Classifier
Text classification in Ruby. Five algorithms, native performance, streaming support.
Documentation · Tutorials · API Reference
Why This Library?
| This Gem | Other Forks | |
|---|---|---|
| Algorithms | ✅ 5 classifiers | ❌ 2 only |
| Incremental LSI | ✅ Brand's algorithm (no rebuild) | ❌ Full SVD rebuild on every add |
| LSI Performance | ✅ Native C extension (5-50x faster) | ❌ Pure Ruby or requires GSL |
| Streaming | ✅ Train on multi-GB datasets | ❌ Must load all data in memory |
| Persistence | ✅ Pluggable (file, Redis, S3, SQL, Custom) | ❌ Marshal only |
Installation
gem 'classifier'Quick Start
Bayesian
classifier = Classifier::Bayes.new(:spam, :ham)
classifier.train(spam: "Buy viagra cheap pills now")
classifier.train(spam: "You won million dollars prize")
classifier.train(ham: ["Meeting tomorrow at 3pm", "Quarterly report attached"])
classifier.classify("Cheap pills!") # => "Spam"Logistic Regression
classifier = Classifier::LogisticRegression.new(:positive, :negative)
classifier.train(positive: "love amazing great wonderful")
classifier.train(negative: "hate terrible awful bad")
classifier.classify("I love it!") # => "Positive"LSI (Latent Semantic Indexing)
lsi = Classifier::LSI.new
lsi.add(dog: "dog puppy canine bark fetch", cat: "cat kitten feline meow purr")
lsi.classify("My puppy barks") # => "dog"k-Nearest Neighbors
knn = Classifier::KNN.new(k: 3)
%w[laptop coding software developer programming].each { |w| knn.add(tech: w) }
%w[football basketball soccer goal team].each { |w| knn.add(sports: w) }
knn.classify("programming code") # => "tech"TF-IDF
tfidf = Classifier::TFIDF.new
tfidf.fit(["Ruby is great", "Python is great", "Ruby on Rails"])
tfidf.transform("Ruby programming") # => {:rubi => 1.0}Key Features
Incremental LSI
Add documents without rebuilding the entire index—400x faster for streaming data:
lsi = Classifier::LSI.new(incremental: true)
lsi.add(tech: ["Ruby is elegant", "Python is popular"])
lsi.build_index
# These use Brand's algorithm—no full rebuild
lsi.add(tech: "Go is fast")
lsi.add(tech: "Rust is safe")Persistence
classifier.storage = Classifier::Storage::File.new(path: "model.json")
classifier.save
loaded = Classifier::Bayes.load(storage: classifier.storage)Streaming Training
classifier.train_from_stream(:spam, File.open("spam_corpus.txt"))Performance
Native C extension provides 5-50x speedup for LSI operations:
| Documents | Speedup |
|---|---|
| 10 | 25x |
| 20 | 50x |
rake benchmark:compare # Run your own comparisonDevelopment
bundle install
rake compile # Build native extension
rake test # Run testsAuthors
- Lucas Carlson - lucas@rufy.com
- David Fayram II - dfayram@gmail.com
- Cameron McBride - cameron.mcbride@gmail.com
- Ivan Acosta-Rubio - ivan@softwarecriollo.com