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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 {
private static final long serialVersionUID = 3275754421525338036L;
/**
* The template for constructing concrete instances of the clusterer to train
*/
protected weka.clusterers.Clusterer m_clustererTemplate;
/** Holds a trained/loaded clusterer */
protected weka.clusterers.Clusterer m_trainedClusterer;
/** Header used to train the clusterer */
protected Instances m_trainedClustererHeader;
/** Handles train test pair routing and synchronization for us */
protected transient PairedDataHelper m_trainTestHelper;
/**
* Optional file to load a pre-trained model to score with (batch, or to score
* and update (incremental) in the case of testSet only (batch) or instance
* (incremental) connections
*/
protected File m_loadModelFileName = new File("");
/** True if the step has just been reset */
protected boolean m_isReset;
/** Re-usable Data object for incrementalClusterer output */
protected Data m_incrementalData;
/** True if there is a single incoming "instance" connection */
protected boolean m_streaming;
/**
* Get the clusterer to train
*
* @return the clusterer to train
*/
public weka.clusterers.Clusterer getClusterer() {
return (weka.clusterers.Clusterer) getWrappedAlgorithm();
}
/**
* Set the clusterer to train
*
* @param clusterer the clusterer to train
*/
@ProgrammaticProperty
public void setClusterer(weka.clusterers.Clusterer clusterer) {
setWrappedAlgorithm(clusterer);
}
/**
* Get the name of the clusterer to load at execution time. This only applies
* in the case where the only incoming connection is a test set connection
* (batch mode) or an instance connection (incremental prediction mode).
*
* @return the name of the file to load the model from
*/
public File getLoadClustererFileName() {
return m_loadModelFileName;
}
/**
* Set the name of the clusterer to load at execution time. This only applies
* in the case where the only incoming connection is a test set connection
* (batch mode) or an instance connection (incremental prediction mode).
*
* @param filename the name of the file to load the model from
*/
@OptionMetadata(
displayName = "Clusterer model to load",
description = "Optional "
+ "path to a clusterer to load at execution time (only applies when using "
+ "testSet connections)")
@FilePropertyMetadata(fileChooserDialogType = KFGUIConsts.OPEN_DIALOG,
directoriesOnly = false)
public void setLoadClustererFileName(File filename) {
m_loadModelFileName = filename;
}
/**
* Get the class of the wrapped algorithm
*
* @return the class of the wrapped algorithm
*/
@Override
public Class getWrappedAlgorithmClass() {
return weka.clusterers.Clusterer.class;
}
/**
* Set the wrapped algorithm
*
* @param algo the algorithm to wrap
*/
@Override
public void setWrappedAlgorithm(Object algo) {
super.setWrappedAlgorithm(algo);
m_defaultIconPath = StepVisual.BASE_ICON_PATH + "DefaultClusterer.gif";
}
/**
* Initialize the step
*
* @throws WekaException if a problem occurs
*/
@Override
public void stepInit() throws WekaException {
if (!(getWrappedAlgorithm() instanceof weka.clusterers.Clusterer)) {
throw new WekaException("Incorrect type of algorithm");
}
try {
m_clustererTemplate =
weka.clusterers.AbstractClusterer
.makeCopy((weka.clusterers.Clusterer) getWrappedAlgorithm());
if (m_clustererTemplate instanceof EnvironmentHandler) {
((EnvironmentHandler) m_clustererTemplate)
.setEnvironment(getStepManager().getExecutionEnvironment()
.getEnvironmentVariables());
}
} catch (Exception ex) {
throw new WekaException(ex);
}
// create and initialize our train/test pair helper if necessary
if (getStepManager().numIncomingConnectionsOfType(
StepManager.CON_TRAININGSET) > 0) {
m_trainTestHelper =
new PairedDataHelper(
this,
this,
StepManager.CON_TRAININGSET,
getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_TESTSET) > 0 ? StepManager.CON_TESTSET
: null);
}
m_isReset = true;
m_streaming = false;
m_incrementalData = new Data(StepManager.CON_INCREMENTAL_CLUSTERER);
if (getLoadClustererFileName() != null
&& getLoadClustererFileName().toString().length() > 0
&& getStepManager().numIncomingConnectionsOfType(
StepManager.CON_TRAININGSET) == 0) {
String resolvedFileName =
getStepManager().environmentSubstitute(
getLoadClustererFileName().toString());
try {
loadModel(resolvedFileName);
} catch (Exception ex) {
throw new WekaException(ex);
}
}
}
/**
* Process an incoming data object
*
* @param data the data to process
* @throws WekaException if a problem occurs
*/
@Override
public void processIncoming(Data data) throws WekaException {
try {
if (m_isReset) {
m_isReset = false;
getStepManager().processing();
Instances incomingStructure = null;
if (data.getConnectionName().equals(StepManager.CON_INSTANCE)) {
incomingStructure =
((Instance) data.getPayloadElement(StepManager.CON_INSTANCE))
.dataset();
} else {
incomingStructure =
(Instances) data.getPayloadElement(data.getConnectionName());
}
if (data.getConnectionName().equals(StepManager.CON_INSTANCE)) {
m_streaming = true;
if (m_trainedClusterer == null) {
m_trainedClusterer =
weka.clusterers.AbstractClusterer.makeCopy(m_clustererTemplate);
if (m_trainedClusterer instanceof EnvironmentHandler) {
((EnvironmentHandler) m_trainedClusterer)
.setEnvironment(getStepManager().getExecutionEnvironment()
.getEnvironmentVariables());
}
// TODO - support incremental training at some point?
}
} else if (data.getConnectionName().equals(StepManager.CON_TRAININGSET)) {
m_trainedClustererHeader = incomingStructure;
}
if (m_trainedClustererHeader != null
&& !incomingStructure.equalHeaders(m_trainedClustererHeader)) {
throw new WekaException("Structure of incoming data does not match "
+ "that of the trained clusterer");
}
}
if (m_streaming) {
// TODO processStreaming()
} else if (m_trainTestHelper != null) {
// train test pairs
m_trainTestHelper.process(data);
} else {
processOnlyTestSet(data);
}
} catch (Exception ex) {
throw new WekaException(ex);
}
}
/**
* Process a Data object in the case where we only have an incoming testSet
* connection
*
* @param data the Data object to process
* @throws WekaException if a problem occurs
*/
protected void processOnlyTestSet(Data data) throws WekaException {
try {
weka.clusterers.Clusterer tempToTest =
weka.clusterers.AbstractClusterer.makeCopy(m_trainedClusterer);
Data batchClusterer =
new Data(StepManager.CON_BATCH_CLUSTERER, tempToTest);
batchClusterer.setPayloadElement(StepManager.CON_AUX_DATA_TESTSET,
data.getPayloadElement(StepManager.CON_AUX_DATA_TESTSET));
batchClusterer.setPayloadElement(StepManager.CON_AUX_DATA_SET_NUM,
data.getPayloadElement(StepManager.CON_AUX_DATA_SET_NUM, 1));
batchClusterer.setPayloadElement(StepManager.CON_AUX_DATA_MAX_SET_NUM,
data.getPayloadElement(StepManager.CON_AUX_DATA_MAX_SET_NUM, 1));
getStepManager().outputData(batchClusterer);
if (isStopRequested()) {
getStepManager().interrupted();
} else {
getStepManager().finished();
}
} catch (Exception ex) {
throw new WekaException(ex);
}
}
/**
* Get a list of connection types that could be made to this Step at this
* point in time
*
* @return a list of incoming connection types that could be made at this time
*/
@Override
public List getIncomingConnectionTypes() {
List result = new ArrayList();
int numTraining =
getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_TRAININGSET);
int numTesting =
getStepManager().numIncomingConnectionsOfType(StepManager.CON_TESTSET);
int numInstance =
getStepManager().numIncomingConnectionsOfType(StepManager.CON_INSTANCE);
if (numTraining == 0) {
result.add(StepManager.CON_TRAININGSET);
}
if (numTesting == 0) {
result.add(StepManager.CON_TESTSET);
}
// streaming prediction only
if (numTraining == 0 && numTesting == 0) {
result.add(StepManager.CON_INSTANCE);
}
return result;
}
/**
* Get a list of outgoing connections that could be made from this step at
* this point in time
*
* @return a list of outgoing connections that could be made at this point in
* time
*/
@Override
public List getOutgoingConnectionTypes() {
int numTraining =
getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_TRAININGSET);
int numTesting =
getStepManager().numIncomingConnectionsOfType(StepManager.CON_TESTSET);
List result = new ArrayList();
if (numTraining > 0 || numTesting > 0) {
result.add(StepManager.CON_BATCH_CLUSTERER);
}
result.add(StepManager.CON_TEXT);
if (getClusterer() instanceof Drawable && numTraining > 0) {
result.add(StepManager.CON_GRAPH);
}
// info connection - downstream steps can get our wrapped clusterer
// for information (configuration) purposes
result.add(StepManager.CON_INFO);
return result;
}
/**
* Load a pre-trained model from the supplied path
*
* @param filePath the path to load the model from
* @throws Exception if a problem occurs
*/
protected void loadModel(String filePath) throws Exception {
ObjectInputStream is = null;
try {
is =
new ObjectInputStream(new BufferedInputStream(new FileInputStream(
new File(filePath))));
m_trainedClusterer = (weka.clusterers.Clusterer) is.readObject();
// try and read the header
try {
m_trainedClustererHeader = (Instances) is.readObject();
} catch (Exception ex) {
getStepManager().logWarning(
"Model file '" + filePath
+ "' does not seem to contain an Instances header");
}
} finally {
if (is != null) {
is.close();
}
}
}
/**
* Output a Data object containing a dot graph, if the model is Drawable and
* we have downstream steps receiving graph connections.
*
* @param clusterer the clusterer to generate the graph from
* @param setNum the set number of the data used to generate the graph
* @throws WekaException if a problem occurs
*/
protected void
outputGraphData(weka.clusterers.Clusterer clusterer, int setNum)
throws WekaException {
if (clusterer instanceof Drawable) {
if (getStepManager().numOutgoingConnectionsOfType(StepManager.CON_GRAPH) == 0) {
return;
}
try {
String graphString = ((Drawable) clusterer).graph();
int graphType = ((Drawable) clusterer).graphType();
String grphTitle = clusterer.getClass().getCanonicalName();
grphTitle =
grphTitle.substring(grphTitle.lastIndexOf('.') + 1,
grphTitle.length());
grphTitle =
"Set " + setNum + " (" + m_trainedClustererHeader.relationName()
+ ") " + grphTitle;
Data graphData = new Data(StepManager.CON_GRAPH);
graphData.setPayloadElement(StepManager.CON_GRAPH, graphString);
graphData.setPayloadElement(StepManager.CON_AUX_DATA_GRAPH_TITLE,
grphTitle);
graphData.setPayloadElement(StepManager.CON_AUX_DATA_GRAPH_TYPE,
graphType);
getStepManager().outputData(graphData);
} catch (Exception ex) {
throw new WekaException(ex);
}
}
}
/**
* Output a Data object containing a textual description of a model to any
* outgoing text connections
*
* @param clusterer the clusterer to get the textual description of
* @param setNum the set number of the training data
* @throws WekaException if a problem occurs
*/
protected void
outputTextData(weka.clusterers.Clusterer clusterer, int setNum)
throws WekaException {
if (getStepManager().numOutgoingConnectionsOfType(StepManager.CON_TEXT) == 0) {
return;
}
Data textData = new Data(StepManager.CON_TEXT);
String modelString = clusterer.toString();
String titleString = clusterer.getClass().getName();
titleString =
titleString.substring(titleString.lastIndexOf('.') + 1,
titleString.length());
modelString =
"=== Clusterer model ===\n\n" + "Scheme: " + titleString + "\n"
+ "Relation: " + m_trainedClustererHeader.relationName() + "\n\n"
+ modelString;
titleString = "Model: " + titleString;
textData.setPayloadElement(StepManager.CON_TEXT, modelString);
textData
.setPayloadElement(StepManager.CON_AUX_DATA_TEXT_TITLE, titleString);
if (setNum != -1) {
textData.setPayloadElement(StepManager.CON_AUX_DATA_SET_NUM, setNum);
}
getStepManager().outputData(textData);
}
/**
* Output batch clusterer data to downstream steps
*
* @param clusterer the clusterer to outpit
* @param setNum the set number of the current dataset
* @param maxSetNum the maximum set number
* @param trainingSplit the training data
* @param testSplit the test data, or null if there is no test data
* @throws WekaException if a problem occurs
*/
protected void outputBatchClusterer(weka.clusterers.Clusterer clusterer,
int setNum, int maxSetNum, Instances trainingSplit, Instances testSplit)
throws WekaException {
Data batchClusterer = new Data(StepManager.CON_BATCH_CLUSTERER, clusterer);
batchClusterer.setPayloadElement(StepManager.CON_AUX_DATA_TRAININGSET,
trainingSplit);
if (testSplit != null) {
batchClusterer.setPayloadElement(StepManager.CON_AUX_DATA_TESTSET,
testSplit);
}
batchClusterer.setPayloadElement(StepManager.CON_AUX_DATA_SET_NUM, setNum);
batchClusterer.setPayloadElement(StepManager.CON_AUX_DATA_MAX_SET_NUM,
maxSetNum);
batchClusterer.setPayloadElement(StepManager.CON_AUX_DATA_LABEL, getName());
getStepManager().outputData(batchClusterer);
}
/**
* Process a training split (primary data handled by the PairedDataHelper)
*
* @param setNum the number of this split/fold
* @param maxSetNum the maximum number of splits/folds in the group
* @param data the actual split/fold data
* @param helper the PairedDataHelper managing the paired data
* @return a Classifier trained on this training split
* @throws WekaException if a problem occurs
*/
@Override
public weka.clusterers.Clusterer processPrimary(Integer setNum,
Integer maxSetNum, Data data,
PairedDataHelper helper) throws WekaException {
Instances trainingData = data.getPrimaryPayload();
try {
weka.clusterers.Clusterer clusterer =
weka.clusterers.AbstractClusterer.makeCopy(m_clustererTemplate);
String clustererDesc = clusterer.getClass().getCanonicalName();
clustererDesc =
clustererDesc.substring(clustererDesc.lastIndexOf('.') + 1);
if (clusterer instanceof OptionHandler) {
String optsString =
Utils.joinOptions(((OptionHandler) clusterer).getOptions());
clustererDesc += " " + optsString;
}
if (clusterer instanceof EnvironmentHandler) {
((EnvironmentHandler) clusterer).setEnvironment(getStepManager()
.getExecutionEnvironment().getEnvironmentVariables());
}
// retain the training data
helper
.addIndexedValueToNamedStore("trainingSplits", setNum, trainingData);
if (!isStopRequested()) {
getStepManager().logBasic(
"Building " + clustererDesc + " on " + trainingData.relationName()
+ " for fold/set " + setNum + " out of " + maxSetNum);
if (maxSetNum == 1) {
// single train/test split - makes sense to retain this trained
// classifier
m_trainedClusterer = clusterer;
}
clusterer.buildClusterer(trainingData);
getStepManager().logDetailed(
"Finished building " + clustererDesc + "on "
+ trainingData.relationName() + " for fold/set " + setNum
+ " out of " + maxSetNum);
outputTextData(clusterer, setNum);
outputGraphData(clusterer, setNum);
if (getStepManager().numIncomingConnectionsOfType(
StepManager.CON_TESTSET) == 0) {
// output a butch clusterer for just the trained model
outputBatchClusterer(clusterer, setNum, maxSetNum, trainingData, null);
}
}
return clusterer;
} catch (Exception ex) {
throw new WekaException(ex);
}
}
/**
* Process a test split/fold (secondary data handled by PairedDataHelper)
*
* @param setNum the set number of this split/fold
* @param maxSetNum the maximum number of splits/folds in the group
* @param data the actual split/fold data
* @param helper the PairedDataHelper managing the paried data
* @throws WekaException if a problem occurs
*/
@Override
public void processSecondary(Integer setNum, Integer maxSetNum, Data data,
PairedDataHelper helper) throws WekaException {
// trained clusterer for this set number
weka.clusterers.Clusterer clusterer =
helper.getIndexedPrimaryResult(setNum);
// test data
Instances testSplit = data.getPrimaryPayload();
// paired training data
Instances trainingSplit =
helper.getIndexedValueFromNamedStore("trainingSplits", setNum);
getStepManager().logBasic(
"Dispatching model for set " + setNum + " out of " + maxSetNum
+ " to output");
outputBatchClusterer(clusterer, setNum, maxSetNum, trainingSplit, testSplit);
}
}
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