Question 1

How does LDA-Ruby compare to Python's Gensim for topic modeling?

Accepted Answer

LDA-Ruby is Ruby-focused with seamless integration but lacks the extensive features and community of Gensim. It's ideal for Ruby projects avoiding language switching, though performance may lag for large-scale uses.

Question 2

How to install LDA-Ruby with the Rust backend enabled?

Accepted Answer

Set LDA_RUBY_RUST_BUILD=always during gem install, but ensure Rust and libclang are installed. On supported platforms, precompiled gems can skip this, but source installs require the toolchain.

Question 3

Can LDA-Ruby handle large datasets efficiently?

Accepted Answer

With native or Rust backends, it manages moderate sizes, but the pure Ruby backend is slow for large corpora. Benchmark with your data using the provided scripts to assess performance.

Question 4

What's the best way to ensure reproducible results in LDA-Ruby?

Accepted Answer

Use the seeded EM initialization by calling lda.em('seeded'), which supports deterministic runs for testing, as documented in the deterministic runs feature and example usage.

Question 5

Is LDA-Ruby suitable for production Rails apps?

Accepted Answer

Yes, but batch processing in background jobs is recommended for larger analyses to avoid blocking web requests. Integrate via the gem, but monitor performance with chosen backend.

Question 6

How to choose between native, Rust, and pure Ruby backends?

Accepted Answer

Benchmark with your data using the ./bin/benchmark-backends script. Use 'pure' for no dependencies, 'native' for stable C performance, or 'rust' for experimental speed if toolchain is available.

lda-ruby

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