/*
* 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,
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* 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.bayes;
import java.util.List;
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.learner.AbstractLearner;
import com.rapidminer.operator.learner.PredictionModel;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.conditions.BooleanParameterCondition;
import com.rapidminer.parameter.conditions.EqualTypeCondition;
/**
* Kernel Naive Bayes learner.
*
* @author Tobias Malbrecht
* @version $Id: KernelNaiveBayes.java,v 1.1.2.1 2009-04-08 14:40:21 tobiasmalbrecht Exp $
*/
public class KernelNaiveBayes extends AbstractLearner {
public static final String PARAMETER_LAPLACE_CORRECTION = "laplace_correction";
public static final String PARAMETER_ESTIMATION_MODE = "estimation_mode";
public static final String[] ESTIMATION_MODES = { "full" , "greedy" };
public static final int ESTIMATION_MODE_FULL = 0;
public static final int ESTIMATION_MODE_GREEDY = 1;
public static final String PARAMETER_BANDWIDTH_SELECTION = "bandwidth_selection";
public static final String[] BANDWIDTH_SELECTION_MODES = { "heuristic" , "fix" };
public static final int BANDWIDTH_SELECTION_MODE_HEURISTIC = 0;
public static final int BANDWIDTH_SELECTION_MODE_FIX = 1;
public static final String PARAMETER_BANDWIDTH = "bandwidth";
public static final String PARAMETER_MINIMUM_BANDWIDTH = "minimum_bandwidth";
public static final String PARAMETER_NUMBER_OF_KERNELS = "number_of_kernels";
public static final String PARAMETER_USE_APPLICATION_GRID = "use_application_grid";
public static final String PARAMETER_APPLICATION_GRID_SIZE = "application_grid_size";
public KernelNaiveBayes(OperatorDescription description) {
super(description);
}
public Model learn(ExampleSet exampleSet) throws OperatorException {
int estimationMode = getParameterAsInt(PARAMETER_ESTIMATION_MODE);
double bandwidth = estimationMode == ESTIMATION_MODE_FULL ? getParameterAsDouble(PARAMETER_BANDWIDTH) : getParameterAsDouble(PARAMETER_MINIMUM_BANDWIDTH);
int gridSize = getParameterAsBoolean(PARAMETER_USE_APPLICATION_GRID) ? getParameterAsInt(PARAMETER_APPLICATION_GRID_SIZE) : 0;
return new KernelDistributionModel(exampleSet,
getParameterAsBoolean(PARAMETER_LAPLACE_CORRECTION),
estimationMode,
getParameterAsInt(PARAMETER_BANDWIDTH_SELECTION),
bandwidth,
getParameterAsInt(PARAMETER_NUMBER_OF_KERNELS),
gridSize);
}
@Override
public Class<? extends PredictionModel> getModelClass() {
return KernelDistributionModel.class;
}
public boolean supportsCapability(OperatorCapability lc) {
if (lc == OperatorCapability.POLYNOMINAL_ATTRIBUTES)
return true;
if (lc == OperatorCapability.BINOMINAL_ATTRIBUTES)
return true;
if (lc == OperatorCapability.NUMERICAL_ATTRIBUTES)
return true;
if (lc == OperatorCapability.POLYNOMINAL_LABEL)
return true;
if (lc == OperatorCapability.BINOMINAL_LABEL)
return true;
if (lc == OperatorCapability.WEIGHTED_EXAMPLES)
return true;
if (lc == OperatorCapability.UPDATABLE)
return true;
return false;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeBoolean(PARAMETER_LAPLACE_CORRECTION, "Use Laplace correction to prevent high influence of zero probabilities.", true);
type.setExpert(false);
types.add(type);
type = new ParameterTypeCategory(PARAMETER_ESTIMATION_MODE, "The kernel density estimation mode.", ESTIMATION_MODES, ESTIMATION_MODE_GREEDY);
type.setExpert(false);
types.add(type);
type = new ParameterTypeCategory(PARAMETER_BANDWIDTH_SELECTION, "The method to set the kernel bandwidth.", BANDWIDTH_SELECTION_MODES, BANDWIDTH_SELECTION_MODE_HEURISTIC);
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_ESTIMATION_MODE, ESTIMATION_MODES, false, ESTIMATION_MODE_FULL));
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_BANDWIDTH, "Kernel bandwidth.", Double.MIN_VALUE, Double.POSITIVE_INFINITY, 0.1d);
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_BANDWIDTH_SELECTION, BANDWIDTH_SELECTION_MODES, false, BANDWIDTH_SELECTION_MODE_FIX));
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_ESTIMATION_MODE, ESTIMATION_MODES, false, ESTIMATION_MODE_FULL));
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_MINIMUM_BANDWIDTH, "Minimum kernel bandwidth.", Double.MIN_VALUE, Double.POSITIVE_INFINITY, 0.1d);
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_ESTIMATION_MODE, ESTIMATION_MODES, false, ESTIMATION_MODE_GREEDY));
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_OF_KERNELS, "Number of kernels.", 1, Integer.MAX_VALUE, 10);
type.registerDependencyCondition(new EqualTypeCondition(this, PARAMETER_ESTIMATION_MODE, ESTIMATION_MODES, false, ESTIMATION_MODE_GREEDY));
type.setExpert(false);
types.add(type);
type = new ParameterTypeBoolean(PARAMETER_USE_APPLICATION_GRID, "Use a kernel density function grid in model application. (Speeds up model application at the expense of the density function precision.)", false);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_APPLICATION_GRID_SIZE, "Size of the application grid.", 10, Integer.MAX_VALUE, 200);
type.registerDependencyCondition(new BooleanParameterCondition(this, PARAMETER_USE_APPLICATION_GRID, false, true));
type.setExpert(false);
types.add(type);
return types;
}
}