diff options
Diffstat (limited to 'lib/algorithm.rb')
| -rw-r--r-- | lib/algorithm.rb | 14 |
1 files changed, 8 insertions, 6 deletions
diff --git a/lib/algorithm.rb b/lib/algorithm.rb index 40cbc84..19666ad 100644 --- a/lib/algorithm.rb +++ b/lib/algorithm.rb @@ -381,10 +381,10 @@ module OpenTox else #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 "set.seed(1)" @r.eval "suppressPackageStartupMessages(library('caret'))" # requires R packages "caret" and "kernlab" @r.eval "suppressPackageStartupMessages(library('doMC'))" # requires R packages "multicore" @r.eval "registerDoMC()" # switch on parallel processing + @r.eval "set.seed(1)" begin # set data @@ -417,7 +417,7 @@ module OpenTox # model + support vectors LOGGER.debug "Creating R SVM model ..." - @r.eval <<-EOR + train_success = @r.eval <<-EOR model = train(prop_matrix,y,method="svmradial",tuneLength=8,trControl=trainControl(method="LGOCV",number=10),preProcess=c("center", "scale")) perf = ifelse ( class(y)!='numeric', max(model$results$Accuracy), model$results[which.min(model$results$RMSE),]$Rsquared ) EOR @@ -431,6 +431,7 @@ module OpenTox # censoring prediction = nil if ( @r.perf.nan? || @r.perf < min_train_performance ) + prediction = nil unless train_success LOGGER.debug "Performance: #{sprintf("%.2f", @r.perf)}" rescue Exception => e LOGGER.debug "#{e.class}: #{e.message}" @@ -459,12 +460,12 @@ module OpenTox # need packs 'randomForest', 'RANN' @r.eval <<-EOR - set.seed(1) suppressPackageStartupMessages(library('caret')) suppressPackageStartupMessages(library('randomForest')) suppressPackageStartupMessages(library('RANN')) suppressPackageStartupMessages(library('doMC')) registerDoMC() + set.seed(1) acts = read.csv(ds_csv_file, check.names=F) feats = read.csv(fds_csv_file, check.names=F) @@ -506,15 +507,16 @@ module OpenTox features = features[,!names(features) %in% nan_col] # determine subsets - subsets = dim(features)[2]*c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7) - subsets = c(2,3,4,5,7,10,subsets) + subsets = dim(features)[2]*c(0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.42, 0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58, 0.6, 0.62, 0.64, 0.66, 0.68, 0.7) + #subsets = dim(features)[2]*c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7) + #subsets = c(2,3,4,5,7,10,subsets) #subsets = c(2,3,4,5,7,10,13,16,19,22,25,28,30) subsets = unique(sort(round(subsets))) subsets = subsets[subsets<=dim(features)[2]] subsets = subsets[subsets>1] # Recursive feature elimination - rfProfile = rfe( x=features, y=y, rfeControl=rfeControl(functions=rfFuncs, method='cv'), sizes=subsets) + rfProfile = rfe( x=features, y=y, rfeControl=rfeControl(functions=rfFuncs, number=150), sizes=subsets) optVar = rfProfile$optVariables if (rfProfile$bestSubset == dim(features)[2]) { newRMSE = rfProfile$results$RMSE |