add super-stratification split for training test splitting

2 files changed, 205 insertions, 0 deletions

diff --git a/lib/r-util.rb b/lib/r-util.rb
new file mode 100644
index 0000000..0d58389
--- /dev/null
+++ b/lib/r-util.rb
@@ -0,0 +1,82 @@
+# pending: package dir hack ---------
+# CONFIG[:base_dir] = "/home/<user>/opentox-ruby/www"
+# PACKAGE_DIR = "/home/<user>/opentox-ruby/r-packages"
+package_dir = CONFIG[:base_dir].split("/")
+package_dir[-1] = "r-packages"
+package_dir = package_dir.join("/")
+PACKAGE_DIR = package_dir
+
+
+
+module Lib
+  
+  module RUtil
+    
+    def self.dataset_to_dataframe( dataset )
+      LOGGER.debug "convert dataset to dataframe #{dataset.uri}"
+      all_features = []
+      dataset.features.each do |f|
+        feat_name = "feature_#{f[0].split("/")[-1]}"
+        LOGGER.debug "- adding feature: #{feat_name}"
+        feat = OpenTox::Feature.find(f[0])
+        nominal = feat.metadata[RDF.type].to_a.flatten.include?(OT.NominalFeature)
+        values = []
+        dataset.compounds.each do |c|
+          val = dataset.data_entries[c][f[0]]
+          raise "not yet implemented" if val!=nil && val.size>1
+          v = val==nil ? "" : val[0].to_s
+          v = "NA" if v.size()==0
+          values << v
+        end
+        all_features << feat_name
+        @@r.assign feat_name,values
+        @@r.eval "#{feat_name} <- as.numeric(#{feat_name})" unless nominal
+      end
+      df_name = "df_#{dataset.uri.split("/")[-1].split("?")[0]}"
+      cmd =  "#{df_name} <- data.frame(#{all_features.join(",")})"
+      @@r.eval cmd
+      #@@r.eval "head(#{df_name})"
+      df_name
+    end
+    
+    def self.stratified_split( dataframe, pct=0.3, seed=42 )
+      @@r.eval "set.seed(#{seed})"
+      @@r.eval "split <- stratified_split(#{dataframe}, ratio=#{pct})"
+      split = @@r.pull 'split'
+      split.collect{|s| s.to_i}
+    end
+    
+    def self.package_installed?( package )
+      @@r.eval ".libPaths(\"#{PACKAGE_DIR}\")"
+      p = @@r.pull "installed.packages()[,1]"
+      p.include?(package) 
+    end
+    
+    def self.install_packages( package )
+      unless package_installed? package
+        @@r.eval "install.packages(\"#{package}\", repos=\"http://cran.r-project.org\", dependencies=T, lib=\"#{PACKAGE_DIR}\")"
+      end
+    end
+    
+    def self.library( package )
+      install_packages( package )
+      @@r.eval "library(\"#{package}\")"
+    end
+    
+    def self.init_r
+      @@r = RinRuby.new(true,false) unless defined?(@@r) and @@r
+      library("sampling")
+      library("gam")
+      @@r.eval "source(\"#{PACKAGE_DIR}/stratification.R\")"
+    end
+    
+    def self.quit_r
+      begin
+        @@r.quit
+        @@r = nil
+      rescue
+      end
+    end
+    
+  end
+end
diff --git a/lib/stratification.R b/lib/stratification.R
new file mode 100644
index 0000000..9aa8d1f
--- /dev/null
+++ b/lib/stratification.R
@@ -0,0 +1,123 @@
+library("sampling")
+library("gam")
+
+nominal_to_binary <- function( orig_data )
+{
+  data = as.data.frame( orig_data )
+  result = NULL
+  for (i in 1:ncol(data))
+  {
+     #print(i)
+     if (is.numeric( data[,i] ) )
+     {
+        if (is.null(result))
+          result = data.frame(data[,i])
+        else
+          result = data.frame(result, data[,i])
+        colnames(result)[ncol(result)] <- colnames(data)[i]
+     }
+     else
+     {
+        vals = unique(data[,i])
+        for (j in 1:length(vals))
+        {
+           #print(j)
+           bins = c()
+           for (k in 1:nrow(data))
+           {
+              if(data[,i][k] == vals[j])
+                bins = c(bins,1)
+              else
+                bins = c(bins,0)
+           }
+           #print(bins)
+           if (is.null(result))
+             result = data.frame(bins)
+           else
+             result = data.frame(result, bins)
+           colnames(result)[ncol(result)] <- paste(colnames(data)[i],"is",vals[j])
+           if (length(vals)==2) break
+        }
+     }
+  }
+  result
+}
+
+process_data <- function( data )
+{
+  if (!is.numeric(data))
+    data.num = nominal_to_binary(data)
+  else
+    data.num = data
+  if(any(is.na(data.num)))
+  	data.repl = na.gam.replace(data.num)
+  else
+  	data.repl = data.num
+  data.repl
+}
+
+stratified_split <- function( data, ratio=0.3 )
+{
+    data.processed = as.matrix(process_data( data ))
+    pik = rep(ratio,times=nrow(data.processed))
+    data.strat = cbind(pik,data.processed)
+    samplecube(data.strat,pik,order=2,comment=F)
+}
+
+stratified_k_fold_split <- function( data, num_folds=10 )
+{
+  print(paste(num_folds,"-fold-split, data-size",nrow(data)))
+  data.processed = as.matrix(process_data( data ))
+  folds = rep(0, times=nrow(data))
+  for (i in 1:(num_folds-1))
+  {
+    prop = 1/(num_folds-(i-1))
+    print(paste("fold",i,"/",num_folds," prop",prop))
+    pik = rep(prop,times=nrow(data))
+    for (j in 1:nrow(data))
+      if(folds[j]!=0)
+        pik[j]=0
+    data.strat = cbind(pik,data.processed)
+    s<-samplecube(data.strat,pik,order=2,comment=F)
+    print(paste("fold size: ",sum(s)))
+    for (j in 1:nrow(data))
+      if (s[j] == 1)
+        folds[j]=i
+  }
+  for (j in 1:nrow(data))
+    if (folds[j] == 0)
+      folds[j]=num_folds
+  folds
+}
+
+plot_split <- function( data, split )
+{
+  data.processed = process_data( data )
+	data.pca <- prcomp(data.processed, scale=TRUE)
+  data.2d =as.data.frame(data.pca$x)[1:2]
+  plot( NULL,
+        xlim = extendrange(data.2d[,1]), ylim = extendrange(data.2d[,2]), 
+        xlab = "pc 1", ylab = "pc 2")
+  for (j in 0:max(split))
+  {
+    set = c()
+    for (i in 1:nrow(data))
+      if (split[i] == j)
+        set = c(set,i)
+    points(data.2d[set,], pch = 2, col=(j+1))
+  }
+}
+
+#a<-matrix(rnorm(100, mean=50,  sd=4), ncol=5)
+#b<-matrix(rnorm(5000, mean=0, sd=10), ncol=5)
+#data<-rbind(a,b)
+#c<-matrix(rnorm(50, mean=-50, sd=2), ncol=5)
+#data<-rbind(data,c)
+#data=iris
+#split = stratified_k_fold_split(data, num_folds=3)
+#split = stratified_split(data, ratio=0.3)
+#plot_split(data,split)
+
+
+
+