/*
 *  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.performance;

import java.util.LinkedList;
import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.math.ROCBias;

/**
 * <p>In contrast to the other performance evaluation methods, this performance 
 * evaluator operator can be used for all types of learning tasks. It will 
 * automatically determine the learning task type and will calculate the most
 * common criteria for this type. For more sophisticated performance calculations,
 * you should check the operators {@link RegressionPerformanceEvaluator},
 * {@link PolynominalClassificationPerformanceEvaluator}, or
 * {@link BinominalClassificationPerformanceEvaluator}. You can even
 * simply write your own performance measure and calculate it with the
 * operator {@link UserBasedPerformanceEvaluator}.</p>
 * 
 * <p>The operator expects a test {@link ExampleSet}
 * as input, whose elements have both true and predicted labels, and delivers as
 * output a list of most common performance values for the provided learning
 * task type (regression or (binary) classification. If an input performance 
 * vector was already given, this is used for keeping the performance values.</p> 
 * 
 * @author Ingo Mierswa
 */
public class SimplePerformanceEvaluator extends AbstractPerformanceEvaluator {

	private ExampleSet testSet = null;
	
	public SimplePerformanceEvaluator(OperatorDescription description) {
		super(description);
	}

	/** Does nothing. */
	@Override
	protected void checkCompatibility(ExampleSet exampleSet) throws OperatorException {}

	/** Returns null. */
	@Override
	protected double[] getClassWeights(Attribute label) throws UndefinedParameterError {
		return null;
	}

	/** Uses this example set in order to create appropriate criteria. */
	@Override
	protected void init(ExampleSet testSet) {
		this.testSet = testSet;
	}
	
	/** Returns false. */
	@Override
	protected boolean showSkipNaNLabelsParameter() {
		return false;
	}

	/** Returns false. */
	@Override
	protected boolean showComparatorParameter() {
		return false;
	}
	
	@Override
	public List<PerformanceCriterion> getCriteria() {
		List<PerformanceCriterion> allCriteria = new LinkedList<PerformanceCriterion>();
		if (this.testSet != null) {
			Attribute label = this.testSet.getAttributes().getLabel();
			if (label != null) {
			    if (label.isNominal()) {
			        if (label.getMapping().size() == 2) {
			            // add most important binominal classification criteria
			            allCriteria.add(new MultiClassificationPerformance(MultiClassificationPerformance.ACCURACY));
			            allCriteria.add(new BinaryClassificationPerformance(BinaryClassificationPerformance.PRECISION));
			            allCriteria.add(new BinaryClassificationPerformance(BinaryClassificationPerformance.RECALL));
			            allCriteria.add(new AreaUnderCurve(ROCBias.OPTIMISTIC));
			            allCriteria.add(new AreaUnderCurve(ROCBias.NEUTRAL));
			            allCriteria.add(new AreaUnderCurve(ROCBias.PESSIMISTIC));			            
			        } else {
			            // add most important polynominal classification criteria
			            allCriteria.add(new MultiClassificationPerformance(MultiClassificationPerformance.ACCURACY));
			            allCriteria.add(new MultiClassificationPerformance(MultiClassificationPerformance.KAPPA));
			        }
			    } else {
			        // add most important regression criteria
			        allCriteria.add(new RootMeanSquaredError());
			        allCriteria.add(new SquaredError());
			    }
			}
		}
		this.testSet = null;
		return allCriteria;
	}

	@Override
	protected boolean canEvaluate(int valueType) {
		return true;
	}
	
	@Override
	public boolean supportsCapability(OperatorCapability capability) {
		switch (capability) {
		case NUMERICAL_LABEL:					
		case BINOMINAL_LABEL:
		case POLYNOMINAL_LABEL:
		case ONE_CLASS_LABEL:
			return true;
		case POLYNOMINAL_ATTRIBUTES:
		case BINOMINAL_ATTRIBUTES:
		case NUMERICAL_ATTRIBUTES:
		case WEIGHTED_EXAMPLES:
		case MISSING_VALUES:
			return true;
		case NO_LABEL:
		case UPDATABLE:
		case FORMULA_PROVIDER:
		default:
			return false;
		}		
	}
}