Style changes

lib/decisiontree/id3_tree.rb

@@ -5,17 +5,17 @@
 
 class Object
   def save_to_file(filename)
-    File.open(filename, 'w+' ) { |f| f << Marshal.dump(self) }
+    File.open(filename, 'w+') { |f| f << Marshal.dump(self) }
   end
 
   def self.load_from_file(filename)
-    Marshal.load( File.read( filename ) )
+    Marshal.load(File.read(filename))
   end
 end
 
 class Array
   def classification
-    collect { |v| v.last }
+    collect(&:last)
   end
 
   # calculate information entropy
@@ -24,11 +24,16 @@ class Array
 
     info = {}
     total = 0
-    each { |i| info[i] = !info[i] ? 1 : (info[i] + 1); total += 1}
+    each do |i|
+      info[i] = !info[i] ? 1 : (info[i] + 1)
+      total += 1
+    end
 
     result = 0
-    info.each do |symbol, count|
+    info.each do |_symbol, count|
-      result += -count.to_f/total*Math.log(count.to_f/total)/Math.log(2.0) if (count > 0)
+      if count > 0
+        result += -count.to_f / total * Math.log(count.to_f / total) / Math.log(2.0)
+      end
     end
     result
   end
@@ -39,16 +44,28 @@ module DecisionTree
 
   class ID3Tree
     def initialize(attributes, data, default, type)
-      @used, @tree, @type = {}, {}, type
+      @used = {}
-      @data, @attributes, @default = data, attributes, default
+      @tree = {}
+      @type = type
+      @data = data
+      @attributes = attributes
+      @default = default
     end
 
-    def train(data=@data, attributes=@attributes, default=@default)
+    def train(data = @data, attributes = @attributes, default = @default)
-      attributes = attributes.map { |e| e.to_s}
+      attributes = attributes.map(&:to_s)
       initialize(attributes, data, default, @type)
 
       # Remove samples with same attributes leaving most common classification
-      data2 = data.inject({}) { |hash, d| hash[d.slice(0..-2)] ||= Hash.new(0); hash[d.slice(0..-2)][d.last] += 1; hash }.map{ |key,val| key + [val.sort_by{ |k, v| v }.last.first]}
+      data2 = data.inject({}) do |hash, d|
+        hash[d.slice(0..-2)] ||= Hash.new(0)
+        hash[d.slice(0..-2)][d.last] += 1
+        hash
+      end
+
+      data2 = data2.map do |key, val|
+        key + [val.sort_by { |_k, v| v }.last.first]
+      end
 
       @tree = id3_train(data2, attributes, default)
     end
@@ -59,12 +76,14 @@ module DecisionTree
 
     def fitness_for(attribute)
       case type(attribute)
-        when :discrete; fitness = proc { |a,b,c| id3_discrete(a,b,c) }
+      when :discrete
-        when :continuous; fitness = proc { |a,b,c| id3_continuous(a,b,c) }
+        proc { |a, b, c| id3_discrete(a, b, c) }
+      when :continuous
+        proc { |a, b, c| id3_continuous(a, b, c) }
       end
     end
 
-    def id3_train(data, attributes, default, used={})
+    def id3_train(data, attributes, default, _used={})
       return default if data.empty?
 
       # return classification if all examples have the same classification
@@ -77,7 +96,7 @@ module DecisionTree
       performance = attributes.collect { |attribute| fitness_for(attribute).call(data, attributes, attribute) }
       max = performance.max { |a,b| a[0] <=> b[0] }
       min = performance.min { |a,b| a[0] <=> b[0] }
-      max = performance.shuffle.first if max[0] == min[0]
+      max = performance.sample if max[0] == min[0]
       best = Node.new(attributes[performance.index(max)], max[1], max[0])
       best.threshold = nil if @type == :discrete
       @used.has_key?(best.attribute) ? @used[best.attribute] += [best.threshold] : @used[best.attribute] = [best.threshold]
@@ -86,15 +105,21 @@ module DecisionTree
       fitness = fitness_for(best.attribute)
       case type(best.attribute)
       when :continuous
-          data.partition { |d| d[attributes.index(best.attribute)] >= best.threshold }.each_with_index  { |examples, i|
+        data.partition do |d|
+          d[attributes.index(best.attribute)] >= best.threshold
+        end.each_with_index do |examples, i|
           tree[best][String.new(l[i])] = id3_train(examples, attributes, (data.classification.mode rescue 0), &fitness)
-          }
+        end
       when :discrete
         values = data.collect { |d| d[attributes.index(best.attribute)] }.uniq.sort
-          partitions = values.collect { |val| data.select { |d| d[attributes.index(best.attribute)] == val } }
+        partitions = values.collect do |val|
-          partitions.each_with_index  { |examples, i|
+          data.select do |d|
-            tree[best][values[i]] = id3_train(examples, attributes-[values[i]], (data.classification.mode rescue 0), &fitness)
+            d[attributes.index(best.attribute)] == val
-          }
+          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)
+        end
       end
 
       tree
@@ -102,19 +127,23 @@ module DecisionTree
 
     # ID3 for binary classification of continuous variables (e.g. healthy / sick based on temperature thresholds)
     def id3_continuous(data, attributes, attribute)
-      values, thresholds = data.collect { |d| d[attributes.index(attribute)] }.uniq.sort, []
+      values = data.collect { |d| d[attributes.index(attribute)] }.uniq.sort
+      thresholds = []
       return [-1, -1] if values.size == 1
-      values.each_index { |i| thresholds.push((values[i]+(values[i+1].nil? ? values[i] : values[i+1])).to_f / 2) }
+      values.each_index do |i|
+        thresholds.push((values[i] + (values[i + 1].nil? ? values[i] : values[i + 1])).to_f / 2)
+      end
       thresholds.pop
       #thresholds -= used[attribute] if used.has_key? attribute
 
-      gain = thresholds.collect { |threshold|
+      gain = thresholds.collect do |threshold|
         sp = data.partition { |d| d[attributes.index(attribute)] >= threshold }
         pos = (sp[0].size).to_f / data.size
         neg = (sp[1].size).to_f / data.size
 
-        [data.classification.entropy - pos*sp[0].classification.entropy - neg*sp[1].classification.entropy, threshold]
+        [data.classification.entropy - pos * sp[0].classification.entropy - neg * sp[1].classification.entropy, threshold]
-      }.max { |a,b| a[0] <=> b[0] }
+      end
+      gain = gain.max { |a, b| a[0] <=> b[0] }
 
       return [-1, -1] if gain.size == 0
       gain
@@ -124,7 +153,7 @@ module DecisionTree
     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 } }
-      remainder = partitions.collect { |p| (p.size.to_f / data.size) * p.classification.entropy}.inject(0) { |i,s| s+=i }
+      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)]
     end
@@ -133,7 +162,7 @@ module DecisionTree
       descend(@tree, test)
     end
 
-    def graph(filename, file_type = "png")
+    def graph(filename, file_type = 'png')
       require 'graphr'
       dgp = DotGraphPrinter.new(build_tree)
       dgp.write_to_file("#{filename}.#{file_type}", file_type)
@@ -145,12 +174,12 @@ module DecisionTree
       rs
     end
 
-    def build_rules(tree=@tree)
+    def build_rules(tree = @tree)
       attr = tree.to_a.first
       cases = attr[1].to_a
       rules = []
-      cases.each do |c,child|
+      cases.each do |c, child|
-        if child.is_a?(Hash) then
+        if child.is_a?(Hash)
           build_rules(child).each do |r|
             r2 = r.clone
             r2.premises.unshift([attr.first, c])
@@ -164,42 +193,46 @@ module DecisionTree
     end
 
     private
+
     def descend(tree, test)
       attr = tree.to_a.first
-      return @default if !attr
+      return @default unless attr
       if type(attr.first.attribute) == :continuous
-        return attr[1]['>='] if !attr[1]['>='].is_a?(Hash) and test[@attributes.index(attr.first.attribute)] >= attr.first.threshold
+        return attr[1]['>='] if !attr[1]['>='].is_a?(Hash) && test[@attributes.index(attr.first.attribute)] >= attr.first.threshold
-        return attr[1]['<'] if !attr[1]['<'].is_a?(Hash) and test[@attributes.index(attr.first.attribute)] < attr.first.threshold
+        return attr[1]['<'] if !attr[1]['<'].is_a?(Hash) && test[@attributes.index(attr.first.attribute)] < attr.first.threshold
-        return descend(attr[1]['>='],test) if test[@attributes.index(attr.first.attribute)] >= attr.first.threshold
+        return descend(attr[1]['>='], test) if test[@attributes.index(attr.first.attribute)] >= attr.first.threshold
-        return descend(attr[1]['<'],test) if test[@attributes.index(attr.first.attribute)] < attr.first.threshold
+        return descend(attr[1]['<'], test) if test[@attributes.index(attr.first.attribute)] < attr.first.threshold
       else
         return attr[1][test[@attributes.index(attr[0].attribute)]] if !attr[1][test[@attributes.index(attr[0].attribute)]].is_a?(Hash)
-        return descend(attr[1][test[@attributes.index(attr[0].attribute)]],test)
+        return descend(attr[1][test[@attributes.index(attr[0].attribute)]], test)
       end
     end
 
     def build_tree(tree = @tree)
       return [] unless tree.is_a?(Hash)
-      return [["Always", @default]] if tree.empty?
+      return [['Always', @default]] if tree.empty?
 
       attr = tree.to_a.first
 
       links = attr[1].keys.collect do |key|
         parent_text = "#{attr[0].attribute}\n(#{attr[0].object_id})"
-        if attr[1][key].is_a?(Hash) then
+        if attr[1][key].is_a?(Hash)
           child = attr[1][key].to_a.first[0]
           child_text = "#{child.attribute}\n(#{child.object_id})"
         else
           child = attr[1][key]
           child_text = "#{child}\n(#{child.to_s.clone.object_id})"
         end
-        label_text = "#{key} #{type(attr[0].attribute) == :continuous ? attr[0].threshold : ""}"
+        label_text = "#{key} ''"
+        if type(attr[0].attribute) == :continuous
+          label_text.gsub!("''", attr[0].threshold)
+        end
 
         [parent_text, child_text, label_text]
       end
       attr[1].keys.each { |key| links += build_tree(attr[1][key]) }
 
-      return links
+      links
     end
   end
 
@@ -208,48 +241,56 @@ module DecisionTree
     attr_accessor :conclusion
     attr_accessor :attributes
 
-    def initialize(attributes,premises=[],conclusion=nil)
+    def initialize(attributes, premises = [], conclusion = nil)
-      @attributes, @premises, @conclusion = attributes, premises, conclusion
+      @attributes = attributes
+      @premises = premises
+      @conclusion = conclusion
     end
 
     def to_s
       str = ''
       @premises.each do |p|
-        str += "#{p.first.attribute} #{p.last} #{p.first.threshold}" if p.first.threshold
+        if p.first.threshold
-        str += "#{p.first.attribute} = #{p.last}" if !p.first.threshold
+          str += "#{p.first.attribute} #{p.last} #{p.first.threshold}"
+        else
+          str += "#{p.first.attribute} = #{p.last}"
+        end
         str += "\n"
       end
       str += "=> #{@conclusion} (#{accuracy})"
     end
 
     def predict(test)
-      verifies = true;
+      verifies = true
       @premises.each do |p|
-        if p.first.threshold then # Continuous
+        if p.first.threshold # Continuous
-          if !(p.last == '>=' && test[@attributes.index(p.first.attribute)] >= p.first.threshold) && !(p.last == '<' && test[@attributes.index(p.first.attribute)] < p.first.threshold) then
+          if !(p.last == '>=' && test[@attributes.index(p.first.attribute)] >= p.first.threshold) && !(p.last == '<' && test[@attributes.index(p.first.attribute)] < p.first.threshold)
-            verifies = false; break
+            verifies = false
+            break
           end
         else # Discrete
-          if test[@attributes.index(p.first.attribute)] != p.last then
+          if test[@attributes.index(p.first.attribute)] != p.last
-            verifies = false; break
+            verifies = false
+            break
           end
         end
       end
       return @conclusion if verifies
-      return nil
+      nil
     end
 
     def get_accuracy(data)
-      correct = 0; total = 0
+      correct = 0
+      total = 0
       data.each do |d|
         prediction = predict(d)
         correct += 1 if d.last == prediction
-        total += 1 if !prediction.nil?
+        total += 1 unless prediction.nil?
       end
       (correct.to_f + 1) / (total.to_f + 2)
     end
 
-    def accuracy(data=nil)
+    def accuracy(data = nil)
       data.nil? ? @accuracy : @accuracy = get_accuracy(data)
     end
   end
@@ -258,14 +299,16 @@ module DecisionTree
     attr_accessor :rules
 
     def initialize(attributes, data, default, type)
-      @attributes, @default, @type = attributes, default, type
+      @attributes = attributes
-      mixed_data = data.sort_by {rand}
+      @default = default
+      @type = type
+      mixed_data = data.sort_by { rand }
       cut = (mixed_data.size.to_f * 0.67).to_i
-      @train_data = mixed_data.slice(0..cut-1)
+      @train_data = mixed_data.slice(0..cut - 1)
       @prune_data = mixed_data.slice(cut..-1)
     end
 
-    def train(train_data=@train_data, attributes=@attributes, default=@default)
+    def train(train_data = @train_data, attributes = @attributes, default = @default)
       dec_tree = DecisionTree::ID3Tree.new(attributes, train_data, default, @type)
       dec_tree.train
       @rules = dec_tree.build_rules
@@ -273,21 +316,23 @@ module DecisionTree
       prune
     end
 
-    def prune(data=@prune_data)
+    def prune(data = @prune_data)
       @rules.each do |r|
         (1..r.premises.size).each do
           acc1 = r.accuracy(data)
           p = r.premises.pop
-          if acc1 > r.get_accuracy(data) then
+          if acc1 > r.get_accuracy(data)
-            r.premises.push(p); break
+            r.premises.push(p)
+            break
           end
         end
       end
-      @rules = @rules.sort_by{ |r| -r.accuracy(data) }
+      @rules = @rules.sort_by { |r| -r.accuracy(data) }
     end
 
     def to_s
-      str = ''; @rules.each { |rule| str += "#{rule}\n\n" }
+      str = ''
+      @rules.each { |rule| str += "#{rule}\n\n" }
       str
     end
 
@@ -296,18 +341,21 @@ module DecisionTree
         prediction = r.predict(test)
         return prediction, r.accuracy unless prediction.nil?
       end
-      return @default, 0.0
+      [@default, 0.0]
     end
   end
 
   class Bagging
     attr_accessor :classifiers
     def initialize(attributes, data, default, type)
-      @classifiers, @type = [], type
+      @classifiers = []
-      @data, @attributes, @default = data, attributes, default
+      @type = type
+      @data = data
+      @attributes = attributes
+      @default = default
     end
 
-    def train(data=@data, attributes=@attributes, default=@default)
+    def train(data = @data, attributes = @attributes, default = @default)
       @classifiers = []
       10.times { @classifiers << Ruleset.new(attributes, data, default, @type) }
       @classifiers.each do |c|
@@ -322,8 +370,8 @@ module DecisionTree
         predictions[p] += accuracy unless p.nil?
       end
       return @default, 0.0 if predictions.empty?
-      winner = predictions.sort_by { |k,v| -v}.first
+      winner = predictions.sort_by { |_k, v| -v }.first
-      return winner[0], winner[1].to_f / @classifiers.size.to_f
+      [winner[0], winner[1].to_f / @classifiers.size.to_f]
     end
   end
 end