# Rumale

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Rumale (**Ru**by **ma**chine **le**arning) is a machine learninig library in Ruby.
Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python.
Rumale supports Linear / Kernel Support Vector Machine,
Logistic Regression, Linear Regression, Ridge, Lasso, Factorization Machine,
Naive Bayes, Decision Tree, AdaBoost, Random Forest, K-nearest neighbor classifier,
K-Means, DBSCAN, Principal Component Analysis, and Non-negative Matrix Factorization.

This project was formerly known as "SVMKit".
If you are using SVMKit, please install Rumale and replace `SVMKit` constants with `Rumale`.

## Installation

Add this line to your application's Gemfile:

```ruby
gem 'rumale'
```

And then execute:

    $ bundle

Or install it yourself as:

    $ gem install rumale

## 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.

```bash
$ 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.

```ruby
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, max_iter: 1000, batch_size: 50, 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.

```ruby
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.

```bash
$ ruby train.rb
$ ruby test.rb
Accuracy: 98.4%
```

### Example 2. Cross-validation

```ruby
require 'rumale'

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

# Define the estimator to be evaluated.
lr = Rumale::LinearModel::LogisticRegression.new(reg_param: 0.0001, random_seed: 1)

# Define the evaluation measure, splitting strategy, and cross validation.
ev = Rumale::EvaluationMeasure::LogLoss.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_logloss = report[:test_score].inject(:+) / kf.n_splits
puts("5-CV mean log-loss: %.3f" % mean_logloss)
```

Execution of the above scripts result in the following.

```bash
$ ruby cross_validation.rb
5-CV mean log-loss: 0.476
```

### Example 3. Pipeline

```ruby
require 'rumale'

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

# Construct pipeline with kernel approximation and SVC.
rbf = Rumale::KernelApproximation::RBF.new(gamma: 0.0001, n_components: 800, random_seed: 1)
svc = Rumale::LinearModel::SVC.new(reg_param: 0.0001, max_iter: 1000, random_seed: 1)
pipeline = Rumale::Pipeline::Pipeline.new(steps: { trns: rbf, clsf: svc })

# 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].inject(:+) / kf.n_splits
puts("5-CV mean accuracy: %.1f %%" % (mean_accuracy * 100.0))
```

Execution of the above scripts result in the following.

```bash
$ ruby pipeline.rb
5-CV mean accuracy: 99.2 %
```

## 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](https://rubygems.org).

## 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](http://contributor-covenant.org) code of conduct.

## License

The gem is available as open source under the terms of the [BSD 2-clause License](https://opensource.org/licenses/BSD-2-Clause).

## 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](https://github.com/yoshoku/Rumale/blob/master/CODE_OF_CONDUCT.md).