/**
 * Copyright (C) 2001-2017 by RapidMiner and the contributors
 * 
 * Complete list of developers available at our web site:
 * 
 * http://rapidminer.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
 *
 * @deprecated Use {@link ParallelDecisionTreeLearner} instead
 *
 * @author Sebastian Land, Ingo Mierswa
 */
@Deprecated
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<>();
		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;
	}
}