1 files changed, 96 insertions, 0 deletions
diff --git a/lib/caret.rb b/lib/caret.rb
new file mode 100644
index 0000000..7e4f771
--- /dev/null
+++ b/lib/caret.rb
@@ -0,0 +1,96 @@
+module OpenTox
+  module Algorithm
+    
+    class Caret
+      # model list: https://topepo.github.io/caret/modelList.html
+
+      def self.create_model_and_predict dependent_variables:, independent_variables:, weights:, method:, query_variables:
+        remove = []
+        # remove independent_variables with single values
+        independent_variables.each_with_index { |values,i| remove << i if values.uniq.size == 1}
+        remove.sort.reverse.each do |i|
+          independent_variables.delete_at i
+          query_variables.delete_at i
+        end
+        if independent_variables.flatten.uniq == ["NA"] or independent_variables.flatten.uniq == [] 
+          prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
+          prediction[:warning] = "No variables for regression model. Using weighted average of similar substances."
+        elsif
+          dependent_variables.size < 3
+          prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
+          prediction[:warning] = "Insufficient number of neighbors (#{dependent_variables.size}) for regression model. Using weighted average of similar substances."
+
+        else
+          dependent_variables.each_with_index do |v,i| 
+            dependent_variables[i] = to_r(v)
+          end
+          independent_variables.each_with_index do |c,i| 
+            c.each_with_index do |v,j|
+              independent_variables[i][j] = to_r(v)
+            end
+          end
+          query_variables.each_with_index do |v,i| 
+            query_variables[i] = to_r(v)
+          end
+          begin
+            R.assign "weights", weights
+            r_data_frame = "data.frame(#{([dependent_variables]+independent_variables).collect{|r| "c(#{r.join(',')})"}.join(', ')})"
+            R.eval "data <- #{r_data_frame}"
+            R.assign "features", (0..independent_variables.size-1).to_a
+            R.eval "names(data) <- append(c('activities'),features)" #
+            R.eval "model <- train(activities ~ ., data = data, method = '#{method}', na.action = na.pass, allowParallel=TRUE)"
+          rescue => e
+            $logger.debug "R caret model creation error for:"
+            $logger.debug dependent_variables
+            $logger.debug independent_variables
+            prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
+            prediction[:warning] = "R caret model creation error. Using weighted average of similar substances."
+            return prediction
+          end
+          begin
+            R.eval "query <- data.frame(rbind(c(#{query_variables.join ','})))"
+            R.eval "names(query) <- features" 
+            R.eval "prediction <- predict(model,query)"
+            value = R.eval("prediction").to_f
+            rmse = R.eval("getTrainPerf(model)$TrainRMSE").to_f
+            r_squared = R.eval("getTrainPerf(model)$TrainRsquared").to_f
+            prediction_interval = value-1.96*rmse, value+1.96*rmse
+            prediction = {
+              :value => value,
+              :rmse => rmse,
+              :r_squared => r_squared,
+              :prediction_interval => prediction_interval
+            }
+          rescue => e
+            $logger.debug "R caret prediction error for:"
+            $logger.debug self.inspect
+            prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
+            prediction[:warning] = "R caret prediction error. Using weighted average of similar substances"
+            return prediction
+          end
+          if prediction.nil? or prediction[:value].nil?
+            prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
+            prediction[:warning] = "Could not create local caret model. Using weighted average of similar substances."
+          end
+        end
+        prediction
+
+      end
+
+      # call caret methods dynamically, e.g. Caret.pls
+      def self.method_missing(sym, *args, &block)
+        args.first[:method] = sym.to_s
+        self.create_model_and_predict args.first
+      end
+
+      def self.to_r v
+        return "F" if v == false
+        return "T" if v == true
+        return nil if v.is_a? Float and v.nan?
+        v
+      end
+
+    end
+  end
+end
+