/**
* Sets the format of output instances. The derived class should use this method once it has
* determined the outputformat. The output queue is cleared.
*
* @param outputFormat the new output format
*/
protected void setOutputFormat(Instances outputFormat) {
if (outputFormat != null) {
m_OutputFormat = outputFormat.stringFreeStructure();
initOutputLocators(m_OutputFormat, null);
// Rename the relation
String relationName = outputFormat.relationName() + "-" + this.getClass().getName();
if (this instanceof OptionHandler) {
String[] options = ((OptionHandler) this).getOptions();
for (int i = 0; i < options.length; i++) {
relationName += options[i].trim();
}
}
m_OutputFormat.setRelationName(relationName);
} else {
m_OutputFormat = null;
}
m_OutputQueue = new Queue();
}
/**
* 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;
}
}
}
/**
* ************************************************** Convert a table to a set of instances, with
* <b>rows</b> representing individual </b>instances</b> and <b>columns</b> representing
* <b>attributes</b>
*/
public Instances tableRowsToNominalInstances(Table t, String relationName) {
System.err.print("Converting table rows to instances...");
// Set up attributes, which for rowInstances will be the colNames...
FastVector atts = new FastVector();
ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...
System.err.print("creating attributes...");
for (int c = 0; c < t.numCols; c++) {
// It's nominal... determine the range of values
isNominal.add(true);
FastVector attVals = getColValues(t, c);
atts.addElement(new Attribute(t.colNames[c], attVals));
// Save it for later
allAttVals.add(attVals);
}
System.err.print("creating instances...");
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
// Fill the instances with data...
// For each instance...
for (int r = 0; r < t.numRows; r++) {
double[] vals = new double[data.numAttributes()];
// for each attribute
for (int c = 0; c < t.numCols; c++) {
String val = (String) t.matrix.getQuick(r, c);
if (val == "?") vals[c] = Instance.missingValue();
else if (isNominal.get(c)) {
vals[c] = allAttVals.get(c).indexOf(val);
} else {
vals[c] = Double.parseDouble((String) val);
}
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
System.err.print("add feature names...");
if (addInstanceNamesAsFeatures) {
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
// We save the instanceNames in a list because it's handy later on...
instanceNames = new ArrayList<String>();
for (int r = 0; r < t.rowNames.length; r++) {
instanceNames.add(t.rowNames[r]);
newData.instance(r).setValue(attrIdx, t.rowNames[r]);
}
data = newData;
}
System.err.println("done.");
return (data);
}
/**
* ************************************************** Convert a table to a set of instances, with
* <b>columns</b> representing individual </b>instances</b> and <b>rows</b> representing
* <b>attributes</b> (e.g. as is common with microarray data)
*/
public Instances tableColsToInstances(Table t, String relationName) {
System.err.print("Converting table cols to instances...");
// Set up attributes, which for colInstances will be the rowNames...
FastVector atts = new FastVector();
ArrayList<Boolean> isNominal = new ArrayList<Boolean>();
ArrayList<FastVector> allAttVals = new ArrayList<FastVector>(); // Save values for later...
System.err.print("creating attributes...");
for (int r = 0; r < t.numRows; r++) {
if (rowIsNumeric(t, r)) {
isNominal.add(false);
atts.addElement(new Attribute(t.rowNames[r]));
allAttVals.add(null); // No enumeration of attribute values.
} else {
// It's nominal... determine the range of values and create a nominal attribute...
isNominal.add(true);
FastVector attVals = getRowValues(t, r);
atts.addElement(new Attribute(t.rowNames[r], attVals));
// Save it for later
allAttVals.add(attVals);
}
}
System.err.print("creating instances...");
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
/** ***** CREATE INSTANCES ************* */
// Fill the instances with data...
// For each instance...
for (int c = 0; c < t.numCols; c++) {
double[] vals =
new double[data.numAttributes()]; // Even nominal values are stored as double pointers.
// For each attribute fill in the numeric or attributeValue index...
for (int r = 0; r < t.numRows; r++) {
String val = (String) t.matrix.getQuick(r, c);
if (val == "?") vals[r] = Instance.missingValue();
else if (isNominal.get(r)) {
vals[r] = allAttVals.get(r).indexOf(val);
} else {
vals[r] = Double.parseDouble((String) val);
}
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
System.err.print("add feature names...");
/** ***** ADD FEATURE NAMES ************* */
// takes basically zero time... all time is in previous 2 chunks.
if (addInstanceNamesAsFeatures) {
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
// We save the instanceNames in a list because it's handy later on...
instanceNames = new ArrayList<String>();
for (int c = 0; c < t.colNames.length; c++) {
instanceNames.add(t.colNames[c]);
newData.instance(c).setValue(attrIdx, t.colNames[c]);
}
data = newData;
}
System.err.println("done.");
return (data);
}
/**
* If we know in advance that the table is numeric, can optimize a lot... For example, on 9803 x
* 294 table, TableFileLoader.readNumeric takes 6s compared to 12s for WekaMine readFromTable.
*/
public static Instances readNumeric(String fileName, String relationName, String delimiter)
throws Exception {
int numAttributes = FileUtils.fastCountLines(fileName) - 1; // -1 exclude heading.
String[] attrNames = new String[numAttributes];
// Read the col headings and figure out the number of columns in the table..
BufferedReader reader = new BufferedReader(new FileReader(fileName), 4194304);
String line = reader.readLine();
String[] instanceNames = parseColNames(line, delimiter);
int numInstances = instanceNames.length;
System.err.print("reading " + numAttributes + " x " + numInstances + " table..");
// Create an array to hold the data as we read it in...
double dataArray[][] = new double[numAttributes][numInstances];
// Populate the matrix with values...
String valToken = "";
try {
int rowIdx = 0;
while ((line = reader.readLine()) != null) {
String[] tokens = line.split(delimiter, -1);
attrNames[rowIdx] = tokens[0].trim();
for (int colIdx = 0; colIdx < (tokens.length - 1); colIdx++) {
valToken = tokens[colIdx + 1];
double value;
if (valToken.equals("null")) {
value = Instance.missingValue();
} else if (valToken.equals("?")) {
value = Instance.missingValue();
} else if (valToken.equals("NA")) {
value = Instance.missingValue();
} else if (valToken.equals("")) {
value = Instance.missingValue();
// }else value = DoubleParser.lightningParse(valToken); // faster double parser with
// MANY assumptions
} else value = Double.parseDouble(valToken);
dataArray[rowIdx][colIdx] = value;
}
rowIdx++;
}
} catch (NumberFormatException e) {
System.err.println(e.toString());
System.err.println("Parsing line: " + line);
System.err.println("Parsing token: " + valToken);
}
// Set up attributes, which for colInstances will be the rowNames...
FastVector atts = new FastVector();
for (int a = 0; a < numAttributes; a++) {
atts.addElement(new Attribute(attrNames[a]));
}
// Create Instances object..
Instances data = new Instances(relationName, atts, 0);
data.setRelationName(relationName);
System.err.print("creating instances..");
// System.err.println("DEBUG: numAttributes "+numAttributes);
/** ***** CREATE INSTANCES ************* */
// Fill the instances with data...
// For each instance...
for (int c = 0; c < numInstances; c++) {
double[] vals =
new double[data.numAttributes()]; // Even nominal values are stored as double pointers.
for (int r = 0; r < numAttributes; r++) {
double val = dataArray[r][c];
vals[r] = val;
}
// Add the a newly minted instance with those attribute values...
data.add(new Instance(1.0, vals));
}
// System.err.println("DEBUG: data.numInstances: "+data.numInstances());
// System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
// System.err.println("DEBUG: data.relationNAme"+data.relationName());
System.err.print("add feature names..");
/** ***** ADD FEATURE NAMES ************* */
// takes basically zero time... all time is in previous 2 chunks.
Instances newData = new Instances(data);
newData.insertAttributeAt(new Attribute("ID", (FastVector) null), 0);
int attrIdx = newData.attribute("ID").index(); // Paranoid... should be 0
for (int c = 0; c < numInstances; c++) {
newData.instance(c).setValue(attrIdx, instanceNames[c]);
}
data = newData;
// System.err.println("DEBUG: data.numInstances: "+data.numInstances());
// System.err.println("DEBUG: data.numAttributes: "+data.numAttributes());
return (data);
}
