Ia fix 377 core (#389)

Add method for using a test/training set
arx-deidentifier · Jun 16, 2022 · 479a1ba · 479a1ba
1 parent 18c3089
commit 479a1ba
Show file tree

Hide file tree

Showing 10 changed files with 930 additions and 207 deletions.
diff --git a/src/example/org/deidentifier/arx/examples/Example39.java b/src/example/org/deidentifier/arx/examples/Example39.java
@@ -19,9 +19,14 @@
 
 import java.io.File;
 import java.io.FilenameFilter;
+import java.io.IOError;
 import java.io.IOException;
 import java.nio.charset.StandardCharsets;
+import java.security.SecureRandom;
 import java.text.ParseException;
+import java.util.HashSet;
+import java.util.Random;
+import java.util.Set;
 import java.util.regex.Matcher;
 import java.util.regex.Pattern;
 
@@ -32,16 +37,24 @@
 import org.deidentifier.arx.AttributeType;
 import org.deidentifier.arx.AttributeType.Hierarchy;
 import org.deidentifier.arx.Data;
+import org.deidentifier.arx.DataSubset;
 import org.deidentifier.arx.DataType;
+import org.deidentifier.arx.aggregates.ClassificationConfigurationLogisticRegression;
+import org.deidentifier.arx.aggregates.ClassificationConfigurationNaiveBayes;
+import org.deidentifier.arx.aggregates.ClassificationConfigurationRandomForest;
+import org.deidentifier.arx.criteria.Inclusion;
 import org.deidentifier.arx.criteria.KAnonymity;
 import org.deidentifier.arx.io.CSVHierarchyInput;
 import org.deidentifier.arx.metric.Metric;
 
 /**
  * This class implements an example on how to compare data mining performance
- *
+ * The evaluation can be used with either K-fold cross validation (default) or with 
+ *     subset for training and different subset for testing
+ *     
  * @author Fabian Prasser
  * @author Florian Kohlmayer
+ * @author Ibraheem Al-Dhamari
  */
 public class Example39 extends Example {
 
@@ -83,7 +96,25 @@ public boolean accept(File dir, String name) {
 
         return data;
     }
+
+    public static  Set<Integer> getRandomDataSubsetIndices(double dataSize, Data inputData, int numRecords) {
+
+        if (dataSize < 0d || dataSize > 1d) {
+                     System.out.println(" data size ratio is out of range");
+                     throw new IOError(new Exception());                  
+         }
 
+         // Create a data subset via sampling based on beta
+         Set<Integer> subsetIndices = new HashSet<Integer>();
+         Random random = new SecureRandom();
+         for (int i = 0; i < numRecords; ++i) {
+             if (random.nextDouble() < dataSize) {
+                 subsetIndices.add(i);
+             }
+         }
+         return subsetIndices;
+     }    
+
     /**
      * Entry point.
      * 
@@ -111,18 +142,65 @@ public static void main(String[] args) throws ParseException, IOException {
         data.getDefinition().setDataType("age", DataType.INTEGER);
         data.getDefinition().setResponseVariable("marital-status", true);
 
+
         ARXAnonymizer anonymizer = new ARXAnonymizer();
+
         ARXConfiguration config = ARXConfiguration.create();
         config.addPrivacyModel(new KAnonymity(5));
         config.setSuppressionLimit(1d);
         config.setQualityModel(Metric.createClassificationMetric());
 
+       // Create a training subset data with a specific percentage of the original data e.g 80%
+       double dataSize = 0.80; 
+
+       // Creating a view from the original dataset 
+       Set<Integer>  subsetIndicesTrain = getRandomDataSubsetIndices(dataSize,  data,  data.getHandle().getNumRows()) ;        
+       DataSubset datasubTrain = DataSubset.create(data.getHandle().getNumRows(), subsetIndicesTrain);
+
+       // Adding the data subset to the current configuration,
+       // this subset will be used for the anonymization, 
+       // other records will be transformed but only suppressed,   
+       // In the training, only the subset will be used 
+        config.addPrivacyModel(new Inclusion (datasubTrain) );
+
+        config.setSuppressionLimit(1d);
+        config.setQualityModel(Metric.createClassificationMetric());
+
+        // Start anonymization process
         ARXResult result = anonymizer.anonymize(data, config);
-        System.out.println("5-anonymous dataset (logistic regression)");
-        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, ARXClassificationConfiguration.createLogisticRegression()));
-        System.out.println("5-anonymous dataset (naive bayes)");
-        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, ARXClassificationConfiguration.createNaiveBayes()));
-        System.out.println("5-anonymous dataset (random forest)");
-        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, ARXClassificationConfiguration.createRandomForest()));
+
+        System.out.println("===============================================");
+        System.out.println("   5-anonymous dataset (logistic regression)");
+        System.out.println("===============================================");
+        ClassificationConfigurationLogisticRegression logisticClassifier = ARXClassificationConfiguration.createLogisticRegression();
+        System.out.println("Evaluation using K-fold cross validation: ...............");        
+        logisticClassifier.setEvaluateWithKfold(true);        
+        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, logisticClassifier));
+        System.out.println("Evaluation using testing subset: ........................");        
+        logisticClassifier.setEvaluateWithKfold(false);        
+        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, logisticClassifier));
+
+        System.out.println("===============================================");     
+        System.out.println("   5-anonymous dataset (naive bayes)");
+        System.out.println("===============================================");
+        System.out.println("Evaluation using K-fold cross validation: ...............");        
+        logisticClassifier.setEvaluateWithKfold(true);        
+        ClassificationConfigurationNaiveBayes naiveBayesClassifier = ARXClassificationConfiguration.createNaiveBayes();
+        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, naiveBayesClassifier));
+        System.out.println("Evaluation using testing subset: ........................");        
+        logisticClassifier.setEvaluateWithKfold(false);        
+        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, naiveBayesClassifier));
+
+        System.out.println("===============================================");     
+        System.out.println("   5-anonymous dataset (random forest)");
+        System.out.println("===============================================");     
+        System.out.println("Evaluation using K-fold cross validation: ...............");        
+        logisticClassifier.setEvaluateWithKfold(true);        
+        ClassificationConfigurationRandomForest randomForestClassifier = ARXClassificationConfiguration.createRandomForest();
+        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, randomForestClassifier));
+        System.out.println("Evaluation using testing subset: ........................");        
+        logisticClassifier.setEvaluateWithKfold(false);        
+        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, randomForestClassifier));
+
     }
-}
+}