/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 by Rapid-I and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapid-i.com
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Affero General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Affero General Public License for more details.
 *
 *  You should have received a copy of the GNU Affero General Public License
 *  along with this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.operator.validation.clustering.exampledistribution;

import java.util.List;

import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.ValueDouble;
import com.rapidminer.operator.clustering.ClusterModel;
import com.rapidminer.operator.performance.EstimatedPerformance;
import com.rapidminer.operator.performance.PerformanceCriterion;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.PassThroughOrGenerateRule;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeStringCategory;
import com.rapidminer.tools.ClassNameMapper;


/**
 * Evaluates flat cluster models on how well the examples are distributed over the clusters.
 * 
 * @author Michael Wurst, Sebastian Land
 * 
 */
public class ExampleDistributionEvaluator extends Operator {

	public static final String PARAMETER_MEASURE = "measure";

	private final static String[] DEFAULT_MEASURES = { "com.rapidminer.operator.validation.clustering.exampledistribution.SumOfSquares", "com.rapidminer.operator.validation.clustering.exampledistribution.GiniCoefficient" };

	private ClassNameMapper MEASURE_MAP;

	private double distribution = 0;

	private InputPort clusterModelInput = getInputPorts().createPort("cluster model", ClusterModel.class);
	private InputPort performanceInput = getInputPorts().createPort("performance vector");

	private OutputPort clusterModelOutput = getOutputPorts().createPort("cluster model");
	private OutputPort performanceOutput = getOutputPorts().createPort("performance vector");

	public ExampleDistributionEvaluator(OperatorDescription description) {
		super(description);

		getTransformer().addPassThroughRule(clusterModelInput, clusterModelOutput);
		getTransformer().addRule(new PassThroughOrGenerateRule(performanceInput, performanceOutput, new MetaData(PerformanceVector.class)));

		addValue(new ValueDouble("item_distribution", "The distribution of items over clusters.", false) {
			@Override
			public double getDoubleValue() {
				return distribution;
			}
		});
	}

	@Override
	public void doWork() throws OperatorException {

		ClusterModel model = clusterModelInput.getData();

		ExampleDistributionMeasure measure = (ExampleDistributionMeasure) MEASURE_MAP.getInstantiation(getParameterAsString(PARAMETER_MEASURE));

		int totalNumberOfItems = 0;
		int[] count = new int[model.getNumberOfClusters()];
		for (int i = 0; i < model.getNumberOfClusters(); i++) {

			int numItemsInCluster = model.getCluster(i).getNumberOfExamples();
			totalNumberOfItems = totalNumberOfItems + numItemsInCluster;
			count[i] = numItemsInCluster;
		}

		PerformanceVector performance = performanceInput.getDataOrNull();
		if (performance == null) {
			// If no performance vector is available create a new one
			performance = new PerformanceVector();
		}

		distribution = measure.evaluate(count, totalNumberOfItems);

		PerformanceCriterion criterion = new EstimatedPerformance("Example distribution", distribution, 1, false);
		performance.addCriterion(criterion);

		clusterModelOutput.deliver(model);
		performanceOutput.deliver(performance);
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		MEASURE_MAP = new ClassNameMapper(DEFAULT_MEASURES);
		List<ParameterType> types = super.getParameterTypes();

		ParameterType type = new ParameterTypeStringCategory(PARAMETER_MEASURE, "the item distribution measure to apply", MEASURE_MAP.getShortClassNames(), "SumOfSquares", true);
		type.setExpert(false);
		types.add(type);

		return types;
	}

}