Numo::Liblinear

Build Status Gem Version BSD 3-Clause License Documentation

Numo::Liblinear is a Ruby gem binding to the LIBLINEAR library. LIBLINEAR is one of the famous libraries for large-scale regularized linear classification and regression. Numo::Liblinear makes to use the LIBLINEAR functions with dataset represented by Numo::NArray.

Note: There are other useful Ruby gems binding to LIBLINEAR: liblinear-ruby by Kei Tsuchiya and liblinear-ruby-swig by Tom Zeng.

Installation

Numo::Liblinear bundles LIBLINEAR. There is no need to install LIBLINEAR in advance.

Add this line to your application's Gemfile:

gem 'numo-liblinear'

And then execute:

$ bundle

Or install it yourself as:

$ gem install numo-liblinear

Usage

Preparation

In the following examples, we use red-datasets to download dataset.

$ gem install red-datasets-numo-narray

Example 1. Cross-validation

We conduct cross validation of the Support Vector Classifier on Iris dataset.

require 'numo/narray'
require 'numo/liblinear'
require 'datasets-numo-narray'

# Download Iris dataset.
puts 'Download dataset.'
iris = Datasets::LIBSVM.new('iris').to_narray
x = iris[true, 1..-1]
y = iris[true, 0]

# Define parameters of L2-regularized L2-loss support vector classification.
param = {
  solver_type: Numo::Liblinear::SolverType::L2R_L2LOSS_SVC_DUAL,
  C: 1
}

# Perform 5-cross validation.
puts 'Perform cross validation.'
n_folds = 5
predicted = Numo::Liblinear::cv(x, y, param, n_folds)

# Print mean accuracy.
mean_accuracy = y.eq(predicted).count.fdiv(y.size)
puts "Accuracy: %.1f %%" % (100 * mean_accuracy)

Execution result in the following:

Download dataset.
Perform cross validation.
Accuracy: 87.3 %

Example 2. Pendigits dataset classification

We first train the Logistic Regression using training pendigits dataset.

require 'numo/liblinear'
require 'datasets-numo-narray'

# Download pendigits training dataset.
puts 'Download dataset.'
pendigits = Datasets::LIBSVM.new('pendigits').to_narray
x = pendigits[true, 1..-1]
y = pendigits[true, 0]

# Define parameters of L2-regularized logistic regression.
param = {
  solver_type: Numo::Liblinear::SolverType::L2R_LR_DUAL,
  C: 1
}

# Perform training procedure.
puts 'Train logistic regression.'
model = Numo::Liblinear.train(x, y, param)

# Save parameters and trained model.
puts 'Save parameters and model with Marshal'
File.open('pendigits.dat', 'wb') { |f| f.write(Marshal.dump([param, model])) }
Download dataset.
Train logistic regression.
Save parameters and model with Marshal

We then predict labels of testing dataset, and evaluate the classifier.

require 'numo/liblinear'
require 'datasets-numo-narray'

# Download pendigits testing dataset.
puts 'Download dataset.'
pendigits_test = Datasets::LIBSVM.new('pendigits', note: 'testing').to_narray
x = pendigits_test[true, 1..-1]
y = pendigits_test[true, 0]

# Load parameter and model.
puts 'Load parameter and model.'
param, model = Marshal.load(File.binread('pendigits.dat'))

# Predict labels.
puts 'Predict labels.'
predicted = Numo::Liblinear.predict(x, param, model)

# Evaluate classification results.
mean_accuracy = y.eq(predicted).count.fdiv(y.size)
puts "Accuracy: %.1f %%" % (100 * mean_accuracy)
Download dataset.
Load parameter and model.
Predict labels.
Accuracy: 87.9 %

Note

The hyperparemter of LIBLINEAR is given with Ruby Hash on Numo::Liblinear. The hash key of hyperparameter and its meaning match the struct parameter of LIBLINEAR. The parameter is detailed in LIBLINEAR README

param = {
  solver_type:                    # [Integer] Type of Solver
    Numo::Liblinear::SolverType::L2R_L2LOSS_SVC_DUAL,
  eps: 0.01,                      # [Float] Stopping criterion
  C: 1,                           # [Float] Cost of constraints violation
  nr_weight: 3,                   # [Integer] Number of weights
  weight_label:                   # [Numo::Int32] Labels to add weight
    Numo::Int32[0, 1, 2],
  weight:                         # [Numo::DFloat] Weight values
    Numo::DFloat[0.4, 0.4, 0.2],
  p: 0.1,                         # [Float] Sensitiveness of loss of support vector regression
  nu: 0.5,                        # [Float] one-class SVM approximates the fraction of data as outliers
  verbose: false,                 # [Boolean] Whether to output learning process message
  random_seed: 1                  # [Integer/Nil] Random seed
}

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/yoshoku/numo-liblinear. 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-3-Clause License.