/**
* 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.functions.kernel.evosvm;
import com.rapidminer.example.Attribute;
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.UserError;
import com.rapidminer.operator.annotation.ResourceConsumptionEstimator;
import com.rapidminer.operator.learner.functions.kernel.AbstractKernelBasedLearner;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.OperatorResourceConsumptionHandler;
import com.rapidminer.tools.RandomGenerator;
import com.rapidminer.tools.math.kernels.Kernel;
import java.util.List;
/**
* This is a SVM implementation using a particle swarm optimization (PSO) approach to solve the dual
* optimization problem of a SVM. It turns out that on many datasets this simple implementation is
* as fast and accurate as the usual SVM implementations.
*
* @rapidminer.index SVM
*
* @author Ingo Mierswa
*/
public class PSOSVM extends AbstractKernelBasedLearner {
/**
* 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";
/**
* The parameter name for "The SVM complexity constant (0: calculates probably good
* value)."
*/
public static final String PARAMETER_C = "C";
/** The parameter name for "Stop after this many evaluations" */
public static final String PARAMETER_MAX_EVALUATIONS = "max_evaluations";
/**
* 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 (initial) weight for the old weighting." */
public static final String PARAMETER_INERTIA_WEIGHT = "inertia_weight";
/** The parameter name for "The weight for the individual's best position during run." */
public static final String PARAMETER_LOCAL_BEST_WEIGHT = "local_best_weight";
/** The parameter name for "The weight for the population's best position during run." */
public static final String PARAMETER_GLOBAL_BEST_WEIGHT = "global_best_weight";
/** The parameter name for "If set to true the inertia weight is improved during run." */
public static final String PARAMETER_DYNAMIC_INERTIA_WEIGHT = "dynamic_inertia_weight";
/**
* Creates a new SVM which uses a particle swarm optimization approach for optimization.
*/
public PSOSVM(OperatorDescription description) {
super(description);
}
/** Learns and returns a model. */
@Override
public Model learn(ExampleSet exampleSet) throws OperatorException {
Attribute label = exampleSet.getAttributes().getLabel();
if (label.getMapping().size() != 2) {
throw new UserError(this, 114, getName(), label.getName());
}
// kernel
Kernel kernel = Kernel.createKernel(this);
// optimization
PSOSVMOptimization optimization = new PSOSVMOptimization(exampleSet, kernel, getParameterAsDouble(PARAMETER_C),
getParameterAsInt(PARAMETER_MAX_EVALUATIONS), getParameterAsInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL),
getParameterAsInt(PARAMETER_POPULATION_SIZE), getParameterAsDouble(PARAMETER_INERTIA_WEIGHT),
getParameterAsDouble(PARAMETER_LOCAL_BEST_WEIGHT), getParameterAsDouble(PARAMETER_GLOBAL_BEST_WEIGHT),
getParameterAsBoolean(PARAMETER_DYNAMIC_INERTIA_WEIGHT),
getParameterAsBoolean(PARAMETER_SHOW_CONVERGENCE_PLOT), RandomGenerator.getRandomGenerator(this), this);
optimization.optimize();
double[] bestAlphas = optimization.getBestValuesEver();
return optimization.getModel(bestAlphas);
}
/**
* Returns true for numerical attributes, binominal classes, and numerical target attributes.
*/
@Override
public boolean supportsCapability(OperatorCapability lc) {
if (lc == OperatorCapability.NUMERICAL_ATTRIBUTES) {
return true;
}
if (lc == OperatorCapability.BINOMINAL_LABEL) {
return true;
}
if (lc == OperatorCapability.FORMULA_PROVIDER) {
return true;
}
return false;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(new ParameterTypeBoolean(PARAMETER_SHOW_CONVERGENCE_PLOT,
"Indicates if a dialog with a convergence plot should be drawn.", false));
// adding Kernel parameter
types.addAll(Kernel.getParameters(this));
ParameterType type = new ParameterTypeDouble(PARAMETER_C,
"The SVM complexity constant (0: calculates probably good value).", 0.0d, Double.POSITIVE_INFINITY, 0.0d);
types.add(type);
types.add(new ParameterTypeInt(PARAMETER_MAX_EVALUATIONS, "Stop after this many evaluations", 1, Integer.MAX_VALUE,
500));
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, 10));
types.add(new ParameterTypeInt(PARAMETER_POPULATION_SIZE, "The population size (-1: number of examples)", -1,
Integer.MAX_VALUE, 10));
types.add(new ParameterTypeDouble(PARAMETER_INERTIA_WEIGHT, "The (initial) weight for the old weighting.", 0.0d,
Double.POSITIVE_INFINITY, 0.1d));
types.add(new ParameterTypeDouble(PARAMETER_LOCAL_BEST_WEIGHT,
"The weight for the individual's best position during run.", 0.0d, Double.POSITIVE_INFINITY, 1.0d));
types.add(new ParameterTypeDouble(PARAMETER_GLOBAL_BEST_WEIGHT,
"The weight for the population's best position during run.", 0.0d, Double.POSITIVE_INFINITY, 1.0d));
types.add(new ParameterTypeBoolean(PARAMETER_DYNAMIC_INERTIA_WEIGHT,
"If set to true the inertia weight is improved during run.", true));
types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
@Override
public ResourceConsumptionEstimator getResourceConsumptionEstimator() {
return OperatorResourceConsumptionHandler.getResourceConsumptionEstimator(getExampleSetInputPort(), PSOSVM.class,
null);
}
}