Changeset 34786

Timestamp:

2021-02-01T18:31:25+13:00 (3 years ago)

Author:

davidb

Message:

Code tidy-up

Location:

main/trunk/model-sites-dev/mars

Files:

• RUN-APPLY-AROUSAL-MODEL.sh (modified) (1 diff)
• src/java/org/greenstone/mars/WekaApplyArousalModel.java (modified) (7 diffs)
• src/java/org/greenstone/mars/WekaTrainArousalModel.java (modified) (2 diffs)
• src/java/org/greenstone/mars/WekaUtil.java (added)

main/trunk/model-sites-dev/mars/RUN-APPLY-AROUSAL-MODEL.sh

@@ -11 +11 @@
      "$output_data_filename" 
 
-echo "****"
-echo "* Saved output data: $output_data_filename"
-echo "****"
-
 
 

main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaApplyArousalModel.java

@@ -28 +28 @@
 class WekaApplyArousalModel
 {
-    public final static String AROUSAL_ATTRIBUTE_NAME = "arousal_sample_26500ms";
-    public final static String VALANCE_ATTRIBUTE_NAME = "valance_sample_26500ms";
-
-    public final static String PREDICT_ATTRIBUTE_NAME = "arousal_sample_26500ms";
-    
-
+    //public final static String AROUSAL_ATTRIBUTE_NAME = "arousal_sample_26500ms";
+    //public final static String VALANCE_ATTRIBUTE_NAME = "valance_sample_26500ms";
+
+    public final static String PREDICT_ATTRIBUTE_NAME = WekaUtil.AROUSAL_ATTRIBUTE_NAME;
+    
+
+    /*
     public static Instances applyFilter(Instances data_instances,String additional_remove)
     {
@@ -58 +59 @@
     return filtered_data_instances; 
     }
-
-
+    */
+    
+    /*
     
     public static void checkDatasetInstancesCompatible(Instances new_instances)
@@ -87 +89 @@
 
     }
+    */
     
     public static void main(String[] args)
@@ -113 +116 @@
         System.out.println("Loading unlabeled instances: " + unclassified_data_input_filename);
 
-        //FileReader fr = new FileReader(unclassified_data_input_filename);
-        //BufferedReader br = new BufferedReader(fr);
-        //Instances unlabeled_instances = new Instances(br);
-        //br.close();
-
         DataSource data_source = new DataSource(unclassified_data_input_filename);
         Instances unlabeled_instances = data_source.getDataSet();
@@ -129 +127 @@
         if (predict_attribute == null) {
 
-        unlabeled_instances = applyFilter(unlabeled_instances,null); // no additional top-up to remove      
+        unlabeled_instances = WekaUtil.applyFilter(unlabeled_instances,null); // no additional top-up to remove     
         int num_attributes = unlabeled_instances.numAttributes();
 
-        Attribute arousal_attribute = new Attribute(AROUSAL_ATTRIBUTE_NAME);
+        Attribute arousal_attribute = new Attribute(WekaUtil.AROUSAL_ATTRIBUTE_NAME);
         unlabeled_instances.insertAttributeAt(arousal_attribute,num_attributes);
-        num_attributes++;
-        
-        //Attribute valance_attribute = new Attribute(VALANCE_ATTRIBUTE_NAME);
-        //unlabeled_instances.insertAttributeAt(valance_attribute,num_attributes);
-        //num_attributes++;
-
-        //predict_attribute = new Attribute(PREDICT_ATTRIBUTE_NAME);
-        //unlabeled_instances.insertAttributeAt(predict_attribute,num_attributes);
-        //unlabeled_instances.setClassIndex(num_attributes);
-        //num_attributes++;
-
-        //predict_attribute = unlabeled_instances.attribute(PREDICT_ATTRIBUTE_NAME);
-        //unlabeled_instances.setClass(predict_attribute);
-
-        //unlabeled_instances.setClassIndex(num_attributes - 1);
+        num_attributes++;       
         }
         else {
@@ -155 +139 @@
         //   => in fact has 'valance' attribute too, which we want to remove
         
-        unlabeled_instances = applyFilter(unlabeled_instances,"472"); // top-up with removal of 'valance'
-        //unlabeled_instances.setClass(predict_attribute);
+        unlabeled_instances = WekaUtil.applyFilter(unlabeled_instances,"472"); // top-up with removal of 'valance'
         
         // reference share this as 'groundtruth_instances' to trigger error calculation and output
@@ -163 +146 @@
 
         int num_attributes = unlabeled_instances.numAttributes();
+        
+        // Set class attribute
         unlabeled_instances.setClassIndex(num_attributes - 1);
         
-        checkDatasetInstancesCompatible(unlabeled_instances);
-        
-        // Set class attribute
-        //unlabeled_instances.setClassIndex(gt_instances.numAttributes() - 2);
-
+        WekaUtil.checkDatasetInstancesCompatible(unlabeled_instances);
 
         // Create copy where the predictions are to be made

main/trunk/model-sites-dev/mars/src/java/org/greenstone/mars/WekaTrainArousalModel.java

@@ -49 +49 @@
         DataSource data_source = new DataSource(input_arff_filename);
         Instances data_instances = data_source.getDataSet();
+        
+        Instances filtered_data_instances = WekaUtil.applyFilter(data_instances,"472"); // top-up with removal of 'valance'
 
-        // Filter out Valence
-        //String[] filter_options = weka.core.Utils.splitOptions("-R 472");
-        //String[] filter_options = weka.core.Utils.splitOptions("-R 472 -R 458-466");
-        String[] filter_options = weka.core.Utils.splitOptions("-R 425,458-466,472"); // remove ordinal attributes and 'valance'
-        Remove filter_remove = new Remove();
-        filter_remove.setOptions(filter_options);
-        filter_remove.setInputFormat(data_instances);
-        Instances filtered_data_instances = Filter.useFilter(data_instances, filter_remove);
-
-        // **** have removed R472, so class to predict is numAttributes()-1
-        filtered_data_instances.setClassIndex(filtered_data_instances.numAttributes() - 1);
+            
+        // Filtering above has removed R472, so class to predict is numAttributes()-1
+        filtered_data_instances.setClassIndex(filtered_data_instances.numAttributes()-1);
         
 
@@ -71 +65 @@
         System.out.println(classifier);
 
-
+        // Evaluate     
         Evaluation eval = new Evaluation(filtered_data_instances);
         Random rand = new Random(1);