tentative random forest classification: hangs unpredictably during caret model generation/optimization for some (inorganic?) compounds.mutanew

5 files changed, 123 insertions, 9 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
+
diff --git a/lib/classification.rb b/lib/classification.rb
index a875903..2668e4a 100644
--- a/lib/classification.rb
+++ b/lib/classification.rb
@@ -19,6 +19,7 @@ module OpenTox
           probabilities[a] = w.sum/weights.sum
         end
         # DG: hack to ensure always two probability values
+        # TODO: does not work for arbitrary feature names FIX!!
         if probabilities.keys.uniq.size == 1
           missing_key = probabilities.keys.uniq[0].match(/^non/) ? probabilities.keys.uniq[0].sub(/non-/,"") : "non-"+probabilities.keys.uniq[0]
           probabilities[missing_key] = 0.0
@@ -26,7 +27,7 @@ module OpenTox
         probabilities = probabilities.collect{|a,p| [a,weights.max*p]}.to_h
         p_max = probabilities.collect{|a,p| p}.max
         prediction = probabilities.key(p_max)
-        {:value => prediction,:probabilities => probabilities}
+        {:value => prediction,:probabilities => probabilities,:warnings => []}
       end
 
     end
diff --git a/lib/compound.rb b/lib/compound.rb
index d80f579..8dc53a1 100644
--- a/lib/compound.rb
+++ b/lib/compound.rb
@@ -319,7 +319,6 @@ module OpenTox
       obconversion.read_string obmol, identifier
       case output_format
       when /smi|can|inchi/
-        #obconversion.write_string(obmol).gsub(/\s/,'').chomp
         obconversion.write_string(obmol).split(/\s/).first
       when /sdf/
         # TODO: find disconnected structures
diff --git a/lib/dataset.rb b/lib/dataset.rb
index b7d9d4e..6ad3215 100644
--- a/lib/dataset.rb
+++ b/lib/dataset.rb
@@ -71,6 +71,8 @@ module OpenTox
 
     # Merge an array of datasets 
     # @param [Array] OpenTox::Dataset Array to be merged
+    # @param [Hash] feature modifications
+    # @param [Hash] value modifications
     # @return [OpenTox::Dataset] merged dataset
     def self.merge datasets, feature_map=nil, value_map=nil
       dataset = self.new(:source => datasets.collect{|d| d.source}.join(", "), :name => datasets.collect{|d| d.name}.uniq.join(", "))
@@ -205,7 +207,7 @@ module OpenTox
       md5 = Digest::MD5.hexdigest(File.read(file)) # use hash to identify identical files
       dataset = self.find_by(:md5 => md5)
       if dataset
-        $logger.debug "Skipping import of #{file}, it is already in the database (id: #{dataset.id})."
+        $logger.debug "Found #{file} in the database (id: #{dataset.id}, md5: #{dataset.md5}), skipping import."
       else
         $logger.debug "Parsing #{file}."
         table = nil
@@ -234,10 +236,10 @@ module OpenTox
             if read_result
               value = line.chomp
               if value.numeric?
-                feature = NumericFeature.find_or_create_by(:name => feature_name)
+                feature = NumericFeature.find_or_create_by(:name => feature_name, :measured => true)
                 value = value.to_f
               else
-                feature = NominalFeature.find_or_create_by(:name => feature_name)
+                feature = NominalFeature.find_or_create_by(:name => feature_name, :measured => true)
               end
               features[feature] = value
               read_result = false
@@ -259,7 +261,7 @@ module OpenTox
       md5 = Digest::MD5.hexdigest(File.read(file)) # use hash to identify identical files
       dataset = self.find_by(:md5 => md5)
       if dataset
-        $logger.debug "Skipping import of #{file}, it is already in the database (id: #{dataset.id})."
+        $logger.debug "Found #{file} in the database (id: #{dataset.id}, md5: #{dataset.md5}), skipping import."
       else
         $logger.debug "Parsing #{file}."
         table = nil
@@ -301,7 +303,7 @@ module OpenTox
 
       # guess feature types
       feature_names.each_with_index do |f,i|
-        metadata = {:name => f}
+        metadata = {:name => f, :measured => true}
         original_id ? j = i+2 : j = i+1
         values = table.collect{|row| val=row[j].to_s.strip; val.blank? ? nil : val }.uniq.compact
         types = values.collect{|v| v.numeric? ? true : false}.uniq
@@ -424,7 +426,7 @@ module OpenTox
       name = File.basename(file,".*")
       batch = self.find_by(:source => source, :name => name)
       if batch
-        $logger.debug "Skipping import of #{file}, it is already in the database (id: #{batch.id})."
+        $logger.debug "Found #{file} in the database (id: #{dataset.id}, md5: #{dataset.md5}), skipping import."
       else
         $logger.debug "Parsing #{file}."
         # check delimiter
diff --git a/lib/model.rb b/lib/model.rb
index 0ed70f2..8901a2c 100644
--- a/lib/model.rb
+++ b/lib/model.rb
@@ -37,7 +37,7 @@ module OpenTox
       # @return [OpenTox::Model::Lazar]
       def self.create prediction_feature:nil, training_dataset:, algorithms:{}
         bad_request_error "Please provide a prediction_feature and/or a training_dataset." unless prediction_feature or training_dataset
-        prediction_feature = training_dataset.features.first unless prediction_feature
+        prediction_feature = training_dataset.features.select{|f| f.measured}.first unless prediction_feature
         # TODO: prediction_feature without training_dataset: use all available data
 
         # guess model type
@@ -199,6 +199,8 @@ module OpenTox
       # @return [Hash]
       def predict_substance substance, threshold = self.algorithms[:similarity][:min]
         
+        p substance.smiles
+        t = Time.now
         @independent_variables = Marshal.load $gridfs.find_one(_id: self.independent_variables_id).data
         case algorithms[:similarity][:method]
         when /tanimoto/ # binary features
@@ -284,6 +286,9 @@ module OpenTox
         else # try again with a lower threshold
           predict_substance substance, 0.2
         end
+        p prediction
+        p Time.now - t
+        prediction
       end
 
       # Predict a substance (compound or nanoparticle), an array of substances or a dataset