/*
 *  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.List;

import com.rapidminer.example.Attributes;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.ExecutionUnit;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.MDTransformationRule;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.SubprocessTransformRule;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;


/**  
 * This class uses n+1 inner learners and generates n different models
 * by using the last n learners. The predictions of these n models are
 * taken to create n new features for the example set, which is finally
 * used to serve as an input of the first inner learner.  
 * 
 * @author Ingo Mierswa, Helge Homburg
 */
public class Stacking extends AbstractStacking {

	private final OutputPort stackingExamplesInnerSource = getSubprocess(1).getInnerSources().createPort("stacking examples");
	private final InputPort stackingModelInnerSink = getSubprocess(1).getInnerSinks().createPort("stacking model", PredictionModel.class);

	public static final String PARAMETER_KEEP_ALL_ATTRIBUTES = "keep_all_attributes";

	public Stacking(OperatorDescription description) {
		super(description, "Base Learner", "Stacking Model Learner");		
		getTransformer().addRule(new MDTransformationRule() {
			public void transformMD() {
				MetaData unmodifiedMetaData = exampleSetInput.getMetaData().clone();
				if (unmodifiedMetaData instanceof ExampleSetMetaData) {
					ExampleSetMetaData emd = (ExampleSetMetaData) unmodifiedMetaData;
					if (!keepOldAttributes()) {
						emd.clearRegular();
					}
					// constructing new meta attributes
					List<MetaData> metaDatas = baseModelExtender.getMetaData(true);
					int numberOfModels = 0;
					for (MetaData md: metaDatas) {
						if (PredictionModel.class.isAssignableFrom(md.getObjectClass())) {
							numberOfModels++;
						}
					}
					// adding stacking attributes
					AttributeMetaData label = emd.getLabelMetaData();
					for (int i = 0; i < numberOfModels; i++) {
						AttributeMetaData newRegular = label.copy();
						newRegular.setName("base_prediction" + i);
						newRegular.setRole(Attributes.ATTRIBUTE_NAME);
						emd.addAttribute(newRegular);
					}
					stackingExamplesInnerSource.deliverMD(emd);
				}
				stackingExamplesInnerSource.deliverMD(unmodifiedMetaData);	
			}
		});
		getTransformer().addRule(new SubprocessTransformRule(getSubprocess(1)));
	}		

	@Override
	public String getModelName() {
		return "Stacking Model";
	}

	@Override
	public boolean keepOldAttributes() {
		return getParameterAsBoolean(PARAMETER_KEEP_ALL_ATTRIBUTES);
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		types.add(new ParameterTypeBoolean(PARAMETER_KEEP_ALL_ATTRIBUTES, 
				"Indicates if all attributes (including the original ones) in order to learn the stacked model.", true));
		return types;
	}

	@Override
	protected ExecutionUnit getBaseModelLearnerProcess() {
		return getSubprocess(0);
	}

	@Override
	protected Model getStackingModel(ExampleSet stackingLearningSet) throws OperatorException {
		stackingExamplesInnerSource.deliver(stackingLearningSet);
		getSubprocess(1).execute();
		return stackingModelInnerSink.getData();
	}
}