Index: /trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/BayesNetWrapper.java
===================================================================
--- /trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/BayesNetWrapper.java (revision 130)
+++ /trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/BayesNetWrapper.java (revision 130)
@@ -0,0 +1,118 @@
+// Copyright 2015 Georg-August-Universität Göttingen, Germany
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package de.ugoe.cs.cpdp.wekaclassifier;
+
+import java.util.HashSet;
+import java.util.Set;
+import java.util.logging.Level;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+
+import de.ugoe.cs.cpdp.util.WekaUtils;
+import de.ugoe.cs.util.console.Console;
+import weka.classifiers.bayes.BayesNet;
+import weka.core.DenseInstance;
+import weka.core.Instance;
+import weka.core.Instances;
+
+/**
+ *
+ * Wrapper to max BayesNet to deal with a problem with Discretize
+ *
+ *
+ * @author Steffen Herbold
+ */
+public class BayesNetWrapper extends BayesNet {
+
+ /**
+ * generated ID
+ */
+ /** */
+ private static final long serialVersionUID = -4835134612921456157L;
+
+ /**
+ * Map that store attributes for upscaling for each classifier
+ */
+ private Set upscaleIndex = new HashSet<>();
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see weka.classifiers.bayes.BayesNet#buildClassifier(weka.core.Instances)
+ */
+ @Override
+ public void buildClassifier(Instances traindata) throws Exception {
+ boolean trainingSuccessfull = false;
+ boolean secondAttempt = false;
+ Instances traindataCopy = null;
+ do {
+ try {
+ if (secondAttempt) {
+ super.buildClassifier(traindataCopy);
+ trainingSuccessfull = true;
+ }
+ else {
+ super.buildClassifier(traindata);
+ trainingSuccessfull = true;
+ }
+ }
+ catch (IllegalArgumentException e) {
+ String regex = "A nominal attribute \\((.*)\\) cannot have duplicate labels.*";
+ Pattern p = Pattern.compile(regex);
+ Matcher m = p.matcher(e.getMessage());
+ if (!m.find()) {
+ // cannot treat problem, rethrow exception
+ throw e;
+ }
+ String attributeName = m.group(1);
+ int attrIndex = traindata.attribute(attributeName).index();
+ if (secondAttempt) {
+ throw new RuntimeException("cannot be handled correctly yet, because upscaleIndex is a Map");
+ // traindataCopy = upscaleAttribute(traindataCopy, attrIndex);
+ }
+ else {
+ traindataCopy = WekaUtils.upscaleAttribute(traindata, attrIndex);
+ }
+
+ upscaleIndex.add(attrIndex);
+ Console.traceln(Level.FINE, "upscaled attribute " + attributeName +
+ "; restarting training of BayesNet");
+ secondAttempt = true;
+ continue;
+ }
+ }
+ while (!trainingSuccessfull); // dummy loop for internal continue
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see weka.classifiers.bayes.BayesNet#distributionForInstance(weka.core.Instance)
+ */
+ @Override
+ public double[] distributionForInstance(Instance instance) throws Exception {
+ Instances traindataCopy;
+ for (int attrIndex : upscaleIndex) {
+ // instance value must be upscaled
+ double upscaledVal = instance.value(attrIndex) * WekaUtils.SCALER;
+ traindataCopy = new Instances(instance.dataset());
+ instance = new DenseInstance(instance.weight(), instance.toDoubleArray());
+ instance.setValue(attrIndex, upscaledVal);
+ traindataCopy.add(instance);
+ instance.setDataset(traindataCopy);
+ }
+ return super.distributionForInstance(instance);
+ }
+}
Index: /trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/DecisionTableWrapper.java
===================================================================
--- /trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/DecisionTableWrapper.java (revision 130)
+++ /trunk/CrossPare/src/de/ugoe/cs/cpdp/wekaclassifier/DecisionTableWrapper.java (revision 130)
@@ -0,0 +1,117 @@
+// Copyright 2015 Georg-August-Universität Göttingen, Germany
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package de.ugoe.cs.cpdp.wekaclassifier;
+
+import java.util.HashSet;
+import java.util.Set;
+import java.util.logging.Level;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+
+import de.ugoe.cs.cpdp.util.WekaUtils;
+import de.ugoe.cs.util.console.Console;
+import weka.classifiers.rules.DecisionTable;
+import weka.core.DenseInstance;
+import weka.core.Instance;
+import weka.core.Instances;
+
+/**
+ *
+ * Wrapper to max DecisionTable to deal with a problem with Discretize
+ *
+ *
+ * @author Steffen Herbold
+ */
+public class DecisionTableWrapper extends DecisionTable {
+
+ /**
+ * generated ID
+ */
+ private static final long serialVersionUID = -483513461292145615L;
+
+ /**
+ * Map that store attributes for upscaling for each classifier
+ */
+ private Set upscaleIndex = new HashSet<>();
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see weka.classifiers.rules.DecisionTable#buildClassifier(weka.core.Instances)
+ */
+ @Override
+ public void buildClassifier(Instances traindata) throws Exception {
+ boolean trainingSuccessfull = false;
+ boolean secondAttempt = false;
+ Instances traindataCopy = null;
+ do {
+ try {
+ if (secondAttempt) {
+ super.buildClassifier(traindataCopy);
+ trainingSuccessfull = true;
+ }
+ else {
+ super.buildClassifier(traindata);
+ trainingSuccessfull = true;
+ }
+ }
+ catch (IllegalArgumentException e) {
+ String regex = "A nominal attribute \\((.*)\\) cannot have duplicate labels.*";
+ Pattern p = Pattern.compile(regex);
+ Matcher m = p.matcher(e.getMessage());
+ if (!m.find()) {
+ // cannot treat problem, rethrow exception
+ throw e;
+ }
+ String attributeName = m.group(1);
+ int attrIndex = traindata.attribute(attributeName).index();
+ if (secondAttempt) {
+ throw new RuntimeException("cannot be handled correctly yet, because upscaleIndex is a Map");
+ // traindataCopy = upscaleAttribute(traindataCopy, attrIndex);
+ }
+ else {
+ traindataCopy = WekaUtils.upscaleAttribute(traindata, attrIndex);
+ }
+
+ upscaleIndex.add(attrIndex);
+ Console.traceln(Level.FINE, "upscaled attribute " + attributeName +
+ "; restarting training of DecisionTable");
+ secondAttempt = true;
+ continue;
+ }
+ }
+ while (!trainingSuccessfull); // dummy loop for internal continue
+ }
+
+ /*
+ * (non-Javadoc)
+ *
+ * @see weka.classifiers.rules.DecisionTable#distributionForInstance(weka.core.Instance)
+ */
+ @Override
+ public double[] distributionForInstance(Instance instance) throws Exception {
+ Instances traindataCopy;
+ for (int attrIndex : upscaleIndex) {
+ // instance value must be upscaled
+ double upscaledVal = instance.value(attrIndex) * WekaUtils.SCALER;
+ traindataCopy = new Instances(instance.dataset());
+ instance = new DenseInstance(instance.weight(), instance.toDoubleArray());
+ instance.setValue(attrIndex, upscaledVal);
+ traindataCopy.add(instance);
+ instance.setDataset(traindataCopy);
+ }
+ return super.distributionForInstance(instance);
+ }
+}