/**
 * 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.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.IOObject;
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.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 com.rapidminer.extension.concurrency.operator.validation.CrossValidationOperator
 * CrossValidationOperator}, 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 {
		getProgress().setIndeterminate(true);

		ExampleSet originalExampleSet = exampleSetInput.getData(ExampleSet.class);

		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(IOObject.class));
	}

	@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;
	}
}