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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 {
/** For serialization */
private static final long serialVersionUID = 8326706942962123155L;
/**
* The template for constructing concrete instances of the classifier to train
*/
protected weka.classifiers.Classifier m_classifierTemplate;
/**
* Holds the trained classifier in the case of single train/test pairs or
* instance stream connections
*/
protected weka.classifiers.Classifier m_trainedClassifier;
protected Instances m_trainedClassifierHeader;
/**
* 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 we should resent an Updateable classifier at the start of
* processing for an incoming "instance" stream
*/
protected boolean m_resetIncrementalClassifier;
/**
* True if we should update an incremental classifier when there is a incoming
* "instance" stream
*/
protected boolean m_updateIncrementalClassifier = true;
/** True if we are processing streaming data */
protected boolean m_streaming;
/** True if the classifier in use is Updateable */
protected boolean m_classifierIsIncremental;
/** Handles train test pair routing and synchronization for us */
protected transient PairedDataHelper m_trainTestHelper;
/** Reusable data for incremental streaming classifiers */
protected Data m_incrementalData = new Data(
StepManager.CON_INCREMENTAL_CLASSIFIER);
/** True if we've been reset */
protected boolean m_isReset;
@Override
public Class getWrappedAlgorithmClass() {
return weka.classifiers.Classifier.class;
}
@Override
public void setWrappedAlgorithm(Object algo) {
super.setWrappedAlgorithm(algo);
m_defaultIconPath = StepVisual.BASE_ICON_PATH + "DefaultClassifier.gif";
}
/**
* Get the classifier to train
*
* @return the classifier to train
*/
public weka.classifiers.Classifier getClassifier() {
return (weka.classifiers.Classifier) getWrappedAlgorithm();
}
/**
* Set the classifier to train
*
* @param classifier the classifier to train
*/
@ProgrammaticProperty
public void setClassifier(weka.classifiers.Classifier classifier) {
setWrappedAlgorithm(classifier);
}
@Override
public void stepInit() throws WekaException {
try {
m_trainedClassifier = null;
m_trainedClassifierHeader = null;
m_trainTestHelper = null;
m_incrementalData = new Data(StepManager.CON_INCREMENTAL_CLASSIFIER);
m_classifierTemplate =
AbstractClassifier
.makeCopy((weka.classifiers.Classifier) getWrappedAlgorithm());
if (m_classifierTemplate instanceof EnvironmentHandler) {
((EnvironmentHandler) m_classifierTemplate)
.setEnvironment(getStepManager().getExecutionEnvironment()
.getEnvironmentVariables());
}
// Check to see if the classifier is one that must run single-threaded
Annotation a =
m_classifierTemplate.getClass().getAnnotation(
SingleThreadedExecution.class);
if (a != null) {
getStepManager().logBasic(
getClassifier().getClass().getCanonicalName() + " "
+ "will be executed in the single threaded executor");
getStepManager().setStepMustRunSingleThreaded(true);
}
} 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_classifierIsIncremental =
m_classifierTemplate instanceof UpdateableClassifier;
if (getLoadClassifierFileName() != null
&& getLoadClassifierFileName().toString().length() > 0
&& getStepManager().numIncomingConnectionsOfType(
StepManager.CON_TRAININGSET) == 0) {
String resolvedFileName =
getStepManager().environmentSubstitute(
getLoadClassifierFileName().toString());
try {
getStepManager().logBasic("Loading classifier: " + resolvedFileName);
loadModel(resolvedFileName);
} catch (Exception ex) {
throw new WekaException(ex);
}
}
if (m_trainedClassifier != null
&& getStepManager()
.numIncomingConnectionsOfType(StepManager.CON_INSTANCE) > 0
&& !m_classifierIsIncremental) {
getStepManager().logWarning(
"Loaded classifier is not an incremental one "
+ "- will only be able to evaluate, and not update, on the incoming "
+ "instance stream.");
}
}
/**
* Get the name of the classifier 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 mode).
*
* @return the name of the file to load the model from
*/
public File getLoadClassifierFileName() {
return m_loadModelFileName;
}
/**
* Set the name of the classifier 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 mode).
*
* @param filename the name of the file to load the model from
*/
@OptionMetadata(
displayName = "Classifier model to load",
description = "Optional "
+ "Path to a classifier to load at execution time (only applies when using "
+ "testSet or instance connections)")
@FilePropertyMetadata(fileChooserDialogType = KFGUIConsts.OPEN_DIALOG,
directoriesOnly = false)
public void setLoadClassifierFileName(File filename) {
m_loadModelFileName = filename;
}
/**
* Get whether to reset an incremental classifier at the start of an incoming
* instance stream
*
* @return true if the classifier should be reset
*/
public boolean getResetIncrementalClassifier() {
return m_resetIncrementalClassifier;
}
/**
* Set whether to reset an incremental classifier at the start of an incoming
* instance stream
*
* @param reset true if the classifier should be reset
*/
@OptionMetadata(
displayName = "Reset incremental classifier",
description = "Reset classifier (if it is incremental) at the start of the incoming "
+ "instance stream")
public
void setResetIncrementalClassifier(boolean reset) {
m_resetIncrementalClassifier = reset;
}
/**
* Get whether to update an incremental classifier on an incoming instance
* stream
*
* @return true if an incremental classifier should be updated
*/
public boolean getUpdateIncrementalClassifier() {
return m_updateIncrementalClassifier;
}
/**
* Set whether to update an incremental classifier on an incoming instance
* stream
*
* @param update true if an incremental classifier should be updated
*/
@OptionMetadata(
displayName = "Update incremental classifier",
description = " Update an incremental classifier on incoming instance stream")
public
void setUpdateIncrementalClassifier(boolean update) {
m_updateIncrementalClassifier = update;
}
@Override
public void processIncoming(Data data) throws WekaException {
try {
getStepManager().processing();
if (m_isReset) {
m_isReset = false;
Instances incomingStructure = null;
if (data.getConnectionName().equals(StepManager.CON_INSTANCE)) {
incomingStructure =
new Instances(
((Instance) data.getPayloadElement(StepManager.CON_INSTANCE))
.dataset(),
0);
} else {
incomingStructure =
(Instances) data.getPayloadElement(data.getConnectionName());
}
if (incomingStructure.classAttribute() == null) {
getStepManager()
.logWarning(
"No class index is set in the data - using last attribute as class");
incomingStructure
.setClassIndex(incomingStructure.numAttributes() - 1);
}
if (data.getConnectionName().equals(StepManager.CON_INSTANCE)) {
m_streaming = true;
if (m_trainedClassifier == null) {
m_trainedClassifier =
weka.classifiers.AbstractClassifier
.makeCopy(m_classifierTemplate);
getStepManager().logBasic("Initialising incremental classifier");
m_trainedClassifier.buildClassifier(incomingStructure);
m_trainedClassifierHeader = incomingStructure;
} else if (m_resetIncrementalClassifier && m_classifierIsIncremental) {
// make a copy here, just in case buildClassifier() implementations
// do not re-initialize the classifier correctly
m_trainedClassifier =
weka.classifiers.AbstractClassifier
.makeCopy(m_classifierTemplate);
m_trainedClassifierHeader = incomingStructure;
getStepManager().logBasic("Resetting incremental classifier");
m_trainedClassifier.buildClassifier(m_trainedClassifierHeader);
}
if (m_trainedClassifier instanceof LogHandler) {
((LogHandler) m_trainedClassifier).setLog(getStepManager().getLog());
}
getStepManager()
.logBasic(
m_updateIncrementalClassifier && m_classifierIsIncremental ? "Training incrementally"
: "Predicting incrementally");
} else if (data.getConnectionName().equals(StepManager.CON_TRAININGSET)) {
m_trainedClassifierHeader = incomingStructure;
} else if (data.getConnectionName().equals(StepManager.CON_TESTSET)
&& getStepManager().numIncomingConnectionsOfType(
StepManager.CON_TRAININGSET) == 0
&& m_classifierTemplate instanceof InputMappedClassifier) {
m_trainedClassifier =
weka.classifiers.AbstractClassifier.makeCopy(m_classifierTemplate);
// force the InputMappedClassifier to load a model (if one has been
// configured)
((InputMappedClassifier) m_trainedClassifier).getModelHeader(null);
}
if (m_trainedClassifierHeader != null
&& !incomingStructure.equalHeaders(m_trainedClassifierHeader)) {
if (!(m_trainedClassifier instanceof InputMappedClassifier)) {
throw new WekaException(
"Structure of incoming data does not match "
+ "that of the trained classifier");
}
}
}
if (m_streaming) {
processStreaming(data);
} else if (m_trainTestHelper != null) {
// train test pairs
m_trainTestHelper.process(data);
} else {
// test only
processOnlyTestSet(data);
}
} catch (Exception ex) {
throw new WekaException(ex);
}
}
/**
* 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.classifiers.Classifier processPrimary(Integer setNum,
Integer maxSetNum, Data data,
PairedDataHelper helper) throws WekaException {
Instances trainingData = data.getPrimaryPayload();
if (m_trainedClassifierHeader == null) {
m_trainedClassifierHeader = new Instances(trainingData, 0);
}
try {
weka.classifiers.Classifier classifier =
AbstractClassifier.makeCopy(m_classifierTemplate);
String classifierDesc = classifier.getClass().getCanonicalName();
classifierDesc =
classifierDesc.substring(classifierDesc.lastIndexOf(".") + 1);
if (classifier instanceof OptionHandler) {
String optsString =
Utils.joinOptions(((OptionHandler) classifier).getOptions());
classifierDesc += " " + optsString;
}
if (classifier instanceof EnvironmentHandler) {
((EnvironmentHandler) classifier).setEnvironment(getStepManager()
.getExecutionEnvironment().getEnvironmentVariables());
}
if (classifier instanceof LogHandler) {
((LogHandler) classifier).setLog(getStepManager().getLog());
}
// retain the training data
helper
.addIndexedValueToNamedStore("trainingSplits", setNum, trainingData);
if (!isStopRequested()) {
getStepManager().logBasic(
"Building " + classifierDesc + " on " + trainingData.relationName()
+ " for fold/set " + setNum + " out of " + maxSetNum);
if (getStepManager().getLoggingLevel().ordinal() > LoggingLevel.LOW
.ordinal()) {
getStepManager().statusMessage(
"Building " + classifierDesc + " on fold/set " + setNum);
}
if (maxSetNum == 1) {
// single train/test split - makes sense to retain this trained
// classifier
m_trainedClassifier = classifier;
}
classifier.buildClassifier((Instances) trainingData);
getStepManager().logDetailed(
"Finished building " + classifierDesc + "on "
+ trainingData.relationName() + " for fold/set " + setNum
+ " out of " + maxSetNum);
outputTextData(classifier, setNum);
outputGraphData(classifier, setNum);
if (getStepManager().numIncomingConnectionsOfType(
StepManager.CON_TESTSET) == 0) {
// output a batch classifier for just the trained model
Data batchClassifier =
new Data(StepManager.CON_BATCH_CLASSIFIER, classifier);
batchClassifier.setPayloadElement(
StepManager.CON_AUX_DATA_TRAININGSET, trainingData);
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_SET_NUM,
setNum);
batchClassifier.setPayloadElement(
StepManager.CON_AUX_DATA_MAX_SET_NUM, maxSetNum);
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_LABEL,
getName());
batchClassifier.setPayloadElement(
StepManager.CON_AUX_DATA_PRIMARY_PAYLOAD_NOT_THREAD_SAFE, true);
getStepManager().outputData(batchClassifier);
}
}
return classifier;
} 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 classifier for this set number
weka.classifiers.Classifier classifier =
helper.getIndexedPrimaryResult(setNum);
// test data
Instances testSplit = data.getPrimaryPayload();
if (m_trainedClassifierHeader != null
&& !testSplit.equalHeaders(m_trainedClassifierHeader)) {
if (!(m_trainedClassifier instanceof InputMappedClassifier)) {
throw new WekaException("Structure of incoming data does not match "
+ "that of the trained classifier");
}
}
// paired training data
Instances trainingSplit =
helper.getIndexedValueFromNamedStore("trainingSplits", setNum);
getStepManager().logBasic(
"Dispatching model for set " + setNum + " out of " + maxSetNum
+ " to output");
Data batchClassifier =
new Data(StepManager.CON_BATCH_CLASSIFIER, classifier);
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_TRAININGSET,
trainingSplit);
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_TESTSET,
testSplit);
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_SET_NUM, setNum);
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_MAX_SET_NUM,
maxSetNum);
batchClassifier
.setPayloadElement(StepManager.CON_AUX_DATA_LABEL, getName());
batchClassifier.setPayloadElement(
StepManager.CON_AUX_DATA_PRIMARY_PAYLOAD_NOT_THREAD_SAFE, true);
getStepManager().outputData(batchClassifier);
}
/**
* 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 {
// avoid any potential thread safety issues...
try {
weka.classifiers.Classifier tempToTest =
weka.classifiers.AbstractClassifier.makeCopy(m_trainedClassifier);
Data batchClassifier = new Data(StepManager.CON_BATCH_CLASSIFIER);
batchClassifier.setPayloadElement(StepManager.CON_BATCH_CLASSIFIER,
tempToTest);
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_TESTSET,
data.getPayloadElement(StepManager.CON_TESTSET));
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_SET_NUM,
data.getPayloadElement(StepManager.CON_AUX_DATA_SET_NUM, 1));
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_MAX_SET_NUM,
data.getPayloadElement(StepManager.CON_AUX_DATA_MAX_SET_NUM, 1));
batchClassifier.setPayloadElement(StepManager.CON_AUX_DATA_LABEL,
getName());
// make sure there are no concurrency issues if we are connected to
// multiple downstream steps
batchClassifier.setPayloadElement(
StepManager.CON_AUX_DATA_PRIMARY_PAYLOAD_NOT_THREAD_SAFE, true);
getStepManager().logBasic("Dispatching classifier");
getStepManager().outputData(batchClassifier);
if (isStopRequested()) {
getStepManager().interrupted();
} else {
getStepManager().finished();
}
} catch (Exception ex) {
throw new WekaException(ex);
}
}
/**
* Process a Data object in the case of an incoming instance (streaming)
* connection
*
* @param data the Data object to process
* @throws WekaException if a problem occurs
*/
protected void processStreaming(Data data) throws WekaException {
if (isStopRequested()) {
return;
}
Instance inst = (Instance) data.getPayloadElement(StepManager.CON_INSTANCE);
if (getStepManager().isStreamFinished(data)) {
// finished
if (m_trainedClassifier instanceof UpdateableBatchProcessor) {
try {
((UpdateableBatchProcessor) m_trainedClassifier).batchFinished();
} catch (Exception ex) {
throw new WekaException(ex);
}
}
// notify any downstream steps consuming incremental classifier
// data that the stream has finished
m_incrementalData.setPayloadElement(
StepManager.CON_INCREMENTAL_CLASSIFIER, m_trainedClassifier);
m_incrementalData.setPayloadElement(
StepManager.CON_AUX_DATA_TEST_INSTANCE, null);
// getStepManager().outputData(m_incrementalData);
outputTextData(m_trainedClassifier, -1);
outputGraphData(m_trainedClassifier, 0);
if (!isStopRequested()) {
getStepManager().throughputFinished(m_incrementalData);
}
return;
}
// test on the instance
m_incrementalData.setPayloadElement(StepManager.CON_AUX_DATA_TEST_INSTANCE,
inst);
m_incrementalData.setPayloadElement(StepManager.CON_INCREMENTAL_CLASSIFIER,
m_trainedClassifier);
getStepManager().outputData(m_incrementalData.getConnectionName(),
m_incrementalData);
// train on the instance?
getStepManager().throughputUpdateStart();
if (m_classifierIsIncremental && m_updateIncrementalClassifier) {
if (!inst.classIsMissing()) {
try {
((UpdateableClassifier) m_trainedClassifier).updateClassifier(inst);
} catch (Exception ex) {
throw new WekaException(ex);
}
}
}
getStepManager().throughputUpdateEnd();
}
/**
* Output a Data object containing a textual description of a model to any
* outgoing text connections
*
* @param classifier the classifier 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.classifiers.Classifier classifier,
int setNum) throws WekaException {
if (getStepManager().numOutgoingConnectionsOfType(StepManager.CON_TEXT) == 0) {
return;
}
Data textData = new Data(StepManager.CON_TEXT);
String modelString = classifier.toString();
String titleString = classifier.getClass().getName();
titleString =
titleString.substring(titleString.lastIndexOf('.') + 1,
titleString.length());
modelString =
"=== Classifier model ===\n\n" + "Scheme: " + titleString + "\n"
+ "Relation: " + m_trainedClassifierHeader.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 a Data object containing a dot graph, if the model is Drawable and
* we have downstream steps receiving graph connections.
*
* @param classifier the classifier 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.classifiers.Classifier classifier,
int setNum) throws WekaException {
if (classifier instanceof Drawable) {
if (getStepManager().numOutgoingConnectionsOfType(StepManager.CON_GRAPH) == 0) {
return;
}
try {
String graphString = ((Drawable) classifier).graph();
int graphType = ((Drawable) classifier).graphType();
String grphTitle = classifier.getClass().getCanonicalName();
grphTitle =
grphTitle.substring(grphTitle.lastIndexOf('.') + 1,
grphTitle.length());
grphTitle =
"Set " + setNum + " (" + m_trainedClassifierHeader.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);
}
}
}
@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 && numTesting == 0) {
result.add(StepManager.CON_INSTANCE);
}
if (numInstance == 0 && numTraining == 0) {
result.add(StepManager.CON_TRAININGSET);
}
if (numInstance == 0 && numTesting == 0) {
result.add(StepManager.CON_TESTSET);
}
if (getStepManager().numIncomingConnectionsOfType(StepManager.CON_INFO) == 0) {
result.add(StepManager.CON_INFO);
}
return result;
}
@Override
public List getOutgoingConnectionTypes() {
List result = new ArrayList();
if (getStepManager().numIncomingConnections() > 0) {
int numTraining =
getStepManager().numIncomingConnectionsOfType(
StepManager.CON_TRAININGSET);
int numTesting =
getStepManager().numIncomingConnectionsOfType(StepManager.CON_TESTSET);
int numInstance =
getStepManager().numIncomingConnectionsOfType(StepManager.CON_INSTANCE);
if (numInstance > 0) {
result.add(StepManager.CON_INCREMENTAL_CLASSIFIER);
} else if (numTraining > 0 || numTesting > 0) {
result.add(StepManager.CON_BATCH_CLASSIFIER);
}
result.add(StepManager.CON_TEXT);
if (getClassifier() instanceof Drawable && numTraining > 0) {
result.add(StepManager.CON_GRAPH);
}
}
// info connection - downstream steps can get our wrapped classifier
// 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 =
SerializationHelper.getObjectInputStream(new FileInputStream(new File(
filePath)));
m_trainedClassifier = (weka.classifiers.Classifier) is.readObject();
if (!(m_trainedClassifier.getClass().getCanonicalName()
.equals(getClassifier().getClass().getCanonicalName()))) {
throw new Exception("The loaded model '"
+ m_trainedClassifier.getClass().getCanonicalName() + "' is not a '"
+ getClassifier().getClass().getCanonicalName() + "'");
}
// try and read the header
try {
m_trainedClassifierHeader = (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();
}
}
}
}
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