq34/vzNGCPpQ2MyfeeZuFn58uXl7++fp2p59OjRPN/SbwRPPfWUPv30UyUnJ6ty5coebz8oKEi33HKLmjVrpsTERDVs2FBvvPGGx9rfunWrjh49qqZNmyogIEABAQFau3atZsyYoYCAAGVnZ3sslsuVLFlS9evX1549ezzWZmRkZJ7kvnbt2h6dEJdr//79Wr16tR555BGPt/3cc89p1KhR6tOnj+rXr6/+/fvr6aef9vh47WrVqmnt2rU6c+aMDh48qB9++EEXL15UbGysR+OQZF/pgM/NSy5evKhevXpp7969SkpKMmV8XcmSJXXLLbeoefPmmjdvngICAhzGERel9evX6+jRo6pSpYr9c3P//v165plnVLVqVY/EAHORZOYjKChITZs2tc9azZWUlKQWLVqYFJXnGYahJ598UsuWLdO3335ryh8uZwzDUEZGhsfau+uuu7Rz506lpKTYt2bNmqlfv35KSUmRv7+/x2K5XEZGhn755RdFRkZ6rM2WLVvmWcbq119/VUxMjMdiyDV//nxVrFjRYdKLp5w7d05+fo4fof7+/h5fwihXyZIlFRkZqZMnT2rVqlXq0aOHx2OIjY1VRESEw+dmZmam1q5de0N9bkr/SzD37Nmj1atXe2w4y7V48rOzf//++vHHHx0+N6OiovTcc89p1apVHokB5qK7/CpGjBih/v37q1mzZoqLi9PcuXN14MABPfbYYx6L4cyZM/rtt9/st/fu3auUlBSVLVu2SGYoXmnIkCH68MMPtXLlSoWGhtorFOHh4QoJCSny9iXp+eefV3x8vKKjo3X69GktWbJEa9as0VdffeWR9qVLY9+uHIdasmRJlStXzqPjU5999ll169ZNVapU0dGjRzVhwgTZbDaPds8+/fTTatGihSZNmqRevXrphx9+0Ny5czV37lyPxSBd6rafP3++BgwYoIAAz3+UdevWTRMnTlSVKlVUt25dbd++XdOmTdOgQYM8GseqVatkGIZq1qyp3377Tc8995xq1qyphx56qEjau9Zn0vDhwzVp0iRVr15d1atX16RJk1SiRAn17dvXYzH8+eefOnDggH1dytwvRREREW5bV/ZqMURFRen+++/Xtm3b9Nlnnyk7O9v+2Vm2bFkFBQUVeQzlypXTxIkT1b17d0VGRurEiROaNWuWDh065Nalvq71s7gyuQ4MDFRERIRq1qzpthiKlBkTcYrr0AJnDFzVW2+9ZcTExBhBQUFGkyZNjLVr13q0/eTkZENSnm3AgAEead9Z25KM+fPne6R9wzCMQYMG2X8GFSpUMO666y7j66+/9lj7+WnTpo0xbNgwj7bZu3dvIzIy0ggMDDSioqKMe++919i1a5dHYzAMw/jXv/5l1KtXz7BarUatWrWMuXPnejyGVatWGZKM3bt3e7xtwzAMm81mDBs2zKhSpYoRHBxs3HzzzcYLL7xgZGRkeDSOpUuXGjfffLMRFBRkREREGEOGDDFOnTpVZO1d6zMpJyfHGD16tBEREWFYrVajdevWxs6dOz0aw/z5853eP3r0aI/EsHfv3nw/O5OTkz0Sw/nz542ePXsaUVFRRlBQkBEZGWl0797d+OGHH9zW/rVicCYmJsZ4/fXX3RpDUUhPTzckGda2Y4zg9pM9ulnbjjEkGenp6Wa/DIXGOpkAAACXsa+T2W6cOetkJr/MOpkAAACAMySZAAAAcDsm/gAAADjDxJ9CoZIJAAAAt6OSCQAA4AxX/CkU33kmAAAA8BokmQWQkZGhMWPGePQKM8RADMRADMRADMRgstwxmZ7efATrZBZA7npZZq5ZRQzEQAzEQAzEQAyeYV8ns/0kc9bJXP18sX3tLkclEwAAAG7HxB8AAACnTJj440P1P59PMnNycnTkyBGFhobKcp3jHGw2m8N/zUAMxEAMxEAMxHAjxWAYhk6fPq2oqCj5+flO4nUj8fkxmYcOHVJ0dLTZYQAAgOtw8OBBVa5c2aNt2sdkdpgiS6CHx2RevKCMpJE+MSbT5yuZoaGhkqSOUz5TYEhJ0+LYvjPVtLZzndiZYnYIqtCwidkhqEVTz35Y5WflmwvNDkEN7u9pdgiqWCbE7BCU4wXftQP9za/UnDht/kzgkyfPmx2CjqaeMjsESVKN2pFmh6A60aVNazvz/Bkt+ttd9r/jKH58PsnM7SIPDCmpwJBSpsXhF1TCtLZzWQKsZocgP6v5r0OQie+Dy1n8g8wOQQHB5n3xyhUYYv57giTzkoCL5v9J8Lea/zr4BWWaHYIk7/j9DCph/ufl9Q51g/nM/0QBAADwRhaLCVf88Z2k2vyvjAAAAPA5VDIBAACc4drlheI7zwQAAABeg0omAACAM2ZcS5wxmQAAAED+SDIBAADgdnSXAwAAOMPEn0LxnWcCAAAAr0ElEwAAwBkm/hRKsahkzpo1S7GxsQoODlbTpk21fv16s0MCAADAVXh9krl06VINHz5cL7zwgrZv365WrVopPj5eBw4cMDs0AAAA5MPrk8xp06bp4Ycf1iOPPKLatWtr+vTpio6O1uzZs80ODQAA+LLciT+e3nyEVz+TzMxMbd26VR07dnTY37FjR23YsMHpYzIyMmSz2Rw2AAAAeJZXJ5nHjx9Xdna2KlWq5LC/UqVKSktLc/qYxMREhYeH27fo6GhPhAoAAHxN7sQfT28+wquTzFyWK15wwzDy7MuVkJCg9PR0+3bw4EFPhAgAAIDLeHWSWb58efn7++epWh49ejRPdTOX1WpVWFiYwwYAAOAqi8ViyuYqV1bhWbZsmTp06KAKFSooLCxMcXFxWrVqlcMxCxYscBrXhQsXXIrLq5PMoKAgNW3aVElJSQ77k5KS1KJFC5OiAgAA8A6ursKzbt06dejQQV988YW2bt2qdu3aqVu3btq+fbvDcWFhYUpNTXXYgoODXYrN6xdjHzFihPr3769mzZopLi5Oc+fO1YEDB/TYY4+ZHRoAAECRuHListVqldVqzXPc5avwSNL06dO1atUqzZ49W4mJiXmOnz59usPtSZMmaeXKlfrXv/6lxo0b2/dbLBZFREQU6jl4dSVTknr37q3p06dr3LhxatSokdatW6cvvvhCMTExZocGAAB8mJnd5dHR0Q4TmZ0ljNezCs+VcnJydPr0aZUtW9Zh/5kzZxQTE6PKlSura9eueSqdBeH1lUxJeuKJJ/TEE0+YHQYAAIBHHDx40GFeibMq5vWswnOl1157TWfPnlWvXr3s+2rVqqUFCxaofv36stlseuONN9SyZUvt2LFD1atXL/BzKBZJJgAAgMdZ/rt5uk3JpcnLrqzCc7nFixdrzJgxWrlypSpWrGjf37x5czVv3tx+u2XLlmrSpInefPNNzZgxo0AxSSSZAAAAxdL1rMKTa+nSpXr44Yf10UcfqX379lc91s/PT7feeqv27NnjUnxePyYTAAAAeV3vKjyLFy/WwIED9eGHH6pLly7XbMcwDKWkpCgyMtKl+KhkAgAAOHG961YWslGXDr/WKjwJCQk6fPiwFi1aJOlSgvnggw/qjTfeUPPmze1V0JCQEIWHh0uSxo4dq+bNm6t69eqy2WyaMWOGUlJS9NZbb7kUG0kmAABAMdW7d2+dOHFC48aNU2pqqurVq+ewCk9qaqrDmplvv/22srKyNGTIEA0ZMsS+f8CAAVqwYIEk6dSpU/rb3/6mtLQ0hYeHq3Hjxlq3bp1uu+02l2IjyQQAAHCiOFQypauvwpObOOZas2bNNc/3+uuv6/XXX3c5jisxJhMAAABuRyUTAADAieJSyfRWVDIBAADgdjdMJXN859oKDS3YoqZF4XCrm01rO9f2P+qbHYLiaxTuOqjuUMrqb3YIkqTOdUaZHYK++PmE2SFoWo86Zoegr3YX7MoYRalFlfJmh6BlPx8xOwS1izH/dZixYZ/ZIUiSqlUoYXYI+vce8z4jss6fNa1tuMcNk2QCAAC4gu7ywqG7HAAAAG5HJRMAAMAZE69d7guoZAIAAMDtSDIBAADgdnSXAwAAOMHEn8KhkgkAAAC3o5IJAADghMUiEyqZnm2uKFHJBAAAgNtRyQQAAHDCIhPGZPpQKZNKJgAAANyOJBMAAABu5/VJ5rp169StWzdFRUXJYrFoxYoVZocEAABuALlLGHl68xVen2SePXtWDRs21MyZM80OBQAAAAXk9RN/4uPjFR8fb3YYAADgRsO1ywvF65NMV2VkZCgjI8N+22azmRgNAADAjcnru8tdlZiYqPDwcPsWHR1tdkgAAAA3HJ9LMhMSEpSenm7fDh48aHZIAACgODJj0o8PTfzxue5yq9Uqq9VqdhgAAAA3NJ9LMgEAANzBjCWFfGkJI69PMs+cOaPffvvNfnvv3r1KSUlR2bJlVaVKFRMjAwAAQH68PsncsmWL2rVrZ789YsQISdKAAQO0YMECk6ICAADA1Xh9ktm2bVsZhmF2GAAA4AZDd3nh+NzscgAAAJjP6yuZAAAApuCKP4VCJRMAAABuRyUTAADACcZkFg6VTAAAALgdSSYAAADcju5yAAAAJ+guLxwqmQAAAHA7KpkAAABOUMksHIvh45fTsdlsCg8P1x8n0hUWFmZaHOczs01rO1f6uYtmh6AKYVazQ5Cfl/z+esNvnp+3vBhQVnaO2SEowN/8zq2LWea/Due84PNaksJCzK8DmZnw2Gw2VSoXrvR0z//9zs0dKg5YJL+gEh5tOyfznI4ufNCU5+1u5n+iAAAAwOeY/zUJAADAC9FdXjhUMgEAAOB2VDIBAACc4drlhUIlEwAAAG5HJRMAAMAJxmQWDpVMAAAAuB1JJgAAANyO7nIAAAAn6C4vHCqZAAAAcDsqmQAAAE5QySwcKpkAAABwO69OMhMTE3XrrbcqNDRUFStW1D333KPdu3ebHRYAAACuwauTzLVr12rIkCH6/vvvlZSUpKysLHXs2FFnz541OzQAAODrLCZtPsKrx2R+9dVXDrfnz5+vihUrauvWrWrdurVJUQEAAOBavDrJvFJ6erokqWzZsvkek5GRoYyMDPttm81W5HEBAADfw8SfwvHq7vLLGYahESNG6I477lC9evXyPS4xMVHh4eH2LTo62oNRAgAAQCpGSeaTTz6pH3/8UYsXL77qcQkJCUpPT7dvBw8e9FCEAADAl+RWMj29+Ypi0V3+1FNP6dNPP9W6detUuXLlqx5rtVpltVo9FBkAAACc8eok0zAMPfXUU1q+fLnWrFmj2NhYs0MCAABAAXh1kjlkyBB9+OGHWrlypUJDQ5WWliZJCg8PV0hIiMnRAQAAX2aRCRN/fGgNI68ekzl79mylp6erbdu2ioyMtG9Lly41OzQAAABchVdXMg3DMDsEAABwg2IJo8Lx6komAAAAiieSTAAAALidV3eXAwAAmMaMa4n7Tm85lUwAAAC4H5VMAAAAJ5j4UzhUMgEAAOB2VDIBAACcoJJZOFQyAQAA4HYkmQAAAHC7G6a7PCs7R1nZOaa17+cF1e/zmdlmh6CTZzPNDsFrVocI8Df/O543vC93p54xOwTVrRxmdggK8IIfRnaO+VdZO3nuotkh6MTpDLNDkCSVtJYyOwRl5Zj3d/PCRfP/ZlkslzZPt+krzP8rBwAAAJ9zw1QyAQAAXHGpkunpiT8eba5IUckEAACA25FkAgAAwO3oLgcAAHDGhIk/XjM71Q2oZAIAAMDtqGQCAAA4wRV/CodKJgAAANyOSiYAAIATLMZeOFQyAQAA4HYkmQAAAHA7ussBAACc8POzyM/Ps/3XhofbK0peXcmcPXu2GjRooLCwMIWFhSkuLk5ffvml2WEBAADgGry6klm5cmVNnjxZt9xyiyRp4cKF6tGjh7Zv3666deuaHB0AAPBlTPwpHK9OMrt16+Zwe+LEiZo9e7a+//57kkwAAAAv5tVJ5uWys7P10Ucf6ezZs4qLi8v3uIyMDGVkZNhv22w2T4QHAACAy3h9krlz507FxcXpwoULKlWqlJYvX646derke3xiYqLGjh3rwQgBAIAv4oo/hePVE38kqWbNmkpJSdH333+vxx9/XAMGDNDPP/+c7/EJCQlKT0+3bwcPHvRgtAAAAJCKQSUzKCjIPvGnWbNm2rx5s9544w29/fbbTo+3Wq2yWq2eDBEAAPggJv4UjtdXMq9kGIbDmEsAAAB4H6+uZD7//POKj49XdHS0Tp8+rSVLlmjNmjX66quvzA4NAAD4OMZkFo5XJ5l//PGH+vfvr9TUVIWHh6tBgwb66quv1KFDB7NDAwAAwFV4dZI5b948s0MAAADAdfDqJBMAAMAsdJcXTrGb+AMAAADvR5IJAADgRO4SRp7eXDVr1izFxsYqODhYTZs21fr16/M9dtmyZerQoYMqVKigsLAwxcXFadWqVXmO++STT1SnTh1ZrVbVqVNHy5cvdzkukkwAAIBiaunSpRo+fLheeOEFbd++Xa1atVJ8fLwOHDjg9Ph169apQ4cO+uKLL7R161a1a9dO3bp10/bt2+3HbNy4Ub1791b//v21Y8cO9e/fX7169dKmTZtcis1iGIZRqGfn5Ww2m8LDw3X46EmFhYWZFkd2jvkv85GTF8wOQaEh5g8D9pbRLgH+5n/H8/OCF2N36hmzQ1DdyuZ9NuQK8IIfhp8XxHDiTKbZIejEae9Yi7l6RCmzQ1CWiX+7bDabYiLKKj093eN/v3Nzh3qjVsrfWtKjbWdnnNVPk3sU+HnffvvtatKkiWbPnm3fV7t2bd1zzz1KTEwsUJt169ZV79699fLLL0uSevfuLZvNpi+//NJ+zN13360yZcpo8eLFBX4u5v+VAwAA8EIWWeyTfzy2/bcUYrPZHDZnF6LJzMzU1q1b1bFjR4f9HTt21IYNGwr0HHNycnT69GmVLVvWvm/jxo15ztmpU6cCnzMXSSYAAICXiY6OVnh4uH1zVpU8fvy4srOzValSJYf9lSpVUlpaWoHaee2113T27Fn16tXLvi8tLa1Q58xlft8lAACAFzLz2uUHDx506C63Wq1XeYxjkIZhFGgppMWLF2vMmDFauXKlKlas6JZzXo4kEwAAwMuEhYVdc0xm+fLl5e/vn6fCePTo0TyVyCstXbpUDz/8sD766CO1b9/e4b6IiIjrOueVbpgkM8Dfz9SJFjlGjmlt54qt6NnBy854wwQofy+Y3ID/aXZzGbNDUI4XvC+9YdKNN6gYln+15kaKwVsE+JvXdmagiY0XE0FBQWratKmSkpLUs2dP+/6kpCT16NEj38ctXrxYgwYN0uLFi9WlS5c898fFxSkpKUlPP/20fd/XX3+tFi1auBTfDZNkAgAAuKI4XPFnxIgR6t+/v5o1a6a4uDjNnTtXBw4c0GOPPSZJSkhI0OHDh7Vo0SJJlxLMBx98UG+88YaaN29ur1iGhIQoPDxckjRs2DC1bt1aU6ZMUY8ePbRy5UqtXr1a3333nUuxMfEHAACgmOrdu7emT5+ucePGqVGjRlq3bp2++OILxcTESJJSU1Md1sx8++23lZWVpSFDhigyMtK+DRs2zH5MixYttGTJEs2fP18NGjTQggULtHTpUt1+++0uxXbDrJP5xwnPr7N1ucws87vLgwLM/05Bdzm8Ed3lgPex2WyqVC7c1HUyG73wL/kHe3idzAtnlTKxmynP293MzzoAAADgcxiTCQAA4ERxGJPpzahkAgAAwO1IMgEAAOB2dJcDAAA4YeYVf3wBlUwAAAC4HZVMAAAAJ5j4UzhUMgEAAOB2JJkAAABwu2KVZCYmJspisWj48OFmhwIAAHyd5X+Tfzy1yXd6y4tPkrl582bNnTtXDRo0MDsUAAAAXEOxSDLPnDmjfv366Z133lGZMmXMDgcAANwAcif+eHrzFcUiyRwyZIi6dOmi9u3bX/PYjIwM2Ww2hw0AAACe5fVLGC1ZskTbtm3T5s2bC3R8YmKixo4dW8RRAQAAX8di7IXj1ZXMgwcPatiwYXr//fcVHBxcoMckJCQoPT3dvh08eLCIowQAAMCVvLqSuXXrVh09elRNmza178vOzta6des0c+ZMZWRkyN/f3+ExVqtVVqvV06ECAADgMl6dZN51113auXOnw76HHnpItWrV0siRI/MkmAAAAO7CFX8Kx6uTzNDQUNWrV89hX8mSJVWuXLk8+wEAAOA9vDrJBAAAMAsTfwqn2CWZa9asMTsEAAAAXINXzy4HAABA8VTsKpkAAACewMSfwqGSCQAAALejkgkAAOAElczCoZIJAAAAt6OSCQAA4ARLGBUOlUwAAAC4HUkmAAAA3I7ucgAAACeY+FM4JJkeEhRgftHYMAyzQ9CpcxfNDkFlSwaaHYIkKTvH/J+Hnxd8mPn5EYPkHe8Hfy94HXK84HXwhvcD4AtIMgEAAJxg4k/hmF9eAwAAgM8hyQQAAIDb0V0OAADgBBN/CodKJgAAANyOSiYAAIATFpkw8cezzRUpKpkAAABwOyqZAAAATvhZLB5fT9gb1i92FyqZAAAAcDuSTAAAALgd3eUAAABOcMWfwqGSCQAAALfz6iRzzJgx9oVQc7eIiAizwwIAADeAK3MQT22+wuu7y+vWravVq1fbb/v7+5sYDQAAAArC65PMgIAAqpcAAADFjFd3l0vSnj17FBUVpdjYWPXp00e///77VY/PyMiQzWZz2AAAAFzlZzFn8xVenWTefvvtWrRokVatWqV33nlHaWlpatGihU6cOJHvYxITExUeHm7foqOjPRgxAAAAJC9PMuPj43Xfffepfv36at++vT7//HNJ0sKFC/N9TEJCgtLT0+3bwYMHPRUuAADwJRbPT/7xpYuXe/2YzMuVLFlS9evX1549e/I9xmq1ymq1ejAqAAAAXMmrK5lXysjI0C+//KLIyEizQwEAAD4udzF2T2++wquTzGeffVZr167V3r17tWnTJt1///2y2WwaMGCA2aEBAADgKlxKMrOyshQQEKCffvqpqOJxcOjQIf3lL39RzZo1de+99yooKEjff/+9YmJiPNI+AAAAro9LYzIDAgIUExOj7OzsoorHwZIlSzzSDgAAwJUs//3n6TZ9hcvd5S+++KISEhL0559/FkU8AAAA8AEuzy6fMWOGfvvtN0VFRSkmJkYlS5Z0uH/btm1uCw4AAMAsZiyO7kuLsbucZN5zzz1FEAYAAAB8ictJ5ujRo4siDgAAAPiQ61rC6NSpU/rHP/7hMDZz27ZtOnz4sFuDAwAAMIunr/Zjv+qPj3C5kvnjjz+qffv2Cg8P1759+/Too4+qbNmyWr58ufbv369FixYVRZwAAAAoRlyuZI4YMUIDBw7Unj17FBwcbN8fHx+vdevWuTU4AAAAs3DFn8JxOcncvHmzBg8enGf/TTfdpLS0NLcEBQAAgOLN5e7y4OBg2Wy2PPt3796tChUquCUoAAAAs/lZLPLzcGnR0+0VJZeTzB49emjcuHH65z//KenSoNgDBw5o1KhRuu+++9weoLsYhiHDMExrPzvHvLZzecNg4nKlgswOwdT3weW84eeBSy5keuYqZlfj7wWL43nD55Q3/H4GWa5rTqzbZWWb/1qY+b7M8YL3IwrH5d+kV199VceOHVPFihV1/vx5tWnTRrfccotCQ0M1ceLEoogRAAAAxYzLlcywsDB99913+vbbb7Vt2zbl5OSoSZMmat++fVHEBwAAYAozJuL4UieXy0lmrjvvvFN33nmnO2MBAACAj7iugSfffPONunbtqmrVqumWW25R165dtXr1anfHBgAAYBoWYy8cl5PMmTNn6u6771ZoaKiGDRumoUOHKiwsTJ07d9bMmTOLIkYAAAAUMy53lycmJur111/Xk08+ad83dOhQtWzZUhMnTnTYDwAAgBuTy5VMm82mu+++O8/+jh07Ol0/EwAAoDjiij+F43KS2b17dy1fvjzP/pUrV6pbt25uCQoAAADFW4G6y2fMmGH//9q1a2vixIlas2aN4uLiJEnff/+9/v3vf+uZZ54pmigBAAA8jCv+FE6BkszXX3/d4XaZMmX0888/6+eff7bvK126tN599129+OKL7o0QAAAAxU6Bksy9e/cWdRwAAADwIde9GDsAAIAvs/x383SbvsLlJNMwDH388cdKTk7W0aNHlZOT43D/smXL3BacJB0+fFgjR47Ul19+qfPnz6tGjRqaN2+emjZt6tZ2AAAA4D4uJ5nDhg3T3Llz1a5dO1WqVKlIV6Y/efKkWrZsqXbt2unLL79UxYoV9X//938qXbp0kbUJAAAg/e+KP55u01e4nGS+//77WrZsmTp37lwU8TiYMmWKoqOjNX/+fPu+qlWrFnm7AAAAKByX18kMDw/XzTffXBSx5PHpp5+qWbNmeuCBB1SxYkU1btxY77zzzlUfk5GRIZvN5rABAAC4ys9izuYrXE4yx4wZo7Fjx+r8+fNFEY+D33//XbNnz1b16tW1atUqPfbYYxo6dKgWLVqU72MSExMVHh5u36Kjo4s8TgAAADiyGIZhuPKAc+fO6d5779W///1vVa1aVYGBgQ73b9u2zW3BBQUFqVmzZtqwYYN939ChQ7V582Zt3LjR6WMyMjKUkZFhv22z2RQdHa2046cUFhbmtthclZ3j0stcJLxhnIe/F3xFc/EtX2S84C3hFbMY/bzgPXEhM9vsELzjd8PsAOQdv59BAS7XX4pEVrb5r4WZ70ubzabICqWVnp7u8b/fNptN4eHheuDt9QoMKeXRti+eP6OPBrcy5Xm7m8tjMgcOHKitW7fqr3/9a5FP/ImMjFSdOnUc9tWuXVuffPJJvo+xWq2yWq1FFhMAALgxMPGncFxOMj///HOtWrVKd9xxR1HE46Bly5bavXu3w75ff/1VMTExRd42AAAArp/LSWZ0dLTHyrdPP/20WrRooUmTJqlXr1764YcfNHfuXM2dO9cj7QMAgBubDxUWPc7lgSevvfaa/v73v2vfvn1FEI6jW2+9VcuXL9fixYtVr149jR8/XtOnT1e/fv2KvG0AAABcP5crmX/961917tw5VatWTSVKlMgz8efPP/90W3CS1LVrV3Xt2tWt5wQAAEDRcjnJnD59ehGEAQAA4F2Y+FM4LieZAwYMKIo4AAAA4ENcTjIPHDhw1furVKly3cEAAAB4CzOuwOMFS+a6jctJZtWqVa9ays3ONn9RYwAAAJjL5SRz+/btDrcvXryo7du3a9q0aZo4caLbAgMAADATYzILx+Uks2HDhnn2NWvWTFFRUXrllVd07733uiUwAAAAFF9uu0BrjRo1tHnzZnedDgAAAMWYy5VMm83mcNswDKWmpmrMmDGqXr262wIDAAAwk+W/m6fb9BUuJ5mlS5fOM17AMAxFR0dryZIlbgsMAAAAxZfLSWZycrLDbT8/P1WoUEG33HKLAgJcPp3HZOcYys4xTGvfGwby/n70rNkhqEq5ELNDkL8vrQ9RSBezzfudyBXgb/7PwxveEz8ftl37oCJWPzrc7BCUlWN2BJLtfJbZIUiSQoPN/5uamW3eD8TMtnP5WSzy8/Dfb0+3V5RcHpPZpk0bh61Vq1aqVauWVyeYAAAAvmrWrFmKjY1VcHCwmjZtqvXr1+d7bGpqqvr27auaNWvKz89Pw4cPz3PMggUL7DPrL98uXLjgUlzXlRn++uuvWrNmjY4ePaqcHMdvGi+//PL1nBIAAAAuWrp0qYYPH65Zs2apZcuWevvttxUfH6+ff/7Z6QVyMjIyVKFCBb3wwgt6/fXX8z1vWFiYdu/e7bAvODjYpdhcTjLfeecdPf744ypfvrwiIiIcuoEtFgtJJgAA8AkWy6XN0226Ytq0aXr44Yf1yCOPSJKmT5+uVatWafbs2UpMTMxzfNWqVfXGG29Ikt59992rxGFRRESEa8FcweUkc8KECZo4caJGjhxZqIYBAADg3JWr+VitVlmtVod9mZmZ2rp1q0aNGuWwv2PHjtqwYUOh2j9z5oxiYmKUnZ2tRo0aafz48WrcuLFL53B5TObJkyf1wAMPuPowAACAYsXZuERPbJIUHR2t8PBw++asKnn8+HFlZ2erUqVKDvsrVaqktLS0637etWrV0oIFC/Tpp59q8eLFCg4OVsuWLbVnzx6XzuNyJfOBBx7Q119/rccee8zVhwIAAKAADh48qLCwMPvtK6uYl3O2tGRhVrVp3ry5mjdvbr/dsmVLNWnSRG+++aZmzJhR4PO4nGTecssteumll/T999+rfv36CgwMdLh/6NChrp4SAADA65g5JjMsLMwhyXSmfPny8vf3z1O1PHr0aJ7qZmH4+fnp1ltvLfpK5ty5c1WqVCmtXbtWa9eudbjPYrGQZAIAAHhAUFCQmjZtqqSkJPXs2dO+PykpST169HBbO4ZhKCUlRfXr13fpcS4nmXv37nX1IQAAACgCI0aMUP/+/dWsWTPFxcVp7ty5OnDggH1YY0JCgg4fPqxFixbZH5OSkiLp0uSeY8eOKSUlRUFBQapTp44kaezYsWrevLmqV68um82mGTNmKCUlRW+99ZZLsbGCOgAAgBPF4Yo/vXv31okTJzRu3DilpqaqXr16+uKLLxQTEyPp0uLrBw4ccHjM5bPEt27dqg8//FAxMTHat2+fJOnUqVP629/+prS0NIWHh6tx48Zat26dbrvtNpdisxiGYf515YqQzWZTeHi4Dh89ec2xDUWJy0pewmUlvUsWl5WUJHnDpyCXlbwky8TL/+Y6n5ltdgiSuKykzWZTTERZpaene/zvd27uMGjRJgWVKOXRtjPPndG7D95uyvN2N/PfwQAAAF6oOCzG7s1cXifT06pWrep0DakhQ4aYHRoAAADy4fWVzM2bNys7+39dFz/99JM6dOjAgvAAAABezOUkc/78+SpVqlSeJO+jjz7SuXPnNGDAALcFJ0kVKlRwuD158mRVq1ZNbdq0cWs7AAAAl7v8CjyebNNXuNxdPnnyZJUvXz7P/ooVK2rSpEluCSo/mZmZev/99zVo0KB8fwgZGRmy2WwOGwAAADzL5SRz//79io2NzbM/JiYmzxR5d1uxYoVOnTqlgQMH5ntMYmKiw7U+o6OjizQmAADgm/xM2nyFy8+lYsWK+vHHH/Ps37Fjh8qVK+eWoPIzb948xcfHKyoqKt9jEhISlJ6ebt8OHjxYpDEBAAAgL5fHZPbp00dDhw5VaGioWrduLUlau3athg0bpj59+rg9wFz79+/X6tWrtWzZsqseZ7Var3oReQAAgIJgTGbhuJxkTpgwQfv379ddd92lgIBLD8/JydGDDz5YpGMy58+fr4oVK6pLly5F1gYAAADcw+UkMygoSEuXLtX48eO1Y8cOhYSEqH79+vbLFxWFnJwczZ8/XwMGDLAntgAAAPBe152x1ahRQzVq1HBnLPlavXq1Dhw4oEGDBnmkPQAAAItF8vSViH2ot7xgSeaIESM0fvx4lSxZUiNGjLjqsdOmTXNLYJfr2LGjfPwS6wAAAD6lQEnm9u3bdfHiRfv/58eXBqsCAIAbm58JlUxPt1eUCpRkJicnO/1/AAAAwJlCrfl58OBBHTp0yF2xAAAAwEe4nGRmZWXppZdeUnh4uKpWraqYmBiFh4frxRdftHepAwAAFHe562R6evMVLs8uf/LJJ7V8+XJNnTpVcXFxkqSNGzdqzJgxOn78uObMmeP2IAEAAFC8uJxkLl68WEuWLFF8fLx9X4MGDVSlShX16dOHJBMAAPgEJv4Ujsvd5cHBwapatWqe/VWrVlVQUJA7YgIAAEAx53KSOWTIEI0fP14ZGRn2fRkZGZo4caKefPJJtwYHAABgFovFnM1XuNxdvn37dn3zzTeqXLmyGjZsKEnasWOHMjMzddddd+nee++1H7ts2TL3RQoAAIBiw+Uks3Tp0rrvvvsc9kVHR7stoKKSlWMoK8fEqwZ5wRWLokoHmx2CsrLNfx38vWTAy9mMbLNDUIC/F7wWOWYHIAV4wXsi7ewFs0NQbEZJs0OQvxeUcX5JtZkdgiSpTlSY2SEoyL9QKx0Wjvl/LlBILieZ8+fPL4o4AAAAvIqfxSI/D3/x8XR7RcnlJDPXsWPHtHv3blksFtWoUUMVKlRwZ1wAAAAoxlyug589e1aDBg1SZGSkWrdurVatWikqKkoPP/ywzp07VxQxAgAAeJyfSZuvcPm5jBgxQmvXrtW//vUvnTp1SqdOndLKlSu1du1aPfPMM0URIwAAAIoZl7vLP/nkE3388cdq27atfV/nzp0VEhKiXr16afbs2e6MDwAAAMWQy0nmuXPnVKlSpTz7K1asSHc5AADwGWasW+lD835c7y6Pi4vT6NGjdeHC/5bbOH/+vMaOHWu/ljkAAABubC5XMqdPn674+Hj7YuwWi0UpKSkKDg7WqlWriiJGAAAAj/OTCUsYyXdKmS4nmfXr19eePXv0/vvv6z//+Y8Mw1CfPn3Ur18/hYSEFEWMAAAAKGZcTjLXrVunFi1a6NFHH3XYn5WVpXXr1ql169ZuCw4AAMAsjMksHJfHZLZr105//vlnnv3p6elq166dW4ICAABA8eZykmkYhixO0uwTJ06oZEnzr3sLAAAA8xW4u/zee++VJFksFg0cOFBWq9V+X3Z2tn788Ue1aNHCrcFlZWVpzJgx+uCDD5SWlqbIyEgNHDhQL774ovz8fGlNfAAA4G38LJc2T7fpKwqcZIaHh0u6VMkMDQ11mOQTFBSk5s2b5xmnWVhTpkzRnDlztHDhQtWtW1dbtmzRQw89pPDwcA0bNsytbQEAAMB9Cpxkzp8/X5JUtWpVPfvssx7pGt+4caN69OihLl262NtevHixtmzZUuRtAwCAG5vFIo8vYXRDT/wZPXq0x8Ze3nHHHfrmm2/066+/SpJ27Nih7777Tp07d873MRkZGbLZbA4bAAAAPMvlJYw8aeTIkUpPT1etWrXk7++v7OxsTZw4UX/5y1/yfUxiYqLGjh3rwSgBAABwJa+ePbN06VK9//77+vDDD7Vt2zYtXLhQr776qhYuXJjvYxISEpSenm7fDh486MGIAQCAr8hdJ9PTm6/w6krmc889p1GjRqlPnz6SLl1taP/+/UpMTNSAAQOcPsZqtTrMfAcAAIDnua2SeeLECU2fPt1dp5MknTt3Ls9SRf7+/srJyXFrOwAAAFfKXcLI05uvKFSSaRiGVq1apV69eikqKkoTJ050V1ySpG7dumnixIn6/PPPtW/fPi1fvlzTpk1Tz5493doOAAAA3Ou6ksx9+/bp5ZdfVkxMjDp37qzg4GB9/vnnSktLc2twb775pu6//3498cQTql27tp599lkNHjxY48ePd2s7AAAAcK8CJ5kZGRlavHix7rrrLtWuXVs//fSTpk2bJj8/P40aNUrt27eXv7+/W4MLDQ3V9OnTtX//fp0/f17/93//pwkTJigoKMit7QAAAFzJYtI/X1HgiT833XST6tSpo7/+9a/6+OOPVaZMGUm66nJCAAAAuDEVOMnMzs6WxWKRxWJxe8USAADA23Dt8sIpcHd5amqq/va3v2nx4sWKiIjQfffdp+XLl8viSws6AQAAwC0KnGQGBwerX79++vbbb7Vz507Vrl1bQ4cOVVZWliZOnKikpCRlZ2cXZawAAAAewxJGhXNds8urVaumCRMmaP/+/frss8+UkZGhrl27qlKlSu6ODwAAAMVQoa744+fnp86dO6tz5846duyY3nvvPXfFBQAAgGLM5SRzz549Wrlypfbt2yeLxaKbb75Z99xzj2JjYzVixIiiiBEAAMDjcic8e7pNX+FSkpmYmKiXX35ZOTk5qlixogzD0LFjxzRy5EhNmjRJzz77bFHFCQAAgGKkwGMyk5OT9eKLL+qFF17Q8ePHlZqaqrS0NB07dkyjRo3SqFGjtG7duqKMFQAAwGOY+FM4Ba5kzpkzR4888ojGjBnjsL9s2bIaN26c0tLSNHv2bLVu3drdMQIAAKCYKXCS+cMPP1x1Yk///v314IMPuiWoohDgZ1GAiV8PjtoyTGs7V3aOYXYIqhBqNTsEGea/DJKkc5nmL/kVEW7+z+NchvmvQ5D/dS204VYNokqbHYL8vaCEcuZCltkh6NbYsmaHIEnKuGj+74Y10LzfDTPbhnsUOMn8448/VLVq1Xzvj42NVVpamjtiAgAAMJ3FcmnzdJu+osBfEy5cuKCgoKB87w8MDFRmZqZbggIAAEDx5tLs8n/84x8qVaqU0/tOnz7tloAAAAC8gZ/FIj8PlxY93V5RKnCSWaVKFb3zzjvXPAYAAAAocJK5b9++IgwDAADAu5ixpJAXzL9zG7dN3Tp48KAGDRrkrtMBAACgGHNbkvnnn39q4cKF7jodAAAAijGXr10OAABwQzBhCSPRXQ4AAADkj0omAACAE36yyM/DpUVPt1eUCpxk3nvvvVe9/9SpU4WNBQAAAD6iwN3l4eHhV91iYmKK5Nrlp0+f1vDhwxUTE6OQkBC1aNFCmzdvdns7AAAAcJ8CVzLnz59flHHk65FHHtFPP/2k9957T1FRUXr//ffVvn17/fzzz7rppptMiQkAAPg+rl1eOF498ef8+fP65JNPNHXqVLVu3Vq33HKLxowZo9jYWM2ePdvs8AAAAJAPr574k5WVpezsbAUHBzvsDwkJ0Xfffef0MRkZGcrIyLDfttlsRRojAADwTVzxp3C8upIZGhqquLg4jR8/XkeOHFF2drbef/99bdq0SampqU4fk5iY6DBWNDo62sNRAwAAwKuTTEl67733ZBiGbrrpJlmtVs2YMUN9+/aVv7+/0+MTEhKUnp5u3w4ePOjhiAEAgC/ws1hM2XyFV3eXS1K1atW0du1anT17VjabTZGRkerdu7diY2OdHm+1WmW1Wj0cJQAAAC7n9ZXMXCVLllRkZKROnjypVatWqUePHmaHBAAAgHx4fSVz1apVMgxDNWvW1G+//abnnntONWvW1EMPPWR2aAAAwIexhFHheH0lMz09XUOGDFGtWrX04IMP6o477tDXX3+twMBAs0MDAABAPry+ktmrVy/16tXL7DAAAMANxk+en4jjS9cu9/pKJgAAAIofkkwAAAC4ndd3lwMAAJiBiT+FQyUTAAAAbkclEwAAwAk/eb4a50vVP196LgAAAPASVDIBAACcsFgssnh4kKSn2ytKVDIBAADgdiSZAAAAcDu6ywEAAJyw/HfzdJu+4oZJMgP8/RTgb17htmKY1bS2c3nDOA/zI5ByDMPsECRJ5UsFmR2CV7wngoP8zQ5Bx89kmh2CsrJzzA5BJa3m/0kINPFzOldOjnd8Rpj5NwtwB/M/UQAAALyQn8WEa5d7wZd/d+FrEgAAANyOJBMAAABuR3c5AABAPnyn89rzqGQCAADA7ahkAgAAOGGxXNo83aavoJIJAAAAtyPJBAAAcCL32uWe3lw1a9YsxcbGKjg4WE2bNtX69evzPTY1NVV9+/ZVzZo15efnp+HDhzs97pNPPlGdOnVktVpVp04dLV++3OW4SDIBAACKqaVLl2r48OF64YUXtH37drVq1Urx8fE6cOCA0+MzMjJUoUIFvfDCC2rYsKHTYzZu3KjevXurf//+2rFjh/r3769evXpp06ZNLsVmMQwvufxJEbHZbAoPD9cfJ9IVFhZmWhzecDUPb7i6i/kReM8Vf7whjMAA879nZnvB1VVOeMEVfzIuZpsdgqLLlTA7BGVmecFnpdkB/Jf5vxlSoL95r4bNZlNE+dJKT/f83+/c3OEf635RiVKhHm373JnTeqR17QI/79tvv11NmjTR7Nmz7ftq166te+65R4mJiVd9bNu2bdWoUSNNnz7dYX/v3r1ls9n05Zdf2vfdfffdKlOmjBYvXlzg52L+XxgAAAAv5GfSJl1KdC/fMjIy8sSXmZmprVu3qmPHjg77O3bsqA0bNlz38964cWOec3bq1Mnlc5qaZK5bt07dunVTVFSULBaLVqxY4XC/YRgaM2aMoqKiFBISorZt22rXrl3mBAsAAOAh0dHRCg8Pt2/OqpLHjx9Xdna2KlWq5LC/UqVKSktLu+6209LS3HJOU5PMs2fPqmHDhpo5c6bT+6dOnapp06Zp5syZ2rx5syIiItShQwedPn3aw5ECAIAbjZkTfw4ePKj09HT7lpCQcNU4L2cYRqGHyLnjnKaukxkfH6/4+Hin9xmGoenTp+uFF17QvffeK0lauHChKlWqpA8//FCDBw/2ZKgAAAAeExYWds0xmeXLl5e/v3+eCuPRo0fzVCJdERER4ZZzeu2YzL179yotLc1hTIDValWbNm2uOiYgIyMjzzgGAAAAXxMUFKSmTZsqKSnJYX9SUpJatGhx3eeNi4vLc86vv/7a5XN67RV/cjNoZ2MC9u/fn+/jEhMTNXbs2CKNDQAA+D6LPL/agKvtjRgxQv3791ezZs0UFxenuXPn6sCBA3rsscckSQkJCTp8+LAWLVpkf0xKSook6cyZMzp27JhSUlIUFBSkOnXqSJKGDRum1q1ba8qUKerRo4dWrlyp1atX67vvvnMpNq9NMnO5OiYgISFBI0aMsN+22WyKjo4usvgAAADM0rt3b504cULjxo1Tamqq6tWrpy+++EIxMTGSLi2+fuWamY0bN7b//9atW/Xhhx8qJiZG+/btkyS1aNFCS5Ys0YsvvqiXXnpJ1apV09KlS3X77be7FJvXJpkRERGSLlU0IyMj7fuvNSbAarXKarUWeXwAAMC3Xe8VeArbpqueeOIJPfHEE07vW7BgQZ59BVki/f7779f999/vciyX89oxmbGxsYqIiHAYE5CZmam1a9cWapwBAAAAip6plcwzZ87ot99+s9/eu3evUlJSVLZsWVWpUkXDhw/XpEmTVL16dVWvXl2TJk1SiRIl1LdvXxOjBgAAN4LLF0f3ZJu+wtQkc8uWLWrXrp39du5YygEDBmjBggX6+9//rvPnz+uJJ57QyZMndfvtt+vrr79WaKhnL/EEAAAA15iaZLZt2/aq4wIsFovGjBmjMWPGeC4oAAAAFJrXTvwBAAAwU3GZ+OOtfKnrHwAAAF6CSiYAAIATxWExdm9GJRMAAABuR5IJAAAAt6O7HAAAwAmL5dLm6TZ9BZVMAAAAuB2VTAAAACf8ZJGfh6fieLq9okQlEwAAAG5HkgkAAAC3o7vcQzKzcswOQdZAf7ND8IoBzcOW7zI7BEnStO51zA7hqpd1vZFUDLOaHYIuXMw2OwSv4OcFnxGppy6YHYIkKbpcCbNDUE6OeZ8R3vDxxMSfwqGSCQAAALejkgkAAOCE5b//PN2mr6CSCQAAALejkgkAAOAEYzILh0omAAAA3I4kEwAAAG5HdzkAAIATFhOu+MPEHwAAAOAqqGQCAAA4wcSfwqGSCQAAALcjyQQAAIDbmZpkrlu3Tt26dVNUVJQsFotWrFjhcP+yZcvUqVMnlS9fXhaLRSkpKabECQAAbjy53eWe3nyFqUnm2bNn1bBhQ82cOTPf+1u2bKnJkyd7ODIAAAAUhqkTf+Lj4xUfH5/v/f3795ck7du3z0MRAQAAXMK1ywvH52aXZ2RkKCMjw37bZrOZGA0AAMCNyecm/iQmJio8PNy+RUdHmx0SAAAohvws5my+wueSzISEBKWnp9u3gwcPmh0SAADADcfnusutVqusVqvZYQAAANzQfC7JBAAAcAcm/hSOqUnmmTNn9Ntvv9lv7927VykpKSpbtqyqVKmiP//8UwcOHNCRI0ckSbt375YkRUREKCIiwpSYAQAAcG2mjsncsmWLGjdurMaNG0uSRowYocaNG+vll1+WJH366adq3LixunTpIknq06ePGjdurDlz5pgWMwAAuDGwGHvhmFrJbNu2rQzDyPf+gQMHauDAgZ4LCAAAAG7hc7PLAQAAYD4m/gAAADhhkecn4vhQbzmVTAAAALgflUwAAAAnzLgCD1f8AQAAAK6CSiYAAIATLMZeOFQyAQAA4HYkmQAAAHA7ussBAACcMOMKPL50xR8qmQAAAHC7G6aSaRjGVS9hWdQC/M3P573hy5HFC76ivdmzntkhSPKOb6ve8PPIyc4xOwT5e8GaIcGB/maH4BW84WcRXa6E2SF4DT8Tfx5mtp3LIs//7TT/WbuP+ZkPAAAAfA5JJgAAANzuhukuBwAAcIWfLPLz8LAiPx/qMKeSCQAAALejkgkAAOAEE38Kh0omAAAA3I5KJgAAgDOUMguFSiYAAADcjiQTAAAAbkd3OQAAgBOW//7zdJu+gkomAAAA3M7UJHPdunXq1q2boqKiZLFYtGLFCvt9Fy9e1MiRI1W/fn2VLFlSUVFRevDBB3XkyBHzAgYAADcOi2Tx8OZDhUxzk8yzZ8+qYcOGmjlzZp77zp07p23btumll17Stm3btGzZMv3666/q3r27CZECAADAFaaOyYyPj1d8fLzT+8LDw5WUlOSw780339Rtt92mAwcOqEqVKp4IEQAAANehWE38SU9Pl8ViUenSpfM9JiMjQxkZGfbbNpvNA5EBAABfwzKZhVNsJv5cuHBBo0aNUt++fRUWFpbvcYmJiQoPD7dv0dHRHowSAAAAUjFJMi9evKg+ffooJydHs2bNuuqxCQkJSk9Pt28HDx70UJQAAMCnWEzafITXd5dfvHhRvXr10t69e/Xtt99etYopSVarVVar1UPRAQAAwBmvTjJzE8w9e/YoOTlZ5cqVMzskAABwg2Ax9sIxNck8c+aMfvvtN/vtvXv3KiUlRWXLllVUVJTuv/9+bdu2TZ999pmys7OVlpYmSSpbtqyCgoLMChsAAADXYGqSuWXLFrVr185+e8SIEZKkAQMGaMyYMfr0008lSY0aNXJ4XHJystq2beupMAEAAOAiU5PMtm3byjCMfO+/2n0AAABFyX4VHg+36SuKxexyAAAAFC9ePfEHAADALCzGXjhUMgEAAOB2JJkAAABwO7rLAQAAnKG/vFCoZAIAAMDtqGQCAAA4wRV/CodKJgAAANyOSiYAAIATLMZeOFQyAQAA4HY3TCUzO8dQdo55l6kM8DP/q0lGVo7ZISg40PzvNX5e8LOQpOOnM8wOQaWCzf8ICA70NzsELf/xkNkhqFvdKLNDkL8X/G6Y+Tmd62K2+Z+VkpTlJXGY5cz5i2aHgEIy/y8MAACAF2IFo8Ixv6wEAAAAn0MlEwAAwBlKmYVCJRMAAABuR5IJAAAAt6O7HAAAwAmu+FM4VDIBAADgdlQyAQAAnOCKP4VDJRMAAABuR5IJAAAAt6O7HAAAwAmWySwcKpkAAABwO1OTzHXr1qlbt26KioqSxWLRihUrHO4fM2aMatWqpZIlS6pMmTJq3769Nm3aZE6wAADgxmIxafMRpiaZZ8+eVcOGDTVz5kyn99eoUUMzZ87Uzp079d1336lq1arq2LGjjh075uFIAQAA4ApTk8z4+HhNmDBB9957r9P7+/btq/bt2+vmm29W3bp1NW3aNNlsNv3444/5njMjI0M2m81hAwAAcJXFpH+umjVrlmJjYxUcHKymTZtq/fr1Vz1+7dq1atq0qYKDg3XzzTdrzpw5DvcvWLBAFoslz3bhwgWX4io2YzIzMzM1d+5chYeHq2HDhvkel5iYqPDwcPsWHR3twSgBAAA8Z+nSpRo+fLheeOEFbd++Xa1atVJ8fLwOHDjg9Pi9e/eqc+fOatWqlbZv367nn39eQ4cO1SeffOJwXFhYmFJTUx224OBgl2Lz+iTzs88+U6lSpRQcHKzXX39dSUlJKl++fL7HJyQkKD093b4dPHjQg9ECAAB4zrRp0/Twww/rkUceUe3atTV9+nRFR0dr9uzZTo+fM2eOqlSpounTp6t27dp65JFHNGjQIL366qsOx1ksFkVERDhsrvL6JLNdu3ZKSUnRhg0bdPfdd6tXr146evRovsdbrVaFhYU5bAAAAK7KveKPpzdJeYb+ZWRk5IkvMzNTW7duVceOHR32d+zYURs2bHD6nDZu3Jjn+E6dOmnLli26ePGifd+ZM2cUExOjypUrq2vXrtq+fbvLr5/XJ5klS5bULbfcoubNm2vevHkKCAjQvHnzzA4LAACgyERHRzsM/0tMTMxzzPHjx5Wdna1KlSo57K9UqZLS0tKcnjctLc3p8VlZWTp+/LgkqVatWlqwYIE+/fRTLV68WMHBwWrZsqX27Nnj0nModouxG4bhNJsHAABwJzMXYz948KBDb6zVas3/MVdc8NwwjDz7rnX85fubN2+u5s2b2+9v2bKlmjRpojfffFMzZswo0POQTE4yz5w5o99++81+e+/evUpJSVHZsmVVrlw5TZw4Ud27d1dkZKROnDihWbNm6dChQ3rggQdMjBoAAKBoFWTIX/ny5eXv75+nann06NE81cpcERERTo8PCAhQuXLlnD7Gz89Pt956q8uVTFO7y7ds2aLGjRurcePGkqQRI0aocePGevnll+Xv76///Oc/uu+++1SjRg117dpVx44d0/r161W3bl0zwwYAADBdUFCQmjZtqqSkJIf9SUlJatGihdPHxMXF5Tn+66+/VrNmzRQYGOj0MYZhKCUlRZGRkS7FZ2ols23btvYSrTPLli3zYDQAAACXKQYXLx8xYoT69++vZs2aKS4uTnPnztWBAwf02GOPSbq06s7hw4e1aNEiSdJjjz2mmTNnasSIEXr00Ue1ceNGzZs3T4sXL7afc+zYsWrevLmqV68um82mGTNmKCUlRW+99ZZLsRW7MZkAAAC4pHfv3jpx4oTGjRun1NRU1atXT1988YViYmIkSampqQ5rZsbGxuqLL77Q008/rbfeektRUVGaMWOG7rvvPvsxp06d0t/+9jelpaUpPDxcjRs31rp163Tbbbe5FJvFuFop0QfYbDaFh4fr8NGTpi5n5HeVAbiekpGVY3YICg40f0GDqw2G9qTjp82fwFYq2PzvmcGB/maHoOU/HjI7BHWrG2V2CPL3M/93IzvH/D9JF7PNj0GSsrLN/8w202mbTdUql1d6errH/37n5g6bd6eqVKhn2z5z2qZba0aa8rzdzfy/+AAAAPA55pcxAAAAvNDli6N7sk1fQSUTAAAAbkeSCQAAALejuxwAAMCJYrCCkVejkgkAAAC3o5IJAADgDKXMQrlhkkzDuLSZ1r4XvGlyvGBJVG9YozLjYrbZIUiSypYMMjsEXfCC18Ib1kVsX935NX49yRt+NzK9YC1db1ij0hvW85Wksxnm/zyCAsx7Lcx/J6CwvOM3CQAAAD7lhqlkAgAAuMLy33+ebtNXUMkEAACA21HJBAAAcMaEK/74UCGTSiYAAADcj0omAACAE6xgVDhUMgEAAOB2JJkAAABwO7rLAQAAnKG/vFCoZAIAAMDtqGQCAAA4wWLshUMlEwAAAG5napK5bt06devWTVFRUbJYLFqxYkW+xw4ePFgWi0XTp0/3WHwAAAC4PqYmmWfPnlXDhg01c+bMqx63YsUKbdq0SVFRUR6KDAAA3OgsFnM2X2HqmMz4+HjFx8df9ZjDhw/rySef1KpVq9SlS5drnjMjI0MZGRn22zabrdBxAgAAwDVePSYzJydH/fv313PPPae6desW6DGJiYkKDw+3b9HR0UUcJQAA8EUWkzZf4dVJ5pQpUxQQEKChQ4cW+DEJCQlKT0+3bwcPHizCCAEAAOCM1y5htHXrVr3xxhvatm2bLC4MULBarbJarUUYGQAAuCGwGHuheG0lc/369Tp69KiqVKmigIAABQQEaP/+/XrmmWdUtWpVs8MDAADAVXhtJbN///5q3769w75OnTqpf//+euihh0yKCgAAAAVhapJ55swZ/fbbb/bbe/fuVUpKisqWLasqVaqoXLlyDscHBgYqIiJCNWvW9HSoAADgBsMVfwrH1CRzy5Ytateunf32iBEjJEkDBgzQggULTIoKAAAAhWVqktm2bVsZhlHg4/ft21d0wQAAAFzGIs8vju47dUwvnvgDAACA4oskEwAAAG7ntbPLAQAAzMQymYVDJRMAAABuRyUTAADACYvFhIk/PlTKpJIJAAAAt6OSCQAA4BSjMguDSiYAAADczucrmbmLvZ8+bTM1Dj8/87+ZZFzMNjsEZVvNf8t5w+sgSYH+5n/Hu+AFr4U10N/sEHQ+I8vsEJTlBb8bWdk5ZoegrOyCX6CjqGQGmv+7KUmnz100OwQFBZj3Wpw+fVqSXLpoC7yL+Z9qRSz3TVr7lhiTIwEAAK46ffq0wsPDTWmbiT+F4/NJZlRUlA4ePKjQ0FBZfOknBwCADzMMQ6dPn1ZUVJTZoeA6+XyS6efnp8qVK5sdBgAAcJFZFcxcTPspHO8YeAIAAACfQpIJAAAAt/P57nIAAIDrwcSfwqGSCQAAALejkgkAAOCE5b//PN2mr6CSCQAAALejkgkAAOAMaxgVCpVMAAAAuB1JJgAAANyO7nIAAAAn6C0vHCqZAAAAcDsqmQAAAE6wGHvhUMkEAACA25FkAgAAwO3oLgcAAHCCK/4UDpVMAAAAuB2VTAAAAGdYw6hQqGQCAADA7UgyAQAA4HZ0lwMAADhBb3nhUMkEAACA21HJBAAAcIIr/hQOlUwAAAC4HZVMAAAApzy/GLsvjcqkkgkAAAC3I8kEAACA29FdDgAA4AQTfwqHSiYAAADcjiQTAAAAbkeSCQAAALcjyQQAAIDbMfEHAADACSb+FA6VTAAAALgdlUwAAAAnLCZc8cfzVxgqOlQyAQAA4HZUMgEAAJxgTGbhUMkEAACA25FkAgAAwO3oLgcAAHDC8t/N0236CiqZAAAAcDsqmQAAAM5QyiwUKpkAAABwO5JMAAAAuB3d5QAAAE5wxZ/CoZIJAAAAt6OSCQAA4ARX/CkcKpkAAABwOyqZAAAATrCCUeFQyQQAAIDbkWQCAADA7eguBwAAcIb+8kKhkgkAAAC3o5IJAADgBIuxFw6VTAAAALgdSSYAAADcju5yAAAAJ7jiT+GQZAIAADhhs9luiDaLCkkmAADAZYKCghQREaHqsdGmtB8REaGgoCBT2nYni2EYhtlBAAAAeJMLFy4oMzPTlLaDgoIUHBxsStvuRJIJAAAAt2N2OQAAANyOJBMAAABuR5IJAAAAtyPJBAAAgNuRZAIAAMDtSDIBAADgdiSZAAAAcLv/BzC8Dgns7G/gAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_common = nmf_topics[:, :1000]\n",
    "y_common = lda_topics[:, :1000]\n",
    "\n",
    "plot_confusion(x = x_common, y = y_common, x_label='NMF topic number',\n",
    "               y_label='LDA topic number',\n",
    "               t_label = 'Word Similarity between LDA and NMF Topics',\n",
    "               sort=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can also sort the topics so that the most similar ones are aligned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_common = nmf_topics[:, :1000]\n",
    "y_common = lda_topics[:, :1000]\n",
    "\n",
    "plot_confusion(x = x_common, y = y_common, x_label='NMF topic number',\n",
    "               y_label='LDA topic number',\n",
    "               t_label = 'Word Similarity between LDA and NMF Topics',\n",
    "               sort=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-info\">\n",
    "    \n",
    "#### Short Answer No.11\n",
    "\n",
    "Look at the LDA and NMF topic words and the confusion matrix, and consider the following questions:\n",
    "- Do any of the topics seem to be the same in both models?\n",
    "- Are some topics in one model but not the other?\n",
    "- Do the topics you get from one of the models make more sense than the ones you get from the other?\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 8: What We Learned\n",
    "- Two statistical methods for topic modeling\n",
    "    - LDA\n",
    "    - NMF\n",
    "- How to think about and interpret the topics our models find\n",
    "- How to compare and relate different topics\n",
    "- Different ways to see the distribution of topics in profiles\n",
    "- Which topics are most popular with social categories of people\n",
    "- Which social categories of people discuss a topic most"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-warning\">\n",
    "\n",
    "#### Reflection Question 1:\n",
    "- How is what we learned in this lab, using topic modeling, different from what we learned in the last lab, using just word frequencies? How is it similar? Write a paragraph explaining. \n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert-warning\">\n",
    "\n",
    "#### Reflection Question 2:\n",
    "- For both the LDA and NMF models, we specified 15 topics. Now, try running the LDA with other numbers of topics.\n",
    "    - If the topics seemed repetitive, you might want to try looking for fewer topics.\n",
    "    - If the topics seem confusing or vague, you might want to try looking for more topics (so that they can be more specific).\n",
    "- Run the code below and answer the following:\n",
    "    - What different numbers of topics did you try?\n",
    "    - How did your interpretations change in response to the number of topics? Were they it easier or more difficult to interpret?\n",
    "    - How did topic quality change in response to the number of topics? Were topics more similar or less similar?\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "🤔 **Write your response here:**\n",
    "...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Decide how many topics we want to find\n",
    "- We must tell LDA how many topics we want it to look for (we did this above with the `ntopics` variable).\n",
    "    - We suggest picking a few values between 2-50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#how many topics we want our model to find\n",
    "ntopics = 12\n",
    "\n",
    "#how many top words we want to display for each topic\n",
    "nshow = 10\n",
    "\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "texts = data_lemmatized\n",
    "\n",
    "corpus = [id2word.doc2bow(text) for text in texts]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Run the LDA algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing LDA on vectors. This may take a while...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Build LDA model\n",
    "print('Performing LDA on vectors. This may take a while...')\n",
    "lda_model = gensim.models.LdaMulticore(corpus=corpus,\n",
    "                                       id2word=id2word,\n",
    "                                       num_topics=ntopics, \n",
    "                                       random_state=100,\n",
    "                                       chunksize=100,\n",
    "                                       passes=10,\n",
    "                                       per_word_topics=True)\n",
    "\n",
    "lda_topics = lda_model.get_topics()\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Show our topics with the top words in each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0: life love good friend people enjoy time person thing work\n",
      "Topic 1: people get thing know think really say make good want\n",
      "Topic 2: relationship love woman man open look also people well sex\n",
      "Topic 3: go love play time work get watch friend enjoy movie\n",
      "Topic 4: com asian youtube adjective watch feature san_franciscan link sadly tumblr\n",
      "Topic 5: love new enjoy people go thing travel try good friend\n",
      "Topic 6: get day make love good thing take eat live time\n",
      "Topic 7: work study learn school play currently student teach college also\n",
      "Topic 8: year move live work bear back city go grow ago\n",
      "Topic 9: m guy fun look girl go nice funny love easy\n",
      "Topic 10: music art love creative film movie read artist photography book\n",
      "Topic 11: life love live look want man woman share year old\n"
     ]
    }
   ],
   "source": [
    "display_topics(lda_model, n_words=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Examine the words that make two topics similar or different\n",
    "- We can also compare two topics to each other by looking at words that are common in both, or words that are common in one but not the other.\n",
    "- Try changing `topic_a` and `topic_b` to different topic numbers.\n",
    "- Notice the `how` option will let you see either the `overlap` or `difference` between two topics\n",
    "    - Notice also that the difference between topics a and b is not the same as between b and a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Common words between Topic 0 and Topic 1:\n",
      "{'thing', 'people', 'good'}\n",
      "Words unique to Topic 0: {'friend', 'work', 'time', 'person', 'love', 'enjoy', 'life'}\n",
      "Words unique to Topic 1: {'get', 'make', 'say', 'want', 'really', 'know', 'think'}\n"
     ]
    }
   ],
   "source": [
    "topic_a = 0\n",
    "topic_b = 1\n",
    "\n",
    "compare_topic_overlap(lda_model, topic_a, topic_b)\n",
    "compare_topic_difference(lda_model, topic_a, topic_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Interpret these topics\n",
    "- This part is for you to do: code can't do it for you.\n",
    "- Try to come up with a short, catchy name for each topic (no writing required)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6: Topic Quality\n",
    "Let's see how good the topics we found are.\n",
    "\n",
    "#### Step 6.1: See if the topics are each about different things.\n",
    "We want each topic to be about something different than the other topics. We can check this by comparing the words in each topic to the words in all the others. How to interpret:\n",
    "- Each square shows how similar two topics are. Darker means more similar, and lighter means more different.\n",
    "    - Notice that some topics may be negatively correlated.\n",
    "- The square in the very top left shows how similar topic 0 is to topic 0 (i.e. how similar it is to itself). \n",
    "- The square next to it in the top row shows how similar topic 0 is to topic 1, and so on. \n",
    "- For any two topics, you can see how similar they are by finding their numbers on the edges and seeing where they intersect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_topics(components):\n",
    "    sim = cosine_similarity(components)\n",
    "    blue_matrix(sim, xl='Topic number', yl='Topic number',\n",
    "                t = 'Word Similarity between Topics')\n",
    "    return\n",
    "\n",
    "plot_topics(lda_topics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6.2: See if different topics show up in different profiles\n",
    "The point is to tell profiles apart based on what topics they're about, so we need to check whether the topics appear in different profiles.\n",
    "- This shows us something that looks similar to the topic similarity we saw before, but this time:\n",
    "    - We **don't** compare topics based on which words they use\n",
    "    - We **do** compare topics based on how often they appear in the same profile as one another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_topics = [lda_model.get_document_topics(bow, minimum_probability=0) for bow in corpus]\n",
    "profile_topics_flattened = np.array([np.array([t[1] for t in profile]) for profile in profile_topics])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def topic_cooccurance(topics):\n",
    "    co = pd.DataFrame(topics).corr()\n",
    "    blue_matrix(co, xl='Topic number', yl='Topic number',\n",
    "               t = 'Topic Co-occurance in Profiles')\n",
    "\n",
    "topic_cooccurance(profile_topics_flattened)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current Time = 05:03:29\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "now = datetime.now()\n",
    "\n",
    "current_time = now.strftime(\"%H:%M:%S\")\n",
    "print(\"Current Time =\", current_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}