topica 0.14.0

Topica: fast, all-purpose topic modeling for Python — a Rust core for LDA, STM, and more

Topica: fast, all-purpose topic modeling for Python

topica is a fast topic-modeling library for Python with more than a dozen models, built for social scientists who want to move from text data to publishable results in a single workflow. It brings together models and tools usually split across JVM software like MALLET and R packages like stm, and runs them on a parallel Rust core competitive with the standard implementations, with every fit reproducible from a fixed seed. Each model comes with the validation, covariate-effect, and reporting tools to meet the standards reviewers expect.

pip install topica

The core needs only NumPy. Optional extras add features without weighing the core down: topica[viz] (matplotlib plots), topica[formula] (R-style formulas), topica[polars] (Polars frames), and topica[llm] (LLM labels and embeddings, OpenAI or local via ollama). PyTorch is never required.

from topica import LDA

model = LDA(num_topics=2, seed=42)
model.fit([["cat", "dog", "fish"]] * 15 + [["planet", "star", "moon"]] * 15, iters=1000)

for i, words in enumerate(model.top_words(3)):
    print(f"Topic {i}:", " ".join(w for w, _ in words))

See the getting-started guide and the worked examples for end-to-end analyses.

Models

Count-based models learn topics from word counts (collapsed Gibbs, variational EM, or an amortized VAE):

Model	What it's for
LDA	Classic topics via fast collapsed-Gibbs (SparseLDA); optional multi-threaded and LightLDA alias samplers
ProdLDA	Sharper, more coherent topics via a product-of-experts word model, fit as an amortized VAE (no PyTorch)
DMR	Topics conditioned on document metadata (Dirichlet-multinomial regression)
LabeledLDA	Supervised topics tied to document labels
CTM	Correlated topics (logistic-normal)
STM	The Structural Topic Model: correlated topics with prevalence and content covariates
SAGE	Content-covariate topics: the same topic worded differently across groups
HDP	Nonparametric LDA that infers the number of topics
DTM	Dynamic topics that evolve across time slices
SupervisedLDA	Topics shaped to predict a per-document response
PT / GSDMM	Short-text models for tweets, survey answers, headlines
SeededLDA / KeyATM	Guided topics steered by seed words
PA / HLDA	Topic hierarchies (Pachinko, nested-CRP)

Embedding-based models start from document embeddings you supply (no PyTorch, no UMAP/numba in the wheel):

Model	What it's for
BERTopic	Cluster document embeddings, label topics by class-TF-IDF; topic reduction and a soft per-document distribution
Top2Vec	Topics as points in the embedding space; topic words are the nearest word vectors
ETM	Generative LDA with the topic-word distribution factored through embeddings (β = softmax(ρ·α)); per-document EM or an amortized VAE (inference="vae")
FASTopic	Topics read off two optimal-transport plans between document, topic, and word embeddings

Every model exposes the same shape: fit(docs, …), then topic_word (φ), doc_topic (θ), top_words(n), and save/load. The count-based variational models (CTM/STM/SupervisedLDA/DTM) parallelize across cores while staying bit-for-bit deterministic. The embedding models split into two kinds: BERTopic and Top2Vec run the reduce → cluster → represent pipeline, while ETM and FASTopic are generative and mixed-membership; all of them take vectors from any embedder (sentence-transformers, an API, a local model such as ollama). Full guides: the models and embedding topics.

Diagnostics & analysis

Model-agnostic: they work on any fitted model's topic_word/doc_topic:

• Quality: coherence (u_mass, c_v, c_uci, c_npmi; computed in the Rust core), exclusivity, topic_diversity, quality_frontier
• Labeling: label_topics (prob / FREX / lift / score), frex, relevance, find_thoughts, topic_table, summary
• Validation: word_intrusion, document_intrusion, bootstrap_stability, search_k
• Comparison: fighting_words (weighted log-odds) for contrasting corpora
• Covariate effects: estimate_effect (method of composition, cluster-robust SEs, GLM links), topic_correlation, and the design helpers spline / interaction / one_hot (an stm-style API); posterior_theta_samples draws θ for the logistic-normal models (STM/CTM)
• Preprocessing: tokenize, learn_phrases / apply_phrases, split_documents, the Corpus class

See diagnostics and covariate effects.

Performance

topica runs on a parallel Rust core. It is several times faster than R stm — the single-threaded field standard — for the structural and other variational models, and it matches the hand-tuned compiled samplers core for core: parity with Java MALLET on plain LDA and with the C++ keyATM on keyword models. On the political-blog corpus (2,000 documents, fit time only, same iterations on both sides):

Model	Reference	topica speedup
STM	R stm	3–6× single-threaded, ~10–22× multithreaded
LDA	Java MALLET	parity single-threaded, ~2× multithreaded
keyATM	R keyATM	parity single-threaded, ~2× multithreaded

Every fit is reproducible from a fixed seed and validated against its reference. See Benchmarks for the full methodology, and reproduce the table with python benchmarks/speed_vs_r.py.

Install from source

pip install maturin
git clone https://github.com/nealcaren/topica && cd topica
python -m venv .venv && source .venv/bin/activate
maturin develop --release --features python

Requires numpy >= 1.21. Use --release (the debug build is much slower).

Acknowledgements

Topica stands on a generation of open topic-modeling research and code. Each entry below lists the reference, its authors and year, and the topica class(es) it underlies; the other models are Rust ports or reimplementations, validated against these reference implementations.

• MALLET (McCallum, 2002) — LDA, DMR, LabeledLDA: the SparseLDA sampler, Dirichlet-multinomial regression, and hyperparameter optimization. LDA binds David Mimno's RustMallet (Apache-2.0), reproducing its train CLI byte-for-byte; against Java MALLET (a different RNG) it recovers the same topics (cosine 1.000)
• stm (Roberts, Stewart & Tingley, 2019) — STM, CTM, SAGE: variational EM, estimateEffect, searchK, FREX, spectral initialization, and the method of composition
• lda-c / ctm-c / dtm and hdp (Blei lab, 2006–2007) — CTM, DTM, HDP: the CTM, Dynamic Topic Model, and HDP samplers
• gensim (Řehůřek & Sojka, 2010) — DTM: coherence measures and the LdaSeqModel DTM reference
• tomotopy (bab2min, 2020) — API conventions (summary, the short-text models)
• keyATM (Eshima, Imai & Sasaki, 2024) — KeyATM: the base, covariate, and dynamic models, the information-theory token weighting, and the Chib (1998) change-point HMM, validated against the package
• seededlda (Watanabe, 2023) — SeededLDA: the seeded-prior scheme
• LightLDA (Yuan et al., 2015) — LDA: the alias-table Metropolis-Hastings sampler
• GSDMM (Yin & Wang, 2014) — GSDMM: the movie-group-process mixture for short text
• ProdLDA / AVITM (Srivastava & Sutton, 2017) — ProdLDA: autoencoding variational inference and the product-of-experts word model
• BERTopic (Grootendorst, 2022) and Top2Vec (Angelov, 2020) — BERTopic, Top2Vec: the embedding-clustering pipeline, class-based TF-IDF, and the reduce → cluster → represent design
• ETM (Dieng, Ruiz & Blei, 2020) — ETM: the Embedded Topic Model (per-document variational EM and an amortized VAE)
• FASTopic (Wu et al., 2024) — FASTopic: the optimal-transport topic model

The embedding-native models build on two pure-Rust crates: petal-clustering for HDBSCAN and umap-rs for the optional UMAP reducer, both BLAS-free.

Full citations for every model and reference implementation, and how to cite topica, are on the Citing page.

License

Apache-2.0 — see LICENSE.