/*
 * ARX: Powerful Data Anonymization
 * Copyright 2012 - 2017 Fabian Prasser, Florian Kohlmayer and contributors
 * 
 * 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 org.deidentifier.arx.examples;

import java.io.File;
import java.io.FilenameFilter;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.text.ParseException;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

import org.deidentifier.arx.ARXAnonymizer;
import org.deidentifier.arx.ARXConfiguration;
import org.deidentifier.arx.ARXLogisticRegressionConfiguration;
import org.deidentifier.arx.ARXNaiveBayesConfiguration;
import org.deidentifier.arx.ARXResult;
import org.deidentifier.arx.ARXSVMConfiguration;
import org.deidentifier.arx.AttributeType;
import org.deidentifier.arx.AttributeType.Hierarchy;
import org.deidentifier.arx.Data;
import org.deidentifier.arx.DataType;
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
 *
 * @author Fabian Prasser
 * @author Florian Kohlmayer
 */
public class Example39 extends Example {
    
    /**
     * Loads a dataset from disk
     * @param dataset
     * @return
     * @throws IOException
     */
    public static Data createData(final String dataset) throws IOException {
        
        // Load data
        Data data = Data.create("data/" + dataset + ".csv", StandardCharsets.UTF_8, ';');
        
        // Read generalization hierarchies
        FilenameFilter hierarchyFilter = new FilenameFilter() {
            @Override
            public boolean accept(File dir, String name) {
                if (name.matches(dataset + "_hierarchy_(.)+.csv")) {
                    return true;
                } else {
                    return false;
                }
            }
        };
        
        // Create definition
        File testDir = new File("data/");
        File[] genHierFiles = testDir.listFiles(hierarchyFilter);
        Pattern pattern = Pattern.compile("_hierarchy_(.*?).csv");
        for (File file : genHierFiles) {
            Matcher matcher = pattern.matcher(file.getName());
            if (matcher.find()) {
                CSVHierarchyInput hier = new CSVHierarchyInput(file, StandardCharsets.UTF_8, ';');
                String attributeName = matcher.group(1);
                data.getDefinition().setAttributeType(attributeName, Hierarchy.create(hier.getHierarchy()));
            }
        }
        
        return data;
    }
    
    /**
     * Entry point.
     * 
     * @param args the arguments
     * @throws ParseException
     * @throws IOException
     */
    public static void main(String[] args) throws ParseException, IOException {
        
        String[] features = new String[] {
                                           "sex",
                                           "age",
                                           "race",
                                           "marital-status",
                                           "education",
                                           "native-country",
                                           "workclass",
                                           "occupation",
                                           "salary-class"
        };
        
        String clazz = "marital-status";
        
        Data data = createData("adult");
        data.getDefinition().setAttributeType("marital-status", AttributeType.INSENSITIVE_ATTRIBUTE);
        data.getDefinition().setDataType("age", DataType.INTEGER);
        
        ARXAnonymizer anonymizer = new ARXAnonymizer();
        ARXConfiguration config = ARXConfiguration.create();
        config.addPrivacyModel(new KAnonymity(5));
        config.setMaxOutliers(1d);
        config.setQualityModel(Metric.createLossMetric());
        
        ARXResult result = anonymizer.anonymize(data, config);
        System.out.println("5-anonymous dataset (logistic regression)");
        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, ARXLogisticRegressionConfiguration.create()));
        System.out.println("5-anonymous dataset (naive bayes)");
        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, ARXNaiveBayesConfiguration.create()));
        System.out.println("5-anonymous dataset (random forest)");
        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, ARXSVMConfiguration.create()));
        System.out.println("5-anonymous dataset (SVM)");
        System.out.println(result.getOutput().getStatistics().getClassificationPerformance(features, clazz, ARXSVMConfiguration.create()));
    }
}

@@ -1162,32 +1162,91 @@
 Configuration;%0D%0A
+import org.deidentifier.arx.ARXRandomForestConfiguration;%0D%0A
 import org.deide
@@ -5305,35 +5305,44 @@
 ures, clazz, ARX
-SVM
+RandomForest
 Configuration.cr

Create correct ARXClassificationConfiguration