CAH_bertTopic_V2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "JfzLlyjiiUWV",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JfzLlyjiiUWV",
    "outputId": "9d0246c4-14e5-4bde-fb37-686c2782182d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.7/dist-packages (2.1.0)\n",
      "Requirement already satisfied: bertopic in /usr/local/lib/python3.7/dist-packages (0.9.3)\n",
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      "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.1.96)\n",
      "Requirement already satisfied: tokenizers>=0.10.3 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.10.3)\n",
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      "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.11.1+cu111)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.0.1)\n",
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      "Requirement already satisfied: umap-learn>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from bertopic) (0.5.2)\n",
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      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2.10)\n",
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      "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (7.1.2)\n",
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     ]
    }
   ],
   "source": [
    "!pip install  sentence-transformers bertopic -U\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "experienced-isolation",
   "metadata": {
    "id": "experienced-isolation"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from bertopic import BERTopic\n",
    "# import math \n",
    "import numpy as np\n",
    "pd.set_option(\"display.precision\", 3)\n",
    "pd.set_option('display.width', 1024)\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "%matplotlib inline \n",
    "\n",
    "TARGET = \"picks_ratio\" ## \"picks\"\n",
    "\n",
    "model_name = \"/content/drive/MyDrive/research/cah/cah_tsdae-model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "xZ84pkSNh-lL",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xZ84pkSNh-lL",
    "outputId": "2ad1dd2f-094a-4265-b379-600d2ef0127d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bridal-paris",
   "metadata": {
    "id": "bridal-paris"
   },
   "source": [
    "Load data\n",
    "\n",
    "* Note there are some near-duplicate cards (e.g. footballers - could minhash - https://github.com/chrisjmccormick/MinHash/blob/master/runMinHashExample.py , LSH/shingles : https://onestopdataanalysis.com/lsh/  (broken library?) , https://github.com/zyocum/dedup/blob/master/dedup.py ,\n",
    "\n",
    "`Datasketch` https://www.learndatasci.com/tutorials/building-recommendation-engine-locality-sensitive-hashing-lsh-python/ ,\n",
    "https://stackoverflow.com/questions/25114338/approximate-string-matching-using-lsh , \n",
    "\n",
    "SKLearn friendly?: http://ethen8181.github.io/machine-learning/recsys/content_based/lsh_text.html\n",
    "\n",
    "`dedupe` library - https://github.com/dedupeio/dedupe\n",
    "\n",
    "* Paraphrase mining using sentence bert - https://www.sbert.net/examples/applications/paraphrase-mining/README.html  (may be too semantic)\n",
    "\n",
    "* Example: \"10 football players with erections *barreling* towards you at full speed\" - 10 football players with erections **barrelling** towards you at full speed.\"\n",
    "\n",
    "Task modelling:\n",
    "* Ranking = most accurate\n",
    "* Regression/classification: can be done just over single cards, or between pairs. \n",
    "    * if pairs-wise: can use sentencebert/SNLI models (which assume pairs of sentences) - https://github.com/UKPLab/sentence-transformers\n",
    "    \n",
    "    \n",
    "Examples:\n",
    "\n",
    "* https://www.sbert.net/examples/training/sts/README.html#training-data\n",
    "* https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli.py\n",
    "* https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli_v2.py\n",
    "\n",
    "\n",
    "* Keras example (not sentenceBert): https://keras.io/examples/nlp/semantic_similarity_with_bert/\n",
    "\n",
    "\n",
    "* Interpretability tool : https://github.com/cdpierse/transformers-interpret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "y3ch8EHEvw5r",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 528
    },
    "id": "y3ch8EHEvw5r",
    "outputId": "fe422828-e9cd-48c7-fd05-4f4035a57f2d"
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>fake_round_id</th>\n",
       "      <th>black_card_text</th>\n",
       "      <th>white_card_text</th>\n",
       "      <th>picks</th>\n",
       "      <th>text</th>\n",
       "      <th>sum_picks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2989541</th>\n",
       "      <td>298955</td>\n",
       "      <td>Oh my god! __ killed Kenny!</td>\n",
       "      <td>Jeff Bezos</td>\n",
       "      <td>1</td>\n",
       "      <td>Oh my god! Jeff Bezos killed Kenny!</td>\n",
       "      <td>3</td>\n",
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       "      <td>Oh my god! __ killed Kenny!</td>\n",
       "      <td>Brief male nudity</td>\n",
       "      <td>0</td>\n",
       "      <td>Oh my god! Brief male nudity killed Kenny!</td>\n",
       "      <td>1</td>\n",
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       "      <th>2989542</th>\n",
       "      <td>298955</td>\n",
       "      <td>Oh my god! __ killed Kenny!</td>\n",
       "      <td>A bird that shits human turds</td>\n",
       "      <td>0</td>\n",
       "      <td>Oh my god! A bird that shits human turds killed Kenny!</td>\n",
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       "      <th>2989543</th>\n",
       "      <td>298955</td>\n",
       "      <td>Oh my god! __ killed Kenny!</td>\n",
       "      <td>Holding up the line at Walgreens by trying to use an expired coupon</td>\n",
       "      <td>0</td>\n",
       "      <td>Oh my god! Holding up the line at Walgreens by trying to use an expired coupon killed Kenny!</td>\n",
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       "      <th>2989544</th>\n",
       "      <td>298955</td>\n",
       "      <td>Oh my god! __ killed Kenny!</td>\n",
       "      <td>Powerful thighs</td>\n",
       "      <td>0</td>\n",
       "      <td>Oh my god! Powerful thighs killed Kenny!</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>A sorry excuse for a father</td>\n",
       "      <td>0</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into A sorry excuse for a father, and I love to have a good time.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>A full-on panic attack</td>\n",
       "      <td>0</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into A full-on panic attack, and I love to have a good time.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>Chunky highlights</td>\n",
       "      <td>0</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into Chunky highlights, and I love to have a good time.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>Your mom</td>\n",
       "      <td>0</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into Your mom, and I love to have a good time.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>Mufasa's death scene</td>\n",
       "      <td>0</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into Mufasa's death scene, and I love to have a good time.</td>\n",
       "      <td>0</td>\n",
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       "<p>1296706 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "         fake_round_id  ... sum_picks\n",
       "2989541         298955  ...         3\n",
       "2989540         298955  ...         1\n",
       "2989542         298955  ...         0\n",
       "2989543         298955  ...         0\n",
       "2989544         298955  ...         0\n",
       "...                ...  ...       ...\n",
       "4                    1  ...         0\n",
       "6                    1  ...         0\n",
       "7                    1  ...         1\n",
       "8                    1  ...         2\n",
       "9                    1  ...         0\n",
       "\n",
       "[1296706 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(\"/content/drive/MyDrive/research/cah/cah_train_min3_games.parquet\").drop(columns=[\"prior_white\"],errors=\"ignore\") \n",
    "df[\"won\"] = df[\"won\"].astype(int)\n",
    "df.rename(columns={\"won\":\"picks\",\"sum_won\":\"sum_picks\"},inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sound-transcript",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 873
    },
    "id": "sound-transcript",
    "outputId": "d4680ed3-0de0-446d-ceee-ba1784b08e1e"
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fake_round_id</th>\n",
       "      <th>black_card_text</th>\n",
       "      <th>white_card_text</th>\n",
       "      <th>picks</th>\n",
       "      <th>text</th>\n",
       "      <th>sum_picks</th>\n",
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       "  </thead>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>A sorry excuse for a father</td>\n",
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       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into A sorry excuse for a father, and I love to have a good time.</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>A full-on panic attack</td>\n",
       "      <td>0</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into A full-on panic attack, and I love to have a good time.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>Chunky highlights</td>\n",
       "      <td>0</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into Chunky highlights, and I love to have a good time.</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Your mom</td>\n",
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       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into Your mom, and I love to have a good time.</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into __, and I love to have a good time.</td>\n",
       "      <td>Mufasa's death scene</td>\n",
       "      <td>0</td>\n",
       "      <td>Hi MTV! My name is Kendra, I live in Malibu, I'm into Mufasa's death scene, and I love to have a good time.</td>\n",
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      ],
      "text/plain": [
       "   fake_round_id  ... sum_picks\n",
       "4              1  ...         0\n",
       "6              1  ...         0\n",
       "7              1  ...         1\n",
       "8              1  ...         2\n",
       "9              1  ...         0\n",
       "\n",
       "[5 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       picks_ratio       picks\n",
      "count   452710.000  452710.000\n",
      "mean         0.087       0.280\n",
      "std          0.197       0.449\n",
      "min          0.000       0.000\n",
      "25%          0.000       0.000\n",
      "50%          0.000       0.000\n",
      "75%          0.100       1.000\n",
      "max          1.000       1.000\n"
     ]
    },
    {
     "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>text</th>\n",
       "      <th>picks_ratio</th>\n",
       "      <th>picks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1815347</th>\n",
       "      <td>What gets me wet? Sperm whales</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703109</th>\n",
       "      <td>I have solved politics. My solution is A salty surprise.</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80801</th>\n",
       "      <td>What killed my boner? A salad for men that's made of metal</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714424</th>\n",
       "      <td>Money can't buy me love, but it can buy me Firing a rifle into the air while balls deep in a squealing hog.</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718885</th>\n",
       "      <td>Okay here's the pitch. James Franco and Seth Rogen are trying to score some weed, and then Twenty bucks happens.</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823347</th>\n",
       "      <td>Do the Dew® with our most extreme flavor yet! Get ready for Mountain Dew Little boy penises!</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698291</th>\n",
       "      <td>After four platinum albums and three Grammys, it's time to get back to my roots, to what inspired me to make music in the first place: Holding up the line at Walgreens by trying to use an expired coupon.</td>\n",
       "      <td>0.100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831862</th>\n",
       "      <td>Girls just wanna have Stalin.</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2814600</th>\n",
       "      <td>Instead of coal, Santa now gives the bad children Establishing dominance.</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2808059</th>\n",
       "      <td>Google Calendar alert: Snorting coke off a clown's boner in 10 minutes.</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>452710 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                text  ...  picks\n",
       "1815347                                                                                                                                                                               What gets me wet? Sperm whales  ...      1\n",
       "703109                                                                                                                                                      I have solved politics. My solution is A salty surprise.  ...      0\n",
       "80801                                                                                                                                                     What killed my boner? A salad for men that's made of metal  ...      0\n",
       "1714424                                                                                                  Money can't buy me love, but it can buy me Firing a rifle into the air while balls deep in a squealing hog.  ...      1\n",
       "718885                                                                                              Okay here's the pitch. James Franco and Seth Rogen are trying to score some weed, and then Twenty bucks happens.  ...      0\n",
       "...                                                                                                                                                                                                              ...  ...    ...\n",
       "823347                                                                                                                  Do the Dew® with our most extreme flavor yet! Get ready for Mountain Dew Little boy penises!  ...      0\n",
       "1698291  After four platinum albums and three Grammys, it's time to get back to my roots, to what inspired me to make music in the first place: Holding up the line at Walgreens by trying to use an expired coupon.  ...      1\n",
       "1831862                                                                                                                                                                                Girls just wanna have Stalin.  ...      1\n",
       "2814600                                                                                                                                    Instead of coal, Santa now gives the bad children Establishing dominance.  ...      0\n",
       "2808059                                                                                                                                      Google Calendar alert: Snorting coke off a clown's boner in 10 minutes.  ...      0\n",
       "\n",
       "[452710 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = pd.read_csv(\"cah_lab_data_for_research_dan_ofer.csv\")\n",
    "# df = pd.read_csv(\"/content/drive/MyDrive/Research/CAH/cah_min6_v2.csv.gz\")\n",
    "\n",
    "display(df.tail())\n",
    "## alt target - weighted % of times picked - regression target - give more weight to those picked more than 1 time. (log2(1) == 0)\n",
    "# df[\"picks_ratio\"] = (df[\"picks_sum\"] + math.log2(df[\"picks_sum\"]))/df[\"pick_opportunities\"]\n",
    "# df[\"picks_ratio\"] = np.where(df['picks_sum']==0, 0, (df[\"picks_sum\"] + np.log2(df[\"picks_sum\"].values))/df[\"pick_opportunities\"]) # ORIG\n",
    "df = df.sort_values(\"picks\",ascending=False)\n",
    "## Traing target: % of times picked as funny + upweigh for cases picked more than once\n",
    "df[\"picks_ratio\"] = df.groupby(\"text\")[\"picks\"].transform(\"mean\")\n",
    "df[\"multi_win\"] = (df.groupby(\"text\")[\"picks\"].transform(\"sum\")>1).astype(int)\n",
    "df[\"picks_ratio\"] = (df[\"picks_ratio\"]+df[\"multi_win\"])/2\n",
    "# df[\"picks_ratio\"] = np.where(df['picks_sum']==0, 0, (df[\"picks_sum\"] + np.log2(df[\"picks_sum\"].values))/df[\"pick_opportunities\"])\n",
    "# df[\"picks_ratio\"] = df[\"picks_ratio\"].clip(upper=1.)\n",
    "# print(df.describe())\n",
    "df = df[[\"text\",'black_card_text', 'white_card_text', 'picks',\"picks_ratio\"]]\n",
    "\n",
    "### keep just text ratios:\n",
    "df = df[[\"text\",\"picks_ratio\",\"picks\"]].drop_duplicates(subset=\"text\").sample(frac=1)\n",
    "# df[\"picks\"] = df[\"picks\"].astype(float)\n",
    "print(df.describe())\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compound-amino",
   "metadata": {
    "id": "compound-amino"
   },
   "source": [
    "## Sentence transformers model(s)\n",
    "* Sentence bert (NLI? semtantic similarity?) \n",
    "https://www.sbert.net/docs/training/overview.html\n",
    "https://www.sbert.net/docs/quickstart.html  , https://github.com/UKPLab/sentence-transformers\n",
    "* Load data from disk ? \n",
    "\n",
    "* list of pretrained sentencebert models: (All are >300 M! slow download) https://docs.google.com/spreadsheets/d/14QplCdTCDwEmTqrn1LH4yrbKvdogK4oQvYO1K1aPR5M/edit#gid=0\n",
    "\n",
    "* `stsb-distilroberta-base-v2` - 305M sized model - semantic similarity\n",
    "* `nli-distilroberta-base-v2` - NLI \n",
    "* `average_word_embeddings_glove.6B.300d`, `average_word_embeddings_glove.840B.300d` - glove/w2v embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c3aa9f9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0c3aa9f9",
    "outputId": "bda2e0f1-9ee7-406e-bbcf-aba951b62ad8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer, SentencesDataset, InputExample, losses, evaluation\n",
    "import torch\n",
    "print(torch.cuda.is_available())\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.utils.data as data_utils\n",
    "## https://stackoverflow.com/questions/50307707/convert-pandas-dataframe-to-pytorch-tensor\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "## example from : https://www.sbert.net/docs/training/overview.html\n",
    "from sentence_transformers import SentenceTransformer, SentencesDataset, InputExample, losses \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "consecutive-journalist",
   "metadata": {
    "id": "consecutive-journalist"
   },
   "outputs": [],
   "source": [
    "## pretrained weights - download vis program often fails, easier to download externally from ftp\n",
    "# model = SentenceTransformer('stsb-distilroberta-base-v2') ## was distilbert-base-nli-mean-tokens\n",
    "# model = SentenceTransformer('average_word_embeddings_glove.6B.300d') ## was distilbert-base-nli-mean-tokens\n",
    "# model = SentenceTransformer(\"nli-distilroberta-base-v2\",device=\"cuda\")\n",
    "\n",
    "# model = SentenceTransformer(\"paraphrase-MiniLM-L12-v2\",device=\"cuda\") ## \"paraphrase-MiniLM-L6-v2\". \"paraphrase-MiniLM-L12-v2\"\n",
    "\n",
    "model = SentenceTransformer(model_name,device=\"cuda\") \n",
    "# model = SentenceTransformer(model_name) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6026b18e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6026b18e",
    "outputId": "20b6e071-9f25-4f92-fb5c-a4adc96db729"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.max_seq_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "legal-exemption",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "legal-exemption",
    "outputId": "b5269b5a-9b48-4054-c97d-e3bcc64dc150"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# len(model.tokenize([\"ad and \"])[\"input_ids\"])\n",
    "model.get_sentence_embedding_dimension() # 768"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "novel-repeat",
   "metadata": {
    "id": "novel-repeat"
   },
   "source": [
    "#### redo above, but with real data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "modified-journal",
   "metadata": {
    "id": "modified-journal"
   },
   "outputs": [],
   "source": [
    "TEST_SIZE = df.shape[0]//12 # 37725\n",
    "print(TEST_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hiXYGjYtwbwi",
   "metadata": {
    "id": "hiXYGjYtwbwi"
   },
   "outputs": [],
   "source": [
    "# df[\"picks\"] = df[\"picks\"].astype(int) # for softmax model \n",
    "df.iloc[TEST_SIZE:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "victorian-relevance",
   "metadata": {
    "id": "victorian-relevance"
   },
   "outputs": [],
   "source": [
    "## is list correct? error otherwise? \n",
    "# train_examples = list(df.iloc[TEST_SIZE:].apply(lambda row: InputExample(texts=[ row[\"black_card_text\"],row[\"white_card_text\"]], label=row[TARGET]),axis=1))\n",
    "# test_examples =  list(df.iloc[0:TEST_SIZE].apply(lambda row: InputExample(texts=[row[\"black_card_text\"],row[\"white_card_text\"]], label=row[TARGET]),axis=1))\n",
    "\n",
    "train_examples = list(df.iloc[TEST_SIZE:].apply(lambda row: InputExample(texts=[ row[\"text\"]], label=row[TARGET]),axis=1))\n",
    "# test_examples =  list(df.iloc[0:TEST_SIZE].apply(lambda row: InputExample(texts=[row[\"black_card_text\"],row[\"white_card_text\"]], label=row[TARGET]),axis=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deadly-lancaster",
   "metadata": {
    "id": "deadly-lancaster"
   },
   "outputs": [],
   "source": [
    "# dev_eval = evaluation.BinaryClassificationEvaluator(sentences1=df.iloc[0:2500,0],\n",
    "#     sentences2= df.iloc[0:2500,1],\n",
    "#     labels=df.iloc[0:2500,2],show_progress_bar=True,batch_size=256)\n",
    "dev_eval = evaluation.BinaryClassificationEvaluator(sentences1=list(df.iloc[0:TEST_SIZE,0].values),\n",
    "    sentences2= list(df.iloc[0:TEST_SIZE,1].values),\n",
    "    labels=list(df.iloc[0:TEST_SIZE,2].values),show_progress_bar=True,batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51be1206",
   "metadata": {
    "id": "51be1206"
   },
   "outputs": [],
   "source": [
    "## https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli.py \n",
    "\n",
    "## evaluator example \n",
    "train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "normal-bones",
   "metadata": {
    "id": "normal-bones",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "train_loss = losses.CosineSimilarityLoss(model) # ORIG.  # label must be float  - regression \n",
    "# train_loss = losses.SoftmaxLoss(model,  # label must be int \n",
    "#     sentence_embedding_dimension= model.get_sentence_embedding_dimension(),\n",
    "#     num_labels= 2,\n",
    "#     concatenation_sent_rep= True,\n",
    "#     concatenation_sent_difference = True,\n",
    "#     concatenation_sent_multiplication = True) # ALT - for classification https://www.sbert.net/docs/package_reference/losses.html#softmaxloss\n",
    "\n",
    "#Tune the model\n",
    "model.fit(train_objectives=[(train_dataloader, train_loss)], \n",
    "          epochs=1, \n",
    "          warmup_steps=(df.shape[0]//64) *2*10, # 100 / or 10% of data?\n",
    "          evaluator=dev_eval,\n",
    "          use_amp=True,\n",
    "          optimizer_params= {'lr': 2e-04}, # 2e-05\n",
    "          output_path=\"./output\",\n",
    "        #   save_best_model = True,\n",
    "         )\n",
    "### On regression target, min 6, ; Cosine : 62% accuracy, 89 % recall, 47% precision, 62% f1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "svKWhW_Dkr_D",
   "metadata": {
    "id": "svKWhW_Dkr_D"
   },
   "source": [
    "# BertTopic\n",
    "* Try to use our (finetuned?) model \n",
    "\n",
    "* base on : https://www.kaggle.com/danofer/topic-modeling-arxiv-abstract-with-bertopic\n",
    "\n",
    "* We can get topics for just the white/answer cards (drop duplicates if so) ;  or the Q&A combination \n",
    "\n",
    "* Semi supervised topic model : https://colab.research.google.com/drive/1bxizKzv5vfxJEB29sntU__ZC7PBSIPaQ?usp=sharing#scrollTo=2FNFTvhadAE5 \n",
    "* Hyperpaams documentation: BERTopic - Advanced Customization.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "-4RoaN_E86Qx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-4RoaN_E86Qx",
    "outputId": "e8479bf3-71f3-450d-df98-d2c44dd93cf2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "452710"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0hFrtMaG-RWb",
   "metadata": {
    "id": "0hFrtMaG-RWb"
   },
   "outputs": [],
   "source": [
    "# WHITE_TOPICS_ONLY = False #True\n",
    "\n",
    "# if WHITE_TOPICS_ONLY:\n",
    "#  # either white only (problematic if as target) , or the combinations \n",
    "#     df2 = df.drop_duplicates([\"white_card_text\"])\n",
    "#     # df2 = df.groupby([\"white_card_text\"]).max().reset_index().drop_duplicates([\"white_card_text\"]) ## can't do - all the white cards have at least \"1\" pick globally\n",
    "#     print(df2.shape)\n",
    "#     abstracts = list(df2[\"white_card_text\"])\n",
    "#     targets = list(df2[\"picks\"].astype(int).values) ##df.drop_duplicates([\"white_card_text\"])[\"picks\"].astype(int).values\n",
    "# else:## alt : use both cols \n",
    "#     abstracts = list(df['black_card_text'] +\" \" + df['white_card_text'])\n",
    "#     # targets = df.drop_duplicates([\"white_card_text\",\"black_card_text\"])[\"picks\"].astype(int).values\n",
    "#     targets = list(df[\"picks\"].astype(int).values)\n",
    "\n",
    "### new:\n",
    "\n",
    "abstracts = list(df[\"text\"].head())\n",
    "targets = list(df[\"picks\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wmZ4BF8s8pXM",
   "metadata": {
    "id": "wmZ4BF8s8pXM"
   },
   "outputs": [],
   "source": [
    "df2 = df.sample(133456).copy()\n",
    "\n",
    "abstracts = list(df2[\"text\"].copy())\n",
    "targets = list(df2[\"picks\"].copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44eb33fa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "44eb33fa",
    "outputId": "8573dc0b-71e7-4a68-9560-a404375c56bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 886 µs, sys: 0 ns, total: 886 µs\n",
      "Wall time: 903 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "##ORIG: \n",
    "# topic_model = BERTopic(verbose=True, embedding_model=model, min_topic_size=10)\n",
    "### Alt: semi supervised - Warning! problematic if used with white only data (labels will conflict)\n",
    "topic_model = BERTopic(verbose=True, embedding_model=model, min_topic_size=12, top_n_words=7,\n",
    "                       n_gram_range=(1,2),nr_topics=100,low_memory=True) ## 1k topics with 100k words by deault/\"auto\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YLLYv24xSaKT",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136,
     "referenced_widgets": [
      "6ba2261e54f84a56b1a0c56c7b48ca79",
      "268897eee3ae48a9b23e555bc95b756c",
      "c58f70895c9f40918e0d650031b93f2d",
      "cf6ae818d8ef4d7ea090be2a4d8f7397",
      "7e3c183203be47b6be5f6f56bc56fecc",
      "711c4e620d0f4098964ee6df96fb294a",
      "91b10b1b07194cf6a683bc2ec46bdb5c",
      "34d7a3bf2f5446f782d551c59a67de29",
      "4209d143aae94ee4af9fa79cf1292c4f",
      "270029e55a1746d7beb978af33c1e493",
      "92de7c1b45fb413ca447f49902aea0ae"
     ]
    },
    "id": "YLLYv24xSaKT",
    "outputId": "41850933-0c7e-47c5-ad39-0a311e09de8d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ba2261e54f84a56b1a0c56c7b48ca79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/1046 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-08 18:25:50,735 - BERTopic - Transformed documents to Embeddings\n",
      "2021-12-08 18:26:29,626 - BERTopic - Reduced dimensionality with UMAP\n",
      "2021-12-08 18:26:35,735 - BERTopic - Clustered UMAP embeddings with HDBSCAN\n",
      "2021-12-08 18:27:36,154 - BERTopic - Reduced number of topics from 719 to 121\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bertopic._bertopic.BERTopic at 0x7f92069da590>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "topic_model.fit(documents=abstracts,y=targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pw_z8Rqq_GbE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
      "21c94236a1b44f0f9059f7635b1ed855",
      "ac1a5e7a83f44308a3930b728f19fe26",
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      "c8c4aebb07cb473a9029ee25ff56570d",
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      "717cd2ea18a747e69fa9cf338be19917",
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      "a67a59323a30463fb76751423f77d239"
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    "id": "pw_z8Rqq_GbE",
    "outputId": "c368403f-5ccb-45a1-9699-a8063d704f41"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21c94236a1b44f0f9059f7635b1ed855",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/1046 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# topics, _ = topic_model.fit_transform(abstracts); len(topic_model.get_topic_info()) \n",
    "# topics, _ = topic_model.fit_transform(documents=abstracts,y=targets)\n",
    "topics, _ = topic_model.transform(documents=abstracts)\n",
    "print(len(topic_model.get_topic_info())) # supervised topic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "AUm0IW_2lJS9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "id": "AUm0IW_2lJS9",
    "outputId": "50d6f9b7-a677-47c6-c6db-522d53031883"
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>Count</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>8473</td>\n",
       "      <td>-1_re_are_out_we</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1466</td>\n",
       "      <td>0_of the_with the_and the_about the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1296</td>\n",
       "      <td>1_full of_no_when_richards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>633</td>\n",
       "      <td>2_laid_you laid_and watching_getting naked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>467</td>\n",
       "      <td>3_developed smaller_efficient version_dear brutus_fault</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>457</td>\n",
       "      <td>4_bellied boy_pot bellied_bellied_my disgusting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>368</td>\n",
       "      <td>5_dog dying_friends dying_my friends_friends</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>366</td>\n",
       "      <td>6_ed absolutely_co ed_what harshing_harshing</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>344</td>\n",
       "      <td>7_can handle_bullshit fuck_card again_did it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>331</td>\n",
       "      <td>8_my solution_solved politics_politics_my fettuccine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>321</td>\n",
       "      <td>9_violated the_directive you_exposed an_have violated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>308</td>\n",
       "      <td>10_officially become_grammys it_three grammys_albums and</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic  Count                                                      Name\n",
       "0      -1   8473                                          -1_re_are_out_we\n",
       "1       0   1466                       0_of the_with the_and the_about the\n",
       "2       1   1296                                1_full of_no_when_richards\n",
       "3       2    633                2_laid_you laid_and watching_getting naked\n",
       "4       3    467   3_developed smaller_efficient version_dear brutus_fault\n",
       "5       4    457           4_bellied boy_pot bellied_bellied_my disgusting\n",
       "6       5    368              5_dog dying_friends dying_my friends_friends\n",
       "7       6    366              6_ed absolutely_co ed_what harshing_harshing\n",
       "8       7    344              7_can handle_bullshit fuck_card again_did it\n",
       "9       8    331      8_my solution_solved politics_politics_my fettuccine\n",
       "10      9    321     9_violated the_directive you_exposed an_have violated\n",
       "11     10    308  10_officially become_grammys it_three grammys_albums and"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_model.get_topic_info().head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0xuFu3f0krpL",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 717
    },
    "id": "0xuFu3f0krpL",
    "outputId": "8740d65c-92b2-4205-8f93-4d8e7b4d4ea1"
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   "outputs": [
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   "source": [
    "topic_model.visualize_barchart(top_n_topics=10, height=700)"
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    "For each topic, we have generated 10 words that best represent that topic. However, are 10 words sufficient? Can we already have a good topic representation with 1 word?\n",
    "\n",
    "To understand the amount of words needed to have a sufficient topic representation, we can show the decline of term score when adding terms. The idea here is that each term that is added has a lower term score than the previous since the first is the best term for the topic. Eventually, we reach the point of diminishing returns, which is very similar to the elbow-method used in k-NN. Below, you can see the decline in term score when adding terms per topic:"
   ]
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forest_hereby sentence_huntin\", \"line\": {\"color\": \"black\", \"width\": 1.5}, \"mode\": \"lines+lines\", \"name\": \"\", \"opacity\": 0.1, \"type\": \"scatter\", \"x\": [1, 2, 3, 4, 5, 6, 7, 8], \"y\": [0.020258439725530892, 0.020258439725530892, 0.020258439725530892, 0.020258439725530892, 0.020208833278004136, 0.01889273209633677, 0.017417389796119263, 0.017417389796119263]}, {\"hovertext\": \"<b>Topic 29</b>:it hungers_hungers for_it lurks_hu\", \"line\": {\"color\": \"black\", \"width\": 1.5}, \"mode\": \"lines+lines\", \"name\": \"\", \"opacity\": 0.1, \"type\": \"scatter\", \"x\": [1, 2, 3, 4, 5, 6, 7, 8], \"y\": [0.017990031139556856, 0.017990031139556856, 0.017990031139556856, 0.017990031139556856, 0.017990031139556856, 0.017990031139556856, 0.017990031139556856, 0.017990031139556856]}, {\"hovertext\": \"<b>Topic 30</b>:week nothing_juice and_kale juice_\", \"line\": {\"color\": \"black\", \"width\": 1.5}, \"mode\": \"lines+lines\", \"name\": \"\", \"opacity\": 0.1, 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topic_model.visualize_term_rank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nG6iKvbzvTUE",
   "metadata": {
    "id": "nG6iKvbzvTUE"
   },
   "outputs": [],
   "source": [
    "# topic_model.visualize_term_rank(log_scale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4Dw3p-VQJ8k",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a4Dw3p-VQJ8k",
    "outputId": "997039cf-4c88-4839-b6e3-71b728273961"
   },
   "outputs": [
    {
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       "  ('redneck well', 0.018363024097782024),\n",
       "  ('dub dub', 0.014878912650882077),\n",
       "  ('rub', 0.014878912650882077),\n",
       "  ('rub dub', 0.014878912650882077)],\n",
       " 35: [('the most', 0.05068068627609048),\n",
       "  ('most problematic', 0.05068068627609048),\n",
       "  ('making things', 0.03505733888418928),\n",
       "  ('sauna', 0.03505733888418928),\n",
       "  ('things awkward', 0.03505733888418928),\n",
       "  ('the sauna', 0.03505733888418928),\n",
       "  ('find disturbing', 0.024256785170415872),\n",
       "  ('oddly charming', 0.024256785170415872)],\n",
       " 36: [('don mean', 0.044752852273080436),\n",
       "  ('brag but', 0.044752852273080436),\n",
       "  ('to brag', 0.044752852273080436),\n",
       "  ('an unattainable', 0.034387823232133685),\n",
       "  ('unattainable myth', 0.034387823232133685),\n",
       "  ('unattainable', 0.034387823232133685),\n",
       "  ('myth was', 0.034387823232133685),\n",
       "  ('but wonder', 0.034387823232133685)],\n",
       " 37: [('statue', 0.02494329958552115),\n",
       "  ('foot statue', 0.02494329958552115),\n",
       "  ('statue to', 0.02494329958552115),\n",
       "  ('erect 50', 0.02494329958552115),\n",
       "  ('billionaire shall', 0.02494329958552115),\n",
       "  ('am billionaire', 0.02494329958552115),\n",
       "  ('to commemorate', 0.02494329958552115),\n",
       "  ('shall erect', 0.02494329958552115)],\n",
       " 38: [('couldn complete', 0.07292124816683364),\n",
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       "  ('sorry professor', 0.07292124816683364),\n",
       "  ('absolutely mesmerized', 0.06530458038788253),\n",
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       "  ('now because', 0.06530458038788253),\n",
       "  ('mesmerized', 0.06530458038788253),\n",
       "  ('pretty sure', 0.05590768902147645)],\n",
       " 39: [('lasts longer', 0.023228970535971217),\n",
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       "  ('an unstoppable', 0.014251667504159675),\n",
       "  ('unstoppable wave', 0.014251667504159675),\n",
       "  ('fire ants', 0.014251667504159675)],\n",
       " 40: [('rapists', 0.035357628637497134),\n",
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       "  ('blasphemes and', 0.020407961104039515),\n",
       "  ('gnaws hungrily', 0.020407961104039515)],\n",
       " 41: [('blood of', 0.01066504801012283),\n",
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       "  ('pervades', 0.008784211558666958)],\n",
       " 42: [('best friend', 0.09488327418323105),\n",
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       "  ('what girl', 0.09488327418323105),\n",
       "  ('girl', 0.07610639711650276),\n",
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       "  ('liven', 0.02640177904694436),\n",
       "  ('to liven', 0.02640177904694436),\n",
       "  ('friend the', 0.015954463659013825)],\n",
       " 43: [('manhattan theatre', 0.043725299176670326),\n",
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       "  ('steppenwolf', 0.029622068705453444),\n",
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       "  ('classic existential', 0.029622068705453444),\n",
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       "  ('existential', 0.029622068705453444),\n",
       "  ('at steppenwolf', 0.029622068705453444)],\n",
       " 44: [('the biggest', 0.02191100659382457),\n",
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       "  ('biggest threat', 0.02191100659382457),\n",
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       "  ('nation 60', 0.02191100659382457),\n",
       "  ('most extreme', 0.01991190555138274)],\n",
       " 45: [('it needs', 0.043176906314190175),\n",
       "  ('customer', 0.030129735940035266),\n",
       "  ('customer is', 0.030129735940035266),\n",
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       "  ('the customer', 0.030129735940035266),\n",
       "  ('business school', 0.030129735940035266),\n",
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       "  ('vagina furious', 0.025401143919251006)],\n",
       " 46: [('breakthrough scientists', 0.05110952481067894),\n",
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       "  ('hailed', 0.05110952481067894),\n",
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       " 47: [('your mother', 0.05093172494420564),\n",
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       "  ('mexican', 0.02252921931893774),\n",
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       " 48: [('op please', 0.08006759510112807),\n",
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       "  ('unlocked the', 0.035195176142871265),\n",
       "  ('theory kind', 0.01153003500188312),\n",
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       "  ('mess in', 0.01153003500188312),\n",
       "  ('betcha can', 0.010408712247690436),\n",
       "  ('betcha', 0.010408712247690436)],\n",
       " 49: [('just solve', 0.035454470102107366),\n",
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       "  ('dammit', 0.035454470102107366),\n",
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       "  ('gary you', 0.03536911485341421),\n",
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       "  ('solve', 0.034562773525156895)],\n",
       " 50: [('racism once', 0.04482247771682272),\n",
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       " 51: [('not delivery', 0.05740245474678441),\n",
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       "  ('pity', 0.027173363691845455),\n",
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       "  ('my butt', 0.0353588822391781),\n",
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       "  ('get high', 0.019787623103977223)],\n",
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       "  ('jesus jesus', 0.013608051936990015),\n",
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       " 54: [('dark web', 0.06363960603580342),\n",
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       "  ('tsa', 0.02311557693610277),\n",
       "  ('guidelines', 0.02311557693610277),\n",
       "  ('now prohibit', 0.02311557693610277)],\n",
       " 55: [('we want', 0.04568404676768321),\n",
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       "  ('want', 0.030234455397746093),\n",
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       "  ('not going', 0.017157002482135895)],\n",
       " 56: [('successful job', 0.030317075391307725),\n",
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       " 57: [('stars falling', 0.0594677760888181),\n",
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       "  ('mountains crumbling', 0.0594677760888181),\n",
       "  ('the sky', 0.05764776147273519)],\n",
       " 58: [('principal office', 0.025237384450053506),\n",
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       " 59: [('thee', 0.09335152850872663),\n",
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       " 60: [('frail white', 0.009908110714635359),\n",
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       "  ('blacker dick', 0.008191763818537048)],\n",
       " 61: [('discontent', 0.025816034601518403),\n",
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       " 62: [('aware', 0.04397659336975807),\n",
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       " 63: [('rhythm ve', 0.045103054107781905),\n",
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       "  ('wasn meant', 0.030526776148910324),\n",
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       " 64: [('soup tastes', 0.0353825626406472),\n",
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       "  ('waiter', 0.0353825626406472),\n",
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       "  ('swanky restaurant', 0.019754479914199537)],\n",
       " 65: [('bloopers trigger', 0.04504108856216321),\n",
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       " 67: [('that why', 0.06921480296428484),\n",
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       " 69: [('west raps', 0.05575385247913363),\n",
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       " 71: [('bearded lady', 0.05862013845225775),\n",
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       " 73: [('booty', 0.042628440051354737),\n",
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       " 74: [('basically tinder', 0.05163361834071529),\n",
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       " 77: [('may not', 0.07959911453245562),\n",
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       " 78: [('dunks love', 0.0700901636111782),\n",
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       "  ('fuckfest when', 0.03828899814448438),\n",
       "  ('tell you', 0.03828899814448438)],\n",
       " 80: [('you think', 0.06974264304954805),\n",
       "  ('really funny', 0.06974264304954805),\n",
       "  ('funny when', 0.06974264304954805),\n",
       "  ('know what', 0.06911443036274632),\n",
       "  ('think about', 0.05649523951240547),\n",
       "  ('those whales', 0.040257152873843925),\n",
       "  ('all those', 0.03872240187242883),\n",
       "  ('believe what', 0.03348279491762028)],\n",
       " 81: [('second date', 0.06879551023791758),\n",
       "  ('have strict', 0.06879551023791758),\n",
       "  ('date dinner', 0.06879551023791758),\n",
       "  ('date kiss', 0.06879551023791758),\n",
       "  ('first date', 0.06879551023791758),\n",
       "  ('dinner second', 0.06879551023791758),\n",
       "  ('third date', 0.06879551023791758),\n",
       "  ('have date', 0.05129212811285433)],\n",
       " 82: [('just say', 0.03193610517499382),\n",
       "  ('netflix don', 0.03193610517499382),\n",
       "  ('you haaaaaaave', 0.03193610517499382),\n",
       "  ('haaaaaaave', 0.03193610517499382),\n",
       "  ('new documentary', 0.03193610517499382),\n",
       "  ('haaaaaaave to', 0.03193610517499382),\n",
       "  ('to give', 0.031842064213657525),\n",
       "  ('documentary', 0.031749320410448006)],\n",
       " 83: [('tested mother', 0.13197910387836503),\n",
       "  ('kid tested', 0.13197910387836503),\n",
       "  ('dawg', 0.09527470132753889),\n",
       "  ('me dawg', 0.09527470132753889),\n",
       "  ('no from', 0.09527470132753889),\n",
       "  ('approved being', 0.013948025163369694),\n",
       "  ('approved having', 0.007607892033500069),\n",
       "  ('nerf being', 0.007607892033500069)],\n",
       " 84: [('too rich', 0.07288587136523322),\n",
       "  ('rest of', 0.07288587136523322),\n",
       "  ('and busy', 0.07288587136523322),\n",
       "  ('rich and', 0.07288587136523322),\n",
       "  ('the rest', 0.07288587136523322),\n",
       "  ('know netflix', 0.06948822224291759),\n",
       "  ('in tonight', 0.06948822224291759),\n",
       "  ('tonight you', 0.06721169875472394)],\n",
       " 85: [('love yourself', 0.035921386660257654),\n",
       "  ('hell you', 0.035921386660257654),\n",
       "  ('gonna love', 0.035921386660257654),\n",
       "  ('you gonna', 0.035921386660257654),\n",
       "  ('yourself how', 0.035823097125463485),\n",
       "  ('yourself', 0.02786464624462776),\n",
       "  ('shooting wizard', 0.025304513755701738),\n",
       "  ('wizard with', 0.025304513755701738)],\n",
       " 86: [('what there', 0.11258485223543954),\n",
       "  ('heaven', 0.11258485223543954),\n",
       "  ('there ton', 0.11258485223543954),\n",
       "  ('in heaven', 0.11258485223543954),\n",
       "  ('ton', 0.10823443960469473),\n",
       "  ('ton of', 0.10823443960469473),\n",
       "  ('there what', 0.035332765963458),\n",
       "  ('there dude', 0.016348192004452297)],\n",
       " 87: [('taking off', 0.05613882011878283),\n",
       "  ('laughing and', 0.05613882011878283),\n",
       "  ('am laughing', 0.05601008973201644),\n",
       "  ('and crying', 0.05601008973201644),\n",
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       "  ('laughing', 0.051347400493655916),\n",
       "  ('be hurt', 0.022994966931238196),\n",
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       " 88: [('wanna have', 0.06994038579109431),\n",
       "  ('wanna', 0.06994038579109431),\n",
       "  ('girls just', 0.06994038579109431),\n",
       "  ('just wanna', 0.06994038579109431),\n",
       "  ('can buy', 0.026163007655402772),\n",
       "  ('the penises', 0.01964298023388273),\n",
       "  ('penises kiss', 0.01964298023388273),\n",
       "  ('penises', 0.016166076963574808)],\n",
       " 89: [('robin egg', 0.04540307996439181),\n",
       "  ('thinking robin', 0.04540307996439181),\n",
       "  ('blue or', 0.04540307996439181),\n",
       "  ('egg blue', 0.04540307996439181),\n",
       "  ('bedroom were', 0.04540307996439181),\n",
       "  ('kids connor', 0.04195071697169509),\n",
       "  ('hey kids', 0.04195071697169509),\n",
       "  ('connor dad', 0.04195071697169509)],\n",
       " 90: [('pregnancy', 0.03942681092593198),\n",
       "  ('teenage pregnancy', 0.03111374537055609),\n",
       "  ('pregnancy what', 0.018214609070640226),\n",
       "  ('emotions what', 0.01624475165548726),\n",
       "  ('solved politics', 0.014856291459639183),\n",
       "  ('my solution', 0.014856291459639183),\n",
       "  ('an unwanted', 0.012704291156119674),\n",
       "  ('unwanted pregnancy', 0.012704291156119674)],\n",
       " 91: [('famine', 0.028713987272072968),\n",
       "  ('this upsetting', 0.02570677539251138),\n",
       "  ('please retweet', 0.02570677539251138),\n",
       "  ('retweet', 0.02570677539251138),\n",
       "  ('upsetting video', 0.02570677539251138),\n",
       "  ('upsetting', 0.02570677539251138),\n",
       "  ('just saw', 0.021662624560020088),\n",
       "  ('saw this', 0.021628966353506744)],\n",
       " 92: [('hitler whooo', 0.045186968423869926),\n",
       "  ('voldemort', 0.013556090527160978),\n",
       "  ('pringle', 0.009469510954697802),\n",
       "  ('voldemort you', 0.008915243894694887),\n",
       "  ('liked getting', 0.008915243894694887),\n",
       "  ('pringle whooo', 0.00624801347249893),\n",
       "  ('liked some', 0.00624801347249893),\n",
       "  ('liked going', 0.00624801347249893)],\n",
       " 93: [('porn star', 0.0657920844267322),\n",
       "  ('is joey', 0.0657920844267322),\n",
       "  ('tahoe', 0.06387294377579696),\n",
       "  ('chevy', 0.06387294377579696),\n",
       "  ('new chevy', 0.06387294377579696),\n",
       "  ('chevy tahoe', 0.06387294377579696),\n",
       "  ('tahoe with', 0.06387294377579696),\n",
       "  ('space to', 0.06371653934007922)],\n",
       " 94: [('all eternity', 0.0831062929877487),\n",
       "  ('sinners must', 0.0831062929877487),\n",
       "  ('hell sinners', 0.0831062929877487),\n",
       "  ('the seventh', 0.0831062929877487),\n",
       "  ('seventh circle', 0.0831062929877487),\n",
       "  ('sinners', 0.0831062929877487),\n",
       "  ('must endure', 0.0831062929877487),\n",
       "  ('circle', 0.07964444783528901)],\n",
       " 95: [('broke', 0.2107199621976184),\n",
       "  ('am broke', 0.2107199621976184),\n",
       "  ('broke the', 0.034711805619235515),\n",
       "  ('arse why', 0.025589895908411122),\n",
       "  ('broke getting', 0.012282097512453433),\n",
       "  ('toilet why', 0.012282097512453433),\n",
       "  ('broke kanye', 0.008607584002723564),\n",
       "  ('broke eating', 0.008607584002723564)],\n",
       " 96: [('amurica if', 0.033438082927960786),\n",
       "  ('your seatbelts', 0.03258391779300042),\n",
       "  ('fasten your', 0.03258391779300042),\n",
       "  ('seatbelts and', 0.03258391779300042),\n",
       "  ('fasten', 0.03258391779300042),\n",
       "  ('seatbelts', 0.03258391779300042),\n",
       "  ('speaking fasten', 0.03258391779300042),\n",
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       " 97: [('me uncontrollable', 0.06676642470185398),\n",
       "  ('uncontrollable', 0.06676642470185398),\n",
       "  ('uncontrollable gas', 0.06676642470185398),\n",
       "  ('grinding halt', 0.06303369493946391),\n",
       "  ('to grinding', 0.06303369493946391),\n",
       "  ('the orgy', 0.06303369493946391),\n",
       "  ('orgy to', 0.06303369493946391),\n",
       "  ('orgy', 0.06303369493946391)],\n",
       " 98: [('gnaws', 0.009393973022758883),\n",
       "  ('speak aloud', 0.009393973022758883),\n",
       "  ('inconceivable', 0.009393973022758883),\n",
       "  ('unlighted', 0.009393973022758883),\n",
       "  ('unlighted chambers', 0.009393973022758883),\n",
       "  ('hungrily in', 0.009393973022758883),\n",
       "  ('inconceivable unlighted', 0.009393973022758883),\n",
       "  ('in inconceivable', 0.009393973022758883)],\n",
       " 99: [('57 in', 0.027879029168905573),\n",
       "  ('all manner', 0.025257895723183876),\n",
       "  ('manner of', 0.025257895723183876),\n",
       "  ('pillows', 0.024593584770492245),\n",
       "  ('decorative', 0.024593584770492245),\n",
       "  ('decorative pillows', 0.024593584770492245),\n",
       "  ('these decorative', 0.024593584770492245),\n",
       "  ('50 mosquito', 0.022329879554722376)],\n",
       " 100: [('month cosmo', 0.08058287071199277),\n",
       "  ('by bringing', 0.08058287071199277),\n",
       "  ('spice up', 0.08058287071199277),\n",
       "  ('cosmo', 0.08058287071199277),\n",
       "  ('cosmo spice', 0.08058287071199277),\n",
       "  ('bedroom this', 0.07009605404238027),\n",
       "  ('bedroom', 0.0691399975283021),\n",
       "  ('the bedroom', 0.0691399975283021)],\n",
       " 101: [('so sweaty', 0.11480376693757155),\n",
       "  ('sweaty', 0.10998454790379011),\n",
       "  ('care why', 0.017600901585254805),\n",
       "  ('sweaty the', 0.00957174326133841),\n",
       "  ('sticky putting', 0.007911773598318902),\n",
       "  ('sweaty being', 0.007911773598318902),\n",
       "  ('sweaty getting', 0.007911773598318902),\n",
       "  ('hammer why', 0.007526167218758119)],\n",
       " 102: [('ennui', 0.040635845306125996),\n",
       "  ('making pouty', 0.038763225166501304),\n",
       "  ('pouty', 0.038763225166501304),\n",
       "  ('pouty face', 0.038763225166501304),\n",
       "  ('making friend', 0.034595293345365465),\n",
       "  ('friend brad', 0.0299940970831606),\n",
       "  ('mutual friend', 0.0299940970831606),\n",
       "  ('our mutual', 0.0299940970831606)],\n",
       " 103: [('fifty continuous', 0.030220515443588215),\n",
       "  ('get fifty', 0.030220515443588215),\n",
       "  ('fifty', 0.030220515443588215),\n",
       "  ('continuous', 0.030220515443588215),\n",
       "  ('continuous hours', 0.030220515443588215),\n",
       "  ('hour energys', 0.030220515443588215),\n",
       "  ('drinking ten', 0.030220515443588215),\n",
       "  ('me garbage', 0.027808450174968247)],\n",
       " 104: [('fried egg', 0.07049530052573394),\n",
       "  ('bbq bacon', 0.07049530052573394),\n",
       "  ('the bbq', 0.07049530052573394),\n",
       "  ('bacon burger', 0.07049530052573394),\n",
       "  ('bbq', 0.07049530052573394),\n",
       "  ('with fried', 0.07049530052573394),\n",
       "  ('burger with', 0.07049530052573394),\n",
       "  ('fried', 0.07049530052573394)],\n",
       " 105: [('smoking blunt', 0.030827620275136063),\n",
       "  ('ass naked', 0.030827620275136063),\n",
       "  ('butt ass', 0.030827620275136063),\n",
       "  ('blunt', 0.030827620275136063),\n",
       "  ('blunt butt', 0.030827620275136063),\n",
       "  ('tripping balls', 0.028531969157568046),\n",
       "  ('flying robots', 0.02632709602800218),\n",
       "  ('taking off', 0.021333486864062154)],\n",
       " 106: [('heart attack', 0.031117112132724882),\n",
       "  ('having stroke', 0.023626908576323456),\n",
       "  ('stroke', 0.023626908576323456),\n",
       "  ('having wonderful', 0.020532373797145412),\n",
       "  ('wonderful time', 0.020532373797145412),\n",
       "  ('sons named', 0.01847913641743087),\n",
       "  ('named chad', 0.01847913641743087),\n",
       "  ('having thirty', 0.01847913641743087)],\n",
       " 107: [('really fit', 0.09069941300282867),\n",
       "  ('school never', 0.09069941300282867),\n",
       "  ('high school', 0.08407023177342438),\n",
       "  ('club during', 0.05619511627097553),\n",
       "  ('errbody in', 0.043381049134185036),\n",
       "  ('errbody', 0.043381049134185036),\n",
       "  ('club errbody', 0.03189372808477265),\n",
       "  ('art during', 0.020686803668365755)],\n",
       " 108: [('my grandfather', 0.041498931701143965),\n",
       "  ('grandfather worked', 0.041498931701143965),\n",
       "  ('worked his', 0.041498931701143965),\n",
       "  ('worked', 0.041498931701143965),\n",
       "  ('the obstacle', 0.03783399959002807),\n",
       "  ('for adding', 0.03783399959002807),\n",
       "  ('gym teacher', 0.03783399959002807),\n",
       "  ('fired for', 0.03783399959002807)],\n",
       " 109: [('country club', 0.12015138304846743),\n",
       "  ('don allow', 0.12015138304846743),\n",
       "  ('sorry sir', 0.10263316336140693),\n",
       "  ('club sorry', 0.10260011099650286),\n",
       "  ('allow', 0.10229291960125889),\n",
       "  ('allow the', 0.02287325209962386),\n",
       "  ('nipples sorry', 0.009661258344872001),\n",
       "  ('allow getting', 0.005428535033365303)],\n",
       " 110: [('lover child', 0.19623921107469833),\n",
       "  ('bitch lover', 0.19623921107469833),\n",
       "  ('africa bitch', 0.03236747909060922),\n",
       "  ('re africa', 0.008780189908846782),\n",
       "  ('dies bitch', 0.008780189908846782),\n",
       "  ('micropenis bitch', 0.008780189908846782),\n",
       "  ('child gay', 0.008780189908846782),\n",
       "  ('micropenis', 0.006298527368304853)],\n",
       " 111: [('big bang', 0.02837147836673191),\n",
       "  ('the flute', 0.026705795020547513),\n",
       "  ('the gays', 0.01942522406518722),\n",
       "  ('size of', 0.011827581488922101),\n",
       "  ('the yogurt', 0.009622768565382123),\n",
       "  ('tina vagina', 0.009622768565382123),\n",
       "  ('of tina', 0.009622768565382123),\n",
       "  ('fucks me', 0.009537783935909825)],\n",
       " 112: [('those buffalo', 0.06435706354960552),\n",
       "  ('buffalo wings', 0.06435706354960552),\n",
       "  ('industry', 0.020503100375916173),\n",
       "  ('virtues are', 0.020503100375916173),\n",
       "  ('temperance industry', 0.020503100375916173),\n",
       "  ('are honor', 0.020503100375916173),\n",
       "  ('temperance', 0.020503100375916173),\n",
       "  ('virtues', 0.020503100375916173)],\n",
       " 113: [('cool hat', 0.04209978432871636),\n",
       "  ('manhole', 0.036065954349438475),\n",
       "  ('motorcycle', 0.028833636018676497),\n",
       "  ('he rides', 0.028833636018676497),\n",
       "  ('on motorcycle', 0.028833636018676497),\n",
       "  ('rides on', 0.028833636018676497),\n",
       "  ('rides', 0.028833636018676497),\n",
       "  ('hat up', 0.015401246320577782)],\n",
       " 114: [('look lot', 0.05365579953873486),\n",
       "  ('it beginning', 0.05365579953873486),\n",
       "  ('beginning to', 0.05365579953873486),\n",
       "  ('lot like', 0.05365579953873486),\n",
       "  ('to look', 0.04492120615072168),\n",
       "  ('yourself how', 0.017449154776292827),\n",
       "  ('stewart he', 0.016362878065662546),\n",
       "  ('and asks', 0.016362878065662546)],\n",
       " 115: [('montana struggles', 0.09068000998329967),\n",
       "  ('hannah montana', 0.09068000998329967),\n",
       "  ('disney channel', 0.09068000998329967),\n",
       "  ('new disney', 0.09068000998329967),\n",
       "  ('original movie', 0.09068000998329967),\n",
       "  ('channel original', 0.09068000998329967),\n",
       "  ('movie hannah', 0.09068000998329967),\n",
       "  ('campaign detroit', 0.02284497791248545)],\n",
       " 116: [('burps smell', 0.05970483279188884),\n",
       "  ('your burps', 0.05970483279188884),\n",
       "  ('burps', 0.05970483279188884),\n",
       "  ('my anaconda', 0.056249810176862404),\n",
       "  ('anaconda', 0.056249810176862404),\n",
       "  ('anaconda don', 0.056249810176862404),\n",
       "  ('hun my', 0.025990105486812968),\n",
       "  ('hun men', 0.02410073590440254)],\n",
       " 117: [('bullshit they', 0.09963307796717327),\n",
       "  ('bed at', 0.034922861321856895),\n",
       "  ('reasonable hour', 0.034922861321856895),\n",
       "  ('reasonable', 0.034922861321856895),\n",
       "  ('at reasonable', 0.034922861321856895),\n",
       "  ('to bed', 0.034922861321856895),\n",
       "  ('bed', 0.025630747041831523),\n",
       "  ('hour that', 0.017142546465060776)],\n",
       " 118: [('cure for', 0.1063511575998062),\n",
       "  ('as cure', 0.1063511575998062),\n",
       "  ('1950s psychologists', 0.1063511575998062),\n",
       "  ('cure', 0.1063511575998062),\n",
       "  ('psychologists', 0.1063511575998062),\n",
       "  ('psychologists prescribed', 0.1063511575998062),\n",
       "  ('the 1950s', 0.1063511575998062),\n",
       "  ('prescribed', 0.1063511575998062)],\n",
       " 119: [('prime of', 0.058383447526601205),\n",
       "  ('life young', 0.058383447526601205),\n",
       "  ('seeing things', 0.016179226612668515),\n",
       "  ('of tuna', 0.015337071924743114),\n",
       "  ('in robe', 0.01128344074227186),\n",
       "  ('robe like', 0.01128344074227186),\n",
       "  ('rich person', 0.01128344074227186),\n",
       "  ('robe', 0.01128344074227186)]}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_model.topics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "WxyJ1OkNl3Pj",
   "metadata": {
    "id": "WxyJ1OkNl3Pj"
   },
   "source": [
    "## Topic Relationships\n",
    "Having extracted the topics and their representations it might be helpful to check out the uniqueness of each topic. Some topics might be quite similar and could be merged or are simply interesting to research further. \n",
    "\n",
    "To do this, we start off by mapping our topics to a 2D representation by reducing the topic vectors with UMAP:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "B4xV16WJl0Yc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 667
    },
    "id": "B4xV16WJl0Yc",
    "outputId": "950ad0c3-04a6-4f9e-8844-ec467da8893e"
   },
   "outputs": [
    {
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    "The distance between topics show, to a certain extend, the similarities between topics. However, to better visualize and understand the similarity between topics, we can use two plots to gives us further insight into that. Namely, visualizing the possible topic hierarchy and its similarity matrix:"
   ]
  },
  {
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   "execution_count": null,
   "id": "DPCXWph_l8AL",
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    "id": "DPCXWph_l8AL",
    "outputId": "8f2417e5-3ec1-4737-99f3-004f2b13b0d6"
   },
   "outputs": [
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   "source": [
    "topic_model.visualize_heatmap(n_clusters=20, top_n_topics=80)"
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    "## Topics per Class\n",
    "Lastly, let's focus on the given categories for each paper. Can we find out which topics are frequently found in certain categories? Typically, topic modeling tends to find more topics than the categories that were previously defined. This helps us find and understand certain subcategories that might exist in the data. "
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     "text": [
      "2it [00:13,  6.73s/it]\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>Words</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>no, re, are, one, out</td>\n",
       "      <td>5379</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>of the, and the, about the, re the, from the</td>\n",
       "      <td>1458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>full of, when, denise richards, denise, richards</td>\n",
       "      <td>1195</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>getting really, getting naked, crunk, getting crunk, watching nickelodeon</td>\n",
       "      <td>382</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>more efficient, smaller more, developed smaller, efficient version, communication communication</td>\n",
       "      <td>343</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>115</td>\n",
       "      <td>original movie, montana struggles, movie hannah, channel original, hannah montana</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>116</td>\n",
       "      <td>burps smell, none unless, hun, want none, anaconda don</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>117</td>\n",
       "      <td>entire day, at reasonable, reasonable, to bed, bed at</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>118</td>\n",
       "      <td>prescribed, cure for, psychologists, psychologists prescribed, as cure</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>119</td>\n",
       "      <td>prime of, the prime, seeing things, of tuna, rich person</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>237 rows × 4 columns</p>\n",
       "</div>"
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      "text/plain": [
       "     Topic  ... Class\n",
       "0       -1  ...     0\n",
       "1        0  ...     0\n",
       "2        1  ...     0\n",
       "3        2  ...     0\n",
       "4        3  ...     0\n",
       "..     ...  ...   ...\n",
       "232    115  ...     1\n",
       "233    116  ...     1\n",
       "234    117  ...     1\n",
       "235    118  ...     1\n",
       "236    119  ...     1\n",
       "\n",
       "[237 rows x 4 columns]"
      ]
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     "execution_count": 38,
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   "source": [
    "topics_per_class = topic_model.topics_per_class(abstracts, topics, classes=targets)\n",
    "# topics_per_class"
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       "      <th></th>\n",
       "      <th>Topic</th>\n",
       "      <th>Words</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Class</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>no, re, are, one, out</td>\n",
       "      <td>5379</td>\n",
       "      <td>0</td>\n",
       "      <td>-1_re_are_out_we</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>of the, and the, about the, re the, from the</td>\n",
       "      <td>1458</td>\n",
       "      <td>0</td>\n",
       "      <td>0_of the_with the_and the_about the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>full of, when, denise richards, denise, richards</td>\n",
       "      <td>1195</td>\n",
       "      <td>0</td>\n",
       "      <td>1_full of_no_when_richards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>my disgusting, bellied, bellied boy, pot bellied, disgusting pot</td>\n",
       "      <td>454</td>\n",
       "      <td>0</td>\n",
       "      <td>4_bellied boy_pot bellied_bellied_my dis...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>getting really, getting naked, crunk, getting crunk, watching nickelodeon</td>\n",
       "      <td>382</td>\n",
       "      <td>0</td>\n",
       "      <td>2_laid_you laid_and watching_getting nak...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>more efficient, smaller more, developed smaller, efficient version, communication communication</td>\n",
       "      <td>343</td>\n",
       "      <td>0</td>\n",
       "      <td>3_developed smaller_efficient version_de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>ed absolutely, co ed, video called, absolutely destroyed, hot smelly</td>\n",
       "      <td>299</td>\n",
       "      <td>0</td>\n",
       "      <td>6_ed absolutely_co ed_what harshing_hars...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>prime directive, you exposed, have violated, violated the, directive</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>9_violated the_directive you_exposed an_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13</td>\n",
       "      <td>moon people, tall people, jumping, person eat, jumping out</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>13_moon people_tall people_black people_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>bullshit fuck, again played, oops did, did it, it again</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>7_can handle_bullshit fuck_card again_di...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>20</td>\n",
       "      <td>nickname, nickname my, did nickname, my genitals, ended my</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>20_nickname_nickname my_did nickname_my ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>solved politics, politics, watching the, my fettuccine, going bald</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>8_my solution_solved politics_politics_m...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "    Topic  ...                                         Name\n",
       "0      -1  ...                             -1_re_are_out_we\n",
       "1       0  ...          0_of the_with the_and the_about the\n",
       "2       1  ...                   1_full of_no_when_richards\n",
       "5       4  ...  4_bellied boy_pot bellied_bellied_my dis...\n",
       "3       2  ...  2_laid_you laid_and watching_getting nak...\n",
       "4       3  ...  3_developed smaller_efficient version_de...\n",
       "7       6  ...  6_ed absolutely_co ed_what harshing_hars...\n",
       "10      9  ...  9_violated the_directive you_exposed an_...\n",
       "14     13  ...  13_moon people_tall people_black people_...\n",
       "8       7  ...  7_can handle_bullshit fuck_card again_di...\n",
       "21     20  ...  20_nickname_nickname my_did nickname_my ...\n",
       "9       8  ...  8_my solution_solved politics_politics_m...\n",
       "\n",
       "[12 rows x 5 columns]"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics_per_class[topics_per_class[\"Class\"]==0].sort_values(\"Frequency\",ascending=False).head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "z-aB_BiWaKD0",
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    "colab": {
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     "height": 676
    },
    "id": "z-aB_BiWaKD0",
    "outputId": "384d99fd-ad9d-4fda-e153-ea6626583f9b"
   },
   "outputs": [
    {
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       "      <th>162</th>\n",
       "      <td>41</td>\n",
       "      <td>pervades all, existence, pervades, basic suffering, the basic</td>\n",
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       "      <td>60_frail white_frail_to frail_bleached a...</td>\n",
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       "      <th>124</th>\n",
       "      <td>3</td>\n",
       "      <td>brutus, dear brutus, brutus is, our stars, fault dear</td>\n",
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       "      <td>3_developed smaller_efficient version_de...</td>\n",
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       "      <th>133</th>\n",
       "      <td>12</td>\n",
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       "      <th>217</th>\n",
       "      <td>98</td>\n",
       "      <td>in inconceivable, dare speak, lips dare, aloud and, inconceivable</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>98_gnaws_speak aloud_inconceivable_unlig...</td>\n",
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       "      <th>139</th>\n",
       "      <td>18</td>\n",
       "      <td>admirable, your persistence, admirable my, prince but, is admirable</td>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>18_is admirable_admirable my_from rowlin...</td>\n",
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       "      <th>142</th>\n",
       "      <td>21</td>\n",
       "      <td>the musical, musical, netflix jiro, jiro dreams, spock cry</td>\n",
       "      <td>113</td>\n",
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       "      <td>21_the musical_musical_this season_netfl...</td>\n",
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       "      <th>181</th>\n",
       "      <td>61</td>\n",
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       "      <td>1</td>\n",
       "      <td>61_discontent_winter of_discontent made_...</td>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>10</td>\n",
       "      <td>roots to, grammys, grammys it, albums and, platinum albums</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
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       "      <th>128</th>\n",
       "      <td>7</td>\n",
       "      <td>douche, douche with, an acoustic, acoustic guitar, some douche</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>7_can handle_bullshit fuck_card again_di...</td>\n",
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       "      <th>129</th>\n",
       "      <td>8</td>\n",
       "      <td>call your, important right, solved politics, wearhouse, guarantee it</td>\n",
       "      <td>103</td>\n",
       "      <td>1</td>\n",
       "      <td>8_my solution_solved politics_politics_m...</td>\n",
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       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1</td>\n",
       "      <td>gay sorcerer, everyone gay, who turns, sorcerer who, turns everyone</td>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>1_full of_no_when_richards</td>\n",
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       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>23</td>\n",
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       "      <td>96</td>\n",
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       "      <th>132</th>\n",
       "      <td>11</td>\n",
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       "      <td>95</td>\n",
       "      <td>1</td>\n",
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       "     Topic  ...                                         Name\n",
       "120     -1  ...                             -1_re_are_out_we\n",
       "126      5  ...  5_dog dying_friends dying_my friends_fri...\n",
       "123      2  ...  2_laid_you laid_and watching_getting nak...\n",
       "153     32  ...  32_the dentist_dentist_the homosexual_ho...\n",
       "162     41  ...  41_blood of_existence_basic suffering_pe...\n",
       "180     60  ...  60_frail white_frail_to frail_bleached a...\n",
       "124      3  ...  3_developed smaller_efficient version_de...\n",
       "133     12  ...  12_covered raccoon_raccoon_raccoon out_n...\n",
       "217     98  ...  98_gnaws_speak aloud_inconceivable_unlig...\n",
       "139     18  ...  18_is admirable_admirable my_from rowlin...\n",
       "142     21  ...  21_the musical_musical_this season_netfl...\n",
       "181     61  ...  61_discontent_winter of_discontent made_...\n",
       "131     10  ...  10_officially become_grammys it_three gr...\n",
       "128      7  ...  7_can handle_bullshit fuck_card again_di...\n",
       "129      8  ...  8_my solution_solved politics_politics_m...\n",
       "122      1  ...                   1_full of_no_when_richards\n",
       "144     23  ...      23_ooo daddy_lie ooo_have gotten_too if\n",
       "132     11  ...  11_becoming_becoming blueberry_blueberry...\n",
       "143     22  ...        22_theory kind_of mess_in theory_mess\n",
       "130      9  ...  9_violated the_directive you_exposed an_...\n",
       "\n",
       "[20 rows x 5 columns]"
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     },
     "execution_count": 44,
     "metadata": {},
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  {
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   "id": "ubFX35xGmami",
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