Classifier

Gem Version CI License: LGPL

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'

Or install via Homebrew for CLI-only usage:

brew install classifier

Command Line

Classify text instantly with pre-trained models—no coding required:

# Detect spam
classifier classify "You won a free iPhone!" -r sms-spam-filter
# => spam

# Analyze sentiment
classifier classify "This movie was absolutely amazing!" -r imdb-sentiment
# => positive

# Detect emotions
classifier classify "I'm so happy today!" -r emotion-detection
# => joy

# List all available models
classifier models

Train your own model:

# Train from files
classifier train positive reviews/good/*.txt
classifier train negative reviews/bad/*.txt

# Classify new text
classifier classify "Great product, highly recommend"
# => positive

CLI Guide →

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"

Bayesian Guide →

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"

Logistic Regression Guide →

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"

LSI Guide →

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"

k-Nearest Neighbors Guide →

TF-IDF

tfidf = Classifier::TFIDF.new
tfidf.fit(["Ruby is great", "Python is great", "Ruby on Rails"])
tfidf.transform("Ruby programming")  # => {:rubi => 1.0}

TF-IDF Guide →

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")

Learn more →

Persistence

classifier.storage = Classifier::Storage::File.new(path: "model.json")
classifier.save

loaded = Classifier::Bayes.load(storage: classifier.storage)

Learn more →

Streaming Training

classifier.train_from_stream(:spam, File.open("spam_corpus.txt"))

Learn more →

Performance

Native C extension provides 5-50x speedup for LSI operations:

Documents Speedup
10 25x
20 50x 
rake benchmark:compare  # Run your own comparison

Development

bundle install
rake compile  # Build native extension
rake test     # Run tests

Authors

License

LGPL 2.1