/*
 *  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.preprocessing.sampling;

import java.util.List;

import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.MappedExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.annotation.ResourceConsumptionEstimator;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.MDInteger;
import com.rapidminer.operator.validation.IteratingPerformanceAverage;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.parameter.conditions.EqualTypeCondition;
import com.rapidminer.tools.OperatorResourceConsumptionHandler;
import com.rapidminer.tools.RandomGenerator;


/**
 * This operator constructs a bootstrapped sample from the given example set. That means
 * that a sampling with replacement will be performed. The usual sample size is the number 
 * of original examples. This operator also offers the possibility to create the inverse
 * example set, i.e. an example set containing all examples which are not part of the 
 * bootstrapped example set. This inverse example set might be used for a bootstrapped
 * validation (together with an {@link IteratingPerformanceAverage} operator.
 * 
 * @author Ingo Mierswa, Tobias Malbrecht
 */
public class BootstrappingOperator extends AbstractSamplingOperator {

	public static final String PARAMETER_SAMPLE = "sample";

	public static final String[] SAMPLE_MODES = { "absolute" , "relative" };

	public static final int SAMPLE_ABSOLUTE = 0;

	public static final int SAMPLE_RELATIVE = 1;

	/** The parameter name for "The fraction of examples which should be sampled" */
	public static final String PARAMETER_SAMPLE_SIZE = "sample_size";

	/** The parameter name for "This ratio determines the size of the new example set." */
	public static final String PARAMETER_SAMPLE_RATIO = "sample_ratio";

	public static final String PARAMETER_USE_WEIGHTS = "use_weights";

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

	@Override
	protected MDInteger getSampledSize(ExampleSetMetaData emd) throws UndefinedParameterError {
		switch (getParameterAsInt(PARAMETER_SAMPLE)) {
		case SAMPLE_ABSOLUTE:
			return new MDInteger(getParameterAsInt(PARAMETER_SAMPLE_SIZE));
		case SAMPLE_RELATIVE:
			MDInteger number = emd.getNumberOfExamples();
			number.multiply(getParameterAsDouble(PARAMETER_SAMPLE_RATIO));
			return number;
		default:
			return new MDInteger();
		}
	}

	@Override
	public ExampleSet apply(ExampleSet exampleSet) throws OperatorException {         
		RandomGenerator random = RandomGenerator.getRandomGenerator(this);
		int size = exampleSet.size();
		switch (getParameterAsInt(PARAMETER_SAMPLE)) {
		case SAMPLE_ABSOLUTE:
			size = getParameterAsInt(PARAMETER_SAMPLE_SIZE);
			break;
		case SAMPLE_RELATIVE:
			size = (int) Math.round(exampleSet.size() * getParameterAsDouble(PARAMETER_SAMPLE_RATIO));
			break;
		}

		int[] mapping = null;
		if (getParameterAsBoolean(PARAMETER_USE_WEIGHTS) && exampleSet.getAttributes().getWeight() != null) {
			mapping = MappedExampleSet.createWeightedBootstrappingMapping(exampleSet, size, random);
		} else {
			mapping = MappedExampleSet.createBootstrappingMapping(exampleSet, size, random);
		}
		return new MappedExampleSet(exampleSet, mapping, true);
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		ParameterType type = new ParameterTypeCategory(PARAMETER_SAMPLE, "Determines how the amount of data is specified.", SAMPLE_MODES, SAMPLE_RELATIVE);  
		type.setExpert(false);
		types.add(type);
		type = new ParameterTypeInt(PARAMETER_SAMPLE_SIZE, "The number of examples which should be sampled", 1, Integer.MAX_VALUE, 100);
		type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_SAMPLE, SAMPLE_MODES, true, SAMPLE_ABSOLUTE));
		type.setExpert(false);
		types.add(type);
		type = new ParameterTypeDouble(PARAMETER_SAMPLE_RATIO, "This ratio determines the size of the new example set.", 0.0d, Double.POSITIVE_INFINITY, 1.0d);
		type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_SAMPLE, SAMPLE_MODES, true, SAMPLE_RELATIVE));
		type.setExpert(false);
		types.add(type);
		type = new ParameterTypeBoolean(PARAMETER_USE_WEIGHTS, "If checked, example weights will be considered during the bootstrapping if such weights are present.", true);
		type.setExpert(false);
		types.add(type);
		types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
		return types;
	}
	
	@Override
	public ResourceConsumptionEstimator getResourceConsumptionEstimator() {
		return OperatorResourceConsumptionHandler.getResourceConsumptionEstimator(getInputPort(), BootstrappingOperator.class, null);
	}
}