playground_topic_modeling.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7kBBbX5Blty"
      },
      "source": [
        "# Open Sustainable Technology Topic Modeling\n",
        "\n",
        "This is a prototype notebook for experimenting with the Open Sustainable Technology dataset in combination with natural language processing. The first goal is to identify and cluster topics within open source projects in the field of environmental sustainability.\n",
        "\n",
        "ToDO:\n",
        "* Create Topic Blacklist\n",
        "* Hierarchical Topic Modeling: https://maartengr.github.io/BERTopic/getting_started/hierarchicaltopics/hierarchicaltopics.html\n",
        "\n",
        "Tips and Tricks given by the BARTopic Maintainer Maarten:\n",
        "* Split README into sentences --> Topic Modeling on every sentence.\n",
        "* Just use the first two paragraphs of the README, which is the actual description of the project.\n",
        "* Remove special character / HTML / badges in the preprocessing   \n",
        "* Representation Model to have \"better\" words\n",
        "* Example to use: https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#keybertinspired --> 10 words per topics -->\n",
        "* Parse -->GPT4\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NToxGi0voKei",
        "outputId": "a4eba1c1-e6b2-4f69-9fcb-e082184f902e"
      },
      "outputs": [],
      "source": [
        "!pip install bertopic keybert pandas\n",
        "!wget https://raw.githubusercontent.com/protontypes/AwesomeCure/main/csv/projects_with_readme.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ikr6z58-JiyI"
      },
      "source": [
        "## Reconfigure CSV Reading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oN1e0vG6oaTN"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import sys\n",
        "import csv\n",
        "\n",
        "maxInt = sys.maxsize\n",
        "\n",
        "# find the largest possible file size\n",
        "while True:\n",
        "    try:\n",
        "        csv.field_size_limit(maxInt)\n",
        "        break\n",
        "    except OverflowError:\n",
        "        maxInt = int(maxInt/10)\n",
        "df = pd.read_csv(\"projects_with_readme.csv\", engine='python')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rzbszp_d_CRt"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y_Z4-ZPv_Dk_",
        "outputId": "998b3e29-a257-4792-bd19-44e16034fa4c"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import dataloader\n",
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "\n",
        "data = pd.DataFrame(columns=[\"Sentence\", \"readme_content\"])\n",
        "readmes = [str(doc) for doc in df.readme_content.tolist()]\n",
        "\n",
        "sentences_cleaned = []\n",
        "for index, readme in enumerate(readmes):\n",
        "  sents = sent_tokenize(re.sub(' +', ' ', BeautifulSoup(readme, \"lxml\").text.replace(r\"\\n\", \"\")))\n",
        "  sentences_cleaned.extend(sents)\n",
        "\n",
        "  for sentence in sents:\n",
        "    data.loc[len(data), :] = [sentence, index]"
      ]
    },
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        },
        "id": "WKhfCmyiCAZS",
        "outputId": "e4daee7c-0137-41ff-cc28-50d76cb0f8f9"
      },
      "outputs": [],
      "source": [
        "from bertopic.representation import KeyBERTInspired\n",
        "from bertopic import BERTopic\n",
        "\n",
        "# Create your representation model\n",
        "representation_model = KeyBERTInspired()\n",
        "\n",
        "# Use the representation model in BERTopic on top of the default pipeline\n",
        "topic_model = BERTopic(representation_model=representation_model, verbose=True).fit(sentences_cleaned)\n",
        "data[\"Topic\"] = topic_model.topics_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "ihhTidFJCRA4",
        "outputId": "8328c638-354c-4485-ceb9-51f4367b1d18"
      },
      "outputs": [],
      "source": [
        "topic_model.get_topic_info().head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4XUj81AqDkLx"
      },
      "outputs": [],
      "source": [
        "# # Calculate the topic distributions on a token-level\n",
        "# topic_distr, topic_token_distr = topic_model.approximate_distribution(docs[:2], calculate_tokens=True)\n",
        "\n",
        "# # Visualize the token-level distributions\n",
        "# df = topic_model.visualize_approximate_distribution(docs[1], topic_token_distr[1])\n",
        "# df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDKVnACEDh2E",
        "outputId": "316ccb9e-c5ea-4801-ac2c-22d5570cae32"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import fetch_20newsgroups\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from bertopic import BERTopic\n",
        "from umap import UMAP\n",
        "\n",
        "# Prepare embeddings\n",
        "docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']\n",
        "sentence_model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
        "embeddings = sentence_model.encode(docs, show_progress_bar=False)\n",
        "\n",
        "# Train BERTopic\n",
        "topic_model = BERTopic().fit(docs, embeddings)\n",
        "\n",
        "# Run the visualization with the original embeddings\n",
        "topic_model.visualize_documents(docs, embeddings=embeddings)\n",
        "\n",
        "# Reduce dimensionality of embeddings, this step is optional but much faster to perform iteratively:\n",
        "reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)\n",
        "topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWJbXSjEJrpQ"
      },
      "source": [
        "## Preprocessing and Cleanup of dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "W55xaLDmpL__"
      },
      "outputs": [],
      "source": [
        "# Replace nan with empty strings in the readmes\n",
        "import numpy as np\n",
        "df = df.replace(np.inf, np.nan)\n",
        "df_readme = df['readme'].fillna('')\n",
        "docs = df_readme.to_list()\n",
        "\n",
        "# Replace nan with empty strings in topics\n",
        "df_topics = df[\"topics\"].fillna('')\n",
        "supervised_topics = df_topics.to_list()\n",
        "\n",
        "# create a subset of the dataset to speedup the processing\n",
        "docs_subset = docs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Q1-eZE9Kzk_1"
      },
      "outputs": [],
      "source": [
        "# Create a flat list of all userdefined topics\n",
        "supervised_topics_flat = []\n",
        "for word in supervised_topics:\n",
        "    word = word.split(\",\")\n",
        "    supervised_topics_flat.extend(word)\n",
        "supervised_topics_flat = [x for x in supervised_topics_flat if x]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "JXim_pSV6Dkz",
        "outputId": "bf45b311-3cc2-42a9-b60c-d430d88e9b6b"
      },
      "outputs": [],
      "source": [
        "supervised_topics_flat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "KSejke8SFBJa"
      },
      "outputs": [],
      "source": [
        "# Define a list of blacklist topics and remove them from the topic list\n",
        "topic_black_list = [\"install\",\"data\",\"python\",\"using\",\"use\"]\n",
        "topic_black_list_set = set(topic_black_list)\n",
        "supervised_topics_flat_cleaned = [x for x in supervised_topics_flat if x not in topic_black_list_set]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "8isrzIxzaIND"
      },
      "outputs": [],
      "source": [
        "from keybert import KeyBERT\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "\n",
        "# Extract keywords\n",
        "kw_model = KeyBERT()\n",
        "keywords = kw_model.extract_keywords(docs)\n",
        "\n",
        "vocabulary = [k[0] for keyword in keywords for k in keyword]\n",
        "vocabulary = list(set(vocabulary))\n",
        "vocabulary = [str(i) for i in vocabulary]\n",
        "\n",
        "vectorizer_model= CountVectorizer(vocabulary=vocabulary,stop_words=\"english\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pmATl1bFqGoi"
      },
      "outputs": [],
      "source": [
        "from bertopic import BERTopic\n",
        "from bertopic.vectorizers import ClassTfidfTransformer\n",
        "\n",
        "ctfidf_model = ClassTfidfTransformer(bm25_weighting=True)\n",
        "\n",
        "topic_model = BERTopic(ctfidf_model=ctfidf_model, min_topic_size=10)\n",
        "topics, probs = topic_model.fit_transform(docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YBDPvrezqLyo"
      },
      "outputs": [],
      "source": [
        "topic_model.get_topic_info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jD861olC8srA"
      },
      "outputs": [],
      "source": [
        "subset_training = topic_model.get_document_info(docs_subset)"
      ]
    },
    {
      "cell_type": "code",
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