Class: Basset::Svm

Inherits:
Object
  • Object
show all
Defined in:
lib/basset/svm.rb

Overview

Overview

A class for SVM document classification. Follows the same basic interface as NaiveBayes; add labeled training documents to the classifier, then use it to classify unlabeled documents. Do test your accuracy before using the classifier in production, there are a lot of knobs to tweak. When testing, it is usually best to use a separate set of documents, i.e., not the training set.

Learning Resources

SVM can be tricky to understand at first, try the following references: en.wikipedia.org/wiki/Support_vector_machine www.igvita.com/2008/01/07/support-vector-machines-svm-in-ruby/ www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

Implementation

This class wraps libsvm-ruby-swig, which is itself a swig based wrapper for libsvm. libsvm-ruby-swig: github.com/tomz/libsvm-ruby-swig libsvm: www.csie.ntu.edu.tw/~cjlin/libsvm verbose version: Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at www.csie.ntu.edu.tw/~cjlin/libsvm

There is also the libsvm-ruby implementation. It was originally available from debian.cilibrar.com/debian/pool/main/libs/libsvm-ruby/libsvm-ruby_2.8.4.orig.tar.gz but was not available from there when I last checked. The Ubuntu package was still available as of this writing.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize ⇒ Svm

Returns a new instance of Svm.



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# File 'lib/basset/svm.rb', line 34

def initialize
  @total_classes = 0
  @feature_dictionary = []
  @class_labels = {}
  @documents_for_class = Hash.new {|docs_hash,key| docs_hash[key] = []}
  @svm_parameter = default_svm_parameter
end

Instance Attribute Details

#class_labels ⇒ Object (readonly)

include YamlSerialization



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# File 'lib/basset/svm.rb', line 32

def class_labels
  @class_labels
end

#feature_dictionary ⇒ Object (readonly)

include YamlSerialization



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# File 'lib/basset/svm.rb', line 32

def feature_dictionary
  @feature_dictionary
end

Instance Method Details

#add_document(classification, feature_vectors) ⇒ Object

Adds a new document to the training set.



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# File 'lib/basset/svm.rb', line 43

def add_document(classification, feature_vectors)
  update_class_labels_with_new(classification) if new_class?(classification)
  @feature_dictionary += feature_vectors.map { |fv| fv.name }
  @feature_dictionary.uniq!
  @documents_for_class[classification] << feature_vectors.map { |fv| fv.name }
  reset_memoized_vars!
end

#classes ⇒ Object 



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# File 'lib/basset/svm.rb', line 79

def classes
  @class_labels.keys
end

#classify(feature_vectors) ⇒ Object 



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# File 'lib/basset/svm.rb', line 75

def classify(feature_vectors)
  class_of_label(model.predict(vectorize_doc(feature_vectors.map { |fv| fv.name })))
end

#labels_and_document_vectors ⇒ Object

Returns the vectorized representation of the training data, suitable for use in the constructor for the libsvm Problem class.



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# File 'lib/basset/svm.rb', line 63

def labels_and_document_vectors
  # {labels => [features1-label, features2-label, ...], :features => [features1, features2, ...]}
  labels_features = {:labels => [], :features => []}
  @class_labels.each do |classification, label|
    vectorized_docs(classification).each do |document_vector|
      labels_features[:labels] << label
      labels_features[:features] << document_vector
    end
  end
  labels_features
end

#parameters ⇒ Object

Exposes the libsvm-ruby-swig Parameter object. If given a block, the parameter object is yielded, otherwise, it’s returned.

For example, to set parameters to their default values:

basset_svm_obj.parameters do |param|
  param.C = 100           
  param.svm_type = NU_SVC
  param.degree = 1
  param.coef0 = 0
  param.eps= 0.001
  param.kernel_type = RBF
end

To access one value:

basset_svm_obj.parameters.svm_type
=> NU_SVC



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# File 'lib/basset/svm.rb', line 101

def parameters
  if block_given?
    yield @svm_parameter
  else
    @svm_parameter
  end
end

#vectorized_docs(classification) ⇒ Object

Gives the vector representation of the training documents of class classification



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# File 'lib/basset/svm.rb', line 53

def vectorized_docs(classification)
  # hardwired to binary representation
  @documents_for_class[classification].map do |features| 
    vectorize_doc(features)
    #@feature_dictionary.map { |dict_feature| features.include?(dict_feature) ? 1 : 0}
  end
end