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|
ENV['JAVA_HOME'] = "/usr/bin" unless ENV['JAVA_HOME']
ENV['PATH'] = ENV['JAVA_HOME']+":"+ENV['PATH'] unless ENV['PATH'].split(":").index(ENV['JAVA_HOME'])
ENV['RANK_PLOTTER_JAR'] = "RankPlotter/RankPlotter.jar" unless ENV['RANK_PLOTTER_JAR']
class Array
def swap!(i,j)
tmp = self[i]
self[i] = self[j]
self[j] = tmp
end
# summing up values of fields where array __groups__ has equal values
# EXAMPLE
# self: [1, 0, 1, 2, 3, 0, 2]
# __groups__: [100, 90, 70, 70, 30, 10, 0]
# returns:
# [ 1, 0, 3, 3, 0, 2]
# (fields with equal value 70 are compressed)
# PRECONDITION
# __groups__ has to be sorted
def compress_sum(groups)
compress(groups) do |a,b|
a+b
end
end
# see compress_sum, replace sum with max
def compress_max(groups)
compress(groups) do |a,b|
a > b ? a : b
end
end
private
def compress(groups)
raise "length not equal" unless self.size==groups.size
raise "to small" unless self.size>=2
a = [ self[0] ]
(1..groups.size-1).each do |i|
if groups[i]!=groups[i-1]
a << self[i]
else
a[-1] = yield a[-1],self[i]
end
end
a
end
end
module Reports
module PlotFactory
def self.create_regression_plot( out_files, validation_set, name_attribute, logscale=true )
out_files = [out_files] unless out_files.is_a?(Array)
LOGGER.debug "Creating regression plot, out-file:"+out_files.to_s
omit_count = 0
names = []
x = []
y = []
validation_set.validations.each do |v|
x_i = v.get_predictions.predicted_values
y_i = v.get_predictions.actual_values
# filter out nil-predictions and <=0 predictions if log-scale wanted
valid_indices = []
x_i.size.times do |i|
if x_i[i]!=nil and y_i[i]!=nil
if !logscale or (x_i[i]>0 and y_i[i]>0)
valid_indices << i
else
omit_count += 1
end
end
end
if valid_indices.size < x_i.size
x_i = valid_indices.collect{ |i| x_i[i] }
y_i = valid_indices.collect{ |i| y_i[i] }
end
names << ( name_attribute==:crossvalidation_fold ? "fold " : "" ) + v.send(name_attribute).to_s
x << x_i
y << y_i
end
names = [""] if names.size==1
omit_str = omit_count>0 ? " ("+omit_count.to_s+" predictions omitted)" : ""
raise "no predictions performed"+omit_str if x.size==0 || x[0].size==0
out_files.each do |out_file|
RubyPlot::regression_point_plot(out_file, "Regression plot", "Predicted values", "Actual values", names, x, y, logscale)
end
omit_count
end
# creates a roc plot (result is plotted into out_file)
# * if (split_set_attributes == nil?)
# * the predictions of all validations in the validation set are plotted as one average roc-curve
# * if (show_single_curves == true) -> the single predictions of each validation are plotted as well
# * if (split_set_attributes != nil?)
# * the validation set is splitted into sets of validation_sets with equal attribute values
# * each of theses validation sets is plotted as a roc-curve
#
def self.create_roc_plot( out_files, validation_set, class_value, split_set_attribute=nil,
x_label="False positive rate", y_label="True Positive Rate" )
out_files = [out_files] unless out_files.is_a?(Array)
LOGGER.debug "creating roc plot for '"+validation_set.size.to_s+"' validations, out-files:"+out_files.inspect
data = []
if split_set_attribute
attribute_values = validation_set.get_values(split_set_attribute)
attribute_values.each do |value|
begin
data << transform_roc_predictions(validation_set.filter({split_set_attribute => value}), class_value, false )
data[-1].name = split_set_attribute.to_s.nice_attr+" "+value.to_s
rescue
LOGGER.warn "could not create ROC plot for "+value.to_s
end
end
else
data << transform_roc_predictions(validation_set, class_value )
end
out_files.each do |out_file|
RubyPlot::plot_lines(out_file, "ROC-Plot", x_label, y_label, data )
end
end
def self.create_confidence_plot( out_files, validation_set, class_value, split_set_attribute=nil, show_single_curves=false )
out_files = [out_files] unless out_files.is_a?(Array)
LOGGER.debug "creating confidence plot for '"+validation_set.size.to_s+"' validations, out-file:"+out_files.inspect
if split_set_attribute
attribute_values = validation_set.get_values(split_set_attribute)
names = []
confidence = []
performance = []
attribute_values.each do |value|
begin
data = transform_confidence_predictions(validation_set.filter({split_set_attribute => value}), class_value, false)
names << split_set_attribute.to_s.nice_attr+" "+value.to_s
confidence << data[:confidence][0]
performance << data[:performance][0]
rescue
LOGGER.warn "could not create confidence plot for "+value.to_s
end
end
#RubyPlot::plot_lines(out_file, "Percent Correct vs Confidence Plot", "Confidence", "Percent Correct", names, fp_rates, tp_rates )
out_files.each do |out_file|
case validation_set.unique_feature_type
when "classification"
RubyPlot::accuracy_confidence_plot(out_file, "Percent Correct vs Confidence Plot", "Confidence", "Percent Correct", names, confidence, performance)
when "regression"
RubyPlot::accuracy_confidence_plot(out_file, "RMSE vs Confidence Plot", "Confidence", "RMSE", names, confidence, performance, true)
end
end
else
data = transform_confidence_predictions(validation_set, class_value, show_single_curves)
out_files.each do |out_file|
case validation_set.unique_feature_type
when "classification"
RubyPlot::accuracy_confidence_plot(out_file, "Percent Correct vs Confidence Plot", "Confidence", "Percent Correct", data[:names], data[:confidence], data[:performance])
when "regression"
RubyPlot::accuracy_confidence_plot(out_file, "RMSE vs Confidence Plot", "Confidence", "RMSE", data[:names], data[:confidence], data[:performance], true)
end
end
end
end
def self.create_bar_plot( out_files, validation_set, title_attribute, value_attributes )
out_files = [out_files] unless out_files.is_a?(Array)
LOGGER.debug "creating bar plot, out-files:"+out_files.inspect
data = []
titles = []
labels = []
validation_set.validations.each do |v|
values = []
value_attributes.each do |a|
accept = validation_set.get_accept_values_for_attr(a)
if accept and accept.size>0
accept.each do |class_value|
value = v.send(a)
if value.is_a?(Hash)
if class_value==nil
avg_value = 0
value.values.each{ |val| avg_value+=val }
value = avg_value/value.values.size.to_f
else
raise "bar plot value is hash, but no entry for class-value ("+class_value.to_s+"); value for "+a.to_s+" -> "+value.inspect unless value.key?(class_value)
value = value[class_value]
end
end
raise "value is nil\nattribute: "+a.to_s+"\nvalidation: "+v.inspect if value==nil
values.push(value)
labels.push(a.to_s.gsub("_","-") + ( class_value==nil ? "" : "("+class_value.to_s+")" ))
end
else
value = v.send(a)
values.push(value)
labels.push(a.to_s.gsub("_","-"))
end
end
titles << v.send(title_attribute).to_s
raise "no title for '"+title_attribute.to_s+"' in validation: "+v.to_yaml if titles[-1].to_s.size==0
data << values
end
titles = titles.remove_common_prefix
(0..titles.size-1).each do |i|
data[i] = [titles[i]] + data[i]
end
LOGGER.debug "bar plot labels: "+labels.inspect
LOGGER.debug "bar plot data: "+data.inspect
out_files.each do |out_file|
RubyPlot::plot_bars('Bar plot', labels, data, out_file)
end
end
def self.create_ranking_plot( out_file, validation_set, compare_attribute, equal_attribute, rank_attribute, class_value=nil )
#compute ranks
#puts "rank attibute is "+rank_attribute.to_s
rank_set = validation_set.compute_ranking([equal_attribute],rank_attribute,class_value)
#puts compare_attribute
#puts rank_set.to_array([:algorithm_uri, :dataset_uri, :percent_correct, :percent_correct_ranking]).collect{|a| a.inspect}.join("\n")
#puts "\n"
#compute avg ranks
merge_set = rank_set.merge([compare_attribute])
#puts merge_set.to_array([:algorithm_uri, :dataset_uri, :percent_correct, :percent_correct_ranking]).collect{|a| a.inspect}.join("\n")
comparables = merge_set.get_values(compare_attribute)
ranks = merge_set.get_values((rank_attribute.to_s+"_ranking").to_sym,false)
plot_ranking( rank_attribute.to_s+" ranking",
comparables,
ranks,
nil, #0.1,
validation_set.num_different_values(equal_attribute),
out_file)
end
protected
def self.plot_ranking( title, comparables_array, ranks_array, confidence = nil, numdatasets = nil, out_file = nil )
(confidence and numdatasets) ? conf = "-q "+confidence.to_s+" -k "+numdatasets.to_s : conf = ""
out_file ? show = "-o" : show = ""
(title and title.length > 0) ? tit = '-t "'+title+'"' : tit = ""
#title = "-t \""+ranking_value_prop+"-Ranking ("+comparables.size.to_s+" "+comparable_prop+"s, "+num_groups.to_s+" "+ranking_group_prop+"s, p < "+p.to_s+")\" "
cmd = "java -jar "+ENV['RANK_PLOTTER_JAR']+" "+tit+" -c '"+
comparables_array.join(",")+"' -r '"+ranks_array.join(",")+"' "+conf+" "+show #+" > /home/martin/tmp/test.svg"
#puts "\nplotting: "+cmd
LOGGER.debug "Plotting ranks: "+cmd.to_s
res = ""
IO.popen(cmd) do |f|
while line = f.gets do
res += line
end
end
raise "rank plot failed" unless $?==0
if out_file
f = File.new(out_file, "w")
f.puts res
end
out_file ? out_file : res
end
def self.demo_ranking_plot
puts plot_ranking( nil, ["naive bayes", "svm", "decision tree"], [1.9, 3, 1.5], 0.1, 50) #, "/home/martin/tmp/test.svg")
end
private
def self.transform_roc_predictions(validation_set, class_value, add_label=true )
if (validation_set.size > 1)
values = { :true_positives => [], :confidence_values => []}
(0..validation_set.size-1).each do |i|
roc_values = validation_set.get(i).get_predictions.get_roc_prediction_values(class_value)
values[:true_positives ] += roc_values[:true_positives ]
values[:confidence_values] += roc_values[:confidence_values]
end
else
values = validation_set.validations[0].get_predictions.get_roc_prediction_values(class_value)
end
tp_fp_rates = get_tp_fp_rates(values)
labels = []
tp_fp_rates[:youden].each do |point,confidence|
labels << ["confidence: "+confidence.to_nice_s, point[0], point[1]]
end if add_label
RubyPlot::LinePlotData.new(:name => "", :x_values => tp_fp_rates[:fp_rate], :y_values => tp_fp_rates[:tp_rate], :labels => labels)
end
def self.transform_confidence_predictions(validation_set, class_value, add_single_folds=false)
if (validation_set.size > 1)
names = []; performance = []; confidence = []; faint = []
sum_confidence_values = { :predicted_values => [], :actual_values => [], :confidence_values => []}
(0..validation_set.size-1).each do |i|
confidence_values = validation_set.get(i).get_predictions.get_prediction_values(class_value)
sum_confidence_values[:predicted_values] += confidence_values[:predicted_values]
sum_confidence_values[:confidence_values] += confidence_values[:confidence_values]
sum_confidence_values[:actual_values] += confidence_values[:actual_values]
if add_single_folds
begin
pref_conf_rates = get_performance_confidence_rates(confidence_values)
names << "fold "+i.to_s
performance << pref_conf_rates[:performance]
confidence << pref_conf_rates[:confidence]
faint << true
rescue
LOGGER.warn "could not get confidence vals for fold "+i.to_s
end
end
end
pref_conf_rates = get_performance_confidence_rates(sum_confidence_values, validation_set.unique_feature_type)
names << nil # "all"
performance << pref_conf_rates[:performance]
confidence << pref_conf_rates[:confidence]
faint << false
return { :names => names, :performance => performance, :confidence => confidence, :faint => faint }
else
confidence_values = validation_set.validations[0].get_predictions.get_prediction_values(class_value)
pref_conf_rates = get_performance_confidence_rates(confidence_values, validation_set.unique_feature_type)
return { :names => [""], :performance => [pref_conf_rates[:performance]], :confidence => [pref_conf_rates[:confidence]] }
end
end
def self.demo_roc_plot
# roc_values = {:confidence_values => [0.1, 0.9, 0.5, 0.6, 0.6, 0.6],
# :predicted_values => [1, 0, 0, 1, 0, 1],
# :actual_values => [0, 1, 0, 0, 1, 1]}
roc_values = {:confidence_values => [0.9, 0.8, 0.7, 0.6, 0.5, 0.4],
:true_positives => [1, 1, 1, 0, 1, 0]}
tp_fp_rates = get_tp_fp_rates(roc_values)
labels = []
tp_fp_rates[:youden].each do |point,confidence|
labels << ["confidence: "+confidence.to_s, point[0], point[1]]
end
plot_data = []
plot_data << RubyPlot::LinePlotData.new(:name => "testname", :x_values => tp_fp_rates[:fp_rate], :y_values => tp_fp_rates[:tp_rate], :labels => labels)
RubyPlot::plot_lines("/tmp/plot.png",
"ROC-Plot",
"False positive rate",
"True Positive Rate", plot_data )
end
def self.get_performance_confidence_rates(roc_values, feature_type)
c = roc_values[:confidence_values]
p = roc_values[:predicted_values]
a = roc_values[:actual_values]
raise "no prediction values for confidence plot" if p.size==0
(0..p.size-2).each do |i|
((i+1)..p.size-1).each do |j|
if c[i]<c[j]
c.swap!(i,j)
a.swap!(i,j)
p.swap!(i,j)
end
end
end
#puts c.inspect+"\n"+a.inspect+"\n"+p.inspect+"\n\n"
perf = []
conf = []
case feature_type
when "classification"
count = 0
correct = 0
(0..p.size-1).each do |i|
count += 1
correct += 1 if p[i]==a[i]
if i>0 && (c[i]>=conf[-1]-0.00001)
perf.pop
conf.pop
end
perf << correct/count.to_f * 100
conf << c[i]
end
when "regression"
count = 0
sum_squared_error = 0
(0..p.size-1).each do |i|
count += 1
sum_squared_error += (p[i]-a[i])**2
if i>0 && (c[i]>=conf[-1]-0.00001)
perf.pop
conf.pop
end
perf << Math.sqrt(sum_squared_error/count.to_f)
conf << c[i]
end
end
#puts perf.inspect
return {:performance => perf,:confidence => conf}
end
def self.get_tp_fp_rates(roc_values)
c = roc_values[:confidence_values]
tp = roc_values[:true_positives]
raise "no prediction values for roc-plot" if tp.size==0
# hack for painting perfect/worst roc curve, otherwhise fp/tp-rate will always be 100%
# determine if perfect/worst roc curve
fp_found = false
tp_found = false
(0..tp.size-1).each do |i|
if tp[i]==0
fp_found |= true
else
tp_found |=true
end
break if tp_found and fp_found
end
unless fp_found and tp_found #if perfect/worst add wrong/right instance with lowest confidence
tp << (tp_found ? 0 : 1)
c << -Float::MAX
end
(0..tp.size-2).each do |i|
((i+1)..tp.size-1).each do |j|
if c[i]<c[j]
c.swap!(i,j)
tp.swap!(i,j)
end
end
end
#puts c.inspect+"\n"+tp.inspect+"\n\n"
tp_rate = [0]
fp_rate = [0]
w = [1]
c2 = [Float::MAX]
(0..tp.size-1).each do |i|
if tp[i]==1
tp_rate << tp_rate[-1]+1
fp_rate << fp_rate[-1]
else
fp_rate << fp_rate[-1]+1
tp_rate << tp_rate[-1]
end
w << 1
c2 << c[i]
end
#puts c2.inspect+"\n"+tp_rate.inspect+"\n"+fp_rate.inspect+"\n"+w.inspect+"\n\n"
tp_rate = tp_rate.compress_max(c2)
fp_rate = fp_rate.compress_max(c2)
w = w.compress_sum(c2)
#puts tp_rate.inspect+"\n"+fp_rate.inspect+"\n"+w.inspect+"\n\n"
youden = []
(0..tp_rate.size-1).each do |i|
tpr = tp_rate[i]/tp_rate[-1].to_f
fpr = fp_rate[i]/fp_rate[-1].to_f
youden << tpr + (1 - fpr)
#puts youden[-1].to_s+" ("+tpr.to_s+" "+fpr.to_s+")"
end
max = youden.max
youden_hash = {}
(0..tp_rate.size-1).each do |i|
if youden[i]==max and i>0
youden_hash[i] = c2[i]
end
end
#puts youden.inspect+"\n"+youden_hash.inspect+"\n\n"
(0..tp_rate.size-1).each do |i|
tp_rate[i] = tp_rate[-1]>0 ? tp_rate[i]/tp_rate[-1].to_f*100 : 100
fp_rate[i] = fp_rate[-1]>0 ? fp_rate[i]/fp_rate[-1].to_f*100 : 100
end
#puts tp_rate.inspect+"\n"+fp_rate.inspect+"\n\n"
youden_coordinates_hash = {}
youden_hash.each do |i,c|
youden_coordinates_hash[[fp_rate[i],tp_rate[i]]] = c
end
#puts youden_coordinates_hash.inspect+"\n\n"
return {:tp_rate => tp_rate,:fp_rate => fp_rate,:youden => youden_coordinates_hash}
end
end
end
#require "rubygems"
#require "ruby-plot"
##Reports::PlotFactory::demo_ranking_plot
#Reports::PlotFactory::demo_roc_plot
#a = [1, 0, 1, 2, 3, 0, 2]
#puts a.compress_sum([100, 90, 70, 70, 30, 10, 0]).inspect
#puts a.compress_max([100, 90, 70, 70, 30, 10, 0]).inspect
|