/*
* 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.test;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.util.Arrays;
import java.util.Collection;
import org.deidentifier.arx.ARXConfiguration;
import org.deidentifier.arx.Data;
import org.deidentifier.arx.DataSubset;
import org.deidentifier.arx.criteria.DPresence;
import org.deidentifier.arx.criteria.KAnonymity;
import org.deidentifier.arx.metric.Metric;
import org.deidentifier.arx.metric.Metric.AggregateFunction;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;
import org.junit.runners.Parameterized.Parameters;
/**
* Test for utility transformations.
*
* @author Fabian Prasser
* @author Florian Kohlmayer
*/
@RunWith(Parameterized.class)
public class TestUtilityMetricsPrecomputation extends AbstractTestUtilityMetricsPrecomputation {
/** A threshold */
private final static double threshold = 1d;
/**
* Returns the test cases
*
* @return
* @throws IOException
*/
@Parameters(name = "{index}:[{0}]")
public static Collection<Object[]> cases() throws IOException {
return Arrays.asList(new Object[][] {
// entropy: criterion monotone metric monotone
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.0d).addPrivacyModel(new KAnonymity(5)), "occupation", "./data/adult.csv", Metric.createEntropyMetric(true), Metric.createPrecomputedEntropyMetric(threshold, true)) },
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.0d).addPrivacyModel(new DPresence(0.05, 0.15, DataSubset.create(Data.create("./data/adult.csv", StandardCharsets.UTF_8, ';'), Data.create("./data/adult_subset.csv", StandardCharsets.UTF_8, ';')))), "occupation", "./data/adult.csv", Metric.createEntropyMetric(true), Metric.createPrecomputedEntropyMetric(threshold, true)) },
// entropy: criterion monotone metric non-monotone
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.0d).addPrivacyModel(new KAnonymity(5)), "occupation", "./data/adult.csv", Metric.createEntropyMetric(false), Metric.createPrecomputedEntropyMetric(threshold, false)) },
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.0d).addPrivacyModel(new DPresence(0.05, 0.15, DataSubset.create(Data.create("./data/adult.csv", StandardCharsets.UTF_8, ';'), Data.create("./data/adult_subset.csv", StandardCharsets.UTF_8, ';')))), "occupation", "./data/adult.csv", Metric.createEntropyMetric(false), Metric.createPrecomputedEntropyMetric(threshold, false)) },
// loss: criterion monotone metric monotone
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.0d).addPrivacyModel(new KAnonymity(5)), "occupation", "./data/adult.csv", Metric.createLossMetric(AggregateFunction.RANK), Metric.createPrecomputedLossMetric(threshold, AggregateFunction.RANK)) },
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.0d).addPrivacyModel(new DPresence(0.05, 0.15, DataSubset.create(Data.create("./data/adult.csv", StandardCharsets.UTF_8, ';'), Data.create("./data/adult_subset.csv", StandardCharsets.UTF_8, ';')))), "occupation", "./data/adult.csv", Metric.createLossMetric(AggregateFunction.RANK), Metric.createPrecomputedLossMetric(threshold, AggregateFunction.RANK)) },
// entropy: criterion non-monotone metric monotone
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.5d).addPrivacyModel(new KAnonymity(5)), "occupation", "./data/adult.csv", Metric.createEntropyMetric(true), Metric.createPrecomputedEntropyMetric(threshold, true)) },
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.5d).addPrivacyModel(new DPresence(0.05, 0.15, DataSubset.create(Data.create("./data/adult.csv", StandardCharsets.UTF_8, ';'), Data.create("./data/adult_subset.csv", StandardCharsets.UTF_8, ';')))), "occupation", "./data/adult.csv", Metric.createEntropyMetric(true), Metric.createPrecomputedEntropyMetric(threshold, true)) },
// entropy: criterion non-monotone metric non-monotone
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.5d).addPrivacyModel(new KAnonymity(5)), "occupation", "./data/adult.csv", Metric.createEntropyMetric(false), Metric.createPrecomputedEntropyMetric(threshold, false)) },
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.5d).addPrivacyModel(new DPresence(0.05, 0.15, DataSubset.create(Data.create("./data/adult.csv", StandardCharsets.UTF_8, ';'), Data.create("./data/adult_subset.csv", StandardCharsets.UTF_8, ';')))), "occupation", "./data/adult.csv", Metric.createEntropyMetric(false), Metric.createPrecomputedEntropyMetric(threshold, false)) },
// loss: criterion non-monotone metric monotone
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.5d).addPrivacyModel(new KAnonymity(5)), "occupation", "./data/adult.csv", Metric.createLossMetric(AggregateFunction.RANK), Metric.createPrecomputedLossMetric(threshold, AggregateFunction.RANK)) },
{ new ARXUtilityMetricsTestCase(ARXConfiguration.create(0.5d).addPrivacyModel(new DPresence(0.05, 0.15, DataSubset.create(Data.create("./data/adult.csv", StandardCharsets.UTF_8, ';'), Data.create("./data/adult_subset.csv", StandardCharsets.UTF_8, ';')))), "occupation", "./data/adult.csv", Metric.createLossMetric(AggregateFunction.RANK), Metric.createPrecomputedLossMetric(threshold, AggregateFunction.RANK)) },
});
}
/**
* Creates a new instance.
*
* @param testCase
*/
public TestUtilityMetricsPrecomputation(final ARXUtilityMetricsTestCase testCase) {
super(testCase);
}
}