/*
 *  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.learner.tree;

import java.util.List;

import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.RandomGenerator;

/**
 * <p>
 * This operator learns decision trees from both nominal and numerical data.
 * Decision trees are powerful classification methods which often can also
 * easily be understood. The random tree learner works similar to Quinlan's
 * C4.5 or CART but it selects a random subset of attributes before it is 
 * applied. The size of the subset is defined by the parameter subset_ratio.
 * </p>
 *  
 * @author Ingo Mierswa
 * @version $Id: RandomTreeLearner.java,v 1.5 2008/05/09 19:22:53 ingomierswa Exp $
 */
public class RandomTreeLearner extends DecisionTreeLearner {
    
	/** The parameter name for "Ratio of randomly chosen attributes to test" */
	public static final String PARAMETER_SUBSET_RATIO = "subset_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 RandomTreeLearner(OperatorDescription description) {
        super(description);
    }
    
    /** Returns a random feature subset sampling. */
    public SplitPreprocessing getSplitPreprocessing() {
    	SplitPreprocessing preprocessing = null;
    	try {
    		preprocessing = new RandomSubsetPreprocessing(getParameterAsDouble(PARAMETER_SUBSET_RATIO), RandomGenerator.getRandomGenerator(getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED)));
    	} catch (UndefinedParameterError e) {
    		// cannot happen
    	}
    	return preprocessing;
    }
    
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		ParameterType type = new ParameterTypeDouble(PARAMETER_SUBSET_RATIO, "Ratio of randomly chosen attributes to test (-1: use log(m) + 1 features)", -1.0d, 1.0d, -1.0d);
		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;
	}
}