/*
* RapidMiner
*
* Copyright (C) 2001-2008 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.generator;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.example.table.DataRow;
import com.rapidminer.example.table.DoubleArrayDataRow;
import com.rapidminer.example.table.ListDataRowReader;
import com.rapidminer.example.table.MemoryExampleTable;
import com.rapidminer.operator.IOObject;
import com.rapidminer.operator.Operator;
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.tools.Ontology;
import com.rapidminer.tools.RandomGenerator;
/**
* Generates a random example set for testing purposes with more than one label.
*
* @author Ingo Mierswa
* @version $Id: MultipleLabelGenerator.java,v 1.10 2006/03/27 13:22:00
* ingomierswa Exp $
*/
public class MultipleLabelGenerator extends Operator {
/** The parameter name for "The number of generated examples." */
public static final String PARAMETER_NUMBER_EXAMPLES = "number_examples";
/** The parameter name for "Defines if multiple labels for regression tasks should be generated." */
public static final String PARAMETER_REGRESSION = "regression";
/** The parameter name for "The minimum value for the attributes." */
public static final String PARAMETER_ATTRIBUTES_LOWER_BOUND = "attributes_lower_bound";
/** The parameter name for "The maximum value for the attributes." */
public static final String PARAMETER_ATTRIBUTES_UPPER_BOUND = "attributes_upper_bound";
/** The parameter name for "Use the given random seed instead of global random numbers (-1: use global)." */
public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
private static final int NUMBER_OF_ATTRIBUTES = 5;
public MultipleLabelGenerator(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
// init
int numberOfExamples = getParameterAsInt(PARAMETER_NUMBER_EXAMPLES);
double lower = getParameterAsDouble(PARAMETER_ATTRIBUTES_LOWER_BOUND);
double upper = getParameterAsDouble(PARAMETER_ATTRIBUTES_UPPER_BOUND);
// create table
List<Attribute> attributes = new LinkedList<Attribute>();
for (int m = 0; m < NUMBER_OF_ATTRIBUTES; m++)
attributes.add(AttributeFactory.createAttribute("att" + (m + 1), Ontology.REAL));
// generate labels
int type = Ontology.NOMINAL;
if (getParameterAsBoolean(PARAMETER_REGRESSION)) {
type = Ontology.REAL;
}
Attribute label1 = AttributeFactory.createAttribute("label1", type);
attributes.add(label1);
Attribute label2 = AttributeFactory.createAttribute("label2", type);
attributes.add(label2);
Attribute label3 = AttributeFactory.createAttribute("label3", type);
attributes.add(label3);
if (!getParameterAsBoolean(PARAMETER_REGRESSION)) {
label1.getMapping().mapString("positive");
label1.getMapping().mapString("negative");
label2.getMapping().mapString("positive");
label2.getMapping().mapString("negative");
label3.getMapping().mapString("positive");
label3.getMapping().mapString("negative");
}
MemoryExampleTable table = new MemoryExampleTable(attributes);
// create data
RandomGenerator random = RandomGenerator.getRandomGenerator(getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED));
List<DataRow> data = new LinkedList<DataRow>();
for (int n = 0; n < numberOfExamples; n++) {
double[] features = new double[NUMBER_OF_ATTRIBUTES];
for (int i = 0; i < features.length; i++)
features[i] = random.nextDoubleInRange(lower, upper);
double[] example = new double[NUMBER_OF_ATTRIBUTES + 3];
System.arraycopy(features, 0, example, 0, features.length);
if (getParameterAsBoolean(PARAMETER_REGRESSION)) {
example[example.length - 3] = example[0] + example[1] + example[2];
example[example.length - 2] = 2 * example[0] + example[3];
example[example.length - 1] = example[3] * example[3];
} else {
example[example.length - 3] = example[0] + example[1] + example[2] > 0 ? label1.getMapping().mapString("positive") : label1.getMapping().mapString("negative");
example[example.length - 2] = 2 * example[0] + example[3] > 0 ? label1.getMapping().mapString("positive") : label1.getMapping().mapString("negative");
example[example.length - 1] = example[3] * example[3] - example[2] * example[2] > 0 ? label1.getMapping().mapString("positive") : label1.getMapping().mapString("negative");
}
data.add(new DoubleArrayDataRow(example));
}
// fill table with data
table.readExamples(new ListDataRowReader(data.iterator()));
// create example set and return it
Map<Attribute, String> specialMap = new HashMap<Attribute, String>();
specialMap.put(label1, "label1");
specialMap.put(label2, "label2");
specialMap.put(label3, "label3");
ExampleSet result = table.createExampleSet(specialMap);
return new IOObject[] { result };
}
public Class<?>[] getInputClasses() {
return new Class[0];
}
public Class<?>[] getOutputClasses() {
return new Class[] { ExampleSet.class };
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_EXAMPLES, "The number of generated examples.", 1, Integer.MAX_VALUE, 100);
type.setExpert(false);
types.add(type);
type = new ParameterTypeBoolean(PARAMETER_REGRESSION, "Defines if multiple labels for regression tasks should be generated.", false);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeDouble(PARAMETER_ATTRIBUTES_LOWER_BOUND, "The minimum value for the attributes.", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, -10));
types.add(new ParameterTypeDouble(PARAMETER_ATTRIBUTES_UPPER_BOUND, "The maximum value for the attributes.", Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 10));
types.add(new ParameterTypeInt(PARAMETER_LOCAL_RANDOM_SEED, "Use the given random seed instead of global random numbers (-1: use global).", -1, Integer.MAX_VALUE, -1));
return types;
}
}