Class: VectorModel

Inherits:
Object
  • Object
show all
Defined in:
lib/rbbt/vector/model.rb

Direct Known Subclasses

RFModel, SVMModel, SpaCyModel, TensorFlowModel

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(directory, extract_features = nil, train_model = nil, eval_model = nil, names = nil, factor_levels = nil) ⇒ VectorModel 



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# File 'lib/rbbt/vector/model.rb', line 100

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
  else
    @factor_levels = factor_levels 
  end

  @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

#factor_levels ⇒ Object

Returns the value of attribute factor_levels.



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# File 'lib/rbbt/vector/model.rb', line 5

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 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

#names ⇒ Object

Returns the value of attribute names.



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# File 'lib/rbbt/vector/model.rb', line 5

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 4

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 288

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 61

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 "features = read.table(\"\#{ feature_file }\", sep =\"\\\\t\", stringsAsFactors=TRUE);\n\#{\"names(features) = make.names(readLines('\#{feature_file + '.names'}'))\" if names }\n\#{ factor_levels.collect do |name,levels|\n  \"features[['\#{name}']] = factor(features[['\#{name}']], levels=\#{R.ruby2R levels})\"\nend * \"\\n\" if factor_levels }\nload(file=\"\#{model_file}\");\n\#{code}\ncat(paste(label, sep=\"\\\\n\", collapse=\"\\\\n\"));\n      EOF\n      txt = io.read\n      res = txt.sub(/WARNING: .*?\\n/s,'').split(/\\s+/)\n\n      if list\n        res\n      else\n        res.first\n      end\n    end\n  end\nend\n"

.R_run(model_file, features, labels, code, names = nil, factor_levels = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 7

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 "features = read.table(\"\#{ feature_file }\", sep =\"\\\\t\", stringsAsFactors=TRUE);\n\#{\"names(features) = make.names(readLines('\#{feature_file + '.names'}'))\" if names }\n\#{ factor_levels.collect do |name,levels|\n  \"features[['\#{name}']] = factor(features[['\#{name}']], levels=\#{R.ruby2R levels})\"\nend * \"\\n\" if factor_levels }\nlabels = scan(\"\#{ feature_file }.label\", what=\#{what});\nfeatures = cbind(features, label = labels);\n\#{code}\n    EOF\n  end\nend\n"

.R_train(model_file, features, labels, code, names = nil, factor_levels = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 34

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 "features = read.table(\"\#{ feature_file }\", sep =\"\\\\t\", stringsAsFactors=TRUE);\nlabels = scan(\"\#{ feature_file }.label\", what=\#{what});\n\#{\"names(features) = make.names(readLines('\#{feature_file + '.names'}'))\" if names }\nfeatures = cbind(features, label = labels);\n\#{ factor_levels.collect do |name,levels|\n  \"features[['\#{name}']] = factor(features[['\#{name}']], levels=\#{R.ruby2R levels})\"\nend * \"\\n\" if factor_levels }\n\#{code}\nsave(model, file='\#{model_file}')\n    EOF\n  end\nend\n"

Instance Method Details

#__load_method(file) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 94

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 166

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 172

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 161

def clear
  @features = []
  @labels = []
end

#cross_validation(folds = 10, good_label = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 331

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 227

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 236

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 223

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 184

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 213

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