physchem regression, correlation_filter for fingerprints

1 files changed, 57 insertions, 127 deletions

diff --git a/lib/caret.rb b/lib/caret.rb
index b999b06..59e02da 100644
--- a/lib/caret.rb
+++ b/lib/caret.rb
@@ -5,33 +5,56 @@ module OpenTox
       # TODO classification
       # model list: https://topepo.github.io/caret/modelList.html
 
-      attr_accessor :descriptors, :neighbors, :method, :relevant_features, :data_frame, :feature_names, :weights, :query_features
-
-      def initialize descriptors:, neighbors:, method:, relevant_features:
-        @descriptors = descriptors
-        @neighbors = neighbors
-        @method = method
-        @relevant_features = relevant_features
-      end
-
-      def self.regression descriptors:, neighbors:, method:, relevant_features:nil 
-
-        caret = new(descriptors:descriptors, neighbors:neighbors, method:method, relevant_features:relevant_features)
-        # collect training data for R
-        if descriptors.is_a? Array
-          caret.fingerprint2R 
-        elsif descriptors.is_a? Hash
-          caret.properties2R
-        else
-          bad_request_error "Descriptors should be a fingerprint (Array) or properties (Hash). Cannot handle '#{descriptors.class}'."
-        end
-        if caret.feature_names.empty? or caret.data_frame.flatten.uniq == ["NA"]
-          prediction = Algorithm::Regression::weighted_average(descriptors: @descriptors, neighbors: neighbors)
+      def self.create_model_and_predict dependent_variables:, independent_variables:, weights:, method:, query_variables:
+        if independent_variables.flatten.uniq == ["NA"] 
+          prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
           prediction[:warning] = "No variables for regression model. Using weighted average of similar substances."
         else
-          prediction = caret.r_model_prediction 
+          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 JSON.pretty_generate(dependent_variables)
+            $logger.debug JSON.pretty_generate(independent_variables)
+            return {:value => nil, :warning => "R caret model cration error."}
+          end
+          begin
+            R.eval "query <- data.frame(rbind(c(#{query_variables.join ','})))"
+            R.eval "names(query) <- features" 
+            R.eval "prediction <- predict(model,query)"
+            value = R.eval("prediction").to_f
+            rmse = R.eval("getTrainPerf(model)$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
+            return nil
+          end
           if prediction.nil? or prediction[:value].nil?
-            prediction = Algorithm::Regression::weighted_average(descriptors: @descriptors, neighbors: neighbors)
+            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
@@ -39,111 +62,18 @@ module OpenTox
 
       end
 
-      def fingerprint2R
-
-        values = []
-        features = {}
-        @weights = []
-        descriptor_ids = neighbors.collect{|n| n["descriptors"]}.flatten.uniq.sort
-
-        neighbors.each do |n|
-          activities = n["measurements"]
-          activities.each do |act|
-            values << act
-            @weights << n["similarity"]
-            descriptor_ids.each do |id|
-              features[id] ||= []
-              features[id] << n["descriptors"].include?(id) 
-            end
-          end if activities
-        end
-
-        @feature_names = []
-        @data_frame = [values]
-
-        features.each do |k,v| 
-          unless v.uniq.size == 1
-            @data_frame << v.collect{|m| m ? "T" : "F"}
-            @feature_names << k
-          end
-        end
-        @query_features = @feature_names.collect{|f| descriptors.include?(f) ? "T" : "F"} 
-
+      # 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 properties2R 
-
-        @weights = []
-        @feature_names = []
-        @query_features = []
-
-        # keep only descriptors with values
-        @relevant_features.keys.each_with_index do |f,i|
-          if @descriptors[f]
-            @feature_names << f
-            @query_features << @descriptors[f].median
-          else
-            neighbors.each do |n|
-              n["descriptors"].delete_at i
-            end
-          end
-        end
-        
-        measurements = neighbors.collect{|n| n["measurements"]}.flatten 
-        # initialize data frame with 'NA' defaults
-        @data_frame = Array.new(@feature_names.size+1){Array.new(measurements.size,"NA") }
-
-        i = 0
-        # parse neighbor activities and descriptors
-        neighbors.each do |n|
-          activities = n["measurements"]
-          activities.each do |act| # multiple measurements are treated as separate instances
-            unless n["descriptors"].include?(nil)
-              data_frame[0][i] = act
-              @weights << n["similarity"]
-              n["descriptors"].each_with_index do |d,j| 
-                @data_frame[j+1][i] = d
-              end
-              i += 1
-            end
-          end if activities # ignore neighbors without measurements
-        end
-
-      end
-
-      def r_model_prediction 
-        begin
-          R.assign "weights", @weights
-          r_data_frame = "data.frame(#{@data_frame.collect{|r| "c(#{r.join(',')})"}.join(', ')})"
-          R.eval "data <- #{r_data_frame}"
-          R.assign "features", @feature_names
-          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:"
-          $logger.debug JSON.pretty_generate(self.inspect)
-          return nil
-        end
-        begin
-          R.eval "query <- data.frame(rbind(c(#{@query_features.join ','})))"
-          R.eval "names(query) <- features" 
-          R.eval "prediction <- predict(model,query)"
-          value = R.eval("prediction").to_f
-          rmse = R.eval("getTrainPerf(model)$TrainRMSE").to_f
-          r_squared = R.eval("getTrainPerf(model)$TrainRsquared").to_f
-          prediction_interval = value-1.96*rmse, value+1.96*rmse
-          {
-            :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
-          return nil
-        end
+      def self.to_r v
+        return "F" if v == false
+        return "T" if v == true
+        return "NA" if v.nil? 
+        return "NA" if v.is_a? Float and v.nan?
+        v
       end
 
     end