A Ruby wrapper for Latent Dirichlet Allocation (LDA) that clusters documents into topics with native, Rust, and pure Ruby backends.
LDA-Ruby is a Ruby library that implements Latent Dirichlet Allocation (LDA) for topic modeling. It allows developers to automatically cluster collections of documents into topics, revealing underlying thematic structures in text data. The wrapper is based on David M. Blei's original C implementation but provides a more Ruby-friendly object-oriented interface.
Ruby developers working on natural language processing, text mining, or document analysis projects who need to perform topic modeling without leaving the Ruby ecosystem.
Developers choose LDA-Ruby for its seamless integration into Ruby applications, multiple backend options (native, Rust, pure Ruby) for performance tuning, and elimination of file-based I/O hurdles present in the original C implementation.
A Ruby wrapper for Latent Dirichlet Allocation (LDA).
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Offers native (C), Rust, and pure Ruby implementations, allowing developers to balance performance and ease of use, with backend selection via parameters or environment variables as detailed in the README.
Replaces the original C code's file-based I/O with Ruby objects, simplifying integration into applications without external file dependencies, as highlighted in the philosophy section.
Supports seeded EM algorithm initialization for consistent results, crucial for testing and research, with examples like em('seeded') provided in the usage section.
Provides precompiled gems for common platforms to avoid build dependencies, and configurable install-time policies for Rust backend setup via LDA_RUBY_RUST_BUILD environment variable.
The Rust extension is scaffolded and requires additional toolchain (Cargo, libclang), with fallback to pure Ruby if unavailable, indicating instability and added setup complexity.
Based on David Blei's 2003 C code, lacking modern LDA optimizations or features found in more recent libraries, which limits its competitiveness in advanced NLP tasks.
Development involves multiple Docker containers, environment variables, and rake tasks for building and testing, which can be overwhelming for new contributors or simple integrations.