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Danielius
2015-11-20 14:22:03 +02:00
parent 310b73bacd
commit 8a8cc1e988
1 changed files with 123 additions and 75 deletions
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152
lib/decisiontree/id3_tree.rb
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@@ -15,7 +15,7 @@ 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|
result += -count.to_f/total*Math.log(count.to_f/total)/Math.log(2.0) if (count > 0)
info.each do |_symbol, count|
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
@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}
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 :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
@@ -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.each_with_index { |examples, i|
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
@@ -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]
}.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)
@@ -150,7 +179,7 @@ module DecisionTree
cases = attr[1].to_a
rules = []
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])
@@ -164,12 +193,13 @@ 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 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
@@ -180,26 +210,29 @@ module DecisionTree
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
@@ -209,42 +242,50 @@ module DecisionTree
attr_accessor :attributes
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
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
@@ -258,7 +299,9 @@ module DecisionTree
attr_accessor :rules
def initialize(attributes, data, default, type)
@attributes, @default, @type = attributes, default, type
@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)
@@ -278,8 +321,9 @@ module DecisionTree
(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
@@ -287,7 +331,8 @@ module DecisionTree
end
def to_s
str = ''; @rules.each { |rule| str += "#{rule}\n\n" }
str = ''
@rules.each { |rule| str += "#{rule}\n\n" }
str
end
@@ -296,15 +341,18 @@ 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)
@@ -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
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