/*
* 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;
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.UserError;
import com.rapidminer.operator.learner.AbstractLearner;
import com.rapidminer.operator.learner.LearnerCapability;
import com.rapidminer.operator.performance.PerformanceVector;
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, Tobias Malbrecht
* @version $Id: LogisticRegression.java,v 1.6 2008/05/09 19:22:56 ingomierswa Exp $
*/
public class LogisticRegression extends AbstractLearner {
/** The parameter name for "Determines whether to include an intercept." */
public static final String PARAMETER_ADD_INTERCEPT = "add_intercept";
/** The parameter name for "Determines whether to return the performance." */
public static final String PARAMETER_RETURN_PERFORMANCE = "return_model_performance";
/** 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";
private PerformanceVector estimatedPerformance;
public LogisticRegression(OperatorDescription description) {
super(description);
}
public Model learn(ExampleSet exampleSet) throws OperatorException {
RandomGenerator random = RandomGenerator.getRandomGenerator(getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED));
LogisticRegressionOptimization optimization =
new LogisticRegressionOptimization(
exampleSet,
getParameterAsBoolean(PARAMETER_ADD_INTERCEPT),
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);
LogisticRegressionModel model = optimization.train();
estimatedPerformance = optimization.getPerformance();
return model;
}
public boolean shouldEstimatePerformance() {
return getParameterAsBoolean(PARAMETER_RETURN_PERFORMANCE);
}
public PerformanceVector getEstimatedPerformance() throws OperatorException {
if (getParameterAsBoolean(PARAMETER_RETURN_PERFORMANCE)) {
if (estimatedPerformance != null) {
return estimatedPerformance;
}
}
throw new UserError(this, 912, getName(), "could not deliver optimization performance.");
}
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();
types.add(new ParameterTypeBoolean(PARAMETER_ADD_INTERCEPT, "Determines whether to include an intercept.", true));
types.add(new ParameterTypeBoolean(PARAMETER_RETURN_PERFORMANCE, "Determines whether to return the performance.", false));
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, 300));
types.add(new ParameterTypeInt(PARAMETER_POPULATION_SIZE, "The population size (-1: number of examples)", -1, Integer.MAX_VALUE, 3));
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;
}
}