/*
* 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.generator;
import java.util.ArrayList;
import java.util.List;
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.table.DoubleArrayDataRow;
import com.rapidminer.example.table.DoubleSparseArrayDataRow;
import com.rapidminer.example.table.MemoryExampleTable;
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.MDReal;
import com.rapidminer.operator.ports.metadata.MetaData;
import com.rapidminer.operator.ports.metadata.SetRelation;
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 huge amounts of data in either sparse or dense format. This
* operator can be used to check if huge amounts of data can be handled by RapidMiner
* for a given process setup without creating the correct format / writing
* special purpose input operators.
*
* @author Ingo Mierswa
*/
public class MassiveDataGenerator extends AbstractExampleSource {
/** The parameter name for "The number of generated examples." */
public static final String PARAMETER_NUMBER_EXAMPLES = "number_examples";
/** The parameter name for "The number of attributes." */
public static final String PARAMETER_NUMBER_ATTRIBUTES = "number_attributes";
/** The parameter name for "The fraction of default attributes." */
public static final String PARAMETER_SPARSE_FRACTION = "sparse_fraction";
/** The parameter name for "Indicates if the example should be internally represented in a sparse format." */
public static final String PARAMETER_SPARSE_REPRESENTATION = "sparse_representation";
public MassiveDataGenerator(OperatorDescription description) {
super(description);
}
@Override
public MetaData getGeneratedMetaData() throws OperatorException {
ExampleSetMetaData emd = new ExampleSetMetaData();
AttributeMetaData amd = new AttributeMetaData("label", Ontology.NOMINAL, Attributes.LABEL_NAME);
emd.addAttribute(amd);
int desirendNumberOfAttributes = getParameterAsInt(PARAMETER_NUMBER_ATTRIBUTES);
double mean = getParameterAsDouble(PARAMETER_SPARSE_FRACTION);
if (desirendNumberOfAttributes > 20) {
emd.attributesAreSuperset();
// first ten
for (int i = 1; i < 11; i++) {
AttributeMetaData newAMD = new AttributeMetaData("att" + i, Ontology.REAL);
newAMD.setValueRange(new Range(0, 1), SetRelation.EQUAL);
newAMD.setMean(new MDReal(mean));
emd.addAttribute(newAMD);
}
// last ten
for (int i = desirendNumberOfAttributes - 10; i <= desirendNumberOfAttributes ; i++) {
AttributeMetaData newAMD = new AttributeMetaData("att" + i, Ontology.REAL);
newAMD.setValueRange(new Range(0, 1), SetRelation.EQUAL);
newAMD.setMean(new MDReal(mean));
emd.addAttribute(newAMD);
}
} else {
for (int i = 0; i < desirendNumberOfAttributes; i++) {
AttributeMetaData newAMD = new AttributeMetaData("att" + (i + 1), Ontology.REAL);
newAMD.setValueRange(new Range(0, 1), SetRelation.EQUAL);
newAMD.setMean(new MDReal(mean));
emd.addAttribute(newAMD);
}
}
return emd;
}
@Override
public ExampleSet createExampleSet() throws OperatorException {
// init
int numberOfExamples = getParameterAsInt(PARAMETER_NUMBER_EXAMPLES);
int numberOfAttributes = getParameterAsInt(PARAMETER_NUMBER_ATTRIBUTES);
double sparseFraction = getParameterAsDouble(PARAMETER_SPARSE_FRACTION);
boolean sparseRepresentation = getParameterAsBoolean(PARAMETER_SPARSE_REPRESENTATION);
// create table
List<Attribute> attributes = new ArrayList<Attribute>();
for (int m = 0; m < numberOfAttributes; m++)
attributes.add(AttributeFactory.createAttribute("att" + (m + 1), Ontology.REAL));
Attribute label = AttributeFactory.createAttribute("label", Ontology.NOMINAL);
label.getMapping().mapString("positive");
label.getMapping().mapString("negative");
attributes.add(label);
MemoryExampleTable table = new MemoryExampleTable(attributes);
// create data
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
for (int n = 0; n < numberOfExamples; n++) {
int counter = 0;
if (sparseRepresentation) {
DoubleSparseArrayDataRow dataRow = new DoubleSparseArrayDataRow(numberOfAttributes + 1);
for (int i = 0; i < numberOfAttributes; i++) {
double value = random.nextDouble() > sparseFraction ? 1.0d : 0.0d;
dataRow.set(attributes.get(i), value);
if (value == 0.0d)
counter++;
}
if (counter < (sparseFraction * numberOfAttributes))
dataRow.set(label, label.getMapping().mapString("positive"));
else
dataRow.set(label, label.getMapping().mapString("negative"));
dataRow.trim();
table.addDataRow(dataRow);
} else {
double[] dataRow = new double[numberOfAttributes + 1];
for (int i = 0; i < numberOfAttributes; i++) {
double value = random.nextDouble() > sparseFraction ? 1.0d : 0.0d;
dataRow[i] = value;
if (value == 0.0d)
counter++;
}
if (counter < (sparseFraction * numberOfAttributes))
dataRow[dataRow.length - 1] = label.getMapping().mapString("positive");
else
dataRow[dataRow.length - 1] = label.getMapping().mapString("negative");
table.addDataRow(new DoubleArrayDataRow(dataRow));
}
}
// create example set and return it
ExampleSet result = table.createExampleSet(label);
return result;
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_EXAMPLES, "The number of generated examples.", 0, Integer.MAX_VALUE, 10000);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_ATTRIBUTES, "The number of attributes.", 0, Integer.MAX_VALUE, 10000);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeDouble(PARAMETER_SPARSE_FRACTION, "The fraction of default attributes.", 0.0d, 1.0d, 0.99d));
types.add(new ParameterTypeBoolean(PARAMETER_SPARSE_REPRESENTATION, "Indicates if the example should be internally represented in a sparse format.", true));
types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
}