https://github.com/yoshoku/numo-libsvm
Numo::Libsvm is a Ruby gem binding to the LIBSVM
https://github.com/yoshoku/numo-libsvm
libsvm machine-learning ml ruby rubyml svm
Last synced: 9 months ago
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Numo::Libsvm is a Ruby gem binding to the LIBSVM
- Host: GitHub
- URL: https://github.com/yoshoku/numo-libsvm
- Owner: yoshoku
- License: bsd-3-clause
- Created: 2019-07-20T17:30:40.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2025-05-31T11:15:38.000Z (10 months ago)
- Last Synced: 2025-05-31T23:25:54.432Z (10 months ago)
- Topics: libsvm, machine-learning, ml, ruby, rubyml, svm
- Language: C++
- Homepage: https://rubygems.org/gems/numo-libsvm
- Size: 357 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
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README
# Numo::Libsvm
[](https://github.com/yoshoku/numo-libsvm/actions?query=workflow%3Abuild)
[](https://badge.fury.io/rb/numo-libsvm)
[](https://github.com/yoshoku/numo-libsvm/blob/main/LICENSE.txt)
[](https://yoshoku.github.io/numo-libsvm/doc/)
Numo::Libsvm is a Ruby gem binding to the [LIBSVM](https://github.com/cjlin1/libsvm) library.
LIBSVM is one of the famous libraries that implemented Support Vector Machines,
and provides functions for support vector classifier, regression, and distribution estimation.
Numo::Libsvm makes to use the LIBSVM functions with dataset represented by [Numo::NArray](https://github.com/ruby-numo/numo-narray).
Note: There are other useful Ruby gems binding to LIBSVM:
[rb-libsvm](https://github.com/febeling/rb-libsvm) by C. Florian Ebeling,
[libsvm-ruby-swig](https://github.com/tomz/libsvm-ruby-swig) by Tom Zeng,
and [jrb-libsvm](https://github.com/andreaseger/jrb-libsvm) by Andreas Eger.
## Installation
Numo::Libsvm bundles LIBSVM. There is no need to install LIBSVM in advance.
Add this line to your application's Gemfile:
```ruby
gem 'numo-libsvm'
```
And then execute:
$ bundle
Or install it yourself as:
$ gem install numo-libsvm
## Usage
### Preparation
In the following examples, we use [red-datasets](https://github.com/red-data-tools/red-datasets) to download dataset.
$ gem install red-datasets-numo-narray
### Example 1. Cross-validation
We conduct cross validation of support vector classifier on [Iris dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#iris).
```ruby
require 'numo/narray'
require 'numo/libsvm'
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 C-SVC with RBF Kernel.
param = {
svm_type: Numo::Libsvm::SvmType::C_SVC,
kernel_type: Numo::Libsvm::KernelType::RBF,
gamma: 1.0,
C: 1
}
# Perform 5-cross validation.
puts 'Perform cross validation.'
n_folds = 5
predicted = Numo::Libsvm.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:
```sh
Download dataset.
Perform cross validation.
Accuracy: 96.0 %
```
### Example 2. Pendigits dataset classification
We first train the support vector classifier with RBF kernel using training [pendigits dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#pendigits).
```ruby
require 'numo/narray'
require 'numo/libsvm'
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 C-SVC with RBF Kernel.
param = {
svm_type: Numo::Libsvm::SvmType::C_SVC,
kernel_type: Numo::Libsvm::KernelType::RBF,
gamma: 0.0001,
C: 10,
shrinking: true
}
# Perform training procedure.
puts 'Train support vector machine.'
model = Numo::Libsvm.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])) }
```
```sh
$ ruby train.rb
Download dataset.
Train support vector machine.
Save paramters and model with Marshal.
```
We then predict labels of testing dataset, and evaluate the classifier.
```ruby
require 'numo/narray'
require 'numo/libsvm'
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::Libsvm.predict(x, param, model)
# Evaluate classification results.
mean_accuracy = y.eq(predicted).count.fdiv(y.size)
puts "Accuracy: %.1f %%" % (100 * mean_accuracy)
```
```sh
$ ruby test.rb
Download dataset.
Load parameter and model.
Predict labels.
Accuracy: 98.3 %
```
### Note
The hyperparameter of SVM is given with Ruby Hash on Numo::Libsvm.
The hash key of hyperparameter and its meaning match the struct svm_parameter of LIBSVM.
The svm_parameter is detailed in [LIBSVM README](https://github.com/cjlin1/libsvm/blob/master/README).
```ruby
param = {
svm_type: # [Integer] Type of SVM
Numo::Libsvm::SvmType::C_SVC,
kernel_type: # [Integer] Type of kernel function
Numo::Libsvm::KernelType::RBF,
degree: 3, # [Integer] Degree in polynomial kernel function
gamma: 0.5, # [Float] Gamma in poly/rbf/sigmoid kernel function
coef0: 1.0, # [Float] Coefficient in poly/sigmoid kernel function
# for training procedure
cache_size: 100, # [Float] Cache memory size in MB
eps: 1e-3, # [Float] Tolerance of termination criterion
C: 1.0, # [Float] Parameter C of C-SVC, epsilon-SVR, and nu-SVR
nr_weight: 3, # [Integer] Number of weights for C-SVC
weight_label: # [Numo::Int32] Labels to add weight in C-SVC
Numo::Int32[0, 1, 2],
weight: # [Numo::DFloat] Weight values in C-SVC
Numo::DFloat[0.4, 0.4, 0.2],
nu: 0.5, # [Float] Parameter nu of nu-SVC, one-class SVM, and nu-SVR
p: 0.1, # [Float] Parameter epsilon in loss function of epsilon-SVR
shrinking: true, # [Boolean] Whether to use the shrinking heuristics
probability: false, # [Boolean] Whether to train a SVC or SVR model for probability estimates
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-libsvm. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [Contributor Covenant](https://contributor-covenant.org) code of conduct.
## License
The gem is available as open source under the terms of the [BSD-3-Clause License](https://opensource.org/licenses/BSD-3-Clause).