diff options
Diffstat (limited to 'lib/algorithm.rb')
| -rw-r--r-- | lib/algorithm.rb | 90 |
1 files changed, 84 insertions, 6 deletions
diff --git a/lib/algorithm.rb b/lib/algorithm.rb index 96b9df1..2652695 100644 --- a/lib/algorithm.rb +++ b/lib/algorithm.rb @@ -138,7 +138,7 @@ module OpenTox # @param [Array] neighbors, each neighbor is a hash with keys `:similarity, :activity` # @param [optional] params Ignored (only for compatibility with local_svm_regression) # @return [Hash] Hash with keys `:prediction, :confidence` - def self.weighted_majority_vote(neighbors,params={}) + def self.weighted_majority_vote(neighbors,params={}, props=nil) conf = 0.0 confidence = 0.0 neighbors.each do |neighbor| @@ -164,7 +164,7 @@ module OpenTox # @param [Array] neighbors, each neighbor is a hash with keys `:similarity, :activity, :features` # @param [Hash] params Keys `:similarity_algorithm,:p_values` are required # @return [Hash] Hash with keys `:prediction, :confidence` - def self.local_svm_regression(neighbors, params) + def self.local_svm_regression(neighbors, params, props=nil) take_logs=true neighbors.each do |n| if (! n[:activity].nil?) && (n[:activity].to_f < 0.0) @@ -178,7 +178,7 @@ module OpenTox sims = neighbors.collect{ |n| Algorithm.gauss(n[:similarity]) } # similarity values btwn q and nbors begin - prediction = local_svm(neighbors, acts, sims, "nu-svr", params) + prediction = (props.nil? ? local_svm(neighbors, acts, sims, "nu-svr", params) : local_svm_prop(props, acts, "nu-svr", params)) prediction = (take_logs ? 10**(prediction.to_f) : prediction.to_f) LOGGER.debug "Prediction is: '" + prediction.to_s + "'." rescue Exception => e @@ -194,15 +194,16 @@ module OpenTox # Local support vector classification from neighbors # @param [Array] neighbors, each neighbor is a hash with keys `:similarity, :activity, :features` # @param [Hash] params Keys `:similarity_algorithm,:p_values` are required + # @param [Array] props, propositionalization of neighbors and query structure e.g. [ Array_for_q, two-nested-Arrays_for_n ] # @return [Hash] Hash with keys `:prediction, :confidence` - def self.local_svm_classification(neighbors, params) + def self.local_svm_classification(neighbors, params, props=nil) acts = neighbors.collect do |n| act = n[:activity] end # activities of neighbors for supervised learning acts_f = acts.collect {|v| v == true ? 1.0 : 0.0} sims = neighbors.collect{ |n| Algorithm.gauss(n[:similarity]) } # similarity values btwn q and nbors begin - prediction = local_svm(neighbors, acts_f, sims, "C-bsvc", params) + prediction = (props.nil? ? local_svm(neighbors, acts_f, sims, "C-bsvc", params) : local_svm_prop(props, acts_f, "C-bsvc", params)) LOGGER.debug "Prediction is: '" + prediction.to_s + "'." rescue Exception => e LOGGER.debug "#{e.class}: #{e.message} #{e.backtrace}" @@ -216,14 +217,17 @@ module OpenTox # Local support vector prediction from neighbors. - # Not to be called directly (use local_svm_regression or local_svm_classification. + # Uses pre-defined Kernel Matrix. + # Not to be called directly (use local_svm_regression or local_svm_classification). # @param [Array] neighbors, each neighbor is a hash with keys `:similarity, :activity, :features` # @param [Array] acts, activities for neighbors. # @param [Array] sims, similarities for neighbors. # @param [String] type, one of "nu-svr" (regression) or "C-bsvc" (classification). # @param [Hash] params Keys `:similarity_algorithm,:p_values` are required + # @param [Array] props, propositionalization of neighbors and query structure e.g. [ Array_for_q, two-nested-Arrays_for_n ] # @return [Numeric] A prediction value. def self.local_svm(neighbors, acts, sims, type, params) + LOGGER.debug "Local SVM (Weighted Tanimoto Kernel)." neighbor_matches = neighbors.collect{ |n| n[:features] } # URIs of matches gram_matrix = [] # square matrix of similarities between neighbors; implements weighted tanimoto kernel if neighbor_matches.size == 0 @@ -285,6 +289,80 @@ module OpenTox prediction end + # Local support vector prediction from neighbors. + # Uses propositionalized setting. + # Not to be called directly (use local_svm_regression or local_svm_classification). + # @param [Array] neighbors, each neighbor is a hash with keys `:similarity, :activity, :features` + # @param [Array] acts, activities for neighbors. + # @param [Array] props, propositionalization of neighbors and query structure e.g. [ Array_for_q, two-nested-Arrays_for_n ] + # @param [String] type, one of "nu-svr" (regression) or "C-bsvc" (classification). + # @param [Hash] params Keys `:similarity_algorithm,:p_values` are required + # @return [Numeric] A prediction value. + def self.local_svm_prop(props, acts, type, params) + + LOGGER.debug "Local SVM (Propositionalization / Kernlab Kernel)." + n_prop = props[0] # is a matrix, i.e. two nested Arrays. + q_prop = props[1] # is an Array. + + #neighbor_matches = neighbors.collect{ |n| n[:features] } # URIs of matches + #gram_matrix = [] # square matrix of similarities between neighbors; implements weighted tanimoto kernel + if n_prop.size == 0 + raise "No neighbors found." + else + # gram matrix + #(0..(neighbor_matches.length-1)).each do |i| + # gram_matrix[i] = [] unless gram_matrix[i] + # # upper triangle + # ((i+1)..(neighbor_matches.length-1)).each do |j| + # sim = eval("#{params[:similarity_algorithm]}(neighbor_matches[i], neighbor_matches[j], params[:p_values])") + # gram_matrix[i][j] = Algorithm.gauss(sim) + # gram_matrix[j] = [] unless gram_matrix[j] + # gram_matrix[j][i] = gram_matrix[i][j] # lower triangle + # end + # gram_matrix[i][i] = 1.0 + #end + + #LOGGER.debug gram_matrix.to_yaml + @r = RinRuby.new(false,false) # global R instance leads to Socket errors after a large number of requests + @r.eval "library('kernlab')" # this requires R package "kernlab" to be installed + LOGGER.debug "Setting R data ..." + # set data + @r.n_prop = n_prop.flatten + @r.n_prop_x_size = n_prop.size + @r.n_prop_y_size = n_prop[0].size + @r.y = acts + @r.q_prop = q_prop + + begin + LOGGER.debug "Preparing R data ..." + # prepare data + @r.eval "y<-matrix(y)" + @r.eval "prop_matrix<-matrix(n_prop, n_prop_x_size, n_prop_y_size, byrow=TRUE)" + @r.eval "q_prop<-matrix(q_prop, 1, n_prop_y_size, byrow=TRUE)" + + # model + support vectors + LOGGER.debug "Creating SVM model ..." + @r.eval "model<-ksvm(prop_matrix, y, type=\"#{type}\", nu=0.5)" + LOGGER.debug "Predicting ..." + if type == "nu-svr" + @r.eval "p<-predict(model,q_prop)[1,1]" + elsif type == "C-bsvc" + @r.eval "p<-predict(model,q_prop)" + end + if type == "nu-svr" + prediction = @r.p + elsif type == "C-bsvc" + prediction = (@r.p.to_f == 1.0 ? true : false) + end + @r.quit # free R + rescue Exception => e + LOGGER.debug "#{e.class}: #{e.message} #{e.backtrace}" + end + end + prediction + end + + end module Substructure |