/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
Vector<String> result = new Vector<String>();
result.add("-C");
result.add("" + getC());
result.add("-N");
result.add("" + m_filterType);
result.add("-I");
result.add(
""
+ getRegOptimizer().getClass().getName()
+ " "
+ Utils.joinOptions(getRegOptimizer().getOptions()));
result.add("-K");
result.add(
"" + getKernel().getClass().getName() + " " + Utils.joinOptions(getKernel().getOptions()));
Collections.addAll(result, super.getOptions());
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Gets the current settings of FuzzyRoughSubsetEval
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions() {
Vector<String> result;
result = new Vector<String>();
result.add("-Z");
result.add(
(m_FuzzyMeasure.getClass().getName() + " " + Utils.joinOptions(m_FuzzyMeasure.getOptions()))
.trim());
result.add("-I");
result.add(
(m_Implicator.getClass().getName() + " " + Utils.joinOptions(m_Implicator.getOptions()))
.trim());
result.add("-T");
result.add(
(m_TNorm.getClass().getName() + " " + Utils.joinOptions(m_TNorm.getOptions())).trim());
result.add("-R");
result.add(
(m_Similarity.getClass().getName() + " " + Utils.joinOptions(m_Similarity.getOptions()))
.trim());
return result.toArray(new String[result.size()]);
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
@Override
public String[] getOptions() {
ArrayList<String> options = new ArrayList<String>();
if (getUseWordFrequencies()) {
options.add("-W");
}
options.add("-P");
options.add("" + getPeriodicPruning());
options.add("-M");
options.add("" + getMinWordFrequency());
if (getNormalizeDocLength()) {
options.add("-normalize");
}
options.add("-norm");
options.add("" + getNorm());
options.add("-lnorm");
options.add("" + getLNorm());
if (getLowercaseTokens()) {
options.add("-lowercase");
}
if (getUseStopList()) {
options.add("-stoplist");
}
if (!getStopwords().isDirectory()) {
options.add("-stopwords");
options.add(getStopwords().getAbsolutePath());
}
options.add("-tokenizer");
String spec = getTokenizer().getClass().getName();
if (getTokenizer() instanceof OptionHandler) {
spec += " " + Utils.joinOptions(((OptionHandler) getTokenizer()).getOptions());
}
options.add(spec.trim());
if (getStemmer() != null) {
options.add("-stemmer");
spec = getStemmer().getClass().getName();
if (getStemmer() instanceof OptionHandler) {
spec += " " + Utils.joinOptions(((OptionHandler) getStemmer()).getOptions());
}
options.add(spec.trim());
}
Collections.addAll(options, super.getOptions());
return options.toArray(new String[1]);
}
/**
* returns the options of the current setup
*
* @return the current options
*/
public String[] getOptions() {
int i;
Vector result;
String[] options;
result = new Vector();
options = super.getOptions();
for (i = 0; i < options.length; i++) result.add(options[i]);
result.add("-A");
result.add("" + getAlgorithm().getSelectedTag().getReadable());
result.add("-P");
result.add("" + getPadding().getSelectedTag().getReadable());
result.add("-F");
if (getFilter() instanceof OptionHandler)
result.add(
getFilter().getClass().getName()
+ " "
+ Utils.joinOptions(((OptionHandler) getFilter()).getOptions()));
else result.add(getFilter().getClass().getName());
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Gets the filter specification string, which contains the class name of the filter and any
* options to the filter
*
* @return the filter string.
*/
protected String getFilterSpec() {
Filter c = getFilter();
if (c instanceof OptionHandler)
return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) c).getOptions());
else return c.getClass().getName();
}
/**
* Gets the current settings of IBk.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions() {
String[] options = new String[11];
int current = 0;
options[current++] = "-K";
options[current++] = "" + getKNN();
options[current++] = "-W";
options[current++] = "" + m_WindowSize;
if (getCrossValidate()) {
options[current++] = "-X";
}
if (getMeanSquared()) {
options[current++] = "-E";
}
if (m_DistanceWeighting == WEIGHT_INVERSE) {
options[current++] = "-I";
} else if (m_DistanceWeighting == WEIGHT_SIMILARITY) {
options[current++] = "-F";
}
options[current++] = "-A";
options[current++] =
m_NNSearch.getClass().getName() + " " + Utils.joinOptions(m_NNSearch.getOptions());
while (current < options.length) {
options[current++] = "";
}
return options;
}
/**
* Gets the current settings of SimpleKMeans.
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions() {
ArrayList<String> options = new ArrayList<String>();
options.add("-I");
options.add("" + getMaxIterations());
options.add("-min");
options.add("" + getMinNumClusters());
options.add("-max");
options.add("" + getMaxNumClusters());
options.add("-restarts");
options.add("" + getRestarts());
if (isManuallySelectNumClusters()) {
options.add("-manual");
}
if (getInitializeUsingKMeansPlusPlusMethod()) {
options.add("-P");
}
if (isPrintDebug()) {
options.add("-debug");
}
options.add("-A");
options.add(
(distanceFunction.getClass().getName()
+ " "
+ Utils.joinOptions(distanceFunction.getOptions()))
.trim());
return (String[]) options.toArray(new String[options.size()]);
}
@Override
public void setOptions(String[] options) throws Exception {
// these are options to the hadoop map task (especially the -names-file)
String existing = Utils.getOption("existing-header", options);
setPathToExistingHeader(existing);
String attNames = Utils.getOption('A', options);
setAttributeNames(attNames);
String namesFile = Utils.getOption("names-file", options);
setAttributeNamesFile(namesFile);
String outputName = Utils.getOption("header-file-name", options);
setOutputHeaderFileName(outputName);
super.setOptions(options);
// any options to pass on to the underlying Weka csv to arff map task?
CSVToARFFHeaderMapTask tempMap = new CSVToARFFHeaderMapTask();
tempMap.setOptions(options);
String optsToWekaMapTask = Utils.joinOptions(tempMap.getOptions());
if (!DistributedJobConfig.isEmpty(optsToWekaMapTask)) {
setCsvToArffTaskOptions(optsToWekaMapTask);
}
}
/**
* Returns description of the bias-variance decomposition results.
*
* @return the bias-variance decomposition results as a string
*/
public String toString() {
String result = "\nBias-Variance Decomposition\n";
if (getClassifier() == null) {
return "Invalid setup";
}
result += "\nClassifier : " + getClassifier().getClass().getName();
if (getClassifier() instanceof OptionHandler) {
result += Utils.joinOptions(((OptionHandler) m_Classifier).getOptions());
}
result += "\nData File : " + getDataFileName();
result += "\nClass Index : ";
if (getClassIndex() == 0) {
result += "last";
} else {
result += getClassIndex();
}
result += "\nTraining Pool: " + getTrainPoolSize();
result += "\nIterations : " + getTrainIterations();
result += "\nSeed : " + getSeed();
result += "\nError : " + Utils.doubleToString(getError(), 6, 4);
result += "\nSigma^2 : " + Utils.doubleToString(getSigma(), 6, 4);
result += "\nBias^2 : " + Utils.doubleToString(getBias(), 6, 4);
result += "\nVariance : " + Utils.doubleToString(getVariance(), 6, 4);
return result + "\n";
}
/**
* Gets the classifier specification string, which contains the class name of the classifier and
* any options to the classifier
*
* @param index the index of the classifier string to retrieve, starting from 0.
* @return the classifier string, or the empty string if no classifier has been assigned (or the
* index given is out of range).
*/
protected String getClassifierSpec(int index) {
if (m_Classifiers.length < index) {
return "";
}
Classifier c = getClassifier(index);
return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) c).getOptions());
}
/**
* Gets the search specification string, which contains the class name of the search method and
* any options to it
*
* @return the search string.
*/
protected String getSearchSpec() {
ASSearch s = getSearch();
if (s instanceof OptionHandler) {
return s.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) s).getOptions());
}
return s.getClass().getName();
}
/**
* Gets the classifier specification string, which contains the class name of the classifier and
* any options to the classifier.
*
* @return the classifier string.
*/
protected String getClassifierSpec() {
Classifier c = getClassifier();
if (c instanceof OptionHandler) {
return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) c).getOptions());
}
return c.getClass().getName();
}
/**
* returns the specification of the given object (class, options if an instance of OptionHandler)
*
* @param o the object to get the specs as string
* @return the specification string
*/
protected String getSpecification(Object o) {
String result;
result = o.getClass().getName();
if (o instanceof OptionHandler)
result += " " + Utils.joinOptions(((OptionHandler) o).getOptions());
return result.trim();
}
/**
* Returns graph describing the classifier (if possible).
*
* @return the graph of the classifier in dotty format
* @throws Exception if the classifier cannot be graphed
*/
public String graph() throws Exception {
if (m_Classifier instanceof Drawable) return ((Drawable) m_Classifier).graph();
else
throw new Exception(
"Classifier: "
+ m_Classifier.getClass().getName()
+ " "
+ Utils.joinOptions(m_BestClassifierOptions)
+ " cannot be graphed");
}
/**
* Apply the complete list of options to the current underlying job
*
* @param opts the options to apply
*/
protected void applyOptionsToJob(List<String> opts) {
String combined = Utils.joinOptions(opts.toArray(new String[opts.size()]));
System.err.println("Combined: " + combined);
if (!combined.equals(m_optionsOrig)) {
m_modifyListener.setModifiedStatus(this, true);
}
m_bean.setJobOptions(combined);
m_parentWindow.dispose();
}
/**
* Parses a given list of options.
*
* @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 tmpStr;
m_GroovyOptions = new String[0];
tmpStr = Utils.getOption('G', options);
if (tmpStr.length() != 0) setGroovyModule(new File(tmpStr));
else setGroovyModule(new File(System.getProperty("user.dir")));
setGroovyOptions(Utils.joinOptions(Utils.partitionOptions(options).clone()));
super.setOptions(options);
}
/** outputs some data about the classifier */
public String toString() {
StringBuffer result;
result = new StringBuffer();
result.append("Weka - Demo\n===========\n\n");
result.append(
"Classifier...: "
+ m_Classifier.getClass().getName()
+ " "
+ Utils.joinOptions(m_Classifier.getOptions())
+ "\n");
if (m_Filter instanceof OptionHandler)
result.append(
"Filter.......: "
+ m_Filter.getClass().getName()
+ " "
+ Utils.joinOptions(((OptionHandler) m_Filter).getOptions())
+ "\n");
else result.append("Filter.......: " + m_Filter.getClass().getName() + "\n");
result.append("Training file: " + m_TrainingFile + "\n");
result.append("\n");
result.append(m_Classifier.toString() + "\n");
result.append(m_Evaluation.toSummaryString() + "\n");
try {
result.append(m_Evaluation.toMatrixString() + "\n");
} catch (Exception e) {
e.printStackTrace();
}
try {
result.append(m_Evaluation.toClassDetailsString() + "\n");
} catch (Exception e) {
e.printStackTrace();
}
return result.toString();
}
protected void updateOptions() {
if (m_clusterer instanceof OptionHandler) {
m_clustererOptions = Utils.joinOptions(((OptionHandler) m_clusterer).getOptions());
} else {
m_clustererOptions = "";
}
if (m_clusterer instanceof Serializable) {
ObjectStreamClass obs = ObjectStreamClass.lookup(m_clusterer.getClass());
m_clustererVersion = "" + obs.getSerialVersionUID();
} else {
m_clustererVersion = "";
}
}
@Override
public void setOptions(String[] options) throws Exception {
String ns = Utils.getOption('N', options);
if (ns.length() > 0) {
setNamespace(ns);
}
// scheme options after the --
String[] schemeOpts = Utils.partitionOptions(options);
if (schemeOpts != null && schemeOpts.length > 0) {
String sO = Utils.joinOptions(schemeOpts);
setSchemeOptions(sO);
}
}
/**
* Returns the options for this node and its subtree.
*
* @param node the node to get the options for
* @return the generated options
*/
protected String getOptions(DefaultMutableTreeNode node) {
Vector<String> options;
int i;
options = new Vector<String>();
// the node itself
if (!node.toString().equals(LABEL_ROOT) && !node.toString().equals(LABEL_NESTED))
options.add(node.toString());
// the node's children
for (i = 0; i < node.getChildCount(); i++)
options.add(getOptions((DefaultMutableTreeNode) node.getChildAt(i)));
return Utils.joinOptions(options.toArray(new String[options.size()]));
}
/**
* Displays a frame with the option tree panel.
*
* @param args displayed in the full options text
*/
public static void main(String[] args) {
OptionTree tree = new OptionTree();
final JFrame jf = new JFrame("Option Tree");
jf.getContentPane().setLayout(new BorderLayout());
jf.getContentPane().add(tree, BorderLayout.CENTER);
jf.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE);
jf.addWindowListener(
new WindowAdapter() {
public void windowClosing(WindowEvent e) {
jf.dispose();
}
});
jf.pack();
jf.setSize(800, 600);
jf.setLocationRelativeTo(null);
jf.setVisible(true);
if (args.length > 0) tree.setOptions(Utils.joinOptions(args));
}
/**
* Returns description of the cross-validated classifier.
*
* @return description of the cross-validated classifier as a string
*/
public String toString() {
if (m_InitOptions == null) return "CVParameterSelection: No model built yet.";
String result =
"Cross-validated Parameter selection.\n"
+ "Classifier: "
+ m_Classifier.getClass().getName()
+ "\n";
try {
for (int i = 0; i < m_CVParams.size(); i++) {
CVParameter cvParam = (CVParameter) m_CVParams.elementAt(i);
result +=
"Cross-validation Parameter: '-"
+ cvParam.m_ParamChar
+ "'"
+ " ranged from "
+ cvParam.m_Lower
+ " to ";
switch ((int) (cvParam.m_Lower - cvParam.m_Upper + 0.5)) {
case 1:
result += m_NumAttributes;
break;
case 2:
result += m_TrainFoldSize;
break;
default:
result += cvParam.m_Upper;
break;
}
result += " with " + cvParam.m_Steps + " steps\n";
}
} catch (Exception ex) {
result += ex.getMessage();
}
result +=
"Classifier Options: "
+ Utils.joinOptions(m_BestClassifierOptions)
+ "\n\n"
+ m_Classifier.toString();
return result;
}
/**
* generates a filename with a certain suffix for the logQuality method
*
* @param suffix the suffix
* @return the generated filename
* @see #logQuality(int,int)
*/
protected String createFilename(String suffix) {
String result;
result =
this.getClass().getName()
+ "-"
+ CollectiveHelper.generateMD5(Utils.joinOptions(this.getOptions()))
+ "-"
+ (m_Trainset == null
? "null"
: m_Trainset.relationName().replaceAll("-weka\\.filters\\..*", ""))
+ "-R"
+ getNumRestarts()
+ "-I"
+ getNumIterations()
+ "-E"
+ m_EvaluationType
+ "-C"
+ m_ComparisonType
+ suffix;
return result;
}
/**
* Gets the current settings of the classifier.
*
* @return an array of strings suitable for passing to setOptions
*/
public String[] getOptions() {
String[] superOptions = super.getOptions();
String[] options = new String[superOptions.length + 6];
int current = 0;
options[current++] = "-U";
options[current++] = "" + getWeightingKernel();
if ((getKNN() == 0) && m_UseAllK) {
options[current++] = "-K";
options[current++] = "-1";
} else {
options[current++] = "-K";
options[current++] = "" + getKNN();
}
options[current++] = "-A";
options[current++] =
m_NNSearch.getClass().getName() + " " + Utils.joinOptions(m_NNSearch.getOptions());
System.arraycopy(superOptions, 0, options, current, superOptions.length);
return options;
}
/**
* A concise description of the model.
*
* @return a concise description of the model
*/
public String toSummaryString() {
String result = "Selected values: " + Utils.joinOptions(m_BestClassifierOptions);
return result + '\n';
}
@Override
protected void notifyJobOutputListeners() {
weka.classifiers.Classifier finalClassifier =
((weka.distributed.spark.WekaClassifierSparkJob) m_runningJob).getClassifier();
Instances modelHeader =
((weka.distributed.spark.WekaClassifierSparkJob) m_runningJob).getTrainingHeader();
String classAtt =
((weka.distributed.spark.WekaClassifierSparkJob) m_runningJob).getClassAttribute();
try {
weka.distributed.spark.WekaClassifierSparkJob.setClassIndex(classAtt, modelHeader, true);
} catch (Exception ex) {
if (m_log != null) {
m_log.logMessage(statusMessagePrefix() + ex.getMessage());
}
ex.printStackTrace();
}
if (finalClassifier == null) {
if (m_log != null) {
m_log.logMessage(statusMessagePrefix() + "No classifier produced!");
}
}
if (modelHeader == null) {
if (m_log != null) {
m_log.logMessage(statusMessagePrefix() + "No training header available for the model!");
}
}
if (finalClassifier != null) {
if (m_textListeners.size() > 0) {
String textual = finalClassifier.toString();
String title = "Spark: ";
String classifierSpec = finalClassifier.getClass().getName();
if (finalClassifier instanceof OptionHandler) {
classifierSpec += " " + Utils.joinOptions(((OptionHandler) finalClassifier).getOptions());
}
title += classifierSpec;
TextEvent te = new TextEvent(this, textual, title);
for (TextListener t : m_textListeners) {
t.acceptText(te);
}
}
if (modelHeader != null) {
// have to add a single bogus instance to the header to trick
// the SerializedModelSaver into saving it (since it ignores
// structure only DataSetEvents) :-)
double[] vals = new double[modelHeader.numAttributes()];
for (int i = 0; i < vals.length; i++) {
vals[i] = Utils.missingValue();
}
Instance tempI = new DenseInstance(1.0, vals);
modelHeader.add(tempI);
DataSetEvent dse = new DataSetEvent(this, modelHeader);
BatchClassifierEvent be = new BatchClassifierEvent(this, finalClassifier, dse, dse, 1, 1);
for (BatchClassifierListener b : m_classifierListeners) {
b.acceptClassifier(be);
}
}
}
}
/**
* returns a string representation of the cluster
*
* @return the cluster definition as string
*/
public String toString() {
return this.getClass().getName() + ": " + Utils.joinOptions(getOptions());
}
/**
* Gets the Groovy module options.
*
* @return the options
*/
public String getGroovyOptions() {
return Utils.joinOptions(m_GroovyOptions);
}
/**
* Takes an evaluation object from a task and aggregates it with the overall one.
*
* @param eval the evaluation object to aggregate
* @param classifier the classifier used by the task
* @param testData the testData from the task
* @param plotInstances the ClassifierErrorsPlotInstances object from the task
* @param setNum the set number processed by the task
* @param maxSetNum the maximum number of sets in this batch
*/
protected synchronized void aggregateEvalTask(
Evaluation eval,
Classifier classifier,
Instances testData,
ClassifierErrorsPlotInstances plotInstances,
int setNum,
int maxSetNum) {
m_eval.aggregate(eval);
if (m_aggregatedPlotInstances == null) {
m_aggregatedPlotInstances = new Instances(plotInstances.getPlotInstances());
m_aggregatedPlotShapes = plotInstances.getPlotShapes();
m_aggregatedPlotSizes = plotInstances.getPlotSizes();
} else {
Instances temp = plotInstances.getPlotInstances();
for (int i = 0; i < temp.numInstances(); i++) {
m_aggregatedPlotInstances.add(temp.get(i));
m_aggregatedPlotShapes.addElement(plotInstances.getPlotShapes().get(i));
m_aggregatedPlotSizes.addElement(plotInstances.getPlotSizes().get(i));
}
}
m_setsComplete++;
// if (ce.getSetNumber() == ce.getMaxSetNumber()) {
if (m_setsComplete == maxSetNum) {
try {
String textTitle = classifier.getClass().getName();
String textOptions = "";
if (classifier instanceof OptionHandler) {
textOptions = Utils.joinOptions(((OptionHandler) classifier).getOptions());
}
textTitle = textTitle.substring(textTitle.lastIndexOf('.') + 1, textTitle.length());
String resultT =
"=== Evaluation result ===\n\n"
+ "Scheme: "
+ textTitle
+ "\n"
+ ((textOptions.length() > 0) ? "Options: " + textOptions + "\n" : "")
+ "Relation: "
+ testData.relationName()
+ "\n\n"
+ m_eval.toSummaryString();
if (testData.classAttribute().isNominal()) {
resultT += "\n" + m_eval.toClassDetailsString() + "\n" + m_eval.toMatrixString();
}
TextEvent te = new TextEvent(ClassifierPerformanceEvaluator.this, resultT, textTitle);
notifyTextListeners(te);
// set up visualizable errors
if (m_visualizableErrorListeners.size() > 0) {
PlotData2D errorD = new PlotData2D(m_aggregatedPlotInstances);
errorD.setShapeSize(m_aggregatedPlotSizes);
errorD.setShapeType(m_aggregatedPlotShapes);
errorD.setPlotName(textTitle + " " + textOptions);
/* PlotData2D errorD = m_PlotInstances.getPlotData(
textTitle + " " + textOptions); */
VisualizableErrorEvent vel =
new VisualizableErrorEvent(ClassifierPerformanceEvaluator.this, errorD);
notifyVisualizableErrorListeners(vel);
m_PlotInstances.cleanUp();
}
if (testData.classAttribute().isNominal() && m_thresholdListeners.size() > 0) {
ThresholdCurve tc = new ThresholdCurve();
Instances result = tc.getCurve(m_eval.predictions(), 0);
result.setRelationName(testData.relationName());
PlotData2D pd = new PlotData2D(result);
String htmlTitle = "<html><font size=-2>" + textTitle;
String newOptions = "";
if (classifier instanceof OptionHandler) {
String[] options = ((OptionHandler) classifier).getOptions();
if (options.length > 0) {
for (int ii = 0; ii < options.length; ii++) {
if (options[ii].length() == 0) {
continue;
}
if (options[ii].charAt(0) == '-'
&& !(options[ii].charAt(1) >= '0' && options[ii].charAt(1) <= '9')) {
newOptions += "<br>";
}
newOptions += options[ii];
}
}
}
htmlTitle +=
" "
+ newOptions
+ "<br>"
+ " (class: "
+ testData.classAttribute().value(0)
+ ")"
+ "</font></html>";
pd.setPlotName(textTitle + " (class: " + testData.classAttribute().value(0) + ")");
pd.setPlotNameHTML(htmlTitle);
boolean[] connectPoints = new boolean[result.numInstances()];
for (int jj = 1; jj < connectPoints.length; jj++) {
connectPoints[jj] = true;
}
pd.setConnectPoints(connectPoints);
ThresholdDataEvent rde =
new ThresholdDataEvent(
ClassifierPerformanceEvaluator.this, pd, testData.classAttribute());
notifyThresholdListeners(rde);
}
if (m_logger != null) {
m_logger.statusMessage(statusMessagePrefix() + "Finished.");
}
} catch (Exception ex) {
if (m_logger != null) {
m_logger.logMessage(
"[ClassifierPerformanceEvaluator] "
+ statusMessagePrefix()
+ " problem constructing evaluation results. "
+ ex.getMessage());
}
ex.printStackTrace();
} finally {
m_visual.setStatic();
// save memory
m_PlotInstances = null;
m_setsComplete = 0;
m_tasks = null;
m_aggregatedPlotInstances = null;
}
}
}
