04C_Modeling_single_defined.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install -c conda-forge pyldavis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in terminal\n",
    "# python -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !conda install gensim -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "import pickle\n",
    "from emoji import UNICODE_EMOJI\n",
    "import pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pickle.load(open(\"Data/clean_df.pickle\",\"rb\"))\n",
    "single_defined_notes = pickle.load(open(\"Data/single_defined_notes.pickle\",\"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a smaller set of notes to practice preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer \n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import re\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_topics(model, feature_names, no_top_words, topic_names=None):\n",
    "    for ix, topic in enumerate(model.components_):\n",
    "        if not topic_names or not topic_names[ix]:\n",
    "            print(\"\\nTopic \", ix)\n",
    "        else:\n",
    "            print(\"\\nTopic: '\",topic_names[ix],\"'\")\n",
    "        print(\", \".join(feature_names[i] + ' ' + f'{topic[i]:9.2f}'\n",
    "                        for i in topic.argsort()[:-no_top_words - 1:-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## single_defined_notes\n",
    "### generate document-term matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/57333183/sk-learn-countvectorizer-keeping-emojis-as-words\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "s= ['👋 Welcome', '👋 Welcome']\n",
    "\n",
    "v = TfidfVectorizer(token_pattern=r'[^\\s]+')\n",
    "v.fit(s)\n",
    "v.get_feature_names()\n",
    "\n",
    "['welcome', '👋']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286464, 660)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(token_pattern=r'[^\\s]+',stop_words='english',min_df=200)\n",
    "dtm = vectorizer.fit_transform(single_defined_notes)\n",
    "dtm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have to convert `.toarray()` because the vectorizer returns a sparse matrix.\n",
    "# For a big corpus, we would skip the dataframe and keep the output sparse.\n",
    "dtm_lsa = pd.DataFrame(dtm.toarray(), columns=vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['admission_tickets', 'airbnb', 'airplane', 'amazon',\n",
       "       'american_football', 'april', 'artist_palette', 'ass', 'august',\n",
       "       'automobile',\n",
       "       ...\n",
       "       '🥟', '🥤', '🥦', '🥩', '🥪', '🥯', '🥰', '🥳', '🦄', '🧀'],\n",
       "      dtype='object', length=660)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtm_lsa.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42014827566833374"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Latent Semantic Analysis (LSA) is just another name for Singular Value Decomposition (SVD) applied to Natural Language Processing (NLP)\n",
    "\n",
    "lsa = TruncatedSVD(150)\n",
    "doc_topic = lsa.fit(dtm[:100000])\n",
    "\n",
    "sum(lsa.explained_variance_ratio_[0:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/lsa_single_defined_100k.pickle', 'wb') as f:\n",
    "    pickle.dump(doc_topic, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb87b82e550>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lsa.explained_variance_ratio_[0:50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "️      0.60, ☕      0.56, hot_beverage      0.56\n",
      "\n",
      "Topic  1\n",
      "pizza      0.72, 🍕      0.69, hamburger      0.01\n",
      "\n",
      "Topic  2\n",
      "‍      0.47, man      0.36, 👨      0.36\n",
      "\n",
      "Topic  3\n",
      "money_with_wings      0.60, 💸      0.60, house      0.34\n",
      "\n",
      "Topic  4\n",
      "hamburger      0.70, 🍔      0.70, 🍟      0.04\n",
      "\n",
      "Topic  5\n",
      "taco      0.58, 🌮      0.56, burrito      0.39\n",
      "\n",
      "Topic  6\n",
      "☕      0.21, hot_beverage      0.21, taco      0.15\n",
      "\n",
      "Topic  7\n",
      "sushi      0.70, 🍣      0.69, taco      0.04\n",
      "\n",
      "Topic  8\n",
      "burrito      0.59, 🌯      0.58, sushi      0.06\n",
      "\n",
      "Topic  9\n",
      "american_football      0.70, 🏈      0.70, fantasy      0.07\n",
      "\n",
      "Topic  10\n",
      "red_heart      0.53, ❤      0.53, ️      0.26\n",
      "\n",
      "Topic  11\n",
      "🎉      0.65, party_popper      0.65, eggplant      0.18\n",
      "\n",
      "Topic  12\n",
      "party_popper      0.21, 🎉      0.21, house      0.09\n",
      "\n",
      "Topic  13\n",
      "light_skin_tone      0.37, 🏻      0.37, ❤      0.18\n",
      "\n",
      "Topic  14\n",
      "food      1.00, medium_light_skin_tone      0.03, 🏼      0.03\n",
      "\n",
      "Topic  15\n",
      "⚡      0.28, high_voltage      0.28, electric_plug      0.27\n",
      "\n",
      "Topic  16\n",
      "️      0.32, ‍      0.22, house      0.21\n",
      "\n",
      "Topic  17\n",
      "🍺      0.68, beer_mug      0.68, clinking_beer_mugs      0.13\n",
      "\n",
      "Topic  18\n",
      "basketball      0.71, 🏀      0.70, trophy      0.03\n",
      "\n",
      "Topic  19\n",
      "💦      0.44, sweat_droplets      0.44, 🔥      0.33\n",
      "\n",
      "Topic  20\n",
      "️      0.32, ⛽      0.30, fuel_pump      0.30\n",
      "\n",
      "Topic  21\n",
      "🍷      0.51, wine_glass      0.51, 🍾      0.24\n",
      "\n",
      "Topic  22\n",
      "sandwich      0.71, 🥪      0.67, 🍗      0.05\n",
      "\n",
      "Topic  23\n",
      "high_voltage      0.19, ⚡      0.19, light_bulb      0.11\n",
      "\n",
      "Topic  24\n",
      "fuel_pump      0.36, ⛽      0.36, ‍      0.20\n",
      "\n",
      "Topic  25\n",
      "clinking_beer_mugs      0.62, 🍻      0.62, 🍹      0.18\n",
      "\n",
      "Topic  26\n",
      "dollar_banknote      0.31, 💵      0.31, house      0.28\n",
      "\n",
      "Topic  27\n",
      "cookie      0.49, 🍪      0.48, poultry_leg      0.41\n",
      "\n",
      "Topic  28\n",
      "🎁      0.52, wrapped_gift      0.52, cookie      0.33\n",
      "\n",
      "Topic  29\n",
      "cookie      0.37, 🍪      0.36, clinking_beer_mugs      0.10\n",
      "\n",
      "Topic  30\n",
      "😘      0.26, face_blowing_a_kiss      0.26, ️      0.11\n",
      "\n",
      "Topic  31\n",
      "😘      0.53, face_blowing_a_kiss      0.53, ⛳      0.28\n",
      "\n",
      "Topic  32\n",
      "tropical_drink      0.60, 🍹      0.60, fork_and_knife      0.18\n",
      "\n",
      "Topic  33\n",
      "money_bag      0.63, 💰      0.63, 🍴      0.19\n",
      "\n",
      "Topic  34\n",
      "medium_skin_tone      0.52, 🏽      0.52, 😘      0.30\n",
      "\n",
      "Topic  35\n",
      "🍴      0.63, fork_and_knife      0.63, clinking_beer_mugs      0.05\n",
      "\n",
      "Topic  36\n",
      "rent      0.96, fork_and_knife      0.15, 🍴      0.15\n",
      "\n",
      "Topic  37\n",
      "automobile      0.68, 🚗      0.68, airplane      0.11\n",
      "\n",
      "Topic  38\n",
      "🇺      0.52, regional_indicator_symbol_letter_u      0.52, 🇸      0.45\n",
      "\n",
      "Topic  39\n",
      "🍾      0.61, bottle_with_popping_cork      0.61, collision      0.08\n",
      "\n",
      "Topic  40\n",
      "female_sign      0.49, ♀      0.49, ‍      0.19\n",
      "\n",
      "Topic  41\n",
      "🔥      0.84, chicken      0.14, 🐔      0.11\n",
      "\n",
      "Topic  42\n",
      "pile_of_poo      0.50, 💩      0.50, dog_face      0.48\n",
      "\n",
      "Topic  43\n",
      "💩      0.47, pile_of_poo      0.47, thanks      0.05\n",
      "\n",
      "Topic  44\n",
      "chicken      0.71, 🐔      0.59, admission_tickets      0.18\n",
      "\n",
      "Topic  45\n",
      "admission_tickets      0.64, 🎟      0.64, ticket      0.24\n",
      "\n",
      "Topic  46\n",
      "strawberry      0.66, banana      0.54, bread      0.19\n",
      "\n",
      "Topic  47\n",
      "👌      0.41, ok_hand      0.41, 🏾      0.39\n",
      "\n",
      "Topic  48\n",
      "poultry_leg      0.31, 🍗      0.31, strawberry      0.28\n",
      "\n",
      "Topic  49\n",
      "thanks      0.96, good      0.05, stuff      0.03\n",
      "\n",
      "Topic  50\n",
      "sign_of_the_horns      0.43, 🤘      0.43, 🏾      0.26\n",
      "\n",
      "Topic  51\n",
      "🍜      0.66, steaming_bowl      0.66, bread      0.09\n",
      "\n",
      "Topic  52\n",
      "smiling_face_with_sunglasses      0.63, 😎      0.63, airplane      0.20\n",
      "\n",
      "Topic  53\n",
      "airplane      0.57, ✈      0.57, taxi      0.18\n",
      "\n",
      "Topic  54\n",
      "tshirt      0.71, 👕      0.66, airplane      0.09\n",
      "\n",
      "Topic  55\n",
      "🙌      0.32, raising_hands      0.32, 🤘      0.30\n",
      "\n",
      "Topic  56\n",
      "💻      0.40, laptop_computer      0.40, raising_hands      0.17\n",
      "\n",
      "Topic  57\n",
      "cooking      0.21, 🍳      0.21, 🤘      0.20\n",
      "\n",
      "Topic  58\n",
      "stuff      0.70, sign_of_the_horns      0.20, 🤘      0.20\n",
      "\n",
      "Topic  59\n",
      "stuff      0.35, woman_dancing      0.24, 💃      0.24\n",
      "\n",
      "Topic  60\n",
      "baseball      0.65, ⚾      0.59, fantasy      0.16\n",
      "\n",
      "Topic  61\n",
      "thank      0.42, 💃      0.38, woman_dancing      0.38\n",
      "\n",
      "Topic  62\n",
      "baby      0.62, 👶      0.41, baseball      0.15\n",
      "\n",
      "Topic  63\n",
      "thank      0.36, u      0.07, 🎉      0.06\n",
      "\n",
      "Topic  64\n",
      "volleyball      0.61, 🏐      0.59, flexed_biceps      0.25\n",
      "\n",
      "Topic  65\n",
      "volleyball      0.36, 🏐      0.35, baby      0.23\n",
      "\n",
      "Topic  66\n",
      "house_with_garden      0.62, 🏡      0.62, ⚽      0.19\n",
      "\n",
      "Topic  67\n",
      "🍟      0.34, french_fries      0.34, 🏡      0.24\n",
      "\n",
      "Topic  68\n",
      "soccer_ball      0.54, ⚽      0.54, ticket      0.22\n",
      "\n",
      "Topic  69\n",
      "ticket      0.51, 🎫      0.33, french_fries      0.20\n",
      "\n",
      "Topic  70\n",
      "money      0.44, 😂      0.43, face_with_tears_of_joy      0.43\n",
      "\n",
      "Topic  71\n",
      "ticket      0.37, money      0.34, 🎫      0.24\n",
      "\n",
      "Topic  72\n",
      "😂      0.25, face_with_tears_of_joy      0.25, cooking      0.19\n",
      "\n",
      "Topic  73\n",
      "face_with_tears_of_joy      0.42, 😂      0.42, ticket      0.24\n",
      "\n",
      "Topic  74\n",
      "money      0.37, gas      0.21, good      0.05\n",
      "\n",
      "Topic  75\n",
      "fantasy      0.78, football      0.39, peach      0.15\n",
      "\n",
      "Topic  76\n",
      "peach      0.67, 🍑      0.66, 😂      0.05\n",
      "\n",
      "Topic  77\n",
      "⚡      0.29, high_voltage      0.29, ⚽      0.18\n",
      "\n",
      "Topic  78\n",
      "good      0.51, 🥰      0.27, smiling_face_with_3_hearts      0.27\n",
      "\n",
      "Topic  79\n",
      "🥰      0.26, smiling_face_with_3_hearts      0.26, 😍      0.20\n",
      "\n",
      "Topic  80\n",
      "😍      0.28, smiling_face_with_hearteyes      0.28, 🥰      0.27\n",
      "\n",
      "Topic  81\n",
      "bills      0.79, splitwisecom      0.32, 🎄      0.16\n",
      "\n",
      "Topic  82\n",
      "🎄      0.33, christmas_tree      0.33, santa_claus      0.28\n",
      "\n",
      "Topic  83\n",
      "🎥      0.63, movie_camera      0.63, clown_face      0.19\n",
      "\n",
      "Topic  84\n",
      "tumbler_glass      0.61, 🥃      0.61, 🍸      0.17\n",
      "\n",
      "Topic  85\n",
      "taxi      0.62, 🚕      0.60, sport_utility_vehicle      0.19\n",
      "\n",
      "Topic  86\n",
      "person_getting_haircut      0.66, 💇      0.66, tumbler_glass      0.09\n",
      "\n",
      "Topic  87\n",
      "clown_face      0.68, 🤡      0.68, ticket      0.03\n",
      "\n",
      "Topic  88\n",
      "laptop_computer      0.35, 💻      0.35, mobile_phone      0.27\n",
      "\n",
      "Topic  89\n",
      "dog      0.62, 🐕      0.44, 🥳      0.15\n",
      "\n",
      "Topic  90\n",
      "love      0.22, money      0.21, 💕      0.17\n",
      "\n",
      "Topic  91\n",
      "venmo_dollar      0.38, money      0.32, 🥰      0.13\n",
      "\n",
      "Topic  92\n",
      "partying_face      0.31, 🥳      0.31, 🥰      0.26\n",
      "\n",
      "Topic  93\n",
      "venmo_dollar      0.49, call_me_hand      0.38, 🤙      0.38\n",
      "\n",
      "Topic  94\n",
      "venmo_dollar      0.76, gas      0.41, 🥳      0.08\n",
      "\n",
      "Topic  95\n",
      "🎃      0.44, jackolantern      0.44, two_hearts      0.33\n",
      "\n",
      "Topic  96\n",
      "💕      0.25, two_hearts      0.25, 🎂      0.20\n",
      "\n",
      "Topic  97\n",
      "green_salad      0.46, 🥗      0.46, shallow_pan_of_food      0.22\n",
      "\n",
      "Topic  98\n",
      "🥳      0.51, partying_face      0.51, smiling_face_with_hearteyes      0.18\n",
      "\n",
      "Topic  99\n",
      "cheese_wedge      0.39, 🧀      0.39, 🥩      0.23\n",
      "\n",
      "Topic  100\n",
      "running_shoe      0.70, 👟      0.70, television      0.08\n",
      "\n",
      "Topic  101\n",
      "dinner      0.94, 🥘      0.13, shallow_pan_of_food      0.13\n",
      "\n",
      "Topic  102\n",
      "🥤      0.49, cup_with_straw      0.49, cut_of_meat      0.07\n",
      "\n",
      "Topic  103\n",
      "snowflake      0.50, ❄      0.50, cup_with_straw      0.36\n",
      "\n",
      "Topic  104\n",
      "television      0.64, 📺      0.64, groceries      0.17\n",
      "\n",
      "Topic  105\n",
      "groceries      0.84, car      0.26, shallow_pan_of_food      0.18\n",
      "\n",
      "Topic  106\n",
      "🥘      0.37, shallow_pan_of_food      0.37, 🥤      0.22\n",
      "\n",
      "Topic  107\n",
      "shallow_pan_of_food      0.27, 🥘      0.27, ❄      0.27\n",
      "\n",
      "Topic  108\n",
      "car      0.75, 🚙      0.20, sport_utility_vehicle      0.20\n",
      "\n",
      "Topic  109\n",
      "🍖      0.23, meat_on_bone      0.23, 🥩      0.22\n",
      "\n",
      "Topic  110\n",
      "love      0.49, balloon      0.28, 🎈      0.28\n",
      "\n",
      "Topic  111\n",
      "sport_utility_vehicle      0.44, 🚙      0.44, telephone      0.41\n",
      "\n",
      "Topic  112\n",
      "balloon      0.45, 🎈      0.44, sport_utility_vehicle      0.13\n",
      "\n",
      "Topic  113\n",
      "telephone      0.37, ☎      0.37, hotel      0.14\n",
      "\n",
      "Topic  114\n",
      "telephone      0.29, ☎      0.28, balloon      0.19\n",
      "\n",
      "Topic  115\n",
      "lunch      0.65, spaghetti      0.33, 🍝      0.33\n",
      "\n",
      "Topic  116\n",
      "lunch      0.52, hundred_points      0.33, 💯      0.33\n",
      "\n",
      "Topic  117\n",
      "🍸      0.41, cocktail_glass      0.41, 🕺      0.18\n",
      "\n",
      "Topic  118\n",
      "hotel      0.60, 🍸      0.41, cocktail_glass      0.41\n",
      "\n",
      "Topic  119\n",
      "man_dancing      0.46, 🕺      0.46, hotel      0.24\n",
      "\n",
      "Topic  120\n",
      "day      0.38, work      0.37, 😊      0.28\n",
      "\n",
      "Topic  121\n",
      "😊      0.53, smiling_face_with_smiling_eyes      0.53, 🍸      0.18\n",
      "\n",
      "Topic  122\n",
      "fish      0.71, 🐟      0.59, 🏃      0.15\n",
      "\n",
      "Topic  123\n",
      "person_running      0.61, 🏃      0.61, 🥩      0.07\n",
      "\n",
      "Topic  124\n",
      "work      0.30, life      0.15, things      0.14\n",
      "\n",
      "Topic  125\n",
      "😁      0.34, beaming_face_with_smiling_eyes      0.34, man      0.20\n",
      "\n",
      "Topic  126\n",
      "oncoming_automobile      0.63, 🚘      0.63, 🍽      0.13\n",
      "\n",
      "Topic  127\n",
      "fork_and_knife_with_plate      0.62, 🍽      0.62, phone      0.10\n",
      "\n",
      "Topic  128\n",
      "🎶      0.53, musical_notes      0.53, microphone      0.37\n",
      "\n",
      "Topic  129\n",
      "phone      0.83, insurance      0.18, utilities      0.14\n",
      "\n",
      "Topic  130\n",
      "clinking_glasses      0.55, 🥂      0.55, beaming_face_with_smiling_eyes      0.16\n",
      "\n",
      "Topic  131\n",
      "beaming_face_with_smiling_eyes      0.48, 😁      0.48, 🎅      0.26\n",
      "\n",
      "Topic  132\n",
      "bacon      0.47, 🥓      0.44, soft_ice_cream      0.28\n",
      "\n",
      "Topic  133\n",
      "🍦      0.41, soft_ice_cream      0.41, clinking_glasses      0.27\n",
      "\n",
      "Topic  134\n",
      "💗      0.39, growing_heart      0.39, bacon      0.11\n",
      "\n",
      "Topic  135\n",
      "🎮      0.53, video_game      0.53, 😷      0.19\n",
      "\n",
      "Topic  136\n",
      "🤑      0.34, moneymouth_face      0.34, 😷      0.26\n",
      "\n",
      "Topic  137\n",
      "face_with_medical_mask      0.39, 😷      0.39, 💗      0.18\n",
      "\n",
      "Topic  138\n",
      "🤑      0.33, moneymouth_face      0.33, 🏌      0.29\n",
      "\n",
      "Topic  139\n",
      "work      0.23, person_golfing      0.23, 🏌      0.23\n",
      "\n",
      "Topic  140\n",
      "p_button      0.59, 🅿      0.59, 😉      0.12\n",
      "\n",
      "Topic  141\n",
      "u      0.61, know      0.30, back_arrow      0.21\n",
      "\n",
      "Topic  142\n",
      "😉      0.31, winking_face      0.31, trophy      0.28\n",
      "\n",
      "Topic  143\n",
      "trophy      0.43, 🏆      0.42, 🔙      0.21\n",
      "\n",
      "Topic  144\n",
      "🔙      0.31, back_arrow      0.31, deposit      0.20\n",
      "\n",
      "Topic  145\n",
      "🤠      0.43, cowboy_hat_face      0.43, u      0.31\n",
      "\n",
      "Topic  146\n",
      "back_arrow      0.25, 🔙      0.25, light_bulb      0.20\n",
      "\n",
      "Topic  147\n",
      "🤠      0.25, cowboy_hat_face      0.25, winking_face      0.18\n",
      "\n",
      "Topic  148\n",
      "payment      0.38, 🤪      0.28, zany_face      0.28\n",
      "\n",
      "Topic  149\n",
      "payment      0.40, fun      0.36, rainbow      0.29\n"
     ]
    }
   ],
   "source": [
    "display_topics(lsa, vectorizer.get_feature_names(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb87b799400>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lsa.components_[15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NMF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmf_model = NMF(20)\n",
    "nmf_doc_topic = nmf_model.fit_transform(dtm[:100000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/nmf_single_defined_100k.pickle', 'wb') as f:\n",
    "    pickle.dump(nmf_doc_topic, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Topic  0\n",
      "☕      6.60, hot_beverage      6.60, ️      6.14\n",
      "\n",
      "Topic  1\n",
      "pizza      7.76, 🍕      7.45, 🍗      0.03\n",
      "\n",
      "Topic  2\n",
      "‍      4.16, man      3.81, 👨      3.75\n",
      "\n",
      "Topic  3\n",
      "💸      5.34, money_with_wings      5.34, house      3.98\n",
      "\n",
      "Topic  4\n",
      "hamburger      6.23, 🍔      6.23, french_fries      0.32\n",
      "\n",
      "Topic  5\n",
      "taco      6.79, 🌮      6.58, tropical_drink      0.83\n",
      "\n",
      "Topic  6\n",
      "light_skin_tone      6.29, 🏻      6.29, ‍      2.51\n",
      "\n",
      "Topic  7\n",
      "sushi      5.83, 🍣      5.71, bento_box      0.10\n",
      "\n",
      "Topic  8\n",
      "burrito      5.35, 🌯      5.28, 🚙      0.08\n",
      "\n",
      "Topic  9\n",
      "american_football      5.68, 🏈      5.68, fantasy      0.58\n",
      "\n",
      "Topic  10\n",
      "❤      4.83, red_heart      4.83, ️      2.75\n",
      "\n",
      "Topic  11\n",
      "party_popper      5.56, 🎉      5.56, 🎂      0.65\n",
      "\n",
      "Topic  12\n",
      "eggplant      5.38, 🍆      5.38, peach      0.35\n",
      "\n",
      "Topic  13\n",
      "medium_light_skin_tone      4.99, 🏼      4.98, ‍      1.38\n",
      "\n",
      "Topic  14\n",
      "food      7.34, chinese      0.14, dog      0.11\n",
      "\n",
      "Topic  15\n",
      "⚡      2.84, high_voltage      2.84, electric_plug      2.26\n",
      "\n",
      "Topic  16\n",
      "wine_glass      6.37, 🍷      6.37, bottle_with_popping_cork      2.95\n",
      "\n",
      "Topic  17\n",
      "beer_mug      4.87, 🍺      4.87, clinking_beer_mugs      0.69\n",
      "\n",
      "Topic  18\n",
      "basketball      4.90, 🏀      4.83, trophy      0.20\n",
      "\n",
      "Topic  19\n",
      "sweat_droplets      4.35, 💦      4.35, 🔥      2.53\n"
     ]
    }
   ],
   "source": [
    "display_topics(nmf_model, vectorizer.get_feature_names(), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/allen/opt/anaconda3/envs/metis/lib/python3.8/site-packages/scipy/sparse/sparsetools.py:21: DeprecationWarning: `scipy.sparse.sparsetools` is deprecated!\n",
      "scipy.sparse.sparsetools is a private module for scipy.sparse, and should not be used.\n",
      "  _deprecated()\n"
     ]
    }
   ],
   "source": [
    "from gensim import matutils, models\n",
    "import scipy.sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdm = dtm.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_counts = scipy.sparse.csr_matrix(tdm)\n",
    "corpus = matutils.Sparse2Corpus(sparse_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.corpora as corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2word2 = corpora.Dictionary([[k for k,v in vectorizer.vocabulary_.items()]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('b', 12), ('food', 153), ('💇', 579), ('person_getting_haircut', 286), ('🎳', 531), ('bowling', 51), ('🍵', 507), ('teacup_without_handle', 380), ('good', 176), ('⛽', 457), ('fuel_pump', 164), ('🏿', 549), ('dark_skin_tone', 110), ('💃', 575), ('woman_dancing', 433), ('🌮', 472), ('taco', 374), ('🥑', 644), ('avocado', 10), ('know', 223), ('tickets', 392), ('🙏', 625), ('folded_hands', 152), ('🏼', 546), ('medium_light_skin_tone', 251), ('uber', 408), ('got', 178), ('ya', 437), ('cash', 69), ('money', 257), ('🔥', 609), ('🍗', 489), ('poultry_leg', 304), ('🐔', 550), ('chicken', 77), ('🎫', 529), ('ticket', 391), ('🍿', 516), ('popcorn', 301), ('airbnb', 1), ('🌶', 477), ('hot_pepper', 202), ('🏻', 545), ('light_skin_tone', 233), ('💯', 597), ('hundred_points', 207), ('💦', 593), ('sweat_droplets', 371), ('lunch', 241), ('\\u200d', 444), ('♀', 449), ('female_sign', 145), ('🍸', 509), ('cocktail_glass', 89), ('🥰', 656), ('smiling_face_with_3_hearts', 345), ('🎂', 518), ('birthday_cake', 41), ('🎉', 523), ('party_popper', 279), ('🌯', 473), ('burrito', 58), ('🍆', 480), ('eggplant', 129), ('🍺', 511), ('beer_mug', 34), ('groceries', 183), ('🥳', 657), ('partying_face', 280), ('🎮', 530), ('video_game', 417), ('💕', 584), ('two_hearts', 405), ('🤑', 631), ('moneymouth_face', 260), ('🍔', 486), ('hamburger', 190), ('🍝', 493), ('spaghetti', 356), ('🍕', 487), ('pizza', 296), ('️', 464), ('🏌', 538), ('person_golfing', 288), ('♂', 450), ('male_sign', 244), ('clothes', 87), ('❤', 462), ('red_heart', 311), ('👁', 557), ('eye', 134), ('😂', 613), ('face_with_tears_of_joy', 139), ('i’m', 212), ('hot', 199), ('house', 204), ('💩', 595), ('pile_of_poo', 294), ('🥤', 651), ('cup_with_straw', 103), ('🐕', 551), ('dog', 116), ('tv', 404), ('phone', 292), ('💵', 600), ('dollar_banknote', 118), ('💸', 601), ('money_with_wings', 259), ('📱', 605), ('mobile_phone', 255), ('hi', 195), ('☕', 447), ('hot_beverage', 200), ('⛳', 455), ('flag_in_hole', 149), ('🏠', 542), ('🍴', 506), ('fork_and_knife', 157), ('🍹', 510), ('tropical_drink', 399), ('march', 247), ('rent', 316), ('kids', 221), ('january', 214), ('make', 243), ('gift', 171), ('happy', 191), ('birthday', 40), ('bitch', 42), ('girl', 173), ('stuff', 366), ('thanks', 387), ('night', 269), ('🍱', 503), ('bento_box', 36), ('things', 388), ('girls', 174), ('parking', 277), ('🍣', 496), ('sushi', 370), ('squares', 361), ('hotel', 203), ('🏨', 544), ('💍', 582), ('ring', 321), ('forgot', 156), ('lol', 238), ('thank', 386), ('test', 384), ('baseball', 25), ('game', 168), ('🚙', 629), ('sport_utility_vehicle', 360), ('poker', 299), ('man', 245), ('refund', 312), ('july', 217), ('gas', 169), ('electric', 130), ('love', 240), ('late', 227), ('fees', 144), ('water', 419), ('school', 329), ('la', 224), ('wifi', 426), ('🤘', 633), ('sign_of_the_horns', 342), ('present', 305), ('little', 236), ('golf', 175), ('😋', 616), ('face_savoring_food', 137), ('🏃', 535), ('person_running', 289), ('g', 167), ('fantasy', 141), ('🏈', 537), ('american_football', 4), ('💗', 586), ('growing_heart', 185), ('dinner', 115), ('yo', 440), ('⚾', 454), ('👍', 559), ('thumbs_up', 389), ('class', 83), ('😘', 619), ('face_blowing_a_kiss', 136), ('payment', 282), ('trophy', 398), ('new', 268), ('year', 438), ('🌭', 471), ('hot_dog', 201), ('venmo_dollar', 415), ('n', 265), ('bbq', 27), ('🔙', 608), ('back_arrow', 16), ('party', 278), ('fund', 166), ('drinks', 124), ('wings', 429), ('bar', 24), ('u', 407), ('christmas', 81), ('day', 112), ('raffle', 307), ('fun', 165), ('ty', 406), ('cookies', 95), ('🍰', 502), ('shortcake', 340), ('🎵', 532), ('musical_note', 263), ('coffee', 90), ('table', 373), ('💪', 596), ('flexed_biceps', 150), ('🇺', 468), ('regional_indicator_symbol_letter_u', 315), ('🇸', 467), ('regional_indicator_symbol_letter_s', 314), ('life', 231), ('🚕', 626), ('taxi', 378), ('✈', 458), ('airplane', 2), ('⭐', 463), ('white_medium_star', 425), ('bed', 32), ('🚗', 627), ('automobile', 9), ('🍻', 512), ('clinking_beer_mugs', 85), ('beer', 33), ('♥', 451), ('heart_suit', 192), ('🎨', 528), ('artist_palette', 6), ('lessons', 230), ('best', 37), ('💥', 592), ('collision', 91), ('🌊', 470), ('water_wave', 420), ('🏆', 536), ('🍾', 515), ('bottle_with_popping_cork', 48), ('🧀', 659), ('cheese_wedge', 75), ('🎶', 533), ('musical_notes', 264), ('help', 193), ('bills', 39), ('dad', 109), ('🏐', 539), ('volleyball', 418), ('🐟', 553), ('fish', 148), ('bread', 53), ('⚡', 452), ('high_voltage', 196), ('shirts', 337), ('😊', 615), ('smiling_face_with_smiling_eyes', 347), ('🤙', 634), ('call_me_hand', 61), ('👌', 558), ('ok_hand', 270), ('time', 393), ('basketball', 26), ('🏀', 534), ('favorite', 142), ('bus', 59), ('service', 332), ('🍞', 494), ('🥪', 654), ('sandwich', 327), ('candy', 64), ('hey', 194), ('da', 108), ('🍷', 508), ('wine_glass', 428), ('chinese', 78), ('fuck', 163), ('🐶', 554), ('dog_face', 117), ('🙃', 623), ('upsidedown_face', 410), ('🍫', 501), ('chocolate_bar', 80), ('🍦', 498), ('soft_ice_cream', 353), ('🏡', 543), ('house_with_garden', 205), ('👀', 556), ('eyes', 135), ('haircut', 188), ('🍓', 485), ('strawberry', 365), ('👕', 561), ('tshirt', 400), ('shirt', 336), ('costco', 97), ('june', 218), ('utilities', 412), ('room', 323), ('cleaning', 84), ('supplies', 369), ('movie', 261), ('ice', 208), ('cat', 70), ('great', 180), ('way', 421), ('t', 372), ('internet', 210), ('work', 435), ('flowers', 151), ('🌼', 479), ('blossom', 44), ('💰', 598), ('money_bag', 258), ('🎟', 525), ('admission_tickets', 0), ('brunch', 57), ('weekend', 424), ('bank', 23), ('today', 396), ('juice', 216), ('cream', 100), ('car', 66), ('vegas', 413), ('🎅', 521), ('santa_claus', 328), ('🎄', 520), ('christmas_tree', 82), ('babysitting', 15), ('🎥', 527), ('movie_camera', 262), ('plus', 298), ('🍎', 483), ('red_apple', 310), ('za', 442), ('👶', 572), ('baby', 13), ('football', 155), ('week', 423), ('🚿', 630), ('shower', 341), ('😎', 618), ('smiling_face_with_sunglasses', 348), ('🏽', 547), ('medium_skin_tone', 252), ('super', 368), ('goods', 177), ('🔌', 607), ('electric_plug', 131), ('festival_floaty', 147), ('🐝', 552), ('honeybee', 198), ('💡', 591), ('light_bulb', 232), ('home', 197), ('boy', 52), ('need', 267), ('💳', 599), ('credit_card', 101), ('🥯', 655), ('bagel', 18), ('🎁', 517), ('wrapped_gift', 436), ('👻', 573), ('ghost', 170), ('🍩', 499), ('doughnut', 121), ('🍤', 497), ('fried_shrimp', 161), ('🙌', 624), ('raising_hands', 309), ('wedding', 422), ('🍑', 484), ('peach', 283), ('📚', 603), ('books', 46), ('chipotle', 79), ('sex', 333), ('it’s', 211), ('🐷', 555), ('pig_face', 293), ('😉', 614), ('winking_face', 430), ('card', 67), ('august', 8), ('tip', 394), ('big', 38), ('🤡', 636), ('clown_face', 88), ('🍟', 495), ('french_fries', 159), ('dues', 126), ('wine', 427), ('pay', 281), ('👨', 568), ('🍳', 505), ('cooking', 96), ('like', 234), ('🎤', 526), ('microphone', 254), ('👦', 566), ('🤤', 637), ('drooling_face', 125), ('👩', 569), ('woman', 432), ('🥂', 642), ('clinking_glasses', 86), ('🍚', 490), ('cooked_rice', 93), ('rest', 318), ('insurance', 209), ('sitting', 343), ('👯', 570), ('people_with_bunny_ears', 284), ('april', 5), ('potato', 303), ('🚘', 628), ('oncoming_automobile', 271), ('tax', 377), ('return', 319), ('🤷', 641), ('person_shrugging', 290), ('shit', 338), ('half', 189), ('🍜', 492), ('steaming_bowl', 364), ('fee', 143), ('☺', 448), ('smiling_face', 344), ('donation', 119), ('💆', 578), ('person_getting_massage', 287), ('😷', 622), ('face_with_medical_mask', 138), ('🥘', 648), ('shallow_pan_of_food', 334), ('foods', 154), ('🦄', 658), ('unicorn_face', 409), ('beach', 29), ('crab', 99), ('cause', 71), ('hair', 187), ('deposit', 113), ('venmo', 414), ('balance', 20), ('you’re', 441), ('🇷', 466), ('regional_indicator_symbol_letter_r', 313), ('🥩', 653), ('cut_of_meat', 106), ('amazon', 3), ('sorry', 355), ('🍖', 488), ('meat_on_bone', 249), ('🍲', 504), ('pot_of_food', 302), ('🎃', 519), ('jackolantern', 213), ('guitar', 186), ('kale', 220), ('salad', 326), ('send', 331), ('🍽', 514), ('fork_and_knife_with_plate', 158), ('🎈', 522), ('balloon', 21), ('🏕', 540), ('camping', 63), ('thai', 385), ('💋', 581), ('kiss_mark', 222), ('📞', 604), ('telephone_receiver', 382), ('📺', 606), ('television', 383), ('paid', 274), ('⛵', 456), ('sailboat', 325), ('❄', 461), ('snowflake', 350), ('👰', 571), ('bride_with_veil', 55), ('🏖', 541), ('beach_with_umbrella', 30), ('target', 376), ('gifts', 172), ('☀', 445), ('sun', 367), ('💐', 583), ('bouquet', 49), ('👟', 565), ('running_shoe', 324), ('👖', 562), ('jeans', 215), ('💙', 587), ('blue_heart', 45), ('💊', 580), ('pill', 295), ('shoes', 339), ('⚽', 453), ('soccer_ball', 352), ('merry', 253), ('💛', 589), ('yellow_heart', 439), ('💜', 590), ('purple_heart', 306), ('shipping', 335), ('🤩', 639), ('starstruck', 363), ('🌴', 476), ('palm_tree', 275), ('just', 219), ('loan', 237), ('ass', 7), ('ur', 411), ('bday', 28), ('lyft', 242), ('extra', 133), ('thx', 390), ('🍼', 513), ('baby_bottle', 14), ('🍇', 481), ('grapes', 179), ('🥓', 645), ('bacon', 17), ('🤠', 635), ('cowboy_hat_face', 98), ('🥃', 643), ('tumbler_glass', 401), ('🤪', 640), ('zany_face', 443), ('pancakes', 276), ('mom', 256), ('ride', 320), ('change', 73), ('egg', 128), ('👗', 563), ('dress', 122), ('🥟', 650), ('dumpling', 127), ('splitwisecom', 359), ('🥖', 646), ('baguette_bread', 19), ('✌', 459), ('victory_hand', 416), ('🌱', 474), ('seedling', 330), ('trip', 397), ('🍛', 491), ('curry_rice', 104), ('🍪', 500), ('cookie', 94), ('turkey', 402), ('🥦', 652), ('broccoli', 56), ('🥗', 647), ('green_salad', 182), ('bowl', 50), ('tix', 395), ('breakfast', 54), ('🎊', 524), ('confetti_ball', 92), ('don’t', 120), ('🕺', 610), ('man_dancing', 246), ('drink', 123), ('festival_beer', 146), ('lawn', 228), ('🌲', 475), ('evergreen_tree', 132), ('care', 68), ('💻', 602), ('laptop_computer', 225), ('cell', 72), ('😭', 621), ('loudly_crying_face', 239), ('pool', 300), ('rental', 317), ('😍', 617), ('smiling_face_with_hearteyes', 346), ('buy', 60), ('rocket_popsicle', 322), ('awesome', 11), ('✨', 460), ('sparkles', 357), ('🏾', 548), ('medium_dark_skin_tone', 250), ('package', 273), ('🍌', 482), ('banana', 22), ('☎', 446), ('telephone', 381), ('🅿', 465), ('p_button', 272), ('starbucks', 362), ('🥞', 649), ('tacos', 375), ('din', 114), ('friend', 162), ('cut', 105), ('🤦', 638), ('person_facepalming', 285), ('💅', 577), ('nail_polish', 266), ('soap', 351), ('👧', 567), ('🌸', 478), ('cherry_blossom', 76), ('🤗', 632), ('hugging_face', 206), ('bell', 35), ('family', 140), ('😜', 620), ('winking_face_with_tongue', 431), ('💁', 574), ('person_tipping_hand', 291), ('masks', 248), ('snacks', 349), ('tea', 379), ('candy_heart', 65), ('👑', 560), ('crown', 102), ('💄', 576), ('lipstick', 235), ('😁', 612), ('beaming_face_with_smiling_eyes', 31), ('💨', 594), ('dashing_away', 111), ('camera', 62), ('friday', 160), ('place', 297), ('check', 74), ('💚', 588), ('green_heart', 181), ('🌈', 469), ('rainbow', 308), ('👚', 564), ('woman’s_clothes', 434), ('booze', 47), ('💖', 585), ('sparkling_heart', 358), ('cuz', 107), ('softball', 354), ('lashes', 226), ('grocery', 184), ('tutoring', 403), ('lesson', 229), ('🖤', 611), ('black_heart', 43)])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.vocabulary_.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "id2word = dict((v, k) for k, v in vectorizer.vocabulary_.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20:52:02\n",
      "21:03:54\n"
     ]
    }
   ],
   "source": [
    "print(datetime.now().strftime(\"%H:%M:%S\"))\n",
    "lda = models.LdaMulticore(corpus=corpus, id2word=id2word, num_topics=20, passes=50, workers=8)\n",
    "lda.print_topics()\n",
    "print(datetime.now().strftime(\"%H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/lda_single_defined_286k.pickle', 'wb') as f:\n",
    "    pickle.dump(lda, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.163*\"money_with_wings\" + 0.163*\"💸\" + 0.160*\"house\" + 0.136*\"🏠\" + 0.056*\"two_hearts\" + 0.056*\"💕\" + 0.025*\"pile_of_poo\" + 0.025*\"💩\" + 0.022*\"trip\" + 0.019*\"💗\"'),\n",
       " (1,\n",
       "  '0.204*\"taco\" + 0.185*\"🌮\" + 0.043*\"boy\" + 0.035*\"♥\" + 0.035*\"heart_suit\" + 0.033*\"👦\" + 0.031*\"sorry\" + 0.030*\"fee\" + 0.029*\"winking_face\" + 0.029*\"😉\"'),\n",
       " (2,\n",
       "  '0.065*\"groceries\" + 0.062*\"chicken\" + 0.060*\"burrito\" + 0.055*\"🌯\" + 0.044*\"🐔\" + 0.037*\"thumbs_up\" + 0.037*\"👍\" + 0.032*\"tickets\" + 0.031*\"face_with_tears_of_joy\" + 0.031*\"😂\"'),\n",
       " (3,\n",
       "  '0.084*\"automobile\" + 0.084*\"🚗\" + 0.072*\"lunch\" + 0.042*\"hair\" + 0.039*\"🎥\" + 0.039*\"movie_camera\" + 0.036*\"sparkles\" + 0.036*\"✨\" + 0.033*\"refund\" + 0.032*\"😷\"'),\n",
       " (4,\n",
       "  '0.062*\"🥰\" + 0.062*\"smiling_face_with_3_hearts\" + 0.056*\"money_bag\" + 0.056*\"💰\" + 0.044*\"sandwich\" + 0.037*\"🥪\" + 0.037*\"🙌\" + 0.037*\"raising_hands\" + 0.034*\"wifi\" + 0.031*\"peach\"'),\n",
       " (5,\n",
       "  '0.092*\"face_blowing_a_kiss\" + 0.092*\"😘\" + 0.069*\"love\" + 0.058*\"🍟\" + 0.058*\"french_fries\" + 0.057*\"poultry_leg\" + 0.057*\"🍗\" + 0.041*\"house_with_garden\" + 0.041*\"🏡\" + 0.040*\"venmo_dollar\"'),\n",
       " (6,\n",
       "  '0.088*\"stuff\" + 0.087*\"uber\" + 0.077*\"🔥\" + 0.067*\"hamburger\" + 0.067*\"🍔\" + 0.046*\"clinking_glasses\" + 0.046*\"🥂\" + 0.042*\"basketball\" + 0.041*\"🏀\" + 0.038*\"🎟\"'),\n",
       " (7,\n",
       "  '0.085*\"american_football\" + 0.085*\"🏈\" + 0.059*\"cooking\" + 0.057*\"🍳\" + 0.038*\"😍\" + 0.038*\"smiling_face_with_hearteyes\" + 0.030*\"just\" + 0.030*\"💙\" + 0.030*\"blue_heart\" + 0.028*\"bacon\"'),\n",
       " (8,\n",
       "  '0.088*\"medium_skin_tone\" + 0.088*\"🏽\" + 0.061*\"dinner\" + 0.051*\"car\" + 0.046*\"nail_polish\" + 0.046*\"💅\" + 0.039*\"baseball\" + 0.036*\"green_salad\" + 0.036*\"🥗\" + 0.034*\"⚾\"'),\n",
       " (9,\n",
       "  '0.100*\"🎁\" + 0.100*\"wrapped_gift\" + 0.073*\"sushi\" + 0.065*\"🍣\" + 0.045*\"tshirt\" + 0.038*\"👕\" + 0.035*\"🍜\" + 0.035*\"steaming_bowl\" + 0.034*\"🎄\" + 0.034*\"christmas_tree\"'),\n",
       " (10,\n",
       "  '0.257*\"pizza\" + 0.216*\"🍕\" + 0.096*\"☕\" + 0.096*\"hot_beverage\" + 0.074*\"clinking_beer_mugs\" + 0.074*\"🍻\" + 0.046*\"money\" + 0.027*\"babysitting\" + 0.022*\"week\" + 0.020*\"️\"'),\n",
       " (11,\n",
       "  '0.080*\"tropical_drink\" + 0.080*\"🍹\" + 0.066*\"birthday\" + 0.062*\"happy\" + 0.054*\"woman_dancing\" + 0.054*\"💃\" + 0.042*\"fun\" + 0.036*\"deposit\" + 0.034*\"night\" + 0.034*\"taxi\"'),\n",
       " (12,\n",
       "  '0.182*\"red_heart\" + 0.182*\"❤\" + 0.111*\"️\" + 0.067*\"fuel_pump\" + 0.067*\"⛽\" + 0.027*\"eggplant\" + 0.027*\"🍆\" + 0.024*\"flag_in_hole\" + 0.024*\"⛳\" + 0.022*\"🚙\"'),\n",
       " (13,\n",
       "  '0.086*\"thanks\" + 0.083*\"\\u200d\" + 0.060*\"♀\" + 0.060*\"female_sign\" + 0.049*\"sweat_droplets\" + 0.049*\"💦\" + 0.038*\"girl\" + 0.038*\"dog\" + 0.029*\"hotel\" + 0.025*\"best\"'),\n",
       " (14,\n",
       "  '0.081*\"fork_and_knife\" + 0.081*\"🍴\" + 0.076*\"dollar_banknote\" + 0.076*\"💵\" + 0.049*\"light_bulb\" + 0.049*\"💡\" + 0.049*\"high_voltage\" + 0.049*\"⚡\" + 0.040*\"electric_plug\" + 0.040*\"🔌\"'),\n",
       " (15,\n",
       "  '0.144*\"medium_light_skin_tone\" + 0.144*\"🏼\" + 0.068*\"beer_mug\" + 0.068*\"🍺\" + 0.051*\"cookie\" + 0.048*\"🍪\" + 0.044*\"person_getting_haircut\" + 0.044*\"💇\" + 0.043*\"gas\" + 0.033*\"bread\"'),\n",
       " (16,\n",
       "  '0.101*\"🏻\" + 0.101*\"light_skin_tone\" + 0.078*\"thank\" + 0.072*\"\\u200d\" + 0.061*\"rent\" + 0.049*\"🍷\" + 0.049*\"wine_glass\" + 0.034*\"♀\" + 0.034*\"female_sign\" + 0.030*\"️\"'),\n",
       " (17,\n",
       "  '0.295*\"food\" + 0.114*\"baby\" + 0.074*\"👶\" + 0.055*\"dog_face\" + 0.055*\"🐶\" + 0.053*\"day\" + 0.040*\"🙏\" + 0.040*\"folded_hands\" + 0.037*\"🥳\" + 0.037*\"partying_face\"'),\n",
       " (18,\n",
       "  '0.124*\"party_popper\" + 0.124*\"🎉\" + 0.068*\"mobile_phone\" + 0.068*\"📱\" + 0.059*\"gift\" + 0.057*\"birthday_cake\" + 0.057*\"🎂\" + 0.055*\"🍾\" + 0.055*\"bottle_with_popping_cork\" + 0.031*\"🧀\"'),\n",
       " (19,\n",
       "  '0.059*\"bills\" + 0.048*\"good\" + 0.047*\"phone\" + 0.047*\"😊\" + 0.047*\"smiling_face_with_smiling_eyes\" + 0.043*\"u\" + 0.032*\"🥘\" + 0.032*\"shallow_pan_of_food\" + 0.031*\"🥤\" + 0.031*\"cup_with_straw\"')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.print_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import CoherenceModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.249840500069645"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.log_perplexity(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Coherence Score:  -12.89004876596872\n"
     ]
    }
   ],
   "source": [
    "coherence_model_lda = CoherenceModel(model=lda, corpus=corpus, dictionary=id2word, coherence='u_mass')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{12: 'b',\n",
       " 153: 'food',\n",
       " 579: '💇',\n",
       " 286: 'person_getting_haircut',\n",
       " 531: '🎳',\n",
       " 51: 'bowling',\n",
       " 507: '🍵',\n",
       " 380: 'teacup_without_handle',\n",
       " 176: 'good',\n",
       " 457: '⛽',\n",
       " 164: 'fuel_pump',\n",
       " 549: '🏿',\n",
       " 110: 'dark_skin_tone',\n",
       " 575: '💃',\n",
       " 433: 'woman_dancing',\n",
       " 472: '🌮',\n",
       " 374: 'taco',\n",
       " 644: '🥑',\n",
       " 10: 'avocado',\n",
       " 223: 'know',\n",
       " 392: 'tickets',\n",
       " 625: '🙏',\n",
       " 152: 'folded_hands',\n",
       " 546: '🏼',\n",
       " 251: 'medium_light_skin_tone',\n",
       " 408: 'uber',\n",
       " 178: 'got',\n",
       " 437: 'ya',\n",
       " 69: 'cash',\n",
       " 257: 'money',\n",
       " 609: '🔥',\n",
       " 489: '🍗',\n",
       " 304: 'poultry_leg',\n",
       " 550: '🐔',\n",
       " 77: 'chicken',\n",
       " 529: '🎫',\n",
       " 391: 'ticket',\n",
       " 516: '🍿',\n",
       " 301: 'popcorn',\n",
       " 1: 'airbnb',\n",
       " 477: '🌶',\n",
       " 202: 'hot_pepper',\n",
       " 545: '🏻',\n",
       " 233: 'light_skin_tone',\n",
       " 597: '💯',\n",
       " 207: 'hundred_points',\n",
       " 593: '💦',\n",
       " 371: 'sweat_droplets',\n",
       " 241: 'lunch',\n",
       " 444: '\\u200d',\n",
       " 449: '♀',\n",
       " 145: 'female_sign',\n",
       " 509: '🍸',\n",
       " 89: 'cocktail_glass',\n",
       " 656: '🥰',\n",
       " 345: 'smiling_face_with_3_hearts',\n",
       " 518: '🎂',\n",
       " 41: 'birthday_cake',\n",
       " 523: '🎉',\n",
       " 279: 'party_popper',\n",
       " 473: '🌯',\n",
       " 58: 'burrito',\n",
       " 480: '🍆',\n",
       " 129: 'eggplant',\n",
       " 511: '🍺',\n",
       " 34: 'beer_mug',\n",
       " 183: 'groceries',\n",
       " 657: '🥳',\n",
       " 280: 'partying_face',\n",
       " 530: '🎮',\n",
       " 417: 'video_game',\n",
       " 584: '💕',\n",
       " 405: 'two_hearts',\n",
       " 631: '🤑',\n",
       " 260: 'moneymouth_face',\n",
       " 486: '🍔',\n",
       " 190: 'hamburger',\n",
       " 493: '🍝',\n",
       " 356: 'spaghetti',\n",
       " 487: '🍕',\n",
       " 296: 'pizza',\n",
       " 464: '️',\n",
       " 538: '🏌',\n",
       " 288: 'person_golfing',\n",
       " 450: '♂',\n",
       " 244: 'male_sign',\n",
       " 87: 'clothes',\n",
       " 462: '❤',\n",
       " 311: 'red_heart',\n",
       " 557: '👁',\n",
       " 134: 'eye',\n",
       " 613: '😂',\n",
       " 139: 'face_with_tears_of_joy',\n",
       " 212: 'i’m',\n",
       " 199: 'hot',\n",
       " 204: 'house',\n",
       " 595: '💩',\n",
       " 294: 'pile_of_poo',\n",
       " 651: '🥤',\n",
       " 103: 'cup_with_straw',\n",
       " 551: '🐕',\n",
       " 116: 'dog',\n",
       " 404: 'tv',\n",
       " 292: 'phone',\n",
       " 600: '💵',\n",
       " 118: 'dollar_banknote',\n",
       " 601: '💸',\n",
       " 259: 'money_with_wings',\n",
       " 605: '📱',\n",
       " 255: 'mobile_phone',\n",
       " 195: 'hi',\n",
       " 447: '☕',\n",
       " 200: 'hot_beverage',\n",
       " 455: '⛳',\n",
       " 149: 'flag_in_hole',\n",
       " 542: '🏠',\n",
       " 506: '🍴',\n",
       " 157: 'fork_and_knife',\n",
       " 510: '🍹',\n",
       " 399: 'tropical_drink',\n",
       " 247: 'march',\n",
       " 316: 'rent',\n",
       " 221: 'kids',\n",
       " 214: 'january',\n",
       " 243: 'make',\n",
       " 171: 'gift',\n",
       " 191: 'happy',\n",
       " 40: 'birthday',\n",
       " 42: 'bitch',\n",
       " 173: 'girl',\n",
       " 366: 'stuff',\n",
       " 387: 'thanks',\n",
       " 269: 'night',\n",
       " 503: '🍱',\n",
       " 36: 'bento_box',\n",
       " 388: 'things',\n",
       " 174: 'girls',\n",
       " 277: 'parking',\n",
       " 496: '🍣',\n",
       " 370: 'sushi',\n",
       " 361: 'squares',\n",
       " 203: 'hotel',\n",
       " 544: '🏨',\n",
       " 582: '💍',\n",
       " 321: 'ring',\n",
       " 156: 'forgot',\n",
       " 238: 'lol',\n",
       " 386: 'thank',\n",
       " 384: 'test',\n",
       " 25: 'baseball',\n",
       " 168: 'game',\n",
       " 629: '🚙',\n",
       " 360: 'sport_utility_vehicle',\n",
       " 299: 'poker',\n",
       " 245: 'man',\n",
       " 312: 'refund',\n",
       " 217: 'july',\n",
       " 169: 'gas',\n",
       " 130: 'electric',\n",
       " 240: 'love',\n",
       " 227: 'late',\n",
       " 144: 'fees',\n",
       " 419: 'water',\n",
       " 329: 'school',\n",
       " 224: 'la',\n",
       " 426: 'wifi',\n",
       " 633: '🤘',\n",
       " 342: 'sign_of_the_horns',\n",
       " 305: 'present',\n",
       " 236: 'little',\n",
       " 175: 'golf',\n",
       " 616: '😋',\n",
       " 137: 'face_savoring_food',\n",
       " 535: '🏃',\n",
       " 289: 'person_running',\n",
       " 167: 'g',\n",
       " 141: 'fantasy',\n",
       " 537: '🏈',\n",
       " 4: 'american_football',\n",
       " 586: '💗',\n",
       " 185: 'growing_heart',\n",
       " 115: 'dinner',\n",
       " 440: 'yo',\n",
       " 454: '⚾',\n",
       " 559: '👍',\n",
       " 389: 'thumbs_up',\n",
       " 83: 'class',\n",
       " 619: '😘',\n",
       " 136: 'face_blowing_a_kiss',\n",
       " 282: 'payment',\n",
       " 398: 'trophy',\n",
       " 268: 'new',\n",
       " 438: 'year',\n",
       " 471: '🌭',\n",
       " 201: 'hot_dog',\n",
       " 415: 'venmo_dollar',\n",
       " 265: 'n',\n",
       " 27: 'bbq',\n",
       " 608: '🔙',\n",
       " 16: 'back_arrow',\n",
       " 278: 'party',\n",
       " 166: 'fund',\n",
       " 124: 'drinks',\n",
       " 429: 'wings',\n",
       " 24: 'bar',\n",
       " 407: 'u',\n",
       " 81: 'christmas',\n",
       " 112: 'day',\n",
       " 307: 'raffle',\n",
       " 165: 'fun',\n",
       " 406: 'ty',\n",
       " 95: 'cookies',\n",
       " 502: '🍰',\n",
       " 340: 'shortcake',\n",
       " 532: '🎵',\n",
       " 263: 'musical_note',\n",
       " 90: 'coffee',\n",
       " 373: 'table',\n",
       " 596: '💪',\n",
       " 150: 'flexed_biceps',\n",
       " 468: '🇺',\n",
       " 315: 'regional_indicator_symbol_letter_u',\n",
       " 467: '🇸',\n",
       " 314: 'regional_indicator_symbol_letter_s',\n",
       " 231: 'life',\n",
       " 626: '🚕',\n",
       " 378: 'taxi',\n",
       " 458: '✈',\n",
       " 2: 'airplane',\n",
       " 463: '⭐',\n",
       " 425: 'white_medium_star',\n",
       " 32: 'bed',\n",
       " 627: '🚗',\n",
       " 9: 'automobile',\n",
       " 512: '🍻',\n",
       " 85: 'clinking_beer_mugs',\n",
       " 33: 'beer',\n",
       " 451: '♥',\n",
       " 192: 'heart_suit',\n",
       " 528: '🎨',\n",
       " 6: 'artist_palette',\n",
       " 230: 'lessons',\n",
       " 37: 'best',\n",
       " 592: '💥',\n",
       " 91: 'collision',\n",
       " 470: '🌊',\n",
       " 420: 'water_wave',\n",
       " 536: '🏆',\n",
       " 515: '🍾',\n",
       " 48: 'bottle_with_popping_cork',\n",
       " 659: '🧀',\n",
       " 75: 'cheese_wedge',\n",
       " 533: '🎶',\n",
       " 264: 'musical_notes',\n",
       " 193: 'help',\n",
       " 39: 'bills',\n",
       " 109: 'dad',\n",
       " 539: '🏐',\n",
       " 418: 'volleyball',\n",
       " 553: '🐟',\n",
       " 148: 'fish',\n",
       " 53: 'bread',\n",
       " 452: '⚡',\n",
       " 196: 'high_voltage',\n",
       " 337: 'shirts',\n",
       " 615: '😊',\n",
       " 347: 'smiling_face_with_smiling_eyes',\n",
       " 634: '🤙',\n",
       " 61: 'call_me_hand',\n",
       " 558: '👌',\n",
       " 270: 'ok_hand',\n",
       " 393: 'time',\n",
       " 26: 'basketball',\n",
       " 534: '🏀',\n",
       " 142: 'favorite',\n",
       " 59: 'bus',\n",
       " 332: 'service',\n",
       " 494: '🍞',\n",
       " 654: '🥪',\n",
       " 327: 'sandwich',\n",
       " 64: 'candy',\n",
       " 194: 'hey',\n",
       " 108: 'da',\n",
       " 508: '🍷',\n",
       " 428: 'wine_glass',\n",
       " 78: 'chinese',\n",
       " 163: 'fuck',\n",
       " 554: '🐶',\n",
       " 117: 'dog_face',\n",
       " 623: '🙃',\n",
       " 410: 'upsidedown_face',\n",
       " 501: '🍫',\n",
       " 80: 'chocolate_bar',\n",
       " 498: '🍦',\n",
       " 353: 'soft_ice_cream',\n",
       " 543: '🏡',\n",
       " 205: 'house_with_garden',\n",
       " 556: '👀',\n",
       " 135: 'eyes',\n",
       " 188: 'haircut',\n",
       " 485: '🍓',\n",
       " 365: 'strawberry',\n",
       " 561: '👕',\n",
       " 400: 'tshirt',\n",
       " 336: 'shirt',\n",
       " 97: 'costco',\n",
       " 218: 'june',\n",
       " 412: 'utilities',\n",
       " 323: 'room',\n",
       " 84: 'cleaning',\n",
       " 369: 'supplies',\n",
       " 261: 'movie',\n",
       " 208: 'ice',\n",
       " 70: 'cat',\n",
       " 180: 'great',\n",
       " 421: 'way',\n",
       " 372: 't',\n",
       " 210: 'internet',\n",
       " 435: 'work',\n",
       " 151: 'flowers',\n",
       " 479: '🌼',\n",
       " 44: 'blossom',\n",
       " 598: '💰',\n",
       " 258: 'money_bag',\n",
       " 525: '🎟',\n",
       " 0: 'admission_tickets',\n",
       " 57: 'brunch',\n",
       " 424: 'weekend',\n",
       " 23: 'bank',\n",
       " 396: 'today',\n",
       " 216: 'juice',\n",
       " 100: 'cream',\n",
       " 66: 'car',\n",
       " 413: 'vegas',\n",
       " 521: '🎅',\n",
       " 328: 'santa_claus',\n",
       " 520: '🎄',\n",
       " 82: 'christmas_tree',\n",
       " 15: 'babysitting',\n",
       " 527: '🎥',\n",
       " 262: 'movie_camera',\n",
       " 298: 'plus',\n",
       " 483: '🍎',\n",
       " 310: 'red_apple',\n",
       " 442: 'za',\n",
       " 572: '👶',\n",
       " 13: 'baby',\n",
       " 155: 'football',\n",
       " 423: 'week',\n",
       " 630: '🚿',\n",
       " 341: 'shower',\n",
       " 618: '😎',\n",
       " 348: 'smiling_face_with_sunglasses',\n",
       " 547: '🏽',\n",
       " 252: 'medium_skin_tone',\n",
       " 368: 'super',\n",
       " 177: 'goods',\n",
       " 607: '🔌',\n",
       " 131: 'electric_plug',\n",
       " 147: 'festival_floaty',\n",
       " 552: '🐝',\n",
       " 198: 'honeybee',\n",
       " 591: '💡',\n",
       " 232: 'light_bulb',\n",
       " 197: 'home',\n",
       " 52: 'boy',\n",
       " 267: 'need',\n",
       " 599: '💳',\n",
       " 101: 'credit_card',\n",
       " 655: '🥯',\n",
       " 18: 'bagel',\n",
       " 517: '🎁',\n",
       " 436: 'wrapped_gift',\n",
       " 573: '👻',\n",
       " 170: 'ghost',\n",
       " 499: '🍩',\n",
       " 121: 'doughnut',\n",
       " 497: '🍤',\n",
       " 161: 'fried_shrimp',\n",
       " 624: '🙌',\n",
       " 309: 'raising_hands',\n",
       " 422: 'wedding',\n",
       " 484: '🍑',\n",
       " 283: 'peach',\n",
       " 603: '📚',\n",
       " 46: 'books',\n",
       " 79: 'chipotle',\n",
       " 333: 'sex',\n",
       " 211: 'it’s',\n",
       " 555: '🐷',\n",
       " 293: 'pig_face',\n",
       " 614: '😉',\n",
       " 430: 'winking_face',\n",
       " 67: 'card',\n",
       " 8: 'august',\n",
       " 394: 'tip',\n",
       " 38: 'big',\n",
       " 636: '🤡',\n",
       " 88: 'clown_face',\n",
       " 495: '🍟',\n",
       " 159: 'french_fries',\n",
       " 126: 'dues',\n",
       " 427: 'wine',\n",
       " 281: 'pay',\n",
       " 568: '👨',\n",
       " 505: '🍳',\n",
       " 96: 'cooking',\n",
       " 234: 'like',\n",
       " 526: '🎤',\n",
       " 254: 'microphone',\n",
       " 566: '👦',\n",
       " 637: '🤤',\n",
       " 125: 'drooling_face',\n",
       " 569: '👩',\n",
       " 432: 'woman',\n",
       " 642: '🥂',\n",
       " 86: 'clinking_glasses',\n",
       " 490: '🍚',\n",
       " 93: 'cooked_rice',\n",
       " 318: 'rest',\n",
       " 209: 'insurance',\n",
       " 343: 'sitting',\n",
       " 570: '👯',\n",
       " 284: 'people_with_bunny_ears',\n",
       " 5: 'april',\n",
       " 303: 'potato',\n",
       " 628: '🚘',\n",
       " 271: 'oncoming_automobile',\n",
       " 377: 'tax',\n",
       " 319: 'return',\n",
       " 641: '🤷',\n",
       " 290: 'person_shrugging',\n",
       " 338: 'shit',\n",
       " 189: 'half',\n",
       " 492: '🍜',\n",
       " 364: 'steaming_bowl',\n",
       " 143: 'fee',\n",
       " 448: '☺',\n",
       " 344: 'smiling_face',\n",
       " 119: 'donation',\n",
       " 578: '💆',\n",
       " 287: 'person_getting_massage',\n",
       " 622: '😷',\n",
       " 138: 'face_with_medical_mask',\n",
       " 648: '🥘',\n",
       " 334: 'shallow_pan_of_food',\n",
       " 154: 'foods',\n",
       " 658: '🦄',\n",
       " 409: 'unicorn_face',\n",
       " 29: 'beach',\n",
       " 99: 'crab',\n",
       " 71: 'cause',\n",
       " 187: 'hair',\n",
       " 113: 'deposit',\n",
       " 414: 'venmo',\n",
       " 20: 'balance',\n",
       " 441: 'you’re',\n",
       " 466: '🇷',\n",
       " 313: 'regional_indicator_symbol_letter_r',\n",
       " 653: '🥩',\n",
       " 106: 'cut_of_meat',\n",
       " 3: 'amazon',\n",
       " 355: 'sorry',\n",
       " 488: '🍖',\n",
       " 249: 'meat_on_bone',\n",
       " 504: '🍲',\n",
       " 302: 'pot_of_food',\n",
       " 519: '🎃',\n",
       " 213: 'jackolantern',\n",
       " 186: 'guitar',\n",
       " 220: 'kale',\n",
       " 326: 'salad',\n",
       " 331: 'send',\n",
       " 514: '🍽',\n",
       " 158: 'fork_and_knife_with_plate',\n",
       " 522: '🎈',\n",
       " 21: 'balloon',\n",
       " 540: '🏕',\n",
       " 63: 'camping',\n",
       " 385: 'thai',\n",
       " 581: '💋',\n",
       " 222: 'kiss_mark',\n",
       " 604: '📞',\n",
       " 382: 'telephone_receiver',\n",
       " 606: '📺',\n",
       " 383: 'television',\n",
       " 274: 'paid',\n",
       " 456: '⛵',\n",
       " 325: 'sailboat',\n",
       " 461: '❄',\n",
       " 350: 'snowflake',\n",
       " 571: '👰',\n",
       " 55: 'bride_with_veil',\n",
       " 541: '🏖',\n",
       " 30: 'beach_with_umbrella',\n",
       " 376: 'target',\n",
       " 172: 'gifts',\n",
       " 445: '☀',\n",
       " 367: 'sun',\n",
       " 583: '💐',\n",
       " 49: 'bouquet',\n",
       " 565: '👟',\n",
       " 324: 'running_shoe',\n",
       " 562: '👖',\n",
       " 215: 'jeans',\n",
       " 587: '💙',\n",
       " 45: 'blue_heart',\n",
       " 580: '💊',\n",
       " 295: 'pill',\n",
       " 339: 'shoes',\n",
       " 453: '⚽',\n",
       " 352: 'soccer_ball',\n",
       " 253: 'merry',\n",
       " 589: '💛',\n",
       " 439: 'yellow_heart',\n",
       " 590: '💜',\n",
       " 306: 'purple_heart',\n",
       " 335: 'shipping',\n",
       " 639: '🤩',\n",
       " 363: 'starstruck',\n",
       " 476: '🌴',\n",
       " 275: 'palm_tree',\n",
       " 219: 'just',\n",
       " 237: 'loan',\n",
       " 7: 'ass',\n",
       " 411: 'ur',\n",
       " 28: 'bday',\n",
       " 242: 'lyft',\n",
       " 133: 'extra',\n",
       " 390: 'thx',\n",
       " 513: '🍼',\n",
       " 14: 'baby_bottle',\n",
       " 481: '🍇',\n",
       " 179: 'grapes',\n",
       " 645: '🥓',\n",
       " 17: 'bacon',\n",
       " 635: '🤠',\n",
       " 98: 'cowboy_hat_face',\n",
       " 643: '🥃',\n",
       " 401: 'tumbler_glass',\n",
       " 640: '🤪',\n",
       " 443: 'zany_face',\n",
       " 276: 'pancakes',\n",
       " 256: 'mom',\n",
       " 320: 'ride',\n",
       " 73: 'change',\n",
       " 128: 'egg',\n",
       " 563: '👗',\n",
       " 122: 'dress',\n",
       " 650: '🥟',\n",
       " 127: 'dumpling',\n",
       " 359: 'splitwisecom',\n",
       " 646: '🥖',\n",
       " 19: 'baguette_bread',\n",
       " 459: '✌',\n",
       " 416: 'victory_hand',\n",
       " 474: '🌱',\n",
       " 330: 'seedling',\n",
       " 397: 'trip',\n",
       " 491: '🍛',\n",
       " 104: 'curry_rice',\n",
       " 500: '🍪',\n",
       " 94: 'cookie',\n",
       " 402: 'turkey',\n",
       " 652: '🥦',\n",
       " 56: 'broccoli',\n",
       " 647: '🥗',\n",
       " 182: 'green_salad',\n",
       " 50: 'bowl',\n",
       " 395: 'tix',\n",
       " 54: 'breakfast',\n",
       " 524: '🎊',\n",
       " 92: 'confetti_ball',\n",
       " 120: 'don’t',\n",
       " 610: '🕺',\n",
       " 246: 'man_dancing',\n",
       " 123: 'drink',\n",
       " 146: 'festival_beer',\n",
       " 228: 'lawn',\n",
       " 475: '🌲',\n",
       " 132: 'evergreen_tree',\n",
       " 68: 'care',\n",
       " 602: '💻',\n",
       " 225: 'laptop_computer',\n",
       " 72: 'cell',\n",
       " 621: '😭',\n",
       " 239: 'loudly_crying_face',\n",
       " 300: 'pool',\n",
       " 317: 'rental',\n",
       " 617: '😍',\n",
       " 346: 'smiling_face_with_hearteyes',\n",
       " 60: 'buy',\n",
       " 322: 'rocket_popsicle',\n",
       " 11: 'awesome',\n",
       " 460: '✨',\n",
       " 357: 'sparkles',\n",
       " 548: '🏾',\n",
       " 250: 'medium_dark_skin_tone',\n",
       " 273: 'package',\n",
       " 482: '🍌',\n",
       " 22: 'banana',\n",
       " 446: '☎',\n",
       " 381: 'telephone',\n",
       " 465: '🅿',\n",
       " 272: 'p_button',\n",
       " 362: 'starbucks',\n",
       " 649: '🥞',\n",
       " 375: 'tacos',\n",
       " 114: 'din',\n",
       " 162: 'friend',\n",
       " 105: 'cut',\n",
       " 638: '🤦',\n",
       " 285: 'person_facepalming',\n",
       " 577: '💅',\n",
       " 266: 'nail_polish',\n",
       " 351: 'soap',\n",
       " 567: '👧',\n",
       " 478: '🌸',\n",
       " 76: 'cherry_blossom',\n",
       " 632: '🤗',\n",
       " 206: 'hugging_face',\n",
       " 35: 'bell',\n",
       " 140: 'family',\n",
       " 620: '😜',\n",
       " 431: 'winking_face_with_tongue',\n",
       " 574: '💁',\n",
       " 291: 'person_tipping_hand',\n",
       " 248: 'masks',\n",
       " 349: 'snacks',\n",
       " 379: 'tea',\n",
       " 65: 'candy_heart',\n",
       " 560: '👑',\n",
       " 102: 'crown',\n",
       " 576: '💄',\n",
       " 235: 'lipstick',\n",
       " 612: '😁',\n",
       " 31: 'beaming_face_with_smiling_eyes',\n",
       " 594: '💨',\n",
       " 111: 'dashing_away',\n",
       " 62: 'camera',\n",
       " 160: 'friday',\n",
       " 297: 'place',\n",
       " 74: 'check',\n",
       " 588: '💚',\n",
       " 181: 'green_heart',\n",
       " 469: '🌈',\n",
       " 308: 'rainbow',\n",
       " 564: '👚',\n",
       " 434: 'woman’s_clothes',\n",
       " 47: 'booze',\n",
       " 585: '💖',\n",
       " 358: 'sparkling_heart',\n",
       " 107: 'cuz',\n",
       " 354: 'softball',\n",
       " 226: 'lashes',\n",
       " 184: 'grocery',\n",
       " 403: 'tutoring',\n",
       " 229: 'lesson',\n",
       " 611: '🖤',\n",
       " 43: 'black_heart'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el22591404277293865129153888631\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el22591404277293865129153888631_data = {\"mdsDat\": {\"x\": [0.3172397584011222, -0.07506635053913022, 0.1597763338978991, -0.09484832147993337, 0.3072853718715301, -0.06790644051163844, 0.0654358880597344, -0.12540738670913545, -0.02365750051867323, -0.08076194741697286, -0.09779057869640341, -0.07101541338392192, -0.07582767540295379, 0.05851910271996877, -0.07603880327757083, 0.056105078562968105, -0.07442596878951634, -0.0765387904599469, 0.04971030360765138, -0.07478665993507924], \"y\": [0.038737248981264676, -0.029376746313378343, -0.07945827684113278, 0.3420886027975591, 0.06753687226606377, -0.036816496879003895, -0.07040399364984293, -0.0639400315042521, 0.35631530539687545, -0.040873170487411556, -0.04561896610208476, -0.03523946423176857, -0.03457203792983816, -0.06386561184379906, -0.05457332645514954, -0.07945653586476333, -0.034304476602245844, -0.032668263280619496, -0.07407965965481969, -0.02943097180165238], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 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\"squares\", \"starbucks\", \"starstruck\", \"steaming_bowl\", \"strawberry\", \"stuff\", \"sun\", \"super\", \"supplies\", \"sushi\", \"sweat_droplets\", \"t\", \"table\", \"taco\", \"tacos\", \"tacos\", \"taxi\", \"tea\", \"teacup_without_handle\", \"telephone\", \"telephone_receiver\", \"television\", \"television\", \"test\", \"thai\", \"thank\", \"thanks\", \"things\", \"thumbs_up\", \"thx\", \"ticket\", \"tickets\", \"time\", \"tip\", \"tix\", \"today\", \"trip\", \"trophy\", \"tropical_drink\", \"tshirt\", \"tumbler_glass\", \"turkey\", \"turkey\", \"two_hearts\", \"ty\", \"u\", \"uber\", \"unicorn_face\", \"ur\", \"utilities\", \"utilities\", \"venmo\", \"venmo_dollar\", \"victory_hand\", \"volleyball\", \"water\", \"water_wave\", \"way\", \"wedding\", \"week\", \"weekend\", \"white_medium_star\", \"wifi\", \"wine\", \"wine_glass\", \"wings\", \"winking_face\", \"winking_face_with_tongue\", \"woman\", \"woman_dancing\", \"woman\\u2019s_clothes\", \"work\", \"wrapped_gift\", \"ya\", \"year\", \"yellow_heart\", \"yo\", \"you\\u2019re\", \"za\", \"zany_face\", \"\\u200d\", \"\\u200d\", \"\\u2600\", \"\\u260e\", \"\\u2615\", \"\\u263a\", \"\\u2640\", \"\\u2640\", \"\\u2642\", \"\\u2665\", \"\\u26a1\", \"\\u26bd\", \"\\u26be\", \"\\u26f3\", \"\\u26f5\", \"\\u26fd\", \"\\u2708\", \"\\u270c\", \"\\u2728\", \"\\u2744\", \"\\u2764\", \"\\u2b50\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ufe0f\", \"\\ud83c\\uddf8\", \"\\ud83c\\uddfa\", \"\\ud83c\\udf08\", \"\\ud83c\\udf0a\", \"\\ud83c\\udf2d\", \"\\ud83c\\udf2e\", \"\\ud83c\\udf2f\", \"\\ud83c\\udf31\", \"\\ud83c\\udf32\", \"\\ud83c\\udf34\", \"\\ud83c\\udf36\", \"\\ud83c\\udf38\", \"\\ud83c\\udf3c\", \"\\ud83c\\udf46\", \"\\ud83c\\udf4c\", \"\\ud83c\\udf4e\", \"\\ud83c\\udf4e\", \"\\ud83c\\udf51\", \"\\ud83c\\udf53\", \"\\ud83c\\udf54\", \"\\ud83c\\udf54\", \"\\ud83c\\udf55\", \"\\ud83c\\udf56\", \"\\ud83c\\udf57\", \"\\ud83c\\udf5a\", \"\\ud83c\\udf5c\", \"\\ud83c\\udf5d\", \"\\ud83c\\udf5e\", \"\\ud83c\\udf5f\", \"\\ud83c\\udf63\", \"\\ud83c\\udf66\", \"\\ud83c\\udf69\", \"\\ud83c\\udf6a\", \"\\ud83c\\udf6b\", \"\\ud83c\\udf70\", \"\\ud83c\\udf71\", \"\\ud83c\\udf72\", \"\\ud83c\\udf73\", \"\\ud83c\\udf74\", \"\\ud83c\\udf75\", \"\\ud83c\\udf77\", \"\\ud83c\\udf78\", \"\\ud83c\\udf78\", \"\\ud83c\\udf79\", \"\\ud83c\\udf7a\", \"\\ud83c\\udf7b\", \"\\ud83c\\udf7c\", \"\\ud83c\\udf7d\", \"\\ud83c\\udf7e\", \"\\ud83c\\udf7f\", \"\\ud83c\\udf81\", \"\\ud83c\\udf82\", \"\\ud83c\\udf83\", \"\\ud83c\\udf84\", \"\\ud83c\\udf85\", \"\\ud83c\\udf88\", \"\\ud83c\\udf88\", \"\\ud83c\\udf89\", \"\\ud83c\\udf8a\", \"\\ud83c\\udf9f\", \"\\ud83c\\udfa4\", \"\\ud83c\\udfa5\", \"\\ud83c\\udfab\", \"\\ud83c\\udfb3\", \"\\ud83c\\udfb5\", \"\\ud83c\\udfb6\", \"\\ud83c\\udfc0\", \"\\ud83c\\udfc3\", \"\\ud83c\\udfc3\", \"\\ud83c\\udfc6\", \"\\ud83c\\udfc8\", \"\\ud83c\\udfcc\", \"\\ud83c\\udfd0\", \"\\ud83c\\udfd6\", \"\\ud83c\\udfe0\", \"\\ud83c\\udfe1\", \"\\ud83c\\udfe8\", \"\\ud83c\\udffb\", \"\\ud83c\\udffc\", \"\\ud83c\\udffd\", \"\\ud83c\\udffe\", \"\\ud83c\\udfff\", \"\\ud83d\\udc14\", \"\\ud83d\\udc15\", \"\\ud83d\\udc1f\", \"\\ud83d\\udc36\", \"\\ud83d\\udc37\", \"\\ud83d\\udc40\", \"\\ud83d\\udc41\", \"\\ud83d\\udc4c\", \"\\ud83d\\udc4d\", \"\\ud83d\\udc51\", \"\\ud83d\\udc55\", \"\\ud83d\\udc56\", \"\\ud83d\\udc57\", \"\\ud83d\\udc5a\", \"\\ud83d\\udc5f\", \"\\ud83d\\udc66\", \"\\ud83d\\udc67\", \"\\ud83d\\udc68\", \"\\ud83d\\udc69\", \"\\ud83d\\udc6f\", \"\\ud83d\\udc70\", \"\\ud83d\\udc76\", \"\\ud83d\\udc7b\", \"\\ud83d\\udc81\", \"\\ud83d\\udc83\", \"\\ud83d\\udc84\", \"\\ud83d\\udc85\", \"\\ud83d\\udc86\", \"\\ud83d\\udc87\", \"\\ud83d\\udc8a\", \"\\ud83d\\udc8b\", \"\\ud83d\\udc8d\", \"\\ud83d\\udc90\", \"\\ud83d\\udc95\", \"\\ud83d\\udc96\", \"\\ud83d\\udc97\", \"\\ud83d\\udc99\", \"\\ud83d\\udc9a\", \"\\ud83d\\udc9b\", \"\\ud83d\\udc9c\", \"\\ud83d\\udca1\", \"\\ud83d\\udca5\", \"\\ud83d\\udca5\", \"\\ud83d\\udca6\", \"\\ud83d\\udca8\", \"\\ud83d\\udca9\", \"\\ud83d\\udcaa\", \"\\ud83d\\udcaf\", \"\\ud83d\\udcb0\", \"\\ud83d\\udcb5\", \"\\ud83d\\udcb8\", \"\\ud83d\\udcb8\", \"\\ud83d\\udcbb\", \"\\ud83d\\udcda\", \"\\ud83d\\udcde\", \"\\ud83d\\udcf1\", \"\\ud83d\\udcfa\", \"\\ud83d\\udcfa\", \"\\ud83d\\udd0c\", \"\\ud83d\\udd0c\", \"\\ud83d\\udd19\", \"\\ud83d\\udd25\", \"\\ud83d\\udd25\", \"\\ud83d\\udda4\", \"\\ud83d\\ude01\", \"\\ud83d\\ude02\", \"\\ud83d\\ude09\", \"\\ud83d\\ude0a\", \"\\ud83d\\ude0b\", \"\\ud83d\\ude0d\", \"\\ud83d\\ude0e\", \"\\ud83d\\ude18\", \"\\ud83d\\ude1c\", \"\\ud83d\\ude2d\", \"\\ud83d\\ude37\", \"\\ud83d\\ude4c\", \"\\ud83d\\ude4f\", \"\\ud83d\\ude95\", \"\\ud83d\\ude97\", \"\\ud83d\\ude98\", \"\\ud83d\\ude99\", \"\\ud83d\\udebf\", \"\\ud83e\\udd11\", \"\\ud83e\\udd17\", \"\\ud83e\\udd18\", \"\\ud83e\\udd19\", \"\\ud83e\\udd20\", \"\\ud83e\\udd21\", \"\\ud83e\\udd24\", \"\\ud83e\\udd26\", \"\\ud83e\\udd29\", \"\\ud83e\\udd2a\", \"\\ud83e\\udd37\", \"\\ud83e\\udd42\", \"\\ud83e\\udd43\", \"\\ud83e\\udd51\", \"\\ud83e\\udd53\", \"\\ud83e\\udd57\", \"\\ud83e\\udd58\", \"\\ud83e\\udd5e\", \"\\ud83e\\udd5e\", \"\\ud83e\\udd5f\", \"\\ud83e\\udd64\", \"\\ud83e\\udd69\", \"\\ud83e\\udd6a\", \"\\ud83e\\udd6f\", \"\\ud83e\\udd70\", \"\\ud83e\\udd73\", \"\\ud83e\\udd84\", \"\\ud83e\\uddc0\", \"\\ud83e\\uddc0\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [17, 16, 13, 1, 14, 10, 11, 6, 15, 19, 12, 8, 3, 9, 7, 20, 18, 4, 2, 5]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el22591404277293865129153888631\", ldavis_el22591404277293865129153888631_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el22591404277293865129153888631\", ldavis_el22591404277293865129153888631_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el22591404277293865129153888631\", ldavis_el22591404277293865129153888631_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
       "topic                                               \n",
       "16     0.317240  0.038737       1        1  8.637576\n",
       "15    -0.075066 -0.029377       2        1  6.202503\n",
       "12     0.159776 -0.079458       3        1  5.750233\n",
       "0     -0.094848  0.342089       4        1  5.714991\n",
       "13     0.307285  0.067537       5        1  5.687773\n",
       "9     -0.067906 -0.036816       6        1  5.381647\n",
       "10     0.065436 -0.070404       7        1  5.327236\n",
       "5     -0.125407 -0.063940       8        1  4.924824\n",
       "14    -0.023658  0.356315       9        1  4.791292\n",
       "18    -0.080762 -0.040873      10        1  4.703551\n",
       "11    -0.097791 -0.045619      11        1  4.547911\n",
       "7     -0.071015 -0.035239      12        1  4.538269\n",
       "2     -0.075828 -0.034572      13        1  4.495395\n",
       "8      0.058519 -0.063866      14        1  4.471558\n",
       "6     -0.076039 -0.054573      15        1  4.405961\n",
       "19     0.056105 -0.079457      16        1  4.401294\n",
       "17    -0.074426 -0.034304      17        1  4.297980\n",
       "3     -0.076539 -0.032668      18        1  4.045718\n",
       "1      0.049710 -0.074080      19        1  3.843009\n",
       "4     -0.074787 -0.029431      20        1  3.831280, topic_info=               Term         Freq        Total Category  logprob  loglift\n",
       "296           pizza  6592.000000  6592.000000  Default  30.0000  30.0000\n",
       "153            food  6125.000000  6125.000000  Default  29.0000  29.0000\n",
       "487               🍕  5541.000000  5541.000000  Default  28.0000  28.0000\n",
       "462               ❤  5036.000000  5036.000000  Default  27.0000  27.0000\n",
       "311       red_heart  5036.000000  5036.000000  Default  26.0000  26.0000\n",
       "..              ...          ...          ...      ...      ...      ...\n",
       "132  evergreen_tree   302.071045   302.989166  Topic20  -4.1136   3.2589\n",
       "475               🌲   302.071045   302.989166  Topic20  -4.1136   3.2589\n",
       "180           great   298.755615   299.673737  Topic20  -4.1246   3.2589\n",
       "335        shipping   296.855682   297.773804  Topic20  -4.1310   3.2589\n",
       "248           masks   280.652435   281.570557  Topic20  -4.1871   3.2587\n",
       "\n",
       "[688 rows x 6 columns], token_table=      Topic      Freq               Term\n",
       "term                                    \n",
       "0        15  0.998563  admission_tickets\n",
       "1         2  0.997329             airbnb\n",
       "2         6  0.998600           airplane\n",
       "3         8  0.997799             amazon\n",
       "4        12  0.999672  american_football\n",
       "...     ...       ...                ...\n",
       "656      20  0.998842                  🥰\n",
       "657      17  0.999173                  🥳\n",
       "658      20  0.995710                  🦄\n",
       "659      10  0.863734                  🧀\n",
       "659      12  0.134802                  🧀\n",
       "\n",
       "[641 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[17, 16, 13, 1, 14, 10, 11, 6, 15, 19, 12, 8, 3, 9, 7, 20, 18, 4, 2, 5])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the topics\n",
    "pyLDAvis.enable_notebook()\n",
    "LDAvis_prepared = pyLDAvis.gensim.prepare(lda, corpus, dictionary=id2word2)\n",
    "LDAvis_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/ldavis_single_defined_286k.pickle', 'wb') as f:\n",
    "    pickle.dump(LDAvis_prepared, f)"
   ]
  }
 ],
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   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m54"
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  "kernelspec": {
   "display_name": "Python [conda env:metis] *",
   "language": "python",
   "name": "conda-env-metis-py"
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