/*
 *  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.LinkedList;
import java.util.List;

import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;

/**
 * <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. This decision tree learner works similar to Quinlan's
 * C4.5 or CART.</p>
 * 
 * <p>The actual type of the tree is determined by the criterion, e.g. using
 * gain_ratio or Gini for CART / C4.5.</p>
 * 
 * @rapidminer.index C4.5
 * @rapidminer.index CART
 * 
 * @author Sebastian Land, Ingo Mierswa
 */
public class DecisionTreeLearner extends AbstractTreeLearner {

    /** The parameter name for the maximum tree depth. */
    public static final String PARAMETER_MAXIMAL_DEPTH = "maximal_depth";

    /** The parameter name for "The confidence level used for pruning." */
    public static final String PARAMETER_CONFIDENCE = "confidence";

    /** The parameter name for "Disables the pruning and delivers an unpruned tree." */
    public static final String PARAMETER_NO_PRUNING = "no_pruning";

    public static final String PARAMETER_NO_PRE_PRUNING = "no_pre_pruning";

    public static final String PARAMETER_NUMBER_OF_PREPRUNING_ALTERNATIVES = "number_of_prepruning_alternatives";


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

    @Override
    public Pruner getPruner() throws OperatorException {
        if (!getParameterAsBoolean(PARAMETER_NO_PRUNING)) {
            return new PessimisticPruner(getParameterAsDouble(PARAMETER_CONFIDENCE), new DecisionTreeLeafCreator());
        } else {
            return null;
        }
    }

    @Override
    public List<Terminator> getTerminationCriteria(ExampleSet exampleSet) throws OperatorException {
        List<Terminator> result = new LinkedList<Terminator>();
        result.add(new SingleLabelTermination());
        result.add(new NoAttributeLeftTermination());
        result.add(new EmptyTermination());
        int maxDepth = getParameterAsInt(PARAMETER_MAXIMAL_DEPTH);
        if (maxDepth <= 0) {
            maxDepth = exampleSet.size();
        }
        result.add(new MaxDepthTermination(maxDepth));
        return result;
    }

    @Override
    public boolean supportsCapability(OperatorCapability capability) {
        switch (capability) {
        case BINOMINAL_ATTRIBUTES:
        case POLYNOMINAL_ATTRIBUTES:
        case NUMERICAL_ATTRIBUTES:
        case POLYNOMINAL_LABEL:
        case BINOMINAL_LABEL:
        case WEIGHTED_EXAMPLES:
        case MISSING_VALUES:
            return true;
        default:
            return false;
        }
    }


    @Override
    protected TreeBuilder getTreeBuilder(ExampleSet exampleSet) throws OperatorException {
        return new TreeBuilder(createCriterion(getParameterAsDouble(PARAMETER_MINIMAL_GAIN)),
                getTerminationCriteria(exampleSet),
                getPruner(),
                getSplitPreprocessing(),
                new DecisionTreeLeafCreator(),
                getParameterAsBoolean(PARAMETER_NO_PRE_PRUNING),
                getParameterAsInt(PARAMETER_NUMBER_OF_PREPRUNING_ALTERNATIVES),
                getParameterAsInt(PARAMETER_MINIMAL_SIZE_FOR_SPLIT),
                getParameterAsInt(PARAMETER_MINIMAL_LEAF_SIZE));
    }

    @Override
    public List<ParameterType> getParameterTypes() {
        List<ParameterType> types = super.getParameterTypes();
        ParameterType type = new ParameterTypeInt(PARAMETER_MAXIMAL_DEPTH, "The maximum tree depth (-1: no bound)", -1, Integer.MAX_VALUE, 20);
        type.setExpert(false);
        types.add(type);
        type = new ParameterTypeDouble(PARAMETER_CONFIDENCE, "The confidence level used for the pessimistic error calculation of pruning.", 0.0000001, 0.5, 0.25);
        type.setExpert(false);
        types.add(type);

        types.add(new ParameterTypeInt(PARAMETER_NUMBER_OF_PREPRUNING_ALTERNATIVES, "The number of alternative nodes tried when prepruning would prevent a split.", 0, Integer.MAX_VALUE, 3));
        types.add(new ParameterTypeBoolean(PARAMETER_NO_PRE_PRUNING, "Disables the pre pruning and delivers a tree without any prepruning.", false));
        types.add(new ParameterTypeBoolean(PARAMETER_NO_PRUNING, "Disables the pruning and delivers an unpruned tree.", false));

        return types;
    }
}