/*
* 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.
*/
/*
* MultiInstanceToPropositional.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.Instance;
import weka.core.Instances;
import weka.core.MultiInstanceCapabilitiesHandler;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RelationalLocator;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.StringLocator;
import weka.core.Tag;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.UnsupervisedFilter;
import java.util.Enumeration;
import java.util.Vector;
import weka.core.DenseInstance;
/**
<!-- globalinfo-start -->
* Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing.<br/>
* Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -A <num>
* The type of weight setting for each prop. instance:
* 0.weight = original single bag weight /Total number of
* prop. instance in the corresponding bag;
* 1.weight = 1.0;
* 2.weight = 1.0/Total number of prop. instance in the
* corresponding bag;
* 3. weight = Total number of prop. instance / (Total number
* of bags * Total number of prop. instance in the
* corresponding bag).
* (default:0)</pre>
*
<!-- options-end -->
*
* @author Lin Dong (ld21@cs.waikato.ac.nz)
* @version $Revision: 5543 $
* @see PropositionalToMultiInstance
*/
public class MultiInstanceToPropositional
extends Filter
implements OptionHandler, UnsupervisedFilter, MultiInstanceCapabilitiesHandler {
/** for serialization */
private static final long serialVersionUID = -4102847628883002530L;
/** the total number of bags */
protected int m_NumBags;
/** Indices of string attributes in the bag */
protected StringLocator m_BagStringAtts = null;
/** Indices of relational attributes in the bag */
protected RelationalLocator m_BagRelAtts = null;
/** the total number of the propositional instance in the dataset */
protected int m_NumInstances;
/** weight method: keep the weight to be the same as the original value */
public static final int WEIGHTMETHOD_ORIGINAL = 0;
/** weight method: 1.0 */
public static final int WEIGHTMETHOD_1 = 1;
/** weight method: 1.0 / Total # of prop. instance in the corresp. bag */
public static final int WEIGHTMETHOD_INVERSE1 = 2;
/** weight method: Total # of prop. instance / (Total # of bags * Total # of prop. instance in the corresp. bag) */
public static final int WEIGHTMETHOD_INVERSE2 = 3;
/** weight methods */
public static final Tag[] TAGS_WEIGHTMETHOD = {
new Tag(WEIGHTMETHOD_ORIGINAL,
"keep the weight to be the same as the original value"),
new Tag(WEIGHTMETHOD_1,
"1.0"),
new Tag(WEIGHTMETHOD_INVERSE1,
"1.0 / Total # of prop. instance in the corresp. bag"),
new Tag(WEIGHTMETHOD_INVERSE2,
"Total # of prop. instance / (Total # of bags * Total # of prop. instance in the corresp. bag)")
};
/** the propositional instance weight setting method */
protected int m_WeightMethod = WEIGHTMETHOD_INVERSE2;
/**
* 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 type of weight setting for each prop. instance:\n"
+ "\t0.weight = original single bag weight /Total number of\n"
+ "\tprop. instance in the corresponding bag;\n"
+ "\t1.weight = 1.0;\n"
+ "\t2.weight = 1.0/Total number of prop. instance in the \n"
+ "\t\tcorresponding bag; \n"
+ "\t3. weight = Total number of prop. instance / (Total number \n"
+ "\t\tof bags * Total number of prop. instance in the \n"
+ "\t\tcorresponding bag). \n"
+ "\t(default:0)",
"A", 1, "-A <num>"));
return result.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -A <num>
* The type of weight setting for each prop. instance:
* 0.weight = original single bag weight /Total number of
* prop. instance in the corresponding bag;
* 1.weight = 1.0;
* 2.weight = 1.0/Total number of prop. instance in the
* corresponding bag;
* 3. weight = Total number of prop. instance / (Total number
* of bags * Total number of prop. instance in the
* corresponding bag).
* (default:0)</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 weightString = Utils.getOption('A', options);
if (weightString.length() != 0) {
setWeightMethod(
new SelectedTag(Integer.parseInt(weightString), TAGS_WEIGHTMETHOD));
} else {
setWeightMethod(
new SelectedTag(WEIGHTMETHOD_INVERSE2, TAGS_WEIGHTMETHOD));
}
}
/**
* 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("-A");
result.add("" + m_WeightMethod);
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 weightMethodTipText() {
return "The method used for weighting the instances.";
}
/**
* The new method for weighting the instances.
*
* @param method the new method
*/
public void setWeightMethod(SelectedTag method){
if (method.getTags() == TAGS_WEIGHTMETHOD)
m_WeightMethod = method.getSelectedTag().getID();
}
/**
* Returns the current weighting method for instances.
*
* @return the current weight method
*/
public SelectedTag getWeightMethod(){
return new SelectedTag(m_WeightMethod, TAGS_WEIGHTMETHOD);
}
/**
* 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 multi-instance dataset into single instance dataset "
+ "so that the Nominalize, Standardize and other type of filters or transformation "
+ " can be applied to these data for the further preprocessing.\n"
+ "Note: the first attribute of the converted dataset is a nominal "
+ "attribute and refers to the bagId.";
}
/**
* 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.disableAllAttributes();
result.enable(Capability.NOMINAL_ATTRIBUTES);
result.enable(Capability.RELATIONAL_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enableAllClasses();
result.enable(Capability.MISSING_CLASS_VALUES);
// other
result.enable(Capability.ONLY_MULTIINSTANCE);
return result;
}
/**
* Returns the capabilities of this multi-instance filter for the
* relational data (i.e., the bags).
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getMultiInstanceCapabilities() {
Capabilities result = new Capabilities(this);
// attributes
result.enableAllAttributes();
result.disable(Capability.RELATIONAL_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
// class
result.enableAllClasses();
result.enable(Capability.MISSING_CLASS_VALUES);
result.enable(Capability.NO_CLASS);
// other
result.setMinimumNumberInstances(0);
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(1).type()!=Attribute.RELATIONAL) {
throw new Exception("Can only handle relational-valued attribute!");
}
super.setInputFormat(instanceInfo);
m_NumBags = instanceInfo.numInstances();
m_NumInstances = 0;
for (int i=0; i<m_NumBags; i++)
m_NumInstances += instanceInfo.instance(i).relationalValue(1).numInstances();
Attribute classAttribute = (Attribute) instanceInfo.classAttribute().copy();
Attribute bagIndex = (Attribute) instanceInfo.attribute(0).copy();
/* create a new output format (propositional instance format) */
Instances newData = instanceInfo.attribute(1).relation().stringFreeStructure();
newData.insertAttributeAt(bagIndex, 0);
newData.insertAttributeAt(classAttribute, newData.numAttributes());
newData.setClassIndex(newData.numAttributes() - 1);
super.setOutputFormat(newData.stringFreeStructure());
m_BagStringAtts = new StringLocator(instanceInfo.attribute(1).relation().stringFreeStructure());
m_BagRelAtts = new RelationalLocator(instanceInfo.attribute(1).relation().stringFreeStructure());
return true;
}
/**
* Input an instance for filtering. Filter requires all
* training instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be
* collected with output().
* @throws IllegalStateException if no input format has been set.
*/
public boolean input(Instance instance) {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
convertInstance(instance);
return true;
}
/**
* 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();
// Convert pending input instances
for(int i = 0; i < input.numInstances(); i++) {
convertInstance(input.instance(i));
}
// Free memory
flushInput();
m_NewBatch = true;
return (numPendingOutput() != 0);
}
/**
* Convert a single bag over. The converted instances is
* added to the end of the output queue.
*
* @param bag the bag to convert
*/
private void convertInstance(Instance bag) {
Instances data = bag.relationalValue(1);
int bagSize = data.numInstances();
double bagIndex = bag.value(0);
double classValue = bag.classValue();
double weight = 0.0;
//the proper weight for each instance in a bag
if (m_WeightMethod == WEIGHTMETHOD_1)
weight = 1.0;
else if (m_WeightMethod == WEIGHTMETHOD_INVERSE1)
weight = (double) 1.0 / bagSize;
else if (m_WeightMethod == WEIGHTMETHOD_INVERSE2)
weight=(double) m_NumInstances / (m_NumBags * bagSize);
else
weight = (double) bag.weight() / bagSize;
Instance newInst;
Instances outputFormat = getOutputFormat().stringFreeStructure();
for (int i = 0; i < bagSize; i++) {
newInst = new DenseInstance (outputFormat.numAttributes());
newInst.setDataset(outputFormat);
newInst.setValue(0,bagIndex);
if (!bag.classIsMissing())
newInst.setClassValue(classValue);
// copy the attribute values to new instance
for (int j = 1; j < outputFormat.numAttributes() - 1; j++){
newInst.setValue(j,data.instance(i).value(j - 1));
}
newInst.setWeight(weight);
// copy strings/relational values
StringLocator.copyStringValues(
newInst, false,
data, m_BagStringAtts,
outputFormat, m_OutputStringAtts);
RelationalLocator.copyRelationalValues(
newInst, false,
data, m_BagRelAtts,
outputFormat, m_OutputRelAtts);
push(newInst);
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5543 $");
}
/**
* 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 MultiInstanceToPropositional(), args);
}
}