Class: Ai4r::Classifiers::Prism

Inherits:
Classifier show all
Defined in:
lib/ai4r/classifiers/prism.rb

Overview

Introduction

This is an implementation of the PRISM algorithm (Cendrowska, 1987) Given a set of preclassified examples, it builds a set of rules to predict the class of other instaces.

  1. Cendrowska (1987). PRISM: An algorithm for inducing modular rules.

International Journal of Man-Machine Studies. 27(4):349-370.

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Data::Parameterizable

#get_parameters, included, #set_parameters

Instance Attribute Details

#data_set ⇒ Object (readonly)

Returns the value of attribute data_set.



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# File 'lib/ai4r/classifiers/prism.rb', line 29

def data_set
  @data_set
end

#rules ⇒ Object (readonly)

Returns the value of attribute rules.



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# File 'lib/ai4r/classifiers/prism.rb', line 29

def rules
  @rules
end

Instance Method Details

#build(data_set) ⇒ Object

Build a new Prism classifier. You must provide a DataSet instance as parameter. The last attribute of each item is considered as the item class.



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# File 'lib/ai4r/classifiers/prism.rb', line 34

def build(data_set)
  data_set.check_not_empty
  @data_set = data_set
  domains = @data_set.build_domains
  instances = @data_set.data_items.collect {|data| data }
  @rules = []
  domains.last.each do |class_value|
    while(has_class_value(instances, class_value))
      rule = build_rule(class_value, instances)
      @rules << rule
      instances = instances.select {|data| !matches_conditions(data, rule[:conditions])}
    end
  end
  return self
end

#eval(instace) ⇒ Object

You can evaluate new data, predicting its class. e.g.

classifier.eval(['New York',  '<30', 'F'])  # => 'Y'



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# File 'lib/ai4r/classifiers/prism.rb', line 53

def eval(instace)
  @rules.each do |rule|
    return rule[:class_value] if matches_conditions(instace, rule[:conditions])
  end
  return nil
end

#get_rules ⇒ Object

This method returns the generated rules in ruby code. e.g.

classifier.get_rules
  # => if age_range == '<30' then marketing_target = 'Y'
 elsif age_range == '>80' then marketing_target = 'Y'
 elsif city == 'Chicago' and age_range == '[30-50)' then marketing_target = 'Y'
 else marketing_target = 'N'
 end

It is a nice way to inspect induction results, and also to execute them:

age_range = '[30-50)'
city = 'New York'
eval(classifier.get_rules) 
puts marketing_target
 'Y'



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# File 'lib/ai4r/classifiers/prism.rb', line 76

def get_rules
  out = "if #{join_terms(@rules.first)} then #{then_clause(@rules.first)}"
  @rules[1...-1].each do |rule| 
    out += "\nelsif #{join_terms(rule)} then #{then_clause(rule)}"
  end
  out += "\nelse #{then_clause(@rules.last)}" if @rules.size > 1
  out += "\nend"
  return out
end