https://github.com/yoshoku/numo-liblinear
Numo::Liblinear is a Ruby gem binding to the LIBLINEAR
https://github.com/yoshoku/numo-liblinear
liblinear machine-learning ml ruby rubyml svm
Last synced: 2 months ago
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Numo::Liblinear is a Ruby gem binding to the LIBLINEAR
- Host: GitHub
- URL: https://github.com/yoshoku/numo-liblinear
- Owner: yoshoku
- License: bsd-3-clause
- Created: 2019-07-25T16:06:51.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2025-01-01T11:46:11.000Z (10 months ago)
- Last Synced: 2025-03-28T15:21:20.591Z (7 months ago)
- Topics: liblinear, machine-learning, ml, ruby, rubyml, svm
- Language: C++
- Homepage: https://rubygems.org/gems/numo-liblinear
- Size: 178 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
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README
# Numo::Liblinear
[](https://github.com/yoshoku/numo-liblinear/actions?query=workflow%3Abuild)
[](https://badge.fury.io/rb/numo-liblinear)
[](https://github.com/yoshoku/numo-liblinear/blob/main/LICENSE.txt)
[](https://yoshoku.github.io/numo-liblinear/doc/)
Numo::Liblinear is a Ruby gem binding to the [LIBLINEAR](https://www.csie.ntu.edu.tw/~cjlin/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](https://github.com/ruby-numo/numo-narray).
Note: There are other useful Ruby gems binding to LIBLINEAR:
[liblinear-ruby](https://github.com/kei500/liblinear-ruby) by Kei Tsuchiya and
[liblinear-ruby-swig](https://github.com/tomz/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:
```ruby
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](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 the Support Vector Classifier on [Iris dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#iris).
```ruby
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:
```sh
Download dataset.
Perform cross validation.
Accuracy: 87.3 %
```
### Example 2. Pendigits dataset classification
We first train the Logistic Regression using training [pendigits dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#pendigits).
```ruby
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])) }
```
```sh
Download dataset.
Train logistic regression.
Save parameters and model with Marshal
```
We then predict labels of testing dataset, and evaluate the classifier.
```ruby
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)
```
```sh
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](https://github.com/cjlin1/liblinear/blob/master/README)
```ruby
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](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).