🚀 Rename to Rumale

.rspec

@@ -1,2 +1,3 @@
 --format documentation
 --color
+--require spec_helper

.rubocop.yml

@@ -1,7 +1,7 @@
 inherit_from: .rubocop_todo.yml
 
 AllCops:
-  TargetRubyVersion: 2.2
+  TargetRubyVersion: 2.3
   DisplayCopNames: true
   DisplayStyleGuide: true
 

.rubocop_todo.yml

@@ -10,21 +10,21 @@
 # Cop supports --auto-correct.
 Layout/ClosingHeredocIndentation:
   Exclude:
-    - 'svmkit.gemspec'
+    - 'rumale.gemspec'
 
 # Offense count: 2
 # Cop supports --auto-correct.
 # Configuration parameters: EnforcedStyle.
 # SupportedStyles: auto_detection, squiggly, active_support, powerpack, unindent
 Layout/IndentHeredoc:
   Exclude:
-    - 'svmkit.gemspec'
+    - 'rumale.gemspec'
 
 # Offense count: 1
 # Cop supports --auto-correct.
 Layout/LeadingBlankLines:
   Exclude:
-    - 'svmkit.gemspec'
+    - 'rumale.gemspec'
 
 # Offense count: 1
 # Configuration parameters: CountComments, ExcludedMethods.

.travis.yml

@@ -3,8 +3,6 @@ os: linux
 dist: trusty
 language: ruby
 rvm:
-  - 2.1
-  - 2.2
   - 2.3
   - 2.4
   - 2.5

CHANGELOG.md

@@ -1,9 +1,6 @@
 # 0.8.0
-## Information
-- Rename the project to [Rumale](https://github.com/yoshoku/Rumale).
-  - SVMKit has been deprecated and has been renamed to Rumale. SVMKit will release only bugfix.
-
 ## Breaking changes
+- Rename SVMKit to Rumale.
 - Rename SGDLienareEstimator class to BaseLienarModel class.
 - Add data type option to load_libsvm_file method. By default, the method represents the feature with Numo::DFloat.
 

Gemfile

@@ -1,6 +1,4 @@
 source 'https://rubygems.org'
 
-git_source(:github) { |repo_name| "https://github.com/#{repo_name}" }
-
-# Specify your gem's dependencies in svmkit.gemspec
+# Specify your gem's dependencies in rumale.gemspec
 gemspec

README.md

@@ -1,29 +1,27 @@
-# SVMKit
-
-[![Build Status](https://travis-ci.org/yoshoku/svmkit.svg?branch=master)](https://travis-ci.org/yoshoku/svmkit)
-[![Coverage Status](https://coveralls.io/repos/github/yoshoku/svmkit/badge.svg?branch=master)](https://coveralls.io/github/yoshoku/svmkit?branch=master)
-[![Gem Version](https://badge.fury.io/rb/svmkit.svg)](https://badge.fury.io/rb/svmkit)
-[![BSD 2-Clause License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://github.com/yoshoku/svmkit/blob/master/LICENSE.txt)
-[![Documentation](http://img.shields.io/badge/docs-rdoc.info-blue.svg)](https://www.rubydoc.info/gems/svmkit/)
-
-SVMKit has been deprecated and has been renamed to [Rumale](https://github.com/yoshoku/rumale).
-Initially, I started developing SVMKit as an experimental library aiming at implementing SVM in Ruby.
-However, since I added many other machine learning algorithms to SVMKit, I decided to change the library name.
-SVMKit will continue releasing for bugfix but will not add new features.
-
-SVMKit is a machine learninig library in Ruby.
-SVMKit provides machine learning algorithms with interfaces similar to Scikit-Learn in Python.
-SVMKit supports Linear / Kernel Support Vector Machine,
+# Rumale
+
+[![Build Status](https://travis-ci.org/yoshoku/rumale.svg?branch=master)](https://travis-ci.org/yoshoku/rumale)
+[![Coverage Status](https://coveralls.io/repos/github/yoshoku/rumale/badge.svg?branch=master)](https://coveralls.io/github/yoshoku/rumale?branch=master)
+[![Gem Version](https://badge.fury.io/rb/rumale.svg)](https://badge.fury.io/rb/rumale)
+[![BSD 2-Clause License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://github.com/yoshoku/rumale/blob/master/LICENSE.txt)
+[![Documentation](http://img.shields.io/badge/docs-rdoc.info-blue.svg)](https://www.rubydoc.info/gems/rumale/)
+
+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 use SVMKit, please replace `SVMKit` constants with `Rumale`.
+
 ## Installation
 
 Add this line to your application's Gemfile:
 
 ```ruby
-gem 'svmkit'
+gem 'rumale'
 ```
 
 And then execute:
@@ -32,13 +30,13 @@ And then execute:
 
 Or install it yourself as:
 
-    $ gem install svmkit
+    $ gem install rumale
 
 ## Usage
 
 ### Example 1. Pendigits dataset classification
 
-SVMKit provides function loading libsvm format dataset file.
+Rumale provides function loading libsvm format dataset file.
 We start by downloading the pendigits dataset from LIBSVM Data web site.
 
 ```bash
@@ -49,17 +47,17 @@ $ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/pendig
 Training of the classifier with Linear SVM and RBF kernel feature map is the following code.
 
 ```ruby
-require 'svmkit'
+require 'rumale'
 
 # Load the training dataset.
-samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits')
+samples, labels = Rumale::Dataset.load_libsvm_file('pendigits')
 
 # Map training data to RBF kernel feature space.
-transformer = SVMKit::KernelApproximation::RBF.new(gamma: 0.0001, n_components: 1024, random_seed: 1)
+transformer = Rumale::KernelApproximation::RBF.new(gamma: 0.0001, n_components: 1024, random_seed: 1)
 transformed = transformer.fit_transform(samples)
 
 # Train linear SVM classifier.
-classifier = SVMKit::LinearModel::SVC.new(reg_param: 0.0001, max_iter: 1000, batch_size: 50, random_seed: 1)
+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.
@@ -70,10 +68,10 @@ File.open('classifier.dat', 'wb') { |f| f.write(Marshal.dump(classifier)) }
 Classifying testing data with the trained classifier is the following code.
 
 ```ruby
-require 'svmkit'
+require 'rumale'
 
 # Load the testing dataset.
-samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits.t')
+samples, labels = Rumale::Dataset.load_libsvm_file('pendigits.t')
 
 # Load the model.
 transformer = Marshal.load(File.binread('transformer.dat'))
@@ -87,7 +85,7 @@ puts("Accuracy: %.1f%%" % (100.0 * classifier.score(transformed, labels)))
 
 # Other evaluating approach
 # results = classifier.predict(transformed)
-# evaluator = SVMKit::EvaluationMeasure::Accuracy.new
+# evaluator = Rumale::EvaluationMeasure::Accuracy.new
 # puts("Accuracy: %.1f%%" % (100.0 * evaluator.score(results, labels)))
 ```
 
@@ -102,18 +100,18 @@ Accuracy: 98.4%
 ### Example 2. Cross-validation
 
 ```ruby
-require 'svmkit'
+require 'rumale'
 
 # Load dataset.
-samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits')
+samples, labels = Rumale::Dataset.load_libsvm_file('pendigits')
 
 # Define the estimator to be evaluated.
-lr = SVMKit::LinearModel::LogisticRegression.new(reg_param: 0.0001, random_seed: 1)
+lr = Rumale::LinearModel::LogisticRegression.new(reg_param: 0.0001, random_seed: 1)
 
 # Define the evaluation measure, splitting strategy, and cross validation.
-ev = SVMKit::EvaluationMeasure::LogLoss.new
-kf = SVMKit::ModelSelection::StratifiedKFold.new(n_splits: 5, shuffle: true, random_seed: 1)
-cv = SVMKit::ModelSelection::CrossValidation.new(estimator: lr, splitter: kf, evaluator: ev)
+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)
@@ -126,19 +124,19 @@ puts("5-CV mean log-loss: %.3f" % mean_logloss)
 ### Example 3. Pipeline
 
 ```ruby
-require 'svmkit'
+require 'rumale'
 
 # Load dataset.
-samples, labels = SVMKit::Dataset.load_libsvm_file('pendigits')
+samples, labels = Rumale::Dataset.load_libsvm_file('pendigits')
 
 # Construct pipeline with kernel approximation and SVC.
-rbf = SVMKit::KernelApproximation::RBF.new(gamma: 0.0001, n_components: 800, random_seed: 1)
-svc = SVMKit::LinearModel::SVC.new(reg_param: 0.0001, max_iter: 1000, random_seed: 1)
-pipeline = SVMKit::Pipeline::Pipeline.new(steps: { trns: rbf, clsf: 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 = SVMKit::ModelSelection::StratifiedKFold.new(n_splits: 5, shuffle: true, random_seed: 1)
-cv = SVMKit::ModelSelection::CrossValidation.new(estimator: pipeline, splitter: kf)
+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)
@@ -163,7 +161,7 @@ To install this gem onto your local machine, run `bundle exec rake install`. To
 
 ## Contributing
 
-Bug reports and pull requests are welcome on GitHub at https://github.com/yoshoku/svmkit.
+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.
 
@@ -173,5 +171,5 @@ The gem is available as open source under the terms of the [BSD 2-clause License
 
 ## Code of Conduct
 
-Everyone interacting in the SVMKit project’s codebases, issue trackers,
-chat rooms and mailing lists is expected to follow the [code of conduct](https://github.com/yoshoku/svmkit/blob/master/CODE_OF_CONDUCT.md).
+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).

Rakefile

@@ -3,4 +3,4 @@ require 'rspec/core/rake_task'
 
 RSpec::Core::RakeTask.new(:spec)
 
-task default: :spec
+task :default => :spec

bin/console

@@ -1,14 +1,14 @@
 #!/usr/bin/env ruby
 
-require "bundler/setup"
-require "svmkit"
+require 'bundler/setup'
+require 'rumale'
 
 # You can add fixtures and/or initialization code here to make experimenting
 # with your gem easier. You can also use a different console, if you like.
 
 # (If you use this, don't forget to add pry to your Gemfile!)
-# require "pry"
+# require 'pry'
 # Pry.start
 
-require "irb"
+require 'irb'
 IRB.start(__FILE__)

lib/rumale.rb

@@ -0,0 +1,70 @@
+# frozen_string_literal: true
+
+require 'numo/narray'
+
+require 'rumale/version'
+require 'rumale/validation'
+require 'rumale/values'
+require 'rumale/utils'
+require 'rumale/pairwise_metric'
+require 'rumale/dataset'
+require 'rumale/probabilistic_output'
+require 'rumale/base/base_estimator'
+require 'rumale/base/classifier'
+require 'rumale/base/regressor'
+require 'rumale/base/cluster_analyzer'
+require 'rumale/base/transformer'
+require 'rumale/base/splitter'
+require 'rumale/base/evaluator'
+require 'rumale/optimizer/sgd'
+require 'rumale/optimizer/rmsprop'
+require 'rumale/optimizer/nadam'
+require 'rumale/optimizer/yellow_fin'
+require 'rumale/pipeline/pipeline'
+require 'rumale/kernel_approximation/rbf'
+require 'rumale/linear_model/base_linear_model'
+require 'rumale/linear_model/svc'
+require 'rumale/linear_model/svr'
+require 'rumale/linear_model/logistic_regression'
+require 'rumale/linear_model/linear_regression'
+require 'rumale/linear_model/ridge'
+require 'rumale/linear_model/lasso'
+require 'rumale/kernel_machine/kernel_svc'
+require 'rumale/polynomial_model/base_factorization_machine'
+require 'rumale/polynomial_model/factorization_machine_classifier'
+require 'rumale/polynomial_model/factorization_machine_regressor'
+require 'rumale/multiclass/one_vs_rest_classifier'
+require 'rumale/nearest_neighbors/k_neighbors_classifier'
+require 'rumale/nearest_neighbors/k_neighbors_regressor'
+require 'rumale/naive_bayes/naive_bayes'
+require 'rumale/tree/node'
+require 'rumale/tree/base_decision_tree'
+require 'rumale/tree/decision_tree_classifier'
+require 'rumale/tree/decision_tree_regressor'
+require 'rumale/ensemble/ada_boost_classifier'
+require 'rumale/ensemble/ada_boost_regressor'
+require 'rumale/ensemble/random_forest_classifier'
+require 'rumale/ensemble/random_forest_regressor'
+require 'rumale/clustering/k_means'
+require 'rumale/clustering/dbscan'
+require 'rumale/decomposition/pca'
+require 'rumale/decomposition/nmf'
+require 'rumale/preprocessing/l2_normalizer'
+require 'rumale/preprocessing/min_max_scaler'
+require 'rumale/preprocessing/standard_scaler'
+require 'rumale/preprocessing/label_encoder'
+require 'rumale/preprocessing/one_hot_encoder'
+require 'rumale/model_selection/k_fold'
+require 'rumale/model_selection/stratified_k_fold'
+require 'rumale/model_selection/cross_validation'
+require 'rumale/model_selection/grid_search_cv'
+require 'rumale/evaluation_measure/accuracy'
+require 'rumale/evaluation_measure/precision'
+require 'rumale/evaluation_measure/recall'
+require 'rumale/evaluation_measure/f_score'
+require 'rumale/evaluation_measure/log_loss'
+require 'rumale/evaluation_measure/r2_score'
+require 'rumale/evaluation_measure/mean_squared_error'
+require 'rumale/evaluation_measure/mean_absolute_error'
+require 'rumale/evaluation_measure/purity'
+require 'rumale/evaluation_measure/normalized_mutual_information'

lib/svmkit/base/base_estimator.rb → lib/rumale/base/base_estimator.rb

@@ -1,9 +1,9 @@
 # frozen_string_literal: true
 
-module SVMKit
+module Rumale
   # This module consists of basic mix-in classes.
   module Base
-    # Base module for all estimators in SVMKit.
+    # Base module for all estimators in Rumale.
     module BaseEstimator
       # Return parameters about an estimator.
       # @return [Hash]

lib/svmkit/base/classifier.rb → lib/rumale/base/classifier.rb

@@ -1,11 +1,11 @@
 # frozen_string_literal: true
 
-require 'svmkit/validation'
-require 'svmkit/evaluation_measure/accuracy'
+require 'rumale/validation'
+require 'rumale/evaluation_measure/accuracy'
 
-module SVMKit
+module Rumale
   module Base
-    # Module for all classifiers in SVMKit.
+    # Module for all classifiers in Rumale.
     module Classifier
       include Validation
 
@@ -28,7 +28,7 @@ def score(x, y)
         check_sample_array(x)
         check_label_array(y)
         check_sample_label_size(x, y)
-        evaluator = SVMKit::EvaluationMeasure::Accuracy.new
+        evaluator = Rumale::EvaluationMeasure::Accuracy.new
         evaluator.score(y, predict(x))
       end
     end

lib/svmkit/base/cluster_analyzer.rb → lib/rumale/base/cluster_analyzer.rb

@@ -1,11 +1,11 @@
 # frozen_string_literal: true
 
-require 'svmkit/validation'
-require 'svmkit/evaluation_measure/purity'
+require 'rumale/validation'
+require 'rumale/evaluation_measure/purity'
 
-module SVMKit
+module Rumale
   module Base
-    # Module for all clustering algorithms in SVMKit.
+    # Module for all clustering algorithms in Rumale.
     module ClusterAnalyzer
       include Validation
 
@@ -23,7 +23,7 @@ def score(x, y)
         check_sample_array(x)
         check_label_array(y)
         check_sample_label_size(x, y)
-        evaluator = SVMKit::EvaluationMeasure::Purity.new
+        evaluator = Rumale::EvaluationMeasure::Purity.new
         evaluator.score(y, fit_predict(x))
       end
     end