initial package import

LICENSE

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+Copyright (c) 2011-2012 Tiejun Cheng
+
+Permission is hereby granted, free of charge, to any person
+obtaining a copy of this software and associated documentation
+files (the "Software"), to deal in the Software without
+restriction, including without limitation the rights to use,
+copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the
+Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.

README.md

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+FSelector: a Ruby package for feature selection and ranking
+===========================================================
+
+**Git**: [https://github.com/need47/fselector](https://github.com/need47/fselector)  
+**Author**: Tiejun Cheng  
+**Email**: [[email protected]](mailto:[email protected])  
+**Copyright**: 2011-2012  
+**License**: MIT License  
+**Latest Version**: 0.1.0  
+**Release Date**: March 1st 2012
+
+Synopsis
+--------
+
+FSelector is an open-access Ruby package that aims to integrate as many 
+feature selection/ranking algorithms as possible. It enables the 
+user to perform feature selection by either a single algorithm or by an 
+ensemble of algorithms. Below is a summary of FSelector's features.
+
+Feature List
+------------
+
+**1. available algorithms**
+
+    algorithm                       alias      feature type
+    -------------------------------------------------------
+    Accuracy                        Acc         discrete
+    AccuracyBalanced                Acc2        discrete
+    BiNormalSeparation              BNS         discrete
+    ChiSquaredTest                  CHI         discrete
+    CorrelationCoefficient          CC          discrete
+    DocumentFrequency               DF          discrete
+    F1Measure                       F1          discrete
+    FishersExactTest                FET         discrete
+    GiniIndex                       GI          discrete
+    GMean                           GM          discrete
+    GSSCoefficient                  GSS         discrete
+    InformationGain                 IG          discrete
+    MatthewsCorrelationCoefficient  MCC, PHI    discrete
+    McNemarsTest                    MNT         discrete
+    OddsRatio                       OR          discrete
+    OddsRatioNumerator              ORN         discrete
+    PhiCoefficient                  Phi         discrete
+    Power                           Power       discrete
+    Precision                       Precision   discrete
+    ProbabilityRatio                PR          discrete
+    Random                          Random      discrete
+    Recall                          Recall      discrete
+    Relief_d                        Relief_d    discrete
+    ReliefF_d                       ReliefF_d   discrete
+    Sensitivity                     SN, Recall  discrete
+    Specificity                     SP          discrete
+    PMetric                         PM          continuous
+    Relief_c                        Relief_c    continuous
+    ReliefF_c                       ReliefF_c   continuous
+    TScore                          TS          continuous
+
+**2. feature selection approaches**
+
+ - by a single algorithm
+ - by multiple algorithms in a tandem manner
+ - by multiple algorithms in a consensus manner
+
+**3. availabe normalization and discretization algorithms for continuous feature**
+
+    algorithm          note
+    --------------------------------------------------------------------
+    log                normalization by logarithmic transformation
+    min_max            normalization by scaling into [min, max]
+    zscore             normalization by converting into zscore
+    equal_width        discretization by equal width among intervals
+    equal_frequency    discretization by equal frequency among intervals
+    ChiMerge           discretization by ChiMerge method
+
+**4. supported input/output file types**
+
+ - csv
+ - libsvm
+ - weka ARFF
+ - random (for test purpose)
+
+Installing
+----------
+
+To install FSelector, use the following command:
+
+    $ gem install fselector
+
+Usage
+-----
+
+**1. feature selection by a single algorithm**
+
+    require 'fselector'
+
+    # use InformationGain as a feature ranking algorithm
+    r1 = FSelector::InformationGain.new
+
+    # read from random data (or csv, libsvm, weka ARFF file)
+    # no. of samples: 100
+    # no. of classes: 2
+    # no. of features: 10
+    # no. of possible values for each feature: 3
+    # allow missing values: true
+    r1.data_from_random(100, 2, 10, 3, true)
+
+    # number of features before feature selection
+    puts "# features (before): "+ r1.get_features.size.to_s
+
+    # select the top-ranked features with scores >0.01
+    r1.select_data_by_score!('>0.01')
+
+    # number of features before feature selection
+    puts "# features (after): "+ r1.get_features.size.to_s
+
+    # you can also use multiple alogirithms in a tandem manner
+    # e.g. use the ChiSquaredTest with Yates' continuity correction
+    # initialize from r1's data
+    r2 = FSelector::ChiSquaredTest.new(:yates, r1.get_data)
+
+    # number of features before feature selection
+    puts "# features (before): "+ r2.get_features.size.to_s
+
+    # select the top-ranked 3 features
+    r2.select_data_by_rank!('<=3')
+
+    # number of features before feature selection
+    puts "# features (after): "+ r2.get_features.size.to_s
+
+    # save data to standard ouput as a weka ARFF file (sparse format)
+    # with selected features only
+    r2.data_to_weka(:stdout, :sparse)
+
+
+**2. feature selection by an ensemble of algorithms**
+
+    require 'fselector'
+
+	# use both Information and ChiSquaredTest
+    r1 = FSelector::InformationGain.new
+    r2 = FSelector::ChiSquaredTest.new
+
+    # ensemble ranker
+    re = FSelector::Ensemble.new(r1, r2)
+
+    # read random data
+    re.data_from_random(100, 2, 10, 3, true)
+
+    # number of features before feature selection
+    puts '# features before feature selection: ' + re.get_features.size.to_s
+
+    # based on the min feature rank among
+    # ensemble feature selection algorithms
+    re.ensemble_by_rank(re.method(:by_min))
+
+    # select the top-ranked 3 features
+    re.select_data_by_rank!('<=3')
+
+    # number of features before feature selection
+    puts '# features before feature selection: ' + re.get_features.size.to_s
+
+
+ **3. normalization and discretization before feature selection**
+
+ In addition to the algorithms designed for continous feature, one
+ can apply those deisgned for discrete feature after (optionally
+ normalization and) discretization
+
+    require 'fselector'
+
+    # for continuous feature
+    r1 = FSelector::BaseContinuous.new
+
+    # read the Iris data set (under the test/ directory)
+    r1.data_from_csv(File.expand_path(File.dirname(__FILE__))+'/iris.csv')
+
+    # normalization by log2 (optional)
+    # r1.normalize_log!(2)
+
+    # discretization by ChiMerge algorithm
+    # chi-squared value = 4.60 for a three-class problem at alpha=0.10
+    r1.discretize_chimerge!(4.60)
+
+    # apply Relief_d for discrete feature
+    # initialize with discretized data from r1
+    r2 = FSelector::ReliefF_d.new(r1.get_sample_size, 10, r1.get_data)
+
+    # print feature ranks
+    r2.print_feature_ranks
+
+Copyright
+---------
+FSelector &copy; 2011-2012 by [Tiejun Cheng](mailto:[email protected]).
+FSelector is licensed under the MIT license. Please see the {file:LICENSE} for
+more information.

Rakefile

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+#
+# make a ruby gem
+#
+
+task :default => :gem
+
+task :gem do
+  Gem::Builder.new(eval(File.read('fselector.gemspec'))).build
+end
+
+#
+# test example
+#
+require 'rake'
+require 'rake/testtask.rb'
+task :test do
+  Rake::TestTask.new do |t|
+    t.libs = ['lib']
+    t.test_files = FileList['test/test_*.rb']
+    t.verbose = true
+  end
+end