Changeset 41 for trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/Oversampling.java

Timestamp:

09/24/15 10:59:05 (9 years ago)

Author:

sherbold

Message:

• formatted code and added copyrights

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/Oversampling.java (modified) (2 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/Oversampling.java

@@ +1 @@
+// 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.dataprocessing;
 
@@ -8 +22 @@
 
 /**
- * Implements oversampling, a strategy for
- * handling bias in data. In case there are less positive samples (i.e.
- * defect-prone) samples in the data than negative samples (i.e.
- * non-defect-prone), the defect-prone entities are over-sampled such that the
- * number of defect-prone and non-defect-prone instances is the same afterwards.
- * This means, that some of the defect-prone entities will be more than once
- * within the data.
+ * Implements oversampling, a strategy for handling bias in data. In case there are less positive
+ * samples (i.e. defect-prone) samples in the data than negative samples (i.e. non-defect-prone),
+ * the defect-prone entities are over-sampled such that the number of defect-prone and
+ * non-defect-prone instances is the same afterwards. This means, that some of the defect-prone
+ * entities will be more than once within the data.
  * 
  * @author Steffen Herbold
  */
-public class Oversampling implements IProcessesingStrategy,
-                ISetWiseProcessingStrategy {
+public class Oversampling implements IProcessesingStrategy, ISetWiseProcessingStrategy {
 
-        /**
-         * Does not have parameters. String is ignored.
-         * 
-         * @param parameters
-         *            ignored
-         */
-        @Override
-        public void setParameter(String parameters) {
-                // dummy
-        }
+    /**
+     * Does not have parameters. String is ignored.
+     * 
+     * @param parameters
+     *            ignored
+     */
+    @Override
+    public void setParameter(String parameters) {
+        // dummy
+    }
 
-        /*
-         * (non-Javadoc)
-         * 
-         * @see
-         * de.ugoe.cs.cpdp.dataprocessing.ISetWiseProcessingStrategy#apply(weka.
-         * core.Instances, org.apache.commons.collections4.list.SetUniqueList)
-         */
-        @Override
-        public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
-                for (Instances traindata : traindataSet) {
-                        apply(testdata, traindata);
-                }
-        }
+    /*
+     * (non-Javadoc)
+     * 
+     * @see de.ugoe.cs.cpdp.dataprocessing.ISetWiseProcessingStrategy#apply(weka. core.Instances,
+     * org.apache.commons.collections4.list.SetUniqueList)
+     */
+    @Override
+    public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
+        for (Instances traindata : traindataSet) {
+            apply(testdata, traindata);
+        }
+    }
 
-        /*
-         * (non-Javadoc)
-         * 
-         * @see
-         * de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy#apply(weka.core.
-         * Instances, weka.core.Instances)
-         */
-        @Override
-        public void apply(Instances testdata, Instances traindata) {
+    /*
+     * (non-Javadoc)
+     * 
+     * @see de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy#apply(weka.core. Instances,
+     * weka.core.Instances)
+     */
+    @Override
+    public void apply(Instances testdata, Instances traindata) {
 
-                final int[] counts = traindata.attributeStats(traindata.classIndex()).nominalCounts;
-                if (counts[1] < counts[0]) {
-                        Instances negatives = new Instances(traindata);
-                        Instances positives = new Instances(traindata);
+        final int[] counts = traindata.attributeStats(traindata.classIndex()).nominalCounts;
+        if (counts[1] < counts[0]) {
+            Instances negatives = new Instances(traindata);
+            Instances positives = new Instances(traindata);
 
-                        for (int i = traindata.size() - 1; i >= 0; i--) {
-                                if (Double.compare(1.0, negatives.get(i).classValue()) == 0) {
-                                        negatives.remove(i);
-                                }
-                                if (Double.compare(0.0, positives.get(i).classValue()) == 0) {
-                                        positives.remove(i);
-                                }
-                        }
+            for (int i = traindata.size() - 1; i >= 0; i--) {
+                if (Double.compare(1.0, negatives.get(i).classValue()) == 0) {
+                    negatives.remove(i);
+                }
+                if (Double.compare(0.0, positives.get(i).classValue()) == 0) {
+                    positives.remove(i);
+                }
+            }
 
-                        Resample resample = new Resample();
-                        // TODO: resample.setSampleSizePercent((100.0*counts[1])/100+0.01);
-                        // Ohne +0.01 wird bei tomcat, xerces-1.2 und jedit-4.0 ein negative
-                        // weniger zurückgegeben
-                        resample.setSampleSizePercent((100.0 * counts[0]) / counts[1]);
-                        try {
-                                resample.setInputFormat(traindata);
-                                positives = Filter.useFilter(positives, resample);
-                        } catch (Exception e) {
-                                throw new RuntimeException(e);
-                        }
-                        traindata.clear();
-                        for (int i = 0; i < negatives.size(); i++) {
-                                traindata.add(negatives.get(i));
-                        }
-                        for (int i = 0; i < positives.size(); i++) {
-                                traindata.add(positives.get(i));
-                        }
-                }
-        }
+            Resample resample = new Resample();
+            // TODO: resample.setSampleSizePercent((100.0*counts[1])/100+0.01);
+            // Ohne +0.01 wird bei tomcat, xerces-1.2 und jedit-4.0 ein negative
+            // weniger zurückgegeben
+            resample.setSampleSizePercent((100.0 * counts[0]) / counts[1]);
+            try {
+                resample.setInputFormat(traindata);
+                positives = Filter.useFilter(positives, resample);
+            }
+            catch (Exception e) {
+                throw new RuntimeException(e);
+            }
+            traindata.clear();
+            for (int i = 0; i < negatives.size(); i++) {
+                traindata.add(negatives.get(i));
+            }
+            for (int i = 0; i < positives.size(); i++) {
+                traindata.add(positives.get(i));
+            }
+        }
+    }
 
 }