Speed improvements for discrete

lib/decisiontree/id3_tree.rb

@@ -3,6 +3,8 @@
 ### Copyright (c) 2007 Ilya Grigorik <ilya AT igvita DOT com>
 ### Modifed at 2007 by José Ignacio Fernández <joseignacio.fernandez AT gmail DOT com>
 
+require 'set'
+
 module DecisionTree
   Node = Struct.new(:attribute, :threshold, :gain)
 
@@ -28,7 +30,7 @@ def train(data = @data, attributes = @attributes, default = @default)
       end
 
       data2 = data2.map do |key, val|
-        key + [val.sort_by { |_k, v| v }.last.first]
+        key + [val.sort_by { |_, v| v }.last.first]
       end
 
       @tree = id3_train(data2, attributes, default)
@@ -41,9 +43,9 @@ def type(attribute)
     def fitness_for(attribute)
       case type(attribute)
       when :discrete
-        proc { |a, b, c| id3_discrete(a, b, c) }
+        proc { |*args| id3_discrete(*args) }
       when :continuous
-        proc { |a, b, c| id3_continuous(a, b, c) }
+        proc { |*args| id3_continuous(*args) }
       end
     end
 
@@ -66,14 +68,13 @@ def id3_train(data, attributes, default, _used={})
       @used.has_key?(best.attribute) ? @used[best.attribute] += [best.threshold] : @used[best.attribute] = [best.threshold]
       tree, l = {best => {}}, ['>=', '<']
 
-      fitness = fitness_for(best.attribute)
       case type(best.attribute)
       when :continuous
         partitioned_data = data.partition do |d|
           d[attributes.index(best.attribute)] >= best.threshold
         end
         partitioned_data.each_with_index do |examples, i|
-          tree[best][String.new(l[i])] = id3_train(examples, attributes, (data.classification.mode rescue 0), &fitness)
+          tree[best][String.new(l[i])] = id3_train(examples, attributes, (data.classification.mode rescue 0))
         end
       when :discrete
         values = data.collect { |d| d[attributes.index(best.attribute)] }.uniq.sort
@@ -83,7 +84,7 @@ def id3_train(data, attributes, default, _used={})
           end
         end
         partitions.each_with_index do |examples, i|
-          tree[best][values[i]] = id3_train(examples, attributes - [values[i]], (data.classification.mode rescue 0), &fitness)
+          tree[best][values[i]] = id3_train(examples, attributes - [values[i]], (data.classification.mode rescue 0))
         end
       end
 
@@ -116,11 +117,14 @@ def id3_continuous(data, attributes, attribute)
 
     # ID3 for discrete label cases
     def id3_discrete(data, attributes, attribute)
-      values = data.collect { |d| d[attributes.index(attribute)] }.uniq.sort
-      partitions = values.collect { |val| data.select { |d| d[attributes.index(attribute)] == val } }
+      index = attributes.index(attribute)
+
+      values = Set.new
+      data.each { |d| values << d[index] }
+      partitions = values.to_a.sort.collect { |val| data.select { |d| d[index] == val } }
       remainder = partitions.collect { |p| (p.size.to_f / data.size) * p.classification.entropy }.inject(0) { |a, e| e += a }
 
-      [data.classification.entropy - remainder, attributes.index(attribute)]
+      [data.classification.entropy - remainder, index]
     end
 
     def predict(test)