/*
 *  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.learner.meta;

import java.util.Iterator;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.ProcessSetupError.Severity;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.MDReal;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.PredictionModelMetaData;
import com.rapidminer.operator.ports.metadata.SetRelation;
import com.rapidminer.operator.ports.metadata.SimpleMetaDataError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.math.container.Range;

/**
 * For a classified dataset (with possibly more than two classes) builds a classifier using a regression method which is
 * specified by the inner operator. For each class {@rapidminer.math i} a regression model is trained after setting the
 * label to {@rapidminer.math +1} if the label equals {@rapidminer.math i} and to {@rapidminer.math -1} if it is not.
 * Then the regression models are combined into a classification model. In order to determine the prediction for an
 * unlabeled example, all models are applied and the class belonging to the regression model which predicts the greatest
 * value is chosen.
 * 
 * @author Ingo Mierswa, Simon Fischer
 */
public class ClassificationByRegression extends AbstractMetaLearner {

	private int numberOfClasses;

	public ClassificationByRegression(OperatorDescription description) {
		super(description);
	}

	@Override
	protected MetaData modifyExampleSetMetaData(ExampleSetMetaData unmodifiedMetaData) {
		switch (unmodifiedMetaData.hasSpecial(Attributes.LABEL_NAME)) {
		case NO:
			getTrainingSetInputPort().addError(new SimpleMetaDataError(Severity.ERROR, getTrainingSetInputPort(), "special_missing", "label"));
			return unmodifiedMetaData;
		case UNKNOWN:
			getTrainingSetInputPort().addError(new SimpleMetaDataError(Severity.WARNING, getTrainingSetInputPort(), "special_unknown", "label"));
			return unmodifiedMetaData;
		case YES:
			AttributeMetaData labelMD = unmodifiedMetaData.getLabelMetaData();
			unmodifiedMetaData.removeAttribute(labelMD);
			AttributeMetaData transformedMD = new AttributeMetaData("regression(" + labelMD.getName() + ")", Ontology.REAL, Attributes.LABEL_NAME);
			transformedMD.setValueRange(new Range(-1d, 1d), SetRelation.EQUAL);
			transformedMD.setValueSetRelation(SetRelation.EQUAL);
			transformedMD.setMean(new MDReal());

			unmodifiedMetaData.addAttribute(transformedMD);
			return unmodifiedMetaData;
		default:
			return unmodifiedMetaData;
		}
	}

	/** Transforms the regression label back into a classification label. */
	@Override
	protected MetaData modifyGeneratedModelMetaData(PredictionModelMetaData unmodifiedMetaData) {
		InputPort in = getTrainingSetInputPort();
		MetaData esetIn = in.getMetaData();
		if ((esetIn != null) && (esetIn instanceof ExampleSetMetaData)) {
			return new PredictionModelMetaData(MultiModelByRegression.class, (ExampleSetMetaData) esetIn);
		}
		return unmodifiedMetaData;
	}

	public Model learn(ExampleSet inputSet) throws OperatorException {
		Attribute classLabel = inputSet.getAttributes().getLabel();
		numberOfClasses = classLabel.getMapping().getValues().size();
		Model[] models = new Model[numberOfClasses];

		ExampleSet eSet = (ExampleSet) inputSet.clone();
		Attribute tempLabel = AttributeFactory.createAttribute("regression(" + classLabel.getName() + ")", Ontology.REAL);
		eSet.getExampleTable().addAttribute(tempLabel);
		eSet.getAttributes().setLabel(tempLabel);

		for (int i = 0; i < numberOfClasses; i++) {
			// 1. Set regression labels
			Iterator<Example> r = eSet.iterator();
			while (r.hasNext()) {
				Example e = r.next();
				if (e.getValue(classLabel) == i) {
					e.setValue(tempLabel, +1.0);
				} else {
					e.setValue(tempLabel, -1.0);
				}
			}
			// 2. Apply learner
			models[i] = applyInnerLearner(eSet);
			inApplyLoop();
		}

		return new MultiModelByRegression(inputSet, models);
	}

	@Override
	public boolean supportsCapability(OperatorCapability lc) {
		switch (lc) {
		case NUMERICAL_LABEL:
		case NO_LABEL:
		case UPDATABLE:
		case FORMULA_PROVIDER:
			return false;
		default:
			return true;
		}
	}
}