.. _release_notes: ================================================================================ Release Notes & Changelog ================================================================================ Version history and changelog for poisson-topicmodels. Current Version =============== **Latest**: 0.2.0 (April 2026) For full changelog, see below. Version 0.2.0 - Inspection & Diagnostics ========================================== *April 1, 2026* **New Methods (all models)**: - ✨ ``summary(n_top_words=5)`` — Formatted text summary of any fitted model - ✨ ``compute_topic_coherence(metric='c_npmi', top_n=10)`` — Per-topic NPMI/UMass coherence - ✨ ``compute_topic_diversity(top_n=25)`` — Unique-word fraction across topics (0–1) - ✨ ``plot_topic_prevalence()`` — Horizontal bar chart of mean topic prevalence - ✨ ``plot_topic_correlation()`` — Cosine-similarity heatmap between topics - ✨ ``plot_document_topic_heatmap()`` — Document × topic heatmap - ✨ Academic-style plotting (``_setup_academic_style``) applied to all built-in figures **New Methods (SPF)**: - ✨ ``plot_seed_effectiveness()`` — Grouped bar chart of seed vs. non-seed word weights **New Methods (CPF & CSPF)**: - ✨ ``return_covariate_effects_ci(ci=0.95)`` — Covariate effects with Bayesian credible intervals - ✨ ``plot_cov_effects(ci=0.95)`` — Forest plot of covariate effects **New Methods (TBIP)**: - ✨ ``return_ideal_points()`` — DataFrame of author positions with uncertainty - ✨ ``return_ideological_words(topic, n)`` — Top ideological words per topic - ✨ ``plot_ideal_points(show_ci=True)`` — Publication-ready 1-D scatter with CIs - ✨ ``return_topics()`` / ``return_beta()`` overrides using LogNormal posterior **New Methods (ETM)**: - ✨ ``return_topics()`` — Neural encoder inference (was NotImplementedError) - ✨ ``return_beta()`` — Embedding-based topic–word computation (was NotImplementedError) **Bug Fixes**: - 🐛 TBIP: Fixed ``sigma_y`` → ``sigma_x`` typo in variational guide - 🐛 CPF: Fixed ``self.covariates`` not set in ``__init__``; fixed ndim validation order - 🐛 PF: Removed duplicate ``return_top_words_per_topic`` override **Breaking Changes**: - ``CSPF2`` renamed to ``CSPF`` (old ``CSPF`` class removed) - ``from poisson_topicmodels import CSPF2`` → ``from poisson_topicmodels import CSPF`` **Documentation**: - All code examples updated to use correct method names (``return_topics``, ``return_beta``, ``return_top_words_per_topic``, ``train_step`` instead of fictional ``get_*``/``train`` names) - API reference rewritten with complete method documentation - Autodoc directives enabled for auto-generated class reference - New methods documented across fundamentals, tutorials, and examples Version 0.1.0 - Initial Release =============================== *February 5, 2026* **New Features**: - ✨ Poisson Factorization (PF) - Unsupervised topic modeling - ✨ Seeded PF (SPF) - Guided topic discovery with keyword priors - ✨ Covariate PF (CPF) - Model topic variation by document metadata - ✨ Covariate Seeded PF (CSPF) - Combine seeds and covariates - ✨ Text-Based Ideal Points (TBIP) - Estimate author positions - ✨ Embedded Topic Models (ETM) - Integration with pre-trained embeddings - ✨ Stochastic Variational Inference (SVI) with mini-batch training - ✨ GPU acceleration via JAX - ✨ Comprehensive type hints (90%+ coverage) - ✨ >70% test coverage - ✨ Complete documentation and tutorials **Documentation**: - Getting Started guide - Fundamentals covering all models - 4 detailed tutorials - How-to guides for common tasks - Complete API reference - Testing guide - Contributing guidelines **Quality**: - Code follows Black, isort, mypy standards - Comprehensive error messages - Input validation - Reproducibility via seeding - GitHub Actions CI/CD **Breaking Changes**: - N/A (first release) **Migration Guide**: - N/A (first release) **Contributors**: - Bernd Prostmaier (Lead) - Bettina Grün - Paul Hofmarcher **Known Issues**: - GPU memory estimation could be improved - Some edge cases in covariate handling - Documentation for advanced features could be more detailed **Future Roadmap**: - Dynamic topic models (time-varying topics) - Online learning from streaming data - Hierarchical topic models (HTM) - Better visualization toolkit - Performance optimizations - Additional embedding support Coming Soon (0.3.0) =================== Planned features: - **Dynamic Topic Models**: Topics that evolve over time - **Streaming Mode**: Learn from new documents continuously - **Better Visualization**: Interactive topic visualization - **Save/Load**: Persist trained models to disk - **Documentation Enhancements**: More examples and tutorials Not Planned for 0.2.0: - Breaking API changes (we'll maintain compatibility) - Complete rewrite of inference (current approach is solid) Deprecation Policy ================== **Stability Guarantees**: - Public API stable across minor versions - Breaking changes only in major versions - Deprecations announced one release ahead **How to stay updated**: - Watch GitHub releases - Subscribe to changelog - Monitor upgrade guides Installation by Version ======================= Install specific version: .. code-block:: bash pip install poisson-topicmodels==0.1.0 List available versions: .. code-block:: bash pip index versions poisson-topicmodels Upgrade to latest: .. code-block:: bash pip install --upgrade poisson-topicmodels Compatibility Matrix ==================== .. list-table:: :widths: 25 25 25 25 :header-rows: 1 * - poisson-topicmodels - Python 3.11 - Python 3.12 - Python 3.13 * - 0.1.0 - ✓ - ✓ - ✓ Dependency Versions =================== Core dependencies for 0.1.0: - jax==0.8.0 - jaxlib==0.8.0 - numpyro==0.19.0 - numpy>=2.2.0,<3.0.0 - scipy>=1.15.0,<2.0.0 - pandas>=2.2.0,<3.0.0 - scikit-learn>=1.6.0,<2.0.0 - matplotlib>=3.10.0,<4.0.0 Optional dependencies: - sphinx>=6.0 (for documentation) - sphinx-rtd-theme>=1.2 (for docs theme) - myst-parser>=1.0 (for markdown docs) - pytest>=9.0.1 (for testing) Citing poisson-topicmodels ============================ If you use poisson-topicmodels in research, please cite: .. code-block:: bibtex @software{prostmaier2026poisson, title={poisson-topicmodels: Probabilistic Topic Modeling with Bayesian Inference}, author={Prostmaier, Bernd and Grün, Bettina and Hofmarcher, Paul}, year={2026}, url={https://github.com/BPro2410/topicmodels_package} } Or in plain text: Prostmaier, B., Grün, B., & Hofmarcher, P. (2026). poisson-topicmodels: Probabilistic Topic Modeling with Bayesian Inference. Retrieved from https://github.com/BPro2410/topicmodels_package Community Feedback ================== Help improve poisson-topicmodels: - 🐛 **Bug reports**: GitHub Issues - 💡 **Feature ideas**: GitHub Discussions - 📚 **Documentation feedback**: GitHub Issues - 💬 **General discussion**: GitHub Discussions - 🤝 **Contributions**: See Contributing Guide Report Issues ============= Found a bug? Please report it: 1. Check existing issues 2. Create detailed bug report 3. Include: - Python version - OS - JAX version - Minimal reproducible example - Full error traceback See :doc:`../contributing_guide/index` for detailed instructions. Acknowledgments =============== **Core Team**: - Bernd Prostmaier - Bettina Grün - Paul Hofmarcher **Built on**: - `JAX `_ - Automatic differentiation - `NumPyro `_ - Probabilistic programming - `SciPy `_ - Scientific computing - `Sphinx `_ - Documentation **Inspired by**: - Gensim - scikit-learn - PyMC - NumPyro License ======= poisson-topicmodels is licensed under the MIT License. See `LICENSE `_ for details. Next Steps ========== - **Update to latest**: ``pip install --upgrade poisson-topicmodels`` - **Get started**: See :doc:`../getting_started/index` - **Report issues**: `GitHub Issues `_ - **Contribute**: :doc:`../contributing_guide/index` **Questions?** Open a discussion or create an issue on GitHub! Questions About Releases ========================= **Q: When is the next release?** A: Quarterly minor releases, monthly patch releases. Check milestones on GitHub. **Q: How do I get a feature in the next release?** A: Open an issue or discussion to propose it, or submit a PR! **Q: Can I use development version?** A: Yes: ``pip install git+https://github.com/BPro2410/topicmodels_package.git`` **Q: What about backwards compatibility?** A: Public API stable within major versions. Breaking changes disclosed ahead of time.