/**
* 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.generator;
import java.util.LinkedHashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import com.rapidminer.example.Attribute;
import com.rapidminer.example.Attributes;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.example.utils.ExampleSetBuilder;
import com.rapidminer.example.utils.ExampleSets;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.io.AbstractExampleSource;
import com.rapidminer.operator.ports.metadata.AttributeMetaData;
import com.rapidminer.operator.ports.metadata.ExampleSetMetaData;
import com.rapidminer.operator.ports.metadata.MetaData;
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;
import com.rapidminer.tools.math.container.Range;
/**
* Generates a random example set for testing purposes with more than one label.
*
* @author Ingo Mierswa
*/
public class MultipleLabelGenerator extends AbstractExampleSource {
/** 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";
private static final int NUMBER_OF_ATTRIBUTES = 5;
public MultipleLabelGenerator(OperatorDescription description) {
super(description);
}
@Override
public MetaData getGeneratedMetaData() throws OperatorException {
int numberOfExamples = getParameterAsInt(PARAMETER_NUMBER_EXAMPLES);
double lower = getParameterAsDouble(PARAMETER_ATTRIBUTES_LOWER_BOUND);
double upper = getParameterAsDouble(PARAMETER_ATTRIBUTES_UPPER_BOUND);
ExampleSetMetaData emd = new ExampleSetMetaData();
for (int i = 0; i < NUMBER_OF_ATTRIBUTES; i++) {
emd.addAttribute(new AttributeMetaData("att" + (i + 1), null, Ontology.REAL, new Range(lower, upper)));
}
if (getParameterAsBoolean(PARAMETER_REGRESSION)) {
emd.addAttribute(new AttributeMetaData("label1", Attributes.LABEL_NAME + 1, Ontology.REAL,
new Range(3 * lower, 3 * upper)));
emd.addAttribute(new AttributeMetaData("label2", Attributes.LABEL_NAME + 2, Ontology.REAL,
new Range(3 * lower, 3 * upper)));
emd.addAttribute(new AttributeMetaData("label3", Attributes.LABEL_NAME + 3, Ontology.REAL,
new Range(Math.max(lower, 0) * Math.max(lower, 0), upper * upper)));
} else {
emd.addAttribute(new AttributeMetaData("label1", Attributes.LABEL_NAME + 1, "positive", "negative"));
emd.addAttribute(new AttributeMetaData("label2", Attributes.LABEL_NAME + 2, "positive", "negative"));
emd.addAttribute(new AttributeMetaData("label3", Attributes.LABEL_NAME + 3, "positive", "negative"));
}
emd.setNumberOfExamples(numberOfExamples);
return emd;
}
@Override
public ExampleSet createExampleSet() throws OperatorException {
// init
int numberOfExamples = getParameterAsInt(PARAMETER_NUMBER_EXAMPLES);
double lower = getParameterAsDouble(PARAMETER_ATTRIBUTES_LOWER_BOUND);
double upper = getParameterAsDouble(PARAMETER_ATTRIBUTES_UPPER_BOUND);
boolean regression = getParameterAsBoolean(PARAMETER_REGRESSION);
// 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 (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 (!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");
}
ExampleSetBuilder builder = ExampleSets.from(attributes).withExpectedSize(numberOfExamples);
// create data
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
// init operator progress
getProgress().setTotal(numberOfExamples);
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 (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");
}
builder.addRow(example);
getProgress().step();
}
// create example set and return it
Map<Attribute, String> specialMap = new LinkedHashMap<Attribute, String>();
specialMap.put(label1, Attributes.LABEL_NAME + 1);
specialMap.put(label2, Attributes.LABEL_NAME + 2);
specialMap.put(label3, Attributes.LABEL_NAME + 3);
ExampleSet result = builder.withRoles(specialMap).build();
getProgress().complete();
return result;
}
@Override
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.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
}