ruby-learn is a library for machine learning.
Now, we support cross-validation and feature extraction.
Add this line to your application's Gemfile:
gem 'rblearn'And then execute:
$ bundle
Or install it yourself as:
$ gem install rblearn
CrossValidation provides two features for cross-validation and train_test_split.
- train_test_split
This method splits your dataset into train_set and test_set.
x\_train, y\_train, x\_test, y\_test = Rblearn::CrossValidation.train_test_split(x, y, 0.7).map(&:dup)- K-Fold
This method is for k-fold cross-validation.
three parameters are required.
- n :: integer
n indicates the size of dataset.
- n_folds :: integer
we specify the k by n_folds.
- shuffle :: boolean
if shuffle is true, dataset are shuffled.
kf = Rblearn::CrossValidation::KFold.new(100, 10, true)
kf.create #=> list<list<train_set_indices, test_set_indices>>CountVectorizer is the feature extractor from texts.
Constructor needs three parameters.
-
tokenizer :: function
-
lowercase :: boolean
-
max_features :: integer
for example,
cv = Rblearn::CountVectorizer.new(lambda{|feature| feature.split.map(&:stem)}, 1, 0.7)
cv.fit_transform(features)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.
Bug reports and pull requests are welcome on GitHub at https://github.com/[USERNAME]/rblearn. 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.
The gem is available as open source under the terms of the MIT License.