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| author | rautenberg <rautenberg@in-silico.ch> | 2012-07-13 09:29:53 +0200 |
|---|---|---|
| committer | rautenberg <rautenberg@in-silico.ch> | 2012-07-13 09:29:53 +0200 |
| commit | 2e57f766ad7aa71755d517ab6070be080a95f4fa (patch) | |
| tree | ab862152f0c2dce97fdd41f246ecd1ed9cd5cd18 | |
| parent | 1e728f326b29b3648ab77aee95306cb20fb2080e (diff) | |
| parent | c0f2c07e652f1ea94ac19c3f104dd71c23a33217 (diff) | |
Merge branch 'release/v3.2.0' of github.com:opentox/opentox-ruby into release/v3.2.0
| -rw-r--r-- | lib/algorithm.rb | 23 |
1 files changed, 20 insertions, 3 deletions
diff --git a/lib/algorithm.rb b/lib/algorithm.rb index e2798b3..78fc447 100644 --- a/lib/algorithm.rb +++ b/lib/algorithm.rb @@ -427,7 +427,7 @@ module OpenTox prediction = acts[0] 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 = RinRuby.new(true,false) # global R instance leads to Socket errors after a large number of requests @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 @@ -447,7 +447,14 @@ module OpenTox # prepare data LOGGER.debug "Preparing R data ..." - @r.eval "if (class(y) == 'character') { y = factor(y); suppressPackageStartupMessages(library('class')) }" # For classification + @r.eval <<-EOR + weights=NULL + if (class(y) == 'character') { + y = factor(y) + suppressPackageStartupMessages(library('class')) + #weights=unlist(as.list(prop.table(table(y)))) + } + EOR @r.eval <<-EOR rem = nearZeroVar(prop_matrix) @@ -465,7 +472,17 @@ module OpenTox # model + support vectors LOGGER.debug "Creating R SVM model ..." train_success = @r.eval <<-EOR - model = train(prop_matrix,y,method="svmradial",tuneLength=8,trControl=trainControl(method="LGOCV",number=10),preProcess=c("center", "scale")) + # AM: TODO: evaluate class weight effect by altering: + # AM: comment in 'weights' above run and class.weights=weights vs. class.weights=1-weights + # AM: vs + # AM: comment out 'weights' above (status quo), thereby disabling weights + model = train(prop_matrix,y, + method="svmradial", + preProcess=c("center", "scale"), + class.weights=weights, + trControl=trainControl(method="LGOCV",number=10), + tuneLength=8 + ) perf = ifelse ( class(y)!='numeric', max(model$results$Accuracy), model$results[which.min(model$results$RMSE),]$Rsquared ) EOR |