/*
 *  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.learner.tree;

import java.util.List;

import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.operator.learner.tree.criterions.AbstractCriterion;
import com.rapidminer.operator.learner.tree.criterions.AccuracyCriterion;
import com.rapidminer.operator.learner.tree.criterions.Criterion;
import com.rapidminer.operator.learner.tree.criterions.GainRatioCriterion;
import com.rapidminer.operator.learner.tree.criterions.GiniIndexCriterion;
import com.rapidminer.operator.learner.tree.criterions.InfoGainCriterion;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.ParameterTypeStringCategory;

/**
 * This is the abstract super class for all decision tree learners. The actual
 * type of the tree is determined by the criterion, e.g. using gain_ratio or Gini
 * for CART / C4.5 and chi_squared for CHAID. 
 * 
 * @author Sebastian Land, Ingo Mierswa
 */
public abstract class AbstractTreeLearner extends AbstractLearner {

	/** The parameter name for "Specifies the used criterion for selecting attributes and numerical splits." */
	public static final String PARAMETER_CRITERION = "criterion";

	/** The parameter name for "The minimal size of all leaves." */
	public static final String PARAMETER_MINIMAL_SIZE_FOR_SPLIT = "minimal_size_for_split";

	/** The parameter name for "The minimal size of all leaves." */
	public static final String PARAMETER_MINIMAL_LEAF_SIZE = "minimal_leaf_size";

	/** The parameter name for the minimal gain. */
	public static final String PARAMETER_MINIMAL_GAIN = "minimal_gain";

	public static final String[] CRITERIA_NAMES = {
		"gain_ratio",
		"information_gain",
		"gini_index",
		"accuracy"
	};

	public static final Class[] CRITERIA_CLASSES = {
		GainRatioCriterion.class,
		InfoGainCriterion.class,
		GiniIndexCriterion.class,
		AccuracyCriterion.class
	};

	public static final int CRITERION_GAIN_RATIO  = 0;

	public static final int CRITERION_INFO_GAIN   = 1;

	public static final int CRITERION_GINI_INDEX  = 2;

	public static final int CRITERION_ACCURACY    = 3;

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

	@Override
	public Class<? extends PredictionModel> getModelClass() {
		return TreeModel.class;
	}

	/** Returns all termination criteria. */
	public abstract List<Terminator> getTerminationCriteria(ExampleSet exampleSet) throws OperatorException;

	/** Returns the pruner for this tree learner. If this method returns null,
	 *  pruning will be disabled. */
	public abstract Pruner getPruner() throws OperatorException;

	/** The split preprocessing is applied before each new split
	 *  The default implementation does nothing and simply returns 
	 *  the given example set. Subclasses might want to override this
	 *  in order to perform some data preprocessing like random subset 
	 *  selections.*/
	public SplitPreprocessing getSplitPreprocessing() {
		return null;
	}

	public Model learn(ExampleSet eSet) throws OperatorException {
		ExampleSet exampleSet = (ExampleSet)eSet.clone();

		// create tree builder
		TreeBuilder builder = getTreeBuilder(exampleSet); 

		// learn tree
		Tree root = builder.learnTree(exampleSet);

		// create and return model
		return new TreeModel(exampleSet, root);
	}

	protected abstract TreeBuilder getTreeBuilder(ExampleSet exampleSet) throws OperatorException;

	protected Criterion createCriterion(double minimalGain) throws OperatorException {
		return AbstractCriterion.createCriterion(this, minimalGain);
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		ParameterType type = new ParameterTypeStringCategory(PARAMETER_CRITERION, "Specifies the used criterion for selecting attributes and numerical splits.", CRITERIA_NAMES, CRITERIA_NAMES[CRITERION_GAIN_RATIO], false);
		type.setExpert(false);
		types.add(type);
		type = new ParameterTypeInt(PARAMETER_MINIMAL_SIZE_FOR_SPLIT, "The minimal size of a node in order to allow a split.", 1, Integer.MAX_VALUE, 4);
		type.setExpert(false);
		types.add(type);
		type = new ParameterTypeInt(PARAMETER_MINIMAL_LEAF_SIZE, "The minimal size of all leaves.", 1, Integer.MAX_VALUE, 2);
		type.setExpert(false);
		types.add(type);
		types.add(new ParameterTypeDouble(PARAMETER_MINIMAL_GAIN, "The minimal gain which must be achieved in order to produce a split.", 0.0d, Double.POSITIVE_INFINITY, 0.1d));
		return types;
	} 
}