/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* PropositionalToMultiInstance.java
* Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.unsupervised.attribute;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RelationalLocator;
import weka.core.RevisionUtils;
import weka.core.StringLocator;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;
import weka.core.DenseInstance;
/**
<!-- globalinfo-start -->
* Converts the propositional instance dataset into multi-instance dataset (with relational attribute). When normalize or standardize a multi-instance dataset, a MIToSingleInstance filter can be applied first to convert the multi-instance dataset into propositional instance dataset. After normalization or standardization, may use this PropositionalToMultiInstance filter to convert the data back to multi-instance format.<br/>
* <br/>
* Note: the first attribute of the original propositional instance dataset must be a nominal attribute which is expected to be bagId attribute.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -S <num>
* The seed for the randomization of the order of bags. (default 1)</pre>
*
* <pre> -R
* Randomizes the order of the produced bags after the generation. (default off)</pre>
*
<!-- options-end -->
*
* @author Lin Dong (ld21@cs.waikato.ac.nz)
* @version $Revision: 5547 $
* @see MultiInstanceToPropositional
*/
public class PropositionalToMultiInstance
extends Filter
implements OptionHandler, UnsupervisedFilter {
/** for serialization */
private static final long serialVersionUID = 5825873573912102482L;
/** the seed for randomizing, default is 1 */
protected int m_Seed = 1;
/** whether to randomize the output data */
protected boolean m_Randomize = false;
/** Indices of string attributes in the bag */
protected StringLocator m_BagStringAtts = null;
/** Indices of relational attributes in the bag */
protected RelationalLocator m_BagRelAtts = null;
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return
"Converts the propositional instance dataset into multi-instance "
+ "dataset (with relational attribute). When normalize or standardize a "
+ "multi-instance dataset, a MIToSingleInstance filter can be applied "
+ "first to convert the multi-instance dataset into propositional "
+ "instance dataset. After normalization or standardization, may use "
+ "this PropositionalToMultiInstance filter to convert the data back to "
+ "multi-instance format.\n\n"
+ "Note: the first attribute of the original propositional instance "
+ "dataset must be a nominal attribute which is expected to be bagId "
+ "attribute.";
}
/**
* Returns an enumeration describing the available options
*
* @return an enumeration of all the available options
*/
public Enumeration listOptions() {
Vector result = new Vector();
result.addElement(new Option(
"\tThe seed for the randomization of the order of bags."
+ "\t(default 1)",
"S", 1, "-S <num>"));
result.addElement(new Option(
"\tRandomizes the order of the produced bags after the generation."
+ "\t(default off)",
"R", 0, "-R"));
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -S <num>
* The seed for the randomization of the order of bags. (default 1)</pre>
*
* <pre> -R
* Randomizes the order of the produced bags after the generation. (default off)</pre>
*
<!-- options-end -->
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String tmpStr;
setRandomize(Utils.getFlag('R', options));
tmpStr = Utils.getOption('S', options);
if (tmpStr.length() != 0)
setSeed(Integer.parseInt(tmpStr));
else
setSeed(1);
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String [] getOptions() {
Vector result;
result = new Vector();
result.add("-S");
result.add("" + getSeed());
if (m_Randomize)
result.add("-R");
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String seedTipText() {
return "The random seed used by the random number generator";
}
/**
* Sets the new seed for randomizing the order of the generated data
*
* @param value the new seed value
*/
public void setSeed(int value) {
m_Seed = value;
}
/**
* Returns the current seed value for randomizing the order of the generated
* data
*
* @return the current seed value
*/
public int getSeed() {
return m_Seed;
}
/**
* Sets whether the order of the generated data is randomized
*
* @param value whether to randomize or not
*/
public void setRandomize(boolean value) {
m_Randomize = value;
}
/**
* Gets whether the order of the generated is randomized
*
* @return true if the order is randomized
*/
public boolean getRandomize() {
return m_Randomize;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String randomizeTipText() {
return "Whether the order of the generated data is randomized.";
}
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.DATE_ATTRIBUTES);
result.enable(Capability.STRING_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enableAllClasses();
result.enable(Capability.MISSING_CLASS_VALUES);
result.enable(Capability.NO_CLASS);
return result;
}
/**
* Sets the format of the input instances.
*
* @param instanceInfo an Instances object containing the input
* instance structure (any instances contained in the object are
* ignored - only the structure is required).
* @return true if the outputFormat may be collected immediately
* @throws Exception if the input format can't be set
* successfully
*/
public boolean setInputFormat(Instances instanceInfo)
throws Exception {
if (instanceInfo.attribute(0).type()!= Attribute.NOMINAL) {
throw new Exception("The first attribute type of the original propositional instance dataset must be Nominal!");
}
super.setInputFormat(instanceInfo);
/* create a new output format (multi-instance format) */
Instances newData = instanceInfo.stringFreeStructure();
Attribute attBagIndex = (Attribute) newData.attribute(0).copy();
Attribute attClass = (Attribute) newData.classAttribute().copy();
// remove the bagIndex attribute
newData.deleteAttributeAt(0);
// remove the class attribute
newData.setClassIndex(-1);
newData.deleteAttributeAt(newData.numAttributes() - 1);
FastVector attInfo = new FastVector(3);
attInfo.addElement(attBagIndex);
attInfo.addElement(new Attribute("bag", newData)); // relation-valued attribute
attInfo.addElement(attClass);
Instances data = new Instances("Multi-Instance-Dataset", attInfo, 0);
data.setClassIndex(data.numAttributes() - 1);
super.setOutputFormat(data.stringFreeStructure());
m_BagStringAtts = new StringLocator(data.attribute(1).relation());
m_BagRelAtts = new RelationalLocator(data.attribute(1).relation());
return true;
}
/**
* adds a new bag out of the given data and adds it to the output
*
* @param input the intput dataset
* @param output the dataset this bag is added to
* @param bagInsts the instances in this bag
* @param bagIndex the bagIndex of this bag
* @param classValue the associated class value
* @param bagWeight the weight of the bag
*/
protected void addBag(
Instances input,
Instances output,
Instances bagInsts,
int bagIndex,
double classValue,
double bagWeight) {
// copy strings/relational values
for (int i = 0; i < bagInsts.numInstances(); i++) {
RelationalLocator.copyRelationalValues(
bagInsts.instance(i), false,
input, m_InputRelAtts,
bagInsts, m_BagRelAtts);
StringLocator.copyStringValues(
bagInsts.instance(i), false,
input, m_InputStringAtts,
bagInsts, m_BagStringAtts);
}
int value = output.attribute(1).addRelation(bagInsts);
Instance newBag = new DenseInstance(output.numAttributes());
newBag.setValue(0, bagIndex);
newBag.setValue(2, classValue);
newBag.setValue(1, value);
newBag.setWeight(bagWeight);
newBag.setDataset(output);
output.add(newBag);
}
/**
* Adds an output instance to the queue. The derived class should use this
* method for each output instance it makes available.
*
* @param instance the instance to be added to the queue.
*/
protected void push(Instance instance) {
if (instance != null) {
super.push(instance);
// set correct references
}
}
/**
* Signify that this batch of input to the filter is finished.
* If the filter requires all instances prior to filtering,
* output() may now be called to retrieve the filtered instances.
*
* @return true if there are instances pending output
* @throws IllegalStateException if no input structure has been defined
*/
public boolean batchFinished() {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
Instances input = getInputFormat();
input.sort(0); // make sure that bagID is sorted
Instances output = getOutputFormat();
Instances bagInsts = output.attribute(1).relation();
Instance inst = new DenseInstance(bagInsts.numAttributes());
inst.setDataset(bagInsts);
double bagIndex = input.instance(0).value(0);
double classValue = input.instance(0).classValue();
double bagWeight = 0.0;
// Convert pending input instances
for(int i = 0; i < input.numInstances(); i++) {
double currentBagIndex = input.instance(i).value(0);
// copy the propositional instance value, except the bagIndex and the class value
for (int j = 0; j < input.numAttributes() - 2; j++)
inst.setValue(j, input.instance(i).value(j + 1));
inst.setWeight(input.instance(i).weight());
if (currentBagIndex == bagIndex){
bagInsts.add(inst);
bagWeight += inst.weight();
}
else{
addBag(input, output, bagInsts, (int) bagIndex, classValue, bagWeight);
bagInsts = bagInsts.stringFreeStructure();
bagInsts.add(inst);
bagIndex = currentBagIndex;
classValue = input.instance(i).classValue();
bagWeight = inst.weight();
}
}
// reach the last instance, create and add the last bag
addBag(input, output, bagInsts, (int) bagIndex, classValue, bagWeight);
if (getRandomize())
output.randomize(new Random(getSeed()));
for (int i = 0; i < output.numInstances(); i++)
push(output.instance(i));
// Free memory
flushInput();
m_NewBatch = true;
m_FirstBatchDone = true;
return (numPendingOutput() != 0);
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5547 $");
}
/**
* Main method for running this filter.
*
* @param args should contain arguments to the filter:
* use -h for help
*/
public static void main(String[] args) {
runFilter(new PropositionalToMultiInstance(), args);
}
}