/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2011 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.validation;

import java.util.List;

import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.set.AttributeWeightedExampleSet;
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.operator.OperatorVersion;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.CapabilityPrecondition;
import com.rapidminer.operator.ports.metadata.Precondition;
import com.rapidminer.operator.ports.quickfix.ParameterSettingQuickFix;
import com.rapidminer.operator.ports.quickfix.QuickFix;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.UndefinedParameterError;
import com.rapidminer.tools.RandomGenerator;

/**
 * This operator evaluates the performance of feature weighting algorithms
 * including feature selection. The first inner operator is the weighting
 * algorithm to be evaluated itself. It must return an attribute weights vector
 * which is applied on the data. Then a new model is created using the second
 * inner operator and a performance is retrieved using the third inner operator.
 * This performance vector serves as a performance indicator for the actual
 * algorithm.
 * 
 * This implementation is described for the {@link RandomSplitValidationChain}.
 * 
 * @author Ingo Mierswa
 */
public class RandomSplitWrapperValidationChain extends WrapperValidationChain {

	public static final String PARAMETER_SPLIT_RATIO = "split_ratio";

	public static final String PARAMETER_SAMPLING_TYPE = "sampling_type";

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

    @Override
    protected Precondition getCapabilityPrecondition() {
        return new CapabilityPrecondition(this, exampleSetInput) {
            @Override
            protected List<QuickFix> getFixesForRegressionWhenClassificationSupported(AttributeMetaData labelMD) {
                List<QuickFix> fixes = super.getFixesForRegressionWhenClassificationSupported(labelMD);
                fixes.add(0, new ParameterSettingQuickFix(RandomSplitWrapperValidationChain.this, PARAMETER_SAMPLING_TYPE, SplittedExampleSet.SHUFFLED_SAMPLING + "", "switch_to_shuffled_sampling"));
                return fixes;
            }
        };
    }
    
	
	@Override
	public void doWork() throws OperatorException {
		double splitRatio = getParameterAsDouble(PARAMETER_SPLIT_RATIO);
		ExampleSet inputSet = exampleSetInput.getData();
		SplittedExampleSet eSet = new SplittedExampleSet(
				inputSet,
				splitRatio,
				getParameterAsInt(PARAMETER_SAMPLING_TYPE),
				getParameterAsBoolean(RandomGenerator.PARAMETER_USE_LOCAL_RANDOM_SEED),
				getParameterAsInt(RandomGenerator.PARAMETER_LOCAL_RANDOM_SEED),
				getCompatibilityLevel().isAtMost(SplittedExampleSet.VERSION_SAMPLING_CHANGED));

		eSet.selectSingleSubset(0);
		AttributeWeights weights = useWeightingMethod(eSet);
		SplittedExampleSet newInputSet = (SplittedExampleSet) eSet.clone();

		// learn on the same data
		Model model = learn(new AttributeWeightedExampleSet(newInputSet,
				weights, 0.0d).createCleanClone());

		// testing
		newInputSet.selectSingleSubset(1);
		PerformanceVector pv = evaluate(new AttributeWeightedExampleSet(
				newInputSet, weights, 0.0d).createCleanClone(), model);
		setResult(pv.getMainCriterion());

		performanceOutput.deliver(pv);
		attributeWeightsOutput.deliver(weights);
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		ParameterType type = new ParameterTypeDouble(PARAMETER_SPLIT_RATIO,
				"Relative size of the training set", 0, 1, 0.7);
		type.setExpert(false);
		types.add(type);
		types.add(new ParameterTypeCategory(
				PARAMETER_SAMPLING_TYPE,
				"Defines the sampling type of the cross validation (linear = consecutive subsets, shuffled = random subsets, stratified = random subsets with class distribution kept constant)",
				SplittedExampleSet.SAMPLING_NAMES,
				SplittedExampleSet.STRATIFIED_SAMPLING, false));
		types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
		return types;
	}

	@Override
	public boolean supportsCapability(OperatorCapability capability) {
		switch (capability) {
		case NO_LABEL:
			return false;
		case NUMERICAL_LABEL:
			try {
				return getParameterAsInt(PARAMETER_SAMPLING_TYPE) != SplittedExampleSet.STRATIFIED_SAMPLING;
			} catch (UndefinedParameterError e) {
				return false;
			}
		default:
			return true;
		}
	}

	@Override
	public OperatorVersion[] getIncompatibleVersionChanges() {
		return new OperatorVersion[] { SplittedExampleSet.VERSION_SAMPLING_CHANGED };
	}
}