{"library":"pyldavis","title":"pyLDAvis","description":"Interactive topic model visualization, port of the R package. Current version 3.4.1. Supports LDA models from gensim, scikit-learn, and other sources. Release cadence: irregular, last update in 2023.","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install pyldavis"],"cli":null},"imports":["import pyLDAvis","from pyLDAvis import gensim_models as gensimvis","from pyLDAvis import sklearn_models as sklearnvis","from pyLDAvis import sklearn_models"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pyLDAvis\nimport pyLDAvis.gensim_models as gensimvis\n\n# Prepare data (example: using gensim LDA model)\n# If using real API key, set it via os.environ\nimport os\napi_key = os.environ.get('OPENAI_API_KEY', 'sk-...')  # not used in this example\n\n# Example with dummy data\nfrom gensim.corpora import Dictionary\nfrom gensim.models import LdaModel\n\n# Create a simple corpus\ndocs = [['apple', 'orange', 'banana'], ['car', 'truck', 'bus']]\ndictionary = Dictionary(docs)\ncorpus = [dictionary.doc2bow(doc) for doc in docs]\nlda_model = LdaModel(corpus, num_topics=2, id2word=dictionary, passes=5)\n\n# Prepare visualization\nvis_data = gensimvis.prepare(lda_model, corpus, dictionary)\n# Save to HTML file\npyLDAvis.save_html(vis_data, 'vis.html')\nprint('Visualization saved as vis.html')","lang":"python","description":"Load a gensim LDA model, prepare interactive visualization, and save as HTML.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}