/*
* 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.ArrayList;
import java.util.List;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.example.table.DoubleArrayDataRow;
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.ParameterTypeInt;
import com.rapidminer.tools.Ontology;
import com.rapidminer.tools.RandomGenerator;
/**
* Generates a random example set for testing purposes. The data represents a team profit
* example set.
*
* @author Ingo Mierswa
* @version $Id: TeamProfitExampleSetGenerator.java,v 1.4 2008/07/07 07:06:42 ingomierswa Exp $
*/
public class TeamProfitExampleSetGenerator extends Operator {
/** The parameter name for "The number of generated examples." */
public static final String PARAMETER_NUMBER_EXAMPLES = "number_examples";
/** 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 Class[] INPUT_CLASSES = new Class[0];
private static final Class[] OUTPUT_CLASSES = { ExampleSet.class };
private static String[] ATTRIBUTE_NAMES = {
"size", "leader", "number of qualified employees", "leader changed", "average years of experience", "structure"
};
private static int[] VALUE_TYPES = {
Ontology.INTEGER,
Ontology.NOMINAL,
Ontology.INTEGER,
Ontology.BINOMINAL,
Ontology.INTEGER,
Ontology.BINOMINAL
};
private static String[][] POSSIBLE_VALUES = {
null,
{ "Mr. Brown", "Mr. Miller", "Mrs. Smith", "Mrs. Hanson", "Mrs. Green", "Mr. Chang" },
null,
{ "yes", "no" },
null,
{ "flat", "hierachical" }
};
public TeamProfitExampleSetGenerator(OperatorDescription description) {
super(description);
}
public IOObject[] apply() throws OperatorException {
// init
int numberOfExamples = getParameterAsInt(PARAMETER_NUMBER_EXAMPLES);
// create table
List<Attribute> attributes = new ArrayList<Attribute>();
Attribute id = AttributeFactory.createAttribute("teamID", Ontology.NOMINAL);
attributes.add(id);
for (int m = 0; m < ATTRIBUTE_NAMES.length; m++) {
Attribute current = AttributeFactory.createAttribute(ATTRIBUTE_NAMES[m], VALUE_TYPES[m]);
String[] possibleValues = POSSIBLE_VALUES[m];
if (possibleValues != null) {
for (int v = 0; v < possibleValues.length; v++)
current.getMapping().mapString(possibleValues[v]);
}
attributes.add(current);
}
Attribute label = AttributeFactory.createAttribute("label", Ontology.BINOMINAL);
label.getMapping().mapString("good");
label.getMapping().mapString("bad");
attributes.add(label);
MemoryExampleTable table = new MemoryExampleTable(attributes);
// create data
RandomGenerator random = RandomGenerator.getRandomGenerator(getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED));
for (int n = 0; n < numberOfExamples; n++) {
double[] values = new double[ATTRIBUTE_NAMES.length + 2];
values[0] = attributes.get(0).getMapping().mapString("team_" + n);
// "size", "leader", "number of qualified employees", "leader changed", "average years of experience", "structure"
values[1] = random.nextIntInRange(5, 20);
values[2] = random.nextInt(POSSIBLE_VALUES[1].length);
values[3] = Math.round((random.nextDouble() * (values[1] - 1)) + 1);
values[4] = random.nextInt(POSSIBLE_VALUES[3].length);
values[5] = random.nextIntInRange(1, 10);
values[6] = random.nextInt(POSSIBLE_VALUES[5].length);
values[7] = label.getMapping().mapString("bad");
if (values[1] > 18) {
if (random.nextDouble() > 0.05)
values[7] = label.getMapping().mapString("good");
} else if (values[1] > 15) {
if (random.nextDouble() > 0.1)
values[7] = label.getMapping().mapString("good");
} else if (values[4] == 1) {
if (random.nextDouble() > 0.1)
values[7] = label.getMapping().mapString("good");
}
table.addDataRow(new DoubleArrayDataRow(values));
}
// create example set and return it
return new IOObject[] { table.createExampleSet(label) };
}
public Class<?>[] getInputClasses() {
return INPUT_CLASSES;
}
public Class<?>[] getOutputClasses() {
return OUTPUT_CLASSES;
}
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);
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;
}
}