bertopic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18c79887-66fd-4e8f-9fa7-12cd21970524",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install -U chromadb bertopic openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "259aad1e-fa40-44be-b709-8815d7587a9c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import chromadb\n",
    "import os\n",
    "import openai\n",
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "from bertopic import BERTopic\n",
    "from bertopic.representation import OpenAI\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "load_dotenv(find_dotenv())\n",
    "\n",
    "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
    "DATABASE_PATH = \"./chroma/\"\n",
    "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9702c61",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "chromadb.configure(allow_reset=True)\n",
    "client = chromadb.PersistentClient(\n",
    "    path=os.path.join(DATABASE_PATH, f\"{EMBEDDING_MODEL}\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da9e7408-5795-47a8-a01d-15d3233a0bc6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "coll = client.get_collection(\"BTW2025\")\n",
    "chroma_docs = coll.get(include=[\"metadatas\", \"documents\", \"embeddings\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2b6fc68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(chroma_docs['metadatas'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6602f2ae-3fd7-4859-ba93-521fff5f8801",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['doc'] =  chroma_docs['documents']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77be6808",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bstbewusste BRICS-Bündnis an, dem sich immer mehr Länder anschließen. Über ein Drittel der globalen Industrieproduktion kommt heute aus China.\\n\\nDer Kampf um Einflusszonen, um Rohstoffe und Absatzmärkte wird von allen Seiten mit ökonomi- schen, geheimdienstlichen, aber zunehmend auch mit militärischen Mitteln geführt. Noch sind es vor allem Stellvertreter-Kriege, über die die Auseinandersetzung ausgetragen wird. Aber die Gefahr einer direkten militärischen Konfrontation zwischen Atommächten ist so groß wie vielleicht noch nie in der Geschichte der Menschheit.\\n\\nEin neues Wettrüsten hat begonnen, nachdem nahezu alle Verträge über Rüstungskontrolle und vertrauensbildende Maßnahmen ausgelau- fen sind oder aufgekündigt wurden. Die Hoch- rüstung verschlingt immer mehr Ressourcen und erhöht die Kriegsgefahr, statt sie zu ver- ringern. Das betrifft auch die Gefahr einer durch ein Missverständnis oder einen Computerfehler ausgelösten Katastrophe. Diese gefährliche Entwicklung wollen wir stoppen.\\n\\nBündnis Sahra Wagenknecht - Vernunft und Gerechtigkeit Glinkastr. 32 · 10117 Berlin · www.bsw-vg.de'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['doc'][10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1a504675-b1a0-451b-b65e-eab323c20a83",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "openai_client = openai.OpenAI(api_key=OPENAI_API_KEY)\n",
    "representation_model = OpenAI(openai_client, delay_in_seconds=5, model=\"gpt-4o-mini\", chat=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "74d89a25-3504-4be1-a734-bf98206b6d76",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Use the representation model in BERTopic on top of the default pipeline\n",
    "topic_model = BERTopic(representation_model=representation_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5cbf2420",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bertopic._bertopic.BERTopic at 0x7f4d2a3db550>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_model.fit(chroma_docs['documents'], chroma_docs['embeddings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1ad3b2af",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "topic_model.save(\"topics.model\", serialization=\"safetensors\", save_embedding_model=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ad85cab5-1946-40cd-a449-a308e99cb2dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-01-25 11:34:44,976 - BERTopic - WARNING: You are loading a BERTopic model without explicitly defining an embedding model. If you want to also load in an embedding model, make sure to use `BERTopic.load(my_model, embedding_model=my_embedding_model)`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bertopic._bertopic.BERTopic at 0x7f4d2bb5a650>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_model.load(\"topics.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8d36e134-61e2-43a4-9435-1cc5fb5632a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "doc_info = topic_model.get_document_info(chroma_docs['documents'], df=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2e0b40c7-f6c2-4ad0-8dee-0507fa67a092",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "..     ...    ...                                                ...   \n",
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       "      <td>[eden Tag nicht nur Nerven, sondern auch Zeit ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>2_Integration und Asylpolitik in Deutschland</td>\n",
       "      <td>[Integration und Asylpolitik in Deutschland]</td>\n",
       "      <td>[ür eine Politik der Ausgrenzung steht. Mit ih...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>108</td>\n",
       "      <td>3_Inclusive Education and Individualized Learn...</td>\n",
       "      <td>[Inclusive Education and Individualized Learni...</td>\n",
       "      <td>[auszurichten.\\n\\nKinder- und jugendzentrierte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>103</td>\n",
       "      <td>4_Verbesserung der Pflege- und Gesundheitsvers...</td>\n",
       "      <td>[Verbesserung der Pflege- und Gesundheitsverso...</td>\n",
       "      <td>[gung begren- zen. Deshalb wollen wir öffentli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>73</td>\n",
       "      <td>5_Bezahlbarer Wohnraum und Mietpreisbremse</td>\n",
       "      <td>[Bezahlbarer Wohnraum und Mietpreisbremse]</td>\n",
       "      <td>[mse fallen und von der Plattform\\n\\nüberprüft...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>71</td>\n",
       "      <td>6_Tierschutz und nachhaltige Landwirtschaft in...</td>\n",
       "      <td>[Tierschutz und nachhaltige Landwirtschaft in ...</td>\n",
       "      <td>[ang die Identität der ländlichen Räume gepräg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>66</td>\n",
       "      <td>7_Antisemitismus und Extremismusbekämpfung in ...</td>\n",
       "      <td>[Antisemitismus und Extremismusbekämpfung in D...</td>\n",
       "      <td>[ment rechtsextremer Einstellungen und drückt ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "      <td>8_Systemwechsel in der Pflege</td>\n",
       "      <td>[Systemwechsel in der Pflege]</td>\n",
       "      <td>[nen Systemwechsel in der Pflege\\n\\n831 ein: F...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Topic  Count                                               Name   \n",
       "0     -1    763    -1_Gerechtigkeit und Solidarität in Deutschland  \\\n",
       "1      0    184  0_Klimaschutz und Netzentgelte in der deutsche...   \n",
       "2      1    142  1_Verkehrsinfrastruktur und Mobilitätswende in...   \n",
       "3      2    130       2_Integration und Asylpolitik in Deutschland   \n",
       "4      3    108  3_Inclusive Education and Individualized Learn...   \n",
       "5      4    103  4_Verbesserung der Pflege- und Gesundheitsvers...   \n",
       "6      5     73         5_Bezahlbarer Wohnraum und Mietpreisbremse   \n",
       "7      6     71  6_Tierschutz und nachhaltige Landwirtschaft in...   \n",
       "8      7     66  7_Antisemitismus und Extremismusbekämpfung in ...   \n",
       "9      8     59                      8_Systemwechsel in der Pflege   \n",
       "\n",
       "                                      Representation   \n",
       "0     [Gerechtigkeit und Solidarität in Deutschland]  \\\n",
       "1  [Klimaschutz und Netzentgelte in der deutschen...   \n",
       "2  [Verkehrsinfrastruktur und Mobilitätswende in ...   \n",
       "3       [Integration und Asylpolitik in Deutschland]   \n",
       "4  [Inclusive Education and Individualized Learni...   \n",
       "5  [Verbesserung der Pflege- und Gesundheitsverso...   \n",
       "6         [Bezahlbarer Wohnraum und Mietpreisbremse]   \n",
       "7  [Tierschutz und nachhaltige Landwirtschaft in ...   \n",
       "8  [Antisemitismus und Extremismusbekämpfung in D...   \n",
       "9                      [Systemwechsel in der Pflege]   \n",
       "\n",
       "                                 Representative_Docs  \n",
       "0  [viele Men-\\n\\n43\\n\\nschen wie möglich in zumu...  \n",
       "1  [und Ängste ausgelöst und neue Fragen aufgewor...  \n",
       "2  [eden Tag nicht nur Nerven, sondern auch Zeit ...  \n",
       "3  [ür eine Politik der Ausgrenzung steht. Mit ih...  \n",
       "4  [auszurichten.\\n\\nKinder- und jugendzentrierte...  \n",
       "5  [gung begren- zen. Deshalb wollen wir öffentli...  \n",
       "6  [mse fallen und von der Plattform\\n\\nüberprüft...  \n",
       "7  [ang die Identität der ländlichen Räume gepräg...  \n",
       "8  [ment rechtsextremer Einstellungen und drückt ...  \n",
       "9  [nen Systemwechsel in der Pflege\\n\\n831 ein: F...  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ca039d64-c469-4ca0-a7b8-8a549b325ebf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[58], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m topics_to_merge \u001b[38;5;241m=\u001b[39m [[\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m6\u001b[39m], [\u001b[38;5;241m7\u001b[39m, \u001b[38;5;241m33\u001b[39m,\u001b[38;5;241m49\u001b[39m], [\u001b[38;5;241m16\u001b[39m, \u001b[38;5;241m44\u001b[39m, \u001b[38;5;241m46\u001b[39m, \u001b[38;5;241m55\u001b[39m], [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m39\u001b[39m, \u001b[38;5;241m56\u001b[39m, \u001b[38;5;241m92\u001b[39m]]\n\u001b[0;32m----> 2\u001b[0m \u001b[43mtopic_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge_topics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchroma_docs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdocuments\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtopics_to_merge\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/bertopic/_bertopic.py:2148\u001b[0m, in \u001b[0;36mBERTopic.merge_topics\u001b[0;34m(self, docs, topics_to_merge, images)\u001b[0m\n\u001b[1;32m   2146\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtopic_mapper_\u001b[38;5;241m.\u001b[39madd_mappings(mapping, topic_model\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   2147\u001b[0m documents \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sort_mappings_by_frequency(documents)\n\u001b[0;32m-> 2148\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_extract_topics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmappings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmappings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2149\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_topic_size(documents)\n\u001b[1;32m   2150\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_save_representative_docs(documents)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/bertopic/_bertopic.py:3991\u001b[0m, in \u001b[0;36mBERTopic._extract_topics\u001b[0;34m(self, documents, embeddings, mappings, verbose)\u001b[0m\n\u001b[1;32m   3989\u001b[0m documents_per_topic \u001b[38;5;241m=\u001b[39m documents\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTopic\u001b[39m\u001b[38;5;124m\"\u001b[39m], as_index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39magg({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDocument\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin})\n\u001b[1;32m   3990\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mc_tf_idf_, words \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_c_tf_idf(documents_per_topic)\n\u001b[0;32m-> 3991\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtopic_representations_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_extract_words_per_topic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3992\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_topic_vectors(documents\u001b[38;5;241m=\u001b[39mdocuments, embeddings\u001b[38;5;241m=\u001b[39membeddings, mappings\u001b[38;5;241m=\u001b[39mmappings)\n\u001b[1;32m   3993\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/bertopic/_bertopic.py:4298\u001b[0m, in \u001b[0;36mBERTopic._extract_words_per_topic\u001b[0;34m(self, words, documents, c_tf_idf, calculate_aspects)\u001b[0m\n\u001b[1;32m   4296\u001b[0m         topics \u001b[38;5;241m=\u001b[39m tuner\u001b[38;5;241m.\u001b[39mextract_topics(\u001b[38;5;28mself\u001b[39m, documents, c_tf_idf, topics)\n\u001b[1;32m   4297\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrepresentation_model, BaseRepresentation):\n\u001b[0;32m-> 4298\u001b[0m     topics \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepresentation_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_topics\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdocuments\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mc_tf_idf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtopics\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4299\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrepresentation_model, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m   4300\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrepresentation_model\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMain\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/bertopic/representation/_openai.py:215\u001b[0m, in \u001b[0;36mOpenAI.extract_topics\u001b[0;34m(self, topic_model, documents, c_tf_idf, topics)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;66;03m# Delay\u001b[39;00m\n\u001b[1;32m    214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelay_in_seconds:\n\u001b[0;32m--> 215\u001b[0m     \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelay_in_seconds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchat:\n\u001b[1;32m    218\u001b[0m     messages \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    219\u001b[0m         {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msystem\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are a helpful assistant.\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    220\u001b[0m         {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: prompt},\n\u001b[1;32m    221\u001b[0m     ]\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "topics_to_merge = [[2, 6], [7, 33,49], [16, 44, 46, 55], [1, 39, 56, 92]]\n",
    "topic_model.merge_topics(chroma_docs['documents'], topics_to_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "02db8bae-6568-4309-b1f4-421448442739",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <td>11.971831</td>\n",
       "      <td>9.859155</td>\n",
       "      <td>9.859155</td>\n",
       "      <td>21.126761</td>\n",
       "      <td>19.718310</td>\n",
       "      <td>14.788732</td>\n",
       "      <td>1_Verkehrsinfrastruktur und Mobilitätswende in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>42.307692</td>\n",
       "      <td>9.230769</td>\n",
       "      <td>13.076923</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.769231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2_Integration und Asylpolitik in Deutschland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3_Inclusive Education and Individualized Learn...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>126_Effizientes und menschenwürdiges Asylsyste...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>127</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>63.636364</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.363636</td>\n",
       "      <td>127_Abtreibungen und Mütterliche Unterstützung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>128_Gendergerechtigkeit und Diversität in Bild...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>129_Flüchtlingsintegration und Unterstützung i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>130_Förderung von Biodiversität und nachhaltig...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "party  Topic    AfD   BSW   CDU   FDP  Grüne  Linke   SPD   Volt  Total   \n",
       "0         -1  165.0  66.0  95.0  52.0   59.0   76.0  93.0  157.0  763.0  \\\n",
       "1          0    0.0  20.0  24.0   8.0   43.0    0.0  46.0   43.0  184.0   \n",
       "2          1    7.0  11.0  17.0  14.0   14.0   30.0  28.0   21.0  142.0   \n",
       "3          2   55.0  12.0  17.0   6.0   13.0    0.0  27.0    0.0  130.0   \n",
       "4          3    0.0   0.0   0.0   0.0    0.0    0.0   0.0  108.0  108.0   \n",
       "..       ...    ...   ...   ...   ...    ...    ...   ...    ...    ...   \n",
       "127      126    0.0   0.0   0.0   0.0    0.0    0.0   0.0   11.0   11.0   \n",
       "128      127    7.0   0.0   0.0   0.0    0.0    0.0   0.0    4.0   11.0   \n",
       "129      128    0.0   0.0   0.0   0.0    0.0    0.0   0.0   11.0   11.0   \n",
       "130      129    0.0   0.0   0.0   0.0    0.0   11.0   0.0    0.0   11.0   \n",
       "131      130    0.0   0.0   0.0   0.0    0.0    0.0   0.0   10.0   10.0   \n",
       "\n",
       "party  AfD_Percentage  BSW_Percentage  CDU_Percentage  FDP_Percentage   \n",
       "0           21.625164        8.650066       12.450852        6.815203  \\\n",
       "1            0.000000       10.869565       13.043478        4.347826   \n",
       "2            4.929577        7.746479       11.971831        9.859155   \n",
       "3           42.307692        9.230769       13.076923        4.615385   \n",
       "4            0.000000        0.000000        0.000000        0.000000   \n",
       "..                ...             ...             ...             ...   \n",
       "127          0.000000        0.000000        0.000000        0.000000   \n",
       "128         63.636364        0.000000        0.000000        0.000000   \n",
       "129          0.000000        0.000000        0.000000        0.000000   \n",
       "130          0.000000        0.000000        0.000000        0.000000   \n",
       "131          0.000000        0.000000        0.000000        0.000000   \n",
       "\n",
       "party  Grüne_Percentage  Linke_Percentage  SPD_Percentage  Volt_Percentage   \n",
       "0              7.732634          9.960682       12.188729        20.576671  \\\n",
       "1             23.369565          0.000000       25.000000        23.369565   \n",
       "2              9.859155         21.126761       19.718310        14.788732   \n",
       "3             10.000000          0.000000       20.769231         0.000000   \n",
       "4              0.000000          0.000000        0.000000       100.000000   \n",
       "..                  ...               ...             ...              ...   \n",
       "127            0.000000          0.000000        0.000000       100.000000   \n",
       "128            0.000000          0.000000        0.000000        36.363636   \n",
       "129            0.000000          0.000000        0.000000       100.000000   \n",
       "130            0.000000        100.000000        0.000000         0.000000   \n",
       "131            0.000000          0.000000        0.000000       100.000000   \n",
       "\n",
       "party                                              title  \n",
       "0        -1_Gerechtigkeit und Solidarität in Deutschland  \n",
       "1      0_Klimaschutz und Netzentgelte in der deutsche...  \n",
       "2      1_Verkehrsinfrastruktur und Mobilitätswende in...  \n",
       "3           2_Integration und Asylpolitik in Deutschland  \n",
       "4      3_Inclusive Education and Individualized Learn...  \n",
       "..                                                   ...  \n",
       "127    126_Effizientes und menschenwürdiges Asylsyste...  \n",
       "128       127_Abtreibungen und Mütterliche Unterstützung  \n",
       "129    128_Gendergerechtigkeit und Diversität in Bild...  \n",
       "130    129_Flüchtlingsintegration und Unterstützung i...  \n",
       "131    130_Förderung von Biodiversität und nachhaltig...  \n",
       "\n",
       "[132 rows x 19 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df = doc_info.groupby(['Topic', 'party']).size().reset_index(name='Count')\n",
    "result_df = count_df.pivot(index='Topic', columns='party', values='Count').fillna(0)\n",
    "percentage_columns = [f'{party}_Percentage' for party in result_df.columns if party != 'Topic' and party != 'Total']\n",
    "result_df['Total'] = result_df.sum(axis=1)\n",
    "for party in result_df.columns[0:-1]:  # Exclude 'Total' column\n",
    "    result_df[f'{party}_Percentage'] = (result_df[party] / result_df['Total']) * 100\n",
    "result_df.reset_index(inplace=True)\n",
    "result_df[\"title\"] = topic_df['Name']\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b97e8f6c-5f08-4a43-a9d5-b786323a65c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>party</th>\n",
       "      <th>Topic</th>\n",
       "      <th>AfD</th>\n",
       "      <th>BSW</th>\n",
       "      <th>CDU</th>\n",
       "      <th>FDP</th>\n",
       "      <th>Grüne</th>\n",
       "      <th>Linke</th>\n",
       "      <th>SPD</th>\n",
       "      <th>Volt</th>\n",
       "      <th>Total</th>\n",
       "      <th>AfD_Percentage</th>\n",
       "      <th>BSW_Percentage</th>\n",
       "      <th>CDU_Percentage</th>\n",
       "      <th>FDP_Percentage</th>\n",
       "      <th>Grüne_Percentage</th>\n",
       "      <th>Linke_Percentage</th>\n",
       "      <th>SPD_Percentage</th>\n",
       "      <th>Volt_Percentage</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>165.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>763.0</td>\n",
       "      <td>21.625164</td>\n",
       "      <td>8.650066</td>\n",
       "      <td>12.450852</td>\n",
       "      <td>6.815203</td>\n",
       "      <td>7.732634</td>\n",
       "      <td>9.960682</td>\n",
       "      <td>12.188729</td>\n",
       "      <td>20.576671</td>\n",
       "      <td>-1_Gerechtigkeit und Solidarität in Deutschland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.869565</td>\n",
       "      <td>13.043478</td>\n",
       "      <td>4.347826</td>\n",
       "      <td>23.369565</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>23.369565</td>\n",
       "      <td>0_Klimaschutz und Netzentgelte in der deutsche...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>4.929577</td>\n",
       "      <td>7.746479</td>\n",
       "      <td>11.971831</td>\n",
       "      <td>9.859155</td>\n",
       "      <td>9.859155</td>\n",
       "      <td>21.126761</td>\n",
       "      <td>19.718310</td>\n",
       "      <td>14.788732</td>\n",
       "      <td>1_Verkehrsinfrastruktur und Mobilitätswende in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>42.307692</td>\n",
       "      <td>9.230769</td>\n",
       "      <td>13.076923</td>\n",
       "      <td>4.615385</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.769231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2_Integration und Asylpolitik in Deutschland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3_Inclusive Education and Individualized Learn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.912621</td>\n",
       "      <td>4.854369</td>\n",
       "      <td>18.446602</td>\n",
       "      <td>8.737864</td>\n",
       "      <td>15.533981</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.543689</td>\n",
       "      <td>0.970874</td>\n",
       "      <td>4_Verbesserung der Pflege- und Gesundheitsvers...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>10.958904</td>\n",
       "      <td>8.219178</td>\n",
       "      <td>12.328767</td>\n",
       "      <td>6.849315</td>\n",
       "      <td>9.589041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.095890</td>\n",
       "      <td>10.958904</td>\n",
       "      <td>5_Bezahlbarer Wohnraum und Mietpreisbremse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>30.985915</td>\n",
       "      <td>12.676056</td>\n",
       "      <td>18.309859</td>\n",
       "      <td>7.042254</td>\n",
       "      <td>19.718310</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.450704</td>\n",
       "      <td>2.816901</td>\n",
       "      <td>6_Tierschutz und nachhaltige Landwirtschaft in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>30.303030</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.696970</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>4.545455</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.787879</td>\n",
       "      <td>7.575758</td>\n",
       "      <td>7_Antisemitismus und Extremismusbekämpfung in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8_Systemwechsel in der Pflege</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>21.052632</td>\n",
       "      <td>14.035088</td>\n",
       "      <td>8.771930</td>\n",
       "      <td>15.789474</td>\n",
       "      <td>7.017544</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9_Steuerreform und Entlastung für die Mittelsc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10_Energiewende und Klimaschutz durch Erneuerb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11_Verbesserung der Arbeitsbedingungen und Bes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.923077</td>\n",
       "      <td>32.692308</td>\n",
       "      <td>13.461538</td>\n",
       "      <td>11.538462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.384615</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12_Politikwechsel für Wohlstand und Stabilität...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13_Verbrechensbekämpfung und Jugendstrafrecht ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.122449</td>\n",
       "      <td>8.163265</td>\n",
       "      <td>20.408163</td>\n",
       "      <td>12.244898</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.571429</td>\n",
       "      <td>24.489796</td>\n",
       "      <td>14_Digitalisierung der öffentlichen Verwaltung...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>27.083333</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>20.833333</td>\n",
       "      <td>15_Unterstützung der Ukraine für Frieden und S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83.333333</td>\n",
       "      <td>10.416667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16_Maßnahmen zur Senkung der Lebenshaltungskos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17_Bildungs- und Chancengleichheit für Kinder ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>15.789474</td>\n",
       "      <td>21.052632</td>\n",
       "      <td>7.894737</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.157895</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.473684</td>\n",
       "      <td>2.631579</td>\n",
       "      <td>18_Rentenversicherung und Altersvorsorge in De...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "party  Topic    AfD   BSW   CDU   FDP  Grüne  Linke   SPD   Volt  Total   \n",
       "0         -1  165.0  66.0  95.0  52.0   59.0   76.0  93.0  157.0  763.0  \\\n",
       "1          0    0.0  20.0  24.0   8.0   43.0    0.0  46.0   43.0  184.0   \n",
       "2          1    7.0  11.0  17.0  14.0   14.0   30.0  28.0   21.0  142.0   \n",
       "3          2   55.0  12.0  17.0   6.0   13.0    0.0  27.0    0.0  130.0   \n",
       "4          3    0.0   0.0   0.0   0.0    0.0    0.0   0.0  108.0  108.0   \n",
       "5          4    3.0   5.0  19.0   9.0   16.0    0.0  50.0    1.0  103.0   \n",
       "6          5    8.0   6.0   9.0   5.0    7.0    0.0  30.0    8.0   73.0   \n",
       "7          6   22.0   9.0  13.0   5.0   14.0    0.0   6.0    2.0   71.0   \n",
       "8          7   20.0   0.0  13.0   6.0    3.0    0.0  19.0    5.0   66.0   \n",
       "9          8    0.0   0.0   0.0   0.0    0.0   59.0   0.0    0.0   59.0   \n",
       "10         9   12.0   8.0   5.0   9.0    4.0    0.0  19.0    0.0   57.0   \n",
       "11        10    0.0   0.0   0.0   0.0    0.0   53.0   0.0    0.0   53.0   \n",
       "12        11    0.0   0.0   0.0   0.0    0.0   52.0   0.0    0.0   52.0   \n",
       "13        12    0.0   1.0  17.0   7.0    6.0    0.0  21.0    0.0   52.0   \n",
       "14        13   25.0   6.0   4.0   6.0    1.0    0.0   8.0    0.0   50.0   \n",
       "15        14    0.0   3.0   4.0  10.0    6.0    0.0  14.0   12.0   49.0   \n",
       "16        15    0.0   2.0  13.0   4.0   10.0    0.0   9.0   10.0   48.0   \n",
       "17        16    0.0   3.0   0.0   0.0    0.0   40.0   5.0    0.0   48.0   \n",
       "18        17    0.0   7.0  11.0   9.0    6.0    0.0   7.0    0.0   40.0   \n",
       "19        18    6.0   8.0   3.0   0.0    5.0    0.0  15.0    1.0   38.0   \n",
       "\n",
       "party  AfD_Percentage  BSW_Percentage  CDU_Percentage  FDP_Percentage   \n",
       "0           21.625164        8.650066       12.450852        6.815203  \\\n",
       "1            0.000000       10.869565       13.043478        4.347826   \n",
       "2            4.929577        7.746479       11.971831        9.859155   \n",
       "3           42.307692        9.230769       13.076923        4.615385   \n",
       "4            0.000000        0.000000        0.000000        0.000000   \n",
       "5            2.912621        4.854369       18.446602        8.737864   \n",
       "6           10.958904        8.219178       12.328767        6.849315   \n",
       "7           30.985915       12.676056       18.309859        7.042254   \n",
       "8           30.303030        0.000000       19.696970        9.090909   \n",
       "9            0.000000        0.000000        0.000000        0.000000   \n",
       "10          21.052632       14.035088        8.771930       15.789474   \n",
       "11           0.000000        0.000000        0.000000        0.000000   \n",
       "12           0.000000        0.000000        0.000000        0.000000   \n",
       "13           0.000000        1.923077       32.692308       13.461538   \n",
       "14          50.000000       12.000000        8.000000       12.000000   \n",
       "15           0.000000        6.122449        8.163265       20.408163   \n",
       "16           0.000000        4.166667       27.083333        8.333333   \n",
       "17           0.000000        6.250000        0.000000        0.000000   \n",
       "18           0.000000       17.500000       27.500000       22.500000   \n",
       "19          15.789474       21.052632        7.894737        0.000000   \n",
       "\n",
       "party  Grüne_Percentage  Linke_Percentage  SPD_Percentage  Volt_Percentage   \n",
       "0              7.732634          9.960682       12.188729        20.576671  \\\n",
       "1             23.369565          0.000000       25.000000        23.369565   \n",
       "2              9.859155         21.126761       19.718310        14.788732   \n",
       "3             10.000000          0.000000       20.769231         0.000000   \n",
       "4              0.000000          0.000000        0.000000       100.000000   \n",
       "5             15.533981          0.000000       48.543689         0.970874   \n",
       "6              9.589041          0.000000       41.095890        10.958904   \n",
       "7             19.718310          0.000000        8.450704         2.816901   \n",
       "8              4.545455          0.000000       28.787879         7.575758   \n",
       "9              0.000000        100.000000        0.000000         0.000000   \n",
       "10             7.017544          0.000000       33.333333         0.000000   \n",
       "11             0.000000        100.000000        0.000000         0.000000   \n",
       "12             0.000000        100.000000        0.000000         0.000000   \n",
       "13            11.538462          0.000000       40.384615         0.000000   \n",
       "14             2.000000          0.000000       16.000000         0.000000   \n",
       "15            12.244898          0.000000       28.571429        24.489796   \n",
       "16            20.833333          0.000000       18.750000        20.833333   \n",
       "17             0.000000         83.333333       10.416667         0.000000   \n",
       "18            15.000000          0.000000       17.500000         0.000000   \n",
       "19            13.157895          0.000000       39.473684         2.631579   \n",
       "\n",
       "party                                              title  \n",
       "0        -1_Gerechtigkeit und Solidarität in Deutschland  \n",
       "1      0_Klimaschutz und Netzentgelte in der deutsche...  \n",
       "2      1_Verkehrsinfrastruktur und Mobilitätswende in...  \n",
       "3           2_Integration und Asylpolitik in Deutschland  \n",
       "4      3_Inclusive Education and Individualized Learn...  \n",
       "5      4_Verbesserung der Pflege- und Gesundheitsvers...  \n",
       "6             5_Bezahlbarer Wohnraum und Mietpreisbremse  \n",
       "7      6_Tierschutz und nachhaltige Landwirtschaft in...  \n",
       "8      7_Antisemitismus und Extremismusbekämpfung in ...  \n",
       "9                          8_Systemwechsel in der Pflege  \n",
       "10     9_Steuerreform und Entlastung für die Mittelsc...  \n",
       "11     10_Energiewende und Klimaschutz durch Erneuerb...  \n",
       "12     11_Verbesserung der Arbeitsbedingungen und Bes...  \n",
       "13     12_Politikwechsel für Wohlstand und Stabilität...  \n",
       "14     13_Verbrechensbekämpfung und Jugendstrafrecht ...  \n",
       "15     14_Digitalisierung der öffentlichen Verwaltung...  \n",
       "16     15_Unterstützung der Ukraine für Frieden und S...  \n",
       "17     16_Maßnahmen zur Senkung der Lebenshaltungskos...  \n",
       "18     17_Bildungs- und Chancengleichheit für Kinder ...  \n",
       "19     18_Rentenversicherung und Altersvorsorge in De...  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "76a26029-33e4-4f4d-98a7-fdd782397c08",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare DataFrame for heatmap\n",
    "\n",
    "heatmap_data = result_df[percentage_columns]\n",
    "\n",
    "# Set index to 'Topic' for proper labeling in the heatmap\n",
    "heatmap_data.index = result_df['Topic']\n",
    "\n",
    "# Plotting the heatmap\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(heatmap_data[1:20], annot=True, cmap='YlGnBu', fmt='.1f', linewidths=.5, cbar_kws={'label': 'Percentage (%)'})\n",
    "plt.title('Party Representation by Topic')\n",
    "plt.xlabel('Party')\n",
    "plt.ylabel('Topic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa6b8582-d1ca-40ea-bb9b-7862dad0e20d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for name, group in doc_info.groupby([\"Topic\", \"party\"]):\n",
    "    print(name, group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "727b0d8c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "all_topics = topic_model.get_topics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "82e40a47",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f54b5e2a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-1: [('Wirtschaft und Zukunftsstrategien für Deutschland', 1)],\n",
       " 0: [('Reform des europäischen Asylrechts und Integration von Geflüchteten in Deutschland',\n",
       "   1)],\n",
       " 1: [('Verbesserung der Pflegebedingungen und Finanzierungssysteme', 1)],\n",
       " 2: [('Infrastruktur und Mobilität in Deutschland', 1)],\n",
       " 3: [('Förderung der Bildung und Ausbildung in Deutschland', 1)],\n",
       " 4: [('Energiepolitik und Klimaschutz in Deutschland', 1)],\n",
       " 5: [('Nachhaltige Landwirtschaft und Agrarförderung', 1)],\n",
       " 6: [('Mietpreisbremse und Bezahlbarer Wohnraum', 1)],\n",
       " 7: [('Steuerreform und Entlastung von Arbeitseinkommen in Deutschland', 1)],\n",
       " 8: [('Energiewende und Klimaschutz durch dezentrale Energieversorgung und soziale Maßnahmen',\n",
       "   1)],\n",
       " 9: [('Verbesserung der Arbeitsbedingungen und soziale Absicherung in Deutschland',\n",
       "   1)],\n",
       " 10: [('Politikwechsel für Wohlstand und Stabilität in Deutschland', 1)],\n",
       " 11: [('Gesetzliche Regelungen zur Bekämpfung von Jugendkriminalität und Ausländerkriminalität',\n",
       "   1)],\n",
       " 12: [('Geschlechtergerechtigkeit und Gesundheitspolitik', 1)],\n",
       " 13: [('Preissteigerungen und soziale Gerechtigkeit in Deutschland', 1)],\n",
       " 14: [('Digitalisierung der öffentlichen Verwaltung und Bürokratieabbau', 1)],\n",
       " 15: [('AfD und technikoffene Politik in Deutschland', 1)],\n",
       " 16: [('Innovation und Forschung für eine wettbewerbsfähige Wirtschaft in Deutschland',\n",
       "   1)],\n",
       " 17: [('Sicherheit und Schutz für diskriminierte Gemeinschaften', 1)],\n",
       " 18: [('Bekämpfung von Extremismus und Schutz der Demokratie in Deutschland',\n",
       "   1)],\n",
       " 19: [('Stabilität und Verbesserung der gesetzlichen Rentenversicherung für Geringverdiener und zukünftige Rentner',\n",
       "   1)],\n",
       " 20: [('Förderung von Kultur und Erinnerung in Deutschland', 1)],\n",
       " 21: [('Euro-System und Deutschlands Austritt aus der Transferunion', 1)],\n",
       " 22: [('Verbesserung der Kita- und Schulsysteme zur Bekämpfung sozialer Ungleichheit',\n",
       "   1)],\n",
       " 23: [('Reform der Grundsicherung und Arbeitsanreize im Bürgergeld-System',\n",
       "   1)],\n",
       " 24: [('Soziale Gerechtigkeit und Inklusives Design in Ostdeutschland', 1)],\n",
       " 25: [('Inklusion und Barrierefreiheit für Menschen mit Behinderungen', 1)],\n",
       " 26: [('Innovative Bildungskonzepte für individuelle Lernwege und Chancengleichheit in Schulen',\n",
       "   1)],\n",
       " 27: [('Selbstbestimmtes Altern und Kinderrechte', 1)],\n",
       " 28: [('Föderale Europäische Republik und EU-Verfassung', 1)],\n",
       " 29: [('Verbesserung der Strafverfolgung und Polizeiarbeit in Deutschland durch Digitalisierung und einheitliche Standards',\n",
       "   1)],\n",
       " 30: [('Familienpolitik und Kinderwunsch in Deutschland', 1)],\n",
       " 31: [('Effiziente und zukunftsorientierte Gesundheitsversorgung in Deutschland',\n",
       "   1)],\n",
       " 32: [('Stärkung der Demokratie und Zivilgesellschaft', 1)],\n",
       " 33: [('Wirtschaftliche Reformen und Infrastrukturmodernisierung', 1)],\n",
       " 34: [('Infrastrukturfinanzierung und generationengerechte Investitionspolitik',\n",
       "   1)],\n",
       " 35: [('EU-Erweiterung und Menschenrechte', 1)],\n",
       " 36: [('Förderung des Sports und der Inklusion in Deutschland', 1)],\n",
       " 37: [('Digitale Mitbestimmung und Mindestlohnregelungen', 1)],\n",
       " 38: [('Gemeinnütziger Wohnungsbau und Mietregulierung', 1)],\n",
       " 39: [('Weiterbildung und BAföG für alle Studierenden', 1)],\n",
       " 40: [('Zuwanderung und soziale Sicherung in Deutschland', 1)],\n",
       " 41: [('Schutz von Wasserressourcen, Meeren und Tieren', 1)],\n",
       " 42: [('Israel and Gaza Conflict Dynamics', 1)],\n",
       " 43: [('Außenpolitik und Friedensbewegung in Deutschland', 1)],\n",
       " 44: [('Reform der Gewerbesteuer und Transparenz für transnationale Unternehmen',\n",
       "   1)],\n",
       " 45: [('Fachkräftegewinnung und Einwanderung für den Arbeitsmarkt', 1)],\n",
       " 46: [('Naturschutz und nachhaltige Entwicklung der Ökosysteme', 1)],\n",
       " 47: [('Gesundheitsrecht und Selbstbestimmung in der Medizin', 1)],\n",
       " 48: [('Europäische Sicherheit und wirtschaftliche Innovation in einer polarisierten Welt',\n",
       "   1)],\n",
       " 49: [('Tarifbindung und Mindestlohn in Ostdeutschland', 1)],\n",
       " 50: [('Klimaschutz durch CO2-Reduktion und Emissionshandel', 1)],\n",
       " 51: [('Kirchen, Religionsfreiheit und Gleichstellung der Weltanschauungsgemeinschaften',\n",
       "   1)],\n",
       " 52: [('Resilienz und Sicherheit in der Gesamtverteidigung', 1)],\n",
       " 53: [('Reform der EZB und des Finanzsektors in Krisenzeiten', 1)],\n",
       " 54: [('Open Data and IT Security in Public Services', 1)],\n",
       " 55: [('Bürokratieabbau und vereinfachte Regelungen für Unternehmen', 1)],\n",
       " 56: [('Frauen und reproductive Selbstbestimmung in der Geburtshilfe', 1)],\n",
       " 57: [('Nachhaltige Landwirtschaft und Lebensmittelkontrollen', 1)],\n",
       " 58: [('Wasserstoff und nachhaltige Energieforschung in der Industrie', 1)],\n",
       " 59: [('Volksabstimmungen und politische Transparenz in Deutschland', 1)],\n",
       " 60: [('Stärkung und Modernisierung der Bundeswehr innerhalb der NATO', 1)],\n",
       " 61: [('Deutsche Außenhandelspolitik und internationale Handelsbeziehungen',\n",
       "   1)],\n",
       " 62: [('Soziale Infrastruktur und kommunale Daseinsvorsorge', 1)],\n",
       " 63: [('Schutz von Frauen vor geschlechtsspezifischer Gewalt und häuslicher Gewalt in der EU',\n",
       "   1)],\n",
       " 64: [('Elternzeit und Elterngeld Reformen für Familie und Väter', 1)],\n",
       " 65: [('EU Friedensunion und soziale Kohäsion in Europa', 1)],\n",
       " 66: [('Polizeireform und Bürgerrechte in Deutschland', 1)],\n",
       " 67: [('Europäische Verteidigungszusammenarbeit und Beschaffung', 1)],\n",
       " 68: [('Globale Zusammenarbeit zur Krisenbewältigung und nachhaltigen Entwicklung',\n",
       "   1)],\n",
       " 69: [('Soziale Sicherheit und gerechte Rentenreform', 1)],\n",
       " 70: [('Künstliche Intelligenz und verantwortungsvolle Entwicklung in Deutschland',\n",
       "   1)],\n",
       " 71: [('Klimaschutz und Ressourcenschutz im globalen Süden', 1)],\n",
       " 72: [('Unterstützung der Ukraine für Frieden und Sicherheit in Europa', 1)],\n",
       " 73: [('Gleichstellung und Schutz vor Diskriminierung in der Gesellschaft',\n",
       "   1)],\n",
       " 74: [('Gerechtere Einkommensteuer und Reform für soziale Gerechtigkeit', 1)],\n",
       " 75: [('Europäische Chinapolitik und globale Sicherheitsinteressen', 1)],\n",
       " 76: [('Gesundheitsförderung und Zuckersteuer im Gesundheitssystem', 1)],\n",
       " 77: [('Leerstand und Obdachlosigkeit in Deutschland', 1)],\n",
       " 78: [('Bekämpfung der Organisierten Kriminalität und Wirtschaftskriminalität durch Sicherheitsbehörden und Datenanalysen',\n",
       "   1)],\n",
       " 79: [('Kindergrundsicherung und Bekämpfung von Kinderarmut', 1)],\n",
       " 80: [('Finanzkriminalität und Übergewinnsteuer in Deutschland', 1)],\n",
       " 81: [('Queere Emanzipation und Antidiskriminierungsarbeit', 1)],\n",
       " 82: [('Steuerreform und Besteuerung von Einkommen in Deutschland', 1)],\n",
       " 83: [('Globale soziale Gerechtigkeit und Entwicklungspolitik', 1)],\n",
       " 84: [('Inklusion und Teilhabe von Menschen mit Behinderungen', 1)],\n",
       " 85: [('Stabilitätsanker und Generationengerechtigkeit in Europa', 1)],\n",
       " 86: [('Förderung der sozialen Teilhabe und Unterstützung von Familien mit Kindern',\n",
       "   1)],\n",
       " 87: [('Sicherer Umgang mit digitalen Sicherheitslücken und Überwachungstechnologien',\n",
       "   1)],\n",
       " 88: [('Stärkung der Medienvielfalt und Schutz der Medienschaffenden im öffentlich-rechtlichen Rundfunk',\n",
       "   1)],\n",
       " 89: [('Klimaschutz und nachhaltige Infrastruktur für eine zukunftsfähige Gesellschaft',\n",
       "   1)],\n",
       " 90: [('Zukunft der Bildung und Gesundheitssysteme, Integration und Antidiskriminierung',\n",
       "   1)],\n",
       " 91: [('Meinungsfreiheit und Zensur in den sozialen Medien', 1)],\n",
       " 92: [('Geschlechtswechsel und Kinder- und Jugendschutz', 1)],\n",
       " 93: [('Reform des öffentlich-rechtlichen Rundfunks und Medienberichterstattung',\n",
       "   1)],\n",
       " 94: [('Stärkung ländlicher Regionen und wirtschaftliche Entwicklung', 1)],\n",
       " 95: [('Heizungs- und Sanierungsmaßnahmen zur Mieter*innenunterstützung und Gentrifizierungsbekämpfung',\n",
       "   1)],\n",
       " 96: [('Soziale und kulturelle Absicherung für Kulturschaffende im digitalen Raum',\n",
       "   1)],\n",
       " 97: [('Reform der Kranken- und Pflegeversicherung in Deutschland', 1)],\n",
       " 98: [('Medienvielfalt und Desinformation im digitalen Zeitalter', 1)],\n",
       " 99: [('Altersvorsorge und Sozialwahlen für Selbständige', 1)],\n",
       " 100: [('Sicherheit und Freiheit in Europa', 1)],\n",
       " 101: [('Nachhaltige Wirtschaft und Klimaschutz durch faire Wettbewerbspolitik',\n",
       "   1)],\n",
       " 102: [('Förderung und Schutz jüdischen Lebens in Deutschland gegen Antisemitismus',\n",
       "   1)],\n",
       " 103: [('Zukunftsfähige Bildung und Lehrkräfteausbildung', 1)],\n",
       " 104: [('Hochschulreform und Forschungssicherung', 1)],\n",
       " 105: [('Verbraucher*innenrechte und Verbraucherschutz stärken', 1)],\n",
       " 106: [('Steuerfinanzierte Öffentlichkeitsarbeit und Haushaltsaufstellung der Regierung',\n",
       "   1)],\n",
       " 107: [('Regionale Agrarpolitik und faire Preisstrukturen für Landwirte', 1)],\n",
       " 108: [('Stärkung der kommunalen Selbstverwaltung und Priorisierung von Daseinsvorsorge',\n",
       "   1)],\n",
       " 109: [('Agenda für Wohlstand und Leistung in Deutschland', 1)],\n",
       " 110: [('Zusammenhalt und Politikwechsel für ein funktionierendes Deutschland',\n",
       "   1)],\n",
       " 111: [('Sicherheit und Freiheit in Deutschland', 1)],\n",
       " 112: [('Drogenpolitik und Entkriminalisierung', 1)],\n",
       " 113: [('Friedensarchitektur für Europa und Diplomatie', 1)],\n",
       " 114: [('Stärkung der direkten Demokratie und Meinungsfreiheit in Deutschland',\n",
       "   1)],\n",
       " 115: [('Gesundheitsversorgung und Arzneimittelpolitik', 1)],\n",
       " 116: [('Entwicklungspolitik und Verfahrenssprachen in der deutschen Entwicklungszusammenarbeit',\n",
       "   1)],\n",
       " 117: [('Elektromobilität und E-Auto Förderung in Deutschland', 1)],\n",
       " 118: [('Nahost-Konflikt und Friedensgebote', 1)],\n",
       " 119: [('Datenpolitik und Datenschutzreform', 1)],\n",
       " 120: [('Schutz demokratischer Werte und wirtschaftliche Beziehungen zu China',\n",
       "   1)],\n",
       " 121: [('Ukraine Krieg und Friedenspolitik', 1)],\n",
       " 122: [('Rechte und Drogenpolitik für den Gebrauch psychoaktiver Substanzen',\n",
       "   1)]}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ac210c3f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|▉         | 12/130 [01:08<11:17,  5.74s/it]\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index (124) out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[69], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtopic_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhierarchical_topics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchroma_docs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdocuments\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_ctfidf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/bertopic/_bertopic.py:1132\u001b[0m, in \u001b[0;36mBERTopic.hierarchical_topics\u001b[0;34m(self, docs, use_ctfidf, linkage_function, distance_function)\u001b[0m\n\u001b[1;32m   1129\u001b[0m clustered_topics \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i, x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(clusters) \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;241m==\u001b[39m clusters[topic]]\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Group bow per cluster, calculate c-TF-IDF and extract words\u001b[39;00m\n\u001b[0;32m-> 1132\u001b[0m grouped \u001b[38;5;241m=\u001b[39m csr_matrix(\u001b[43mbow\u001b[49m\u001b[43m[\u001b[49m\u001b[43mclustered_topics\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m))\n\u001b[1;32m   1133\u001b[0m c_tf_idf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mctfidf_model\u001b[38;5;241m.\u001b[39mtransform(grouped)\n\u001b[1;32m   1134\u001b[0m selection \u001b[38;5;241m=\u001b[39m documents\u001b[38;5;241m.\u001b[39mloc[documents\u001b[38;5;241m.\u001b[39mTopic\u001b[38;5;241m.\u001b[39misin(clustered_topics), :]\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/scipy/sparse/_index.py:47\u001b[0m, in \u001b[0;36mIndexMixin.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[0;32m---> 47\u001b[0m     row, col \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;66;03m# Dispatch to specialized methods.\u001b[39;00m\n\u001b[1;32m     50\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(row, INT_TYPES):\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/scipy/sparse/_index.py:159\u001b[0m, in \u001b[0;36mIndexMixin._validate_indices\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    157\u001b[0m         row \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m M\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(row, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m--> 159\u001b[0m     row \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_asindices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mM\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isintlike(col):\n\u001b[1;32m    162\u001b[0m     col \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(col)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/scipy/sparse/_index.py:191\u001b[0m, in \u001b[0;36mIndexMixin._asindices\u001b[0;34m(self, idx, length)\u001b[0m\n\u001b[1;32m    189\u001b[0m max_indx \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mmax()\n\u001b[1;32m    190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_indx \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m length:\n\u001b[0;32m--> 191\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex (\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m) out of range\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m max_indx)\n\u001b[1;32m    193\u001b[0m min_indx \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mmin()\n\u001b[1;32m    194\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m min_indx \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "\u001b[0;31mIndexError\u001b[0m: index (124) out of range"
     ]
    }
   ],
   "source": [
    "topic_model.hierarchical_topics(chroma_docs['documents'], use_ctfidf=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f087a2b5-6102-44fb-813b-35d1064f6b32",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hierarchy = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49a8c788-7247-403a-b58e-bd046412b128",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hierarchy.to_csv(\"hierarchy.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "69f2ec92-1089-44f3-b25b-79a0800e8bc1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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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",
       "      <th>Representation</th>\n",
       "      <th>Representative_Docs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>214</td>\n",
       "      <td>0_Energie- und Klimapolitik für eine wettbewer...</td>\n",
       "      <td>[Energie- und Klimapolitik für eine wettbewerb...</td>\n",
       "      <td>[is.\\n\\nBesonders im Norden und Osten, wo ein ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>1_Integration und Migration in Deutschland</td>\n",
       "      <td>[Integration und Migration in Deutschland]</td>\n",
       "      <td>[traftätern. Die Externalisierung von Asylverf...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>95</td>\n",
       "      <td>2_Unterstützung der Ukraine und Stärkung der E...</td>\n",
       "      <td>[Unterstützung der Ukraine und Stärkung der Eu...</td>\n",
       "      <td>[erneuten Wahl von Donald Trump als US-Präside...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>89</td>\n",
       "      <td>3_Mobilität und infrastrukturelle Erneuerung i...</td>\n",
       "      <td>[Mobilität und infrastrukturelle Erneuerung in...</td>\n",
       "      <td>[e Autoindustrie wäre wirtschafts- und klimapo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>4_Antisemitismus und Extremismusbekämpfung in ...</td>\n",
       "      <td>[Antisemitismus und Extremismusbekämpfung in D...</td>\n",
       "      <td>[in öffentlichen und digitalen Räumen durch Re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>58</td>\n",
       "      <td>5_Systemwechsel in der Pflege und Verbesserung...</td>\n",
       "      <td>[Systemwechsel in der Pflege und Verbesserung ...</td>\n",
       "      <td>[nen Systemwechsel in der Pflege\\n\\n831 ein: F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>55</td>\n",
       "      <td>6_Klimaschutz und Energiewende in Deutschland</td>\n",
       "      <td>[Klimaschutz und Energiewende in Deutschland]</td>\n",
       "      <td>[ssen sie\\n\\n1418\\n\\nkonsequent abgeschöpft we...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>7_Arbeitsmarktpolitik und soziale Sicherheit i...</td>\n",
       "      <td>[Arbeitsmarktpolitik und soziale Sicherheit in...</td>\n",
       "      <td>[n für veränderte Anforderungen und Aufgaben\\n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>52</td>\n",
       "      <td>8_Geschlechtsspezifische Gewalt und Gleichstel...</td>\n",
       "      <td>[Geschlechtsspezifische Gewalt und Gleichstell...</td>\n",
       "      <td>[tem, das Sicherheit im analogen und digitalen...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Topic  Count                                               Name   \n",
       "1      0    214  0_Energie- und Klimapolitik für eine wettbewer...  \\\n",
       "2      1    109         1_Integration und Migration in Deutschland   \n",
       "3      2     95  2_Unterstützung der Ukraine und Stärkung der E...   \n",
       "4      3     89  3_Mobilität und infrastrukturelle Erneuerung i...   \n",
       "5      4     64  4_Antisemitismus und Extremismusbekämpfung in ...   \n",
       "6      5     58  5_Systemwechsel in der Pflege und Verbesserung...   \n",
       "7      6     55      6_Klimaschutz und Energiewende in Deutschland   \n",
       "8      7     52  7_Arbeitsmarktpolitik und soziale Sicherheit i...   \n",
       "9      8     52  8_Geschlechtsspezifische Gewalt und Gleichstel...   \n",
       "\n",
       "                                      Representation   \n",
       "1  [Energie- und Klimapolitik für eine wettbewerb...  \\\n",
       "2         [Integration und Migration in Deutschland]   \n",
       "3  [Unterstützung der Ukraine und Stärkung der Eu...   \n",
       "4  [Mobilität und infrastrukturelle Erneuerung in...   \n",
       "5  [Antisemitismus und Extremismusbekämpfung in D...   \n",
       "6  [Systemwechsel in der Pflege und Verbesserung ...   \n",
       "7      [Klimaschutz und Energiewende in Deutschland]   \n",
       "8  [Arbeitsmarktpolitik und soziale Sicherheit in...   \n",
       "9  [Geschlechtsspezifische Gewalt und Gleichstell...   \n",
       "\n",
       "                                 Representative_Docs  \n",
       "1  [is.\\n\\nBesonders im Norden und Osten, wo ein ...  \n",
       "2  [traftätern. Die Externalisierung von Asylverf...  \n",
       "3  [erneuten Wahl von Donald Trump als US-Präside...  \n",
       "4  [e Autoindustrie wäre wirtschafts- und klimapo...  \n",
       "5  [in öffentlichen und digitalen Räumen durch Re...  \n",
       "6  [nen Systemwechsel in der Pflege\\n\\n831 ein: F...  \n",
       "7  [ssen sie\\n\\n1418\\n\\nkonsequent abgeschöpft we...  \n",
       "8  [n für veränderte Anforderungen und Aufgaben\\n...  \n",
       "9  [tem, das Sicherheit im analogen und digitalen...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_model.get_topic_info()[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2aa8adc9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'datamapplot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[39], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mtopic_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisualize_document_datamap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchroma_docs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdocuments\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchroma_docs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43membeddings\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m fig\u001b[38;5;241m.\u001b[39mwrite_html(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheatmap.html\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Show the figure\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/bertopic/_bertopic.py:2615\u001b[0m, in \u001b[0;36mBERTopic.visualize_document_datamap\u001b[0;34m(self, docs, topics, embeddings, reduced_embeddings, custom_labels, title, sub_title, width, height, **datamap_kwds)\u001b[0m\n\u001b[1;32m   2613\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m   2614\u001b[0m check_documents_type(docs)\n\u001b[0;32m-> 2615\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplotting\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvisualize_document_datamap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2616\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2617\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdocs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2618\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtopics\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2619\u001b[0m \u001b[43m    \u001b[49m\u001b[43membeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2620\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreduced_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2621\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcustom_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2622\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2623\u001b[0m \u001b[43m    \u001b[49m\u001b[43msub_title\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2624\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2625\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2626\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdatamap_kwds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2627\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/bertopic/plotting/_datamap.py:148\u001b[0m, in \u001b[0;36mvisualize_document_datamap\u001b[0;34m(topic_model, docs, topics, embeddings, reduced_embeddings, custom_labels, title, sub_title, width, height, **datamap_kwds)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;66;03m# Map in topic names and plot\u001b[39;00m\n\u001b[1;32m    146\u001b[0m named_topic_per_doc \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mSeries(topic_per_doc)\u001b[38;5;241m.\u001b[39mmap(topic_name_mapping)\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m--> 148\u001b[0m figure, axes \u001b[38;5;241m=\u001b[39m \u001b[43mdatamapplot\u001b[49m\u001b[38;5;241m.\u001b[39mcreate_plot(\n\u001b[1;32m    149\u001b[0m     embeddings_2d,\n\u001b[1;32m    150\u001b[0m     named_topic_per_doc,\n\u001b[1;32m    151\u001b[0m     figsize\u001b[38;5;241m=\u001b[39m(width \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m, height \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m),\n\u001b[1;32m    152\u001b[0m     dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m,\n\u001b[1;32m    153\u001b[0m     title\u001b[38;5;241m=\u001b[39mtitle,\n\u001b[1;32m    154\u001b[0m     sub_title\u001b[38;5;241m=\u001b[39msub_title,\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdatamap_kwds,\n\u001b[1;32m    156\u001b[0m )\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m figure\n",
      "\u001b[0;31mNameError\u001b[0m: name 'datamapplot' is not defined"
     ]
    }
   ],
   "source": [
    "fig = topic_model.visualize_document_datamap(chroma_docs['documents'], embeddings=chroma_docs['embeddings'])\n",
    "fig.write_html(\"heatmap.html\")\n",
    "\n",
    "# Show the figure\n",
    "from IPython.display import HTML\n",
    "HTML(filename=\"heatmap.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac3c05bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "document_info = topic_model.get_document_info(docs['documents'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "56f0c003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Document</th>\n",
       "      <th>Topic</th>\n",
       "      <th>Name</th>\n",
       "      <th>Representation</th>\n",
       "      <th>Representative_Docs</th>\n",
       "      <th>Top_n_words</th>\n",
       "      <th>Probability</th>\n",
       "      <th>Representative_document</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zweitstimme\\n\\nUnser Land verdient mehr!\\n\\nWa...</td>\n",
       "      <td>10</td>\n",
       "      <td>10_land_wieder_jetzt_politik</td>\n",
       "      <td>[land, wieder, jetzt, politik, unser, es, kraf...</td>\n",
       "      <td>[rer Amtszeit Land und\\n\\nLeuten besser geht. ...</td>\n",
       "      <td>land - wieder - jetzt - politik - unser - es -...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eunruhigt die Talfahrt unserer Industrie, der ...</td>\n",
       "      <td>49</td>\n",
       "      <td>49_krieg_krieges_militrausgaben_russland</td>\n",
       "      <td>[krieg, krieges, militrausgaben, russland, sin...</td>\n",
       "      <td>[gelösten Katastrophe. Diese gefährliche Entwi...</td>\n",
       "      <td>krieg - krieges - militrausgaben - russland - ...</td>\n",
       "      <td>0.532807</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tan werden, dass Deutschland nicht in einen Kr...</td>\n",
       "      <td>49</td>\n",
       "      <td>49_krieg_krieges_militrausgaben_russland</td>\n",
       "      <td>[krieg, krieges, militrausgaben, russland, sin...</td>\n",
       "      <td>[gelösten Katastrophe. Diese gefährliche Entwi...</td>\n",
       "      <td>krieg - krieges - militrausgaben - russland - ...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aktuelle Hochrüstung ebenso konsequent ablehnt...</td>\n",
       "      <td>49</td>\n",
       "      <td>49_krieg_krieges_militrausgaben_russland</td>\n",
       "      <td>[krieg, krieges, militrausgaben, russland, sin...</td>\n",
       "      <td>[gelösten Katastrophe. Diese gefährliche Entwi...</td>\n",
       "      <td>krieg - krieges - militrausgaben - russland - ...</td>\n",
       "      <td>0.746483</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>, in dem die Fleißigen belohnt werden und nich...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1_und_fr_die_der</td>\n",
       "      <td>[und, fr, die, der, zu, in, wir, eine, von, ein]</td>\n",
       "      <td>[t haben, sich einzubringen und Teil unse- rer...</td>\n",
       "      <td>und - fr - die - der - zu - in - wir - eine - ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4530</th>\n",
       "      <td>port fördert besondere sportliche Talente und ...</td>\n",
       "      <td>44</td>\n",
       "      <td>44_sport_breitensport_spiele_sports</td>\n",
       "      <td>[sport, breitensport, spiele, sports, bewegung...</td>\n",
       "      <td>[istige Perspektive.\\n\\nGames wollen wir als g...</td>\n",
       "      <td>sport - breitensport - spiele - sports - beweg...</td>\n",
       "      <td>0.452899</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4531</th>\n",
       "      <td>s Modell eingeführt, das berufliche Perspektiv...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1_und_fr_die_der</td>\n",
       "      <td>[und, fr, die, der, zu, in, wir, eine, von, ein]</td>\n",
       "      <td>[t haben, sich einzubringen und Teil unse- rer...</td>\n",
       "      <td>und - fr - die - der - zu - in - wir - eine - ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4532</th>\n",
       "      <td>nd in unserer Freizeit. Deshalb setzt sich Vol...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1_und_fr_die_der</td>\n",
       "      <td>[und, fr, die, der, zu, in, wir, eine, von, ein]</td>\n",
       "      <td>[t haben, sich einzubringen und Teil unse- rer...</td>\n",
       "      <td>und - fr - die - der - zu - in - wir - eine - ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4533</th>\n",
       "      <td>rganisiert, um gesundheitliche Vorteile für al...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1_und_fr_die_der</td>\n",
       "      <td>[und, fr, die, der, zu, in, wir, eine, von, ein]</td>\n",
       "      <td>[t haben, sich einzubringen und Teil unse- rer...</td>\n",
       "      <td>und - fr - die - der - zu - in - wir - eine - ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>n gefördert. Durch stärkere Anreize und einfac...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1_und_fr_die_der</td>\n",
       "      <td>[und, fr, die, der, zu, in, wir, eine, von, ein]</td>\n",
       "      <td>[t haben, sich einzubringen und Teil unse- rer...</td>\n",
       "      <td>und - fr - die - der - zu - in - wir - eine - ...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4535 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Document  Topic  \\\n",
       "0     Zweitstimme\\n\\nUnser Land verdient mehr!\\n\\nWa...     10   \n",
       "1     eunruhigt die Talfahrt unserer Industrie, der ...     49   \n",
       "2     tan werden, dass Deutschland nicht in einen Kr...     49   \n",
       "3     aktuelle Hochrüstung ebenso konsequent ablehnt...     49   \n",
       "4     , in dem die Fleißigen belohnt werden und nich...     -1   \n",
       "...                                                 ...    ...   \n",
       "4530  port fördert besondere sportliche Talente und ...     44   \n",
       "4531  s Modell eingeführt, das berufliche Perspektiv...     -1   \n",
       "4532  nd in unserer Freizeit. Deshalb setzt sich Vol...     -1   \n",
       "4533  rganisiert, um gesundheitliche Vorteile für al...     -1   \n",
       "4534  n gefördert. Durch stärkere Anreize und einfac...     -1   \n",
       "\n",
       "                                          Name  \\\n",
       "0                 10_land_wieder_jetzt_politik   \n",
       "1     49_krieg_krieges_militrausgaben_russland   \n",
       "2     49_krieg_krieges_militrausgaben_russland   \n",
       "3     49_krieg_krieges_militrausgaben_russland   \n",
       "4                            -1_und_fr_die_der   \n",
       "...                                        ...   \n",
       "4530       44_sport_breitensport_spiele_sports   \n",
       "4531                         -1_und_fr_die_der   \n",
       "4532                         -1_und_fr_die_der   \n",
       "4533                         -1_und_fr_die_der   \n",
       "4534                         -1_und_fr_die_der   \n",
       "\n",
       "                                         Representation  \\\n",
       "0     [land, wieder, jetzt, politik, unser, es, kraf...   \n",
       "1     [krieg, krieges, militrausgaben, russland, sin...   \n",
       "2     [krieg, krieges, militrausgaben, russland, sin...   \n",
       "3     [krieg, krieges, militrausgaben, russland, sin...   \n",
       "4      [und, fr, die, der, zu, in, wir, eine, von, ein]   \n",
       "...                                                 ...   \n",
       "4530  [sport, breitensport, spiele, sports, bewegung...   \n",
       "4531   [und, fr, die, der, zu, in, wir, eine, von, ein]   \n",
       "4532   [und, fr, die, der, zu, in, wir, eine, von, ein]   \n",
       "4533   [und, fr, die, der, zu, in, wir, eine, von, ein]   \n",
       "4534   [und, fr, die, der, zu, in, wir, eine, von, ein]   \n",
       "\n",
       "                                    Representative_Docs  \\\n",
       "0     [rer Amtszeit Land und\\n\\nLeuten besser geht. ...   \n",
       "1     [gelösten Katastrophe. Diese gefährliche Entwi...   \n",
       "2     [gelösten Katastrophe. Diese gefährliche Entwi...   \n",
       "3     [gelösten Katastrophe. Diese gefährliche Entwi...   \n",
       "4     [t haben, sich einzubringen und Teil unse- rer...   \n",
       "...                                                 ...   \n",
       "4530  [istige Perspektive.\\n\\nGames wollen wir als g...   \n",
       "4531  [t haben, sich einzubringen und Teil unse- rer...   \n",
       "4532  [t haben, sich einzubringen und Teil unse- rer...   \n",
       "4533  [t haben, sich einzubringen und Teil unse- rer...   \n",
       "4534  [t haben, sich einzubringen und Teil unse- rer...   \n",
       "\n",
       "                                            Top_n_words  Probability  \\\n",
       "0     land - wieder - jetzt - politik - unser - es -...     1.000000   \n",
       "1     krieg - krieges - militrausgaben - russland - ...     0.532807   \n",
       "2     krieg - krieges - militrausgaben - russland - ...     1.000000   \n",
       "3     krieg - krieges - militrausgaben - russland - ...     0.746483   \n",
       "4     und - fr - die - der - zu - in - wir - eine - ...     0.000000   \n",
       "...                                                 ...          ...   \n",
       "4530  sport - breitensport - spiele - sports - beweg...     0.452899   \n",
       "4531  und - fr - die - der - zu - in - wir - eine - ...     0.000000   \n",
       "4532  und - fr - die - der - zu - in - wir - eine - ...     0.000000   \n",
       "4533  und - fr - die - der - zu - in - wir - eine - ...     0.000000   \n",
       "4534  und - fr - die - der - zu - in - wir - eine - ...     0.000000   \n",
       "\n",
       "      Representative_document  \n",
       "0                       False  \n",
       "1                       False  \n",
       "2                       False  \n",
       "3                       False  \n",
       "4                       False  \n",
       "...                       ...  \n",
       "4530                    False  \n",
       "4531                    False  \n",
       "4532                    False  \n",
       "4533                    False  \n",
       "4534                    False  \n",
       "\n",
       "[4535 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "document_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ae6ce1ec-b987-4fb7-9ded-cfb9b6443b5c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fig = topic_model.visualize_documents(chroma_docs['documents'], embeddings=chroma_docs['embeddings'])\n",
    "fig.write_html(\"topics.html\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef644743-89b9-42a7-995a-2185102c994c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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