Class: VectorModel
- Inherits:
-
Object
- Object
- VectorModel
- Defined in:
- lib/rbbt/vector/model.rb
Direct Known Subclasses
Instance Attribute Summary collapse
-
#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.
-
#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.
-
#train_model ⇒ Object
Returns the value of attribute train_model.
Class Method Summary collapse
- .R_eval(model_file, features, list, code) ⇒ Object
- .R_train(model_file, features, labels, code) ⇒ Object
Instance Method Summary collapse
- #add(element, label = nil) ⇒ Object
- #cross_validation(folds = 10) ⇒ Object
- #eval(element) ⇒ Object
- #eval_list(elements, extract = true) ⇒ Object
-
#initialize(directory, extract_features = nil, train_model = nil, eval_model = nil) ⇒ VectorModel
constructor
A new instance of VectorModel.
- #train ⇒ Object
Constructor Details
#initialize(directory, extract_features = nil, train_model = nil, eval_model = nil) ⇒ VectorModel
Returns a new instance of VectorModel.
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# File 'lib/rbbt/vector/model.rb', line 49 def initialize(directory, extract_features = nil, train_model = nil, eval_model = nil) @directory = directory FileUtils.mkdir_p @directory unless File.exists? @directory @model_file = File.join(@directory, "model") extract_features = @extract_features train_model = @train_model eval_model = @eval_model @features = [] @labels = [] end |
Instance Attribute Details
#directory ⇒ Object
Returns the value of attribute directory.
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# File 'lib/rbbt/vector/model.rb', line 4 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 4 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 4 def extract_features @extract_features end |
#features ⇒ Object
Returns the value of attribute features.
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# File 'lib/rbbt/vector/model.rb', line 5 def features @features end |
#labels ⇒ Object
Returns the value of attribute labels.
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# File 'lib/rbbt/vector/model.rb', line 5 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 4 def model_file @model_file end |
#train_model ⇒ Object
Returns the value of attribute train_model.
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# File 'lib/rbbt/vector/model.rb', line 4 def train_model @train_model end |
Class Method Details
.R_eval(model_file, features, list, code) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 22 def self.R_eval(model_file, features, list, code) TmpFile.with_file do |feature_file| TmpFile.with_file do |results| if list Open.write(feature_file, features.collect{|feat| feat * "\t"} * "\n" + "\n") else Open.write(feature_file, features * "\t" + "\n") end io = R.run <<-EOF features = read.table("#{ feature_file }", sep ="\\t", stringsAsFactors=FALSE); load(file="#{model_file}"); #{code} cat(paste(label, sep="\\n")); EOF res = io.read.sub(/WARNING: .*?\n/s,'').split(/\s+/).collect{|l| l.to_f} if list res else res.first end end end end |
.R_train(model_file, features, labels, code) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 7 def self.R_train(model_file, features, labels, code) TmpFile.with_file do |feature_file| Open.write(feature_file, features.collect{|feats| feats * "\t"} * "\n") Open.write(feature_file + '.class', labels * "\n") R.run <<-EOF features = read.table("#{ feature_file }", sep ="\\t", stringsAsFactors=FALSE); labels = scan("#{ feature_file }.class"); features = cbind(features, class = labels); #{code} save(model, file='#{model_file}') EOF end end |
Instance Method Details
#add(element, label = nil) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 60 def add(element, label = nil) @features << extract_features.call(element) @labels << label unless label.nil? end |
#cross_validation(folds = 10) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 92 def cross_validation(folds = 10) saved_features = @features saved_labels = @labels seq = (0..features.length - 1).to_a chunk_size = features.length / folds acc = [] folds.times do seq = seq.shuffle eval_chunk = seq[0..chunk_size] train_chunk = seq[chunk_size.. -1] eval_features = @features.values_at *eval_chunk eval_labels = @labels.values_at *eval_chunk @features = @features.values_at *train_chunk @labels = @labels.values_at *train_chunk train predictions = eval_list eval_features, false acc << predictions.zip(eval_labels).collect{|pred,lab| pred - lab < 0.5 ? 1 : 0}.inject(0){|acc,e| acc +=e} / chunk_size @features = saved_features @labels = saved_labels end acc end |
#eval(element) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 74 def eval(element) case when Proc === eval_model eval_model.call(@model_file, extract_features.call(element), false) when String === eval_model SVMModel.R_eval(@model_file, extract_features.call(element), false, eval_model) end end |
#eval_list(elements, extract = true) ⇒ Object
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# File 'lib/rbbt/vector/model.rb', line 83 def eval_list(elements, extract = true) case when Proc === eval_model eval_model.call(@model_file, extract ? elements.collect{|element| extract_features.call(element)} : elements, true) when String === eval_model SVMModel.R_eval(@model_file, extract ? elements.collect{|element| extract_features.call(element)} : elements, true, eval_model) end end |