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Merge branch 'dvisockas-master'

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Ilya Grigorik
2015-11-22 07:46:25 -08:00
parent eaa9a2f5d7 2155771683
commit 15394f85a3
7 changed files with 229 additions and 132 deletions
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40
examples/continuous-id3.rb
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@@ -2,15 +2,25 @@ require 'rubygems'
require 'decisiontree'
include DecisionTree
# ---Continuous-----------------------------------------------------------------------------------------
# ---Continuous---
# Read in the training data
training, attributes = [], nil
File.open('data/continuous-training.txt','r').each_line { |line|
training = []
File.open('data/continuous-training.txt', 'r').each_line do |line|
data = line.strip.chomp('.').split(',')
attributes ||= data
training.push(data.collect {|v| (v == 'healthy') || (v == 'colic') ? (v == 'healthy' ? 1 : 0) : v.to_f})
}
training_data = data.collect do |v|
case v
when 'healthy'
1
when 'colic'
0
else
v.to_f
end
end
training.push(training_data)
end
# Remove the attribute row from the training data
training.shift
@@ -19,15 +29,25 @@ training.shift
dec_tree = ID3Tree.new(attributes, training, 1, :continuous)
dec_tree.train
#---- Test the tree....
# ---Test the tree---
# 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 { |line|
File.open('data/continuous-test.txt', 'r').each_line do |line|
data = line.strip.chomp('.').split(',')
test.push(data.collect {|v| (v == 'healthy') || (v == 'colic') ? (v == 'healthy' ? 1 : 0) : v.to_f})
}
test_data = data.collect do |v|
if v == 'healthy' || v == 'colic'
v == 'healthy' ? 1 : 0
else
v.to_f
end
end
test.push(test_data)
end
# Let the tree predict the output and compare it to the true specified value
test.each { |t| predict = dec_tree.predict(t); puts "Predict: #{predict} ... True: #{t.last}"}
test.each do |t|
predict = dec_tree.predict(t)
puts "Predict: #{predict} ... True: #{t.last}"
end

46
examples/discrete-id3.rb
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@@ -1,15 +1,25 @@
require 'rubygems'
require 'decisiontree'
# ---Discrete-----------------------------------------------------------------------------------------
# ---Discrete---
# Read in the training data
training, attributes = [], nil
File.open('data/discrete-training.txt','r').each_line { |line|
training = []
File.open('data/discrete-training.txt', 'r').each_line do |line|
data = line.strip.split(',')
attributes ||= data
training.push(data.collect {|v| (v == 'will buy') || (v == "won't buy") ? (v == 'will buy' ? 1 : 0) : v})
}
training_data = data.collect do |v|
case v
when 'will buy'
1
when "won't buy"
0
else
v
end
end
training.push(training_data)
end
# Remove the attribute row from the training data
training.shift
@@ -18,17 +28,31 @@ training.shift
dec_tree = DecisionTree::ID3Tree.new(attributes, training, 1, :discrete)
dec_tree.train
#---- Test the tree....
# ---Test the tree---
# 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 { |line| data = line.strip.split(',')
test.push(data.collect {|v| (v == 'will buy') || (v == "won't buy") ? (v == 'will buy' ? 1 : 0) : v})
}
File.open('data/discrete-test.txt', 'r').each_line do |line|
data = line.strip.split(',')
test_data = data.collect do |v|
case v
when 'will buy'
1
when "won't buy"
0
else
v
end
end
training.push(test_data)
end
# Let the tree predict the output and compare it to the true specified value
test.each { |t| predict = dec_tree.predict(t); puts "Predict: #{predict} ... True: #{t.last}"; }
test.each do |t|
predict = dec_tree.predict(t)
puts "Predict: #{predict} ... True: #{t.last}"
end
# Graph the tree, save to 'discrete.png'
dec_tree.graph("discrete")
dec_tree.graph('discrete')

8
examples/simple.rb
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@@ -10,7 +10,7 @@ training = [
[38, 'sick'],
[36.7, 'healthy'],
[40, 'sick'],
[50, 'really sick'],
[50, 'really sick']
]
# Instantiate the tree, and train it based on the data (set default to '1')
@@ -20,9 +20,7 @@ dec_tree.train
test = [37, 'sick']
decision = dec_tree.predict(test)
puts "Predicted: #{decision} ... True decision: #{test.last}";
puts "Predicted: #{decision} ... True decision: #{test.last}"
# Graph the tree, save to 'tree.png'
dec_tree.graph("tree")
dec_tree.graph('tree')

29
lib/core_extensions/array.rb Normal file
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@@ -0,0 +1,29 @@
class Array
def classification
collect(&:last)
end
# calculate information entropy
def entropy
return 0 if empty?
info = {}
each do |i|
info[i] = !info[i] ? 1 : (info[i] + 1)
end
result(info, length)
end
private
def result(info, total)
final = 0
info.each do |_symbol, count|
next unless count > 0
percentage = count.to_f / total
final += -percentage * Math.log(percentage) / Math.log(2.0)
end
final
end
end

9
lib/core_extensions/object.rb Normal file
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@@ -0,0 +1,9 @@
class Object
def save_to_file(filename)
File.open(filename, 'w+') { |f| f << Marshal.dump(self) }
end
def self.load_from_file(filename)
Marshal.load(File.read(filename))
end
end

2
lib/decisiontree.rb
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@@ -1 +1,3 @@
require File.dirname(__FILE__) + '/decisiontree/id3_tree.rb'
require 'core_extensions/object'
require 'core_extensions/array'

201
lib/decisiontree/id3_tree.rb
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@@ -3,50 +3,33 @@
### Copyright (c) 2007 Ilya Grigorik <ilya AT igvita DOT com>
### Modifed at 2007 by José Ignacio Fernández <joseignacio.fernandez AT gmail DOT com>
class Object
def save_to_file(filename)
File.open(filename, 'w+' ) { |f| f << Marshal.dump(self) }
end
def self.load_from_file(filename)
Marshal.load( File.read( filename ) )
end
end
class Array
def classification; collect { |v| v.last }; end
# calculate information entropy
def entropy
return 0 if empty?
info = {}
total = 0
each {|i| info[i] = !info[i] ? 1 : (info[i] + 1); total += 1}
result = 0
info.each do |symbol, count|
result += -count.to_f/total*Math.log(count.to_f/total)/Math.log(2.0) if (count > 0)
end
result
end
end
module DecisionTree
Node = Struct.new(:attribute, :threshold, :gain)
class ID3Tree
def initialize(attributes, data, default, type)
@used, @tree, @type = {}, {}, type
@data, @attributes, @default = data, attributes, default
@used = {}
@tree = {}
@type = type
@data = data
@attributes = attributes
@default = default
end
def train(data=@data, attributes=@attributes, default=@default)
attributes = attributes.map {|e| e.to_s}
def train(data = @data, attributes = @attributes, default = @default)
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
@@ -57,12 +40,14 @@ module DecisionTree
def fitness_for(attribute)
case type(attribute)
when :discrete; fitness = proc{|a,b,c| id3_discrete(a,b,c)}
when :continuous; fitness = proc{|a,b,c| id3_continuous(a,b,c)}
when :discrete
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
@@ -75,7 +60,7 @@ 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]
@@ -84,15 +69,22 @@ 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|
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)
}
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.each_with_index { |examples, i|
tree[best][values[i]] = id3_train(examples, attributes-[values[i]], (data.classification.mode rescue 0), &fitness)
}
partitions = values.collect do |val|
data.select do |d|
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
@@ -100,19 +92,23 @@ 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]
}.max { |a,b| a[0] <=> b[0] }
[data.classification.entropy - pos * sp[0].classification.entropy - neg * sp[1].classification.entropy, threshold]
end
gain = gain.max { |a, b| a[0] <=> b[0] }
return [-1, -1] if gain.size == 0
gain
@@ -122,7 +118,7 @@ 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
@@ -131,7 +127,7 @@ 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)
@@ -143,12 +139,12 @@ 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|
if child.is_a?(Hash) then
cases.each do |c, child|
if child.is_a?(Hash)
build_rules(child).each do |r|
r2 = r.clone
r2.premises.unshift([attr.first, c])
@@ -162,42 +158,46 @@ 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) and 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 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
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
@@ -206,48 +206,56 @@ module DecisionTree
attr_accessor :conclusion
attr_accessor :attributes
def initialize(attributes,premises=[],conclusion=nil)
@attributes, @premises, @conclusion = attributes, premises, conclusion
def initialize(attributes, premises = [], conclusion = nil)
@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
str += "#{p.first.attribute} = #{p.last}" if !p.first.threshold
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.last == '>=' && test[@attributes.index(p.first.attribute)] >= p.first.threshold) && !(p.last == '<' && test[@attributes.index(p.first.attribute)] < p.first.threshold) then
verifies = false; break
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)
verifies = false
break
end
else # Discrete
if test[@attributes.index(p.first.attribute)] != p.last then
verifies = false; break
if test[@attributes.index(p.first.attribute)] != p.last
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
@@ -256,14 +264,16 @@ module DecisionTree
attr_accessor :rules
def initialize(attributes, data, default, type)
@attributes, @default, @type = attributes, default, type
mixed_data = data.sort_by {rand}
@attributes = attributes
@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
@@ -271,21 +281,23 @@ 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
r.premises.push(p); break
if acc1 > r.get_accuracy(data)
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
@@ -294,18 +306,21 @@ 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
@data, @attributes, @default = data, attributes, default
@classifiers = []
@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|
@@ -320,8 +335,8 @@ module DecisionTree
predictions[p] += accuracy unless p.nil?
end
return @default, 0.0 if predictions.empty?
winner = predictions.sort_by {|k,v| -v}.first
return winner[0], winner[1].to_f / @classifiers.size.to_f
winner = predictions.sort_by { |_k, v| -v }.first
[winner[0], winner[1].to_f / @classifiers.size.to_f]
end
end
end