Changeset 44 for trunk/CrossPare/src/de/ugoe/cs

Timestamp:

11/17/15 20:42:08 (9 years ago)

Author:

atrautsch

Message:

metric matching hinzu

Location:

trunk/CrossPare/src/de/ugoe/cs/cpdp

Files:

• ExperimentConfiguration.java (modified) (8 diffs)
• eval/AbstractWekaEvaluation.java (modified) (4 diffs)
• execution/CrossProjectExperiment.java (modified) (3 diffs)
• execution/RelaxedCrossProjectExperiment.java (modified) (2 diffs)
• training/ISetWiseTestdataAwareTrainingStrategy.java (added)
• training/MetricMatchingTraining.java (added)

trunk/CrossPare/src/de/ugoe/cs/cpdp/ExperimentConfiguration.java

@@ -1 @@
-// Copyright 2015 Georg-August-Universität Göttingen, Germany
+// Copyright 2015 Georg-August-Universit�t G�ttingen, Germany
 //
 //   Licensed under the Apache License, Version 2.0 (the "License");
@@ -40 +40 @@
 import de.ugoe.cs.cpdp.eval.IEvaluationStrategy;
 import de.ugoe.cs.cpdp.loader.IVersionLoader;
+import de.ugoe.cs.cpdp.training.ISetWiseTestdataAwareTrainingStrategy;
 import de.ugoe.cs.cpdp.training.ISetWiseTrainingStrategy;
 import de.ugoe.cs.cpdp.training.ITrainingStrategy;
@@ -114 +115 @@
 
     /**
+     * setwise testdata aware trainers, i.e., trainers that require the selected training data to be separate from
+     * each other and the current testdata 
+     */
+    private List<ISetWiseTestdataAwareTrainingStrategy> setwiseTestdataAwareTrainers;
+    
+    /**
      * data processors that are applied before the pointwise data selection
      */
@@ -178 +185 @@
         setwisepostprocessors = new LinkedList<>();
         setwiseTrainers = new LinkedList<>();
+        setwiseTestdataAwareTrainers = new LinkedList<>();
         preprocessors = new LinkedList<>();
         pointwiseselectors = new LinkedList<>();
@@ -324 +332 @@
     }
 
+    /**
+     * returns the setwise training algorithms
+     * 
+     * @return setwise training algorithms
+     */
+    public List<ISetWiseTestdataAwareTrainingStrategy> getSetWiseTestdataAwareTrainers() {
+        return setwiseTestdataAwareTrainers;
+    }
+    
     /**
      * returns the processors applied before the pointwise data selection
@@ -465 +482 @@
                 trainer.setParameter(attributes.getValue("param"));
                 setwiseTrainers.add(trainer);
+            }
+            else if (qName.equals("setwisetestdataawaretrainer")) {
+                final ISetWiseTestdataAwareTrainingStrategy trainer =
+                    (ISetWiseTestdataAwareTrainingStrategy) Class.forName("de.ugoe.cs.cpdp.training." +
+                                                                 attributes.getValue("name"))
+                        .newInstance();
+                trainer.setParameter(attributes.getValue("param"));
+                setwiseTestdataAwareTrainers.add(trainer);
             }
             else if (qName.equals("preprocessor")) {
@@ -566 +591 @@
         setwisepostprocessors.addAll(other.setwisepostprocessors);
         setwiseTrainers.addAll(other.setwiseTrainers);
+        setwiseTestdataAwareTrainers.addAll(other.setwiseTestdataAwareTrainers);
         preprocessors.addAll(other.preprocessors);
         pointwiseselectors.addAll(other.pointwiseselectors);
@@ -609 +635 @@
             StringTools.ENDLINE);
         builder.append("Setwise trainers: " + setwiseTrainers.toString() + StringTools.ENDLINE);
+        builder.append("Setwise Testdata Aware trainers: " + setwiseTestdataAwareTrainers.toString() + StringTools.ENDLINE);
         builder
             .append("Pointwise preprocessors: " + preprocessors.toString() + StringTools.ENDLINE);

trunk/CrossPare/src/de/ugoe/cs/cpdp/eval/AbstractWekaEvaluation.java

@@ -123 +123 @@
                 output.append(",tn_" + ((IWekaCompatibleTrainer) trainer).getName());
                 output.append(",fp_" + ((IWekaCompatibleTrainer) trainer).getName());
-                output.append(",trainerror_" + ((IWekaCompatibleTrainer) trainer).getName());
-                output.append(",trainrecall_" + ((IWekaCompatibleTrainer) trainer).getName());
-                output.append(",trainprecision_" + ((IWekaCompatibleTrainer) trainer).getName());
-                output.append(",trainsuccHe_" + ((IWekaCompatibleTrainer) trainer).getName());
+                //output.append(",trainerror_" + ((IWekaCompatibleTrainer) trainer).getName());
+                //output.append(",trainrecall_" + ((IWekaCompatibleTrainer) trainer).getName());
+                //output.append(",trainprecision_" + ((IWekaCompatibleTrainer) trainer).getName());
+                //output.append(",trainsuccHe_" + ((IWekaCompatibleTrainer) trainer).getName());
             }
             output.append(StringTools.ENDLINE);
@@ -136 +136 @@
 
         Evaluation eval = null;
-        Evaluation evalTrain = null;
+        //Evaluation evalTrain = null;
         for (Classifier classifier : classifiers) {
             eval = createEvaluator(testdata, classifier);
-            evalTrain = createEvaluator(traindata, classifier);
+            //evalTrain = createEvaluator(traindata, classifier);
 
             double pf =
@@ -195 +195 @@
             output.append("," + eval.numTrueNegatives(1));
             output.append("," + eval.numFalsePositives(1));
-            output.append("," + evalTrain.errorRate());
+            /*output.append("," + evalTrain.errorRate());
             output.append("," + evalTrain.recall(1));
             output.append("," + evalTrain.precision(1));
@@ -203 +203 @@
             else {
                 output.append(",0");
-            }
+            }*/
         }
 

trunk/CrossPare/src/de/ugoe/cs/cpdp/execution/CrossProjectExperiment.java

@@ -31 +31 @@
 import de.ugoe.cs.cpdp.eval.IEvaluationStrategy;
 import de.ugoe.cs.cpdp.loader.IVersionLoader;
+import de.ugoe.cs.cpdp.training.ISetWiseTestdataAwareTrainingStrategy;
 import de.ugoe.cs.cpdp.training.ISetWiseTrainingStrategy;
 import de.ugoe.cs.cpdp.training.ITrainer;
@@ -168 +169 @@
                     setwiseTrainer.apply(traindataSet);
                 }
+                for (ISetWiseTestdataAwareTrainingStrategy setwiseTestdataAwareTrainer : config.getSetWiseTestdataAwareTrainers()) {
+                    Console.traceln(Level.FINE, String
+                        .format("[%s] [%02d/%02d] %s: applying testdata aware setwise trainer %s",
+                                config.getExperimentName(), versionCount, testVersionCount,
+                                testVersion.getVersion(), setwiseTestdataAwareTrainer.getName()));
+                    setwiseTestdataAwareTrainer.apply(traindataSet, testdata);
+                }
                 Instances traindata = makeSingleTrainingSet(traindataSet);
                 for (IProcessesingStrategy processor : config.getPreProcessors()) {
@@ -210 +218 @@
                     for (ISetWiseTrainingStrategy setwiseTrainer : config.getSetWiseTrainers()) {
                         allTrainers.add(setwiseTrainer);
+                    }
+                    for (ISetWiseTestdataAwareTrainingStrategy setwiseTestdataAwareTrainer : config.getSetWiseTestdataAwareTrainers()) {
+                        allTrainers.add(setwiseTestdataAwareTrainer);
                     }
                     for (ITrainingStrategy trainer : config.getTrainers()) {

trunk/CrossPare/src/de/ugoe/cs/cpdp/execution/RelaxedCrossProjectExperiment.java

@@ -31 +31 @@
 import de.ugoe.cs.cpdp.eval.IEvaluationStrategy;
 import de.ugoe.cs.cpdp.loader.IVersionLoader;
+import de.ugoe.cs.cpdp.training.ISetWiseTestdataAwareTrainingStrategy;
 import de.ugoe.cs.cpdp.training.ISetWiseTrainingStrategy;
 import de.ugoe.cs.cpdp.training.ITrainer;
@@ -173 +174 @@
                     setwiseTrainer.apply(traindataSet);
                 }
+                for (ISetWiseTestdataAwareTrainingStrategy setwiseTestdataAwareTrainer : config.getSetWiseTestdataAwareTrainers()) {
+                    Console.traceln(Level.FINE, String
+                        .format("[%s] [%02d/%02d] %s: applying testdata aware setwise trainer %s",
+                                config.getExperimentName(), versionCount, testVersionCount,
+                                testVersion.getVersion(), setwiseTestdataAwareTrainer.getName()));
+                    setwiseTestdataAwareTrainer.apply(traindataSet, testdata);
+                }
                 Instances traindata = makeSingleTrainingSet(traindataSet);
                 for (IProcessesingStrategy processor : config.getPreProcessors()) {