diff options
Diffstat (limited to 'lib/model.rb')
| -rw-r--r-- | lib/model.rb | 127 |
1 files changed, 75 insertions, 52 deletions
diff --git a/lib/model.rb b/lib/model.rb index f0fd46b..4321646 100644 --- a/lib/model.rb +++ b/lib/model.rb @@ -91,7 +91,7 @@ module OpenTox include Model include Algorithm - attr_accessor :compound, :prediction_dataset, :features, :effects, :activities, :p_values, :fingerprints, :feature_calculation_algorithm, :similarity_algorithm, :prediction_algorithm, :min_sim, :subjectid + attr_accessor :compound, :prediction_dataset, :features, :effects, :activities, :p_values, :fingerprints, :feature_calculation_algorithm, :similarity_algorithm, :prediction_algorithm, :min_sim, :subjectid, :prop_kernel def initialize(uri=nil) @@ -114,6 +114,7 @@ module OpenTox @prediction_algorithm = "Neighbors.weighted_majority_vote" @min_sim = 0.3 + @prop_kernel = false end @@ -236,17 +237,22 @@ module OpenTox neighbors_best=nil begin - for i in 1..modulo[0] do - (i == modulo[0]) && (slack>0) ? lr_size = s.size + slack : lr_size = s.size + addon # determine fraction - LOGGER.info "BLAZAR: Neighbors round #{i}: #{position} + #{lr_size}." - neighbors_balanced(s, l, position, lr_size) # get ratio fraction of larger part - prediction = eval("#{@prediction_algorithm}(@neighbors,{:similarity_algorithm => @similarity_algorithm, :p_values => @p_values})") - if prediction_best.nil? || prediction[:confidence].abs > prediction_best[:confidence].abs - prediction_best=prediction - neighbors_best=@neighbors + for i in 1..modulo[0] do + (i == modulo[0]) && (slack>0) ? lr_size = s.size + slack : lr_size = s.size + addon # determine fraction + LOGGER.info "BLAZAR: Neighbors round #{i}: #{position} + #{lr_size}." + neighbors_balanced(s, l, position, lr_size) # get ratio fraction of larger part + if @prop_kernel && @prediction_algorithm.include?("svm") + props = get_props + else + props = nil + end + prediction = eval("#{@prediction_algorithm}(@neighbors,{:similarity_algorithm => @similarity_algorithm, :p_values => @p_values}, props)") + if prediction_best.nil? || prediction[:confidence].abs > prediction_best[:confidence].abs + prediction_best=prediction + neighbors_best=@neighbors + end + position = position + lr_size end - position = position + lr_size - end rescue Exception => e LOGGER.error "BLAZAR failed in prediction: "+e.class.to_s+": "+e.message end @@ -255,10 +261,15 @@ module OpenTox @neighbors=neighbors_best ### END AM balanced predictions - else # regression case: no balancing + else # AM: no balancing LOGGER.info "LAZAR: Unbalanced." neighbors - prediction = eval("#{@prediction_algorithm}(@neighbors,{:similarity_algorithm => @similarity_algorithm, :p_values => @p_values})") + if @prop_kernel && @prediction_algorithm.include?("svm") + props = get_props + else + props = nil + end + prediction = eval("#{@prediction_algorithm}(@neighbors,{:similarity_algorithm => @similarity_algorithm, :p_values => @p_values}, props)") end value_feature_uri = File.join( @uri, "predicted", "value") @@ -266,7 +277,7 @@ module OpenTox prediction_feature_uris = {value_feature_uri => prediction[:prediction], confidence_feature_uri => prediction[:confidence]} prediction_feature_uris[value_feature_uri] = nil if @neighbors.size == 0 or prediction[:prediction].nil? - + @prediction_dataset.metadata[OT.dependentVariables] = @metadata[OT.dependentVariables] @prediction_dataset.metadata[OT.predictedVariables] = [value_feature_uri, confidence_feature_uri] @@ -333,54 +344,66 @@ module OpenTox @prediction_dataset end - # Find neighbors and store them as object variable - def neighbors_balanced(s, l, start, offset) - @compound_features = eval("#{@feature_calculation_algorithm}(@compound,@features)") if @feature_calculation_algorithm - - @neighbors = [] - begin - #@fingerprints.each do |training_compound,training_features| # AM: this is original by CH - [ l[start, offset ] , s ].flatten.each do |training_compound| # AM: access only a balanced subset - training_features = @fingerprints[training_compound] - sim = eval("#{@similarity_algorithm}(@compound_features,training_features,@p_values)") - if sim > @min_sim - @activities[training_compound].each do |act| - this_neighbor = { - :compound => training_compound, - :similarity => sim, - :features => training_features, - :activity => act - } - @neighbors << this_neighbor + # Calculate the propositionalization matrix aka instantiation matrix (0/1 entries for features) + # Same for the vector describing the query compound + def get_props + matrix = Array.new + begin + @neighbors.each do |n| + n = n[:compound] + row = [] + @features.each do |f| + if ! @fingerprints[n].nil? + row << (@fingerprints[n].include?(f) ? 0.0 : @p_values[f]) + else + row << 0.0 end end + matrix << row + end + row = [] + @features.each do |f| + row << (@compound.match([f]).size == 0 ? 0.0 : @p_values[f]) end rescue Exception => e - LOGGER.error "BLAZAR failed in neighbors: "+e.class.to_s+": "+e.message + LOGGER.debug "get_props failed with '" + $! + "'" end - + [ matrix, row ] end + # Find neighbors and store them as object variable, access only a subset of compounds for that. + def neighbors_balanced(s, l, start, offset) + @compound_features = eval("#{@feature_calculation_algorithm}(@compound,@features)") if @feature_calculation_algorithm + @neighbors = [] + [ l[start, offset ] , s ].flatten.each do |training_compound| # AM: access only a balanced subset + training_features = @fingerprints[training_compound] + add_neighbor training_features, training_compound + end + + end - # Find neighbors and store them as object variable + # Find neighbors and store them as object variable, access all compounds for that. def neighbors - - @compound_features = eval("#{@feature_calculation_algorithm}(@compound,@features)") if @feature_calculation_algorithm - - @neighbors = [] - @fingerprints.each do |training_compound,training_features| - sim = eval("#{@similarity_algorithm}(@compound_features,training_features,@p_values)") - if sim > @min_sim - @activities[training_compound].each do |act| - @neighbors << { - :compound => training_compound, - :similarity => sim, - :features => training_features, - :activity => act - } - end + @compound_features = eval("#{@feature_calculation_algorithm}(@compound,@features)") if @feature_calculation_algorithm + @neighbors = [] + @fingerprints.each do |training_compound,training_features| # AM: access all compounds + add_neighbor training_features, training_compound + end + end + + # Adds a neighbor to @neighbors if it passes the similarity threshold. + def add_neighbor(training_features, training_compound) + sim = eval("#{@similarity_algorithm}(@compound_features,training_features,@p_values)") + if sim > @min_sim + @activities[training_compound].each do |act| + @neighbors << { + :compound => training_compound, + :similarity => sim, + :features => training_features, + :activity => act + } end - end + end end # Find database activities and store them in @prediction_dataset |