https://github.com/asafschers/scoruby

Ruby Scoring API for PMML
https://github.com/asafschers/scoruby

classification decision-tree gbm gradient-boosted-models gradient-boosting-classifier machine-learning naive-bayes pmml random-forest ruby ruby-gem rubyml

Last synced: 9 days ago
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Ruby Scoring API for PMML

• Host: GitHub
• URL: https://github.com/asafschers/scoruby
• Owner: asafschers
• License: mit
• Created: 2016-11-12T16:54:47.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2022-10-19T04:55:03.000Z (about 2 years ago)
• Last Synced: 2024-05-22T13:15:51.524Z (6 months ago)
• Topics: classification, decision-tree, gbm, gradient-boosted-models, gradient-boosting-classifier, machine-learning, naive-bayes, pmml, random-forest, ruby, ruby-gem, rubyml
• Language: Ruby
• Homepage:
• Size: 2.39 MB
• Stars: 68
• Watchers: 3
• Forks: 12
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt
  • Code of conduct: CODE_OF_CONDUCT.md

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• machine-learning-with-ruby - scoruby - (Machine Learning Libraries / Frameworks)

README

        


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# Scoruby

Ruby scoring API for Predictive Model Markup Language (PMML).

Currently supports - 

* Decision Tree

* Naive Bayes

* Logistic Regression

* Random Forest 

* Gradient Boosted Trees

Will be happy to implement new models by demand, or assist with any other issue.

Contact me here or at [email protected].

[Tutorial - Deploy Machine Learning Models from R Research to Ruby Production with PMML](https://medium.com/@aschers/deploy-machine-learning-models-from-r-research-to-ruby-go-production-with-pmml-b41e79445d3d)

## Installation

Add this line to your application's Gemfile:

```ruby

gem 'scoruby'

```

And then execute:

    $ bundle

Or install it yourself as:

    $ gem install scoruby

## Usage

### Naive Bayes

```ruby

naive_bayes = Scoruby.load_model 'naive_bayes.pmml'

features = { f1: v1 , ... } 

naive_bayes.lvalues(features)

naive_bayes.score(features, 'l1')

```

### Logistic Regression

```ruby

logistic_regression = Scoruby.load_model 'logistic_regression.pmml'

features = { f1: v1 , ... } 

logistic_regression.score(features)

```

### Decision Tree

```ruby

decision_tree = Scoruby.load_model 'decision_tree.pmml'

features = { f1 : v1, ... } 

decision_tree.decide(features)

=> #"0.999615579933873", "1"=>"0.000384420066126561"}>

```

### Random Forest

[Generate PMML - R](https://github.com/asafschers/scoruby/wiki/Random-Forest) 

```ruby

random_forest = Scoruby.load_model 'titanic_rf.pmml'

features =  {

        Sex: 'male',

        Parch: 0,

        Age: 30,

        Fare: 9.6875,

        Pclass: 2,

        SibSp: 0,

        Embarked: 'Q'       

    }

random_forest.score(features)

=> {:label=>"0", :score=>0.882}

random_forest.decisions_count(features)

=> {"0"=>441, "1"=>59}

```

### Gradient Boosted model

[Generate PMML - R](https://github.com/asafschers/scoruby/wiki/Gradient-Boosted-Model) 

```ruby

gbm = Scoruby.load_model 'gbm.pmml'

features =  {

        Sex: 'male',

        Parch: 0,

        Age: 30,

        Fare: 9.6875,

        Pclass: 2,

        SibSp: 0,

        Embarked: 'Q'       

    }

gbm.score(features)

=> 0.3652639329522468

```

## Development

After checking out the repo, run `bin/setup` to install dependencies. Then, run `rake rspec` 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/asafschers/scoruby. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the [Contributor Covenant](contributor-covenant.org) code of conduct.

## License

The gem is available as open source under the terms of the [MIT License](http://opensource.org/licenses/MIT).