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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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 m_waitingTestData =
new HashMap();
/** Holds selected attribute indices corresponding to training folds */
protected Map m_selectedAttsStore =
new HashMap();
/**
* Holds the calculated number of attributes to select (may depend on
* thresholds) for each training fold
*/
protected Map m_numToSelectStore =
new HashMap();
/**
* Holds the evaluator trained per fold in the case when it is a transformer
* (such as PCA)
*/
protected Map m_transformerStore =
new HashMap();
/** True if we've been reset */
protected boolean m_isReset;
/**
* True if we are processing cross-validation folds to produce a summary over
* the folds (as opposed to producing separate results per fold).
*/
protected boolean m_isDoingXVal;
/** Keeps count of the folds processed */
protected AtomicInteger m_setCount;
/**
* Whether to output separate evaluation results for each fold of a xval or
* report the cross-validation summary
*/
protected boolean m_treatXValFoldsSeparately;
/** Whether a ranking is being produced by the attribute selection */
protected boolean m_isRanking;
/**
* Eval to use when performing a cross-validation and not outputting separate
* results for each fold
*/
protected AttributeSelection m_eval;
/**
* Get the class of Weka algorithm wrapped by this wrapper
*
* @return the wrapped algorithm class
*/
@Override
public Class getWrappedAlgorithmClass() {
return weka.attributeSelection.ASEvaluation.class;
}
/**
* Set an instance of the wrapped algorithm to use
*
* @param algo the algorithm to use
*/
@Override
public void setWrappedAlgorithm(Object algo) {
super.setWrappedAlgorithm(algo);
m_defaultIconPath = StepVisual.BASE_ICON_PATH
+ "filters.supervised.attribute.AttributeSelection.gif";
}
/**
* Get the evaluator wrapped by this step
*
* @return the attribute or subset evaluator wrapped by this step
*/
public ASEvaluation getEvaluator() {
return (weka.attributeSelection.ASEvaluation) getWrappedAlgorithm();
}
/**
* Set the evaluator to wrap (just calls setWrappedAlgorithm)
*
* @param eval the evaluator to use
*/
@ProgrammaticProperty
public void setEvaluator(ASEvaluation eval) {
setWrappedAlgorithm(eval);
}
/**
* Set whether to output separate results for each fold of a cross-validation,
* rather than averaging over folds.
*
* @param treatSeparately true if each fold will have results output
*/
@OptionMetadata(displayName = "Treat x-val folds separately",
description = "Output separate attribute selection results for each fold "
+ "of a cross-validation (rather than averaging across folds)")
public void setTreatXValFoldsSeparately(boolean treatSeparately) {
m_treatXValFoldsSeparately = treatSeparately;
}
/**
* Get whether to output separate results for each fold of a cross-validation,
* rather than averaging over folds.
*
* @return true if each fold will have results output
*/
public boolean getTreatXValFoldsSeparately() {
return m_treatXValFoldsSeparately;
}
/**
* Initialize at the start of a run
*
* @throws WekaException if there is an illegal configuration (i.e. Ranker
* search with subset evaluator or regular search with attribute
* evaluator
*/
@Override
public void stepInit() throws WekaException {
if (!(getWrappedAlgorithm() instanceof ASEvaluation)) {
throw new WekaException("Incorrect type of algorithm");
}
try {
m_evaluatorTemplate =
ASEvaluation.makeCopies((ASEvaluation) getWrappedAlgorithm(), 1)[0];
} catch (Exception ex) {
throw new WekaException(ex);
}
List infos = getStepManager()
.getIncomingConnectedStepsOfConnectionType(StepManager.CON_INFO);
if (infos.size() == 0) {
throw new WekaException(
"A search strategy needs to be supplied via an 'info' "
+ "connection type");
}
ASSearchStrategy searchStrategy =
(ASSearchStrategy) infos.get(0).getInfoStep(ASSearchStrategy.class);
m_searchTemplate = searchStrategy.getSearchStrategy();
if (m_searchTemplate instanceof RankedOutputSearch) {
m_isRanking =
((RankedOutputSearch) m_searchTemplate).getGenerateRanking();
}
if (m_evaluatorTemplate instanceof SubsetEvaluator
&& m_searchTemplate instanceof Ranker) {
throw new WekaException(
"The Ranker search strategy cannot be used with a "
+ "subset evaluator");
}
if (m_evaluatorTemplate instanceof AttributeEvaluator
&& !(m_searchTemplate instanceof Ranker)) {
throw new WekaException("The Ranker search strategy must be used in "
+ "conjunction with an attribute evaluator");
}
m_isReset = true;
m_waitingTestData.clear();
m_selectedAttsStore.clear();
m_numToSelectStore.clear();
m_transformerStore.clear();
m_eval = new AttributeSelection();
}
/**
* Process an incoming Data object
*
* @param data the data object to process
* @throws WekaException if a problem occurs
*/
@Override
public void processIncoming(Data data) throws WekaException {
Instances train = data.getPayloadElement(StepManager.CON_TRAININGSET);
Instances test = data.getPayloadElement(StepManager.CON_TESTSET);
Integer setNum = data.getPayloadElement(StepManager.CON_AUX_DATA_SET_NUM);
Integer maxSetNum =
data.getPayloadElement(StepManager.CON_AUX_DATA_MAX_SET_NUM);
if (m_isReset) {
m_isReset = false;
getStepManager().processing();
m_setCount = new AtomicInteger(maxSetNum != null ? maxSetNum : 1);
if (setNum != null && maxSetNum != null) {
m_isDoingXVal = maxSetNum > 1 && !m_treatXValFoldsSeparately;
if (m_evaluatorTemplate instanceof AttributeTransformer && m_isDoingXVal
&& !m_treatXValFoldsSeparately) {
throw new WekaException(
"Can't cross-validate an attribute transformer");
}
if (m_isDoingXVal) {
m_eval.setFolds(maxSetNum);
}
if (m_isRanking) {
m_eval.setRanking(m_isRanking);
}
}
}
if (m_isDoingXVal) {
processXVal(train, test, setNum, maxSetNum);
} else {
processNonXVal(train, test, setNum, maxSetNum);
}
if (isStopRequested()) {
getStepManager().interrupted();
} else if (m_setCount.get() == 0) {
if (m_isDoingXVal) {
// output xval summary
try {
StringBuilder builder = new StringBuilder();
builder.append("Search method: ");
String evalS = m_evaluatorTemplate.getClass().getCanonicalName();
evalS = evalS.substring(evalS.lastIndexOf('.') + 1, evalS.length());
builder.append(evalS).append(" ")
.append(m_evaluatorTemplate instanceof OptionHandler ? Utils
.joinOptions(((OptionHandler) m_evaluatorTemplate).getOptions())
: "")
.append("\nEvaluator: ");
String searchS = m_searchTemplate.getClass().getCanonicalName();
searchS =
searchS.substring(searchS.lastIndexOf('.') + 1, searchS.length());
builder.append(searchS).append(" ")
.append(m_searchTemplate instanceof OptionHandler ? Utils
.joinOptions(((OptionHandler) m_searchTemplate).getOptions())
: "")
.append("\n");
builder.append(m_eval.CVResultsString());
outputTextData(builder.toString(), null);
} catch (Exception ex) {
throw new WekaException(ex);
}
}
getStepManager().finished();
// save memory
m_waitingTestData.clear();
m_selectedAttsStore.clear();
m_numToSelectStore.clear();
}
}
/**
* Output Data to outgoing text connections
*
* @param text the text to output
* @param setNum the fold/set number that this text is associated with
* @throws WekaException if a problem occurs
*/
protected void outputTextData(String text, Integer setNum)
throws WekaException {
if (isStopRequested()) {
return;
}
if (getStepManager()
.numOutgoingConnectionsOfType(StepManager.CON_TEXT) == 0) {
return;
}
Data textData = new Data(StepManager.CON_TEXT, text);
String titleString = m_evaluatorTemplate.getClass().getCanonicalName();
titleString = titleString.substring(titleString.lastIndexOf('.') + 1,
titleString.length());
String searchString = m_searchTemplate.getClass().getCanonicalName();
searchString = searchString.substring(searchString.lastIndexOf('.') + 1,
searchString.length());
titleString += " (" + searchString + ")";
textData.setPayloadElement(StepManager.CON_AUX_DATA_TEXT_TITLE,
titleString);
if (setNum != null) {
textData.setPayloadElement(StepManager.CON_AUX_DATA_SET_NUM, setNum);
}
getStepManager().outputData(textData);
}
/**
* Handles processing for single train sets, single train/test pairs or
* cross-validation folds when results are output for each separately
*
* @param train the training data
* @param test the test data
* @param setNum the set number for the training/test data
* @param maxSetNum the maximum set number
* @throws WekaException if a problem occurs
*/
protected void processNonXVal(Instances train, Instances test, Integer setNum,
Integer maxSetNum) throws WekaException {
if (train != null) {
try {
AttributeSelection eval = new AttributeSelection();
ASEvaluation evalCopy =
ASEvaluation.makeCopies(m_evaluatorTemplate, 1)[0];
ASSearch searchCopy = ASSearch.makeCopies(m_searchTemplate, 1)[0];
eval.setEvaluator(evalCopy);
eval.setSearch(searchCopy);
eval.setRanking(m_isRanking);
if (!isStopRequested()) {
String message = "Selecting attributes (" + train.relationName();
if (setNum != null && maxSetNum != null) {
message += ", set " + setNum + " of " + maxSetNum;
}
message += ")";
getStepManager().statusMessage(message);
getStepManager().logBasic(message);
eval.SelectAttributes(train);
if (evalCopy instanceof AttributeTransformer) {
m_transformerStore.put(setNum != null ? setNum : -1,
((AttributeTransformer) evalCopy));
}
// this will be the final set of selected (and potentially ranked)
// attributes including class attribute (if appropriate)
int[] selectedAtts = eval.selectedAttributes();
if (m_isRanking) {
m_numToSelectStore.put(setNum != null ? setNum : -1,
((RankedOutputSearch) searchCopy).getCalculatedNumToSelect());
}
// > 2 here as the info connection counts as 1
if (getStepManager().numIncomingConnections() > 2) {
m_selectedAttsStore.put(setNum != null ? setNum : -1, selectedAtts);
}
String results = eval.toResultsString();
outputTextData(results, setNum);
applyFiltering(StepManager.CON_TRAININGSET, selectedAtts, train,
setNum, maxSetNum);
// > 2 here because the info connection counts as 1
if (getStepManager().numIncomingConnections() > 2) {
Instances waitingTest =
m_waitingTestData.get(setNum != null ? setNum : -1);
if (waitingTest != null) {
checkTestFiltering(waitingTest, setNum != null ? setNum : -1,
maxSetNum);
}
} else {
m_setCount.decrementAndGet();
}
evalCopy.clean();
}
} catch (Exception ex) {
throw new WekaException(ex);
}
} else {
checkTestFiltering(test, setNum != null ? setNum : -1, maxSetNum);
}
}
/**
* Handles processing in the cross-validation case when results are to be
* accumulated/averaged over the folds
*
* @param train the training data
* @param test the test data
* @param setNum the set number of this train/test pair
* @param maxSetNum the maximum set number
* @throws WekaException if a problem occurs
*/
protected void processXVal(Instances train, Instances test, Integer setNum,
Integer maxSetNum) throws WekaException {
if (train != null) {
try {
ASEvaluation evalCopy =
ASEvaluation.makeCopies(m_evaluatorTemplate, 1)[0];
ASSearch searchCopy = ASSearch.makeCopies(m_searchTemplate, 1)[0];
if (!isStopRequested()) {
String message =
"Selecting attributes x-val mode (" + train.relationName();
if (setNum != null && maxSetNum != null) {
message += ", set " + setNum + " of " + maxSetNum;
}
message += ")";
getStepManager().statusMessage(message);
getStepManager().logBasic(message);
evalCopy.buildEvaluator(train);
if (evalCopy instanceof AttributeTransformer) {
m_transformerStore.put(setNum != null ? setNum : -1,
((AttributeTransformer) evalCopy));
}
int[] selectedAtts = searchCopy.search(evalCopy, train);
selectedAtts = evalCopy.postProcess(selectedAtts);
if (m_isRanking) {
double[][] ranked =
((RankedOutputSearch) searchCopy).rankedAttributes();
selectedAtts = new int[ranked.length];
for (int i = 0; i < ranked.length; i++) {
selectedAtts[i] = (int) ranked[i][0];
}
}
updateXValStats(train, evalCopy, searchCopy, selectedAtts);
// > 2 here because the info connection counts as 1
if (getStepManager().numIncomingConnections() > 2) {
m_selectedAttsStore.put(setNum, selectedAtts);
}
if (m_isRanking) {
m_numToSelectStore.put(setNum,
((RankedOutputSearch) searchCopy).getCalculatedNumToSelect());
}
applyFiltering(StepManager.CON_TRAININGSET, selectedAtts, train,
setNum, maxSetNum);
// > 2 here because the info connection counts as 1
if (getStepManager().numIncomingConnections() > 2) {
Instances waitingTest = m_waitingTestData.get(setNum);
if (waitingTest != null) {
checkTestFiltering(waitingTest, setNum, maxSetNum);
}
} else {
m_setCount.decrementAndGet();
}
evalCopy.clean();
}
} catch (Exception ex) {
throw new WekaException(ex);
}
} else {
checkTestFiltering(test, setNum, maxSetNum);
}
}
/**
* Check to see if there is a waiting set of selected attributes that can be
* used to reduce the dimensionality of the supplied test set
*
* @param test the test set to potentially filter
* @param setNum the set number of the test set
* @param maxSetNum the maximum set number
* @throws WekaException if a problem occurs
*/
protected synchronized void checkTestFiltering(Instances test, Integer setNum,
Integer maxSetNum) throws WekaException {
if (isStopRequested()) {
return;
}
int[] selectedForSet = m_selectedAttsStore.get(setNum);
if (selectedForSet == null) {
m_waitingTestData.put(setNum, test);
} else {
applyFiltering(StepManager.CON_TESTSET, selectedForSet, test, setNum,
maxSetNum);
m_setCount.decrementAndGet();
}
}
/**
* Apply a filter to reduce the dimensionality of the supplied data. Outputs
* the reduced data to downstream steps on the given connection type
*
* @param connType the connection type to output on
* @param selectedAtts selected attribute indices to use when filtering
* @param data the instances to filter
* @param setNum the set number of the instances
* @param maxSetNum the maximum set number
* @throws WekaException if a problem occurs
*/
protected void applyFiltering(String connType, int[] selectedAtts,
Instances data, Integer setNum, Integer maxSetNum) throws WekaException {
if (isStopRequested()) {
return;
}
if (getStepManager().numOutgoingConnectionsOfType(connType) == 0) {
return;
}
int[] finalSet = new int[selectedAtts.length];
boolean adjust = (m_isDoingXVal || m_isRanking)
&& ((!(m_evaluatorTemplate instanceof UnsupervisedSubsetEvaluator)
&& !(m_evaluatorTemplate instanceof UnsupervisedAttributeEvaluator))
|| m_evaluatorTemplate instanceof AttributeTransformer);
if (m_isRanking) {
int numToSelect = m_numToSelectStore.get(setNum != null ? setNum : -1);
finalSet = new int[numToSelect];
if (data.classIndex() >= 0) {
if (adjust) {
// one more for the class
finalSet = new int[numToSelect + 1];
finalSet[numToSelect] = data.classIndex();
} else {
finalSet = new int[numToSelect];
}
}
for (int i = 0; i < numToSelect; i++) {
finalSet[i] = selectedAtts[i];
}
} else {
if (adjust) {
// one more for the class
finalSet = new int[selectedAtts.length + 1];
finalSet[selectedAtts.length] = data.classIndex();
}
for (int i = 0; i < selectedAtts.length; i++) {
finalSet[i] = selectedAtts[i];
}
}
try {
Instances reduced = null;
AttributeTransformer transformer =
m_transformerStore.get(setNum != null ? setNum : -1);
if (transformer != null) {
reduced =
new Instances(transformer.transformedHeader(), data.numInstances());
for (int i = 0; i < data.numInstances(); i++) {
reduced.add(transformer.convertInstance(data.instance(i)));
}
} else {
Remove r = new Remove();
r.setAttributeIndicesArray(finalSet);
r.setInvertSelection(true);
r.setInputFormat(data);
reduced = weka.filters.Filter.useFilter(data, r);
}
if (!isStopRequested()) {
String message = "Filtering " + connType + " (" + data.relationName();
if (setNum != null && maxSetNum != null) {
message += ", set " + setNum + " of " + maxSetNum;
}
message += ")";
getStepManager().statusMessage(message);
getStepManager().logBasic(message);
Data output = new Data(connType, reduced);
output.setPayloadElement(StepManager.CON_AUX_DATA_SET_NUM, setNum);
output.setPayloadElement(StepManager.CON_AUX_DATA_MAX_SET_NUM,
maxSetNum);
getStepManager().outputData(output);
}
} catch (Exception ex) {
throw new WekaException(ex);
}
}
/**
* Updates stats in the cross-validation case
*
* @param train the training data processed
* @param evaluator the evaluator used
* @param search the search strategy
* @param selectedAtts the attributes selected on this training data
* @throws Exception if a problem occurs
*/
protected synchronized void updateXValStats(Instances train,
ASEvaluation evaluator, ASSearch search, int[] selectedAtts)
throws Exception {
m_eval.updateStatsForModelCVSplit(train, evaluator, search, selectedAtts,
m_isRanking);
}
/**
* Get incoming connections accepted given the current state of the step
*
* @return a list of acceptable incoming connections
*/
@Override
public List getIncomingConnectionTypes() {
List result = new ArrayList();
if (getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_TRAININGSET) == 0) {
result.add(StepManager.CON_TRAININGSET);
}
if (getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_TESTSET) == 0
&& getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_TRAININGSET) == 1) {
result.add(StepManager.CON_TESTSET);
}
if (getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_INFO) == 0) {
result.add(StepManager.CON_INFO);
}
return result;
}
/**
* Get a list of output connections that can be produced given the current
* state of the step
*
* @return a list of output connections
*/
@Override
public List getOutgoingConnectionTypes() {
List result = new ArrayList();
if (getStepManager().numIncomingConnections() > 1 && getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_INFO) == 1) {
result.add(StepManager.CON_TEXT);
}
if (getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_TRAININGSET) == 1
&& getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_INFO) == 1) {
result.add(StepManager.CON_TRAININGSET);
}
if (getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_TESTSET) == 1
&& getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_INFO) == 1) {
result.add(StepManager.CON_TESTSET);
}
return result;
}
/**
* Get the class name of the custom editor for this step
*
* @return the class name of the custom editor for this step
*/
@Override
public String getCustomEditorForStep() {
return "weka.gui.knowledgeflow.steps.ASEvaluatorStepEditorDialog";
}
}
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