/*
* 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 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/*
* AttributeSelection.java
* Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.filters.supervised.attribute;
import java.util.Enumeration;
import java.util.Vector;
import weka.attributeSelection.ASEvaluation;
import weka.attributeSelection.ASSearch;
import weka.attributeSelection.AttributeEvaluator;
import weka.attributeSelection.AttributeTransformer;
import weka.attributeSelection.BestFirst;
import weka.attributeSelection.CfsSubsetEval;
import weka.attributeSelection.Ranker;
import weka.attributeSelection.UnsupervisedAttributeEvaluator;
import weka.attributeSelection.UnsupervisedSubsetEvaluator;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.DenseInstance;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.SupervisedFilter;
/**
<!-- globalinfo-start -->
* A supervised attribute filter that can be used to select attributes. It is very flexible and allows various search and evaluation methods to be combined.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -S <"Name of search class [search options]">
* Sets search method for subset evaluators.
* eg. -S "weka.attributeSelection.BestFirst -S 8"</pre>
*
* <pre> -E <"Name of attribute/subset evaluation class [evaluator options]">
* Sets attribute/subset evaluator.
* eg. -E "weka.attributeSelection.CfsSubsetEval -L"</pre>
*
* <pre>
* Options specific to evaluator weka.attributeSelection.CfsSubsetEval:
* </pre>
*
* <pre> -M
* Treat missing values as a seperate value.</pre>
*
* <pre> -L
* Don't include locally predictive attributes.</pre>
*
* <pre>
* Options specific to search weka.attributeSelection.BestFirst:
* </pre>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.</pre>
*
* <pre> -D <0 = backward | 1 = forward | 2 = bi-directional>
* Direction of search. (default = 1).</pre>
*
* <pre> -N <num>
* Number of non-improving nodes to
* consider before terminating search.</pre>
*
* <pre> -S <num>
* Size of lookup cache for evaluated subsets.
* Expressed as a multiple of the number of
* attributes in the data set. (default = 1)</pre>
*
<!-- options-end -->
*
* @author Mark Hall (mhall@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public class AttributeSelection
extends Filter
implements SupervisedFilter, OptionHandler {
/** for serialization */
static final long serialVersionUID = -296211247688169716L;
/** 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;
/**
* Returns a string describing this filter
*
* @return a description of the filter suitable for
* displaying in the explorer/experimenter gui
*/
public String globalInfo() {
return "A supervised attribute filter that can be used to select "
+ "attributes. It is very flexible and allows various search "
+ "and evaluation methods to be combined.";
}
/**
* 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.\n"
+ "\teg. -S \"weka.attributeSelection.BestFirst -S 8\"",
"S", 1,
"-S <\"Name of search class [search options]\">"));
newVector.addElement(new Option(
"\tSets attribute/subset evaluator.\n"
+ "\teg. -E \"weka.attributeSelection.CfsSubsetEval -L\"",
"E", 1,
"-E <\"Name of attribute/subset evaluation class [evaluator options]\">"));
if ((m_ASEvaluator != null) && (m_ASEvaluator instanceof OptionHandler)) {
Enumeration enu = ((OptionHandler)m_ASEvaluator).listOptions();
newVector.addElement(new Option("", "", 0, "\nOptions specific to "
+ "evaluator " + m_ASEvaluator.getClass().getName() + ":"));
while (enu.hasMoreElements()) {
newVector.addElement((Option)enu.nextElement());
}
}
if ((m_ASSearch != null) && (m_ASSearch instanceof OptionHandler)) {
Enumeration enu = ((OptionHandler)m_ASSearch).listOptions();
newVector.addElement(new Option("", "", 0, "\nOptions specific to "
+ "search " + m_ASSearch.getClass().getName() + ":"));
while (enu.hasMoreElements()) {
newVector.addElement((Option)enu.nextElement());
}
}
return newVector.elements();
}
/**
* Parses a given list of options. <p/>
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -S <"Name of search class [search options]">
* Sets search method for subset evaluators.
* eg. -S "weka.attributeSelection.BestFirst -S 8"</pre>
*
* <pre> -E <"Name of attribute/subset evaluation class [evaluator options]">
* Sets attribute/subset evaluator.
* eg. -E "weka.attributeSelection.CfsSubsetEval -L"</pre>
*
* <pre>
* Options specific to evaluator weka.attributeSelection.CfsSubsetEval:
* </pre>
*
* <pre> -M
* Treat missing values as a seperate value.</pre>
*
* <pre> -L
* Don't include locally predictive attributes.</pre>
*
* <pre>
* Options specific to search weka.attributeSelection.BestFirst:
* </pre>
*
* <pre> -P <start set>
* Specify a starting set of attributes.
* Eg. 1,3,5-7.</pre>
*
* <pre> -D <0 = backward | 1 = forward | 2 = bi-directional>
* Direction of search. (default = 1).</pre>
*
* <pre> -N <num>
* Number of non-improving nodes to
* consider before terminating search.</pre>
*
* <pre> -S <num>
* Size of lookup cache for evaluated subsets.
* Expressed as a multiple of the number of
* attributes in the data set. (default = 1)</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 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;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String evaluatorTipText() {
return "Determines how attributes/attribute subsets are evaluated.";
}
/**
* set attribute/subset evaluator
*
* @param evaluator the evaluator to use
*/
public void setEvaluator(ASEvaluation evaluator) {
m_ASEvaluator = evaluator;
}
/**
* Returns the tip text for this property
*
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String searchTipText() {
return "Determines the search method.";
}
/**
* Set search class
*
* @param search the search class to use
*/
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;
}
/**
* Returns the Capabilities of this filter.
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result;
if (m_ASEvaluator == null) {
result = super.getCapabilities();
result.disableAll();
} else {
result = m_ASEvaluator.getCapabilities();
// class index will be set if necessary, so we always allow the dataset
// to have no class attribute set. see the following method:
// weka.attributeSelection.AttributeSelection.SelectAttributes(Instances)
result.enable(Capability.NO_CLASS);
}
result.setMinimumNumberInstances(0);
return result;
}
/**
* 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().
* @throws IllegalStateException if no input format has been defined.
* @throws 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.
* @throws IllegalStateException if no input structure has been defined.
* @throws 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.
*
* @throws Exception if something goes wrong
*/
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).transformedHeader();
} 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
* @throws Exception if something goes wrong
*/
protected void convertInstance(Instance instance) throws Exception {
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 DenseInstance(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;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method for testing this class.
*
* @param argv should contain arguments to the filter: use -h for help
*/
public static void main(String [] argv) {
runFilter(new AttributeSelection(), argv);
}
}