/*
* RapidMiner
*
* Copyright (C) 2001-2008 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.functions.kernel;
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.LearnerCapability;
import com.rapidminer.operator.learner.functions.kernel.functions.Kernel;
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.tools.RandomGenerator;
import com.rapidminer.tools.math.optimization.ec.es.ESOptimization;
/**
* This operator determines a logistic regression model.
*
* @author Ingo Mierswa
* @version $Id: KernelLogisticRegression.java,v 1.4 2008/05/09 19:23:01 ingomierswa Exp $
*/
public class KernelLogisticRegression extends AbstractLearner {
/** The parameter name for "The SVM kernel type" */
public static final String PARAMETER_KERNEL_TYPE = "kernel_type";
/** The parameter name for "The SVM kernel parameter gamma (RBF, anova)." */
public static final String PARAMETER_KERNEL_GAMMA = "kernel_gamma";
/** The parameter name for "The SVM kernel parameter sigma1 (Epanechnikov, Gaussian Combination, Multiquadric)." */
public static final String PARAMETER_KERNEL_SIGMA1 = "kernel_sigma1";
/** The parameter name for "The SVM kernel parameter sigma2 (Gaussian Combination)." */
public static final String PARAMETER_KERNEL_SIGMA2 = "kernel_sigma2";
/** The parameter name for "The SVM kernel parameter sigma3 (Gaussian Combination)." */
public static final String PARAMETER_KERNEL_SIGMA3 = "kernel_sigma3";
/** The parameter name for "The SVM kernel parameter degree (polynomial, anova, Epanechnikov)." */
public static final String PARAMETER_KERNEL_DEGREE = "kernel_degree";
/** The parameter name for "The SVM kernel parameter shift (polynomial, Multiquadric)." */
public static final String PARAMETER_KERNEL_SHIFT = "kernel_shift";
/** The parameter name for "The SVM kernel parameter a (neural)." */
public static final String PARAMETER_KERNEL_A = "kernel_a";
/** The parameter name for "The SVM kernel parameter b (neural)." */
public static final String PARAMETER_KERNEL_B = "kernel_b";
/** The parameter name for "The SVM complexity constant (0: calculates probably good value)." */
public static final String PARAMETER_C = "C";
/** The parameter name for "The type of start population initialization." */
public static final String PARAMETER_START_POPULATION_TYPE = "start_population_type";
/** The parameter name for "Stop after this many evaluations" */
public static final String PARAMETER_MAX_GENERATIONS = "max_generations";
/** The parameter name for "Stop after this number of generations without improvement (-1: optimize until max_iterations)." */
public static final String PARAMETER_GENERATIONS_WITHOUT_IMPROVAL = "generations_without_improval";
/** The parameter name for "The population size (-1: number of examples)" */
public static final String PARAMETER_POPULATION_SIZE = "population_size";
/** The parameter name for "The fraction of the population used for tournament selection." */
public static final String PARAMETER_TOURNAMENT_FRACTION = "tournament_fraction";
/** The parameter name for "Indicates if the best individual should survive (elititst selection)." */
public static final String PARAMETER_KEEP_BEST = "keep_best";
/** The parameter name for "The type of the mutation operator." */
public static final String PARAMETER_MUTATION_TYPE = "mutation_type";
/** The parameter name for "The type of the selection operator." */
public static final String PARAMETER_SELECTION_TYPE = "selection_type";
/** The parameter name for "The probability for crossovers." */
public static final String PARAMETER_CROSSOVER_PROB = "crossover_prob";
/** The parameter name for "Use the given random seed instead of global random numbers (-1: use global)." */
public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
/** The parameter name for "Indicates if a dialog with a convergence plot should be drawn." */
public static final String PARAMETER_SHOW_CONVERGENCE_PLOT = "show_convergence_plot";
public KernelLogisticRegression(OperatorDescription description) {
super(description);
}
public Model learn(ExampleSet exampleSet) throws OperatorException {
// kernel
int kernelType = getParameterAsInt(PARAMETER_KERNEL_TYPE);
Kernel kernel = Kernel.createKernel(kernelType, this);
RandomGenerator random = RandomGenerator.getRandomGenerator(getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED));
KernelLogisticRegressionOptimization optimization =
new KernelLogisticRegressionOptimization(
exampleSet,
kernel,
getParameterAsDouble(PARAMETER_C),
getParameterAsInt(PARAMETER_START_POPULATION_TYPE),
getParameterAsInt(PARAMETER_MAX_GENERATIONS), getParameterAsInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL),
getParameterAsInt(PARAMETER_POPULATION_SIZE), getParameterAsInt(PARAMETER_SELECTION_TYPE),
getParameterAsDouble(PARAMETER_TOURNAMENT_FRACTION),
getParameterAsBoolean(PARAMETER_KEEP_BEST), getParameterAsInt(PARAMETER_MUTATION_TYPE), getParameterAsDouble(PARAMETER_CROSSOVER_PROB),
getParameterAsBoolean(PARAMETER_SHOW_CONVERGENCE_PLOT),
random,
this);
return optimization.train();
}
public boolean supportsCapability(LearnerCapability lc) {
if (lc == LearnerCapability.NUMERICAL_ATTRIBUTES)
return true;
if (lc == LearnerCapability.BINOMINAL_CLASS)
return true;
if (lc == LearnerCapability.WEIGHTED_EXAMPLES)
return true;
return false;
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeCategory(PARAMETER_KERNEL_TYPE, "The kernel type", Kernel.KERNEL_TYPES, Kernel.KERNEL_RADIAL);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_GAMMA, "The kernel parameter gamma (RBF, anova).", 0.0d, Double.POSITIVE_INFINITY, 1.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_SIGMA1, "The kernel parameter sigma1 (Epanechnikov, Gaussian Combination, Multiquadric).", 0.0d, Double.POSITIVE_INFINITY, 1.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_SIGMA2, "The kernel parameter sigma2 (Gaussian Combination).", 0.0d, Double.POSITIVE_INFINITY, 0.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_SIGMA3, "The kernel parameter sigma3 (Gaussian Combination).", 0.0d, Double.POSITIVE_INFINITY, 2.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_DEGREE, "The kernel parameter degree (polynomial, anova, Epanechnikov).", 0.0d, Double.POSITIVE_INFINITY, 3.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_SHIFT, "The kernel parameter shift (polynomial, Multiquadric).", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 1.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_A, "The kernel parameter a (neural).", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 1.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_KERNEL_B, "The kernel parameter b (neural).", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0.0d);
type.setExpert(false);
types.add(type);
type = new ParameterTypeDouble(PARAMETER_C, "The complexity constant.", 0.00000001d, Double.POSITIVE_INFINITY, 1.0d);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeCategory(PARAMETER_START_POPULATION_TYPE, "The type of start population initialization.", ESOptimization.POPULATION_INIT_TYPES, ESOptimization.INIT_TYPE_RANDOM));
types.add(new ParameterTypeInt(PARAMETER_MAX_GENERATIONS, "Stop after this many evaluations", 1, Integer.MAX_VALUE, 10000));
types.add(new ParameterTypeInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL, "Stop after this number of generations without improvement (-1: optimize until max_iterations).", -1, Integer.MAX_VALUE, 30));
types.add(new ParameterTypeInt(PARAMETER_POPULATION_SIZE, "The population size (-1: number of examples)", -1, Integer.MAX_VALUE, 1));
types.add(new ParameterTypeDouble(PARAMETER_TOURNAMENT_FRACTION, "The fraction of the population used for tournament selection.", 0.0d, Double.POSITIVE_INFINITY, 0.75d));
types.add(new ParameterTypeBoolean(PARAMETER_KEEP_BEST, "Indicates if the best individual should survive (elititst selection).", true));
types.add(new ParameterTypeCategory(PARAMETER_MUTATION_TYPE, "The type of the mutation operator.", ESOptimization.MUTATION_TYPES, ESOptimization.GAUSSIAN_MUTATION));
types.add(new ParameterTypeCategory(PARAMETER_SELECTION_TYPE, "The type of the selection operator.", ESOptimization.SELECTION_TYPES, ESOptimization.TOURNAMENT_SELECTION));
types.add(new ParameterTypeDouble(PARAMETER_CROSSOVER_PROB, "The probability for crossovers.", 0.0d, 1.0d, 1.0d));
types.add(new ParameterTypeInt(PARAMETER_LOCAL_RANDOM_SEED, "Use the given random seed instead of global random numbers (-1: use global).", -1, Integer.MAX_VALUE, -1));
types.add(new ParameterTypeBoolean(PARAMETER_SHOW_CONVERGENCE_PLOT, "Indicates if a dialog with a convergence plot should be drawn.", false));
return types;
}
}