/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2007 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 General Public License as 
 *  published by the Free Software Foundation; either version 2 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
 *  General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
 *  USA.
 */
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.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;


/**
 * Stratified sampling operator. This operator performs a random sampling of a
 * given fraction. In contrast to the simple sampling operator, this operator
 * performs a stratified sampling for data sets with nominal label attributes,
 * i.e. the class distributions remains (almost) the same after sampling. Hence,
 * this operator cannot be applied on data sets without a label or with a
 * numerical label. In these cases a simple sampling without stratification
 * is performed.
 * 
 * @author Ingo Mierswa
 * @version $Id: StratifiedSampling.java,v 1.2 2006/04/05 08:57:27 ingomierswa
 *          Exp $
 */
public class StratifiedSampling extends Operator {


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

	/** 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 StratifiedSampling(OperatorDescription description) {
		super(description);
	}

	public IOObject[] apply() throws OperatorException {
		ExampleSet exampleSet = getInput(ExampleSet.class);

		// perform stratified sampling
		SplittedExampleSet splittedExampleSet = new SplittedExampleSet(exampleSet, getParameterAsDouble(PARAMETER_SAMPLE_RATIO), 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();
		ParameterType type = new ParameterTypeDouble(PARAMETER_SAMPLE_RATIO, "The fraction of examples which should be sampled", 0.0d, 1.0d, 0.1d);
		type.setExpert(false);
		types.add(type);
        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;
	}
}