1 files changed, 107 insertions, 0 deletions
diff --git a/lib/caret-classification.rb b/lib/caret-classification.rb
new file mode 100644
index 0000000..fefe6b6
--- /dev/null
+++ b/lib/caret-classification.rb
@@ -0,0 +1,107 @@
+module OpenTox
+  module Algorithm
+    
+    # Ruby interface for the R caret package
+    # Caret model list: https://topepo.github.io/caret/modelList.html
+    class Caret
+
+      # Create a local R caret model and make a prediction
+      # @param [Array<Float,Bool>] dependent_variables
+      # @param [Array<Array<Float,Bool>>] independent_variables
+      # @param [Array<Float>] weights
+      # @param [String] Caret method
+      # @param [Array<Float,Bool>] query_variables
+      # @return [Hash]
+      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::Classification::weighted_majority_vote dependent_variables:dependent_variables, weights:weights
+          prediction[:warnings] << "No variables for classification model. Using weighted average of similar substances."
+        elsif dependent_variables.uniq.size == 1
+          prediction = Algorithm::Classification::weighted_majority_vote dependent_variables:dependent_variables, weights:weights
+          prediction[:warnings] << "All neighbors have the same measured activity. Cannot create random forest model, using weighted average of similar substances."
+        elsif dependent_variables.size < 3
+          prediction = Algorithm::Classification::weighted_majority_vote dependent_variables:dependent_variables, weights:weights
+          prediction[:warnings] << "Insufficient number of neighbors (#{dependent_variables.size}) for classification model. Using weighted average of similar substances."
+        else
+          dependent_variables.collect!{|v| to_r(v)}
+          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.collect!{|v| to_r(v)}
+          begin
+            R.assign "weights", weights
+            #r_data_frame = "data.frame(#{([dependent_variables.collect{|v| to_r(v)}]+independent_variables).collect{|r| "c(#{r.collect{|v| to_r(v)}.join(',')})"}.join(', ')})"
+            r_data_frame = "data.frame(#{([dependent_variables]+independent_variables).collect{|r| "c(#{r.join(',')})"}.join(', ')})"
+            #p r_data_frame
+            R.eval "data <- #{r_data_frame}"
+            R.assign "features", (0..independent_variables.size-1).to_a
+            R.eval "names(data) <- append(c('activities'),features)" #
+            p "train"
+            R.eval "model <- train(activities ~ ., data = data, method = '#{method}', na.action = na.pass, allowParallel=TRUE)"
+            p "done"
+          rescue => e
+            $logger.debug "R caret model creation error for: #{e.message}"
+            $logger.debug dependent_variables
+            $logger.debug independent_variables
+            prediction = Algorithm::Classification::weighted_majority_vote dependent_variables:dependent_variables, weights:weights
+            prediction[:warnings] << "R caret model creation error. Using weighted average of similar substances."
+            return prediction
+          end
+          begin
+            R.eval "query <- data.frame(rbind(c(#{query_variables.collect{|v| to_r(v)}.join ','})))"
+            R.eval "names(query) <- features" 
+            R.eval "prediction <- predict(model,query, type=\"prob\")"
+            names = R.eval("names(prediction)").to_ruby
+            probs = R.eval("prediction").to_ruby
+            probabilities = {}
+            names.each_with_index { |n,i| probabilities[n] = probs[i] }
+            value = probabilities.sort_by{|n,p| -p }[0][0]
+            prediction = {
+              :value => value,
+              :probabilities => probabilities,
+              :warnings => [],
+            }
+          rescue => e
+            $logger.debug "R caret prediction error for: #{e.inspect}"
+            $logger.debug self.inspect
+            prediction = Algorithm::Classification::weighted_majority_vote dependent_variables:dependent_variables, weights:weights
+            prediction[:warnings] << "R caret prediction error. Using weighted average of similar substances"
+            return prediction
+          end
+          if prediction.nil? or prediction[:value].nil?
+            prediction = Algorithm::Classification::weighted_majority_vote dependent_variables:dependent_variables, weights:weights
+            prediction[:warnings] << "Empty R caret prediction. 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
+
+      # Convert Ruby values to R values
+      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?
+        return "\"#{v}\"" if v.is_a? String
+        v
+      end
+
+    end
+  end
+end
+