How to add your own feature selection algorithms

Baisc steps

1. Require the FSelector gem

2. Derive a subclass from a base class (Base, BaseDiscrete, BaseContinuous and etc.)  

3. Set your own feature selection algorithm with one of the following two types:  
  :feature_weighting         # if it outputs weight for each feature
  :feature_subset_selection  # if it outputs a subset of original feature set

4. Depending on your algorithm type, override one of the following two interfaces:
  calc_contribution()   # if it belongs to the type of feature weighting algorithms
  get_feature_subset()  # if it belongs to the type of feature subset selection algorithms

Example

require 'fselector'  # step 1

module FSelector
  # create a new algorithm belonging to the type of feature weighting
  # to this end, simply override the calc_contribution() in Base class
  class NewAlgo_Weight < Base  # step 2
    # set the algorithm type
    @algo_type = :feature_weighting  # step 3

    # add your own initialize() here if necessary

    private

    # the algorithm assigns feature weight randomly
    def calc_contribution(f)  # step 4
      s = rand

      # set the score (s) of feature (f) for class (:BEST is the best score among all classes)
      set_feature_score(f, :BEST, s)
    end
  end # NewAlgo_Weight


  # create a new algorithm belonging to the type of feature subset selection
  # to this end, simly override the get_feature_subset() in Base class
  class NewAlgo_Subset < Base  # step 2
    # set the algorithm type
    @algo_type = :feature_subset_selection  # step 3

    # add your own initialize() here if necessary

    private

    # the algorithm returns a random half-size subset of the orignal one
    def get_feature_subset  # step 4
      org_features = get_features
      subset = org_features.sample(org_features.size/2)

      subset
    end

  end # NewAlgo_Subset
end # module

Test your algorithms

require 'fselector'

# example 1

# use NewAlgo_Weighting
r1 = FSelector::NewAlgo_Weight.new
r1.data_from_csv('test/iris.csv')
r1.select_feature_by_rank!('<=1')
puts r1.get_features

# example 2

# use NewAlgo_Subset
r2 = FSelector::NewAlgo_Subset.new
r2.data_from_csv('test/iris.csv')
r2.select_feature!
puts r2.get_features

How to become a contributor of FSelector on GitHub

Set up your repository

1. Go to https://github.com/need47/fselector and click the "Fork" button

2. Clone your fork to your local machine:
  git clone git@github.com:yourGitUserName/fselector.git

3. Assign the original repository to a remote called "upstream":  
  cd fselector  
  git remote add upstream git://github.com/need47/fselector.git

4. Get updates from the "upstream" and merge to your local repository:  
  git fetch upstream  
  git merge upstream/master

Develop features

1. Create and checkout a feature branch to house your edits:
  git checkout -b branchName

2. Add your own feature selection algorithm:
  git add yourAlgorithm.rb  
  git commit -m "your commit message"

3. Push your branch to GitHub:  
  git push origin branchName

4. Visit your forked project on GitHub and switch to your branhName branch  

5. Click the "Pull Request" button to request that your features to be merged to the "upstream" master