Class: R::Model

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

Constant Summary collapse

R_METHOD = 
:eval

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(name, formula, data = nil, options = {}) ⇒ Model

Returns a new instance of Model.



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# File 'lib/rbbt/util/R/model.rb', line 21

def initialize(name, formula, data = nil, options = {})
  @name = name
  @formula = formula
  @options = options || {}
  @model_file = options[:model_file] if options[:model_file]
  @model_file ||= Misc.sanitize_filename(File.join(options[:model_dir], name)) if options[:model_dir]

  if data and not model_file.exists?
    method = Misc.process_options options, :fit
    fit(data, method || "lm", options)
  end
end

Instance Attribute Details

#formula ⇒ Object

Returns the value of attribute formula.



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# File 'lib/rbbt/util/R/model.rb', line 20

def formula
  @formula
end

#model_file ⇒ Object

Returns the value of attribute model_file.



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# File 'lib/rbbt/util/R/model.rb', line 20

def model_file
  @model_file
end

#name ⇒ Object

Returns the value of attribute name.



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# File 'lib/rbbt/util/R/model.rb', line 20

def name
  @name
end

Class Method Details

.groom(tsv, formula) ⇒ Object 



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# File 'lib/rbbt/util/R/model.rb', line 68

def self.groom(tsv, formula)
  tsv = tsv.to_list if tsv.type == :single

  if formula.include? tsv.key_field and not tsv.fields.include? tsv.key_field
    tsv = tsv.add_field tsv.key_field do |k,v|
      k
    end
  end

  tsv
end

.load(model_file) ⇒ Object 



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

def self.load(model_file)
  model = Model.new nil, nil, nil, :model_file => model_file
  formula = Open.read(model_file + '.formula')
  model.formula = formula
  model
end

Instance Method Details

#colClasses(tsv) ⇒ Object 



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# File 'lib/rbbt/util/R/model.rb', line 42

def colClasses(tsv)
  return nil unless TSV === tsv
  "c('character', " << 
  (tsv.fields.collect{|f| R.ruby2R(@options[f] ? @options[f].to_s : "NA") } * ", ") <<
  ")"
end

#exists? ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/rbbt/util/R/model.rb', line 124

def exists?
  File.exist? model_file
end

#fit(tsv, method = 'lm', args = {}) ⇒ Object 



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# File 'lib/rbbt/util/R/model.rb', line 128

def fit(tsv, method='lm', args = {})
  args_str = ""
  args_str = args.collect{|name,value| [name,R.ruby2R(value)] * "=" } * ", "
  args_str = ", " << args_str unless args_str.empty?

  tsv = Model.groom(tsv, formula)

  FileUtils.mkdir_p File.dirname(model_file) unless File.exist?(File.dirname(model_file))
  roptions = r_options(tsv)
  tsv.R <<-EOF, roptions
model = rbbt.model.fit(data, #{formula}, method=#{method}#{args_str})
save(model, file='#{model_file}')
data = NULL
  EOF
  Open.write(model_file + '.formula', formula)
end

#predict(tsv, field = "Prediction") ⇒ Object 



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# File 'lib/rbbt/util/R/model.rb', line 93

def predict(tsv, field = "Prediction")
  case tsv
  when TSV
    tsv = Model.groom tsv, formula 
    tsv.R <<-EOF, r_options(tsv)
model = rbbt.model.load('#{model_file}');
data.groomed = rbbt.model.groom(data,formula=#{formula})
data$#{field} = predict(model, data.groomed);
    EOF
  when Hash
    res = R.eval_a <<-EOF
model = rbbt.model.load('#{model_file}');
predict(model, data.frame(#{R.ruby2R tsv}));
    EOF
    Array === tsv.values.first ? res : res.first
  when Numeric, Array, String
    field = formula.split("~").last.strip
    field.gsub!(/log\((.*)\)/,'\1')

    script = <<-EOF
model = rbbt.model.load('#{model_file}');
predict(model, data.frame(#{field} = #{R.ruby2R tsv}));
    EOF

    res = R.eval_a script
    Array === tsv ? res : res.first
  else
    raise "Unknown object for predict: #{Misc.fingerprint tsv}"
  end
end

#predict_interval(value, interval = 'confidence') ⇒ Object 



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# File 'lib/rbbt/util/R/model.rb', line 80

def predict_interval(value, interval='confidence')
  field = formula.split("~").last.strip
  field.gsub!(/log\((.*)\)/,'\1')

  script = <<-EOF
model = rbbt.model.load('#{model_file}');
predict(model, data.frame(#{field} = #{R.ruby2R value}), interval=#{R.ruby2R interval}, level=0.90);
  EOF

  res = R.eval_a script
  Hash[*%w(fit lower upper).zip(res).flatten]
end

#r_options(tsv) ⇒ Object 



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# File 'lib/rbbt/util/R/model.rb', line 49

def r_options(tsv)
  {:R_open => "colClasses=#{colClasses(tsv)}", 
    :R_method => (@options[:R_method] || R_METHOD), 
      :source => @options[:source]}
end

#update(tsv, field = "Prediction") ⇒ Object 



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# File 'lib/rbbt/util/R/model.rb', line 59

def update(tsv, field = "Prediction")
  tsv.R <<-EOF, r_options(tsv)
model = rbbt.model.load('#{model_file}');
model = update(model, data);
save(model, file='#{model_file}');
data = NULL
  EOF
end