From 91787edb3682900bc5a2feeca66e5142f387fcc6 Mon Sep 17 00:00:00 2001
From: Christoph Helma <helma@in-silico.ch>
Date: Fri, 7 Oct 2016 10:25:58 +0200
Subject: unified interface for prediction algorithms

---
 lib/caret.rb | 152 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 152 insertions(+)
 create mode 100644 lib/caret.rb

(limited to 'lib/caret.rb')

diff --git a/lib/caret.rb b/lib/caret.rb
new file mode 100644
index 0000000..b999b06
--- /dev/null
+++ b/lib/caret.rb
@@ -0,0 +1,152 @@
+module OpenTox
+  module Algorithm
+    
+    class Caret
+      # 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)
+          prediction[:warning] = "No variables for regression model. Using weighted average of similar substances."
+        else
+          prediction = caret.r_model_prediction 
+          if prediction.nil? or prediction[:value].nil?
+            prediction = Algorithm::Regression::weighted_average(descriptors: @descriptors, neighbors: neighbors)
+            prediction[:warning] = "Could not create local caret model. Using weighted average of similar substances."
+          end
+        end
+        prediction
+
+      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"} 
+
+      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
+      end
+
+    end
+  end
+end
+
-- 
cgit v1.2.3