/**
 * 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.meta;

import java.util.List;
import java.util.Random;

import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.MappedExampleSet;
import com.rapidminer.example.set.SplittedExampleSet;
import com.rapidminer.operator.Model;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.parameter.ParameterTypeMatrix;
import com.rapidminer.tools.RandomGenerator;


/**
 * This operator uses a given cost matrix to compute label predictions according to classification
 * costs. The method used by this operator is similar to MetaCost as described by Pedro Domingos.
 *
 * @author Helge Homburg
 */
public class MetaCost extends AbstractMetaLearner {

	/** The parameter name for "The cost matrix in Matlab single line format" */
	public static final String PARAMETER_COST_MATRIX = "cost_matrix";

	/** The parameter name for "File" */
	public static final String PARAMETER_COST_MATRIX_FILE_LOCATION = "cost_matrix_file_location";

	/**
	 * The parameter name for "Fraction of examples used for training. Must be greater than 0
	 * and should be lower than 1."
	 */
	public static final String PARAMETER_USE_SUBSET_FOR_TRAINING = "use_subset_for_training";

	/** The parameter name for "The number of iterations (base models)." */
	public static final String PARAMETER_ITERATIONS = "iterations";

	/**
	 * The parameter name for "Use sampling with replacement (true) or without (false)"
	 */
	public static final String PARAMETER_SAMPLING_WITH_REPLACEMENT = "sampling_with_replacement";

	public MetaCost(OperatorDescription description) {
		super(description);
	}

	@Override
	public Model learn(ExampleSet inputSet) throws OperatorException {
		int iterations = getParameterAsInt(PARAMETER_ITERATIONS);
		double subsetRatio = getParameterAsDouble(PARAMETER_USE_SUBSET_FOR_TRAINING);
		Model[] models = new Model[iterations];

		// get cost matrix
		double[][] costMatrix = getParameterAsMatrix(PARAMETER_COST_MATRIX);

		// perform bagging operation
		if (getParameterAsBoolean(PARAMETER_SAMPLING_WITH_REPLACEMENT)) {
			// sampling with replacement
			Random randomGenerator = RandomGenerator.getRandomGenerator(this);
			int size = (int) (inputSet.size() * subsetRatio);
			for (int i = 0; i < iterations; i++) {
				int[] mapping = MappedExampleSet.createBootstrappingMapping(inputSet, size, randomGenerator);
				MappedExampleSet currentSampleSet = new MappedExampleSet(inputSet, mapping);
				models[i] = applyInnerLearner(currentSampleSet);
				inApplyLoop();
			}
		} else {
			// sampling without replacement
			boolean useLocalRandomSeed = getParameterAsBoolean(RandomGenerator.PARAMETER_USE_LOCAL_RANDOM_SEED);
			int localRandomSeed = getParameterAsInt(RandomGenerator.PARAMETER_LOCAL_RANDOM_SEED);
			for (int i = 0; i < iterations; i++) {
				SplittedExampleSet splitted = new SplittedExampleSet(inputSet, subsetRatio,
						SplittedExampleSet.SHUFFLED_SAMPLING, useLocalRandomSeed, localRandomSeed);
				splitted.selectSingleSubset(0);
				models[i] = applyInnerLearner(splitted);
				inApplyLoop();
			}
		}

		return new MetaCostModel(inputSet, models, costMatrix);
	}

	/**
	 * Support polynominal labels. For all other capabilities, it checks for the underlying operator
	 * to see which capabilities are supported by them.
	 */
	@Override
	public boolean supportsCapability(OperatorCapability capability) {
		switch (capability) {
			case NUMERICAL_LABEL:
			case NO_LABEL:
			case UPDATABLE:
			case FORMULA_PROVIDER:
				return false;
			default:
				return true;
		}
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		types.add(new ParameterTypeMatrix(PARAMETER_COST_MATRIX, "The cost matrix in Matlab single line format",
				"Cost Matrix", "Predicted Class", "True Class", true, false));
		types.add(new ParameterTypeDouble(PARAMETER_USE_SUBSET_FOR_TRAINING,
				"Fraction of examples used for training. Must be greater than 0 and should be lower than 1.", 0, 1, 1.0));
		types.add(new ParameterTypeInt(PARAMETER_ITERATIONS, "The number of iterations (base models).", 1, Integer.MAX_VALUE,
				10));
		types.add(new ParameterTypeBoolean(PARAMETER_SAMPLING_WITH_REPLACEMENT,
				"Use sampling with replacement (true) or without (false)", true));

		types.addAll(RandomGenerator.getRandomGeneratorParameters(this));

		return types;
	}
}