/*
 * ARX Data Anonymization Tool
 * Copyright 2012 - 2024 Fabian Prasser 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.util.regex.Matcher;
import java.util.regex.Pattern;
import java.util.Arrays;

import org.deidentifier.arx.ARXAnonymizer;
import org.deidentifier.arx.ARXConfiguration;
import org.deidentifier.arx.ARXCostBenefitConfiguration;
import org.deidentifier.arx.ARXLattice.ARXNode;
import org.deidentifier.arx.ARXResult;
import org.deidentifier.arx.AttributeType.Hierarchy;
import org.deidentifier.arx.Data;
import org.deidentifier.arx.DataHandle;
import org.deidentifier.arx.criteria.ProfitabilityProsecutorNoAttack;
import org.deidentifier.arx.io.CSVHierarchyInput;
import org.deidentifier.arx.metric.Metric;
import org.deidentifier.arx.metric.v2.MetricSDNMPublisherPayout;
import org.deidentifier.arx.metric.v2.QualityMetadata;

/**
 * Examples of using the no-attack variant of the game-theoretic approach for
 * performing a monetary cost/benefit analysis using prosecutor risk.
 *
 * @author Fabian Prasser
 */
public class Example49 extends Example {

	/**
	 * Loads a dataset from disk
	 * 
	 * @param dataset
	 * @return
	 * @throws IOException
	 */
	public static Data createData(final String dataset) throws IOException {

		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 IOException
	 */
	public static void main(String[] args) throws IOException {

		// Load the dataset
		Data data = createData("adult");

		// Config from PLOS|ONE paper
		solve(data, ARXCostBenefitConfiguration.create().setAdversaryCost(4d).setAdversaryGain(300d)
				.setPublisherLoss(300d).setPublisherBenefit(1200d));

		// Fewer costs
		solve(data, ARXCostBenefitConfiguration.create().setAdversaryCost(2d).setAdversaryGain(600d)
				.setPublisherLoss(300d).setPublisherBenefit(1200d));

		// More costs, more gain
		solve(data, ARXCostBenefitConfiguration.create().setAdversaryCost(1d).setAdversaryGain(1200d)
				.setPublisherLoss(300d).setPublisherBenefit(1200d));

	}

	/**
	 * Solve the privacy problem
	 * 
	 * @param data
	 * @param config
	 * @throws IOException
	 */
	private static void solve(Data data, ARXCostBenefitConfiguration config) throws IOException {

		// Release
		data.getHandle().release();

		// Configure
		ARXConfiguration arxconfig = ARXConfiguration.create();
		arxconfig.setCostBenefitConfiguration(config);

		// Create model for measuring publisher's benefit
		MetricSDNMPublisherPayout maximizePublisherPayout = Metric.createPublisherPayoutMetric(false);

		// Create privacy model for the game-theoretic approach
		ProfitabilityProsecutorNoAttack profitability = new ProfitabilityProsecutorNoAttack();

		// Configure ARX
		arxconfig.setSuppressionLimit(1d);
		arxconfig.setQualityModel(maximizePublisherPayout);
		arxconfig.addPrivacyModel(profitability);

		// Anonymize
		ARXAnonymizer anonymizer = new ARXAnonymizer();
		ARXResult result = anonymizer.anonymize(data, arxconfig);
		ARXNode node = result.getGlobalOptimum();
		DataHandle handle = result.getOutput(node, false).getView();

		// Print stuff
		System.out.println("Data: " + data.getHandle().getView().getNumRows() + " records with "
				+ data.getDefinition().getQuasiIdentifyingAttributes().size() + " quasi-identifiers");
		System.out.println(" - Configuration: " + config.toString());
		System.out.println(" - Policies available: " + result.getLattice().getSize());
		System.out.println(" - Solution: " + Arrays.toString(node.getTransformation()));
		System.out.println("   * Optimal: " + result.getProcessStatistics().getStep(0).isOptimal());
		System.out.println("   * Time needed: " + result.getTime() + "[ms]");
		for (QualityMetadata<?> metadata : node.getLowestScore().getMetadata()) {
			System.out.println("   * " + metadata.getParameter() + ": " + metadata.getValue());
		}
		System.out.println("   * Suppressed records: "
				+ handle.getStatistics().getEquivalenceClassStatistics().getNumberOfSuppressedRecords());

	}
}