Class: VectorModel

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

Direct Known Subclasses

PythonModel, RFModel, SVMModel, SpaCyModel, TensorFlowModel

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(directory = nil, model_options = {}) ⇒ VectorModel

Returns a new instance of VectorModel.



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# File 'lib/rbbt/vector/model.rb', line 143

def initialize(directory = nil, model_options = {})
  @directory = directory
  @model_options = IndiferentHash.setup(model_options)

  if @directory
    FileUtils.mkdir_p @directory unless File.exist?(@directory)

    @model_path            = File.join(@directory, "model")

    @extract_features_file = File.join(@directory, "features")
    @init_model_path       = File.join(@directory, "init_model")

    @train_model_path      = File.join(@directory, "train_model")
    @train_model_path_R    = File.join(@directory, "train_model.R")

    @eval_model_path       = File.join(@directory, "eval_model")
    @eval_model_path_R     = File.join(@directory, "eval_model.R")

    @post_process_file     = File.join(@directory, "post_process")
    @post_process_file_R   = File.join(@directory, "post_process.R")

    @names_file            = File.join(@directory, "feature_names")
    @levels_file           = File.join(@directory, "levels")
    @options_file          = File.join(@directory, "options.json")

    if File.exist?(@options_file)
      @model_options = JSON.parse(Open.read(@options_file)).merge(@model_options || {})
      IndiferentHash.setup(@model_options)
    end
  end
  
  if extract_features.nil?
    if @extract_features_file && File.exist?(@extract_features_file)
      @extract_features = __load_method @extract_features_file
    end
  else
    @extract_features = extract_features 
  end

  if init_model.nil?
    if @init_model_path && File.exist?(@init_model_path)
      @init_model = __load_method @init_model_path
    end
  else
    @init_model = init_model 
  end

  if train_model.nil?
    if @train_model_path && File.exist?(@train_model_path)
      @train_model = __load_method @train_model_path
    elsif @train_model_path_R && File.exist?(@train_model_path_R)
      @train_model = Open.read(@train_model_path_R)
    end
  else
    @train_model = train_model 
  end

  if eval_model.nil?
    if @eval_model_path && File.exist?(@eval_model_path)
      @eval_model = __load_method @eval_model_path
    elsif @eval_model_path_R && File.exist?(@eval_model_path_R)
      @eval_model = Open.read(@eval_model_path_R)
    end
  else
    @eval_model = eval_model
  end

  if post_process.nil?
    if @post_process_file && File.exist?(@post_process_file)
      @post_process = __load_method @post_process_file
    elsif @post_process_file_R && File.exist?(@post_process_file_R)
      @post_process = Open.read(@post_process_file_R)
    end
  else
    @post_process = post_process
  end


  if names.nil?
    if @names_file && File.exist?(@names_file)
      @names = Open.read(@names_file).split("\n")
    end
  else
    @extract_features = names 
  end

  if factor_levels.nil?
    if @levels_file && File.exist?(@levels_file)
      @factor_levels = YAML.load(Open.read(@levels_file))
    end
    if @model_path && File.exist?(@model_path + '.factor_levels')
      @factor_levels = TSV.open(@model_path + '.factor_levels')
    end
  else
    @factor_levels = factor_levels 
  end

  @features = []
  @labels = []
end

Instance Attribute Details

#balance ⇒ Object

Returns the value of attribute balance.



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# File 'lib/rbbt/vector/model.rb', line 9

def balance
  @balance
end

#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
  @bar
end

#directory ⇒ Object

Returns the value of attribute directory.



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# File 'lib/rbbt/vector/model.rb', line 9

def directory
  @directory
end

#eval_model(&block) ⇒ Object

Returns the value of attribute eval_model.



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# File 'lib/rbbt/vector/model.rb', line 9

def eval_model
  @eval_model
end

#extract_features(&block) ⇒ Object

Returns the value of attribute extract_features.



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# File 'lib/rbbt/vector/model.rb', line 9

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 10

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 10

def features
  @features
end

#init_model(&block) ⇒ Object

Returns the value of attribute init_model.



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# File 'lib/rbbt/vector/model.rb', line 9

def init_model
  @init_model
end

#labels ⇒ Object

Returns the value of attribute labels.



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# File 'lib/rbbt/vector/model.rb', line 10

def labels
  @labels
end

#model ⇒ Object

Returns the value of attribute model.



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# File 'lib/rbbt/vector/model.rb', line 11

def model
  @model
end

#model_options ⇒ Object

Returns the value of attribute model_options.



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# File 'lib/rbbt/vector/model.rb', line 11

def model_options
  @model_options
end

#model_path ⇒ Object

Returns the value of attribute model_path.



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# File 'lib/rbbt/vector/model.rb', line 9

def model_path
  @model_path
end

#names ⇒ Object

Returns the value of attribute names.



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# File 'lib/rbbt/vector/model.rb', line 10

def names
  @names
end

#post_process(&block) ⇒ Object

Returns the value of attribute post_process.



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# File 'lib/rbbt/vector/model.rb', line 9

def post_process
  @post_process
end

#train_model(&block) ⇒ Object

Returns the value of attribute train_model.



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# File 'lib/rbbt/vector/model.rb', line 9

def train_model
  @train_model
end

Class Method Details

.f1_metrics(test, predicted, good_label = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 377

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_path, features, list, code, names = nil, factor_levels = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 104

def self.R_eval(model_path, 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_path}");
#{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_path, features, labels, code, names = nil, factor_levels = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 43

def self.R_run(model_path, 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_path, features, labels, code, names = nil, factor_levels = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 70

def self.R_train(model_path, 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_path}.factor_levels", factor_levels, names=c('Levels'), type='flat')
save(model, file='#{model_path}')
    EOF
  end
end

Instance Method Details

#__load_method(file) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 137

def __load_method(file)
  code = Open.read(file)
  code.sub!(/.*(\sdo\b|{)/, 'Proc.new\1')
  instance_eval code, file
end

#add(element, label = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 249

def add(element, label = nil)
  features = @extract_features ? self.instance_exec(element, &@extract_features) : element
  @features << features
  @labels << label 
end

#add_list(elements, labels = nil) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 255

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 = self.instance_exec(nil, elements, &@extract_features)
    @features.concat  features
    @labels.concat labels if labels
  end
end

#balance_labels ⇒ Object 



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# File 'lib/rbbt/vector/model/util.rb', line 13

def balance_labels
  counts = Misc.counts(@labels)
  min = counts.values.min

  used = {}
  new_labels = []
  new_features = []
  @labels.zip(@features).shuffle.each do |label, features|
    used[label] ||= 0
    next if used[label] > min
    used[label] += 1
    new_labels << label
    new_features << features
  end
  @labels = new_labels
  @features = new_features
end

#clear ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 244

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

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



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# File 'lib/rbbt/vector/model.rb', line 420

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).flatten(1)

      test_labels = labels_folds[fix]
      train_labels = labels_folds.values_at(*rest).flatten(1)

      @features = train_set
      @labels = train_labels

      self.reset_model if self.respond_to? :reset_model
      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 unless folds == -1

  self.reset_model if self.respond_to? :reset_model
  self.train unless folds == 1
  res
end

#eval(element) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 336

def eval(element)
  features = @extract_features.nil? ? element : self.instance_exec(element, &@extract_features)

  result = case 
           when Proc === @eval_model
             self.instance_exec(features, false, nil, @names, @factor_levels, &@eval_model)
           when String === @eval_model
             VectorModel.R_eval(@model_path, features, false, eval_model, @names, @factor_levels)
           else
             raise "No @eval_model function or R script"
           end

  result = self.instance_exec(result, false, &@post_process) if Proc === @post_process 

  result
end

#eval_list(elements, extract = true) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 353

def eval_list(elements, extract = true)

  if extract && ! @extract_features.nil? 
    features = if @extract_features.arity == 1
                 elements.collect{|element| self.instance_exec(element, &@extract_features) }
               else
                 self.instance_exec(nil, elements, &@extract_features)
               end
  else
    features = elements
  end

  result = case 
           when Proc === eval_model
             self.instance_exec(features, true, nil, @names, @factor_levels, &@eval_model)
           when String === eval_model
             VectorModel.R_eval(@model_path, features, true, eval_model, @names, @factor_levels)
           end

  result = self.instance_exec(result, true, &@post_process) if Proc === @post_process 

  result
end

#init ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 33

def init
  @model ||= self.instance_exec &@init_model
end

#run(code) ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 332

def run(code)
  VectorModel.R_run(@model_path,  @features, @labels, code, @names, @factor_levels)
end

#save_models ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 267

def save_models
  require 'method_source'

  case 
  when Proc === train_model
    begin
      Open.write(@train_model_path, train_model.source)
    rescue
    end
  when String === train_model
    Open.write(@train_model_path_R, @train_model)
  end

  Open.write(@extract_features_file, @extract_features.source) if @extract_features
  Open.write(@init_model_path, @init_model.source) if @init_model

  case 
  when Proc === eval_model
    begin
      Open.write(@eval_model_path, eval_model.source)
    rescue
    end
  when String === eval_model
    Open.write(@eval_model_path_R, eval_model)
  end

  case 
  when Proc === post_process
    begin
      Open.write(@post_process_file, post_process.source)
    rescue
    end
  when String === post_process
    Open.write(@post_process_file_R, post_process)
  end

  Open.write(@levels_file, @factor_levels.to_yaml) if @factor_levels
  Open.write(@names_file, @names * "\n" + "\n") if @names
  Open.write(@options_file, @model_options.to_json) if @model_options
end

#train ⇒ Object 



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# File 'lib/rbbt/vector/model.rb', line 308

def train
  begin
    if @balance
      @original_features = @features
      @original_labels = @labels
      self.balance_labels
    end

    case 
    when Proc === @train_model
      self.instance_exec(@features, @labels, @names, @factor_levels, &@train_model)
    when String === @train_model
      VectorModel.R_train(@model_path, @features, @labels, train_model, @names, @factor_levels)
    end
  ensure
    if @balance
      @features =  @original_features
      @labels = @original_labels
    end
  end

  save_models if @directory
end