Changeset 5 for trunk/CrossPare/src/de/ugoe/cs/cpdp/eval

Timestamp:

08/05/14 11:42:12 (10 years ago)

Author:

sherbold

Message:

• extended WEKA evaluation with training error, recall, precision and success rate after He et al.

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/eval/AbstractWekaEvaluation.java (modified) (3 diffs)

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

@@ -95 +95 @@
                                 output.append(",tn_" + ((WekaCompatibleTrainer) trainer).getName());
                                 output.append(",fp_" + ((WekaCompatibleTrainer) trainer).getName());
+                                output.append(",trainerror_" + ((WekaCompatibleTrainer) trainer).getName());
+                                output.append(",trainrecall_" + ((WekaCompatibleTrainer) trainer).getName());
+                                output.append(",trainprecision_" + ((WekaCompatibleTrainer) trainer).getName());
+                                output.append(",trainsuccHe_" + ((WekaCompatibleTrainer) trainer).getName());
                         }
                         output.append(StringTools.ENDLINE);
@@ -104 +108 @@
                 
                 Evaluation eval = null;
+                Evaluation evalTrain = null;
                 for( Classifier classifier : classifiers ) {
                         eval = createEvaluator(testdata, classifier);
+                        evalTrain = createEvaluator(traindata, classifier);
                         
                         double pf = eval.numFalsePositives(1)/(eval.numFalsePositives(1)+eval.numTrueNegatives(1));
@@ -150 +156 @@
                         output.append("," + eval.numTrueNegatives(1));
                         output.append("," + eval.numFalsePositives(1));
+                        output.append("," + evalTrain.errorRate());
+                        output.append("," + evalTrain.recall(1));
+                        output.append("," + evalTrain.precision(1));
+                        if( evalTrain.recall(1)>=0.7 && evalTrain.precision(1) >= 0.5 ) {
+                                output.append(",1");
+                        } else {
+                                output.append(",0");
+                        }
                 }