/*
* 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.
*/
/*
* AttributeSelection.java
* Copyright (C) 1999 Mark Hall
*
*/
package weka.filters.supervised.attribute;
import weka.filters.*;
import java.io.*;
import java.util.*;
import weka.core.*;
import weka.attributeSelection.*;
/**
* Filter for doing attribute selection.<p>
*
* Valid options are:<p>
*
* -S <"Name of search class [search options]"> <br>
* Set search method for subset evaluators. <br>
* eg. -S "weka.attributeSelection.BestFirst -S 8" <p>
*
* -E <"Name of attribute/subset evaluation class [evaluator options]"> <br>
* Set the attribute/subset evaluator. <br>
* eg. -E "weka.attributeSelection.CfsSubsetEval -L" <p>
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 1.1.1.1 $
*/
public class AttributeSelection extends Filter
implements SupervisedFilter, OptionHandler {
/** the attribute selection evaluation object */
private weka.attributeSelection.AttributeSelection m_trainSelector;
/** the attribute evaluator to use */
private ASEvaluation m_ASEvaluator;
/** the search method if any */
private ASSearch m_ASSearch;
/** holds a copy of the full set of valid options passed to the filter */
private String [] m_FilterOptions;
/** holds the selected attributes */
private int [] m_SelectedAttributes;
/**
* Constructor
*/
public AttributeSelection () {
resetOptions();
}
/**
* Returns an enumeration describing the available options.
* @return an enumeration of all the available options.
*/
public Enumeration listOptions() {
Vector newVector = new Vector(6);
newVector.addElement(new Option("\tSets search method for subset "
+ "evaluators.", "S", 1,
"-S <\"Name of search class"
+ " [search options]\">"));
newVector.addElement(new Option("\tSets attribute/subset evaluator.",
"E", 1,
"-E <\"Name of attribute/subset "
+ "evaluation class [evaluator "
+ "options]\">"));
if ((m_ASEvaluator != null) && (m_ASEvaluator instanceof OptionHandler)) {
Enumeration enum = ((OptionHandler)m_ASEvaluator).listOptions();
newVector.addElement(new Option("", "", 0, "\nOptions specific to "
+ "evaluator " + m_ASEvaluator.getClass().getName() + ":"));
while (enum.hasMoreElements()) {
newVector.addElement((Option)enum.nextElement());
}
}
if ((m_ASSearch != null) && (m_ASSearch instanceof OptionHandler)) {
Enumeration enum = ((OptionHandler)m_ASSearch).listOptions();
newVector.addElement(new Option("", "", 0, "\nOptions specific to "
+ "search " + m_ASSearch.getClass().getName() + ":"));
while (enum.hasMoreElements()) {
newVector.addElement((Option)enum.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options. Valid options are:<p>
*
* -S <"Name of search class [search options]"> <br>
* Set search method for subset evaluators. <br>
* eg. -S "weka.attributeSelection.BestFirst -S 8" <p>
*
* -E <"Name of attribute/subset evaluation class [evaluator options]"> <br>
* Set the attribute/subset evaluator. <br>
* eg. -E "weka.attributeSelection.CfsSubsetEval -L" <p>
*
* @param options the list of options as an array of strings
* @exception Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String optionString;
resetOptions();
if (Utils.getFlag('X',options)) {
throw new Exception("Cross validation is not a valid option"
+ " when using attribute selection as a Filter.");
}
optionString = Utils.getOption('E',options);
if (optionString.length() != 0) {
optionString = optionString.trim();
// split a quoted evaluator name from its options (if any)
int breakLoc = optionString.indexOf(' ');
String evalClassName = optionString;
String evalOptionsString = "";
String [] evalOptions=null;
if (breakLoc != -1) {
evalClassName = optionString.substring(0, breakLoc);
evalOptionsString = optionString.substring(breakLoc).trim();
evalOptions = Utils.splitOptions(evalOptionsString);
}
setEvaluator(ASEvaluation.forName(evalClassName, evalOptions));
}
if (m_ASEvaluator instanceof AttributeEvaluator) {
setSearch(new Ranker());
}
optionString = Utils.getOption('S',options);
if (optionString.length() != 0) {
optionString = optionString.trim();
int breakLoc = optionString.indexOf(' ');
String SearchClassName = optionString;
String SearchOptionsString = "";
String [] SearchOptions=null;
if (breakLoc != -1) {
SearchClassName = optionString.substring(0, breakLoc);
SearchOptionsString = optionString.substring(breakLoc).trim();
SearchOptions = Utils.splitOptions(SearchOptionsString);
}
setSearch(ASSearch.forName(SearchClassName, SearchOptions));
}
Utils.checkForRemainingOptions(options);
}
/**
* Gets the current settings for the attribute selection (search, evaluator)
* etc.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String [] getOptions() {
String [] EvaluatorOptions = new String[0];
String [] SearchOptions = new String[0];
int current = 0;
if (m_ASEvaluator instanceof OptionHandler) {
EvaluatorOptions = ((OptionHandler)m_ASEvaluator).getOptions();
}
if (m_ASSearch instanceof OptionHandler) {
SearchOptions = ((OptionHandler)m_ASSearch).getOptions();
}
String [] setOptions = new String [10];
setOptions[current++]="-E";
setOptions[current++]= getEvaluator().getClass().getName()
+" "+Utils.joinOptions(EvaluatorOptions);
setOptions[current++]="-S";
setOptions[current++]=getSearch().getClass().getName()
+ " "+Utils.joinOptions(SearchOptions);
while (current < setOptions.length) {
setOptions[current++] = "";
}
return setOptions;
}
/**
* set a string holding the name of a attribute/subset evaluator
*/
public void setEvaluator(ASEvaluation evaluator) {
m_ASEvaluator = evaluator;
}
/**
* Set as string holding the name of a search class
*/
public void setSearch(ASSearch search) {
m_ASSearch = search;
}
/**
* Get the name of the attribute/subset evaluator
*
* @return the name of the attribute/subset evaluator as a string
*/
public ASEvaluation getEvaluator() {
return m_ASEvaluator;
}
/**
* Get the name of the search method
*
* @return the name of the search method as a string
*/
public ASSearch getSearch() {
return m_ASSearch;
}
/**
* Input an instance for filtering. Ordinarily the instance is processed
* and made available for output immediately. Some filters require all
* instances be read before producing output.
*
* @param instance the input instance
* @return true if the filtered instance may now be
* collected with output().
* @exception IllegalStateException if no input format has been defined.
* @exception Exception if the input instance was not of the correct format
* or if there was a problem with the filtering.
*/
public boolean input(Instance instance) throws Exception {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (m_NewBatch) {
resetQueue();
m_NewBatch = false;
}
if (isOutputFormatDefined()) {
convertInstance(instance);
return true;
}
bufferInput(instance);
return false;
}
/**
* 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.
* @exception IllegalStateException if no input structure has been defined.
* @exception Exception if there is a problem during the attribute selection.
*/
public boolean batchFinished() throws Exception {
if (getInputFormat() == null) {
throw new IllegalStateException("No input instance format defined");
}
if (!isOutputFormatDefined()) {
m_trainSelector.setEvaluator(m_ASEvaluator);
m_trainSelector.setSearch(m_ASSearch);
m_trainSelector.SelectAttributes(getInputFormat());
// System.out.println(m_trainSelector.toResultsString());
m_SelectedAttributes = m_trainSelector.selectedAttributes();
if (m_SelectedAttributes == null) {
throw new Exception("No selected attributes\n");
}
setOutputFormat();
// Convert pending input instances
for (int i = 0; i < getInputFormat().numInstances(); i++) {
convertInstance(getInputFormat().instance(i));
}
flushInput();
}
m_NewBatch = true;
return (numPendingOutput() != 0);
}
/**
* Set the output format. Takes the currently defined attribute set
* m_InputFormat and calls setOutputFormat(Instances) appropriately.
*/
protected void setOutputFormat() throws Exception {
Instances informat;
if (m_SelectedAttributes == null) {
setOutputFormat(null);
return;
}
FastVector attributes = new FastVector(m_SelectedAttributes.length);
int i;
if (m_ASEvaluator instanceof AttributeTransformer) {
informat = ((AttributeTransformer)m_ASEvaluator).transformedData();
} else {
informat = getInputFormat();
}
for (i=0;i < m_SelectedAttributes.length;i++) {
attributes.
addElement(informat.attribute(m_SelectedAttributes[i]).copy());
}
Instances outputFormat =
new Instances(getInputFormat().relationName(), attributes, 0);
if (!(m_ASEvaluator instanceof UnsupervisedSubsetEvaluator) &&
!(m_ASEvaluator instanceof UnsupervisedAttributeEvaluator)) {
outputFormat.setClassIndex(m_SelectedAttributes.length - 1);
}
setOutputFormat(outputFormat);
}
/**
* Convert a single instance over. Selected attributes only are transfered.
* The converted instance is added to the end of
* the output queue.
*
* @param instance the instance to convert
*/
protected void convertInstance(Instance instance) throws Exception {
int index = 0;
Instance newInstance;
double[] newVals = new double[getOutputFormat().numAttributes()];
if (m_ASEvaluator instanceof AttributeTransformer) {
Instance tempInstance = ((AttributeTransformer)m_ASEvaluator).
convertInstance(instance);
for (int i = 0; i < m_SelectedAttributes.length; i++) {
int current = m_SelectedAttributes[i];
newVals[i] = tempInstance.value(current);
}
} else {
for (int i = 0; i < m_SelectedAttributes.length; i++) {
int current = m_SelectedAttributes[i];
newVals[i] = instance.value(current);
}
}
if (instance instanceof SparseInstance) {
push(new SparseInstance(instance.weight(), newVals));
} else {
push(new Instance(instance.weight(), newVals));
}
}
/**
* set options to their default values
*/
protected void resetOptions() {
m_trainSelector = new weka.attributeSelection.AttributeSelection();
setEvaluator(new CfsSubsetEval());
setSearch(new BestFirst());
m_SelectedAttributes = null;
m_FilterOptions = null;
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter: use -h for help
*/
public static void main(String [] argv) {
try {
if (Utils.getFlag('b', argv)) {
Filter.batchFilterFile(new AttributeSelection(), argv);
} else {
Filter.filterFile(new AttributeSelection(), argv);
}
} catch (Exception ex) {
System.out.println(ex.getMessage());
}
}
}