Class: OpenTox::Algorithm::Fminer

Inherits:
Object
  • Object
show all
Includes:
OpenTox::Algorithm
Defined in:
lib/algorithm.rb

Overview

Fminer algorithms (github.com/amaunz/fminer2)

Direct Known Subclasses

BBRC, LAST

Instance Attribute Summary collapse

Attributes included from OpenTox

#metadata, #uri

Instance Method Summary collapse

Methods included from OpenTox::Algorithm

effect, gauss, get_pc_descriptors, isnull_or_singular?, load_ds_csv, min_frequency, numeric?, pc_descriptors, #run, sum_size, #to_rdfxml, zero_variance?

Methods included from OpenTox

#add_metadata, all, #delete, #initialize, #load_metadata, sign_in, text_to_html, #to_rdfxml

Instance Attribute Details

#all_activities ⇒ Object

Returns the value of attribute all_activities.



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# File 'lib/algorithm.rb', line 55

def all_activities
  @all_activities
end

#compounds ⇒ Object

Returns the value of attribute compounds.



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# File 'lib/algorithm.rb', line 55

def compounds
  @compounds
end

#db_class_sizes ⇒ Object

Returns the value of attribute db_class_sizes.



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# File 'lib/algorithm.rb', line 55

def db_class_sizes
  @db_class_sizes
end

#minfreq ⇒ Object

Returns the value of attribute minfreq.



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# File 'lib/algorithm.rb', line 55

def minfreq
  @minfreq
end

#prediction_feature ⇒ Object

Returns the value of attribute prediction_feature.



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# File 'lib/algorithm.rb', line 55

def prediction_feature
  @prediction_feature
end

#smi ⇒ Object

Returns the value of attribute smi.



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# File 'lib/algorithm.rb', line 55

def smi
  @smi
end

#training_dataset ⇒ Object

Returns the value of attribute training_dataset.



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# File 'lib/algorithm.rb', line 55

def training_dataset
  @training_dataset
end

Instance Method Details

#add_fminer_data(fminer_instance, params, value_map) ⇒ Object 



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# File 'lib/algorithm.rb', line 72

def add_fminer_data(fminer_instance, params, value_map)

  id = 1 # fminer start id is not 0
  @training_dataset.data_entries.each do |compound,entry|
    begin
      smiles = OpenTox::Compound.smiles(compound.to_s)
    rescue
      LOGGER.warn "No resource for #{compound.to_s}"
      next
    end
    if smiles == '' or smiles.nil?
      LOGGER.warn "Cannot find smiles for #{compound.to_s}."
      next
    end

    value_map=params[:value_map] unless params[:value_map].nil?
    entry.each do |feature,values|
      if feature == @prediction_feature.uri
        values.each do |value|
          if value.nil? 
            LOGGER.warn "No #{feature} activity for #{compound.to_s}."
          else
            if @prediction_feature.feature_type == "classification"
              activity= value_map.invert[value.to_s].to_i # activities are mapped to 1..n
              @db_class_sizes[activity-1].nil? ? @db_class_sizes[activity-1]=1 : @db_class_sizes[activity-1]+=1 # AM effect
            elsif @prediction_feature.feature_type == "regression"
              activity= value.to_f 
            end
            begin
              fminer_instance.AddCompound(smiles,id)
              fminer_instance.AddActivity(activity, id)
              @all_activities[id]=activity # DV: insert global information
              @compounds[id] = compound
              @smi[id] = smiles
              id += 1
            rescue Exception => e
              LOGGER.warn "Could not add " + smiles + "\t" + value.to_s + " to fminer"
              LOGGER.warn e.backtrace
            end
          end
        end
      end
    end
  end
end

#check_params(params, per_mil, subjectid = nil) ⇒ Object 



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# File 'lib/algorithm.rb', line 57

def check_params(params,per_mil,subjectid=nil)
  raise OpenTox::NotFoundError.new "Please submit a dataset_uri." unless params[:dataset_uri] and  !params[:dataset_uri].nil?
  raise OpenTox::NotFoundError.new "Please submit a prediction_feature." unless params[:prediction_feature] and  !params[:prediction_feature].nil?
  @prediction_feature = OpenTox::Feature.find params[:prediction_feature], subjectid
  @training_dataset = OpenTox::Dataset.find "#{params[:dataset_uri]}", subjectid
  raise OpenTox::NotFoundError.new "No feature #{params[:prediction_feature]} in dataset #{params[:dataset_uri]}" unless @training_dataset.features and @training_dataset.features.include?(params[:prediction_feature])

  unless params[:min_frequency].nil? 
    @minfreq=params[:min_frequency].to_i
    raise "Minimum frequency must be a number >0!" unless @minfreq>0
  else
    @minfreq=OpenTox::Algorithm.min_frequency(@training_dataset,per_mil) # AM sugg. 8-10 per mil for BBRC, 50 per mil for LAST
  end
end