validation utilizes/requires acceptValue for classification

2 files changed, 27 insertions, 20 deletions

diff --git a/lib/ot_predictions.rb b/lib/ot_predictions.rb
index 5033425..1fd601c 100755
--- a/lib/ot_predictions.rb
+++ b/lib/ot_predictions.rb
@@ -54,13 +54,20 @@ module Lib
         @compounds = test_dataset.compounds
         LOGGER.debug "test dataset size: "+@compounds.size.to_s
         raise "test dataset is empty "+test_dataset_uri.to_s unless @compounds.size>0
-        class_values = feature_type=="classification" ? OpenTox::Feature.find(prediction_feature, subjectid).domain : nil
+        
+        if feature_type=="classification"
+          accept_values = test_target_dataset.features[prediction_feature][OT.acceptValue]
+          raise "'"+OT.acceptValue.to_s+"' missing/invalid for feature '"+prediction_feature.to_s+"' in dataset '"+
+            test_target_dataset_uri.to_s+"', acceptValues are: '"+accept_values.inspect+"'" if accept_values==nil or accept_values.length<2 
+        else
+          accept_values=nil
+        end
         
         actual_values = []
         @compounds.each do |c|
           case feature_type
           when "classification"
-            actual_values << classification_value(test_target_dataset, c, prediction_feature, class_values)
+            actual_values << classification_value(test_target_dataset, c, prediction_feature, accept_values)
           when "regression"
             actual_values << regression_value(test_target_dataset, c, prediction_feature)
           end
@@ -108,7 +115,7 @@ module Lib
             case feature_type
             when "classification"
               # TODO: remove LAZAR_PREDICTION_DATASET_HACK
-              predicted_values << classification_value(prediction_dataset, c, no_prediction_feature ? nil : predicted_variable, class_values)
+              predicted_values << classification_value(prediction_dataset, c, no_prediction_feature ? nil : predicted_variable, accept_values)
             when "regression"
               predicted_values << regression_value(prediction_dataset, c, no_prediction_feature ? nil : predicted_variable)
             end
@@ -126,7 +133,7 @@ module Lib
         end
         task.progress(80) if task # loaded predicted values and confidence
         
-        super(predicted_values, actual_values, confidence_values, feature_type, class_values)
+        super(predicted_values, actual_values, confidence_values, feature_type, accept_values)
         raise "illegal num compounds "+num_info if  @compounds.size != @predicted_values.size
         task.progress(100) if task # done with the mathmatics
     end
@@ -143,11 +150,11 @@ module Lib
       end
     end
     
-    def classification_value(dataset, compound, feature, class_values)
+    def classification_value(dataset, compound, feature, accept_values)
       v = value(dataset, compound, feature)
-      i = class_values.index(v)
-      raise "illegal class_value of prediction (value is '"+v.to_s+"', class is '"+v.class.to_s+"'), possible values are "+
-        class_values.inspect unless v==nil or i!=nil
+      i = accept_values.index(v.to_s)
+      raise "illegal class_value of prediction (value is '"+v.to_s+"'), accept values are "+
+        accept_values.inspect unless v==nil or i!=nil
       i
     end
     
diff --git a/lib/predictions.rb b/lib/predictions.rb
index 5850024..db3c60c 100755
--- a/lib/predictions.rb
+++ b/lib/predictions.rb
@@ -23,13 +23,13 @@ module Lib
                     actual_values, 
                     confidence_values, 
                     feature_type, 
-                    class_domain=nil )
+                    accept_values=nil )
                     
       @predicted_values = predicted_values
       @actual_values = actual_values
       @confidence_values = confidence_values
       @feature_type = feature_type
-      @class_domain = class_domain
+      @accept_values = accept_values
       @num_classes = 1
       
       #puts "predicted:  "+predicted_values.inspect
@@ -58,15 +58,15 @@ module Lib
       
       case @feature_type
       when "classification"
-        raise "class_domain missing while performing classification" unless @class_domain
-        @num_classes = @class_domain.size
+        raise "accept_values missing while performing classification" unless @accept_values
+        @num_classes = @accept_values.size
         raise "num classes < 2" if @num_classes<2
         { "predicted"=>@predicted_values, "actual"=>@actual_values }.each do |s,values|
           values.each{ |v| raise "illegal "+s+" classification-value ("+v.to_s+"),"+
             "has to be either nil or index of predicted-values" if v!=nil and (!v.is_a?(Numeric) or v<0 or v>@num_classes)}
         end
       when "regresssion"
-        raise "class_domain != nil while performing regression" if @class_domain
+        raise "accept_values != nil while performing regression" if @accept_values
         { "predicted"=>@predicted_values, "actual"=>@actual_values }.each do |s,values|
           values.each{ |v| raise "illegal "+s+" regression-value ("+v.to_s+"),"+
             "has to be either nil or number" unless v==nil or v.is_a?(Numeric)}
@@ -89,7 +89,7 @@ module Lib
       case @feature_type
       when "classification"
         @confusion_matrix = []
-        @class_domain.each do |v|
+        @accept_values.each do |v|
           @confusion_matrix.push( Array.new( @num_classes, 0 ) )
         end
         
@@ -235,8 +235,8 @@ module Lib
       res = {}
       (0..@num_classes-1).each do |actual|
           (0..@num_classes-1).each do |predicted|
-            res[{:confusion_matrix_actual => @class_domain[actual],
-                 :confusion_matrix_predicted => @class_domain[predicted]}] = @confusion_matrix[actual][predicted]
+            res[{:confusion_matrix_actual => @accept_values[actual],
+                 :confusion_matrix_predicted => @accept_values[predicted]}] = @confusion_matrix[actual][predicted]
         end
       end
       return res
@@ -495,7 +495,7 @@ module Lib
       raise "no confidence values" if @confidence_values==nil
       raise "no class-value specified" if class_value==nil
       
-      class_index = @class_domain.index(class_value)
+      class_index = @accept_values.index(class_value)
       raise "class not found "+class_value.to_s if class_index==nil
       
       c = []; p = []; a = []
@@ -529,7 +529,7 @@ module Lib
     def predicted_value(instance_index)
       case @feature_type 
       when "classification"
-        @predicted_values[instance_index]==nil ? nil : @class_domain[@predicted_values[instance_index]]
+        @predicted_values[instance_index]==nil ? nil : @accept_values[@predicted_values[instance_index]]
       when "regression"
         @predicted_values[instance_index]
       end
@@ -542,7 +542,7 @@ module Lib
     def actual_value(instance_index)
       case @feature_type 
       when "classification"
-        @actual_values[instance_index]==nil ? nil : @class_domain[@actual_values[instance_index]]
+        @actual_values[instance_index]==nil ? nil : @accept_values[@actual_values[instance_index]]
       when "regression"
         @actual_values[instance_index]
       end
@@ -576,7 +576,7 @@ module Lib
     def prediction_feature_value_map(proc)
       res = {}
       (0..@num_classes-1).each do |i|
-        res[@class_domain[i]] = proc.call(i)
+        res[@accept_values[i]] = proc.call(i)
       end
       return res
     end