/**
 * Copyright (C) 2001-2017 by RapidMiner and the contributors
 * 
 * Complete list of developers available at our web site:
 * 
 * http://rapidminer.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 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;

import java.util.List;


/**
 * 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() {

			@Override
			public void transformMD() {
				MetaData metaData = exampleSetInput.getMetaData();
				if (metaData != null) {
					MetaData unmodifiedMetaData = metaData.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);
				} else {
					stackingExamplesInnerSource.deliverMD(metaData);
				}
			}
		});
		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(Model.class);
	}
}