SVMKit

SVMKit is a machine learninig library in Ruby. SVMKit provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. SVMKit currently supports Linear / Kernel Support Vector Machine, Logistic Regression, Factorization Machine, Naive Bayes, Decision Tree, Random Forest, K-nearest neighbor classifier, and cross-validation.

Installation

Add this line to your application’s Gemfile:

ruby gem 'svmkit'

And then execute:

$ bundle

Or install it yourself as:

$ gem install svmkit

Usage

Training phase:

```ruby require ‘svmkit’

samples, labels = SVMKit::Dataset.load_libsvm_file(‘pendigits’)

normalizer = SVMKit::Preprocessing::MinMaxScaler.new normalized = normalizer.fit_transform(samples)

transformer = SVMKit::KernelApproximation::RBF.new(gamma: 2.0, n_components: 1024, random_seed: 1) transformed = transformer.fit_transform(normalized)

classifier = SVMKit::LinearModel::SVC.new(reg_param: 1.0, max_iter: 1000, batch_size: 20, random_seed: 1) classifier.fit(transformed, labels)

File.open(‘trained_normalizer.dat’, ‘wb’) { |f| f.write(Marshal.dump(normalizer)) } File.open(‘trained_transformer.dat’, ‘wb’) { |f| f.write(Marshal.dump(transformer)) } File.open(‘trained_classifier.dat’, ‘wb’) { |f| f.write(Marshal.dump(classifier)) } ```

Testing phase:

```ruby require ‘svmkit’

samples, labels = SVMKit::Dataset.load_libsvm_file(‘pendigits.t’)

normalizer = Marshal.load(File.binread(‘trained_normalizer.dat’)) transformer = Marshal.load(File.binread(‘trained_transformer.dat’)) classifier = Marshal.load(File.binread(‘trained_classifier.dat’))

normalized = normalizer.transform(samples) transformed = transformer.transform(normalized)

puts(sprintf(“Accuracy: %.1f%%”, 100.0 * classifier.score(transformed, labels))) ```

5-fold cross-validation:

```ruby require ‘svmkit’

samples, labels = SVMKit::Dataset.load_libsvm_file(‘pendigits’)

kernel_svc = SVMKit::KernelMachine::KernelSVC.new(reg_param: 1.0, max_iter: 1000, random_seed: 1)

kf = SVMKit::ModelSelection::StratifiedKFold.new(n_splits: 5, shuffle: true, random_seed: 1) cv = SVMKit::ModelSelection::CrossValidation.new(estimator: kernel_svc, splitter: kf)

kernel_mat = SVMKit::PairwiseMetric::rbf_kernel(samples, nil, 0.005) report = cv.perform(kernel_mat, labels)

mean_accuracy = report[:test_score].inject(:+) / kf.n_splits puts(sprintf(“Mean Accuracy: %.1f%%”, 100.0 * mean_accuracy)) ```

Development

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/yoshoku/svmkit. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

License

The gem is available as open source under the terms of the BSD 2-clause License.

Code of Conduct

Everyone interacting in the SVMKit project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.