/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2013 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.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.functions.LogisticRegressionOptimization;
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.kernels.Kernel;
import com.rapidminer.tools.math.optimization.ec.es.ESOptimization;


/**
 * This operator determines a logistic regression model.
 * 
 * @author Ingo Mierswa
 */
public class KernelLogisticRegression extends AbstractKernelBasedLearner {

	/** The parameter name for &quot;The SVM kernel type&quot; */
	public static final String PARAMETER_KERNEL_TYPE = "kernel_type";

	/** The parameter name for &quot;The SVM kernel parameter gamma (RBF, anova).&quot; */
	public static final String PARAMETER_KERNEL_GAMMA = "kernel_gamma";

	/** The parameter name for &quot;The SVM kernel parameter sigma1 (Epanechnikov, Gaussian Combination, Multiquadric).&quot; */
	public static final String PARAMETER_KERNEL_SIGMA1 = "kernel_sigma1";

	/** The parameter name for &quot;The SVM kernel parameter sigma2 (Gaussian Combination).&quot; */
	public static final String PARAMETER_KERNEL_SIGMA2 = "kernel_sigma2";

	/** The parameter name for &quot;The SVM kernel parameter sigma3 (Gaussian Combination).&quot; */
	public static final String PARAMETER_KERNEL_SIGMA3 = "kernel_sigma3";

	/** The parameter name for &quot;The SVM kernel parameter degree (polynomial, anova, Epanechnikov).&quot; */
	public static final String PARAMETER_KERNEL_DEGREE = "kernel_degree";

	/** The parameter name for &quot;The SVM kernel parameter shift (polynomial, Multiquadric).&quot; */
	public static final String PARAMETER_KERNEL_SHIFT = "kernel_shift";

	/** The parameter name for &quot;The SVM kernel parameter a (neural).&quot; */
	public static final String PARAMETER_KERNEL_A = "kernel_a";

	/** The parameter name for &quot;The SVM kernel parameter b (neural).&quot; */
	public static final String PARAMETER_KERNEL_B = "kernel_b";

	/** The parameter name for &quot;The SVM complexity constant (0: calculates probably good value).&quot; */
	public static final String PARAMETER_C = "C";

	/** The parameter name for &quot;The type of start population initialization.&quot; */
	public static final String PARAMETER_START_POPULATION_TYPE = "start_population_type";

	/** The parameter name for &quot;Stop after this many evaluations&quot; */
	public static final String PARAMETER_MAX_GENERATIONS = "max_generations";

	/** The parameter name for &quot;Stop after this number of generations without improvement (-1: optimize until max_iterations).&quot; */
	public static final String PARAMETER_GENERATIONS_WITHOUT_IMPROVAL = "generations_without_improval";

	/** The parameter name for &quot;The population size (-1: number of examples)&quot; */
	public static final String PARAMETER_POPULATION_SIZE = "population_size";

	/** The parameter name for &quot;The fraction of the population used for tournament selection.&quot; */
	public static final String PARAMETER_TOURNAMENT_FRACTION = "tournament_fraction";

	/** The parameter name for &quot;Indicates if the best individual should survive (elititst selection).&quot; */
	public static final String PARAMETER_KEEP_BEST = "keep_best";

	/** The parameter name for &quot;The type of the mutation operator.&quot; */
	public static final String PARAMETER_MUTATION_TYPE = "mutation_type";

	/** The parameter name for &quot;The type of the selection operator.&quot; */
	public static final String PARAMETER_SELECTION_TYPE = "selection_type";

	/** The parameter name for &quot;The probability for crossovers.&quot; */
	public static final String PARAMETER_CROSSOVER_PROB = "crossover_prob";


	/** The parameter name for &quot;Indicates if a dialog with a convergence plot should be drawn.&quot; */
	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
		Kernel kernel = Kernel.createKernel(this);
		RandomGenerator random = RandomGenerator.getRandomGenerator(this);
		if (kernel.getType() != Kernel.KERNEL_DOT) {
			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();
		} else {
			LogisticRegressionOptimization optimization = 
				new LogisticRegressionOptimization(
						exampleSet,
						true,
						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(OperatorCapability lc) {
		if (lc == OperatorCapability.NUMERICAL_ATTRIBUTES)
			return true;
		if (lc == OperatorCapability.BINOMINAL_LABEL)
			return true;
		if (lc == OperatorCapability.WEIGHTED_EXAMPLES)
			return true;
		return false;
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		types.addAll(Kernel.getParameters(this));
		ParameterType 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.addAll(RandomGenerator.getRandomGeneratorParameters(this));
		types.add(new ParameterTypeBoolean(PARAMETER_SHOW_CONVERGENCE_PLOT, "Indicates if a dialog with a convergence plot should be drawn.", false));
		return types;
	}
}
