# Author::    Sergio Fierens (Implementation only)
# License::   MPL 1.1
# Project::   ai4r
# Url::       http://www.ai4r.org/
#
# You can redistribute it and/or modify it under the terms of 
# the Mozilla Public License version 1.1  as published by the 
# Mozilla Foundation at http://www.mozilla.org/MPL/MPL-1.1.txt

require 'set'
require File.dirname(__FILE__) + '/../data/data_set'
require File.dirname(__FILE__) + '/../classifiers/classifier'

module Ai4r
  module Classifiers

    # = Introduction
    # 
    # The idea of the OneR algorithm is identify the single
    # attribute to use to classify data that makes 
    # fewest prediction errors.
    # It generates rules based on a single attribute.
    class OneR < Classifier
      
      attr_reader :data_set, :rule

      # Build a new OneR classifier. You must provide a DataSet instance
      # as parameter. The last attribute of each item is considered as 
      # the item class.
      def build(data_set)
        data_set.check_not_empty
        @data_set = data_set
        if (data_set.num_attributes == 1) 
          @zero_r = ZeroR.new.build(data_set)
          return self;
        else
          @zero_r = nil;
        end
        domains = @data_set.build_domains
        @rule = nil
        domains[1...-1].each_index do |attr_index|
          rule = build_rule(@data_set.data_items, attr_index, domains)
          @rule = rule if !@rule || rule[:correct] > @rule[:correct]
        end
        return self
      end
      
      # You can evaluate new data, predicting its class.
      # e.g.
      #   classifier.eval(['New York',  '<30', 'F'])  # => 'Y'      
      def eval(data)
        return @zero_r.eval(data) if @zero_r
        attr_value = data[@rule[:attr_index]]
        return @rule[:rule][attr_value]
      end
      
      # 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 == '[30-50)' then marketing_target = 'N'
      #           elsif age_range == '[50-80]' then marketing_target = 'N'
      #           end
      #
      # It is a nice way to inspect induction results, and also to execute them:  
      #     marketing_target = nil
      #     eval classifier.get_rules   
      #     puts marketing_target
      #       # =>  'Y'
      def get_rules
        return @zero_r.get_rules if @zero_r
        sentences = []
        attr_label = @data_set.data_labels[@rule[:attr_index]]
        class_label = @data_set.data_labels.last
        @rule[:rule].each_pair do |attr_value, class_value|
          sentences << "#{attr_label} == '#{attr_value}' then #{class_label} = '#{class_value}'"
        end
        return "if " + sentences.join("\nelsif ") + "\nend"        
      end
      
      protected
      
      def build_rule(data_examples, attr_index, domains)
        domain = domains[attr_index]
        value_freq = Hash.new
        domain.each do |attr_value| 
          value_freq[attr_value] = Hash.new { |hash, key| hash[key] = 0 }
        end
        data_examples.each do |data|
          value_freq[data[attr_index]][data.last] = value_freq[data[attr_index]][data.last] + 1
        end
        rule = {}
        correct_instances = 0
        value_freq.each_pair do |attr, class_freq_hash|  
          max_freq = 0
          class_freq_hash.each_pair do |class_value, freq| 
            if max_freq < freq
              rule[attr] = class_value 
              max_freq = freq
            end
          end
          correct_instances += max_freq
        end
        return {:attr_index => attr_index, :rule => rule, :correct => correct_instances}
      end

    end
  end
end