/*
* 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.ExampleSet;
import com.rapidminer.example.table.AttributeFactory;
import com.rapidminer.example.table.DoubleArrayDataRow;
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.MetaData;
import com.rapidminer.parameter.ParameterType;
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. The data represents a team profit
* example set.
*
* @author Ingo Mierswa
*/
public class TransactionClustersExampleSetGenerator extends AbstractExampleSource {
/** The parameter name for "The number of generated examples." */
public static final String PARAMETER_NUMBER_TRANSACTIONS = "number_transactions";
/** The parameter name for "The number of generated examples." */
public static final String PARAMETER_NUMBER_CUSTOMERS = "number_customers";
/** The parameter name for "The number of generated examples." */
public static final String PARAMETER_NUMBER_ITEMS = "number_items";
/** The parameter name for "The number of generated examples." */
public static final String PARAMETER_NUMBER_CLUSTERS = "number_clusters";
public TransactionClustersExampleSetGenerator(OperatorDescription description) {
super(description);
}
@Override
public ExampleSet createExampleSet() throws OperatorException {
// init
int numberOfTransactions = getParameterAsInt(PARAMETER_NUMBER_TRANSACTIONS);
int numberOfCustomers = getParameterAsInt(PARAMETER_NUMBER_CUSTOMERS);
int numberOfClusters = getParameterAsInt(PARAMETER_NUMBER_CLUSTERS);
int numberOfItems = getParameterAsInt(PARAMETER_NUMBER_ITEMS);
// create table
List<Attribute> attributes = new ArrayList<Attribute>();
Attribute id = AttributeFactory.createAttribute("Id", Ontology.NOMINAL);
for (int i = 1; i <= numberOfCustomers; i++) {
id.getMapping().mapString("Id " + i);
}
attributes.add(id);
Attribute item = AttributeFactory.createAttribute("Item", Ontology.NOMINAL);
for (int i = 1; i <= numberOfItems; i++) {
item.getMapping().mapString("Item " + i);
}
attributes.add(item);
Attribute amount = AttributeFactory.createAttribute("Amount", Ontology.INTEGER);
attributes.add(amount);
MemoryExampleTable table = new MemoryExampleTable(attributes);
// create data
RandomGenerator random = RandomGenerator.getRandomGenerator(this);
double[][] probs = new double[numberOfClusters][numberOfItems];
int[] maxItems = new int[numberOfClusters];
for (int c = 0; c < numberOfClusters; c++) {
double sum = 0.0d;
for (int i = 0; i < numberOfItems; i++) {
probs[c][i] = random.nextDouble();
sum += probs[c][i];
}
for (int i = 0; i < numberOfItems; i++) {
probs[c][i] /= sum;
}
maxItems[c] = random.nextIntInRange(5, 20);
}
double clusterSize = Math.ceil(numberOfCustomers / (double)numberOfClusters);
for (int n = 0; n < numberOfCustomers; n++) {
double[] values = new double[3];
values[0] = id.getMapping().mapString("Id " + (n + 1));
int clusterIndex = Math.max(0, Math.min(numberOfClusters - 1, (int)Math.floor((double)(n + 1) / clusterSize)));
double p = random.nextDouble();
double sum = 0.0d;
int itemIndex = 0;
double itemProb = 0.0d;
for (int i = 0; i < probs[clusterIndex].length; i++) {
if (p <= sum) {
itemIndex = i;
itemProb = probs[clusterIndex][i];
break;
}
sum += probs[clusterIndex][i];
}
values[1] = item.getMapping().mapString("Item " + (itemIndex + 1));
values[2] = Math.round(Math.max(1, random.nextGaussian() * itemProb * maxItems[clusterIndex]));
table.addDataRow(new DoubleArrayDataRow(values));
}
for (int n = numberOfCustomers; n < numberOfTransactions; n++) {
double[] values = new double[3];
int idNumber = random.nextIntInRange(1, numberOfCustomers + 1);
values[0] = values[0] = id.getMapping().mapString("Id " + idNumber);
int clusterIndex = Math.max(0, Math.min(numberOfClusters - 1, (int)Math.floor((double)idNumber / clusterSize)));
double p = random.nextDouble();
double sum = 0.0d;
int itemIndex = 0;
double itemProb = 0.0d;
if (random.nextDouble() < 0.05) {
itemIndex = random.nextIntInRange(0, numberOfItems);
} else {
for (int i = 0; i < probs[clusterIndex].length; i++) {
if (p <= sum) {
itemIndex = i;
itemProb = probs[clusterIndex][i];
break;
}
sum += probs[clusterIndex][i];
}
}
values[1] = item.getMapping().mapString("Item " + (itemIndex + 1));
values[2] = Math.round(Math.max(1, random.nextGaussian() * itemProb * maxItems[clusterIndex]));
table.addDataRow(new DoubleArrayDataRow(values));
}
return table.createExampleSet(null, null, id);
}
@Override
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_NUMBER_TRANSACTIONS, "The number of generated transactions.", 1, Integer.MAX_VALUE, 10000);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_CUSTOMERS, "The number of generated customers.", 1, Integer.MAX_VALUE, 1000);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_ITEMS, "The number of generated items.", 1, Integer.MAX_VALUE, 80);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_NUMBER_CLUSTERS, "The number of generated clusters.", 1, Integer.MAX_VALUE, 10);
type.setExpert(false);
types.add(type);
types.addAll(RandomGenerator.getRandomGeneratorParameters(this));
return types;
}
@Override
public MetaData getGeneratedMetaData() throws OperatorException {
ExampleSetMetaData emd = new ExampleSetMetaData();
String[] possibleValues = new String[getParameterAsInt(PARAMETER_NUMBER_CUSTOMERS)];
for (int i = 0; i < possibleValues.length; i++) {
possibleValues[i] = "Id " + (i + 1);
}
emd.addAttribute(new AttributeMetaData("Id", null, possibleValues));
possibleValues = new String[getParameterAsInt(PARAMETER_NUMBER_ITEMS)];
for (int i = 0; i < possibleValues.length; i++) {
possibleValues[i] = "Item " + (i + 1);
}
emd.addAttribute(new AttributeMetaData("Item", null, possibleValues));
emd.addAttribute(new AttributeMetaData("Amount", null, Ontology.INTEGER, new Range(0, Double.POSITIVE_INFINITY)));
emd.setNumberOfExamples(getParameterAsInt(PARAMETER_NUMBER_TRANSACTIONS));
return emd;
}
}