KNNLearner.java

/*
 *  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.lazy;

import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.annotation.ResourceConsumptionEstimator;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.operator.learner.bayes.NaiveBayes;
import com.rapidminer.operator.ports.metadata.DistanceMeasurePrecondition;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.OperatorResourceConsumptionHandler;
import com.rapidminer.tools.math.container.GeometricDataCollection;
import com.rapidminer.tools.math.container.LinearList;
import com.rapidminer.tools.math.similarity.DistanceMeasure;
import com.rapidminer.tools.math.similarity.DistanceMeasureHelper;
import com.rapidminer.tools.math.similarity.DistanceMeasures;


/**
 * A k nearest neighbor implementation.
 * 
 * @author Sebastian Land
 * 
 */
public class KNNLearner extends AbstractLearner {


	/** The parameter name for "The used number of nearest neighbors." */
	public static final String PARAMETER_K = "k";

	/** The parameter name for "Indicates if the votes should be weighted by similarity." */
	public static final String PARAMETER_WEIGHTED_VOTE = "weighted_vote";

	private DistanceMeasureHelper measureHelper = new DistanceMeasureHelper(this);

	public KNNLearner(OperatorDescription description) {
		super(description);	
		
		getExampleSetInputPort().addPrecondition(new DistanceMeasurePrecondition(getExampleSetInputPort(), this));
	}

	public Model learn(ExampleSet exampleSet) throws OperatorException {		
		DistanceMeasure measure = measureHelper.getInitializedMeasure(exampleSet);
		Attribute label = exampleSet.getAttributes().getLabel();
		if (label.isNominal()) {
			// classification
			GeometricDataCollection<Integer> samples = new LinearList<Integer>(measure);

			Attributes attributes = exampleSet.getAttributes();

			int valuesSize = attributes.size();
			for(Example example: exampleSet) {
				double[] values = new double[valuesSize];
				int i = 0;
				for (Attribute attribute: attributes) {
					values[i] = example.getValue(attribute);
					i++;
				}
				int labelValue = (int) example.getValue(label);
				samples.add(values, labelValue);
				checkForStop();
			}
			return new KNNClassificationModel(exampleSet, samples, getParameterAsInt(PARAMETER_K), getParameterAsBoolean(PARAMETER_WEIGHTED_VOTE));
		} else {
			// regression
			GeometricDataCollection<Double> samples = new LinearList<Double>(measure);
			Attributes attributes = exampleSet.getAttributes();

			int valuesSize = attributes.size();
			for (Example example: exampleSet) {
				double[] values = new double[valuesSize];
				int i = 0;
				for (Attribute attribute: attributes) {
					values[i] = example.getValue(attribute);
					i++;
				}
				double labelValue = example.getValue(label);
				samples.add(values, labelValue);
				checkForStop();
			}
			return new KNNRegressionModel(exampleSet, samples, getParameterAsInt(PARAMETER_K), getParameterAsBoolean(PARAMETER_WEIGHTED_VOTE));
		}
	}

	@Override
	public Class<? extends PredictionModel> getModelClass() {
		//TODO: Needs to unify models in order to return common class
		return super.getModelClass();
	}
	
	public boolean supportsCapability(OperatorCapability capability) {
		int measureType = DistanceMeasures.MIXED_MEASURES_TYPE;
		try {
			measureType = measureHelper.getSelectedMeasureType();
		} catch (Exception e) {
			
		}
		switch (capability) {
		case BINOMINAL_ATTRIBUTES:
		case POLYNOMINAL_ATTRIBUTES:
			return (measureType == DistanceMeasures.MIXED_MEASURES_TYPE) ||
			(measureType == DistanceMeasures.NOMINAL_MEASURES_TYPE);
		case NUMERICAL_ATTRIBUTES:
			return (measureType == DistanceMeasures.MIXED_MEASURES_TYPE) ||
			(measureType == DistanceMeasures.DIVERGENCES_TYPE) ||
			(measureType == DistanceMeasures.NUMERICAL_MEASURES_TYPE);
		case POLYNOMINAL_LABEL:
		case BINOMINAL_LABEL:
		case NUMERICAL_LABEL:
		case WEIGHTED_EXAMPLES:
		case MISSING_VALUES:
			return true;
		default:
			return false;
		}
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();

		ParameterType type = new ParameterTypeInt(PARAMETER_K, "The used number of nearest neighbors.", 1, Integer.MAX_VALUE, 1);
		type.setExpert(false);
		types.add(type);

		types.add(new ParameterTypeBoolean(PARAMETER_WEIGHTED_VOTE, "Indicates if the votes should be weighted by similarity.", false, false));

		types.addAll(DistanceMeasures.getParameterTypes(this));
		return types;
	}
	
	@Override
	public ResourceConsumptionEstimator getResourceConsumptionEstimator() {
		return OperatorResourceConsumptionHandler.getResourceConsumptionEstimator(getExampleSetInputPort(), KNNLearner.class, null);
	}
}