/*
 *  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.meta;

import java.util.List;

import com.rapidminer.example.Attributes;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SplittedExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorChain;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.UserError;
import com.rapidminer.operator.ports.InputPort;
import com.rapidminer.operator.ports.OutputPort;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetPassThroughRule;
import com.rapidminer.operator.ports.metadata.ExampleSetPrecondition;
import com.rapidminer.operator.ports.metadata.PassThroughRule;
import com.rapidminer.operator.ports.metadata.SetRelation;
import com.rapidminer.operator.ports.metadata.SubprocessTransformRule;
import com.rapidminer.operator.validation.XValidation;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.RandomGenerator;


/**
 * This operator works similar to the {@link LearningCurveOperator}. 
 * In contrast to this, it just splits the ExampleSet according to the
 * parameter "fraction" and learns a model only on the subset. It can be used, 
 * for example, in conjunction with {@link GridSearchParameterOptimizationOperator} 
 * which sets the fraction parameter to values between 0 and 1. The advantage 
 * is, that this operator can then be used inside of a {@link XValidation},
 * which delivers more stable result estimations.
 * 
 * @author Martin Mauch, Ingo Mierswa
 */
public class PartialExampleSetLearner extends OperatorChain {

	private final InputPort exampleSetInput = getInputPorts().createPort("example set");
	private final OutputPort modelOutput = getOutputPorts().createPort("model");
	private final OutputPort exampleSubsetInnerSource = getSubprocess(0).getInnerSources().createPort("example subset");
	private final InputPort modelInnerSink = getSubprocess(0).getInnerSinks().createPort("model", Model.class);

	/** The parameter name for "The fraction of examples which shall be used." */
	public static final String PARAMETER_FRACTION = "fraction";

	/** The parameter name for "Defines the sampling type (linear = consecutive subsets, shuffled = random subsets, stratified = random subsets with class distribution kept constant)" */
	public static final String PARAMETER_SAMPLING_TYPE = "sampling_type";


	public PartialExampleSetLearner(OperatorDescription description) {
		super(description, "Learning Process");

		exampleSetInput.addPrecondition(new ExampleSetPrecondition(exampleSetInput, new String[0], Ontology.VALUE_TYPE, Attributes.LABEL_NAME));

		getTransformer().addRule(new ExampleSetPassThroughRule(exampleSetInput, exampleSubsetInnerSource, SetRelation.EQUAL) {
			@Override
			public ExampleSetMetaData modifyExampleSet(ExampleSetMetaData metaData) throws UndefinedParameterError {
				metaData.getNumberOfExamples().multiply(getParameterAsDouble(PARAMETER_FRACTION));
				return super.modifyExampleSet(metaData);
			}
		});
		getTransformer().addRule(new SubprocessTransformRule(getSubprocess(0)));
		getTransformer().addRule(new PassThroughRule(modelInnerSink, modelOutput, false));
	}

	@Override
	public void doWork() throws OperatorException { 
		ExampleSet originalExampleSet = exampleSetInput.getData();

		double fraction = getParameterAsDouble(PARAMETER_FRACTION); 
		if (fraction < 0 || fraction > 1.0) 
			throw new UserError(this, 207, new Object[] { fraction, "fraction", "Cannot use fractions of less than 0.0 or more than 1.0" }); 
		SplittedExampleSet splitted = new SplittedExampleSet(originalExampleSet, fraction, 
				getParameterAsInt(PARAMETER_SAMPLING_TYPE), 
				getParameterAsBoolean(RandomGenerator.PARAMETER_USE_LOCAL_RANDOM_SEED), getParameterAsInt(RandomGenerator.PARAMETER_LOCAL_RANDOM_SEED)); 
		splitted.selectSingleSubset(0); 

		exampleSubsetInnerSource.deliver(splitted);
		getSubprocess(0).execute();                
		modelOutput.deliver(modelInnerSink.getData());
	} 

	@Override
	public List<ParameterType> getParameterTypes() { 
		List<ParameterType> types = super.getParameterTypes(); 
		ParameterType type = new ParameterTypeDouble(PARAMETER_FRACTION, "The fraction of examples which shall be used.", 0.0d, 1.0d, 0.05); 
		type.setExpert(false); 
		types.add(type);
		types.add(new ParameterTypeCategory(PARAMETER_SAMPLING_TYPE, "Defines the sampling type (linear = consecutive subsets, shuffled = random subsets, stratified = random subsets with class distribution kept constant)", SplittedExampleSet.SAMPLING_NAMES, SplittedExampleSet.STRATIFIED_SAMPLING));
		types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
		return types; 
	}
}