module OpenTox module Algorithm class Regression def self.local_weighted_average compound, params weighted_sum = 0.0 sim_sum = 0.0 neighbors = params[:neighbors] neighbors.each do |row| sim = row["tanimoto"] sim ||= 1 # TODO: sim f nanoparticles if row["toxicities"][params[:prediction_feature_id].to_s] row["toxicities"][params[:prediction_feature_id].to_s][params[:training_dataset_id].to_s].each do |act| weighted_sum += sim*act sim_sum += sim end end end sim_sum == 0 ? prediction = nil : prediction = weighted_sum/sim_sum {:value => prediction} end def self.local_fingerprint_regression compound, params, method='pls'#, method_params="sigma=0.05" neighbors = params[:neighbors] return {:value => nil, :confidence => nil, :warning => "No similar compounds in the training data"} unless neighbors.size > 0 activities = [] fingerprints = {} weights = [] fingerprint_ids = neighbors.collect{|row| Compound.find(row["_id"]).fingerprint}.flatten.uniq.sort neighbors.each_with_index do |row,i| neighbor = Compound.find row["_id"] fingerprint = neighbor.fingerprint if row["toxicities"][params[:prediction_feature_id].to_s] row["toxicities"][params[:prediction_feature_id].to_s][params[:training_dataset_id].to_s].each do |act| activities << act weights << row["tanimoto"] fingerprint_ids.each_with_index do |id,j| fingerprints[id] ||= [] fingerprints[id] << fingerprint.include?(id) end end end end variables = [] data_frame = [activities] fingerprints.each do |k,v| unless v.uniq.size == 1 data_frame << v.collect{|m| m ? "T" : "F"} variables << k end end if variables.empty? result = local_weighted_average(compound, params) result[:warning] = "No variables for regression model. Using weighted average of similar compounds." return result else compound_features = variables.collect{|f| compound.fingerprint.include?(f) ? "T" : "F"} prediction = r_model_prediction method, data_frame, variables, weights, compound_features if prediction.nil? or prediction[:value].nil? prediction = local_weighted_average(compound, params) prediction[:warning] = "Could not create local PLS model. Using weighted average of similar compounds." return prediction else prediction[:prediction_interval] = [prediction[:value]-1.96*prediction[:rmse], prediction[:value]+1.96*prediction[:rmse]] prediction[:value] = prediction[:value] prediction[:rmse] = prediction[:rmse] prediction end end end def self.local_physchem_regression compound, params, method="pls"#, method_params="ncomp = 4" neighbors = params[:neighbors].select{|n| n["toxicities"][params[:prediction_feature_id].to_s] and n["toxicities"][params[:prediction_feature_id].to_s][params[:training_dataset_id].to_s]} # use only neighbors with measured activities return {:value => nil, :confidence => nil, :warning => "No similar compounds in the training data"} unless neighbors.size > 0 return {:value => neighbors.first["toxicities"][params[:prediction_feature_id].to_s][params[:training_dataset_id].to_s], :confidence => nil, :warning => "Only one similar compound in the training set"} unless neighbors.size > 1 activities = [] weights = [] pc_ids = neighbors.collect{|n| Substance.find(n["_id"]).physchem_descriptors.keys}.flatten.uniq data_frame = [] data_frame[0] = [] neighbors.each_with_index do |n,i| neighbor = Substance.find(n["_id"]) n["toxicities"][params[:prediction_feature_id].to_s][params[:training_dataset_id].to_s].each do |act| data_frame[0][i] = act n["tanimoto"] ? weights << n["tanimoto"] : weights << 1.0 # TODO cosine ? neighbor.physchem_descriptors.each do |pid,values| values.uniq! warn "More than one value for '#{Feature.find(pid).name}': #{values.join(', ')}. Using the median." unless values.size == 1 j = pc_ids.index(pid)+1 data_frame[j] ||= [] data_frame[j][i] = values.for_R end end (0..pc_ids.size+1).each do |j| # for R: fill empty values with NA data_frame[j] ||= [] data_frame[j][i] ||= "NA" end end remove_idx = [] data_frame.each_with_index do |r,i| remove_idx << i if r.uniq.size == 1 # remove properties with a single value end remove_idx.reverse.each do |i| data_frame.delete_at i pc_ids.delete_at i end if pc_ids.empty? result = local_weighted_average(compound, params) result[:warning] = "No variables for regression model. Using weighted average of similar compounds." return result else query_descriptors = pc_ids.collect do |i| compound.physchem_descriptors[i] ? compound.physchem_descriptors[i].for_R : "NA" end remove_idx = [] query_descriptors.each_with_index do |v,i| remove_idx << i if v == "NA" end remove_idx.reverse.each do |i| data_frame.delete_at i pc_ids.delete_at i query_descriptors.delete_at i end prediction = r_model_prediction method, data_frame, pc_ids.collect{|i| "\"#{i}\""}, weights, query_descriptors if prediction.nil? prediction = local_weighted_average(compound, params) prediction[:warning] = "Could not create local PLS model. Using weighted average of similar compounds." return prediction else prediction end end end def self.r_model_prediction method, training_data, training_features, training_weights, query_feature_values R.assign "weights", training_weights r_data_frame = "data.frame(#{training_data.collect{|r| "c(#{r.join(',')})"}.join(', ')})" rlib = File.expand_path(File.join(File.dirname(__FILE__),"..","R")) =begin File.open("tmp.R","w+"){|f| f.puts "suppressPackageStartupMessages({ library(iterators,lib=\"#{rlib}\") library(foreach,lib=\"#{rlib}\") library(ggplot2,lib=\"#{rlib}\") library(grid,lib=\"#{rlib}\") library(gridExtra,lib=\"#{rlib}\") library(pls,lib=\"#{rlib}\") library(caret,lib=\"#{rlib}\") library(doMC,lib=\"#{rlib}\") registerDoMC(#{NR_CORES}) })" f.puts "data <- #{r_data_frame}\n" f.puts "weights <- c(#{training_weights.join(', ')})" f.puts "features <- c(#{training_features.join(', ')})" f.puts "names(data) <- append(c('activities'),features)" # f.puts "model <- train(activities ~ ., data = data, method = '#{method}')" f.puts "fingerprint <- data.frame(rbind(c(#{query_feature_values.join ','})))" f.puts "names(fingerprint) <- features" f.puts "prediction <- predict(model,fingerprint)" } =end R.eval "data <- #{r_data_frame}" R.assign "features", training_features begin R.eval "names(data) <- append(c('activities'),features)" # R.eval "model <- train(activities ~ ., data = data, method = '#{method}', na.action = na.pass)" R.eval "fingerprint <- data.frame(rbind(c(#{query_feature_values.join ','})))" R.eval "names(fingerprint) <- features" R.eval "prediction <- predict(model,fingerprint)" { :value => R.eval("prediction").to_f, :rmse => R.eval("getTrainPerf(model)$TrainRMSE").to_f, :r_squared => R.eval("getTrainPerf(model)$TrainRsquared").to_f, } rescue return nil end end end end end