Class: VectorModel
- Inherits:
-
Object
- Object
- VectorModel
- Defined in:
- lib/rbbt/vector/model.rb,
lib/rbbt/vector/model/util.rb
Direct Known Subclasses
Instance Attribute Summary collapse
-
#bar(max = nil, desc = nil) ⇒ Object
Returns the value of attribute bar.
-
#directory ⇒ Object
Returns the value of attribute directory.
-
#eval_model ⇒ Object
Returns the value of attribute eval_model.
-
#extract_features ⇒ Object
Returns the value of attribute extract_features.
-
#factor_levels ⇒ Object
Returns the value of attribute factor_levels.
-
#features ⇒ Object
Returns the value of attribute features.
-
#labels ⇒ Object
Returns the value of attribute labels.
-
#model_file ⇒ Object
Returns the value of attribute model_file.
-
#names ⇒ Object
Returns the value of attribute names.
-
#train_model ⇒ Object
Returns the value of attribute train_model.
Class Method Summary collapse
-
.f1_metrics(test, predicted, good_label = nil) ⇒ Object
acc end.
- .R_eval(model_file, features, list, code, names = nil, factor_levels = nil) ⇒ Object
- .R_run(model_file, features, labels, code, names = nil, factor_levels = nil) ⇒ Object
- .R_train(model_file, features, labels, code, names = nil, factor_levels = nil) ⇒ Object
Instance Method Summary collapse
- #__load_method(file) ⇒ Object
- #add(element, label = nil) ⇒ Object
- #add_list(elements, labels = nil) ⇒ Object
- #clear ⇒ Object
- #cross_validation(folds = 10, good_label = nil) ⇒ Object
- #eval(element) ⇒ Object
- #eval_list(elements, extract = true) ⇒ Object
-
#initialize(directory, extract_features = nil, train_model = nil, eval_model = nil, names = nil, factor_levels = nil) ⇒ VectorModel
constructor
A new instance of VectorModel.
- #run(code) ⇒ Object
- #save_models ⇒ Object
- #train ⇒ Object
Constructor Details
#initialize(directory, extract_features = nil, train_model = nil, eval_model = nil, names = nil, factor_levels = nil) ⇒ VectorModel
Returns a new instance of VectorModel.
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# File 'lib/rbbt/vector/model.rb', line 108 def initialize(directory, extract_features = nil, train_model = nil, eval_model = nil, names = nil, factor_levels = nil) @directory = directory FileUtils.mkdir_p @directory unless File.exists? @directory @model_file = File.join(@directory, "model") @extract_features_file = File.join(@directory, "features") @train_model_file = File.join(@directory, "train_model") @eval_model_file = File.join(@directory, "eval_model") @train_model_file_R = File.join(@directory, "train_model.R") @eval_model_file_R = File.join(@directory, "eval_model.R") @names_file = File.join(@directory, "feature_names") @levels_file = File.join(@directory, "levels") if extract_features.nil? if File.exists?(@extract_features_file) @extract_features = __load_method @extract_features_file end else @extract_features = extract_features end if train_model.nil? if File.exists?(@train_model_file) @train_model = __load_method @train_model_file elsif File.exists?(@train_model_file_R) @train_model = Open.read(@train_model_file_R) end else @train_model = train_model end if eval_model.nil? if File.exists?(@eval_model_file) @eval_model = __load_method @eval_model_file elsif File.exists?(@eval_model_file_R) @eval_model = Open.read(@eval_model_file_R) end else @eval_model = eval_model end if names.nil? if File.exists?(@names_file) @names = Open.read(@names_file).split("\n") end else @extract_features = names end if factor_levels.nil? if File.exists?(@levels_file) @factor_levels = YAML.load(Open.read(@levels_file)) end if File.exists?(@model_file + '.factor_levels') @factor_levels = TSV.open(@model_file + '.factor_levels') end else @factor_levels = factor_levels end @features = [] @labels = [] end |
Instance Attribute Details
#bar(max = nil, desc = nil) ⇒ Object
Returns the value of attribute bar.
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# File 'lib/rbbt/vector/model/util.rb', line 2 def @bar end |
#directory ⇒ Object
Returns the value of attribute directory.
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# File 'lib/rbbt/vector/model.rb', line 5 def directory @directory end |
#eval_model ⇒ Object
Returns the value of attribute eval_model.
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# File 'lib/rbbt/vector/model.rb', line 5 def eval_model @eval_model end |
#extract_features ⇒ Object
Returns the value of attribute extract_features.
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# File 'lib/rbbt/vector/model.rb', line 5 def extract_features @extract_features end |
#factor_levels ⇒ Object
Returns the value of attribute factor_levels.
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# File 'lib/rbbt/vector/model.rb', line 6 def factor_levels @factor_levels end |
#features ⇒ Object
Returns the value of attribute features.
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# File 'lib/rbbt/vector/model.rb', line 6 def features @features end |
#labels ⇒ Object
Returns the value of attribute labels.
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# File 'lib/rbbt/vector/model.rb', line 6 def labels @labels end |
#model_file ⇒ Object
Returns the value of attribute model_file.
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# File 'lib/rbbt/vector/model.rb', line 5 def model_file @model_file end |
#names ⇒ Object
Returns the value of attribute names.
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# File 'lib/rbbt/vector/model.rb', line 6 def names @names end |
#train_model ⇒ Object
Returns the value of attribute train_model.
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# File 'lib/rbbt/vector/model.rb', line 5 def train_model @train_model end |
Class Method Details
.f1_metrics(test, predicted, good_label = nil) ⇒ Object
acc end
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# File 'lib/rbbt/vector/model.rb', line 299 def self.f1_metrics(test, predicted, good_label = nil) tp, tn, fp, fn, pr, re, f1 = [0, 0, 0, 0, nil, nil, nil] labels = (test + predicted).uniq if labels.length == 2 || good_label good_label = labels.uniq.select{|l| l.to_s == "true"}.first if good_label.nil? good_label = labels.uniq.select{|l| l.to_s == "1"}.first if good_label.nil? good_label = labels.uniq.sort.first if good_label.nil? good_label = good_label.to_s test.zip(predicted).each do |gs,pred| gs = gs.to_s pred = pred.to_s tp += 1 if pred == good_label && gs == good_label fp += 1 if pred == good_label && gs != good_label tn += 1 if pred != good_label && gs != good_label fn += 1 if pred != good_label && gs == good_label end p = tp + fn pp = tp + fp pr = tp.to_f / pp re = tp.to_f / p f1 = (2.0 * tp) / (2.0 * tp + fp + fn) [tp, tn, fp, fn, pr, re, f1] else num = labels.length acc = [] labels.each do |good_label| values = VectorModel.f1_metrics(test, predicted, good_label) tp, tn, fp, fn, pr, re, f1 = values Log.debug "Partial CV #{good_label} - P:#{"%.3f" % pr} R:#{"%.3f" % re} F1:#{"%.3f" % f1} - #{[tp.to_s, tn.to_s, fp.to_s, fn.to_s] * " "}" acc << values end Misc.zip_fields(acc).collect{|s| Misc.mean(s)} end end |
.R_eval(model_file, features, list, code, names = nil, factor_levels = nil) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 69 def self.R_eval(model_file, features, list, code, names = nil, factor_levels = nil) TmpFile.with_file do |feature_file| if list Open.write(feature_file, features.collect{|feat| feat * "\t"} * "\n" + "\n") else Open.write(feature_file, features * "\t" + "\n") end Open.write(feature_file + '.names', names * "\n" + "\n") if names TmpFile.with_file do |results| io = R.run <<-EOF features = read.table("#{ feature_file }", sep ="\\t", stringsAsFactors=TRUE); #{"names(features) = make.names(readLines('#{feature_file + '.names'}'))" if names } #{ factor_levels.collect do |name,levels| "features[['#{name}']] = factor(features[['#{name}']], levels=#{R.ruby2R levels})" end * "\n" if factor_levels } load(file="#{model_file}"); #{code} cat(paste(label, sep="\\n", collapse="\\n")); EOF txt = io.read res = txt.sub(/WARNING: .*?\n/s,'').split(/\s+/) if list res else res.first end end end end |
.R_run(model_file, features, labels, code, names = nil, factor_levels = nil) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 8 def self.R_run(model_file, features, labels, code, names = nil, factor_levels = nil) TmpFile.with_file do |feature_file| Open.write(feature_file, features.collect{|feats| feats * "\t"} * "\n") Open.write(feature_file + '.label', labels * "\n" + "\n") Open.write(feature_file + '.names', names * "\n" + "\n") if names what = case labels.first when Numeric, Integer, Float 'numeric()' else 'character()' end R.run <<-EOF features = read.table("#{ feature_file }", sep ="\\t", stringsAsFactors=TRUE); #{"names(features) = make.names(readLines('#{feature_file + '.names'}'))" if names } #{ factor_levels.collect do |name,levels| "features[['#{name}']] = factor(features[['#{name}']], levels=#{R.ruby2R levels})" end * "\n" if factor_levels } labels = scan("#{ feature_file }.label", what=#{what}); features = cbind(features, label = labels); #{code} EOF end end |
.R_train(model_file, features, labels, code, names = nil, factor_levels = nil) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 35 def self.R_train(model_file, features, labels, code, names = nil, factor_levels = nil) TmpFile.with_file do |feature_file| Open.write(feature_file, features.collect{|feats| feats * "\t"} * "\n") Open.write(feature_file + '.label', labels * "\n" + "\n") Open.write(feature_file + '.names', names * "\n" + "\n") if names what = case labels.first when Numeric, Integer, Float 'numeric()' else 'character()' end R.run <<-EOF features = read.table("#{ feature_file }", sep ="\\t", stringsAsFactors=TRUE); labels = scan("#{ feature_file }.label", what=#{what}); #{"names(features) = make.names(readLines('#{feature_file + '.names'}'))" if names } features = cbind(features, label = labels); #{ factor_levels.collect do |name,levels| "features[['#{name}']] = factor(features[['#{name}']], levels=#{R.ruby2R levels})" end * "\n" if factor_levels } #{code} # Save used factor levels factor_levels = c() for (c in names(features)){ if (is.factor(features[[c]])) factor_levels[c] = paste(levels(features[[c]]), collapse="\t") } rbbt.tsv.write("#{model_file}.factor_levels", factor_levels, names=c('Levels'), type='flat') save(model, file='#{model_file}') EOF end end |
Instance Method Details
#__load_method(file) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 102 def __load_method(file) code = Open.read(file) code.sub!(/.*Proc\.new/, "Proc.new") instance_eval code, file end |
#add(element, label = nil) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 177 def add(element, label = nil) features = @extract_features ? extract_features.call(element) : element @features << features @labels << label end |
#add_list(elements, labels = nil) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 183 def add_list(elements, labels = nil) if @extract_features.nil? || @extract_features.arity == 1 elements.zip(labels || [nil]).each do |elem,label| add(elem, label) end else features = @extract_features.call(nil, elements) @features.concat features @labels.concat labels if labels end end |
#clear ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 172 def clear @features = [] @labels = [] end |
#cross_validation(folds = 10, good_label = nil) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 342 def cross_validation(folds = 10, good_label = nil) orig_features = @features orig_labels = @labels multiclass = @labels.uniq.length > 2 if multiclass res = TSV.setup({}, "Fold~P,R,F1#:type=:list") else res = TSV.setup({}, "Fold~TP,TN,FP,FN,P,R,F1#:type=:list") end begin if folds == 1 feature_folds = [@features] labels_folds = [@labels] else feature_folds = Misc.divide(@features, folds) labels_folds = Misc.divide(@labels, folds) end folds.times do |fix| if folds == 1 rest = [fix] else rest = (0..(folds-1)).to_a - [fix] end test_set = feature_folds[fix] train_set = feature_folds.values_at(*rest).inject([]){|acc,e| acc += e; acc} test_labels = labels_folds[fix] train_labels = labels_folds.values_at(*rest).flatten @features = train_set @labels = train_labels self.train predictions = self.eval_list test_set, false raise "Number of predictions (#{predictions.length}) and test labels (#{test_labels.length}) do not match" if predictions.length != test_labels.length different_labels = test_labels.uniq Log.debug do "Accuracy Fold #{fix}: #{(100 * test_labels.zip(predictions).select{|t,p| t == p }.length.to_f / test_labels.length).round(2)}%" end tp, tn, fp, fn, pr, re, f1 = VectorModel.f1_metrics(test_labels, predictions, good_label) if multiclass Log.low "Multi-class CV Fold #{fix} - Average P:#{"%.3f" % pr} R:#{"%.3f" % re} F1:#{"%.3f" % f1}" res[fix] = [pr,re,f1] else Log.low "CV Fold #{fix} P:#{"%.3f" % pr} R:#{"%.3f" % re} F1:#{"%.3f" % f1} - #{[tp.to_s, tn.to_s, fp.to_s, fn.to_s] * " "}" res[fix] = [tp,tn,fp,fn,pr,re,f1] end end ensure @features = orig_features @labels = orig_labels end self.train unless folds == 1 res end |
#eval(element) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 238 def eval(element) case when Proc === @eval_model @eval_model.call(@model_file, @extract_features.call(element), false, nil, @names, @factor_levels) when String === @eval_model VectorModel.R_eval(@model_file, @extract_features.call(element), false, eval_model, @names, @factor_levels) end end |
#eval_list(elements, extract = true) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 247 def eval_list(elements, extract = true) if extract && ! @extract_features.nil? features = if @extract_features.arity == 1 elements.collect{|element| @extract_features.call(element) } else @extract_features.call(nil, elements) end else features = elements end case when Proc === eval_model eval_model.call(@model_file, features, true, nil, @names, @factor_levels) when String === eval_model VectorModel.R_eval(@model_file, features, true, eval_model, @names, @factor_levels) end end |
#run(code) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 234 def run(code) VectorModel.R_run(@model_file, @features, @labels, code, @names, @factor_levels) end |
#save_models ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 195 def save_models require 'method_source' case when Proc === train_model begin Open.write(@train_model_file, train_model.source) rescue end when String === train_model Open.write(@train_model_file_R, @train_model) end Open.write(@extract_features_file, @extract_features.source) if @extract_features case when Proc === eval_model begin Open.write(@eval_model_file, eval_model.source) rescue end when String === eval_model Open.write(@eval_model_file_R, eval_model) end Open.write(@levels_file, @factor_levels.to_yaml) if @factor_levels Open.write(@names_file, @names * "\n" + "\n") if @names end |
#train ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 224 def train case when Proc === train_model train_model.call(@model_file, @features, @labels, @names, @factor_levels) when String === train_model VectorModel.R_train(@model_file, @features, @labels, train_model, @names, @factor_levels) end save_models end |