/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 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.features.weighting;

import java.util.Iterator;
import java.util.List;

import com.rapidminer.example.Attribute;
import com.rapidminer.example.AttributeWeights;
import com.rapidminer.example.Example;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorCapability;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeString;


/**
 * This operator uses a corpus of examples to characterize a single class by
 * setting feature weights. Characteristic features receive higher weights than
 * less characteristic features. The weight for a feature is determined by
 * calculating the average value of this feature for all examples of the target
 * class. This operator assumes that the feature values characterize the
 * importance of this feature for an example (e.g. TFIDF or others). Therefore,
 * this operator is mainly used on textual data based on TFIDF weighting
 * schemes. To extract such feature values from text collections you can use the
 * Text plugin.
 * 
 * @author Michael Wurst, Ingo Mierswa
 */
public class CorpusBasedFeatureWeighting extends AbstractWeighting {

	/** The parameter name for "The target class for which to find characteristic feature weights." */
	private static final String PARAMETER_CLASS_TO_CHARACTERIZE = "class_to_characterize";

	public CorpusBasedFeatureWeighting(OperatorDescription description) {
		super(description);
		//TODO: Add Dictionary Quickfix for parameter.
	}	

	@Override
	protected AttributeWeights calculateWeights(ExampleSet es) throws OperatorException {
		String targetValue = getParameterAsString(PARAMETER_CLASS_TO_CHARACTERIZE);

		double[] weights = generateWeightsForClass(es, targetValue);

		double maxWeight = Double.NEGATIVE_INFINITY;
		for (double w : weights) {
			maxWeight = Math.max(maxWeight, w);
		}

		AttributeWeights attWeights = new AttributeWeights();

		int i = 0;
		for (Attribute attribute : es.getAttributes()) {
			if (maxWeight > 0.0d) {
				attWeights.setWeight(attribute.getName(), weights[i++] / maxWeight);
			} else {
				attWeights.setWeight(attribute.getName(), 0.0d);
			}
		}

		return attWeights;
	}

	private double[] generateWeightsForClass(ExampleSet es, String value) {
		double[] result = new double[es.getAttributes().size()];
		for (int i = 0; i < es.getAttributes().size(); i++)
			result[i] = 0.0;
		Iterator<Example> er = es.iterator();
		Attribute labelAttribute = es.getAttributes().getLabel();
		while (er.hasNext()) {
			Example e = er.next();
			if (e.getValueAsString(labelAttribute).equalsIgnoreCase(value)) {
				int index = 0;
				for (Attribute attribute : es.getAttributes()) {
					result[index] += e.getValue(attribute);
					index++;
				}
			}
		}
		return result;
	}

	@Override
	public boolean supportsCapability(OperatorCapability capability) {
		switch (capability) {
		case BINOMINAL_LABEL:
		case POLYNOMINAL_LABEL:
		case NUMERICAL_ATTRIBUTES:
			return true;
		default: 
			return false;
		}
	}

	@Override
	public List<ParameterType> getParameterTypes() {
		List<ParameterType> types = super.getParameterTypes();
		types.add(new ParameterTypeString(PARAMETER_CLASS_TO_CHARACTERIZE, "The target class for which to find characteristic feature weights.", false, false)); 
		return types;
	}
}