/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2008 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.Arrays;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeRole;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.SimpleExampleSet;
import com.rapidminer.example.set.SplittedExampleSet;
import com.rapidminer.example.table.DataRow;
import com.rapidminer.example.table.ExampleTable;
import com.rapidminer.example.table.ListDataRowReader;
import com.rapidminer.example.table.MemoryExampleTable;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeInt;


/**
 * Abstract superclass of stratified sampling operators. Provides funcionallity for samping,
 * subclasses have to provide ratio via getRatio()
 * 
 * @author Ingo Mierswa, Sebastian Land
 * @version $Id: StratifiedSampling.java,v 1.2 2006/04/05 08:57:27 ingomierswa
 *          Exp $
 */
public abstract class AbstractStratifiedSampling extends Operator {


	/** The parameter name for "Use the given random seed instead of global random numbers (-1: use global)." */
	public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
	public AbstractStratifiedSampling(OperatorDescription description) {
		super(description);
	}

	/**
	 * This method should return the ratio used for stratifiedSampling
	 */
	public abstract double getRatio(ExampleSet exampleSet) throws OperatorException;
	
	public IOObject[] apply() throws OperatorException {
		ExampleSet exampleSet = getInput(ExampleSet.class);

		// perform stratified sampling
		SplittedExampleSet splittedExampleSet = new SplittedExampleSet(exampleSet, getRatio(exampleSet), SplittedExampleSet.STRATIFIED_SAMPLING, getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED));
		splittedExampleSet.selectSingleSubset(0);

		// fill new table
		List<DataRow> dataList = new LinkedList<DataRow>();
		Iterator<Example> reader = splittedExampleSet.iterator();
		while (reader.hasNext()) {
			Example example = reader.next();
			dataList.add(example.getDataRow());
			checkForStop();
		}

		List<Attribute> attributes = Arrays.asList(splittedExampleSet.getExampleTable().getAttributes());
		ExampleTable exampleTable = new MemoryExampleTable(attributes, new ListDataRowReader(dataList.iterator()));

		// regular attributes
		List<Attribute> regularAttributes = new LinkedList<Attribute>();
		for (Attribute attribute : exampleSet.getAttributes()) {
			regularAttributes.add(attribute);
		}

		// special attributes
		ExampleSet result = new SimpleExampleSet(exampleTable, regularAttributes);
		Iterator<AttributeRole> special = exampleSet.getAttributes().specialAttributes();
		while (special.hasNext()) {
			AttributeRole role = special.next();
			result.getAttributes().setSpecialAttribute(role.getAttribute(), role.getSpecialName());
		}

		return new IOObject[] { result };
	}

	public Class<?>[] getInputClasses() {
		return new Class[] { ExampleSet.class };
	}

	public Class<?>[] getOutputClasses() {
		return new Class[] { ExampleSet.class };
	}

	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
        types.add(new ParameterTypeInt(PARAMETER_LOCAL_RANDOM_SEED, "Use the given random seed instead of global random numbers (-1: use global).", -1, Integer.MAX_VALUE, -1));
		return types;
	}
}