Tidy code style

Gemfile

@@ -1,4 +1,4 @@
-source 'https://rubygems.org'
+source "https://rubygems.org"
 
 # Specify your gem's dependencies in ..gemspec
 gemspec

Rakefile

@@ -1,7 +1,7 @@
-require 'bundler'
+require "bundler"
 Bundler::GemHelper.install_tasks
 
-require 'rspec/core/rake_task'
+require "rspec/core/rake_task"
 RSpec::Core::RakeTask.new
 
-task :default => :spec
+task default: :spec

decisiontree.gemspec

@@ -1,4 +1,3 @@
-# -*- encoding: utf-8 -*-
 $:.push File.expand_path("../lib", __FILE__)
 
 Gem::Specification.new do |s|
@@ -8,7 +7,7 @@ Gem::Specification.new do |s|
   s.authors = ["Ilya Grigorik"]
   s.email = ["ilya@igvita.com"]
   s.homepage = "https://github.com/igrigorik/decisiontree"
-  s.summary     = %q{ID3-based implementation of the M.L. Decision Tree algorithm}
+  s.summary = "ID3-based implementation of the M.L. Decision Tree algorithm"
   s.description = s.summary
   s.license = "MIT"
 
@@ -21,6 +20,6 @@ Gem::Specification.new do |s|
 
   s.files = `git ls-files`.split("\n")
   s.test_files = `git ls-files -- {test,spec,features}/*`.split("\n")
-  s.executables   = `git ls-files -- bin/*`.split("\n").map{ |f| File.basename(f) }
+  s.executables = `git ls-files -- bin/*`.split("\n").map { |f| File.basename(f) }
   s.require_paths = ["lib"]
 end

examples/continuous-id3.rb

@@ -1,5 +1,5 @@
-require 'rubygems'
+require "rubygems"
-require 'decisiontree'
+require "decisiontree"
 include DecisionTree
 
 # ---Continuous---
@@ -8,14 +8,14 @@ include DecisionTree
 training = []
 attributes = nil
 
-File.open('data/continuous-training.txt', 'r').each_line do |line|
+File.open("data/continuous-training.txt", "r").each_line do |line|
-  data = line.strip.chomp('.').split(',')
+  data = line.strip.chomp(".").split(",")
   attributes ||= data
   training_data = data.collect do |v|
     case v
-    when 'healthy'
+    when "healthy"
       1
-    when 'colic'
+    when "colic"
       0
     else
       v.to_f
@@ -36,11 +36,11 @@ dec_tree.train
 # Read in the test cases
 # Note: omit the attribute line (first line), we know the labels from the training data
 test = []
-File.open('data/continuous-test.txt', 'r').each_line do |line|
+File.open("data/continuous-test.txt", "r").each_line do |line|
-  data = line.strip.chomp('.').split(',')
+  data = line.strip.chomp(".").split(",")
   test_data = data.collect do |v|
-    if v == 'healthy' || v == 'colic'
+    if v == "healthy" || v == "colic"
-      v == 'healthy' ? 1 : 0
+      v == "healthy" ? 1 : 0
     else
       v.to_f
     end

examples/discrete-id3.rb

@@ -1,5 +1,5 @@
-require 'rubygems'
+require "rubygems"
-require 'decisiontree'
+require "decisiontree"
 
 # ---Discrete---
 
@@ -7,12 +7,12 @@ require 'decisiontree'
 training = []
 attributes = nil
 
-File.open('data/discrete-training.txt', 'r').each_line do |line|
+File.open("data/discrete-training.txt", "r").each_line do |line|
-  data = line.strip.split(',')
+  data = line.strip.split(",")
   attributes ||= data
   training_data = data.collect do |v|
     case v
-    when 'will buy'
+    when "will buy"
       1
     when "won't buy"
       0
@@ -35,11 +35,11 @@ dec_tree.train
 # Read in the test cases
 # Note: omit the attribute line (first line), we know the labels from the training data
 test = []
-File.open('data/discrete-test.txt', 'r').each_line do |line|
+File.open("data/discrete-test.txt", "r").each_line do |line|
-  data = line.strip.split(',')
+  data = line.strip.split(",")
   test_data = data.collect do |v|
     case v
-    when 'will buy'
+    when "will buy"
       1
     when "won't buy"
       0
@@ -57,4 +57,4 @@ test.each do |t|
 end
 
 # Graph the tree, save to 'discrete.png'
-dec_tree.graph('discrete')
+dec_tree.graph("discrete")

examples/simple.rb

@@ -1,26 +1,26 @@
 #!/usr/bin/ruby
 
-require 'rubygems'
+require "rubygems"
-require 'decisiontree'
+require "decisiontree"
 
-attributes = ['Temperature']
+attributes = ["Temperature"]
 training = [
-  [36.6, 'healthy'],
+  [36.6, "healthy"],
-  [37, 'sick'],
+  [37, "sick"],
-  [38, 'sick'],
+  [38, "sick"],
-  [36.7, 'healthy'],
+  [36.7, "healthy"],
-  [40, 'sick'],
+  [40, "sick"],
-  [50, 'really sick']
+  [50, "really sick"]
 ]
 
 # Instantiate the tree, and train it based on the data (set default to '1')
-dec_tree = DecisionTree::ID3Tree.new(attributes, training, 'sick', :continuous)
+dec_tree = DecisionTree::ID3Tree.new(attributes, training, "sick", :continuous)
 dec_tree.train
 
-test = [37, 'sick']
+test = [37, "sick"]
 
 decision = dec_tree.predict(test)
 puts "Predicted: #{decision} ... True decision: #{test.last}"
 
 # Graph the tree, save to 'tree.png'
-dec_tree.graph('tree')
+dec_tree.graph("tree")

lib/core_extensions/array.rb

@@ -17,4 +17,3 @@ module ArrayClassification
     end
   end
 end
-

lib/core_extensions/object.rb

@@ -1,6 +1,6 @@
 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)

lib/decisiontree.rb

@@ -1,3 +1,3 @@
-require 'core_extensions/object'
+require "core_extensions/object"
-require 'core_extensions/array'
+require "core_extensions/array"
-require File.dirname(__FILE__) + '/decisiontree/id3_tree.rb'
+require File.dirname(__FILE__) + "/decisiontree/id3_tree.rb"

lib/decisiontree/id3_tree.rb

@@ -23,14 +23,13 @@ module DecisionTree
       initialize(attributes, data, default, @type)
 
       # Remove samples with same attributes leaving most common classification
-      data2 = data.inject({}) do |hash, d|
+      data2 = data.each_with_object({}) do |d, hash|
         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 { |_, v| v }.last.first]
+        key + [val.max_by { |_, v| v }.first]
       end
 
       @tree = id3_train(data2, attributes, default)
@@ -49,7 +48,7 @@ module DecisionTree
       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
@@ -60,13 +59,13 @@ module DecisionTree
       # 2. Pick best attribute
       # 3. If attributes all score the same, then pick a random one to avoid infinite recursion.
       performance = attributes.collect { |attribute| fitness_for(attribute).call(data, attributes, attribute) }
-      max = performance.max { |a,b| a[0] <=> b[0] }
+      max = performance.max_by { |a| a[0] }
-      min = performance.min { |a,b| a[0] <=> b[0] }
+      min = performance.min_by { |a| a[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]
-      tree, l = {best => {}}, ['>=', '<']
+      tree, l = {best => {}}, [">=", "<"]
 
       case type(best.attribute)
       when :continuous
@@ -74,7 +73,11 @@ module DecisionTree
           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))
+          tree[best][String.new(l[i])] = id3_train(examples, attributes, begin
+            data.classification.mode
+          rescue
+            0
+          end)
         end
       when :discrete
         values = data.collect { |d| d[attributes.index(best.attribute)] }.uniq.sort
@@ -84,7 +87,11 @@ module DecisionTree
           end
         end
         partitions.each_with_index do |examples, i|
-          tree[best][values[i]] = id3_train(examples, attributes - [values[i]], (data.classification.mode rescue 0))
+          tree[best][values[i]] = id3_train(examples, attributes - [values[i]], begin
+            data.classification.mode
+          rescue
+            0
+          end)
         end
       end
 
@@ -100,16 +107,16 @@ module DecisionTree
         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
+      # thresholds -= used[attribute] if used.has_key? attribute
 
       gain = thresholds.collect do |threshold|
         sp = data.partition { |d| d[attributes.index(attribute)] >= threshold }
-        pos = (sp[0].size).to_f / data.size
+        pos = sp[0].size.to_f / data.size
-        neg = (sp[1].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]
       end
-      gain = gain.max { |a, b| a[0] <=> b[0] }
+      gain = gain.max_by { |a| a[0] }
 
       return [-1, -1] if gain.size == 0
       gain
@@ -135,16 +142,16 @@ module DecisionTree
       descend(@tree, test)
     end
 
-    def graph(filename, file_type = 'png')
+    def graph(filename, file_type = "png")
-      require 'graphr'
+      require "graphr"
       dgp = DotGraphPrinter.new(build_tree)
-      dgp.size = ''
+      dgp.size = ""
       dgp.node_labeler = proc { |n| n.split("\n").first }
       dgp.write_to_file("#{filename}.#{file_type}", file_type)
     rescue LoadError
-      STDERR.puts "Error: Cannot generate graph."
+      warn "Error: Cannot generate graph."
-      STDERR.puts "       The 'graphr' gem doesn't seem to be installed."
+      warn "       The 'graphr' gem doesn't seem to be installed."
-      STDERR.puts "       Run 'gem install graphr' or add it to your Gemfile."
+      warn "       Run 'gem install graphr' or add it to your Gemfile."
     end
 
     def ruleset
@@ -177,19 +184,19 @@ module DecisionTree
       attr = tree.to_a.first
       return @default unless attr
       if type(attr.first.attribute) == :continuous
-        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) && 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) && 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 attr[1][test[@attributes.index(attr[0].attribute)]] unless attr[1][test[@attributes.index(attr[0].attribute)]].is_a?(Hash)
-        return descend(attr[1][test[@attributes.index(attr[0].attribute)]], test)
+        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
 
@@ -203,10 +210,10 @@ module DecisionTree
           child_text = "#{child}\n(#{child.to_s.clone.object_id})"
         end
 
-        if type(attr[0].attribute) == :continuous
+        label_text = if type(attr[0].attribute) == :continuous
-          label_text = "#{key} #{attr[0].threshold}"
+          "#{key} #{attr[0].threshold}"
         else
-          label_text = key
+          key
         end
 
         [parent_text, child_text, label_text]
@@ -229,12 +236,12 @@ module DecisionTree
     end
 
     def to_s
-      str = ''
+      str = ""
       @premises.each do |p|
-        if p.first.threshold
+        str += if p.first.threshold
-          str += "#{p.first.attribute} #{p.last} #{p.first.threshold}"
+          "#{p.first.attribute} #{p.last} #{p.first.threshold}"
         else
-          str += "#{p.first.attribute} = #{p.last}"
+          "#{p.first.attribute} = #{p.last}"
         end
         str += "\n"
       end
@@ -245,17 +252,15 @@ module DecisionTree
       verifies = true
       @premises.each do |p|
         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)
+          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
           end
-        else # Discrete
+        elsif test[@attributes.index(p.first.attribute)] != p.last # Discrete
-          if test[@attributes.index(p.first.attribute)] != p.last
           verifies = false
           break
         end
       end
-      end
       return @conclusion if verifies
       nil
     end
@@ -312,7 +317,7 @@ module DecisionTree
     end
 
     def to_s
-      str = ''
+      str = ""
       @rules.each { |rule| str += "#{rule}\n\n" }
       str
     end
@@ -355,9 +360,8 @@ 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.min_by { |_k, v| -v }
       [winner[0], winner[1].to_f / @classifiers.size.to_f]
     end
   end
 end
-

spec/id3_spec.rb

@@ -1,19 +1,18 @@
-require 'spec_helper'
+require "spec_helper"
 
 describe describe DecisionTree::ID3Tree do
-
   describe "simple discrete case" do
-    Given(:labels) { ["sun", "rain"]}
+    Given(:labels) { ["sun", "rain"] }
     Given(:data) do
       [
-        [1,0,1],
+        [1, 0, 1],
-        [0,1,0]
+        [0, 1, 0]
       ]
     end
     Given(:tree) { DecisionTree::ID3Tree.new(labels, data, 1, :discrete) }
     When { tree.train }
-    Then { expect(tree.predict([1,0])).to eq 1 }
+    Then { expect(tree.predict([1, 0])).to eq 1 }
-    Then { expect(tree.predict([0,1])).to eq 0 }
+    Then { expect(tree.predict([0, 1])).to eq 0 }
   end
 
   describe "discrete attributes" do
@@ -84,7 +83,7 @@ describe describe DecisionTree::ID3Tree do
     end
     Given(:tree) { DecisionTree::ID3Tree.new(labels, data, "RED", :discrete) }
     When { tree.train }
-    Then { expect(tree.predict(["a1","b0","c0"])).to eq "RED" }
+    Then { expect(tree.predict(["a1", "b0", "c0"])).to eq "RED" }
   end
 
   describe "numerical labels case" do
@@ -109,11 +108,11 @@ describe describe DecisionTree::ID3Tree do
       File.delete("#{FIGURE_FILENAME}.png") if File.file?("#{FIGURE_FILENAME}.png")
     end
 
-    Given(:labels) { ["sun", "rain"]}
+    Given(:labels) { ["sun", "rain"] }
     Given(:data) do
       [
-        [1,0,1],
+        [1, 0, 1],
-        [0,1,0]
+        [0, 1, 0]
       ]
     end
     Given(:tree) { DecisionTree::ID3Tree.new(labels, data, 1, :discrete) }

spec/spec_helper.rb

@@ -1,5 +1,5 @@
-require 'rspec/given'
+require "rspec/given"
-require 'decisiontree'
+require "decisiontree"
-require 'pry'
+require "pry"
 
 FIGURE_FILENAME = "just_a_spec"