Rumale

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Rumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Support Vector Machine, Logistic Regression, Ridge, Lasso, Multi-layer Perceptron, Naive Bayes, Decision Tree, Gradient Tree Boosting, Random Forest, K-Means, Gaussian Mixture Model, DBSCAN, Spectral Clustering, Mutidimensional Scaling, t-SNE, Fisher Discriminant Analysis, Neighbourhood Component Analysis, Principal Component Analysis, Non-negative Matrix Factorization, and many other algorithms.

Installation

Add this line to your application's Gemfile:

gem 'rumale'

And then execute:

$ bundle

Or install it yourself as:

$ gem install rumale

Documentation

• Rumale API Documentation

Usage

Example 1. Pendigits dataset classification

Rumale provides function loading libsvm format dataset file. We start by downloading the pendigits dataset from LIBSVM Data web site.

$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/pendigits
$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/pendigits.t

Training of the classifier with Linear SVM and RBF kernel feature map is the following code.

require 'rumale'

# Load the training dataset.
samples, labels = Rumale::Dataset.load_libsvm_file('pendigits')

# Map training data to RBF kernel feature space.
transformer = Rumale::KernelApproximation::RBF.new(gamma: 0.0001, n_components: 1024, random_seed: 1)
transformed = transformer.fit_transform(samples)

# Train linear SVM classifier.
classifier = Rumale::LinearModel::SVC.new(reg_param: 0.0001, random_seed: 1)
classifier.fit(transformed, labels)

# Save the model.
File.open('transformer.dat', 'wb') { |f| f.write(Marshal.dump(transformer)) }
File.open('classifier.dat', 'wb') { |f| f.write(Marshal.dump(classifier)) }

Classifying testing data with the trained classifier is the following code.

require 'rumale'

# Load the testing dataset.
samples, labels = Rumale::Dataset.load_libsvm_file('pendigits.t')

# Load the model.
transformer = Marshal.load(File.binread('transformer.dat'))
classifier = Marshal.load(File.binread('classifier.dat'))

# Map testing data to RBF kernel feature space.
transformed = transformer.transform(samples)

# Classify the testing data and evaluate prediction results.
puts("Accuracy: %.1f%%" % (100.0 * classifier.score(transformed, labels)))

# Other evaluating approach
# results = classifier.predict(transformed)
# evaluator = Rumale::EvaluationMeasure::Accuracy.new
# puts("Accuracy: %.1f%%" % (100.0 * evaluator.score(results, labels)))

Execution of the above scripts result in the following.

$ ruby train.rb
$ ruby test.rb
Accuracy: 98.7%

Example 2. Cross-validation

require 'rumale'

# Load dataset.
samples, labels = Rumale::Dataset.load_libsvm_file('pendigits')

# Define the estimator to be evaluated.
lr = Rumale::LinearModel::LogisticRegression.new

# Define the evaluation measure, splitting strategy, and cross validation.
ev = Rumale::EvaluationMeasure::Accuracy.new
kf = Rumale::ModelSelection::StratifiedKFold.new(n_splits: 5, shuffle: true, random_seed: 1)
cv = Rumale::ModelSelection::CrossValidation.new(estimator: lr, splitter: kf, evaluator: ev)

# Perform 5-cross validation.
report = cv.perform(samples, labels)

# Output result.
mean_accuracy = report[:test_score].sum / kf.n_splits
puts "5-CV mean accuracy: %.1f%%" % (100.0 * mean_accuracy)

Execution of the above scripts result in the following.

$ ruby cross_validation.rb
5-CV mean accuracy: 95.4%

Example 3. Pipeline

require 'rumale'

# Load dataset.
samples, labels = Rumale::Dataset.load_libsvm_file('pendigits')

# Construct pipeline with kernel approximation and LogisticRegression.
rbf = Rumale::KernelApproximation::RBF.new(gamma: 1e-4, n_components: 800, random_seed: 1)
lr = Rumale::LinearModel::LogisticRegression.new(reg_param: 1e-3)
pipeline = Rumale::Pipeline::Pipeline.new(steps: { trns: rbf, clsf: lr })

# Define the splitting strategy and cross validation.
kf = Rumale::ModelSelection::StratifiedKFold.new(n_splits: 5, shuffle: true, random_seed: 1)
cv = Rumale::ModelSelection::CrossValidation.new(estimator: pipeline, splitter: kf)

# Perform 5-cross validation.
report = cv.perform(samples, labels)

# Output result.
mean_accuracy = report[:test_score].sum / kf.n_splits
puts("5-CV mean accuracy: %.1f %%" % (mean_accuracy * 100.0))

Execution of the above scripts result in the following.

$ ruby pipeline.rb
5-CV mean accuracy: 99.6 %

Speed up

Numo::Linalg

Rumale uses Numo::NArray for typed arrays. Loading the Numo::Linalg allows to perform matrix product of Numo::NArray using BLAS libraries. For example, using the OpenBLAS speeds up many estimators in Rumale.

Install OpenBLAS library.

macOS:

$ brew install openblas

Ubuntu:

$ sudo apt-get install libopenblas-dev liblapacke-dev

Fedora:

$ sudo dnf install openblas-devel lapack-devel

Windows (MSYS2):

$ pacman -S mingw-w64-x86_64-ruby mingw-w64-x86_64-openblas mingw-w64-x86_64-lapack

Install Numo::Linalg gem.

$ gem install numo-linalg

In ruby script, you only need to require the autoloader module of Numo::Linalg.

require 'numo/linalg/autoloader'
require 'rumale'

Numo::OpenBLAS

Numo::OpenBLAS downloads and builds OpenBLAS during installation and uses that as a background library for Numo::Linalg.

Install compilers for building OpenBLAS.

macOS:

$ brew install gcc gfortran make

Ubuntu:

$ sudo apt-get install gcc gfortran make

Fedora:

$ sudo dnf install gcc gcc-gfortran make

Install Numo::OpenBLAS gem.

$ gem install numo-openblas

Load Numo::OpenBLAS gem instead of Numo::Linalg.

require 'numo/openblas'
require 'rumale'

Numo::BLIS

Numo::BLIS downloads and builds BLIS during installation and uses that as a background library for Numo::Linalg. BLIS is one of the high-performance BLAS as with OpenBLAS, and using that can be expected to speed up of processing in Rumale.

Install Numo::BLIS gem.

$ gem install numo-blis

Load Numo::BLIS gem instead of Numo::Linalg.

require 'numo/blis'
require 'rumale'

Parallel

Several estimators in Rumale support parallel processing. Parallel processing in Rumale is realized by Parallel gem, so install and load it.

$ gem install parallel

require 'parallel'
require 'rumale'

Estimators that support parallel processing have n_jobs parameter. When -1 is given to n_jobs parameter, all processors are used.

estimator = Rumale::Ensemble::RandomForestClassifier.new(n_jobs: -1, random_seed: 1)

Related Projects

• Rumale::SVM provides support vector machine algorithms in LIBSVM and LIBLINEAR with Rumale interface.
• Rumale::Torch provides the learning and inference by the neural network defined in torch.rb with Rumale interface.

Novelties

• Rumale SHOP

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/yoshoku/rumale. 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 Rumale project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.